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The role of vascular dysfunction in cognitive impairment

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The role of vascular dysfunction in cognitive impairment

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Content THE ROLE OF VASCULAR DYSFUNCTION IN COGNITIVE IMPAIRMENT

by
Giuseppe Barisano

A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(NEUROSCIENCE)

August 2022

Copyright 2022 Giuseppe Barisano

ii
Dedication
This dissertation is dedicated to my family for their endless support, encouragement, and love.

iii
Acknowledgements
I thank my Ph.D. advisor, Dr. Berislav V. Zlokovic, for his guidance, support, and the
opportunities he offered me throughout my Ph.D. training.
I thank the members of my dissertation committee, Drs. Arthur Toga (Committee Chair),
William Mack, Justin Ichida and Marcelo Coba, for their assistance throughout graduate school.
I thank the current and former members of the Zlokovic laboratory for the training,
availability, and help in several experiments and analyses. In particular, I thank William Gilliam,
Abhay Sagare, Mikko Huuskonen, Ararat Chakhoyan, Kassandra Kisler, Maricarmen Pachicano,
Carina Torres-Sepulveda, Jasmine Stanley, Yaoming Wang, Zhonghua Dai, Axel Montagne,
Mariangela Nikolakopoulou, Yumei Guo, Edward Zuniga, and Dongsheng Bai.
I thank former and current members of the Stevens Neuroimaging and Informatics Institute at
USC for the training, opportunities, kindness, and friendship you gave me in these years. In
particular, I thank Drs. Meng Law, Farshid Sepehrband, Jeiran Choupan, Ryan Cabeen, Kirsten
Lynch, Francesca Sibilia, Marianna La Rocca, Dominique Duncan, Danny Wang, Kay Jann,
Samantha Ma, Xingfeng Shao, Kai Wang, Judy Pa, Ashwin Sakhare, Katherin Martin.
I thank the USC Neuroscience Graduate Program and my Ph.D. cohort: Julia Juliano, Michelle
Seo, Nancy Tran, Anna Pushkin, Samantha Betts, Brandon Butler, Erin Donahue, Adam Jones,
Tanisha London, Elizabeth Loxterkamp, Brock Pluimer, Luren Eisenman. Thank you for being
such great and welcoming fellows and for making my Ph.D. experience in the U.S. enjoyable.
I thank my friends from San Severo and Milano for always encouraging me.
My deepest and sincere thanks to my family and the Ichino’s family: the love of your words
went always beyond the distance between us.
Finally, I thank Lucia Ichino, for always being there for me.
iv
Table of Contents
Dedication ....................................................................................................................................... ii
Acknowledgements ....................................................................................................................... iii
List of Publications .......................................................................................................................... v
List of Figures ................................................................................................................................. ix
List of Tables .................................................................................................................................. xi
Abstract ......................................................................................................................................... xii
Chapter 1: Introduction on blood-brain barrier and perivascular spaces ......................................... 1
Blood-brain barrier ..................................................................................................... 2
Perivascular space .................................................................................................... 15
Perivascular spaces in the white matter are affected by body mass index, time of
day and genetics ....................................................................................................... 39
Perivascular spaces in spaceflight ............................................................................ 71
Acknowledgements .................................................................................................. 86
Chapter 2: APOE4 leads to blood-brain barrier dysfunction predicting cognitive decline ........... 89
Introduction .............................................................................................................. 89
Methods .................................................................................................................... 90
Results .................................................................................................................... 103
Discussion .............................................................................................................. 115
Acknowledgement ................................................................................................. 115
Chapter 3: APOE4 cell-specific mechanisms underlying cerebrovascular disorder, neuronal
and synaptic dysfunction, and cognitive deficits in mice ........................................... 117
Introduction ............................................................................................................ 117
Methods .................................................................................................................. 118
Results .................................................................................................................... 125
Discussion .............................................................................................................. 134
Acknowledgement ................................................................................................. 135
Bibliography ................................................................................................................................ 136

v
List of Publications
This list includes peer-reviewed articles published during my Ph.D.
* denotes equally contributed first co-authors
Published or in press
1 Barisano, G., Sepehrband, F., Collins, H. R., Jillings, S., Jeurissen, B., Taylor, A. J.,
Schoenmaekers, C., De Laet, C., Rukavishnikov, I., Nosikova, I., Litvinova, L., Rumshiskaya, A.,
Annen, J., Sijbers, J., Laureys, S., Van Ombergen, A., Petrovichev, V., Sinitsyn, V., Pechenkova,
E. V., Grishin, A., zu Eulenburg, P., Law, M., Sunaert,

S., Parizel, P. M., Tomilovskaya, E.,
Roberts,

D. R., Wuyts, F. L. The effect of prolonged Spaceflight on Cerebrospinal Fluid and
Perivascular Spaces of Astronauts and Cosmonauts. Proceedings of the National Academy of
Sciences. In press.
2 Zavaliangos-Petropulu, A., Lo, B., Donnelly, M. R., Schweighofer, N., Lohse, K., Jahanshad, N.,
Barisano, G., Banaj, N., Borich, M. R., Boyd, L. A., Buetefisch, C. M., Byblow, W. D., Cassidy,
J. M., Charalambous, C. C., Conforto, A. B., DiCarlo, J. A., Dula, A. N., Egorova-Brumley, N.,
Etherton, M. R., Feng, W., Fercho, K. A., Geranmayeh, F., Hanlon, C. A., Hayward, K. S.,
Hordacre, B., Kautz, S. A., Khlif, M. S., Kim, H., Kuceyeski, A., Lin, D. J., Lotze, M., Liu, J.,
MacIntosh, B. J., Margetis, J. L., Piras, F., Ramos-Murguialday, A., Revill, K. P., Roberts, P. S.,
Robertson, A. D., Schambra, H. M., Seo, N. J., Shiroishi, M. S., Soekadar, S. R., Spalletta, G.,
Taga, M., Tang, W. K., Thielman, G. T., Vecchio, D., Ward, N. S., Westlye, L. T., Werden, E.,
Winstein, C., Wittenberg, G. F., Wolf, S. L., Wong, K. A., Yu, C., Brodtmann, A., Cramer, S. C.,
Thompson, P. M., Liew, S. L. Chronic stroke sensorimotor impairment is related to smaller
hippocampal volumes: An ENIGMA analysis. Journal of the American Heart Association. In
press.
3 Barisano, G.,* Montagne, A.,* Kisler, K., Schneider, J. A., Wardlaw, J. M., Zlokovic, B. V.
Blood-brain barrier link to human cognitive impairment and Alzheimer’s Disease. Nature
Cardiovascular Research. 1(2), 108–115 (2022).
4 Gatz, M., Mack, W. J., Chui, H. C., Law, M. E., Barisano, G., Sutherland, L. M., Sutherland, J.
D., Rodriguez, D. E., Gutierrez, R. Q., Borenstein, A. R., Walters, E. E., Irimia, A., Rowan, C. J.,
Wann, L. S., Allam, A. H., Thompson, R. C., Miyamoto, M. I., Michalik, D. E., Cummings, D.
K., Seabright, E., Garcia, A. R., Hooper, P. L., Kraft, T. S., Finch, C. E., Thomas, G. S., Stieglitz,
J., Trumble, B. C., Gurven, M. D., Kaplan, H. Prevalence of dementia and mild cognitive
impairment in indigenous Bolivian Tsimane and Moseten. Alzheimer’s & Dementia. (2022).
5 La Rocca, M., Barisano, G., Bennett, A., Garner, R., Engel, J., Gilmore, E.J., McArthur, D.L.,
Rosenthal, E., Stanis, J., Vespa, P., Willyerd, F., Zimmerman, L., Toga, A.W., Duncan, D.
Distribution and volume analysis of early hemorrhagic contusions by MRI after traumatic brain
injury: a preliminary report of the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy
(EpiBioS4Rx). Brain Imaging and Behavior, 15(6), 2804–2812 (2022).
vi
6 Strickland, B., Barisano, G., Abedi, A., Shiroishi, M., Cen, S., Emanuel, B., Bulic, S., Kim-
Tenser, M., Nguyen, P., Giannotta, S., Mack, W. & Russin, J. Minocycline decreases blood-brain
barrier permeability following aneurysmal subarachnoid hemorrhage. Journal of Neurosurgery.
Ahead of print. https://thejns.org/view/journals/j-neurosurg/aop/article-10.3171-
2021.6.JNS211270/article-10.3171-2021.6.JNS211270.xml
7 Akbar, M.N., Ruf, S., La Rocca, M., Garner, R., Barisano, G., Cua, R., Vespa, P., Erdoğmuş, D.,
Duncan, D. Lesion Normalization and Supervised Learning in Post-traumatic Seizure
Classification with Diffusion MRI. In: Cetin-Karayumak S. et al. (eds) Computational Diffusion
MRI. CDMRI 2021. Lecture Notes in Computer Science, vol 13006 (2021).
8 Liew, S., Zavaliangos-Petropulu, A., Schweighofer, N., Jahanshad, N., Lang, C. E., Lohse, K. R.,
Banaj, N., Barisano, G., Baugh, L. A., Bhattacharya, A. K., Bigjahan, B., Borich, M. R., Boyd,
L. A., Bordtmann, A., Buetefisch, C. M., Ciullo, V., Conforto, A. B., Craddock, R. C., Dula, A.
N., Egorova, N., Feng, W., Fercho, K. A., Gregory, C. M., Hanlon, C. A., Hayward, K., S.,
Holguin, J. A., Hordacre, B., Hwang, D. H., Kautz, S. A., Khlif, M. S., Kim B., Kim, H.,
Kuceyeski, A., Liu, J., Lin, D., MacIntosh, B. J., Margetis, J. L., Mohamed, F. B., Nordvik, J. E.,
Petoe, M. A., Piras, F., Raju, S., Ramos-Murguialday, A., Revill, K. P., Roberts, P., Robertson, A.
D., Schambra, H. M., Seo, N. J., Shiroishi, M. S., Soekadar, S. R., Spalletta, G., Stinear, C. M.,
Suri, A., Tang, W. K., Thielman, G. T., Thijs, V. N., Vecchio, D., Ward, N. S., Westlye, L. T.,
Winstein, C. J., Wittenberg, G. F., Wong, K. A., Yu, C., Wolf, S. L., Cramer, S. C., Thompson, P.
M. Smaller spared subcortical nuclei are associated with worse post-stroke sensorimotor outcomes
in 28 cohorts worldwide. Brain Commun. 3, 74 (2021).
9 Huuskonen, M., Barisano, G., Chakhoyan, A., Zlokovic, B.V. Editorial for “MRI-Based
Investigation of Association Between Cerebrovascular Structural Alteration and White Matter
Hyperintensity Induced by High Blood Pressure”. J. Magn. Reson. Imaging 54, 1527–1528
(2021).
10 Jann, K., Shao, X., Ma, S. J., Cen, S. Y., D’Orazio, L., Barisano, G., Yan, L., Casey, M., Lamas,
J., Staffaroni, A. M., Kramer, J. H., Ringman, J. M. & Wang, D. J. J. Evaluation of Cerebral Blood
Flow Measured by 3D PCASL as Biomarker of Vascular Cognitive Impairment and Dementia
(VCID) in a Cohort of Elderly Latinx Subjects at Risk of Small Vessel Disease. Front. Neurosci.
15, 35 (2021).
11 Sepehrband, F., Barisano, G., Sheikh-Bahaei, N., Choupan, J., Cabeen, R. P., Lynch, K. M.,
Crawford, M. S., Lan, H., Mack, W. J., Chui, H. C., Ringman, J. M. & Toga, A. W. Volumetric
Distribution of Perivascular Space in Relation to Mild Cognitive Impairment. Neurobiol. Aging
99, 28–43 (2021).
12 Barisano, G., Law, M., Custer, R. M., Toga, A. W. & Sepehrband, F. Perivascular Space Imaging
at Ultrahigh Field MR Imaging. Magn. Reson. Imaging Clin. N. Am. 29, 67–75 (2021).
13 Barisano, G., Sheikh-Bahaei, N., Law, M., Toga, A. W. & Sepehrband, F. Body mass index, time
of day, and genetics affect perivascular spaces in the white matter. J. Cereb. Blood Flow Metab.
41, 1563–1578 (2021).
vii
14 Shkirkova, K., Lamorie-Foote, K., Connor, M., Patel, A., Barisano, G., Baertsch, H., Liu, Q.,
Morgan, T. E., Sioutas, C. & Mack, W. J. Effects of ambient particulate matter on vascular tissue:
a review. J. Toxicol. Environ. Heal. - Part B Crit. Rev. 23, 319–350 (2020).
15 Montagne, A.,* Nation, D. A.,* Sagare, A. P.,* Barisano, G.,* Sweeney, M. D.,* Chakhoyan, A.,
Pachicano, M., Joe, E., Nelson, A. R., D’Orazio, L. M., Buennagel, D. P., Harrington, M. G.,
Benzinger, T. L. S., Fagan, A. M., Ringman, J. M., Schneider, L. S., Morris, J. C., Reiman, E. M.,
Caselli, R. J., Chui, H. C., TCW, J., Chen, Y., Pa, J., Conti, P. S., Law, M., Toga, A. W. &
Zlokovic, B. V. APOE4 leads to blood–brain barrier dysfunction predicting cognitive decline.
Nature 581, 71–76 (2020).
16 Sepehrband, F., Barisano, G., Sheikh-Bahaei, N., Cabeen, R. P., Choupan, J., Law, M. & Toga,
A. W. Image processing approaches to enhance perivascular space visibility and quantification
using MRI. Sci. Rep. 9, 12351 (2019).
17 Barisano, G.,* Bigjahan, B.,* Metting, S., Cen, S., Amezcua, L., Lerner, A., Toga, A. W. & Law,
M. Signal Hyperintensity on Unenhanced T1-Weighted Brain and Cervical Spinal Cord MR
Images after Multiple Doses of Linear Gadolinium-Based Contrast Agent. Am. J. Neuroradiol. 40,
1274–1281 (2019).
18 Sepehrband, F., Cabeen, R. P., Choupan, J., Barisano, G., Law, M. & Toga, A. W. Perivascular
space fluid contributes to diffusion tensor imaging changes in white matter. Neuroimage 197, 243–
254 (2019).
19 Garner, R., La Rocca, M., Barisano, G., Toga, A. W., Duncan, D. & Vespa, P. A machine learning
model to predict seizure susceptibility from resting-state fMRI connectivity. in Simul. Ser. 51, 1–
11 (IEEE, 2019).
20 Sepehrband, F., Cabeen, R. P., Barisano, G., Sheikh-Bahaei, N., Choupan, J., Law, M. & Toga,
A. W. Nonparenchymal fluid is the source of increased mean diffusivity in preclinical Alzheimer’s
disease. Alzheimer’s Dement. Diagnosis, Assess. Dis. Monit. 11, 348–354 (2019).
21 Sakhare, A. R., Barisano, G. & Pa, J. Assessing test–retest reliability of phase contrast MRI for
measuring cerebrospinal fluid and cerebral blood flow dynamics. Magn. Reson. Med. 82, 658–670
(2019).
22 Barisano, G., Culo, B., Shellock, F. G., Sepehrband, F., Martin, K., Stevens, M., Wang, D. J.,
Toga, A. W. & Law, M. 7-Tesla MRI of the brain in a research subject with bilateral, total knee
replacement implants: Case report and proposed safety guidelines. Magn. Reson. Imaging 57, 313–
316 (2019).
23 Barisano, G., Sepehrband, F., Ma, S., Jann, K., Cabeen, R., Wang, D. J., Toga, A. W. & Law, M.
Clinical 7 T MRI: Are we there yet? A review about magnetic resonance imaging at ultra-high
field. Br. J. Radiol. 92, 20180492 (2019).
24 Duncan, D., Barisano, G., Cabeen, R., Sepehrband, F., Garner, R., Braimah, A., Vespa, P.,
Pitkänen, A., Law, M. & Toga, A. W. Analytic Tools for Post-traumatic Epileptogenesis
viii
Biomarker Search in Multimodal Dataset of an Animal Model and Human Patients. Front.
Neuroinform. 12, 86 (2018).

Under review
25 Barisano, G.,* Wilkinson, B.,* Nikolakopoulou, A. M.,* Gilliam, W.,* Sagare, A. P., Wang, Y.,
Huuskonen, M. T., Kisler, K., Hung, S., Ichida, J. K., Gao, F., Coba, M. P., Zlokovic, B. V. APOE4
cell-specific mechanisms underlying cerebrovascular disorder precede neuronal and synaptic
dysfunction and cognitive deficits in mice. Under review.
26 Barisano, G., Lynch, K. M., Sibilia, F., Lan, H., Shih, N-C., Sepehrband, F., Choupan, J. Imaging
perivascular space structure and function using brain MRI. Under review.
27 Shih, N-C., Lincoln, K., Barisano, G., Sepehrband, F., Choupan, J. Effects of Sleep on Brain
Perivascular Space in a Healthy Population. Under review.
28 Liew, S. L., Lo, B., Donnelly, M. R., Zavaliangos-Petropulu, A., Jeong, J. N., Barisano, G.,
Hutton, A., Simon, J. P., Juliano, J. A., Suri, A., Ard, T., Banaj, N., Borich, M. R., Boyd, L. A.,
Brodtmann, A., Buetefisch, C. M., Cao, L., Cassidy, J. M., Ciullo, V., Conforto, A. B., Cramer, S.
C., Dacosta-Aguayo, R., de la Rosa, E., Domin, M., Dula, A. N., Feng, W., Franco, A. R.,
Geranmayeh, F., Gramfort, A., Gregory, C. M, Hanlon, C. A., Hordacre, B. G., Kautz, S. A., Khlif,
M. S., Kim, H., Kirschke, J. S., Liu, J., Lotze, M., MacIntosh, B. J., Mataró, M., Mohamed, F. B.,
Nordvik, J. E., Park, G., Pienta, A., Piras, F., Redman, S. M., Revill, K. P., Reyes, M., Robertson,
A. D., Seo, N. J., Soekadar, S. R., Spalletta, G., Sweet, A., Telenczuk, M., Thielman, G., Westlye,
L. T., Winstein, C. J., Wittenberg, G. F., Wong, K. A., Yu, C. A large, curated, open-source stroke
neuroimaging dataset to improve lesion segmentation algorithms. Under review.

ix
List of Figures
Figure 1.1 The BBB and associated cell types. ............................................................................... 2
Figure 1.2 Visualization and measures of PVS on MRI depend on the image resolution ............ 20
Figure 1.3 Examples showing the increase in perivascular spaces (PVS) in healthy
participants with different ages .......................................................................................... 29
Figure 1.4 Schematic of our pipeline for PVS segmentation. ....................................................... 44
Figure 1.5 Examples showing the high inter-subject variability of PVS in healthy
participants ......................................................................................................................... 49
Figure 1.6 Distribution of the PVS in the white matter and relationship between PVS and
white matter ....................................................................................................................... 51
Figure 1.7 3D rendering of the white matter regions with more than 5% of the total PVS
volume (a), and with PVS over white matter volume ratios higher than 3% (b) .............. 52
Figure 1.8 The perivascular space (PVS) ratio is influenced by age, body mass index
(BMI), and gender ............................................................................................................. 58
Figure 1.9 Scatterplots showing the relationship of years of education with body mass
index (BMI) (a) and perivascular space (PVS) ratio (b) ................................................... 59
Figure 1.10 The perivascular space (PVS) volume in the single individual changes
throughout the day ............................................................................................................. 62
Figure 1.11 PVS and BMI in siblings ............................................................................................ 63
Figure 1.12 The perivascular space (PVS) ratio is influenced by genetics. .................................. 64
Figure 1.13 Study design ............................................................................................................... 75
Figure 1.14 PVS volume increase after long-duration spaceflight on the ISS. ............................. 81
Figure 1.15 Associations between WM-PVS dilation, brain upward shift, and spaceflight
data ..................................................................................................................................... 82
Figure 1.16 Analysis of WM-PVS, VSA, and LV in NASA ISS astronauts by SANS
status .................................................................................................................................. 82
Figure 2.1 BBB breakdown in the HC and PHG in APOE4 carriers increases with
cognitive impairment, independently of CSF Aβ and tau status ..................................... 104
Figure 2.2 Regional BBB Ktrans constant in eight additional brain regions in APOE4
carriers and non-carriers (APOE3) with CDR status 0 and 0.5 ....................................... 106
x
Figure 2.3 BBB breakdown in the hippocampus and parahippocampal gyrus in APOE4
carriers increases with cognitive domain impairment ..................................................... 107
Figure 2.4 Regional BBB Ktrans constant in eight additional brain regions in APOE4
carriers and non-carriers (APOE3) with different degree of cognitive domain
impairment ....................................................................................................................... 108
Figure 2.5 Regional BBB Ktrans constant in all studied brain regions in APOE4 carriers
and non-carriers (APOE3) in relation to vascular risk factors ........................................ 109
Figure 2.6 Blood-brain barrier breakdown in APOE4 carriers is independent of amyloid
and tau accumulation in the brain .................................................................................... 112
Figure 3.1 snRNA-sequencing analysis ....................................................................................... 122
Figure 3.2 APOE4 disrupts the blood-brain barrier transcriptome ............................................. 128
Figure 3.3 Blood-brain barrier breakdown in E4F mice ............................................................. 129
Figure 3.4 DEG analysis in neurons of E4F and E3F mice comparing 9-12-month-old
versus 2-3-month-old mice .............................................................................................. 131
Figure 3.5 DEG analysis in astrocytes and microglia of 9-12-month-old versus 2-3-
month-old E4F mice. ....................................................................................................... 133

xi
List of Tables

Table 1.1 BBB breakdown in MCI and AD dementia detected by neuroimaging .......................... 9
Table 1.2 BBB breakdown in MCI and AD dementia detected by CSF biomarkers .................... 10
Table 1.3 Main neuroradiological characteristics of perivascular spaces (PVS), white
matter hyperintensities (WMH), and lacunes useful for differential diagnosis. ................ 28
Table 1.4 Conditions associated with perivascular space (PVS) burden. ..................................... 38
Table 1.5 Inclusion and exclusion criteria for the participants enrolled in the Human
Connectome Project (S900 release)
180
. .............................................................................. 41
Table 1.6 Demographic and clinical characteristics of participants from the Human
Connectome Project (S900 Release) included in this study. ............................................. 48
Table 1.7 Perivascular space ratio in each region of interest (ROI) .............................................. 55
Table 1.8 Univariate and multivariate general linear models results ............................................ 57
Table 1.9 Results of the ANCOVA model testing the effects of gender and BMI on PVS
ratio after controlling for age ............................................................................................. 58
Table 1.10 Demographic characteristics of the controls, NASA astronauts, and ROS
cosmonauts ........................................................................................................................ 74
Table 1.11 Mixed model ANOVA testing the effects of long-duration spaceflight on the
International Space Station and SANS on WM-PVS, VSA, and LV volumes of
NASA astronauts ............................................................................................................... 83
Table 1.12 Changes in WM-PVS, VSA, and LV from preflight to postflight in NASA
astronauts after long-duration spaceflight on the International Space Station .................. 84
Table 2.1 APOE3 and APOE4 participants studied for regional blood-brain barrier
permeability changes by dynamic contrast-enhanced magnetic resonance imaging
(DCE-MRI). ..................................................................................................................... 105
Table 2.2 APOE3 and APOE4 participants studied for regional amyloid brain
accumulation by PET and blood-brain barrier permeability changes by DCE-MRI ...... 111
Table 2.3 APOE3 and APOE4 participants studied for regional tau brain accumulation by
PET and blood-brain barrier permeability changes by DCE-MRI .................................. 111
xii
Abstract
In this dissertation, I investigated the brain vasculature, and specifically the blood-brain
barrier (BBB) and the perivascular spaces (PVS), in humans using Magnetic Resonance Imaging
(MRI) and in pre-clinical models using single-nuclei RNA-sequencing.
The first chapter includes a description of the anatomy and physiology of the BBB and the
PVS, as well as a literature review about the pathological changes affecting these vascular
components in neurological disorders. I also reported the results of my original studies of PVS in
healthy young adults and in spaceflight: these studies demonstrate that multiple factors influence
the visibility of PVS on MRI, including body mass index, time of day, and genetics, and constitute
a resource for researchers and clinicians interested in the quantitative analysis of PVS.
In the second chapter, I analyzed in humans the relationship between BBB permeability and
the E4 variant of apolipoprotein E (APOE4), the main susceptibility gene for Alzheimer’s disease.
We found that cognitively unimpaired individuals bearing APOE4 have higher BBB permeability
in the hippocampus and parahippocampal gyrus compared with APOE3 homozygous participants.
This breakdown is more severe in patients with mild cognitive impairment and is not related to
amyloid-β or tau pathology measured in cerebrospinal fluid or by positron emission tomography.
These results suggest that BBB breakdown occurs early in the pathogenesis of Alzheimer ‘s
disease and is independent of amyloid plaques and tau tangles.
Finally, in the last chapter, I investigated the molecular mechanisms underlying APOE4
cerebrovascular disorder using single-nuclei RNA-sequencing in human APOE3 and APOE4
knock-in mice. We found dysregulation of multiple genes induced by human APOE4, including
genes regulating cell junctions and cytoskeleton in brain endothelium, and transcription and RNA-
splicing suggestive of DNA damage in pericytes.
1
Chapter 1:
Introduction on blood-brain barrier and perivascular spaces
Adapted from:
Barisano G*, Montagne A*, et al., Nature Cardiovascular Research, 2022
Barisano G, et al., Journal of Cerebral Blood Flow & Metabolism, 2021
Barisano G, et al., Magnetic Resonance Imaging Clinics of North America, 2021
Barisano G, et al., The effect of prolonged Spaceflight on Cerebrospinal Fluid and Perivascular
Spaces of Astronauts and Cosmonauts. Proceedings of the National Academy of Sciences, 2022
Barisano G, et al., Imaging perivascular space structure and function using brain MRI. Under
review.

In this chapter, I introduce the blood-brain barrier (BBB) and perivascular spaces (PVS) and
report the latest results in the field of BBB and PVS analyzed in vivo in humans with brain
Magnetic Resonance Imaging (MRI) in the context of neurodegenerative diseases and healthy
aging. The studies included in this literature review were identified via searches of PubMed. The
search terms included: “Alzheimer disease” or “Alzheimer’s disease”, “vascular dementia”,
“vascular cognitive impairment”, cross-referenced with “blood brain barrier” or “BBB”,
“perivascular spaces” or “PVS” or “Virchow-Robin spaces”, “neurovascular coupling”, “cerebral
blood flow” or “CBF”. A particular attention was dedicated to the works published by key authors
in the field, and the final selection of the studies reported in the reference list was made based on
the relevance to the topics as well as the impact factor of the journals.
Additionally, the PVS section includes my original investigation of PVS in healthy adults and
in spaceflight.
2
Blood-brain barrier
Anatomy and physiology of the blood-brain barrier
The BBB was discovered more than 100 years ago. Initial studies with vital dyes injected into
the blood stream were shown to permeate all peripheral organs while the brain remained
uncoloured. This led to definition of the BBB as a biological membrane between blood and brain,
which in contrast to relatively “leaky” capillaries in peripheral organs, does not allow free solute
exchanges across the capillary endothelium
1
. Today, the barrier function remains one of the many
critical functions that BBB plays for the brain. Physiological, cellular and molecular studies, and
recent studies in the living human brain have revealed that the BBB plays a key role in brain
metabolism and function, has an important role in disease process, and is yet poorly explored as a
therapeutic target
2
.
The BBB is formed by a continuous endothelial monolayer at the level of brain capillaries
2

(Figure 1.1), which provides 85% of endothelial surface area of the brain or 12 m
2
in the human
brain
2
.

Figure 1.1 The BBB and associated cell types.
The brain is among the most highly vascularized organs in the body. Oxygenated blood, nutrients and
regulatory molecules are delivered to the brain via arterial and arteriolar blood vessels that branch out into
brain capillaries. Carbon dioxide and metabolic end products are removed from the brain by a venous
drainage system. The tightly sealed brain capillary endothelium is the key site of the BBB. The endothelial
Neuron
Capillary bed
Neuronal projection
Astrocyte
Microglia
Astrocytic end-feet
Arteriole
Vascular smooth muscle cell
Pericyte
Neuronal projection
Lumen
Red blood cell
Basement membrane
Astrocytic end-feet
Endothelial cell
3
BBB monolayer extends along the arterioles, small arterial vessels and venules. Middle inset, an arteriole
branching out into small capillaries. Vascular smooth muscle cells and pericytes wrap around the arterioles
and capillaries, respectively. Pericytes are embedded into the basement membrane encircling endothelial
cells of the capillary vessel wall. Astrocyte endfeet wrap around the capillary wall and, in places not covered
by pericytes, are separated from endothelial cells by the basement membrane. Together with perivascular
microglia, macrophages and neurons, these different cell types form the neurovascular unit. Right inset,
capillary cross-section illustrates the cellular composition of the neurovascular unit at the level of a brain
capillary.

The endothelial monolayer extends along the arterioles, small arterial vessels, and venules.
The pioneering electron microscopy studies in rodents revealed the presence of tight junctions
(TJs) between the neighbouring endothelial cells of the BBB that helped establish the anatomical
basis of the BBB as a tightly sealed endothelial monolayer. The follow-up molecular and genetic
studies identified several TJ proteins in endothelium including zonula occludens-1 (ZO1), a critical
node in the organization of many protein complexes associated with TJs such as occludins, which
regulate paracellular endothelial permeability, and claudins, including low molecular weight size-
selective claudin-5. Other contacts between endothelial cells include adherens junctions (AJs),
typified by proteins such as VE-cadherin. Importantly, a recent single-nucleus RNA-sequencing
study of human brain vasculature has shown for the first time that human BBB endothelium
expresses all key TJ and AJ proteins that previously had only been shown in the brains of other
mammalian species
3
. These include adhesion molecules such as different cadherins and proto-
cadherins, contactins and catenins that contribute to BBB integrity
2
. For further details on TJs and
AJs proteins and original articles describing their function and how they were discovered over the
last few decades, please refer to recent reviews
2,4,5
.
In contrast to the relatively permeable systemic capillaries, healthy and normal brain
capillaries exhibit a low rate of trans-endothelial bulk flow by transcytosis. This together with
expression of TJs and AJs restricts the entry of most blood-derived molecules into the brain, unless
they have specialized carriers and/or receptors in the brain endothelium that facilitate their
4
transport across the BBB. In this sense, the BBB can be viewed as a selective semipermeable
barrier allowing specific molecules to pass in and out of the brain. Recent studies have identified
over 10,000 transcripts in the murine BBB endothelium with preferential expression of transporters
in the capillary endothelium
6,7
. Similar data sets on BBB transporters have recently been reported
in human brain endothelium
3
.
The selective substrate-specific transport systems at the BBB include carrier-mediated
transport (CMT) of carbohydrates (e.g., glucose), amino acids, monocarboxylic acids (e.g., lactate,
ketone bodies), hormones, fatty acids, nucleotides, inorganic anions, amines, choline and vitamins.
These CMT systems enable transport of their respective substrates to cross BBB bi-directionally
according to their concentration gradients. Some larger molecules including certain proteins and
peptides can use receptor-mediated transport (RMT) to cross the BBB from blood-or brain, as for
example insulin, insulin-like growth factors, transferrin, leptin and some others. RMT systems
including lipoprotein receptors mediate clearance from brain of proteinaceous neurotoxic
molecules that are produced in the brain such Alzheimer’s amyloid-b (Ab) or Parkinson’s a-
synuclein. Endothelial ATP-binding cassette transporters prevent brain accumulation of drugs,
xenobiotics, drug conjugates, and nucleosides in the brain by active efflux from endothelium to
blood. And endothelial ion transporters, such as sodium pumps, control ion concentrations in the
brain. Thus, in addition to protecting the brain parenchyma from blood-derived toxic molecules,
cells and microorganisms, the endothelial monolayer of the BBB regulates transport of nutrients
and essential molecules across brain endothelium into the brain, and clearance into the blood of
metabolic end products and endogenous neurotoxins produced by the brain. For more details, on
BBB transport systems in healthy brain, and how they are affected by the disease process see recent
reviews
2,4,5
.
5
Pericytes, mural cells that lie along brain capillaries, share a common basement membrane
with endothelial cells. As reviewed recently
2,4,5
, N-cadherin forms peg-and-socket contacts
between endothelial cells and pericytes, whereas the gap junction connexin (CX) 43 hemichannels
mediate intercellular communications between pericytes and endothelial cells. Astrocytes also
express gap junction proteins, some of which are important for maintaining BBB integrity, such
as CX30 and CX43. These adhesion molecules originally found in murine pericytes and astrocytes
were recently confirmed in human pericytes and astrocytes
3
.
Finally, in contrast to peripheral organs such as liver, brain does not have a storage capability
for larger energy-saving molecules
1,2
. Its energy metabolism depends on delivery of metabolites
such as glucose by CBF and transport across the BBB. Vascular smooth muscle cells and pericytes
regulate CBF by constricting and dilating arterioles and capillaries, respectively
1,8–10
(Figure 1.1).
Importantly, pericytes maintain BBB integrity, and their loss leads to BBB disruption
11–15
.

BBB dysfunction and neurological disorders in humans
That intact BBB is required for normal brain function is best illustrated by examples of rare
monogenic human neurological disorders where the genetic mutations or defects are found to
originate exclusively within brain endothelial cells, and/or BBB-associated pericytes and vascular
smooth muscle cells. For example, inactivating mutations in the Solute Carrier Family 2 Member
1 (SLC2A1) gene encoding GLUT1 glucose transporter in brain endothelial cells, lead to GLUT1-
deficiency syndrome, a paediatric neurological disease with early onset of seizures and
microcephaly, BBB breakdown, and neuron loss
16
. Inactivating mutations in the Major Facilitator
Superfamily Domain Containing 2A (MSFD2A) gene encoding transporter for essential omega-3
fatty acids that is enriched in brain endothelial cells, lead to BBB breakdown
17,18
and microcephaly
syndrome
19,20
. Mutations in genes encoding the BBB TJ proteins, cerebral cavernous malformation
6
proteins or collagens lead to uncontrolled leakage of proteins and other content from blood into
the brain causing neuroinflammatory response, increased microvessel fragility, cerebral
haemorrhages and cerebral small vessels disease (CSVD), resulting in focal neurological deficits,
seizures and headaches, and/or lacunar ischemic strokes
2
. Mutations in NOTCH3 gene that is
expressed in vascular smooth muscle cells and pericytes lead to cerebral autosomal dominant
arteriopathy with subcortical infracts and leukoencephalopathy (CADASIL), a major cause of
genetically inherited stroke in humans associated with loss of blood vessels integrity
21
.
Altogether, about 20 rare neurological monogenic disorders identified offer insights into
causal pathogenic links between BBB dysfunction and neurological disease in humans, supporting
the idea that BBB dysfunction can have neurological consequences.

BBB dysfunction and cognitive dysfunction
Several neuropathological studies have shown BBB breakdown in AD
2
. However, the point
at which individuals suffering from mild cognitive impairment (MCI) and AD develop BBB
breakdown has not been clear until recently. Using dynamic contrast-enhanced (DCE) magnetic
resonance imaging (MRI) with gadolinium-based contrast agents (GBCA), recent studies indicated
that BBB breakdown occurs early in individuals with MCI and AD-type dementia, and is an early
biomarker of cognitive dysfunction
22,23
. The presence of gadolinium in brain reflects subtle BBB
“leaks” of plasma components, and is typically caused by loss of TJ or AJ proteins, and/or
increased trans-endothelial fluid transcytosis of plasma components across the BBB. According to
experimental studies BBB leaks could be related to loss of pericyte coverage
11,14
.
Loss of BBB integrity has also been shown in MCI and AD dementia by susceptibility
weighted imaging (SWI) MRI detecting early cerebral microhaemorrhages
24,25
. This greater and
7
more focal degree of BBB breakdown leads to extravasation of red blood cells into the brain. Lobar
microhaemorrhages seen by SWI are often due to cerebral amyloid angiopathy (CAA) that is
present in many cases of MCI and AD along with amyloid deposition in brain
26
. However,
microhaemorrhages are also seen in deep infratentorial regions related to hypertensive CSVD and
vascular dementia
27
.
The BBB dysfunction in MCI and AD is not limited only to “mechanical” types of BBB
breakdown described above, and may also include dysfunction in the BBB transporters and/or
receptors, such as, to name a few, loss of GLUT1 glucose transporter and P-glycoprotein 1 (P-gp)
key efflux transporter of toxins, as discussed below. For details of how BBB transport systems are
affected by different neurological disorders see recent review
5
.
The link between BBB and AD is further supported by a recent nuclear RNA sequencing study
of major human brain vascular and perivascular cell types from hippocampus and cortex, which
revealed that 30 of the top 45 AD genes identified by genome wide association studies (GWAS)
are expressed in brain vasculature
3
. Vascular GWAS genes mapped to endothelial protein
transport, adaptive immune and extracellular matrix pathways.
Below, I discuss recent neuroimaging studies demonstrating BBB breakdown in individuals
with early cognitive impairment, MCI and AD-type dementia, and in relation to AD biomarkers
Ab, tau, and neurodegeneration
28
. Earlier neuroimaging studies using computed tomography
(CT)
29,30
, positron emission tomography (PET) with [
68
Ga]EDTA
31
, and DCE-MRI semi-
quantitative analysis
32
failed to detect higher BBB permeability in AD. In contrast to these earlier
studies, more recent neuroimaging studies from several groups over the last five to six years have
shown age-related BBB breakdown
22,33–39
, BBB breakdown in MCI
22,23,33,34,40,41
, AD
42–45
,
CSVD
46–52
and in other neurodegenerative disorders
53,54
. The discrepancy between earlier and
8
recent studies could likely be attributed to use of more advanced techniques and analysis in recent
studies. This includes use of MRI sequences with higher spatial and temporal resolution
22,23,33,42,55
,
direct measurements of individual vascular input functions from the arterial inflow
22,23,33,34
or the
venous outflow
35,36,46,48,51,56,37–41,43–45
, and use of quantification methods, such as the Patlak
model
57
, which has not been used in previous studies
29–32
. Some earlier studies
30,32
measured only
signal changes after contrast injection without applying the pharmacokinetic analysis that takes
into account the tracer’s concentration in blood. One CT study
29
and one PET study
31
adopted
pharmacokinetic models, but did not detect BBB leaks likely due to a lower ability of CT and PET
to resolve cerebral anatomical structures compared to recent MRI sequences, and generally much
lower spatial resolution of PET.

BBB breakdown and mild cognitive impairment
DCE-MRI studies revealed that individuals with MCI develop BBB breakdown in the
hippocampus, a centre for learning and memory
22
(Table 1.1), which correlated with increased
levels of biochemical biomarkers of BBB breakdown in the cerebrospinal fluid (CSF) such as
CSF/serum albumin ratio, Qalb, fibrinogen and plasminogen
22,23
(Table 1.2).

9

Ref.
BBB Breakdown Sampl
e size
Key risk factors
AT(N) biomarkers
DCE-MRI SWI-MRI Age VRFs APOE4 A T N
MCI
44
, C GM, NAWM 33 ✓ ✓
Not
studied
Not studied Yes*
23
, C
HC, PHG,
CN
73 ✓ ✓
Not
studied
Yes*
CSF,
PET
Yes*
CSF,
PET
Yes*
33
, C
HC, PHG,
CN

245 ✓ ✓ ✓
Yes*
CSF,
PET
Yes*
CSF,
PET
Yes*
40
, C
GM, NAWM,
HC
80 ✓ ✓ ✓ Not studied Yes*
58
, C

Cortex
GM (deep)
Infratentorial
67 ✓ Not studied Not studied Yes*
59
, C

Cortex
(siderosis)
809 ✓
Not
studied
✓ Not studied Yes
26
, L Lobes 174 ✓ ✓ ✓
Yes
PET
Not studied
24
, C

Lobes
GM (deep)
Infratentorial
1504 ✓ ✓
Not
studied
Not studied
60
, C
Lobes
GM (deep)
Infratentorial
136 ✓
Not
studied
✓
Yes*
CSF
Yes*
CSF
Yes*
Early
AD
44
, C GM, NAWM 33 ✓ ✓
Not
studied
Not studied Yes
58
, C
Lobes
GM (deep)
Infratentorial
67 ✓ Not studied Not studied Yes*
AD
40
, C
GM, NAWM,
HC
80 ✓ ✓ ✓ Not studied Yes
59
, C
Lobes
Cortex
(siderosis)
809 ✓
Not
studied
✓ Not studied Yes
26
, L Lobes 174 ✓ ✓ ✓
Yes
PET
Not studied
24
, C
Lobes
GM (deep)
Infratentorial
1504 ✓ ✓
Not
studied
Not studied
Table 1.1 BBB breakdown in MCI and AD dementia detected by neuroimaging
The AT(N) system monitors changes in amyloid-b (A), tau (T) and neurodegeneration (N) biomarkers.
“Yes” indicates that BBB breakdown was found in individuals positive for the A and T biomarkers in the
cerebrospinal fluid (CSF) and/or brain by positron emission tomography (PET), and/or N by MRI. ”Yes*”
with asterisks indicates that BBB breakdown was found in individuals positive for the A, T and/or N
biomarkers as well as in those that have not developed AT(N) biomarkers abnormalities. C, and L, indicate
cross-sectional and longitudinal study, respectively. ✓, factor has been studied. Mild cognitive impairment
(MCI) was defined by clinical dementia rating scale of 0.5 and impairment in neuropsychological test scores
in one or more cognitive domains selected from memory, attention/executive function, language tests, and
global cognition; AD dementia was defined by the clinical criteria of the National Institute of Neurological
and Communicative Disorders, Stroke–Alzheimer’s Disease and Related Disorders Association and/or the
10
National Institute on Aging–Alzheimer’s Association guidelines. Abbreviations: AD, Alzheimer disease;
APOE4, variant of apolipoprotein E; BBB, blood-brain barrier; CN, caudate nucleus; DCE-MRI, dynamic
contrast-enhanced magnetic resonance imaging; GM, the entire grey matter; HC, hippocampus; NAWM,
the entire normal appearing white matter; PHG, parahippocampal gyrus; SWI, susceptibility-weighted
imaging sequence; VRFs, vascular risk factors.

Table 1.2 BBB breakdown in MCI and AD dementia detected by CSF biomarkers
Abbreviations: CypA, cyclophilin A; MMP9, matrix metalloproteinase-9; Qalb, CSF/plasma and/or
CSF/serum albumin quotient; sPDGFRβ, soluble platelet-derived growth factor receptor-β; sVEGFR,
soluble vascular endothelial growth factor receptor; VEGF, vascular endothelial growth factor; For
definitions of MCI, AD, AT(N) biomarkers system, CSF, PET, APOE, VRFs, “Yes” and “Yes*” see
footnote to Table 1.1. ✓: factor has been studied. C, L, and M indicate cross-sectional, longitudinal, or
meta-analysis study, respectively.

Increased BBB leaks (i.e., Ktrans values) correlated with increased CSF levels of soluble
platelet-derived growth factor b (sPDGFRb), a biomarker of pericyte injury
22,23,64
. DCE-MRI
approach also revealed a more widespread BBB breakdown in MCI in the grey and normally-
appearing white matter
44
. BBB breakdown in the hippocampus was also found during
physiological aging, but to a lesser degree than in MCI
22
, and in grey and white matter regions
vulnerable to age-related deteriorations, suggesting it is likely an underlying mechanism of age-
related cognitive decline
33,35,38
, particularly associated with loss of memory retrieval
37
.

Ref. CSF
Sample
size
Key risk factors AT(N) biomarkers
Age VRFs APOE4
A T N
MCI
22
, C
↑ sPDGFRβ
↑ Q alb
64 ✓ Not studied Not studied Yes*
23
, C

↑ sPDGFRβ

141
✓ ✓
Not
studied
Yes*
CSF, PET
Yes*
CSF,
PET
Yes*

33
, C
↑ sPDGFRβ,
↑CypA, ↑MMP9
APOE4 vs.
APOE3
350 ✓ ✓ ✓
Yes*
CSF, PET
Yes*
CSF,
PET
Yes
AD
61
, M ↑ Q alb 1295 Not studied Not studied
62
, L
↑ sPDGFRβ,
↑ VEGF,
↑ VEGF/sVEGFR-
1 ratio,
↑ Q alb
1015 ✓ Not studied ✓
Yes*
CSF, PET
Not studied
63
, C
↑ sPDGFRβ (CSF
and serum)
↑ albumin
78 ✓ Not studied
Yes*
CSF
Yes
CSF
Not
studied
11
Since Ab
1
and tau
65,66
are both vasculotoxic, several studies have investigated the relationship
between BBB permeability and Ab and tau CSF biomarkers
23,33,67
. These studies revealed that
neither increase in the BBB permeability in the hippocampus and parahippocampal gyrus by DCE-
MRI, nor increased levels of pericyte injury biomarker sPDGFRb in the CSF, depended on Ab
and tau CSF status
23
, and were found both in individuals with and without positive AD biomarkers
in CSF and/or brain by PET
23,33
(Table 1.1, Table 1.2). These data suggest a link between
early BBB dysfunction and cognitive impairment in individuals that are in early stages in the AD
continuum, but also in those that have not yet developed alterations in Ab and tau biomarkers.
Whether this latter group will develop vascular dementia, AD or mixed dementia at a later stage
remains presently unknown. This should be investigated by future longitudinal studies.
A few MCI studies reported that the BBB breakdown was not influenced by vascular risk
factor (VRF) burden
23,33
. Since the studied cohorts excluded participants with substantial
cerebrovascular pathology, it is possible that interactions between traditional VRFs and BBB
dysfunction in cohorts with more severe vascular lesions and vascular cognitive impairment will
lead to synergistic effects. Again, this remains to be determined by future studies. Some studies
have shown that BBB breakdown in MCI individuals precedes hippocampal degeneration
23,33
,
suggesting that early BBB dysfunction may occur prior to brain atrophy. These cross-sectional
findings remain to be confirmed, however, by longitudinal studies.
A recent DCE-MRI study indicated that BBB breakdown in the hippocampus and
parahippocampal gyrus begins in cognitively unimpaired (CU) APOE4 carriers (ε3/ε4 and ε4/ε4),
which further increases with cognitive impairment, irrespective of Aβ and tau biomarker changes
in the CSF or brain by PET
33
. Since hippocampal volumes were not different between CU APOE4
and APOE3 carriers, these findings additionally suggest that BBB breakdown in CU APOE4
12
carriers preceded hippocampal atrophy that was observed only in APOE4 carriers at MCI stage
33
.
Again, future longitudinal studies should confirm and extend these cross-sectional findings.
Interestingly, high baseline CSF levels of sPDGFRb, a BBB pericyte injury biomarker,
predicted future cognitive decline in APOE4 carriers, but not APOE3 homozygotes, and remained
a significant predictor of cognitive decline after correcting for Ab and tau status
33
. Elevated levels
of sPDGFRb correlated with activation of the BBB-degrading cyclophilin A (CypA)-matrix
metalloproteinase 9 (MMP9) pathway in the CSF
33
, similar as shown before in APOE4 knock-in
mice
68
. Since pharmacologic inhibitors of CypA have been used for non-neurological applications
in humans
69
, it is possible that CypA inhibitors may also suppress CypA in cerebral blood vessels
of APOE4 carriers, which in turn could improve vascular integrity and the associated neuronal and
synaptic deficits, potentially slowing cognitive impairment.
MCI patients develop microbleeds that can be detected by SWI-MRI sequences and T2* as
small, round hypointense foci representing perivascular deposits of blood-derived hemosiderin
phagocytosed by macrophages. Table 1.1 lists MCI studies showing early microhaemorrhages
reflecting breakdown in the BBB mainly in the cortex and deep grey matter regions
24,26,58–60
.
Several studies in AD linked lobar microbleeds to CAA
24,26
, whereas deep infratentorial
microbleeds have been linked to hypertensive arteriopathy
27
. On 7T MRI, >75% of MCI
individuals were found to develop microhaemorrhages likely of capillary and/or pre-capillary
origin
70,71
. These are typically missed when studied by lower resolution 3T MR scanners, detecting
only 21-45% microbleeds in MCI
24,26,58–60
, or on 1.5T detecting microbleeds in 10-15% of MCI
patients.

13
The prevalence of microbleeds was higher in APOE4 carriers
26,59,60
, and was associated with
increased CSF/serum albumin (Qalb) ratio
60
, suggesting a link between microbleeds and BBB
dysfunction. Recent studies indicated that the appearance of microhaemorrhages was associated
with cognitive decline and/or higher risk for dementia
72–74
.
Interestingly, the occurrence of microbleeds was not influenced by tau
60
, and in some studies
preceded medial temporal lobe atrophy
24,58,60
. In a few studies, the incidence of lobar microbleeds
was higher in participants with higher Ab brain load on PET
26
, but was not associated with lower
CSF Ab42 levels
60
. Since most studies on microbleeds in MCI did not evaluate simultaneously Ab
pathology by PET or in the CSF, the association between regional BBB permeability changes on
DCE-MRI, microbleeds, and AD biomarkers needs to be investigated by future studies.

BBB breakdown and Alzheimer’s disease
BBB breakdown in the cortex, white matter, and some deep grey matter regions has been
shown by DCE-MRI during early stages of AD
44
. Compared to MCI, early AD patients present
with a higher prevalence of cerebral microbleeds on 3T MRI
58
, often localized in the occipital and
parietal lobes, sites of CAA (Table 1.1). Cerebral microbleeds are commonly found with more
advanced AD with the prevalence as high as 45% at 3T
24,26,59
and up to 78% at 7T
71
. Although,
the majority of microbleeds was typically lobar and CAA-related, the CAA-unrelated
microhaemorrhages in the subcortical grey matter and infratentorial regions were also found.
Recent studies found that patients with epilepsy and AD, as well as aging mice, develop BBB
leaks associated with slower cortical activity
42
. Moreover, these BBB leaks were related to
activation of transforming growth factor-β (TGFβ) in astrocytes, as shown in humans and mice
55
.

14
P-gp, an active efflux transporter at the luminal side of the BBB endothelium removes drugs,
xenobiotics and A β from brain
75
. Studies using
11
C-verapamil, a PET ligand for P-gp, indicated
diminished P-gp activity in early AD in multiple region including hippocampus and cortex
76
,
suggesting impaired BBB clearance.
In addition to increased CSF sPDGFR β and Qalb in large cohort studies in AD
61,62
, increased
CSF sPDGFRβ correlated with increased sPDGFRβ in the serum and increased CSF/serum Qalb
ratio suggestive of BBB breakdown
63
was also found (Table 1.2). Increased CSF levels of
biomarkers of angiogenesis and endothelial dysfunction, including vascular endothelial growth
factor (VEGF) and VEGF/soluble VEGF receptor 1 (sVEGFR-1) ratio, were also found in AD
62
.
These biomarkers were not associated with Aβ load
62
, suggesting that BBB endothelial
dysfunction is likely independent of amyloid pathology (Table 1.2).
Reduced FDG-PET is often interpreted as brain hypometabolism. However, several
investigators support the view that reduced transport across the BBB also contributes to reduced
FDG-PET as recently reviewed
77
. In brief, glucose enters the brain via transport across the BBB
mediated by GLUT1 glucose transporter, and if GLUT1 is deficient, deleted from the BBB,
blocked genetically, inhibited pharmacologically or suppressed by disease, glucose cannot reach
the brain
2
. Several earlier FDG dynamic PET studies have shown diminished BBB transport of
glucose in AD, as reviewed elsewhere
77
.

BBB and perivascular spaces
BBB breakdown during early cognitive decline in people at risk for AD
33
could lead to
increased PVS as in CADASIL
21,78
. The suggestion of direct leakage across the perforating vessel
wall into the PVS
79
is supported by work in pericyte-deficient mice which develop BBB leakage
15
associated with increase in the size and number of PVS
80
. Subtle diffuse BBB leaks on DCE-MRI
correlated with increasing numbers of PVS
79
. When enlarged, PVS in the white and deep grey
matter become visible by MRI
81
. PVS increases at older age, with CSVD
82
, and BBB breakdown
80
,
indicating that they are likely markers of BBB-related vascular dysfunction. Systematic reviews
of population, vascular, and neurodegenerative diseases indicate that higher number of PVS is
associated in cross-sectional studies with cognitive decline, AD-type dementia, and executive
dysfunction
83–86
.

Perivascular space
In the last decade, several studies have demonstrated that perivascular spaces (PVS) are
critically involved in the drainage of cerebral fluid, and both functional and structural alterations
to PVS have been found to be associated with multiple neurological diseases as well as other non-
neurological conditions
87
. Despite the recent advancements in PVS research, a full understanding
of the physiological functions of PVS and their pathophysiological implications for clinical
disorders remains elusive. Imaging PVS in pre-clinical models and clinical studies can be used not
only to better understand the mechanisms underlying the cerebral fluid dynamics of perivascular
flow and their alterations in diseases, but also to explore the potential use of PVS as a neuroimaging
diagnostic biomarker and for therapeutic purposes. In fact, PVS can represent both a route for the
delivery of therapeutic agents into the brain and a therapeutic target, since variations in the
perivascular flow occur in several neurological disorders and are linked to neurodegeneration
88
.
For example, the development of cerebral edema after stroke, a detrimental complication whose
severity is a critical prognostic factor and predicts the patients’ functional outcome
89,90
, has been
shown to be dependent on an increase of the perivascular flow accompanied by PVS
16
enlargement
91
. Additionally, pre-clinical studies have shown that PVS is a major component of
the brain clearance system, whose impairment can lead to the accumulation of metabolic waste
products, formation of Aβ plaques, protein aggregation, and subsequent cellular damage
92–94
. The
results from these and other studies provide conceptual bases for the development of therapeutic
strategies specifically targeting PVS and highlight the importance of neuroimaging as a
fundamental tool for the investigation of PVS.

Anatomy and physiology of PVS
PVS are fluid-filled spaces that surround cerebral blood vessels penetrating or leaving the
brain parenchyma and are limited externally by the glia limitans, mesh of astrocyte endfeet covered
by an outer basal lamina
95
. PVS were described for the first time by Durand-Fardel and Pestalozzi
in the 1840s
96,97
, but they are commonly called Virchow-Robin spaces from the names of the
German pathologist Rudolf Virchow and the French anatomist Charles Philippe Robin, who
described them in the 1850s
98,99
. PVS surround most of the perforating blood vessels in the brain,
but on MRI they are most commonly visible at the level of the basal ganglia and the centrum
semiovale
100
. According to Zhang et al., PVS around superficial perforating arteries in the centrum
semiovale have only one layer of pia mater and communicate with the subpial space
101
, whereas
in lenticulostriate arteries at the level of the basal ganglia PVS present two layers of
leptomeningeal membranes and communicate with the subarachnoid space
95
. Moreover, in
contrast with periarterial spaces, the perivenous spaces lack the outer layer of leptomeninges
95
.
The PVS are a key compartment at the interface between the blood vessels and the brain, and
different types of cells interact with PVS, including endothelial cells, pericytes, astrocytes,
neurons, and microglia
102
. In particular, the endothelial cells and pericytes are a critical element of
17
the blood-brain barrier (BBB) structure, and they are involved in the exchange of macromolecules
and solutes from the vascular compartment to the brain parenchyma, and vice versa. Astrocytes
interact with neurons by exchanging metabolites between different brain compartments. Microglia
activate in response to inflammatory factors, and, together with perivascular macrophages,
represent the main immune defense in the brain. Increase of these cellular populations have been
seen in neurovascular or neuroinflammatory pathological conditions, such as Alzheimer’s disease
(AD), obesity, and hypertension
103
. Altogether, the communication among these cellular
populations is fundamental to maintain the brain chemical, structural, and functional
homeostasis
102
.
In 2012, the glymphatic system was first described as the system responsible for brain
clearance of toxic and waste metabolites via cerebrospinal fluid (CSF)–interstitial fluid (ISF)
exchange, drainage of fluids, as well as transport of other molecules, such as glucose and lipids,
necessary for the correct cerebral functioning
104
. According to the glymphatic system model, the
CSF flows to the brain parenchyma through the PVS, where it exchanges with the ISF
105
. ISF and
CSF represent 12-20% and 10% of the brain fluid, respectively
106
. The amount of CSF in human
is approximately 140 mL, and each day around 500 mL of CSF are produced by the choroid
plexus
107
. The CSF flows from the subarachnoid space into the PVS driven by an arterial pulsation
that pushes the fluid along the penetrating arteries diving into the brain
104
. From PVS, the CSF
enters the brain parenchyma where it mixes with ISF. This process is facilitated by the polarization
of aquaporin-4 (AQP4), Ca
2+
-dependent water channels that are located on the perivascular endfeet
of astrocytes
102
. A loss of such polarization can lead to alterations in the water homeostasis and
waste clearance process
108
. The correct functioning of AQP4 and PVS plays a key role in the
glymphatic clearance activity
102
. Previous studies have shown how physiological factors, such as
18
blood pressure, respiration, sleep and body posture can influence the efficiency of the glymphatic
system
105
. A decrease in heart rate is associated with 20% more of accumulation of toxic waste
soluble molecules, such as β-amyloid
109
, whereas sleeping in the lateral position has been
demonstrated to have a higher clearance rate during sleep compared with the supine or prone
positions
110
. A decrease in sleep quality or total sleep time (< 7 hours) has been related to two-
fold increase of toxic molecules in the ISF
104,107
. The CSF-ISF containing solutes and waste
products subsequently reaches the perivenous space and drains away from the cerebral
parenchyma via three potential pathways: the arachnoid (or Pacchioni) granulations, which
connects the subarachnoid space to the venous system; the meningeal lymphatics, lymphatic
vessels located in the dura mater in proximity of the venous sinuses which drain to the extracranial
deep cervical lymph nodes
111,112
; the perineural space along cranial and spinal nerves
113–116
. Since
PVS represent a critical component of the glymphatic system, it has been proposed that alterations
in the glymphatic flow mechanism could lead to PVS enlargement, which in turn has been found
related to a number of neurological disorders, including cerebrovascular diseases
117
,
neurodegenerative diseases, and neuroinflammatory pathologies
118–121
.
Another brain clearance system model known as the “intramural periarterial drainage
pathway” (IPAD) reports that the transport of ISF and solutes, but not cells, occur along the
basement membranes of the capillaries and within the tunica media of arterioles and arteries in the
opposite direction to the arterial blood flow, towards the cervical lymph nodes via major cerebral
arteries in the neck
122,123
. Since this fluid movement occurs at microscopic level along the arterial
walls, it is currently unclear whether alterations in the IPAD system would correspond to an
enlargement of MRI-visible PVS.

19
Imaging PVS structure
Given the high sensitivity of MRI in detecting cerebral fluid structural and biophysical
properties, it is an ideal neuroimaging modality to capture PVS characteristics. PVS on structural
MRI appear as tubular, fluid-filled structures. The principal locations of MRI-visible PVS include
the basal ganglia and the whole white matter, with the highest prominence typically found in the
centrum semiovale (i.e., the part of white matter superior to the lateral ventricles and corpus
callosum) and the highest concentration (i.e., PVS-to-white-matter ratio) in the subinsular white
matter
124
. Other brain regions where PVS are visible on MRI include the hippocampus, midbrain,
pons and the cerebellum (usually dentate nuclei and cerebellar white matter)
100
. While the
composition of the PVS fluid is not completely known, PVS on MRI typically have a signal
intensity similar to that of cerebrospinal fluid (CSF), i.e., low on T1-weighted and high on T2-
weighted images, suggesting that the relaxation properties of the fluid inside PVS and CSF are
comparable. It should be noted that the visibility of PVS on MRI depends on the presence of fluid
inside PVS; in fact, structural MRI allows for the visualization of the fluid inside PVS rather than
the PVS themselves, which would have a different MRI signal profile, challenging to detect, if
they were empty, due to the collapse of PVS
125
and/or the contrast between PVS and parenchyma
not sufficiently high. PVS on MRI appear as lines when they are parallel to the image acquisition
plane, and as dots when they are perpendicular to the image acquisition plane. Normally, PVS
cross-sectional diameter on MRI is less than 2 mm
126
. As the blood vessels penetrate deeper in the
cerebral parenchyma, the diameter of PVS decreases
127
. The visibility of PVS on MRI is
significantly affected by the magnetic field strength and the image resolution
128
. In fact, while it
might be difficult to visualize PVS with 1.5 Tesla MRI when they are not enlarged, higher
20
magnetic field (≥3 Tesla) provides sufficient resolution and signal-to-noise ratio (SNR) to
visualize PVS morphology, even in healthy adolescents and young adults
124,129
.

Figure 1.2 Visualization and measures of PVS on MRI depend on the image resolution
A. Axial T2-weighted 3D SPACE image at 7T from a healthy 26-year-old male volunteer acquired at 0.32
x 0.32 x 0.4 mm resolution (interpolated to 0.16 x 0.16 x 0.4mm) with the following parameters:
GRAPPA=3, TR/TE=2320/299ms, flip angle=120, 2 averages. Scan time: 24 minutes.
B. Manual segmentation of the PVS across a sub-portion (8 cm slab) of the white matter.
C-D. Comparison of PVS segmentation at high resolution (C: 0.16 x 0.16 x 0.4 mm) and moderate
resolution (D: 0.6mm
3
) on 7T MRI. PVS characteristics and morphologic features were overestimated when
0.6 mm
3
resolution image was used, especially for the smaller PVSs.
The image in D has been acquired from the same volunteer using the following parameters: GRAPPA=3,
TR/TE=2140/221ms. Scan time: 11minutes.
A B
C D
21
Despite histological studies that described PVS around both arteries and veins, some recent
studies performed with 7 Tesla MRI suggest that the majority of MRI-visible PVS in the centrum
semiovale are periarterial rather than perivenous
130–132
. The reason why perivenous spaces are less
visible than periarterial spaces on MRI is currently unclear, but it might be related to the smaller
size of perivenous spaces as well as differences in the amount and/or composition of the perivenous
fluid. For example, perivenous space signal intensity might be similar to that of the adjacent
cerebral parenchyma, making their visualization on MRI difficult.
Visual rating scales have been thus far the most used approach to investigate PVS on MRI. In
the past three decades, several types of visual rating systems have been adopted
133,134,143,135–142
.
Most of them classify PVS in different areas of the brain based on their count. For example, a
commonly used and validated system rates PVS in each region with the following scores: 0 if PVS
are not visible, 1 if 1-10 PVS are visible, 2 for 11-20 PVS, 3 for 21-40, and 4 if >40 are counted
136
.
Typically, basal ganglia and centrum semiovale are independently scored. Sometimes other
regions are assessed as well, including the mesencephalon, hippocampus, and the subinsular white
matter. For each region, one or few slices (usually those including the highest number of PVS) are
evaluated and PVS are scored separately on each side or hemisphere: the higher score among the
two hemispheres is then utilized. Notably, PVS in basal ganglia are usually scored above the
anterior commissurae, as PVS below it, in the anterior perforated substance, are not considered
pathologic
133,134,136,139,144,145
. In fact, these PVS reflect the entry of the lenticulostriate arteries from
the subarachnoid space of the Sylvian cistern into the brain parenchyma and are commonly found
on 1.5 Tesla and even more on 3 Tesla MRI in healthy people, including young and adults.
22
In some cases, the scoring system takes into account the PVS size as well
133,135,146
. One recent
study, for example, scored PVS based on the presence or absence of PVS with cross-sectional
diameter larger than 3 mm, which they called large PVS
146
.
Visual rating scales present several advantages: they are relatively easy to learn and adopt,
can be used both on T1-weighted and on T2-weighted images, do not require a 3D or isotropic
acquisition, and can be performed directly on the images without the need of specialized
workstation or computational tools.
While these scoring systems provide a qualitative estimate of the extent of PVS burden,
manual counting is still required to assign a rating and lacks granularity, particularly in cases where
the PVS count is close to the limit threshold differentiating two categories (e.g., 20 which
differentiates the scores 2 and 3 in the 5-scale rating system described above). Additional
limitations of the visual rating scales are low sensitivity
147,148
, the inter- and intra-rater variability,
and the limited number of PVS features that could be derived. Moreover, the assessment of small
PVS with visual scales remain difficult, the application of the visual scales on large datasets is
burdensome, and counting PVS can be time-consuming
149
. To overcome some of these issues,
recently an automatic classifier for the rating of PVS burden in the basal ganglia as low or high
was described: this system uses a support vector machine classifier on descriptors based on bag of
visual words model, using keypoints obtained from a dense grid characterized with the scale-
invariant feature transform (SIFT) characteristics
150
. The achieved accuracy was approximately
80%
150
.
Recent technological advancements have allowed for the development of a number of
automatic and semi-automatic PVS segmentation and quantification strategies
120,147,148,150–155
. To
the best of my knowledge, Descombes et al. were the first to perform a computer-aided
23
segmentation of PVS
156
. Their approach was based on the marked point process framework, which
couples the typical tubular shape of PVS with their localization and tendency to cluster, and was
optimized via the Markov chain Monte Carlo method
156
. Uchiyama et al. used the white top-hat
transformation to enhance the intensities of tubular structures and extract them via intensity
thresholding
157
. A similar approach has been adopted more recently by several groups who
employed the Frangi filter
158
for the detection of tubular structures on 2D and 3D images
147,148,153
.
Wuerfel and colleagues were the first to perform a clinical study where the PVS were
segmented
120
. They used a threshold-based semi-automatic method, originally developed for the
quantification of brain lesions
159
, where connectivity and threshold values were adjusted based on
manually determined PVS
120
. Similarly, Ramirez et al. segmented PVS via a modified lesion
extraction algorithm employing T1-weighted, T2-weighted, and proton-density (PD) weighted
images
160
: as PVS have a relative CSF intensity on PD images compared with lesions, they
decreased the PD localized threshold weighting to 0 in order to specifically segment PVS
152
.
Wang et al. were able to segment PVS on one single slice in centrum semiovale and in a
manually-traced ovoid region of the basal ganglia after performing a linear intensity adaptive
adjustment in 3 stages (normalization, gamma correction, and linear mapping) as PVS have
intensity levels ranging from 30% to 90% of the maximum signal intensity on T2-weighted
images
149
. Park et al. proposed the first learning-based method for PVS segmentation on 7T
MRI
155
. In their approach, they first determine a region of interest after computing the vesselness
map in the white matter using a relatively low threshold value in order to include as many PVS
voxels as possible; then they extract in this region of interest randomized Haar features normalized
with respect to the principal directions of the underlying image derivatives and use the random
forest model to determine the features and threshold that maximizes the informative gain;
24
additionally, they adopted the auto-context model to further integrate the contextual features of
tubular structure into the classifier; finally, a sequential learning strategy with five sequential
classifiers is used to further enforce various contextual patterns around the thin tubular structures
into the classifier
155
.
Once the PVS have been segmented, several metrics and morphological features of PVS can
be computed from the segmentation masks. The PVS volume is one of these metrics, and provides
an estimate of the amount of fluid present in the PVS. Since the PVS volume in healthy individuals
is strongly correlated with the brain size
124
, it is recommended to correct for it when implementing
statistical models aimed at comparing PVS volumes across different individuals. It is also possible
to compute the volume fraction of PVS in each brain region, calculated by dividing the total
volume of segmented PVS cluster voxels in the region by the total volume of the region
153
.
The mean cross-sectional diameter is another important PVS feature that can be used to
distinguish PVS from other lesions related to CSVD
161
. One approach to measure the mean cross-
sectional diameter is the following
153
. Each connected cluster of segmented voxels is defined as
one PVS. Then, a thinning algorithm is applied to define the path of each cluster
162,163
. The
Euclidean distance among all pair of voxels within a cluster is measured, and then the longest
pathway between any two voxels within the thinned cluster is considered as the PVS path. For
each voxel on the PVS path, the shortest path is measured by counting the number of voxels in the
original cluster that were closer to that voxel than to any other voxel in the cluster. Then, the
diameter is calculated as:
𝐷 =2
$
N×(𝑣𝑜𝑥𝑒𝑙 𝑠𝑖𝑧𝑒 𝑖𝑛 𝑚𝑖𝑙𝑙𝑖𝑚𝑖𝑡𝑒𝑟)
!
𝑙𝜋

25
where N is the number of voxels associated with that voxel on the PVS path, and 𝑙 is the mean
distance of the voxel on the PVS path to its two neighbor voxels on the PVS path. Finally, the last
voxels on the PVS path are excluded from the diameter measurements since their diameters were
artificially increased due to the thinning algorithm
153
.
Another morphological characteristic that can be assessed in segmented PVS is the linearity,
i.e., the resemblance of a specific PVS cluster to the tubular morphology. Boefsplug et al. recently
presented an approach to assess PVS linearity. First, each segmented PVS cluster coordinate set
(𝑋) is subtracted by the mean of each cluster coordinate to localize the coordinates of PVS clusters
and translate them to the origin. Then, singular value decomposition is used to define the central
PVS cluster principal axis vector and the largest eigenvalue vector (𝑉
"
) is calculated such that the
cluster could be rotated around the principal axis 𝑉
"
and the magnitude of perpendicular norm
vectors from each cluster voxels coordinate to 𝑉
"
(𝑋
#$$
) is minimized, as in orthogonal regression.
The coordinate of 𝑉
"
that lays on the same norm vector is defined as 𝑋
9
. The minimum Euclidean
distance to the origin is measured for each cluster coordinate in 𝑋 and in 𝑋
9
, (𝑋
%&'(
and 𝑋
%)'(
:
,
respectively), and the Pearson correlation coefficient (𝑟) between the two distance vectors 𝑋
%&'(

and 𝑋
%)'(
:
is measured. A coefficient 𝑟 greater than 0.8 has been considered necessary for a PVS
cluster to meet the linearity constraint
154
. This approach also allows to measure the maximum
width of each PVS cluster, which is calculated as the sum of the largest norm, plus a norm whose
vector has opposite direction in the same plane, plus the distance of corner-to-corner of the voxel
(e.g., 1.7 mm for a 1 mm
3
voxel). Based on the existing literature, 15 millimeter is considered the
maximum width of a single MRI-visible perivascular space
164
.
Solidity is another morphological feature of PVS that can be derived from PVS segmentation
masks: it corresponds to the shape complexity of PVS, where a low solidity tends to describe a
26
more tortuous course and less compact shape, and is calculated as the ratio between the
area/volume of the voxels of a single PVS and the area/volume of the smallest convex hull
containing that single PVS.
The computation of PVS morphological features presents some limitations. Due to partial
volume effects that result from low spatial resolution, the segmented PVS clusters may not provide
accurate morphological feature estimates. In such cases, the use of a probabilistic segmentation
approach overcomes some of the issues associated with partial volume effects
165
. Another solution
is to dilate the segmented cluster by 1 voxel, which results in a relatively conservative estimate of
the overall PVS and limits the utility of pixel-wise evaluation of the segmented PVS cluster
147
. It
should be noted that most of the structural metrics of PVS are technically overestimated and
comprise both the actual perivascular space and the enclosed vessel. In fact, the vessel inside the
PVS is usually not visible on MRI due to partial volume effects, noise, their similar signal
intensities especially on T1-weighted images, and the limited resolution
128,131
. Imaging both PVS
and their enclosed vessel would grant a more precise estimate of PVS volume, which could be
important for a better understanding of the physiology and functioning of PVS. The possibility to
use time-of-flight angiography and susceptibility-weighted imaging at ultra-high field (UHF, ≥7
Tesla) to image the small penetrating arteries and veins
70,166–168
, enables exploration of the spatial
correlation between PVS and their enclosed vessels. In addition, normal physiological changes of
PVS in the same subject, such as potential effects of time-of-day, sleep, and hydration, may result
in changes on morphometric estimates of PVS
124,169–172
.
The identification and segmentation of PVS on structural MRI can be sometimes challenging,
especially in the context of aging, CSVD, and other neurodegenerative conditions, as the presence
of white matter hyperintensities (WMH), lacunes, and cysts can hide or mimic PVS. Knowing the
27
anatomical distribution, structure, and the normal radiological appearance of PVS is critical for
this task. In fact, the most important characteristics to distinguish PVS from other type of lesions are
the signal intensity, shape, and location/distribution.
WMH appear hyperintense on fluid-attenuated inversion recovery (FLAIR) sequences, while
PVS appear dark on FLAIR when visible. However, on T1-weighted and T2-weighted images,
PVS and WMH have a similar signal and therefore other characteristics need to be considered in
order to distinguish them when FLAIR is not available. Similar to PVS, WMH tend to be
symmetrically distributed, but the location could be different. WMH are frequently found in the
periventricular area and follow the shape of the borders of the lateral ventricles, while PVS are
usually not visible around the lateral ventricles. WMH in the deep white matter (dWMH) can be
found in areas where also PVS are visible and may have various appearance and sizes. When the
dWMH size is relatively big (more than 5 mm) and the shape is irregular and not rounded or
tubular, it is easier to define it as dWMH rather than as PVS, but the task becomes difficult in case
of rounded/tubular dWMH with small size. In these cases, one way to distinguish them from PVS
consists in evaluating their relationship with the cortical layer: usually PVS in the white matter
originate from the lower border of the cortical layer and advance towards the lateral ventricles,
following the course of the penetrating vessels, whereas dWMH are usually not in direct contact
with the cortical layer. Unfortunately, in some cases, it will still not be possible to confidently
distinguish WMH from PVS, especially when FLAIR is not available.
Lacunes are defined as round or ovoid, subcortical, fluid-filled cavity consistent with a
previous acute small subcortical infarct or hemorrhage in the territory of one perforating
arteriole
161
. Their location, signal intensity, and sometimes shape are similar to PVS. However,
they are usually larger than PVS (typically between 3 mm and about 15 mm in diameter, but they
can be bigger), tend to be asymmetrical, and the shape is often not tubular as in PVS. Moreover,
28
they are more common in the upper two thirds of the basal ganglia, whereas basal ganglia PVS on
MRI are more commonly found in the lower third and closer to the anterior commissure
173
. FLAIR
can also be helpful to distinguish PVS from lacunes, since the latter may have a hyperintense
rim thought to be related to reactive gliosis and/or siderosis, which are not usually found around
PVS
173
.
Since both lacunes and WMH can be frequently found in older adults, acquiring FLAIR
images is recommended when studying PVS in this type of population.
These neuroradiological features are summarized in Table 1.3.
Characteristic PVS WMH Lacune
Signal intensity
T1-weighted
T2-weighted
FLAIR

Dark (CSF-like)
Bright (CSF-like)
Dark (CSF-like)

Dark (cortex-like)
Bright
Bright

Dark (CSF-like)
Bright (CSF-like)
Dark (CSF-like)
Size Usually < 2 mm Usually > 3 mm 3-15 mm
Shape Tubular or dot-like,
depending on the
plane and the course
of the vessel
Rounded,
periventricular, or
irregular
Variable: dot-like,
triangular, irregular
Location WM
BG (lower portion)
WM
Periventricular

WM
BG (upper portion)
Distribution Symmetric Symmetric Asymmetric
Course Specific:
BG: Lenticulostriate
arteries
WM: Medullary arteries
Aspecific.
Tend to coalesce.
Aspecific
Table 1.3 Main neuroradiological characteristics of perivascular spaces (PVS), white matter
hyperintensities (WMH), and lacunes useful for differential diagnosis.
Abbreviations: Basal ganglia: BG; Cerebrospinal fluid: CSF; White matter: WM.

29
PVS across the normative lifespan
While previously considered a marker of neurological disease, MRI-visible PVS are also
found in healthy individuals. Several studies have demonstrated that a greater number of PVS are
observed in the elderly
174,175
and increases with advancing age
87,124,152,174–179
(Figure 1.3).

Figure 1.3 Examples showing the increase in perivascular spaces (PVS) in healthy participants with
different ages
The participant on the top is a 23-year-old male, while the participant on the bottom is a 73-year-old female.
The MRI scans were obtained from the Human Connectome Project (HCP) dataset (S900 release)
180
and
processed as in my previously published work
124
. are shown on the left column and the PVS masks were
overlaid in the center (green). The images on the right are the corresponding 3D maps of the PVS masks.
The orientation of the 3D maps is reported on the top right corner.

PVS are present in considerable numbers in young adults aged 21 to 35 years of age
124
and a
recent study also found PVS were observed in a much larger proportion of adolescents
129
than
previously described
139,181
, which may be attributed to improvements to the spatial resolution
afforded with clinical MRI. A recent study sought to characterize the trajectory of PVS
morphology from childhood through advancing age and found PVS increases nonlinearly with age
73 F
23 M
PVS mask PVS 3D render
30
across the lifespan. Furthermore, age-related changes to PVS morphological features in the white
matter and basal ganglia follow different time courses, which suggests normative PVS trajectories
are structure-specific
182
. In general, white matter regions with high PVS burden in childhood, such
as cingulate regions, tend to change minimally over the lifespan, while regions with low PVS
burden in childhood, including temporal white matter, undergo rapid enlargement. Together, these
findings may indicate a preferential vulnerability of some brain regions to pathological PVS
enlargement with age.
The relative distribution of PVS across the lifespan does not appear to change, with the highest
PVS burden observed in frontal and parietal lobes in adolescents
129
, young adults
124
, and aging
175
.
Additionally, the distribution of PVS between the hemispheres in adolescents
129
and the elderly
175

is generally symmetric, although asymmetries in certain brain regions are possible
124
. There is high
inter-individual variability regarding the amount of PVS in the normative brain, which may be
partly explained by sex, body mass index (BMI), systolic blood pressure, time of day and genetic
factors
124,178
. PVS burden is also significantly associated with larger intracranial volume
124,176
. It
has been suggested that larger craniums requires larger vasculature to supply blood to the brain,
which would result in more and/or bigger MRI-visible PVS
183
; however, it is also possible that
larger brain volumes can simply accommodate more PVS. PVS quantification may also be
obscured by the presence of pathology commonly observed in aging. WMH burden increases with
aging
184
and PVS enlargement is significantly associated with WMH, lacunes, and microbleeds
87,175,177,178
. PVS have also been found associated with hypertension
185
.
Recently, several groups looked at the association between personal lifestyle and glymphatic
activity. For example, some studies show that sleep behaviors, BMI, and stress may affect PVS
morphology and function
107
. PVS has been reported to play an important role in the glymphatic
31
activity during sleep
186,187
. In an animal model, researchers found that CSF-ISF exchange increases
during sleep
186
. Other studies found that sleep quality and sleep efficiency are associated with
enlarged PVS
170,188,189
. Del Brutto et al. found that poor sleep quality is associated with enlarged
PVS in the basal ganglia
189
. Similarly, Berezuk et al. found that lower sleep efficiency and deep
sleep is correlated with higher PVS burden, especially in the basal ganglia
170
. Furthermore,
sleeping position may influence PVS function. Levendowski et al. showed that body posture and
head position during sleep possibly affects the movement of blood and CSF through the brain
190
.
The enlargement of PVS was also associated with sleep disorders, such as obstructive sleep
apnea
191
. The clinical implications of these findings are currently not known, but it is interesting
to consider them in the context of other sleep-related results, such as an increase in amyloid-β in
PET imaging and higher amyloid-β in lumbar CSF observed after one night of sleep deprivation
or deep sleep interruption
192,193
. These observations point towards a critical role of sleep for an
efficient glymphatic activity, which can result in morphological alterations of MRI-visible PVS.
A mouse study also showed that chronic stress is associated with reduced glymphatic influx and
outflow regulated by AQP4 channel, suggesting that stress impaired AQP4-mediated glymphatic
function
194
.
Recently, I demonstrated with my colleagues that BMI is associated with PVS volume in
healthy young adults, and this association was different based on the sex of the individuals
124
.
Similarly, Ozato et al. showed that visceral fat was associated with PVS and white matter
lesions
195
. The mechanistic link between BMI and PVS is not known, but it is possibly related to
higher intracranial pressure
124
, as linear positive relationship exists between CSF pressure and
BMI, and/or to a reduced vascular contractility and vascular dysfunction, which is often found in
obese people
196
.
32

PVS in disease populations
Over the past decade, technological advancements in MR imaging have facilitated the study
of PVS structure in health and disease. MRI-visible PVS enlargement is associated with several
neurological conditions, including neurodegenerative, neurovascular, and neuroinflammatory
diseases. Below, we discuss accumulating evidence from MRI studies that sought to understand
the influence of PVS burden on neurological disorders and their associated symptoms. Despite the
substantial variation in segmentation approaches, methodologies, and populations, converging
evidence suggests the influence of PVS enlargement on neurological diseases differs across
anatomical structures. For example, amyloidopathies and neuroinflammatory conditions are most
strongly associated with PVS enlargement in the subcortical white matter, while neurovascular
disease and neurodegenerative disorders such as Parkinson’s disease predominantly affect PVS
structure in the basal ganglia. These differences may suggest the pathophysiological mechanisms
that give rise to PVS enlargement may differ according to their spatial location.
Vasculopathies
Enlarged PVS are traditionally considered MRI biomarkers of CSVD, that encompasses
arteriole and venule vasculopathies that commonly accompany stroke or cognitive decline and has
a high comorbidity with other neurodegenerative diseases
161
. The majority of studies that have
investigated the association between PVS burden and CSVD use a visual rating scale that scores
the severity of PVS enlargement according to manual counts. Using this approach, studies have
found vascular pathology is most commonly associated with PVS severity in the basal ganglia.
The prevalence of severe PVS in the basal ganglia, but not subcortical white matter, is significantly
associated with lacunar stroke
197,198
, WMH load
175,197
, cerebral microbleeds
199
and cerebral
33
atrophy
198
. In patients with acute lacunar stroke, PVS severity was associated with larger infarct
size, WMH burden, and cerebral atrophy
200
. In a longitudinal study conducted in older adults,
enlarged PVS with diameters greater than 3 mm were manually counted on T2w FLAIR images.
The authors found the global PVS count was associated with the presence of cerebral microbleeds,
subcortical infarcts, and accelerated progression of WMH
146
. Furthermore, PVS burden was
associated with worse information processing speed and a four-fold increase in vascular dementia
risk
146
.
Several studies, including a meta-analysis of 11 studies that include over 7,000 hypertensive
subjects
87
, showed that hypertension is also associated with PVS enlargement preferentially in the
basal ganglia, but not white matter
177–179,201,202
. That PVS enlargement in the BG, but not white
matter, is associated with CSVD and hypertension may be attributed to structural and functional
vascular differences, including arterial diameter and blood pressure
117
. However, future studies
should seek out the precise pathophysiological mechanisms that contribute to the unique
vulnerability of PVS in the basal ganglia to CSVD.
Amyloidopathies and other proteinopathies
There is broad evidence to support amyloidopathies, which encompass a variety of
neurodegenerative disorders characterized by atypical deposition of Aβ in the brain, is
preferentially associated with PVS enlargement in the subcortical white matter. AD is significantly
associated with increased PVS volume
151,152
, count
203
, and visually-rated severity
119,204
in the
subcortical white matter. In a study that utilized an automated segmentation strategy, patients with
MCI had a higher PVS volume fraction compared to cognitively normal controls in the centrum
semiovale of the white matter, but only in females
86
. Furthermore, PVS severity increases with age
in normal aging adults and patients with MCI, but not in patients with AD
204
, which suggests PVS
34
alterations associated with dementia arise prior to the clinical onset of symptoms. PVS
enlargement is also associated with the severity of symptoms, as MMSE was negatively correlated
with PVS severity
204
. Cerebral amyloid angiopathy (CAA) is a neurodegenerative CSVD
characterized by Aβ deposition in cerebral arteries. Previous studies have demonstrated that, like
AD, CAA is characterized by a significantly greater burden of PVS in white matter compared to
controls
205,206
. Strictly lobar microbleeds, which are more characteristic of CAA than vascular
arteriopathy, are significantly associated with increased PVS severity in the white matter, but not
the basal ganglia
201,207
.
A major undertaking of the glymphatic system is clearance of toxic metabolic waste from the
brain, including Aβ. Therefore, Aβ deposition observed in amyloidopathies may be related, at least
in part, to inefficient glymphatic drainage reflected by pathological enlargement of the PVS
208
.
However, evidence of the relationship between Aβ deposition and PVS pathology in AD and CAA
are less clear. In one study using a visual rating scale, PVS severity was not associated with PET-
PiB
119
. In another study, lower PVS volume fraction in the medial temporal lobe of MCI patients
was independently associated with Tau-PET uptake, a separate pathological feature of AD
86
.
Lastly, in a histological study, the frequency and severity of enlarged PVS was positively
associated with cortical Aβ
203
. Similar inconsistent findings were observed in studies of patients
with CAA. In one such study, PET-PiB binding was positively associated with PVS burden in the
white matter of patients with CAA
209
. However, in a combined post-mortem MRI and histological
study, PVS dilation was not significantly associated with the presence of Aβ plaques
206
. These
divergent findings may be due to differences in methodologies and measurements. While
histological studies can provide unprecedented insight into cellular mechanisms, it has a restricted
field of view. It is possible that the relationship between Aβ deposition and PVS enlargement may
35
not be uniform across the white matter, and may therefore not be present in all histological samples.
Additionally, these differences may also point towards the heterogeneity in AD and CAA
presentation. Future studies should aim to reconcile the differences found in histological and MRI
studies to better understand the etiology of neurodegenerative disorders characterized by Aβ
deposition.
There is abundant evidence from histological and neuroimaging case studies that show
pathological PVS dilation in the basal ganglia of patients with Parkinson’s disease (PD),
particularly within the putamen and pallidum
210–215
. However, there is some evidence to suggest
that PVS in certain regions of the subcortical white matter is also associated with PD diagnosis.
Recently, a study used an automated method to segment and quantify PVS in patients with PD and
found the fraction of space occupied by white matter underlying the medial orbitofrontal cortex
and banks of the superior temporal cortex were significantly higher in PD compared to controls
216
.
Furthermore, genetic factors may also influence the distribution of atypical PVS in patients with
PD, as patients with a family history of PD showed increased PVS volume fraction in the white
matter underlying the cuneus and lateral occipital cortex compared to nonmanifest carriers.
Several studies have demonstrated a relationship between PVS burden and symptom severity
in patients with PD. PVS enlargement in the basal ganglia is associated with cognitive
impairment
217,218
and is predictive of future conversion to cognitive decline and dementia
219
.
Furthermore, the number of PVS in the basal ganglia is associated with motor symptoms and
significantly differentiated patients with postural instability and gait disorder subtypes of PD from
those without
220
. Lastly, enlarged PVS in the basal ganglia were observed on the hemisphere
contralateral to the side of symptom onset, consistent with the anatomical motor pathways of
decussating fibers
221,222
. However, there is limited evidence that suggests PVS enlargement is
36
related to the pathophysiological mechanisms of PD. In one study utilizing PET, PVS severity in
the basal ganglia was not significantly associated with dopamine transporter reuptake in patients
with PD
217
. Together, these results suggest enlarged PVS in the basal ganglia of patients with PD
may play a more significant role in cognitive impairment instead of the etiological mechanisms of
the disease.
Neuroinflammatory disease
Histopathological studies have shown that PVS are sites of inflammatory infiltrates in
CSVD
82
. In a critical step for the evolution of inflammatory disease, perivascular macrophages
regulate the entry of inflammatory cells, which can damage the extracellular matrix, degrade the
integrity of the blood-brain barrier, and lead to neuronal death and demyelination
223
. Multiple
sclerosis (MS) is a neuroinflammatory disease of unknown etiology characterized by
demyelinating white matter lesions. Previous studies have shown that patients with MS have
significantly increased PVS burden in white matter compared to controls
120,224–228
. However, there
is some disagreement regarding the morphological PVS features that characterize MS. In one
study, patients with MS had significantly increased PVS volume, but not number, compared to
controls that was not explained by brain atrophy
120
. However, in a study using UHF MRI, authors
found that the number of PVS, but not the total volume, was significantly greater in patients with
MS, particularly in supratentorial regions
227
. These differences could be partially explained by the
MR field strength. At 1.5 T, it is possible that partial volume effect and reduced SNR may have
obscured smaller PVS that are more readily observed at 7 T. Additionally, Kilsdonk et al. explored
PVS burden in different regions
227
, while Wuelfer et al. considered the totality of PVS in the basal
ganglia and white matter together
120
. The association between PVS morphology and MS may
therefore depend on the topographical distribution of PVS in the brain. Indeed, others have found
37
significantly higher PVS count in localized white matter regions in patients with MS, including
high convexity areas
228
, anterior perforating substance, and atypical anatomical regions
215
.
It is unclear if the presence of PVS coincides with MS lesions or disease severity. Previous
studies have shown that PVS burden was not associated with conversion to moderate-severe
disability in relapsing-remitting MS
229
or accrual of white matter lesions
227,229
; however, one study
found that MRI-visible PVS tend to be spatially aligned with MS lesions
224
. Additionally, global
brain atrophy does not appear to be correlated with PVS burden in patients with MS
120,226,229
.
PVS enlargement is associated with several other conditions, including systemic lupus
erythematosus
121
, amyotrophic lateral sclerosis
230
, myotonic dystrophy
135
, traumatic brain
injury
231–233
, autism spectrum disorders
234
and pediatric idiopathic generalized epilepsy
235
.
Together, these findings suggest that higher MRI-visible PVS may be a non-specific indicator of
impaired brain health. However, there is limited evidence across neurological conditions that PVS
enlargement is associated with the molecular mechanisms that characterize disease pathology and
may therefore represent a secondary consequence of neurological dysfunction.
The vast majority of studies discussed quantify PVS enlargement using severity scores based
on a visual rating scale
119,121,206,207,209,218,219,225,232,135,175,198–201,204,205
or manual counts on a subset
of the data
146,197,220,227
. Therefore, future studies should aim to further investigate these findings
using segmentation techniques with the ability to probe morphological characteristics in different
brain regions
86,152,216
.
Table 4 summarizes the main results of this section.

38
Table 1.4 Conditions associated with perivascular space (PVS) burden.
Abbreviations: AD: Alzheimer’s Disease; BMI: body mass index; CAA: cerebral amyloid angiopathy;
MCI: mild cognitive impairment; MS: multiple sclerosis; PD: Parkinson’s disease.

Condition PVS analysis Finding
Healthy Aging
Visual
87,174–179

Quantitative
124,152

Increased PVS burden is associated with aging
Gender
Visual
174,178

Quantitative
124,152

Higher PVS burden is associated with male sex
BMI
Visual
195

Quantitative
124

Higher PVS burden is associated with higher BMI
124
and
higher visceral fat
195

Time of day Quantitative
124

Higher PVS burden is visible on MRI at later time of day
Genetics Quantitative
124

Healthy young twins show similar amount of PVS
Sleep
Visual
188,189,191

Quantitative
170

Higher PVS burden is associated with sleep
dysfunction
170,188,189
and obstructive sleep apnea
191

Hypertension
Visual
177–179,201

Quantitative
86,202

Meta-analysis
87

Hypertension is associated with higher PVS burden,
especially in the basal ganglia
MCI Quantitative
86

Female MCI subjects have higher PVS volume fraction in
centrum semiovale. MCI subjects have lower PVS volume
fraction in the anterosuperior medial temporal lobe.
AD
Visual
119,203,204

Quantitative
151,152

AD is significantly associated with increased PVS
volume
151,152
, count
203
, and visually-rated severity
119,204
in
the subcortical white matter
CAA Visual
205,206

CAA is associated with greater burden of PVS in white
matter
205,206

MS
Visual
224–228

Quantitative
120

MS is associated with higher PVS burden in white matter
PD
Visual
210–215

Quantitative
216

PD is associated PVS dilation in the basal ganglia
210–215

and certain regions of the subcortical white matter
216

39
Perivascular spaces in the white matter are affected by body mass index, time of day and
genetics
Introduction
Detecting pathological PVS changes is of high clinical significance because it provides
mechanistic insight into disease pathology, aids in diagnosis, and can be used for disease
monitoring, as PVS alterations may precede and be more reversible than demyelination and axonal
loss in neurodegenerative disorders
117,236
. However, the physiological profile of the PVS is not
fully understood, limiting the ability to identify and recognize PVS abnormalities in neurological
disorders, especially in subclinical phases of the disease.
Despite the increased interest in the role of PVS within the scientific community, there are
several unsolved controversies regarding in vivo PVS analysis using MRI. Resolving these issues
is critical for the interpretation of the results derived from PVS studies
97,117
. Some of the main
problems include: 1) the definition of the enlarged PVS: traditionally, the increased number of
detected PVS on MRI has been interpreted as an enlargement of PVS, but there is no agreement
regarding the radiological definition of enlarged PVS, as there is no quantitative measure of PVS
in healthy people; 2) the visual scoring used in most studies focus on basal ganglia and centrum
semiovale, but the distribution of PVS in other regions of the white matter is unknown; 3) the role
and effect of clinical and genetic factors on the physiological amount of PVS have not been
thoroughly investigated.
In this study, I provide the first quantitative analysis of PVS performed using submilliter MRI
in a large population of 897 healthy adults from the HCP
180
. I describe the regional distribution
and extent of PVS in the white matter of the human brain, which can be used by researchers and
clinicians as a resource for the quantitative analysis of physiological PVS. The age range of
participants was 22-37 years old and was chosen to represent healthy adults beyond the age of
40
major neurodevelopmental changes and before the onset of neurodegenerative alterations
180
. I also
investigated the relationship between PVS and multiple demographic, clinical, and genetic
parameters in order to understand which factors may significantly influence the amount of PVS in
healthy adults.

Methods
Study Population
A total of 897 participants were identified from the HCP study (S900 release)
180
. According
to how the HCP study has been designed and performed, recruiting efforts were aimed at ensuring
that participants broadly reflect the ethnic and racial composition of the U.S. population as
represented in the 2000 decennial census
237
. The goal was to recruit a pool of individuals that is
generally representative of the population at large, in order to capture a wide range of variability
in healthy individuals with respect to behavioral, ethnic, and socioeconomic diversity
237
. The study
protocol was approved by the Institutional Review Board at the University of Southern California
(IRB# HS-19-00448) conforming with the World Medical Association Declaration of Helsinki.
Written consent was obtained from all participants at the beginning of the first day of involvement
in the project
237
. Only healthy individuals were included in the study (inclusion and exclusion
criteria listed in Table 1.5).

41
Inclusion criteria
- Age between 22-37
- No prior history of psychiatric disorders, substance abuse, neurological or
cardiovascular diseases, as indicated by no report of medical diagnosis, no
hospitalization, and no pharmacologic or behavioral treatment.
Exclusion criteria
- Any genetic disorder
- Current use of chemotherapy or immunomodulatory agents
- History of radiation or chemotherapy that could affect the brain
- Sickle cell disease
- Thyroid hormone treatment within 12 months before the enrollment
- Treatment for diabetes
- Head injury followed by neurological symptoms, such as loss of consciousness for
more than 30 minutes or amnesia or change in mental status for more than 24
hours, and/or CT findings consistent with traumatic brain injury.
- Ineligible to undergo an MRI scan: pregnancy, metal or devices in the body not
compatible with MRI (e.g., cardiac pacemaker, cochlear implant, aneurism clip),
and/or suffering moderate to severe claustrophobia were reasons for being
excluded from the study.
- Non-twins born prior to 37 weeks of gestation and twins born prior to 34 weeks of
gestation have been excluded since preterm birth has been shown to perturb the
development of the brain.
237

Table 1.5 Inclusion and exclusion criteria for the participants enrolled in the Human Connectome
Project (S900 release)
180
.

Clinical and behavioral data
Collected demographic and clinical data included: age, sex, height and weight with the
corresponding body mass index (BMI), blood pressure, years of education, hematocrit, glycated
hemoglobin, and thyroid-stimulating hormone in blood. Information about alcohol consumption
and tobacco smoking was collected through the Semi-Structured Assessment for the Genetics of
Alcoholism interview (SSAGA)
238
. The NIH Toolbox (http://www.nihtoolbox.org) was used to
assess the domains of cognition, emotion, motor function, and sensation
180
. Additionally, each
participant underwent the Mini-Mental State Examination (MMSE)
239
. The Pittsburgh Sleep
Quality Index was used to evaluate sleep quality and quantity
240
. The sleep quality was computed
as the sum of 7 analyzed components including subjective sleep quality, sleep latency, sleep
duration, habitual sleep efficiency, sleep disturbances, use of sleeping medication, and daytime
dysfunction; the sleep quantity was assessed by asking the participant what the average number of
42
hours of actual sleep per night in the past month was, not counting time falling asleep and getting
out of bed.
Pittsburgh Sleep Quality Index
The sleep quality was computed as the sum of 7 analyzed components including subjective
sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbances, use of
sleeping medication, and daytime dysfunction; the sleep quantity was assessed by asking the
participant what the average number of hours of actual sleep per night in the past month was, not
counting time falling asleep and getting out of bed.
MRI methods and analysis
The preprocessed T1-weighted (TR 2400 ms, TE 2.14 ms, TI 1000 ms, FOV 224x224 mm)
and T2-weighted (TR 3200 ms, TE 5.65 ms, FOV 224x224 mm) images of the HCP
241
, acquired
at 0.7 mm
3
resolution on a Siemens 3T Skyra scanner (Siemens Medical Solutions, Erlangen,
Germany), were used for the PVS analysis. Multiple quality control steps have been applied before,
during, and after each scan session, in order to acquire high quality MRI data with minimal
occurrence of motion artifacts
242
. The preprocessing steps included: correction for gradient
nonlinearity, readout, and bias field; alignment to anterior commissure-posterior commissure
subject space; registration to MNI 152 space using the FNIRT function in FSL
243
; generation of
individual cortical, white matter, and pial surfaces and volumes using the FreeSurfer software
244

and the HCP pipelines
241
.
PVS analysis
For PVS quantification and mapping, I performed the segmentation of PVS via an automated
quantification pipeline that I developed with Dr. Farshid Sepehrband (Figure 1.4)
148
. We first
enhanced the visibility of PVS and then automatically segmented PVS across the white matter.
We combined T1- and T2-weighted images that were adaptively filtered to remove non-structured
43
high-frequency spatial noise by using a filtering patch which removes the noise at a single-voxel
level and preserves signal intensities that are spatially repeated, thus preserving PVS voxels
148,245
.
Non-local mean was used for removing high frequency noise, which measures the image intensity
similarities by considering the neighboring voxels in a block-wise fashion, where filtered image is
∑ 𝜔
*
!
∈,
!
(𝑥
&
,𝑥
-
)𝑢(𝑥
-
). For each voxel (𝑥
-
) the weight (𝜔) is measured using the Euclidean distance
between 3D patches. The adaptive non-local mean filtering technique adds a regularization term
to the above formulation to remove bias intensity of the Rician noise observed in MRI.
We then used n-tissue parcellation technique of the Advanced Normalization Tools (ANTs)
package
246,247
. Parcellated white matter was used as a mask for PVS analysis. For PVS
segmentation, we first applied Frangi filter
158
, using Quantitative Imaging Toolkit
248
, which
extracts the likelihood of a voxel belonging to a PVS. The Frangi filter has been shown to be an
adequate tool for PVS segmentation
147,148,249–252
. Frangi filter estimates a vesselness measure for
each voxel from eigenvectors of the Hessian matrix of the image. Default parameters of 𝛼 =
0.5,𝛽 =0.5 and c were used, as recommended in
158
. The parameter c was set to half the value of
the maximum Hessian norm. Frangi filter estimated vesselness measures at different scales and
provided the maximum likeliness. The scale was set to a large range of 0.1-5 voxels in order to
maximize the vessel inclusion. The output of this step is a quantitative maximum likelihood map
of vessels in regions of interest (ROIs)
158
. We selected a previously optimized scaled threshold of
1.5 (equal to raw threshold of 1e-6) in the vessel map in order to obtain a binary mask of PVS
regions, which is required for obtaining PVS volumetric measurements and spatial distribution
148
.
The periventricular voxels were excluded via a dilated mask of the lateral ventricles in order to remove
the incorrectly segmented PVS at the lateral ventricles-white matter boundary
148
. This PVS segmentation
technique has been previously validated on the same MRI dataset
148
. Two expert PVS readers independently
counted the PVS in the centrum semiovale of 100 MRI scans according to a validated visual rating scale
136
.
44
The number of PVS obtained with our segmentation algorithm was compared with the numbers obtained
by the experts: Lin’s concordance coefficient between the automated and the experts’ PVS counts was 0.81,
the bias correction value was 0.88, and the Pearson correlation coefficient was 0.61
148
.
Furthermore, the expert PVS readers reviewed 30 additional PVS masks and manually edited them
removing the false positives PVS and segmenting the false negatives PVS in order to estimate the sensitivity
and specificity of our segmentation technique to PVS voxels. The sensitivity and specificity were
97.7±1.6% and 97.8±1.8% (mean ± standard deviation), respectively. Among the 130/897 MRI scans and
PVS masks visually analyzed by the expert readers, none of the images presented motion artifacts affecting
the PVS segmentation. Finally, PVS ratio was extracted across the white matter regions, parcellated based
on Desikan-Killiany atlas using FreeSurfer software
253
. The total PVS-white matter ratio was also
estimated.

Figure 1.4 Schematic of our pipeline for PVS segmentation.
Adapted from
148
.

Genome-Wide Association Analysis
Genome-wide single nucleotide polymorphisms (SNPs) genotyping was performed in
831/897 participants with useable blood or saliva-based genetic material. For this study, we used
only samples processed with one custom microarray chip consisting of the Illumina Mega Chip (2
million multiethnic SNPs). Clinical and demographic data, PVS measurements, and SNPs were
combined to yield a single data set for every individual. Genotype information was available for
45
2,119,803 typed SNPs across 831 individuals with clinical data and PVS measurements. The high
number of SNPs available allowed us to perform a stringent SNP-level filtering: we filtered out
SNPs for which the minor allele frequency was less than 1%, in order to ensure adequate power to
infer a statistically significant relationship between the SNP and the PVS. After this step, 471,068
typed SNPs across 831 individuals persisted and underwent further pre-processing steps. We
subsequently performed a sample-level filtering: a call rate of 100% was applied in order to include
only participants’ samples with 100% of genetic data available; additionally, we excluded samples
exhibiting deviations from the Hardy-Weinberg equilibrium with an inbreeding coefficient higher
than 0.1, since excess heterozygosity across typed SNPs within an individual may be an indication
of poor sample quality
254
. For ancestry filtering, we first applied linkage disequilibrium pruning
using a threshold value of 0.2, which eliminates a large degree of redundancy in the data and
reduces the influence of chromosomal artifacts
255,256
. Then, we used the Method of Moments
procedure to calculate the identity by descent (IBD) kinship coefficient: pairwise IBD distances
were computed to search for sample relatedness and participants with the highest number of
pairwise kinship coefficients >0.1, which typically suggest relatedness, duplicates, or sample
mixture, were iteratively removed
256
. This resulted in the exclusion of 455 samples. Among non-
twin and twin siblings included in this study, all but one member of each biologically independent
sibship was filtered out at this step. At the end of the pre-processing procedure, 471,068 typed
SNPs across 376 individuals were considered in the final genome-wide association analysis. A
Bonferonni-corrected genome-wide significance threshold of 5×10
−8
and a suggestive association
significance threshold of 5×10
−6
were adopted
256
.
46
Statistical Analysis
The statistical analysis was done using the R package version 1.2.5 (R Development Core
Team, 2019).
The Shapiro-Wilk test for normality was used to assess data distribution. All data analyzed
exhibited a distribution that was significantly different from normal distribution. Therefore, the
following non-parametric tests were applied: the Wilcoxon matched-pairs signed rank test to
compare differences across paired groups; the Wilcoxon rank sum (Mann-Whitney) test to
compare two unmatched groups; the Kruskal-Wallis test to compare three or more unmatched
group; correlations were measured using the Spearman’s coefficient.
In order to assess which demographic and clinical parameters influenced the amount of PVS
measured in the brain, general linear models were applied, using one clinical factor at a time as
independent variable, and the PVS ratio as dependent variable. The following factors were
investigated: age, BMI, gender, systolic blood pressure, diastolic blood pressure, thyroid-
stimulating hormone level, hematocrit, and glycated hemoglobin. After the identification of
potentially significant factors, we performed a new general linear model analysis including all of
them together as independent variables and the PVS ratio as the dependent variable. The two-way
ANCOVA model was used to test the effect of gender and BMI on the PVS ratio controlling for
age. On a separate analysis, we also investigated whether cigarette smoking and alcohol affect
PVS.
When analyzing the relationship between PVS ratio and the results of the behavioral tests, a
principal component analysis was initially applied to convert and reduce this set of variables into
a set of linearly uncorrelated variables, since many of the behavioral scores were expected to have
multi-collinearity. The first principal component, explaining most of the variance in behavioral
47
measures, was then used to identify the most influential neurocognitive scores, which were
employed in a series of linear models as dependent variables to investigate whether the PVS ratio
is a predictor of cognitive performance. Regression models were fitted using the ordinary least
square technique. The Benjamini-Hochberg method was adopted to correct for multiple
comparisons with a false discovery rate of 0.05. All p-values were 2-sided and considered
significant at <0.05.
Results
Analysis of PVS volume, ratio, and distribution across white matter regions
We were able to compute PVS volume in 897 participants (demographic and clinical data are
reported in Table 1.6).
The mean PVS volume in the white matter was 5.03±2.15 cm
3
, with a high inter-subject
variability (median: 4.68 cm
3
; 1
st
quartile (𝑞
. .01
): 3.40 cm
3
; 3
rd
quartile (𝑞
. .21
): 6.21 cm
3
; range:
1.31-14.49 cm
3
) (Figure 1.5). The interquartile range (IQR) criterion was used to identify potential
outliers: PVS values below 𝑞
. .01
−1.5 × 𝐼𝑄𝑅 or above 𝑞
. .21
+1.5 × 𝐼𝑄𝑅 were considered
potential outliers. Based on this criterion, we identified 16/897 cases with total PVS volume in the
white matter above 𝑞
. .21
+1.5 × 𝐼𝑄𝑅 (range: 10.88-14.49 cm
3
). The corresponding PVS masks
were visually analyzed by the expert readers. Since none of the PVS masks presented any major
false positive which could explain the high value, we have not excluded them from the subsequent
analyses.

48
Population
Male Female Overall
Age 28 ± 3.65 29.46 ± 3.58 28.82 ± 3.68
BMI, kg/m² (n=896) 26.95 ± 4.52 26.42 ± 5.84 26.65 ± 5.3
Education (years) 14.77 ± 1.79 14.98 ± 1.84 14.89 ± 1.82
Mini-mental state
examination score (out
of 30) 28.96 ± 1.1 29.05 ± 0.96 29.01 ± 1.03
Smoking history (n=896)
- Non-smokers 186 304 490
- Occasional
smokers
87 89 176
- Regular smokers 121 109 230
Average number of
cigarettes/day 10.08 ± 5.66 9.72 ± 6.41 9.91 ± 6.02
Average alcoholic
drinks/week (n=880) 6.47 ± 8.29 3.06 ± 4.34 4.54 ± 6.58
Systolic Blood pressure
(mmHg) (n=884) 129.29 ± 13.6 120.13 ± 13.66 124.17 ± 14.36
Diastolic Blood pressure
(mmHg) (n=884) 79.11 ± 10.34 75.22 ± 10.38 76.94 ± 10.54
Hematocrit (%) (n=823) 46.03 ± 3.57 40.87 ± 4.43 43.12 ± 4.81
Blood Thyroid Hormone
(mU/L) (n=610) 1.73 ± 0.91 1.82 ± 1.31 1.78 ± 1.13
Glycated Hemoglobin (%)
(n=603) 5.26 ± 0.41 5.26 ± 0.39 5.26 ± 0.4
Average amount of sleep
hours per night 6.81 ± 1.15 6.82 ± 1.14 6.81 ± 1.15
Neuroimaging data
Total intracranial volume
(cm
3
) 1702.86 ± 148.21 1470.94 ± 149.79 1572.81 ± 188.33
Whole brain volume
(cm
3
) 1264.98 ± 103.12 1106.78 ± 85.79 1176.28 ± 122.31
White matter volume
(cm
3
) 480.28 ± 50.41 415.27 ± 40.70 443.83 ± 55.54
PVS Volume (mm
3
) 5842.93 ± 2356.69 4393.76 ± 1733.64 5029.39 ± 2153.15
PVS Ratio (%) 1.22 ± 0.45 1.07 ± 0.39 1.14 ± 0.43
Table 1.6 Demographic and clinical characteristics of participants from the Human Connectome
Project (S900 Release) included in this study.
N=897 unless otherwise specified. Data are mean ± standard deviation.

49

Figure 1.5 Examples showing the high inter-subject variability of PVS in healthy participants
The participant on the top is a 32 years old male, while the participant on the bottom is a 22 years old female
(two extreme cases are intentionally presented to highlight the high inter-subject variability in PVS). The
MRI scans are shown on the left column and the PVS mask were overlaid in the center (orange). The images
on the right are the corresponding 3D maps of the PVS masks. The orientation of the 3D maps is reported
on the top right corner.

Among the ROIs segmented in the white matter, the superior frontal and parietal regions
showed the highest percentage of PVS, including on average more than 8% and 6% of the total
PVS volume, respectively (Figure 1.6A and Figure 1.7A).
We observed a significant relationship between the PVS volume and the measured white
matter volume, as assumed a priori (r=0.52, p < 0.0001) (Figure 1.6B). Therefore, we calculated
the PVS ratio, corresponding to the ratio between the PVS volume and the white matter volume.
The average PVS ratio in the whole white matter was 1.14±0.43% (range: 0.34-3.13%). The
regions with the highest PVS ratios were the white matter areas adjacent to the cingulate cortex,
insula, and supramarginal gyrus (above 3%); on the other hand, the regions with the smallest PVS
50
ratios were the white matter areas underlying the cuneus, entorhinal cortex, and the frontal pole
cortex (Figure 1.6C, Figure 1.7B, and Table 1.7).
When comparing one side of each ROI with its contralateral part in the same subject, the
relative difference in PVS ratio was 18% on average, variably exhibiting more PVS on the right
or on the left side. All the regions showed a significant asymmetric distribution of PVS (Wilcoxon
matched-pairs, p<0.01), except the white matter areas underlying the frontal pole, pars orbitalis
and opercularis, anterior cingulate, precentral, transverse temporal, cuneus, and pericalcarine
regions. The white matter regions showing on average the highest asymmetric distribution of PVS
were those underlying the lingual gyrus (50% higher PVS ratio on the right side) and the entorhinal
cortex (120% higher PVS ratio on the left hemisphere) (Figure 1.6D and Table 1.7).
Together, these results show that an asymmetric distribution of PVS across the two cerebral
hemispheres can be considered physiological in most of the ROIs of healthy adults. Additionally,
the entity of the asymmetry can be of great extent in some ROIs, with one side having a PVS ratio
up to 120% higher than the contralateral side.

51

Figure 1.6 Distribution of the PVS in the white matter and relationship between PVS and white
matter
A. Boxplot showing the percentage of perivascular space (PVS) in each region of interest (ROI). The ROIs
showing more than 5% of total PVS volume are highlighted in red. X-axis labels are white matter regions,
parcellated based on Desikan-Killiany atlas using FreeSurfer software. “Bankssts”: Banks of the Superior
Temporal Sulcus.
B. Scatterplot showing the significant positive relationship between the measured perivascular space (PVS)
and white matter volumes. Spearman's rank correlation coefficient.
C. Boxplot showing the PVS ratio (i.e., PVS volume/white matter volume) in each bilateral region of
interest (ROI). The reported value in each ROI is the PVS ratio measured on the right and left side of the
specific ROI combined. The ROIs with a PVS ratio higher than 3% are highlighted in red.
D. Boxplot showing the PVS ratio in each unilateral region of interest (ROI). For each ROI, the left boxplot
represents the corresponding ROI on the left hemisphere (blue line), while the right boxplot is the
corresponding ROI on the right hemisphere (red line). The adjusted p-values refer to the Wilcoxon matched-
pairs signed rank test performed in each ROI to compare the two sides. The ROIs with a significant
asymmetric distribution of PVS are in white boxes; the ROIs with a significantly asymmetric distribution
of PVS having 50% higher PVS ratio on one side compared with its contralateral part are in yellow boxes;
the ROIs with a symmetric distribution of PVS ratio across the two hemispheres (i.e., adjusted p-value >
0.01) are in black boxes. Outliers in boxplots show PVS values below 𝑞
!.#$
−1.5 × 𝐼𝑄𝑅 or above 𝑞
!.%$
+
1.5 × 𝐼𝑄𝑅 (panels A, B, and D).

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0
5
10
15
Bankssts
Caudalanteriorcingulate
Caudalmiddlefrontal
Cuneus
Entorhinal
Frontalpole
Fusiform
Inferiorparietal
Inferiortemporal
Insula
Isthmuscingulate
Lateraloccipital
Lateralorbitofrontal
Lingual
Medialorbitofrontal
Middletemporal
Paracentral
Parahippocampal
Parsopercularis
Parsorbitalis
Parstriangularis
Pericalcarine
Postcentral
Posteriorcingulate
Precentral
Precuneus
Rostralanteriorcingulate
Rostralmiddlefrontal
Superiorfrontal
Superiorparietal
Superiortemporal
Supramarginal
Temporalpole
Transversetemporal
PVS Volume in ROI/PVS total volume (%)
PVS > 5%
Percentage of PVS across ROIs A
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R = 0.52 , p < 2.2e−16
5000
10000
15000
300000 400000 500000 600000
White matter volume (mm
3
)
PVS volume (mm
3
)
B
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10
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Caudalanteriorcingulate
Caudalmiddlefrontal
Cuneus
Entorhinal
Frontalpole
Fusiform
Inferiorparietal
Inferiortemporal
Insula
Isthmuscingulate
Lateraloccipital
Lateralorbitofrontal
Lingual
Medialorbitofrontal
Middletemporal
Paracentral
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Parsopercularis
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Parstriangularis
Pericalcarine
Postcentral
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Precentral
Precuneus
Rostralanteriorcingulate
Rostralmiddlefrontal
Superiorfrontal
Superiorparietal
Superiortemporal
Supramarginal
Temporalpole
Transversetemporal
PVS Ratio (%)
PVS Ratio > 3%
PVS Ratio (PVS Volume/WM volume) in each ROI C
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Bankssts
Caudalanteriorcingulate
Caudalmiddlefrontal
Cuneus
Entorhinal
Fusiform
Inferiorparietal
Inferiortemporal
Isthmuscingulate
Lateraloccipital
Lateralorbitofrontal
Lingual
Medialorbitofrontal
Middletemporal
Parahippocampal
Paracentral
Parsopercularis
Parsorbitalis
Parstriangularis
Pericalcarine
Postcentral
Posteriorcingulate
Precentral
Precuneus
Rostralanteriorcingulate
Rostralmiddlefrontal
Superiorfrontal
Superiorparietal
Superiortemporal
Supramarginal
Frontalpole
Temporalpole
Transversetemporal
Insula
PVS Ratio (%)
Left Right Non significant Adj. p < 0.01 and asymmetry > 50% Adjusted p < 0.01
PVS Ratio (PVS Volume/WM volume) in each ROI D
52

Figure 1.7 3D rendering of the white matter regions with more than 5% of the total PVS volume (a),
and with PVS over white matter volume ratios higher than 3% (b)

Superior frontal
Superior parietal
Inferior parietal
Precentral
Supramarginal
Insula
Caudal anterior cingulate
Isthmus cingulate
A
B
Regions with PVS > 5%
Regions with PVS/WM ratio > 3%
53
ROI Side Male (n=394) Female (n=503) Overall (n=897)
Adjusted
p-value
Bankssts 2.11 ± 1.08 (0.24-7.13) 2.06 ± 0.96 (0.29-5.71) 2.08 ± 1.01 (0.24-7.13)

Right 1.17 ± 0.67 (0.02-4.61) 1.14 ± 0.58 (0.05-3.42) 1.15 ± 0.62 (0.02-4.61)
2.50E-30

Left 0.94 ± 0.52 (0-3.19) 0.92 ± 0.53 (0-2.72) 0.93 ± 0.53 (0-3.19)
Caudalanteriorcingulate 3.55 ± 2.02 (0.28-9.96) 2.74 ± 1.56 (0.17-9.98) 3.09 ± 1.82 (0.17-9.98)

Right 1.7 ± 1.08 (0.14-5.14) 1.31 ± 0.83 (0.03-5.42) 1.48 ± 0.96 (0.03-5.42)
2.00E-11

Left 1.85 ± 1.03 (0.13-5.31) 1.43 ± 0.84 (0.05-4.86) 1.61 ± 0.95 (0.05-5.31)
Caudalmiddlefrontal 2.39 ± 1.26 (0.18-6.75) 2.08 ± 1.07 (0.29-6.15) 2.22 ± 1.17 (0.18-6.75)

Right 1.05 ± 0.63 (0.08-3.33) 0.93 ± 0.52 (0.07-2.82) 0.98 ± 0.57 (0.07-3.33)
1.60E-70

Left 1.34 ± 0.69 (0.05-3.67) 1.15 ± 0.6 (0.09-3.33) 1.24 ± 0.65 (0.05-3.67)
Cuneus 0.55 ± 0.54 (0-4.81) 0.46 ± 0.46 (0-2.77) 0.5 ± 0.5 (0-4.81)

Right 0.29 ± 0.31 (0-3.17) 0.23 ± 0.27 (0-1.62) 0.26 ± 0.29 (0-3.17)
0.24

Left 0.26 ± 0.29 (0-1.82) 0.22 ± 0.24 (0-1.2) 0.24 ± 0.26 (0-1.82)
Entorhinal 0.5 ± 0.6 (0-3.48) 0.24 ± 0.33 (0-2.2) 0.35 ± 0.48 (0-3.48)

Right 0.15 ± 0.26 (0-1.86) 0.08 ± 0.16 (0-1.39) 0.11 ± 0.21 (0-1.86)
2.40E-30

Left 0.34 ± 0.45 (0-2.91) 0.16 ± 0.27 (0-2.06) 0.24 ± 0.37 (0-2.91)
Fusiform 1.56 ± 0.81 (0.12-5.16) 1.46 ± 0.78 (0.12-6.88) 1.51 ± 0.8 (0.12-6.88)

Right 0.8 ± 0.45 (0.06-2.72) 0.77 ± 0.44 (0.02-3.76) 0.78 ± 0.44 (0.02-3.76)
6.40E-08

Left 0.76 ± 0.43 (0.05-2.91) 0.7 ± 0.41 (0.03-3.13) 0.72 ± 0.42 (0.03-3.13)
Inferiorparietal 2.67 ± 1.29 (0.35-8.01) 2.58 ± 1.23 (0.26-7.29) 2.62 ± 1.25 (0.26-8.01)

Right 1.47 ± 0.7 (0.18-4.33) 1.4 ± 0.68 (0.2-4.07) 1.43 ± 0.69 (0.18-4.33)
2.00E-64

Left 1.2 ± 0.63 (0.14-4.39) 1.18 ± 0.6 (0.07-3.56) 1.19 ± 0.61 (0.07-4.39)
Inferiortemporal 1.48 ± 0.83 (0.11-5.22) 1.39 ± 0.77 (0.08-5.01) 1.43 ± 0.8 (0.08-5.22)

Right 0.69 ± 0.43 (0.01-2.83) 0.68 ± 0.4 (0.01-2.53) 0.69 ± 0.41 (0.01-2.83)
2.30E-07

Left 0.78 ± 0.45 (0.01-2.74) 0.71 ± 0.43 (0-2.69) 0.74 ± 0.44 (0-2.74)
Isthmuscingulate
3.63 ± 1.77 (0.39-
10.15)
2.71 ± 1.27 (0.24-8.12)
3.11 ± 1.58 (0.24-
10.15)

Right 1.96 ± 0.99 (0.25-5.31) 1.4 ± 0.7 (0.07-4.84) 1.65 ± 0.89 (0.07-5.31)
5.00E-30

Left 1.67 ± 0.83 (0.12-4.84) 1.3 ± 0.63 (0.12-3.77) 1.47 ± 0.75 (0.12-4.84)
Lateraloccipital 1.06 ± 0.73 (0.03-4.61) 1 ± 0.64 (0.03-3.89) 1.03 ± 0.68 (0.03-4.61)

Right 0.59 ± 0.41 (0-2.33) 0.56 ± 0.37 (0.02-2.4) 0.57 ± 0.39 (0-2.4)
3.40E-49

Left 0.47 ± 0.36 (0.02-2.28) 0.44 ± 0.3 (0-1.63) 0.45 ± 0.33 (0-2.28)
Lateralorbitofrontal 2.54 ± 1.44 (0.39-8.15) 2.26 ± 1.27 (0.32-8.67) 2.38 ± 1.36 (0.32-8.67)

Right 1.19 ± 0.71 (0.15-4.23) 1.11 ± 0.64 (0.16-4.59) 1.14 ± 0.67 (0.15-4.59)
1.60E-08

Left 1.35 ± 0.8 (0.14-4.31) 1.15 ± 0.7 (0.12-5.29) 1.24 ± 0.75 (0.12-5.29)
Lingual 1.06 ± 0.6 (0.07-4.29) 1 ± 0.57 (0-3.52) 1.03 ± 0.59 (0-4.29)

Right 0.64 ± 0.38 (0.02-2.36) 0.6 ± 0.36 (0-2.28) 0.62 ± 0.37 (0-2.36)
3.50E-69

Left 0.42 ± 0.32 (0-2.09) 0.4 ± 0.3 (0-1.98) 0.41 ± 0.31 (0-2.09)
Medialorbitofrontal 1.46 ± 1.09 (0.05-7.15) 1.24 ± 0.88 (0.08-6.16) 1.33 ± 0.98 (0.05-7.15)
54

Right 0.64 ± 0.61 (0-3.89) 0.55 ± 0.51 (0-3.7) 0.59 ± 0.56 (0-3.89)
1.90E-41

Left 0.81 ± 0.54 (0-3.42) 0.69 ± 0.45 (0.03-2.78) 0.74 ± 0.49 (0-3.42)
Middletemporal 1.51 ± 0.81 (0.11-4.78) 1.43 ± 0.76 (0.11-4.62) 1.46 ± 0.78 (0.11-4.78)

Right 0.73 ± 0.43 (0.02-2.7) 0.7 ± 0.4 (0.06-2.73) 0.71 ± 0.42 (0.02-2.73)
0.001

Left 0.79 ± 0.44 (0.07-2.84) 0.73 ± 0.42 (0.02-2.66) 0.75 ± 0.43 (0.02-2.84)
Parahippocampal 0.83 ± 0.62 (0-4.12) 0.58 ± 0.56 (0-3.76) 0.69 ± 0.6 (0-4.12)

Right 0.37 ± 0.32 (0-1.98) 0.28 ± 0.35 (0-2.86) 0.32 ± 0.34 (0-2.86)
0.005

Left 0.45 ± 0.41 (0-3) 0.3 ± 0.35 (0-2.73) 0.36 ± 0.38 (0-3)
Paracentral 1.42 ± 0.86 (0.04-5.19) 1.24 ± 0.77 (0.03-4.41) 1.32 ± 0.82 (0.03-5.19)

Right 0.8 ± 0.5 (0.02-3.21) 0.68 ± 0.44 (0.03-2.55) 0.73 ± 0.47 (0.02-3.21)
3.80E-35

Left 0.62 ± 0.42 (0-2.09) 0.56 ± 0.39 (0-2.34) 0.59 ± 0.4 (0-2.34)
Parsopercularis 1.96 ± 1.26 (0.03-7.17) 1.62 ± 1.11 (0.06-7.45) 1.77 ± 1.19 (0.03-7.45)

Right 0.99 ± 0.69 (0-3.75) 0.84 ± 0.6 (0-3.18) 0.91 ± 0.65 (0-3.75)
0.034

Left 0.97 ± 0.65 (0-3.43) 0.77 ± 0.59 (0-4.28) 0.86 ± 0.63 (0-4.28)
Parsorbitalis 0.85 ± 0.76 (0-3.9) 0.78 ± 0.71 (0-4) 0.81 ± 0.73 (0-4)

Right 0.41 ± 0.42 (0-2.37) 0.37 ± 0.36 (0-1.89) 0.39 ± 0.39 (0-2.37)
0.28

Left 0.44 ± 0.44 (0-2.25) 0.41 ± 0.46 (0-2.87) 0.42 ± 0.45 (0-2.87)
Parstriangularis 1.5 ± 1.08 (0-5.84) 1.28 ± 0.86 (0-4.81) 1.37 ± 0.96 (0-5.84)

Right 0.64 ± 0.53 (0-2.85) 0.57 ± 0.44 (0-2.94) 0.6 ± 0.48 (0-2.94)
2.00E-36

Left 0.86 ± 0.62 (0-3.21) 0.71 ± 0.5 (0-3.2) 0.78 ± 0.56 (0-3.21)
Pericalcarine 2.1 ± 1.28 (0-6.57) 2.14 ± 1.26 (0-7.37) 2.12 ± 1.27 (0-7.37)

Right 1.03 ± 0.69 (0-3.45) 1.05 ± 0.7 (0-3.42) 1.04 ± 0.69 (0-3.45)
1

Left 1.07 ± 0.86 (0-4.81) 1.09 ± 0.88 (0-4.65) 1.08 ± 0.87 (0-4.81)
Postcentral 1.61 ± 0.96 (0.06-5.82) 1.4 ± 0.82 (0.06-4.88) 1.49 ± 0.89 (0.06-5.82)

Right 0.79 ± 0.49 (0.02-3.07) 0.69 ± 0.42 (0.02-2.53) 0.73 ± 0.45 (0.02-3.07)
0.017

Left 0.83 ± 0.53 (0.03-3.56) 0.71 ± 0.45 (0-2.6) 0.76 ± 0.49 (0-3.56)
Posteriorcingulate 2.6 ± 1.32 (0.33-6.97) 2.11 ± 1.07 (0.34-6.47) 2.33 ± 1.21 (0.33-6.97)

Right 1.35 ± 0.71 (0.15-3.77) 1.08 ± 0.58 (0.15-3.36) 1.2 ± 0.65 (0.15-3.77)
3.30E-06

Left 1.25 ± 0.65 (0.12-3.61) 1.03 ± 0.55 (0.1-3.46) 1.13 ± 0.61 (0.1-3.61)
Precentral 2.23 ± 1.2 (0.23-7.12) 1.98 ± 1.06 (0.25-6.68) 2.09 ± 1.13 (0.23-7.12)

Right 1.11 ± 0.62 (0.12-3.8) 0.98 ± 0.53 (0.06-3.06) 1.04 ± 0.58 (0.06-3.8)
0.57

Left 1.11 ± 0.61 (0.11-3.78) 1 ± 0.55 (0.05-3.62) 1.05 ± 0.58 (0.05-3.78)
Precuneus 2.65 ± 1.11 (0.37-7.24) 2.45 ± 1.06 (0.52-6.35) 2.54 ± 1.09 (0.37-7.24)

Right 1.47 ± 0.61 (0.24-3.85) 1.33 ± 0.59 (0.28-3.69) 1.39 ± 0.61 (0.24-3.85)
5.10E-74

Left 1.18 ± 0.55 (0.13-3.39) 1.11 ± 0.52 (0.22-3.3) 1.14 ± 0.53 (0.13-3.39)
Rostralanteriorcingulate 3.15 ± 2.05 (0.3-13.05) 2.53 ± 1.61 (0.15-11.6) 2.8 ± 1.84 (0.15-13.05)

Right 1.57 ± 1.1 (0.07-6.04) 1.25 ± 0.86 (0-5.88) 1.39 ± 0.99 (0-6.04)
0.57

Left 1.58 ± 1.05 (0.09-7.26) 1.28 ± 0.84 (0-5.72) 1.41 ± 0.95 (0-7.26)
Rostralmiddlefrontal 1.97 ± 1.26 (0.09-7.52) 1.81 ± 1.06 (0.15-6.13) 1.88 ± 1.16 (0.09-7.52)
55

Right 0.84 ± 0.59 (0.03-3.67) 0.8 ± 0.51 (0.04-3.39) 0.82 ± 0.55 (0.03-3.67)
2.50E-96

Left 1.13 ± 0.7 (0.02-4.28) 1.01 ± 0.59 (0.09-4.11) 1.06 ± 0.64 (0.02-4.28)
Superiorfrontal 2.49 ± 1.2 (0.33-7.13) 2.22 ± 1.09 (0.25-6.55) 2.33 ± 1.15 (0.25-7.13)

Right 1.21 ± 0.61 (0.13-3.45) 1.09 ± 0.55 (0.1-3.59) 1.14 ± 0.58 (0.1-3.59)
3.40E-09

Left 1.27 ± 0.61 (0.13-3.73) 1.13 ± 0.57 (0.12-3.3) 1.19 ± 0.59 (0.12-3.73)
Superiorparietal 2.62 ± 1.22 (0.52-6.31) 2.41 ± 1.07 (0.45-6) 2.5 ± 1.14 (0.45-6.31)

Right 1.33 ± 0.63 (0.23-3.54) 1.23 ± 0.56 (0.2-3.27) 1.27 ± 0.6 (0.2-3.54)
2.10E-05

Left 1.29 ± 0.62 (0.28-3.4) 1.18 ± 0.54 (0.13-2.9) 1.23 ± 0.58 (0.13-3.4)
Superiortemporal 1.44 ± 0.81 (0.11-4.2) 1.26 ± 0.74 (0.06-4.52) 1.34 ± 0.78 (0.06-4.52)

Right 0.59 ± 0.4 (0.02-2.45) 0.54 ± 0.38 (0.01-2.49) 0.57 ± 0.39 (0.01-2.49)
3.80E-68

Left 0.85 ± 0.47 (0.01-2.73) 0.71 ± 0.43 (0.02-2.46) 0.77 ± 0.45 (0.01-2.73)
Supramarginal 3.25 ± 1.58 (0.25-8.69) 2.9 ± 1.37 (0.44-7.75) 3.06 ± 1.48 (0.25-8.69)

Right 1.6 ± 0.8 (0.14-4.35) 1.4 ± 0.68 (0.1-3.72) 1.49 ± 0.74 (0.1-4.35)
3.10E-07

Left 1.66 ± 0.85 (0.03-4.79) 1.5 ± 0.76 (0.19-4.16) 1.57 ± 0.8 (0.03-4.79)
Frontalpole 0.32 ± 0.49 (0-2.96) 0.24 ± 0.4 (0-2.77) 0.28 ± 0.44 (0-2.96)

Right 0.17 ± 0.32 (0-1.83) 0.13 ± 0.27 (0-1.68) 0.15 ± 0.29 (0-1.83)
0.063

Left 0.15 ± 0.32 (0-2.4) 0.11 ± 0.27 (0-2.77) 0.13 ± 0.3 (0-2.77)
Temporalpole 0.89 ± 0.86 (0-4.32) 0.62 ± 0.71 (0-6.45) 0.74 ± 0.79 (0-6.45)

Right 0.36 ± 0.45 (0-3.43) 0.27 ± 0.38 (0-2.78) 0.31 ± 0.41 (0-3.43)
2.10E-12

Left 0.53 ± 0.56 (0-3.19) 0.35 ± 0.47 (0-3.67) 0.43 ± 0.52 (0-3.67)
Transversetemporal 1.52 ± 1.26 (0-7.15) 1.7 ± 1.37 (0-8.34) 1.62 ± 1.33 (0-8.34)

Right 0.74 ± 0.81 (0-5.09) 0.88 ± 0.87 (0-4.87) 0.82 ± 0.85 (0-5.09)
1

Left 0.78 ± 0.72 (0-3.76) 0.82 ± 0.78 (0-4.88) 0.81 ± 0.75 (0-4.88)
Insula 3.25 ± 1.12 (0.82-7.76) 2.87 ± 0.95 (0.96-7.43) 3.04 ± 1.05 (0.82-7.76)

Right 1.58 ± 0.6 (0.41-4.52) 1.43 ± 0.52 (0.33-3.87) 1.5 ± 0.56 (0.33-4.52)
0.0013

Left 1.67 ± 0.58 (0.41-3.92) 1.44 ± 0.49 (0.38-3.56) 1.54 ± 0.55 (0.38-3.92)
Table 1.7 Perivascular space ratio in each region of interest (ROI)
The PVS ratio was obtained by dividing the perivascular space volume computed in the ROI by the white
matter volume of the same ROI. For each ROI, the first row represents the sum of the PVS ratio computed
in the two hemispheres, while the PVS ratio of each side is reported in the second and third row (right and
left side, respectively). Data are mean ± standard deviation (range: minumum-maximum value). The
adjusted p-values refer to the Wilcoxon matched-pairs signed rank test performed to compare the PVS ratio
on the right side of each ROI with the corresponding contralateral side. The adjusted p-values that are not
significant after controlling for the false discovery rate are reported in red.

The PVS ratio is influenced by body mass index, age, and gender
Next, we investigated which demographic and clinical factors affect the amount of PVS in the
brain under physiological conditions. In 897 participants (503 females and 394 males) included in
56
the analysis, the mean age was 29.5 in females and 28 in males. The mean BMI was 26.7 kg/m
2

and was slightly higher in males (26.95) compared with females (26.42). The univariate general
linear models testing the clinical factors potentially related with the PVS ratio revealed 4
statistically significant variables: age, BMI, gender and systolic blood pressure (p<0.01 in all
cases; Table 1.8 and Figure 1.8A-C). Diastolic blood pressure, thyroid-stimulating hormone level,
hematocrit, and glycated hemoglobin were not significant (Table 1.8). We included the significant
factors as independent variables in a multivariate model testing PVS ratio as dependent variable:
higher BMI, older age, and male gender, but not systolic blood pressure, are significant predictors
of higher amount of PVS, although the average effect of these factors to PVS is relatively low
(Table 1.8). To further analyze the effects of gender and BMI on PVS, we used a two-way
ANCOVA model with 4 BMI groups (<20, 20-25, 25-30, >30), adjusting for age. The two-way
interaction term between gender and BMI did not reach the statistical significance after controlling
for the false discovery rate (Table 1.9, p=0.045). However, on the main effect analyses, we noted
that the difference in PVS ratio between males and females is statistically significant in participants
with BMI higher than 20 (Figure 1.8D). Interestingly, while in males the relationship between the
increase in PVS ratio and the increase in BMI follows a linear trend, in females the increase in
PVS is noted exclusively when the BMI is >30 (obese people) (Figure 1.8D). This result suggests
that the relationship between BMI and PVS is distinct in males and females and not solely
determined by a difference in BMI in the two groups.
We also investigated the role that cigarette smoking and alcohol could play in modulating
PVS. The PVS ratio was not significantly different in regular smokers, occasional smokers, and
non-smokers (Kruskal-Wallis, p=0.49), and the number of cigarettes per day did not significantly
57
correlate with the PVS ratio (r=-0.04, p=0.55). The total number of alcoholic drinks consumed in
one week on average was not significantly correlated with the PVS ratio (r=0.55, p=0.1).
In summary, this analysis shows that age, gender, and BMI influence the total volume of PVS,
and that the relationship between BMI and PVS is different in males versus females.

Series of univariate general linear models with PVS ratio as dependent variable
Estimate Confidence Interval (95%) p-value
Age 0.0162 0.008-0.023 7.18E-05*
BMI 0.0218 0.017-0.027 1.46E-15*
Gender (male) 0.1571 0.102-0.213 1.35E-07*
Diastolic blood pressure 0.0026 -0.00008-0.0053 0.0914
Systolic blood pressure 0.0039 0.002-0.006 0.0002*
Hematocrit 0.0031 -0.003-0.009 0.4204
Thyroid Hormone -0.0012 -0.031-0.028 0.9348
HbA1C 0.0388 -0.046-0.123 0.4204
Multivariate general linear model with PVS ratio as dependent variable
Estimate Confidence Interval (95%) p-value
Age 0.0206 0.010-0.025 7.18E-05*
BMI 0.0176 0.015-0.026 1.46E-15*
Gender (male) 0.1787 0.121-0.236 1.35E-07*
Systolic blood pressure -0.0009 -0.003-0.001 0.0914
Multivariate general linear model with age-adjusted “Cognitive Function Composite
Score” as dependent variable
Estimate Confidence Interval (95%) p-value
Gender (male) 5.6255 3.200-8.051 6.06E-06*
Education 5.0297 4.369-5.691 < 2E-16*
BMI -0.4131 -0.648-0.178 0.000597*
PVS Ratio -1.7726 -4.703-1.158 0.235489
Multivariate general linear model with age-adjusted “Early Childhood Cognitive Score”
as dependent variable
Estimate Confidence Interval (95%) p-value
Gender (male) 2.41763 0.404-4.431 0.01864*
Education 3.12245 2.573-3.672 < 2E-16*
BMI -0.29904 -0.495-0.104 0.00277*
PVS Ratio -1.95181 -4.382-0.478 0.11527
Table 1.8 Univariate and multivariate general linear models results
Significant p-values after controlling for the false discovery rate are marked with *.

58

Figure 1.8 The perivascular space (PVS) ratio is influenced by age, body mass index (BMI), and
gender
Scatterplots showing the relationship between PVS and age (A) and BMI (B) (Spearman’s rank correlation
coefficient). (C) Violin plot showing the statistically significant difference in PVS ratio between males
(green) and female (red) participants (Wilcoxon rank sum test). (D) Estimated marginal means of PVS ratio
in males and females represented in each BMI group. Significance by ANCOVA for main effects (black *)
and post-hoc comparisons (green and red *) controlling for age. The error bars are lower and upper bounds
on a 95% confidence interval of the estimate. *: adjusted p-value<0.05; **: adjusted p-value<0.01; ***:
adjusted p-value<1x10
-3
; ****: adjusted p- value<1x10
-4
. The following post-hoc comparisons were not
significant after controlling for the false discovery rate: PVS ratio difference in males between BMI groups
“20-25” and “25-30”; PVS ratio difference in males between BMI groups “< 20” and “25-30”.

F-test statistic p-value η
2

Age 22.209 2.85E-06* 0.025
Gender 42.572 1.16E-10* 0.047
BMI groups 15.552 7.34E-10* 0.051
Gender:BMI groups interaction 2.698 0.045 0.009
Table 1.9 Results of the ANCOVA model testing the effects of gender and BMI on PVS ratio after
controlling for age
BMI groups: 1. BMI < 20; 2. BMI between 20 and 25; 3. BMI between 25 and 30; 4. BMI > 30. Significant
p-values after controlling for the false discovery rate are marked with *.
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R = 0.14 , p = 2.3e−05
1
2
3
25 30 35
Age
PVS Ratio (%)
A
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R = 0.26 , p = 5.7e−15
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BMI
PVS Ratio (%)
B
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Wilcoxon, p = 6.2e−08
0
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Female Male
Gender
PVS Ratio (%)
Gender
F
M
C
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**
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***
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0.9
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< 20 20 − 25 25 − 30 > 30
BMI
PVS Ratio (estimated marginal mean)
Gender
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M
D
59
Cognitive functions in healthy adults are not influenced by PVS
Whether the occurrence of MRI-visible PVS in the general elderly population is associated
with cognitive dysfunction remains unclear
164,257
. We analyzed the effect of the PVS ratio to
cognition in healthy young adults. The average years of education in this population are 14.9±1.8
(range: 11-17) and the mean MMSE is 29±1 (range: 23-30); the education level is slightly higher
in females (14.98) compared with males (14.77), and MMSE is not significantly different in
females compared with males (29.05 and 28.96, respectively; Wilcoxon, p=0.44). The PVS ratio
is not significantly correlated with the level of education (r=-0.04, p=0.24, Figure 1.9) and the
MMSE (r=0.01, p=0.73).

Figure 1.9 Scatterplots showing the relationship of years of education with body mass index (BMI)
(a) and perivascular space (PVS) ratio (b)
Spearman’s rank correlation coefficient.

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Education (years)
BMI
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Education (years)
PVS Ratio (%)
B
60
We performed a principal component analysis on a set of 19 NIH Toolbox age-adjusted
behavioral tests to identify the tests explaining most of the variance: within the first component,
explaining 30% of the variance, the most influential tests (loadings > 0.35) are the Cognitive
Function Composite score (loading: 0.40) and the Early Childhood Composite score (loading:
0.37). These age-adjusted scores were included in a linear model (each at a time), corrected by
gender and education, as dependent variables to investigate whether the PVS ratio affects cognitive
performance. The models showed a significant trend towards the PVS ratio as a factor affecting
both the Cognitive Function and the Early Childhood Composite scores (p=0.0321 and p=0.0150,
respectively). However, when BMI was added as a covariate in both models, PVS ratio did not
reach the statistical significance, while the BMI was found to be a significant factor for both the
analyzed cognitive scores (p<0.01 in both cases), where a higher BMI was associated with lower
scores (Table 1.8). These results suggest that a higher amount of PVS in the brain of young adults
does not significantly affect cognition, and that higher BMI is associated with lower cognitive
scores. Therefore, the apparent association between the greater amount of PVS and worse
cognitive performance in a healthy young population is potentially caused by the linear
relationship between BMI and PVS.
The PVS ratio is influenced by the time of day
Next, we investigated whether the sleep quality and quantity as well as the time of day play a
role in the extent of PVS detectable on MRI. In the whole cohort (n=897), we did not find a
significant relationship between PVS ratio and the average number of hours of sleep (r=-0.05,
p=0.11) or the sleep quality index (r=0.04, p=0.2). 45 participants (31 females, 14 males, mean
age: 30.3±3.3) from the HCP S900 release underwent a second MRI scan, with the same scanner
and protocol, after 134±63 days (median: 132; q0.25: 94; q0.75: 154; range: 18-328). We analyzed
61
these scans in order to investigate whether the time of day influences the PVS volume. We tested
whether in this dataset the designation of the time of day for the first and second MRI scans could
be considered random or not. Based on the time of the earliest and the latest scan (acquired at 7:41
AM and 9:01 PM, respectively), we supposed that the MRI scanner was available from 7:30 AM
to 9:30 PM. We assumed time slots of 30 minutes and an equal probability of being scanned in
one of the possible 28 slots (from 7:30 AM to 9:00 PM), i.e. 0.04. Therefore, the expected number
of participants (n=45) being scanned in each time slot is 1.61 per time slot. For both the first and
the second MRI scan, the observed frequency of acquisition times (rounded to the nearest half
hour) was not significantly different from the expected (random) frequency of acquisition times
(Chi-square test at 1% level of significance, 27 degrees of freedom, p=0.78 and p=0.68,
respectively), hence we considered the designation of the time of day for the MRI scans to be
random.
The mean BMI (26.9±5.8) and amount of sleep (7.1±0.9 hours) before the first MRI scan were
not significantly different from those before the second MRI session (26.6±5.7 and 7.2±0.9, p=0.23
and 0.41, respectively). The difference in minutes between the MRI scan performed at a later time
of day and the MRI scan performed at an earlier time of day was computed (Figure 1.10A). The
intra-individual difference in PVS volume between the MRI scan performed at a later time of day
and the MRI scan performed at an earlier time of day was computed. We found a statistically
significant relationship between the time difference and the PVS volume change (r=0.34, p=0.022.
Figure 1.10B): the increase in PVS volume was greater when the difference between the time-of-
day of the two MRI scans was larger. These results suggest that, in people with stable sleep habits,
the amount of fluid within the PVS physiologically changes throughout the day, with more fluid
detectable at later times of the day.
62

Figure 1.10 The perivascular space (PVS) volume in the single individual changes throughout the day
A. Boxplot showing the difference in time of day between the MRI scan performed at a later time of day
(right) and the MRI scan performed at an earlier time of day (left) in each participant (n=45).
B. Scatterplot showing the relationship between the difference in time-of-day the two MRI scans have been
performed (in minutes) and the corresponding changes measured in the perivascular space volume (PVS).
Spearman's rank correlation coefficient. None of the values included in this plot is a significant outlier
(Extreme studentized deviate method, p>0.01).

The PVS ratio is influenced by genetic factors
Finally, to study the relationship between PVS and genetic factors, we focused on 3 groups:
51 couples of monozygotic twins (62 females and 40 males, mean age: 29.3±3.4), 29 couples of
dizygotic twins (36 females and 22 males, mean age: 29.3±3.3), and 143 couples of non-twin
siblings (148 females and 138 males, mean age: 28.4±3.9) available on the HCP dataset. The
correlation between the PVS ratio of each participant with the PVS ratio of the corresponding
sibling was statistically significant in monozygotic twins and non-twin siblings (p<0.01 in both
cases, Figure 1.12A and Figure 1.12C), but did not reach statistical significance in dizygotic twins
after controlling for the false discovery rate, possibly due to the lower sample size (p=0.037. Figure
1.12B). The correlation was still significant when all couples of siblings (twins and non-twins)
were grouped together (r=0.54, p<0.01). After randomization of the pairs, achieved by exchanging
one member of the siblings with another member from a different couple, the correlation between
the 2 PVS ratios in the new randomized couples was not significant anymore (r=0.027, p=0.64,
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63
Figure 1.12D). In any of the 3 groups, the difference in the PVS ratio measured across matched
siblings was not correlated with the corresponding difference in BMI between each member of the
pairs (p=0.91, 0.34, and 0.97, in monozygotic, dizygotic, and non-twin siblings, respectively.
Figure 1.11). These results suggest that genetic factors influence the amount of PVS in the brain.
To gain insights on the specific genetic elements that could affect PVS, we performed a
genome-wide association analysis in the 831 participants for which genetic data was available,
with the goal of finding single nucleotide polymorphisms (SNPs) associated with PVS ratio. A
SNP located in the OR10T2 gene (Olfactory Receptor Family 10 Subfamily T Member 2) in
chromosome 1 was found to be significantly associated with PVS ratio at a suggestive association
threshold (p=3E-6. Figure 1.12E).

Figure 1.11 PVS and BMI in siblings
Scatterplots showing the relationship between the difference in body mass index (BMI) and the difference
in perivascular space (PVS) in each couple of monozygotic twins (a), dizygotic twins (b), non-twin siblings
(c). Spearman’s rank correlation coefficient.

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Relationship between PVS Ratio difference and BMI difference in monozygotic twins A
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Relationship between PVS Ratio difference and BMI difference in non−twin siblings C
64

Figure 1.12 The perivascular space (PVS) ratio is influenced by genetics.
Scatterplots showing the relationship of the PVS ratio in each member of the couples plotted against the
PVS ratio of the corresponding sibling, in monozygotic twins (A), dizygotic twins (B), and non-twin
siblings (C). D. The correlation is not significant after randomization of one member in each couple,
including twins and non-twin siblings. Spearman's rank correlation coefficient. E. Manhattan plot showing
the association p-values between SNPs and PVS ratio across the genome.

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E
65
Discussion
My findings demonstrate that perivascular spaces display a significant inter-subject variability
in a healthy young population and that several factors contribute to the amount of PVS measured
on MRI. We confirmed that the absolute volume of PVS is strongly correlated with the white
matter volume, corroborating the importance of computing a white-matter-adjusted measure of
PVS (PVS ratio). This is usually impracticable in the analysis performed with visual rating scales,
representing a significant limiting factor for the correct interpretation of the results they can
provide.
Concerning the distribution of PVS in the brain, the centrum semiovale and basal ganglia are
typically recognized as the area where most of the PVS are usually visible
145
, but the physiological
regional division in the white matter is not known. We showed that the majority of PVS are visible
in the white matter below the superior frontal and parietal cortices, while the highest PVS ratio
was found in the white matter adjacent to the cingulate and insular cortices (capsulae extrema and
externa). Moreover, an asymmetric distribution of PVS across the two hemispheres was found in
our healthy population, with some regions presenting more than 50% times higher PVS ratio on
one side compared with the contralateral part. These results suggest that an asymmetric distribution
of PVS across the white matter is possibly physiological and not related to pathology. Numerous
types of data support the hypothesis that the human brain present asymmetries at multiple levels,
including gross anatomy, cytoarchitecture, and functional level
258,259
. The cerebral blood flow and
metabolic rate measured using Positron Emission Tomography and
15
O-labeled radiotracers, for
example, were found to be asymmetric in multiple brain regions of healthy subjects, which was
interpreted as a physiological asymmetry
259
, possibly caused by anatomical differences in the
distribution of blood vessels and/or by the lateralization of brain functions. Similar asymmetries
66
have also been found on functional MRI studies, based on blood oxygenation level-dependent
contrast, in healthy people
260
. On the other hand, recent works in stroke and post-traumatic
epilepsy show that the asymmetry in perivascular flow may play a key pathogenetic role in those
diseases
91,233,261
. Further studies are required to clarify the significance of the asymmetric
distribution of PVS volume in the white matter, to understand for example whether it corresponds
to an asymmetric distribution of the blood vessels or to asymmetries in the flow of the perivascular
fluid.
To our knowledge, this neuroimaging-based PVS map is the most structurally complete atlas
of the human PVS to date and can be used as a resource for future quantitative investigations of
PVS.
We also investigated factors potentially affecting PVS in a healthy population.
While aging has already been shown to be associated with more MRI-visible PVS
175
, we
confirmed this finding even in a population with a relatively narrow age range (22-37).
Interestingly, BMI represents a novel factor influencing the amount of PVS: BMI was the most
significant variable correlated with PVS ratio in our population, although the correlation was
relatively weak. Previous studies have shown that BMI has a linear relationship with CSF pressure
in a population with normal CSF pressure values (8-15 mmHg)
262
. Even though it was not possible
to measure the CSF pressure in our population, this result suggests that the correlation between
PVS and BMI could be a consequence of higher CSF pressure in participants with high BMI.
Additionally, obesity is known to critically affect vascular function, including the vascular
contractile response
196
, which is thought to be one of the main factors driving fluid movement
through the PVS
263
. Hence, vascular contractility could represent another link between BMI and
PVS. Nevertheless, since BMI is a non-specific index which can be equally influenced by lean
67
body mass, fat, and body fluid, the biological mechanisms explaining the relationship between
BMI and PVS remain to be investigated.
Another interesting finding is that males showed higher PVS ratio than females. Previous
studies using visual rating scores have reported greater prevalence of MRI-visible PVS in men
compared with women, both in normal elderly and dementia cohorts
152,174,178
. Intriguingly, we
observed an age-corrected gender difference in PVS ratio in all BMI groups except in participants
with BMI less than 20 and the effect of BMI on PVS ratio is more pronounced in males compared
with females, especially in people with BMI between 20 and 30. BMI is positively correlated with
plasma biomarkers of inflammation
264
. The astrocytic response to inflammation has been
previously demonstrated to be higher in males compared with females, possibly due to the perinatal
testosterone which programs astrocytes for a different response to inflammatory challenges
265
.
Therefore, the higher amount of PVS we found in male participants compared with BMI-matched
females when BMI is higher than 20 might be related to a more vigorous inflammatory response
in males, which can affect the perivascular flow and the size of PVS
265
. It would be interesting for
future studies to explore the effects of high-fat diet on the cerebrovasculature and the perivascular
flow, comparing males and females, and to verify the potentially different changes in the
perivascular flow and whether these changes lead to pathological modifications at the cellular and
cognitive levels.
Regarding cognitive function, our results do not substantiate a significant relationship between
neuropsychological test scores and PVS ratio in young adults, although there is a trend showing
more PVS in people who scored worse in some cognitive tests. Remarkably, this trend is mostly
explained by the BMI, which appeared as a critical factor in relation with some cognitive scores,
where a higher BMI was associated with lower scores. This finding, however, cannot support a
68
biological causal connection between BMI and specific aspects of cognition. For example, it
should be noted that in our population higher BMI was inversely correlated with the level of
education (Figure 1.9A). In fact, several other epidemiological and cultural factors, potentially
associated with BMI and not included in our analysis, could at least partially affect the scores
obtained in the neuropsychological tests administered.
Concerning the analysis of sleep, previous human studies showed that people with impaired
sleep efficiency and obstructive sleep apnea present increased PVS visibility, which was indirectly
interpreted as PVS dysfunction
170,191
. Higher PVS volumes were correlated with
objective polysomnography-derived measures of poor sleep quality
170
. Our findings do not show
any significant difference in PVS ratio between people on different hours of sleep. This might be
due not only to the high inter-subject variability on PVS related to other factors, but also to
different body postures exhibited by each participant during sleep
266
, which were not considered
in our study. In fact, animal studies showed that the position assumed during sleep is another
critical factor affecting the CSF transport in the PVS
266
. Additionally, influence of sleep problems
on the brain has been reported to occur in midlife and older ages
267,268
, so the effects on PVS might
still be undetectable in the young population that we analyzed.
On the other hand, our results showed the time-of-day as an important element affecting the
PVS volume. Specifically, a subset of people that underwent MRI scans twice in different days at
different time showed a significantly higher PVS volume in the afternoon and the evening
compared with the PVS volume measured on the same individual’ scan acquired at an earlier time
of day, which is indicative of a circadian fluctuation in the perivascular flow. However, since the
interval time between the two scans is relatively large, we cannot fully exclude that the changes
detected in PVS volume might be determined by other factors, including aging. Increased uptake
69
of CSF tracer gadobutrol into the entorhinal cortex overnight has been recently shown in patients
with idiopathic normal pressure hydrocephalus and controls
269,270
, suggesting a critical role of
natural sleep for glymphatic function, as indicated by studies in rodents
187
. A similar fluctuation
has been also demonstrated in diffusivity measures of brain tissue derived from diffusion tensor
imaging: mean diffusivity, which is significantly influenced by perivascular spaces as well
271
, was
found to systematically increase from morning to afternoon scans
272
. More recently, the
perivascular flow was also shown to be under circadian control in mice
273
. Here we further
validated these findings, showing an increased amount of fluid within the PVS in the white matter
at later time of day in the same person. These changes might be related to circadian oscillations in
blood pressure and/or respiration, two regulators of the perivascular flow
263,274
, or to circadian
regulation of the flow by aquaporin-4, a water channel supporting the fluid transport from the PVS
to the cerebral parenchyma
273
.
Finally, we analyzed for the first time the influence of genetic factors on the PVS ratio in a
healthy population. We found that couples of siblings have more similar PVS ratio compared with
couples of non-siblings. The similarity was more pronounced in monozygotic twins and was not
explained by the difference in BMI. Subsequently, we looked for genome-wide significant
association between SNPs and PVS ratio. Although none of the SNPs passed the Bonferroni
threshold for GWAS, possibly because of the relatively small sample size of the analyzed cohort,
SNPs that are associated with suggestive significance also provide crucial biological insights,
given the polygenic and multifactorial nature of many complex phenotypes such as PVS
275
.
Interestingly, the SNP showing the most significant association with PVS ratio was located in the
OR10T2 gene, a highly conserved region which encodes for one type of olfactory receptors.
Previous studies in humans and mammalians have shown an intimate connection between CSF
70
circulation and olfactory-associated perineural, perivascular, and lymphatic compartments, which
represent a significant drainage pathway and access route to the brain
276–280
. Olfactory receptors
may therefore represent an important regulator of the inflow and outflow of molecules in the
perivascular spaces, consequently affecting the amount of fluid within the PVS.
Of note, since the measure and detectability of perivascular spaces depend on the resolution
of the images, acquisition parameters, and the field strength of the MRI system
70
and can be
significantly influenced by the presence of motion artifacts, a direct comparison of our results with
data acquired at different field strength and/or resolution might be inappropriate. In addition to the
quality and type of image, the tool used to segment PVS is another critical variable to take into
consideration. Several approaches for PVS segmentation have been developed recently
147,148,152–
154
: although some of them, including ours, make use of the Frangi filter to segment PVS
147,148,153
,
it is important to note that different parameters and settings may lead to different outputs and
measures of PVS.
This study represents the largest quantitative analysis of PVS in humans using MRI and the
only one performed in healthy young adults. These findings can be used as a resource for clinicians
and researchers investigating PVS: we provide PVS volumes, PVS/white matter ratios, and their
regional distribution that can be helpful when studying PVS under pathological conditions and
when attempting to identify patients with abnormal PVS size, location, and asymmetry. Moreover,
we report several novel factors that significantly contribute to the observed high inter-subject
variability of PVS visibility in healthy participants, that should be taken into consideration in future
research studies analyzing PVS.

71
Perivascular spaces in spaceflight
Introduction
A human mission to Mars and the building of a lunar outpost are two main goals several space
agencies are aiming to achieve. This requires understanding how the human brain adapts to long-
term exposure to reduced-gravity environments.
Widespread changes in brain structure and CSF redistribution were observed on postflight
MRI in space-flyers, including ventricular enlargement with no parenchymal atrophy
281–286
, brain
upward displacement with VSA narrowing
281,283,285
, and alterations in water diffusivity
287
. These
changes correlate with spaceflight duration
288,289
, persist for several months after return to Earth
281–
284
, and suggest altered CSF homeostasis associated with spaceflight.
The clinical relevance of these alterations is unknown, but they might be related to SANS, a
disorder characterized by ocular structural changes affecting approximately 40-60% of NASA
astronauts undergoing long-duration missions aboard the ISS
290
. Visual alterations after
spaceflight were noted in ROS cosmonauts
281,291,292
, but no published reports described them using
the SANS classification.
Here, I aim to determine if spaceflight induces volumetric changes to PVS, a brain-wide
network of perivascular channels along which CSF-ISF exchange occurs
82
; to investigate the
relationship between PVS dilation and spaceflight-associated alterations in VSA and LV; to
analyze the relationship between these alterations and SANS. Furthermore, as differences in the
adoption of microgravity countermeasures may influence the degree of the spaceflight-associated
changes, we exploratively compared the alterations in these compartments for American,
European, and Russian crews in the first joint international study of pre- and post-spaceflight brain
MRI.
72
Methods
Experimental Design
This observational study includes brain MRI data that were collected per routine medical
operations protocol in NASA astronauts, as well as from an ESA-endorsed prospective MRI study
(“BRAIN-DTI”) in Roscosmos (ROS) cosmonauts, ESA astronauts, and control participants. Data
collected in 24 NASA astronauts who participated in long-duration space missions to the ISS and
in 7 NASA astronauts involved in missions of short duration in the Space Shuttle Program were
provided to the investigators by the NASA Lifetime Surveillance of Astronaut Health Program.
Brain MRI scans were acquired in 13 ROS cosmonauts and in a small group of ESA astronauts
who participated in long-duration missions to the ISS. The number of ESA astronauts included in
this study is not reported to maintain astronaut anonymity. The mean (SD) mission duration of all
long-duration spaceflight subjects combined for the NASA, ROS, and ESA crews was 179.6 (47.8)
days, while for the 7 NASA Shuttle astronauts was 14.7 (1.6) days. Brain MRI data were also
available for 13 healthy control participants, who were of similar age and education level as the
space crews. Demographic characteristics of the participants are reported in Dataset S1. The age
was not significantly different between the five groups (P=.17, one-way ANOVA). In space-flyers
who underwent long-duration missions aboard the ISS, age was not correlated with WM-PVS
changes (ρ=0.07, P=.65), BG-PVS changes (ρ=0.17, P=.29), VSA changes (ρ=0.14, P=.37), LV
changes (ρ=-0.19, P=.23), mission duration (ρ=0.20, P=.21), previous spaceflight experience
(ρ=0.13, P=.49) and interval landing-postflight MRI (ρ=-0.26, P=.09).
The study comprising brain MRI data acquisition in ESA and ROS crews and control subjects
was approved by the ESA Medical Board, the Committee of Biomedicine Ethics of the Institute
of Biomedical Problems of the Russian Academy of Sciences, and the Human Research
73
Multilateral Review Board. The study comprising brain MRI data review of NASA crews was
approved by the Institutional Review Boards at the NASA Johnson Space Center and the Medical
University of South Carolina.
NASA astronauts and ROS cosmonauts were scanned a mean (SD) of 517.2 (148.7) and 121.0
(27.9) days before launch to the ISS (preflight MRI) and 2.6 (1.7) and 9.1 (3.2) days after return
to Earth (postflight MRI), respectively. ESA astronauts were scanned 110.3 (55.8) days before
launch and 5.8 (2.2) days after landing. NASA Shuttle astronauts were scanned 140.9 (58.7) days
before launch and 14.4 (6.6) days after landing. Control participants were scanned twice, with a
mean interval of 368.2 (101.1) days between scans (Figure 1.13).
All participants provided written informed consent to take part in this study.
Demographic characteristics of the participants are reported in Table 1.10.

74

Controls
NASA astronauts
ROS
cosmonauts
P value
(ROS
versus
NASA ISS)
Shuttle ISS
Number of
participants
(females)
13 (0) 7 (1) 24 (5) 13 (0) N/A
Age (years)
46.2 (4.8)
46.1
(2.8)
48.6
(5.4)
47.4 (5.2) .52
Spaceflight
duration (days)
N/A
14.7
(1.6)
174.4
(49.5)
184.4 (52.6) .57
Previous time in
space (days)
N/A
112.4
(130.4)
88.8
(121.6)
266.3 (172.2) .004
Time between
preflight MRI
scan and launch
(days)
N/A
140.9
(58.7)
517.2
(148.7)
121.0 (27.9) <.001
Time between
landing and
postflight MRI
(days)
N/A
14.4
(6.6)
2.6 (1.7) 9.1 (3.2) <.001
Table 1.10 Demographic characteristics of the controls, NASA astronauts, and ROS cosmonauts
The P values refer to independent samples t-test comparing data from NASA ISS astronauts and ROS
cosmonauts. Values represent means (standard deviations), except those in the “number of participants”
row. Due to the small sample size, data pertaining to ESA astronauts have not been included in the statistical
analyses exploring the differences between the space crews. Moreover, their demographic characteristics
are not reported in this table in order to protect the ESA astronauts’ identity.

Ophthalmologic data
Ophthalmologic records were available for the NASA ISS astronauts. We identified NASA
astronauts who developed SANS based on the current diagnostic criteria for SANS, including optic
disc edema, globe flattening, choroidal folds, hyperopic refractive error shifts and cotton wool
spots
293,294
. While the definition of SANS is evolving and a new definition has recently been
75
proposed based on incorporating changes in total retinal thickness
295
, this measurement was not
performed historically and, as such, was not available for all of the astronauts in our cohort.
Therefore, for this study, we followed the current clinical diagnostic criteria for SANS
classification.

Figure 1.13 Study design
Schematic overview of the study design for the long-duration spaceflight group (top panel), including
NASA astronauts, ESA astronauts, and ROS cosmonauts undergoing long-duration missions (~6 months)
aboard the International Space Station (ISS), the short-duration spaceflight group (middle panel), including
NASA astronauts involved in missions of short duration (~2 weeks) in the Space Shuttle Program, and
controls on Earth (bottom panel). The number of ESA astronauts included in this study is not reported in
order to protect the astronauts’ identity.
Preflight MRI Postflight MRI
LONG-DURATION SPACEFLIGHT GROUP
Mission on ISS
ESA
NASA
ROS
CONTROLS ON EARTH
13
First MRI Second MRI
7
Preflight MRI
SHORT-DURATION SPACEFLIGHT GROUP
Mission on Space Shuttle
NASA
(~2 weeks)
(~6 months)
Postflight MRI Launch Landing
Launch Landing
24
13
13
24
13
7
Controls
(~1 year)
76
MRI protocol
All brain scans were performed on 3T MRI machines equipped with 16-channel receiver head
coils. The MRI protocol included the acquisition of 3D high-resolution T1-weighted structural
images (voxel size 1×1×1 mm). NASA astronauts were scanned on a 3T Siemens Verio system
(Siemens, Erlangen, Germany) in Houston, Texas, with the following parameters: T1-weighted
magnetization-prepared rapid acquisition of gradient-echo sequence; 176 sagittal slices;
TR=2300ms; TE=2.98ms; TI=900ms; flip angle=7°. ROS cosmonauts and 9 controls were
scanned on a GE Discovery MR750 3T MRI system (GE Healthcare, Milwaukee, Wisconsin) at
the National Medical Research Treatment and Rehabilitation Center of the Ministry of Health of
Russia in Moscow, Russia with the following parameters: T1-weighted fast spoiled gradient echo;
176 slices; TR=7.9ms; TE=3.06ms; TI=450ms; flip angle=12°.
The ESA astronauts and 4 controls were scanned on a 3T Siemens Biograph mMR system
(Siemens, Erlangen, Germany) located at the German Aerospace Center (DLR) in Cologne,
Germany, with the following parameters: T1-weighted magnetization-prepared rapid acquisition
of gradient-echo sequence; 176 sagittal slices; TR=1900ms; TE=2.43ms; TI=900ms; flip
angle=12°).

MRI data analysis
Preprocessing and parcellation of the T1-weighted images were performed using the recon-
all module of FreeSurfer (v5.3.0, https://surfer.nmr.mgh.harvard.edu/)
244
. Briefly, the following pre-
processing steps were applied: motion correction, non-parametric non-uniform intensity
normalization, Talairach transform computation, intensity normalization and skull stripping
296
.
Grey matter, white matter, and ventricular system parcellations were derived from the Desikan-
77
Killiany atlas
253
. The lateral ventricle (LV) volume was computed by summing the left and right
LV volumes obtained from the ventricular system parcellation in the native space.
Basal ganglia PVS (BG-PVS) and white matter PVS (WM-PVS) segmentation was performed
on the parcellations in the native space previously derived from FreeSurfer via an automated
quantification pipeline
148
, as in previous studies
86,124,216
. Briefly, an adaptive non-local mean
filtering method
245
was applied on the T1-weighted images to remove bias intensity caused by the
Rician noise in the MRI data. The PVS voxels were preserved by applying the filter only on high
frequency spatial noise and by using a filtering patch with a radius of 1 voxel, which allows to
preserve the signal intensities that are spatially repeated
245
. The Frangi filter with the default,
recommended parameters of α, β, and c
158
implemented in the Quantitative Imaging Toolkit
(http://cabeen.io/qitwiki)
248
was subsequently applied to the denoised T1-weighted images for
estimating the probability-like measure of “vesselness” in the native space
158
. In order to maximize
the PVS inclusion, we set the scale to a large range of 0.1–5 voxels. Finally, we applied a
previously optimized threshold of 0.00002
86,148
to the vessel map in order to obtain a binary mask
of PVS. The segmentation masks were reviewed blindly to the clinical and demographic
information and manual corrections were applied with ITK-SNAP (version 3.8.0, www.itksnap.org)
in case of mistakes in the segmentation.
Advanced Normalization Tools (ANTs v2.2.0, http://stnava.github.io/ANTs/)
246
was used to
register all the T1-weighted images to a reference template, using a diffeomorphic image
registration
297,298
, and to automatically segment the subarachnoid CSF space. From this
segmentation mask, the fraction of the subarachnoid CSF space at the vertex (VSA) was obtained
by selecting the first 30 slices (i.e., 30 mm) from the cranial vertex of the reference template
towards the brain, as per the observations reported in previous publications
281,283,285
. The volume
78
of the VSA mask space was subsequently computed in the template space and used in statistical
analyses.
The percentage change in WM-PVS, BG-PVS, LV, and VSA between the preflight and the
postflight values was calculated as:
%𝐶ℎ𝑎𝑛𝑔𝑒 =
𝑉𝑜𝑙𝑢𝑚𝑒
34'(
−𝑉𝑜𝑙𝑢𝑚𝑒
3$#
𝑉𝑜𝑙𝑢𝑚𝑒
3$#
×100
Due to the different scanners adopted among groups, we used the percentage changes when
comparing NASA with ROS MRI data, and absolute volumes when comparing intra-individual
differences from the first to the second timepoint and when comparing groups of individuals
scanned with the same machine (i.e., NASA ISS astronauts who developed SANS compared with
those who did not develop SANS). All the segmentation software used here showed consistent and
robust performance for T1-weighted images acquired on Siemens and GE scanners
86,299–301
.

Statistical analysis
Data were tested for normality using Shapiro-Wilk test. Pre- to post-flight changes in WM-
PVS, BG-PVS, VSA, and LV were evaluated for the control, ROS, and NASA Shuttle groups with
paired t-tests, and with mixed model ANOVAs for the NASA ISS group to identify changes
separately for subjects with and without SANS. Group differences between ROS and NASA ISS
were evaluated with independent samples t-tests. Due to the small sample size, ESA astronaut data
were not included in this analysis and were characterized with descriptive rather than inferential
statistics. To account for physiological brain changes over time on Earth and to accommodate the
variability in time between the preflight MRI scan and launch, preflight values were adjusted to
represent values on launch day based on the mean annual percentage change observed in our
control group for WM-PVS, BG-PVS, VSA, and LV:
79
𝑥
6%-
=𝑥+(𝑥×𝑦×𝑎)
where x is the value measured at the preflight scan, y is the interval between the preflight MRI
scan and the launch in years, and a is the mean annual percentage change of the variable of interest
(WM-PVS, BG-PVS, VSA, or LV) measured in the controls.
Spearman’s correlation coefficients measured associations between changes in WM-PVS,
BG-PVS, VSA, LV, spaceflight experience, mission duration and interval landing-postflight MRI.
Statistical significance was set at α<0.05, two-tailed P values are reported, and post-hoc
comparisons were conducted using the Šídák method
302
to correct for multiple comparisons.
Analyses were conducted with SPSS (v25, IBM: Armonk, NY).

Results
We analyzed brain MRI scans acquired before and within two weeks after long-duration
spaceflight on the ISS (~180 days) in 24 NASA astronauts (48.6±5.4-y-old), 13 ROS cosmonauts
(47.4±5.2-y-old), and a small group of ESA astronauts (number not reported to protect the
astronauts’ identity). Since on Earth physiological changes occur over time in PVS, VSA, and LV,
we also analyzed scans acquired with one-year interval in 13 age-matched healthy volunteers
(46.2±4.8-y-old) who stayed on Earth: their WM-PVS, BG-PVS, VSA, and LV volume changes
between the first and second scan (Figure 1.14A-D) were not significant (5.3%, 1.0%, -0.9%, and
0.7%, respectively). To correct for the time differences between the preflight scan and the launch
day in all space-flyers, these percentage changes were used to estimate the WM-PVS, BG-PVS,
VSA, and LV volumes at the launch day (indicated as “preflight” from now on).
We observed increased PVS volume, decreased VSA, and LV enlargement after long-duration
spaceflight on the ISS (Figure 1.14E-H,M). No significant changes occurred in 7 NASA astronauts
(46.1±2.8-y-old) who participated in 2-week missions on the Space Shuttle (Figure 1.14I-L). The
80
pre-to-post-flight WM-PVS increase was significantly correlated with VSA reduction and mission
duration (Figure 1.15A and B). Since experienced space-flyers adapt better to microgravity than
first-time flyers
281
, we also investigated the relationship between previous spaceflight experience
and the PVS changes: BG-PVS (ρ=-0.47, P=.01), but not WM-PVS changes (Figure 1.15E), were
inversely correlated with previous spaceflight experience.
The long-duration-spaceflight-associated percentage increase in PVS, but not VSA and LV,
was significantly higher in NASA astronauts than ROS cosmonauts (Figure 1.14N). Since
postflight scans in NASA astronauts were acquired closer to landing compared to ROS cosmonauts
and the spaceflight-associated alterations in LV and VSA usually reverse months after return to
Earth
281
, we tested whether the changes in PVS were correlated with the interval landing-postflight
scan: a significant inverse correlation was found for BG-PVS (ρ=-0.39, P=.01), but not for WM-
PVS (Figure 1.15F), suggesting that the higher WM-PVS increase in NASA astronauts is not
explained by a shorter landing-postflight MRI interval.
Ophthalmologic records were available for the NASA astronauts who traveled on the ISS: 8
(33.3%) developed clinical signs of SANS and presented greater pre- and post-flight WM-PVS
volumes than those unaffected, but similar VSA and LV (Figure 1.16A-D, Table 1.11, and Table
1.12). The postflight increase of PVS and VSA was comparable in the two groups, but larger in
LV of the non-SANS group (Figure 1.16E-H, Table 1.11, and Table 1.12), as reported
303
.
81

Figure 1.14 PVS volume increase after long-duration spaceflight on the ISS.
Controls on Earth do not show significant changes in PVS, VSA, or LV volumes after 1-year follow-up (A-
D). After long-duration spaceflight on ISS, we observed a significant increase in PVS and LV and decrease
in VSA volumes (E-H). Short-duration spaceflight on Space Shuttle was not associated with significant
changes in PVS, VSA, or LV volumes (I-L). Examples of 3D masks (cyan) of WM-PVS, BG-PVS, VSA,
and LV before and after long-duration spaceflight on ISS (M). The postflight changes in PVS, but not in
VSA and LV, were significantly higher in NASA astronauts than ROS cosmonauts (N). All data represent
mean±SEM. *P≤.01; **P<.001. Paired (A-L) or independent-samples t-test (N).

Preflight
ISS
Postflight
ISS
WM-PVS VSA LV BG-PVS M
N
** **
**
WM−PVS BG−PVS VSA LV
ROS NASA ISS ROS NASA ISS ROS NASA ISS ROS NASA ISS
−20
0
20
40
Pre− to post−flight change (%)
* *
0
500
1000
1500
2000
MRI 1 MRI 2
Volume (mm
3
)
Earth − WM−PVS
A
0
100
200
MRI 1 MRI 2
Volume (mm
3
)
Earth − BG−PVS
B
0
5000
10000
15000
20000
MRI 1 MRI 2
Volume (mm
3
)
Earth − VSA
C
0
5000
10000
15000
20000
MRI 1 MRI 2
Volume (mm
3
)
Earth − LV
D
0
500
1000
1500
Preflight Postflight
Volume (mm
3
)
ISS − WM−PVS
E
0
50
100
150
Preflight Postflight
Volume (mm
3
)
ISS − BG−PVS
F
0
5000
10000
15000
Preflight Postflight
Volume (mm
3
)
ISS − VSA
G
0
5000
10000
15000
20000
Preflight Postflight
Volume (mm
3
)
ISS − LV
H
0
250
500
750
1000
Preflight Postflight
Volume (mm
3
)
Shuttle − WM−PVS
I
0
25
50
75
Preflight Postflight
Volume (mm
3
)
Shuttle − BG−PVS
J
0
5000
10000
15000
Preflight Postflight
Volume (mm
3
)
Shuttle − VSA
K
0
5000
10000
15000
Preflight Postflight
Volume (mm
3
)
Shuttle − LV
L
**
82

Figure 1.15 Associations between WM-PVS dilation, brain upward shift, and spaceflight data
(A, E, and F) WM-PVS volume changes detected after long-duration spaceflight on ISS are inversely
correlated with VSA volume changes (A), but not with previous spaceflight experience (E) or the interval
between landing and postflight MRI (F). (B to D) Mission duration was significantly correlated with WM-
PVS enlargement (B) and VSA reduction (E), but not with spaceflight-associated changes in LV (D). All
data represent Spearman’s correlation. The regression line and 95% confidence bands are shown. Individual
data points are not plotted in order to protect astronauts’ identity.

Figure 1.16 Analysis of WM-PVS, VSA, and LV in NASA ISS astronauts by SANS status
Preflight and postflight WM-PVS volume (A) were significantly higher in NASA astronauts who developed
SANS than those unaffected. A significant PVS (A-B) and LV (D) enlargement and VSA reduction (C)
were observed in both groups (post-hoc comparisons, mixed model ANOVA). The spaceflight-associated
PVS dilation (E-F) and VSA reduction (G) were not significantly different between the groups, but a
significantly greater LV enlargement (H) was observed in the non-SANS group (time-by-SANS interaction,
mixed model ANOVA). Data represent mean±SEM. *P<.05, **P≤.001.

r
s
= -0.53
p < 0.001
r
s
= -0.28
p = 0.08
r
s
= -0.32
p = 0.04
r
s
= -0.26
p = 0.17
r
s
= -0.14
p = 0.40
A B C
D E F
WM-PVS: Pre- to post-flight change (%)
r
s
= 0.33
p = 0.04
VSA: Pre- to post-flight change (%) Mission duration (days) Mission duration (days)
Mission duration (days) Previous spaceflight experience (days) Landing-postflight MRI interval (days)
VSA: Pre- to post-flight change (%)
LV: Pre- to post-flight change (%)
WM-PVS: Pre- to post-flight change (%) WM-PVS: Pre- to post-flight change (%)
WM-PVS: Pre- to post-flight change (%)
-55 -45 -35 -25 -15 -5 5
80
70
60
50
40
30
20
10
80
70
60
50
40
30
20
10
100 150 200 250 300 350 100 150 200 250 300 350
10
0
-10
-20
-30
-40
-50
100 150 200 250 300 350
40
30
20
10
0
0 100 200 300 400 500 600 700
80
70
60
50
40
30
20
10
0
-10
80
70
60
50
40
30
20
10
0 2 4 6 8 10 12 14
NASA ISS non−SANS NASA ISS SANS
Preflight Postflight Preflight Postflight
0
500
1000
1500
2000
2500
PVS Volume (mm
3
)
WM−PVS
A
NASA ISS non−SANS NASA ISS SANS
Preflight Postflight Preflight Postflight
0
50
100
PVS Volume (mm
3
)
BG−PVS
B
NASA ISS non−SANS NASA ISS SANS
Preflight Postflight Preflight Postflight
0
5000
10000
15000
20000
VSA Volume (mm
3
)
VSA
C
NASA ISS non−SANS NASA ISS SANS
Preflight Postflight Preflight Postflight
0
10000
20000
30000
LV Volume (mm
3
)
LV
D
WM−PVS BG−PVS VSA LV
NASA ISS non−SANS NASA ISS SANS NASA ISS non−SANS NASA ISS SANS NASA ISS non−SANS NASA ISS SANS NASA ISS non−SANS NASA ISS SANS
−20
0
20
40
−20
0
20
40
−20
0
20
40
−20
0
20
40
Pre− to post−flight change (%)
E F G H
**
**
*
*
**
*
** **
**
*
*
83
Compartment Effect
F-test
statistic
Degree of
freedom
P value η
2
p
WM-PVS
Time 42.245 1, 22 <.001 0.658
SANS 5.380
1, 22
.03 0.196
Time x SANS 2.453 1, 22 .13 0.100
BG-PVS
Time 21.902 1, 22 < .001 0.499
SANS 0.865 1, 22 .36 0.038
Time x SANS 1.210 1, 22 .28 0.052
VSA
Time 31.553 1, 22 <.001 0.589
SANS 0.009 1, 22 .93 0.000
Time x SANS 0.360 1, 22 .56 0.016
LV
Time 41.097 1, 22 <.001 0.651
SANS 0.028 1, 22 .869 0.001
Time x SANS 5.663 1, 22 .03 0.205

Table 1.11 Mixed model ANOVA testing the effects of long-duration spaceflight on the International
Space Station and SANS on WM-PVS, VSA, and LV volumes of NASA astronauts
“Time” represents the pre- to post-flight changes in volumes. CI: confidence interval; LV: lateral ventricle;
M: mean; n: number of participants; WM-PVS: perivascular space in the white matter; SD: standard
deviations; VSA: subarachnoid space at the vertex.

84
Compart
ment
Group n
Preflight Postflight Pre- to post-flight
difference
M (SD) P value
(SANS
vs non-
SANS)
M
(SD)
P value
(SANS
vs non-
SANS)
M
(95% CI)
P
value
(pre- to
post-
flight
differe
nce)
WM-PVS
(mm
3
)
NASA ISS
Non-SANS
16
986
(391)
.04
1219
(568)
.03
233
(120 to 347)
<.001
NASA ISS
SANS
8
1485
(715)
1866
(770)
381
(221 to 542)
<.001
BG-PVS
(mm
3
)
NASA ISS
Non-SANS
16
68.93
(37.43)
.24
92.19
(48.45)
.52
23.26
(11.8 to 34.7)
<.001
NASA ISS
SANS
8
92.34
(57.14)
106.75
(56.77)
14.41
(6.1 to 22.8)
.04
VSA
(mm
3
)
NASA ISS
Non-SANS 16
16299
(3587)
.78
13485
(4099)
.91
-2813
(-4156 to
-1471)
.001
NASA ISS
SANS 8
16782
(4444)
13296
(3403)
-3486
(-5385 to
-1587)
<.001
LV
(mm
3
)
NASA ISS
Non-SANS 16
18051
(6355)
.72
21073
(7540)
.98
3022
(2199 to
3845)
<.001
NASA ISS
SANS 8
19580
(14100)
20966
(14204
)
1386
(222 to 2550)
.02
Table 1.12 Changes in WM-PVS, VSA, and LV from preflight to postflight in NASA astronauts after
long-duration spaceflight on the International Space Station
All P values represent post-hoc comparisons of the mixed model ANOVA in
Table 1.11 corrected for multiple comparisons. See also Figure 1.16. CI: confidence interval; LV: lateral
ventricle; M: mean; n: number of participants; WM-PVS: perivascular space in the white matter; SD:
standard deviations; VSA: subarachnoid space at the vertex.

85
Discussion
We showed that long-duration, but not short-duration, spaceflight is associated with PVS
enlargement. The larger pre- and post-flight WM-PVS volumes observed in NASA astronauts who
developed SANS suggests that fluid accumulation in WM-PVS might play a pathophysiological
role in SANS and that a higher WM-PVS volume at baseline might correspond to an increased risk
of SANS. Higher body weight was found associated with an increased risk of SANS in
astronauts
304
; conversely, WM-PVS burden in healthy individuals is correlated with body mass
index
124
, which is also associated with higher ICP
262
. Since WM-PVS and VSA changes were
negatively correlated, we speculate that the brain upward shift may contribute to WM-PVS dilation
by obstructing major CSF-ISF efflux routes (e.g., arachnoid granulations, superior sagittal sinus,
bridging veins, and meningeal lymphatics). Space-flyers’ sleep deprivation
305
and the elevated
CO2 on the ISS
306
may also contribute to PVS changes observed, as on Earth PVS are associated
with poor sleep quality
170
and carbogen inhalation (95%-O2/5%-CO2)
307
. PVS enlargement is
considered a nonspecific indicator of impaired brain health, being associated with several
neurological conditions, including Alzheimer and small vessel disease
82
. Our findings indicate that
long-duration exposure to microgravity on the ISS may alter the CSF-ISF circulation in PVS,
possibly impairing cerebral drainage systems like the paravascular/glymphatic pathway
308
and/or
the intramural periarterial drainage pathway
123
, and highlight the importance of a gravitationally-
maintained brain fluid homeostasis. PVS were also identified in the optic nerve
309
, and their
dilation was speculated to be related to optic disc swelling in astronauts
310
.
While LV expansion and VSA reduction were similar in ROS and NASA crews, the postflight
WM-PVS enlargement was more prominent in NASA astronauts. Since age, mission duration, and
environmental conditions were similar in NASA astronauts and ROS cosmonauts aboard the ISS,
86
other factors must play a role in this difference. We hypothesize that differences in the adoption
of microgravity countermeasures and/or exercise protocols may have influenced the extent of
WM-PVS enlargement. For example, ROS cosmonauts undergo 6 LBNP sessions starting 2 weeks
prior to landing, while NASA and ESA astronauts do not typically do it. LBNP induces caudal
displacement of fluids from the upper-body by placing legs and pelvis in a semi-airtight chamber
with negative pressure. ARED is regularly used by space-flyers to perform free-weight exercises
on the ISS, but the load and frequency of use are lower for ROS cosmonauts compared with NASA
and ESA astronauts. Lifting heavy loads during resistive exercise is often accompanied by a brief
Valsalva maneuver, inducing increased ICP and decreased cerebral blood flow and
cerebrovascular transmural pressure
311
, which can result in PVS fluid accumulation
82,124
. Although
the effects of LBNP and ARED on the brain during spaceflight are unknown, they could partly
explain the different WM-PVS changes detected in astronauts and cosmonauts. Further studies are
required to confirm these hypotheses.
Our results reveal novel spaceflight-associated structural changes of WM-PVS, which
differentially affect NASA and ROS crews and are linked to SANS, with significant implications
for designing countermeasure strategies to support human health on future long-duration
spaceflights and on Earth.
Acknowledgements
I would like to acknowledge the contributions of several co-authors of the studies presented
in this chapter who helped with different aspects of data generation and analyses. Specifically:
• “Blood-brain barrier” section: Dr. Axel Montagne (Chancellor's Fellow & UK DRI Group
Leader at The University of Edinburgh), Dr. Kassandra Kisler (Research Associate and
Assistant Director of the Optical Imaging Core at the Zilkha Neurogenetic Institute at
87
USC), Dr. Joanna Wardlaw (Professor of Applied Neuroimaging at The University of
Edinburgh), and Dr. Berislav Zlokovic (Director of the Zilkha Neurogenetic Institute at
USC) with whom I performed the literature review and prepared the figure.
• “Perivascular space” section: Drs. Kirsten Lynch and Francesca Sibilia (Postdoctoral
Research Associates at Stevens Neuroimaging and Informatics Institute at USC), Haoyu
Lan (graduate student in the Neuroscience Graduate Program at USC), Nien-Chu Shih
(Project Assistant at Stevens Neuroimaging and Informatics Institute at USC), and Dr.
Jeiran Choupan (Assistant Professor of Research Neurology at Stevens Neuroimaging and
Informatics Institute at USC) with whom I performed the literature review. Dr. Farshid
Sepehrband (Adjunct Professor at Stevens Neuroimaging and Informatics Institute at USC)
with whom I worked for the acquisition and analysis of the 7T MRI data.
• “Perivascular spaces in the white matter are affected by body mass index, time of day and
genetics” section: Dr. Farshid Sepehrband (Adjunct Professor at Stevens Neuroimaging
and Informatics Institute at USC) and Dr. Nasim Sheikh-Bahaei (Assistant Professor of
Clinical Radiology and Neurology at USC) for helping me with the data analysis. Dr. Meng
Law (Director of Radiology and Nuclear Medicine at Alfred Health) and Dr. Arthur Toga
(Director of Stevens Neuroimaging and Informatics Institute at USC) for helping me with
the study design. The Neuroscience Graduate Program at USC for supporting me during
this project. Data were provided in part by the HCP, WU-Minn Consortium
(1U54MH091657) and by the McDonnell Center for Systems Neuroscience at Washington
University (MRI and clinical data can be accessed from
https://www.humanconnectome.org).
88
• “Perivascular spaces in spaceflight”: Dr. Elena Tomilovskaya (Head of the laboratory of
gravitational physiology of sensory-motor system, Institute of Biomedical Problems of the
Russian Academy of Science), Dr. Donna R. Roberts (Professor of Radiology and
Radiological Science at Medical University of South Carolina), and Dr. Floris L. Wuyts
(Head of Lab for Equilibrium Investigations and Aerospace at University of Antwerp) and
their teams for providing the MRI and clinical data and helping with the study design; Dr.
Heather Collins (Biostatistician at Medical University of South Carolina) for helping me
with the statistical analysis; and all the volunteers, astronauts, and cosmonauts for
participating in this study. The data collection was supported by the following grants: ESA
ISLRA-2009-1062 (FLW), NASA NNX13AJ92G (DRR), Russian Academy of Sciences
grant #63.1 (ET, FLW), and Belgian Science Policy Prodex (FLW).

89
Chapter 2:
APOE4 leads to blood-brain barrier dysfunction predicting cognitive
decline
Adapted from:
Montagne A*, Nation DA*, Sagare AP*, Barisano G*, Sweeney MD*, et al., Nature, 2020

Introduction
Vascular contributions to dementia and Alzheimer’s disease are increasingly
recognized
1,77,88,161,312,313
. Recent studies have suggested that breakdown of the BBB is an early
biomarker of human cognitive dysfunction
23
, including the early clinical stages of Alzheimer’s
disease
22,43,44,88
. The E4 variant of apolipoprotein E (APOE4), the main susceptibility gene for
Alzheimer’s disease
314–317
, leads to accelerated breakdown of the BBB and degeneration of brain
capillary pericytes
68,318–321
, which maintain BBB integrity
11,14,15
. It is unclear, however, whether
the cerebrovascular effects of APOE4 contribute to cognitive impairment. Here we show that
individuals bearing APOE4 (with the ε3/ε4 or ε4/ε4 alleles) are distinguished from those without
APOE4 (ε3/ε3) by breakdown of the BBB in the hippocampus and medial temporal lobe. This
finding is apparent in cognitively unimpaired APOE4 carriers and more severe in those with
cognitive impairment, but is not related to amyloid-β or tau pathology measured in cerebrospinal
fluid or by positron emission tomography
28
. High baseline levels of the BBB pericyte injury
biomarker soluble PDGFRβ
22,23
in the cerebrospinal fluid predicted future cognitive decline in
APOE4 carriers but not in non-carriers, even after controlling for amyloid-β and tau status, and
were correlated with increased activity of the BBB-degrading cyclophilin A-matrix
90
metalloproteinase-9 pathway
68
in cerebrospinal fluid. Our findings suggest that breakdown of the
BBB contributes to APOE4-associated cognitive decline independently of Alzheimer’s disease
pathology, and might be a therapeutic target in APOE4 carriers.

Methods
Study participants
Participants were recruited from three sites: the University of Southern California (USC), Los
Angeles, CA; Washington University (WashU), St. Louis, MO; and Banner Alzheimer’s Institute
Phoenix, AZ and Mayo Clinic Arizona, Scottsdale, AZ as a single site. At the USC site,
participants were recruited through the USC Alzheimer’s Disease Research Center (ADRC):
combined USC and the Huntington Medical Research Institutes (HMRI), Pasadena, CA. At the
WashU site, participants were recruited through the Washington University Knight ADRC. At
Banner Alzheimer’s Institute and Mayo Clinic Arizona site, participants were recruited through
the Arizona Apolipoprotein E (APOE) cohort. The study and procedures were approved by the
Institutional Review Boards of USC ADRC, Washington University Knight ADRC, and Banner
Good Samaritan Medical Center and Mayo Clinic Scottsdale, indicating compliance with all
ethical regulations. Informed consent was obtained from all participants before study enrolment.
All participants (n = 435) underwent neurological and neuropsychological evaluations performed
using the Uniform Data Set (UDS)
322
and additional neuropsychological tests, as described below,
and received a venipuncture for collection of blood for biomarker studies. An LP was performed
in 350 participants (81%) for collection of CSF. DCE-MRI for assessment of BBB permeability
was performed in 245 participants (56%) who had no contraindications for contrast injection. Both
LP and DCE-MRI were conducted in 172 participants. Among the 245 DCE-MRI participants, 74
and 96 were additionally studied for brain uptake of amyloid and tau PET radiotracers,
91
respectively, as described below. No statistical methods were used to predetermine sample size.
All biomarker assays, MRI, and PET scans were analyzed by investigators blinded to the clinical
status of the participants.
Participant inclusion and exclusion criteria
Included participants (≥45 years of age) were confirmed by clinical and cognitive assessments
to be either cognitively normal or at the earliest symptomatic stage of AD. A current or prior
history of any neurological or psychiatric conditions that might confound cognitive assessment,
including organ failure, brain tumors, epilepsy, hydrocephalus, schizophrenia, and major
depression, was exclusionary. Participants were stratified by APOE genotype as APOE4 carriers
(ε3/ε4 and ε4/ε4) or APOE4 non-carriers (ε3/ε3), also defined as APOE3 homozygotes, who were
cognitively normal or had mild cognitive dysfunction, as determined by CDR scores
323
and the
presence of cognitive impairment in one or more cognitive domains based on comprehensive
neuropsychological evaluation, including performance on ten neuropsychological tests assessing
memory, attention/executive function, language and global cognition. For all analyses individuals
with ε3/ε4 and ε4/ε4 alleles were pooled together in a single APOE4 group, as we did not find in
the pre- sent cohort (82–86% ε3/ε4 and 14–18% ε4/ε4 participants, depending on the outcome
measure) a significant difference between individuals with two versus one ε4 allele for the studied
parameters, including the BBB Ktrans and sPDGFRβ CSF values (see statistical section below).
Individuals were additionally stratified by Aβ and pTau CSF analysis as either Aβ1–42+ (<190
pg/ml) or Aβ1–42− (>190 pg/ml), and pTau+ (>78 pg/ml) or pTau− (<78 pg/ml), using accepted
cutoff values.
23,67,324

Participants were excluded if they were diagnosed with vascular cognitive impairment or
vascular dementia. Clinical diagnoses were made by neurologists and criteria included whether the
92
patient had a known vascular brain injury, and whether the clinician judged that the vascular brain
injury played a role in their cognitive impairment, and/or pattern and course of symptoms. In
addition to clinical diagnosis, the presence of vascular lesions was confirmed by moderate-to-
severe white matter changes and lacunar infarcts by fluid-attenuated inversion recovery (FLAIR)
MRI and/or subcortical microbleeds by T2*-weighted MRI.
161
Participants were also excluded if
they were diagnosed with Parkinson’s disease, Lewy body dementia or frontotemporal dementia.
History of a single stroke or transient ischemic attack was not an exclusion unless it was related to
symptomatic onset of cognitive impairment. Participants also did not have current
contraindications to MRI and were not currently using medications that might better account for
any observed cognitive impairment.
Clinical exam
Participants underwent clinical assessments according to UDS procedures harmonized across
all study sites, including clinical interview and review of any neurocognitive symptoms and health
history with the participant and a knowledgeable informant. A general physical and neurologic
exam was conducted. The CDR assessment was conducted in accordance with published
standardization procedures, including standardized interview and assessment with the participant
and a knowledgeable informant. In accordance with current diagnostic models for cognitive and
biological research criteria for cognitive impairment and AD,
28
participants were separately
stratified by cognitive impairment and AD biomarker abnormality using established cutoffs for
CSF Aβ1–42 and pTau.
23,67,324
Cognitive impairment was determined on the basis of global CDR
score and neuropsychological impairment in one or more cognitive domains.
93
Vascular risk factors
The vascular risk factor (VRF) burden in each participant was evaluated through physical
examination, blood tests, and clinical interviews with the participant and informant; history of
cardiovascular disease (heart failure, angina, stent placement, coronary artery bypass graft,
intermittent claudication), hypertension, hyperlipidemia, type 2 diabetes, atrial fibrillation, and
transient ischemic attack or minor stroke were investigated. The total VRF burden was defined by
the sum of these risk factors, as previously described.
23
We assigned an elevated VRF burden to
individuals with two or more VRFs. This threshold was adopted because previous studies showed
that the presence of two or more VRFs is associated with occult cerebrovascular disease at autopsy
in older adults with AD, whereas a single VRF is common and not necessarily associated with
increased cerebrovascular disease in this population.
325,326

Cognitive domain impairment evaluation
Impairment in one or more cognitive domain was judged by performance on comprehensive
neuropsychological testing, using previously described neuropsychological criteria for cognitive
impairment.
23
All participants underwent neuropsychological testing that included the UDS
battery (version 2.0 or 3.0) plus supplementary neuropsychological tests at each site. Raw test
scores were converted to age-, sex- and education-corrected z scores using the National
Alzheimer’s Coordinating Center (NACC) regression-based norming procedures
(https://www.alz.washington.edu/). Normalized z-scores from ten neuropsychological tests were
evaluated in determining domain impairment, including three tests per cognitive domain (memory,
attention/executive function and language) and one test of global cognition. Impairment in one or
more cognitive domains was determined using previously described neuropsychological criteria,
and was defined as a score >1 s.d. below norm-referenced values on two or more tests within a
94
single cognitive domain or three or more tests across cognitive domains.
327
Prior studies have
established improved sensitivity and specificity of these criteria relative to those employing a
single test score, as well as adaptability of this diagnostic approach to various neuropsychological
batteries.
327,328
Participants were excluded from cognitive domain
analyses if they had less than 90% complete neuropsychological test data (53, 24, and 82
participants were excluded for MRI, PET, and CSF analyses, respectively). Included participants
were classified as 0, 1, or 2+ based on the number of cognitive domains for which they had two or
more impaired test scores. Test battery specifics for each UDS version and recruitment site are as
follows: i) Global cognition: MMSE for UDS version 2
329
and MoCA for UDS version 3
330
. ii)
Memory: The Logical Memory Story A Immediate and Delayed free recall tests (modified from
the original Wechsler Memory Scales, Third Edition (WMS-III)) for UDS version 2 and the Craft
Stories Immediate and Delayed free recall for UDS version 3. For supplementary tests the USC
participants underwent the California Verbal Learning Test, Second Edition (CVLT-II) and the
Selective Reminding Test (SRT) sum of free recall trials. Norm-referenced scores for these
supplementary test scores were derived from a nationally representative sample published with the
test manual (CVLT-II)
331
and in studies of normally ageing adults (SRT). iii) Attention and
executive function: The Trails A, Trails B, and Wechsler Adult Intelligence Scale—Revised
(WAIS-R) Digit Span Backwards tests for UDS version 2 and the Trails A, Trails B and Digit
Span Backwards tests for UDS version 3. iv) Language: The Animal Fluency, Vegetable Fluency,
and Boston Naming Tests for UDS version 2 and Animal Fluency, Vegetable Fluency, and
Multilingual Naming Test (MINT) for UDS version 3.
95
Magnetic resonance imaging and analysis
The MRI data sets were obtained at the Mark and Mary Stevens Neuro- imaging and
Informatics Institute of USC and Washington University of St. Louis. We developed a
standardized high-resolution 3T MRI brain scan protocol. At the USC site, a Siemens 3T Prisma
scanner was used with a product 32-channel head receive coil and body transmit coil. At the
WashU site, a Siemens 3T mMR with 20-channel head coil and Siemens 3T Vida with 64-channel
head coil were used. Anatomical coronal spin echo T2-weighted scans were first obtained through
the hip- pocampi (TR/TE 8020/50 ms, NEX = 1, slice thickness 2 mm with 2 mm gap between
slices, FOV = 175 × 175 mm, matrix size = 448 × 448). Baseline coronal T1-weighted maps were
then acquired using a T1-weighted 3D volumetric interpolated breath-hold (VIBE) sequence and
variable flip angle method using flip angles of 2°, 5°, 10°, 12°, and 15°. Coronal DCE-MRI
covering the hippocampi and temporal lobes was acquired using a T1-weighted 3D VIBE sequence
(FA = 15°, TR/TE = 5.14/2.18 ms, NEX = 1, slice thickness 5 mm with no gap, FOV 175 × 175
mm, matrix size 320 × 320, voxel size 0.550 × 0.550 × 5 mm
3
). This sequence was repeated for a
total of 16 min with an approximate time resolution of 15.4 s. Gadolinium-based contrast agent
(GBCA), gadoterate meglumine (Dotarem, Guerbet, France) (0.05 mmol/kg), was administered
intravenously into the antecubital vein using a power injector, at a rate of 3 ml/s followed by a 25-
ml saline flush, 30 s into the DCE scan. The standardization and optimization of the MRI protocol
required several tests performed on a phantom. Specifically, scanner charac- terization and
calibration sequences including B0, T1, and variable flip-angle mapping were implemented,
optimized, and applied. After the achievement of good results in terms of quality control and
reproducibility, we standardized and employed the same pre-contrast and dynamic T1-weighted
protocols at both USC and Washington University sites. Of note, all the other MR sequences were
96
also identical on both scanners. In order to minimize inter-site variability, the entire MRI protocol
including the anatomical and DCE pulse sequences were 100% mirrored from one site to another
and the same contrast agent gadoterate meglumine (Dotarem) was injected into participants at the
same concentration (0.05 mmol/kg). Finally, exactly the same pre- and post-processing analysis
pipeline was applied for both sites, including T1 multi-FA mapping using linear fitting and Patlak-
based DCE modelling using the arterial input function determined in each individual from the
internal carotid artery. Applying all the above cited factors greatly limited inter-site variability.
The consistency of the results from the two sites was additionally confirmed by our previous
publication
23
. In brief, we performed the analysis of the combined DCE data sets from both USC
and WashU sites, and additionally site-specific analysis for each of the two sites separately, which
showed no statistically significant differences across sites. Recently, we invited a subset of 52
participants for an additional T1-weighted scan without contrast (using the same scanner and same
MR pulse sequences) after their first DCE-MRI
34
and measured both B0 and T1 values at a two-
year interval. This study showed that the results were unchanged and consistent across the scans,
supporting minimal intra-site variability.
Quantification of BBB permeability
Post-processing analysis was performed using Rocketship
332
software running with Matlab.
To account for a possible confounding effect of blood flow on DCE-MRI measurements, we
determined in each studied individual the arterial input function (AIF) curve from the internal
carotid artery (ICA), which provides a dynamic profile of a gadolinium tracer concentration in the
arterial blood after the i.v. injection, instead of using an average value from the superior sagittal
venous sinus to determine tracer concentration in blood
333–336
. Although not as ideal as
simultaneous measurements of the blood flow on the same subjects, using the individual AIF
97
dynamic profile measurements of the tracer concentration in the arterial blood self-corrects for
possible differences in the blood flow that may affect delivery of the tracer to the brain via flow
across the ICA, which tends to minimize possible confounding effects of changes in blood volume
and blood flow that could potentially affect the Ktrans measurements, we reported
22,23
. The AIF,
which was extracted from a region-of-interest (ROI) positioned at the ICA, was fitted with a bi-
exponential function prior to fitting with the Patlak model
337
. In a few cases when the ICA was not
clearly visible a nearby large arterial vessel was used. The Patlak linearized regression
mathematical analysis was used to generate the BBB permeability Ktrans maps, as we previously
reported
22,23,332
. The high spatiotemporal resolution allowed not only simultaneous measurements
of the regional BBB permeability in different white and gray matter regions, but also accurate
calculations of the Ktrans values in small anatomical regions as thin as cortical gray matter areas.
The present analysis requires that the tracer’s diffusion across the BBB remains unidirectional
during the acquisition time. The total tracer concentration in the tissue, Ctissue (t), can be described
as a function of the blood concentration, CAIF (t), the intravascular blood volume, vp, and a blood-
to-brain transfer constant, Ktrans, that represents the flow from the intravascular to the extravascular
extracellular space using equation below:
𝐶
(&''7#
(𝑡)=𝐾
($68'
R 𝐶
9:;
(𝑢)
(
.
𝑑𝑢+𝑣
3
𝐶
9:;
(𝑡)
We did not observe statistically significant intersubject variability in the measurement of vp
value. For instance, vp (mean ± SEM) in hippocampus was 0.0166 ± 0.0003 (n=128; CDR 0
APOE3), 0.0167± 0.0005 (n=68; CDR 0 APOE4), 0.0183 ± 0.0009 (n=14; CDR 0.5 APOE3), and
0.0164 ± 0.0009 (n=25; CDR 0.5 APOE4). In parahippocampal gyrus, vp was 0.0172 ± 0.0003
(n=128; CDR 0 APOE3), 0.0171 ± 0.0004 (n=68; CDR 0 APOE4), 0.0180 ± 0.0009 (n=14; CDR
0.5 APOE3), and 0.0180 ± 0.0008 (n=25; CDR 0.5 APOE4). ROI-averaged analysis of DCE-MRI
98
output maps was performed by an experienced neuroradiologist who manually drew ROIs on T1-
weighted (FA 12°) pre-contrast MR images for each participant based on their own anatomy to
minimize variability between individuals as seen at a macroscopic level (e.g., enlarged ventricles,
cortical atrophy, hippocampal shrinkage). Thus, the regional BBB Ktrans permeability were
measured in 10 different gray matter ROIs including the hippocampus (HC), parahippocampal
gyrus (PHG), caudate nucleus, thalamus, striatum, orbital frontal cortex (OFC), and inferior
temporal gyrus (ITG), and white matter ROIs including subcortical watershed white matter fibers,
corpus callosum, and internal capsule.
Quantification of regional brain volumes
HC and PHG morphometry were performed using the FreeSurfer (v5.3.0) software package
(http://surfer.nmr.mgh.harvard.edu/)
244
, as previously described
23
. The HC and PHG were segmented
using FreeSurfer Desikan-Killiany and subcortical atlases.
253,338
Then, regional volumes (mm
3
)
were derived accordingly. The technical details of this procedure have been described
previously.
339,340
Data processing and visualization were performed using the Laboratory of Neuro
Imaging (LONI) pipeline system (http://pipeline.loni.usc.edu) and Quantitative Imaging
Toolkit.
248,341,342

Positron emission tomography and analysis
PET image acquisition was performed at the Molecular Imaging Center of USC or
Mallinckrodt Institute of Radiology of WashU. Amyloid and tau PET studies were conducted
using
18
F-florbetaben (FBB) or
18
F-florbetapir (FBP) and
18
F-flortaucipir (AV1451), respectively.
FBB (Life Molecular Imaging, Inc.) was obtained from SOPHIE, Inc. for the USC site, while FBP
was provided by Eli Lilly and Company for the WashU site. For all amyloid PET analysis the FBP
and FBB data sets were combined. AV1451 was provided by Avid Radiopharmaceuticals, Inc. for
99
the USC site and was produced by the Mallinckrodt Institute of Radiology for the WashU site. A
Siemens Biograph 64 PET scanner was used at the USC site. At the WashU site, FBP scans were
acquired on a Siemens mMR and AV1451 scans were acquired on a Siemens Biograph mCT. The
mCT session was used for attenuation correction of the mMR scans. Participants were injected
with 300 MBq (±10%) of FBB or 370 MBq (±10%) of FBP. FBB and FBP images were acquired
from 90 to 110 min and 50 to 70 min, respectively, after injection in accordance with the
manufacturers’ recommendations. Individuals who participated in amyloid and tau PET studies
also had their DCE-MRI scan within 2.2 ± 0.9 and 2.1 ± 0.6 months of their amyloid and tau PET
scans, respectively. In brief, a computed tomography (CT) scan was performed first for attenuation
correction before each PET imaging session. The down- loaded PET images from FBB, FBP, and
AV1451 tracers were processed by using standard uptake value maps (SUV in g/ml). All PET
images were co-registered to structural high-resolution 3D T1-weighted Magnetization Prepared
Rapid Acquisition Gradient Echo (MP-RAGE) MRI images using FSL-FLIRT (FMRIB’s Linear
Image Registration Tool).
343
The FreeSurfer-segmented cerebellum was used as a reference tissue
to normalize for both amyloid and tau.
344
After co-registration of PET images into an anatomical
reference image (MNI152 standard-space), Statistical Parametric Mapping (SPM12) was used for
group comparison on a voxel-by-voxel basis. Age at time of PET imaging session, sex, and
education were introduced in a multiple regression model as covariates. Level of significance was
set to P < 0.001 for amyloid and P < 0.005 for tau (uncorrected P values) with the minimum number
of voxels (Ke) in a cluster of 50. Additionally, given the known AV1451 off-target ligand binding
in the choroid plexus (CP)
345,346
, which can contribute to HC regional AV1451 signal owing to the
close proximity of the CP to the HC and relatively low spatial resolution of PET scans (that is, ~6-
mm voxel size), we took advantage of visualizing CP by DCE-MRI, also performed in these
100
individuals, which allowed us to subtract the contribution of the CP signal to the HC AV1451
proper signal. The following steps were used to correct for off-target ligand binding to the CP.
Step 1: HC masks were generated from the 3D T1-weighted MP-RAGE. Step 2: CP masks were
generated from the T1-weighted VIBE post-GBCA (FA = 15°) image. Step 3: HC and CP masks
were overlaid. Step 4: The overlap of the CP and HC masks was subtracted to obtain a CP-
corrected HC PET signal after adding 6-mm voxel size on top of the CP mask generated from DCE
data. We next quantified regional changes in amyloid and tau SUV ratio (SUVR) in relation to
regional DCE-MRI Ktrans values in all participants stratified by APOE genotype. The regional
SUVR values were taken from the FreeSurfer-segmented HC, PHG, OFC,
347
and ITG.
348
The BBB
Ktrans constant (DCE-MRI) was determined in all participants. This includes those who were
analysed for both amyloid and tau (n = 58), only amyloid (n = 9) or only tau (n = 29).
Lumbar puncture and venipuncture
Participants underwent a lumbar puncture and venipuncture in the morning after an overnight
fast. The CSF was collected in polypropylene tubes, processed (centrifuged at 2,000g, 4 °C, 10
min USC site; 5 min WashU site), aliquoted into polypropylene tubes and stored at −80 °C until
assay. Blood was collected into EDTA (EDTA) tubes and processed (centrifuged at 2,000g, 4 °C,
10 min USC site; 5 min WashU site). Plasma and buffy coat were aliquoted in polypropylene tubes
and stored at −80 °C; buffy coat was used for DNA extraction and APOE genotyping.
APOE genotyping
DNA was extracted from buffy coat using the Quick-gDNA Blood Miniprep Kit (catalogue
no. D3024, Zymo Research, Irvine, CA). APOE genotyping was performed via polymerase chain
reaction (PCR)-based retention fragment length polymorphism analysis, as previously reported.
23

101
Aβ peptides
An MSD multiplex assay (cat. no. K15200E, MSD, Rockville, MD) was used to determine
CSF levels of Aβ1–42. Participants were stratified based on CSF analysis as either Aβ+ (<190
pg/ml) or Aβ− (>190 pg/ml) using the accepted cutoff values as previously reported for the MSD
6E10 Aβ peptide assay.
67

Tau
Phosphorylated tau (pT181) was determined by ELISA (cat. no. 81581, Innotest, Fujirebio
US, Inc., Malvern, PA). Participants were stratified based on CSF analysis as either pTau+ (>78
pg/ml) or pTau− (<78 pg/ml), using the accepted cutoff value as previously reported.
324

Statistical analyses
Prior to performing statistical analyses, we first screened for outliers using the Grubbs’ test,
also called the ESD (extreme studentized deviate) method, applying a significance level of α =
0.01 (https://www.graphpad.com/quickcalcs/grubbs1/). For each of the outliers identified, a secondary
index of outlier influence was applied using the degree of deviation from the mean (greater than ±
3 s.d.). Using these stringent criteria, a total of five outliers were removed from analyses, as
indicated in the legends of the corresponding figures. Continuous variables were also evaluated
for departures from normality through quantitative examination of skewness and kurtosis, in
addition to visual inspection of frequency distributions. Where departures of normality were
identified, log10transformations were applied, and distribution normalization was confirmed before
parametric analyses. As the use of log10 transformations accounts for any non-normality, this
obviated the need for outliers exclusion.

102
DCE-MRI Ktrans, and CSF sPDGFRβ and CypA
Regional DCE-MRI Ktrans values and CSF sPDGFRβ, CypA and MMP9 levels were
compared across the entire sample stratified by APOE status. As in the APOE4 group relatively
few participants were homozygotes ε4/ε4 compared to heterozygous ε3/ε4 (14% for DCE-MRI
analysis, and 18% for sPDGFRβ analysis), and initial comparisons between ε4/ε4 and ε3/ε4
carriers did not show any significant differences in regional HC and PHG DCE-MRI Ktrans values
(CDR 0, PHC= 0.19 and PPHG = 0.54 (PHG); CDR 0.5, PHC = 0.22 and PPHG = 0.84) or CSF
sPDGFRβ levels (CDR 0, P = 0.23; CDR 0.5, P = 0.47), all subsequent analyses combined APOE4
carriers (ε3/ε4 and ε4/ε4), and compared these participants to APOE3 carriers (ε3/ε3) stratified by
cognitive impairment status (CDR 0 versus 0.5 and 0 versus 1 versus 2+ cognitive domain
impairment using ANCOVA with FDR correction for multiple comparisons (see details below).
For CDR analyses, model covariates included age, sex, and education. Cognitive domain
impairment was determined using age-, sex-, and education-corrected values, so these covariates
were not additionally included in the analyses. Additional post hoc ANCOVA analyses evaluated
whether the observed differences remained significant after stratifying APOE4 carriers by CSF
Aβ1–42 and pTau status, and after statistically controlling for CSF Aβ1–42 and pTau status and
regional brain volume in APOE4 non-carriers and carriers. These findings were also confirmed by
hierarchical logistic regression models using the same covariates.
PET AD biomarkers
In a subset of participants who underwent amyloid and tau PET imaging together with DCE-
MRI studies, we used ANCOVA models controlled for age, sex, and education to compare regional
amyloid and tau ligand binding and DCE-MRI values in a set of APOE4 non-carriers and carriers
within a priori regions of interest, based on prior imaging studies, to determine whether distinct
regional pathologies differed by APOE4 carrier status.
103
Multiple comparison correction and missing data
Given the large number of analyses, FDR correction was applied to P values for primary study
outcomes (DCE-MRI, sPDGFRβ) evaluated in the entire sample by APOE4 carrier status and CDR
status using the Benjamini–Hochberg method
349
in ANCOVA and logistic regression models
controlling for age, sex, education, brain volume, and CSF Aβ1–42 and pTau status (for DCE-MRI
analyses). Post hoc confirmatory analyses in participant subsets further evaluating independence
of CSF and PET markers of amyloid and tau, evaluation of mechanistic markers (that is, CypA
and MMP9), and longitudinal analysis of predictive value of CSF sPDGFRβ were not corrected
for multiple comparisons. For longitudinal data with variable follow up, we used linear mixed
model analyses with and accounted for missing data via the missing at random assumption.
Code availability
All software used in this study are publicly available: Rocketship v1.2
(https://github.com/petmri/ROCKETSHIP/blob/master/dce/), FreeSurfer (v5.3.0)
(http://surfer.nmr.mgh.harvard.edu/), FSL-FLIRT (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FLIRT), SPM12
(https://www.fil.ion.ucl.ac.uk/spm/software/spm12/), and Quantitative Imaging Toolkit
(https://cabeen.io/about/publication/cabeen2018quantitative/).
Results
Analysis of BBB permeability by dynamic contrast-enhanced magnetic resonance imaging
(DCE-MRI)
22,23
(Figure 2.1a) in 245 participants (Table 2.1) indicated that there was increased
BBB breakdown in the hippocampus (HC) and parahippocampal gyrus (PHG) in cognitively
normal APOE4 (ε3/ε4 and ε4/ε4) carriers, compared to cognitively normal APOE3 homozygotes
(ε3/ε3), both with clinical dementia rating (CDR) scores of 0. The BBB breakdown in the HC and
PHG in APOE4 carriers increased further with cognitive impairment at a CDR score of 0.5 (Figure
104
2.1b–d). This increase was independent of differences in amyloid-β (Aβ) and phosphorylated tau
(pTau) in the cerebrospinal fluid (CSF) (Figure 2.1e–h); that is, whether individuals were Aβ+ or
Aβ− and pTau+ or pTau− using the accepted cut-off values
23,67,324
, where Aβ+ and pTau+ status
indicates classical Alzheimer’s disease (AD)-associated pathways
28
. By contrast, APOE3 carriers
with cognitive impairment developed less pronounced BBB changes in the HC and PHG (Figure
2.1b–d). We found no significant BBB differences in other grey or white matter brain regions
between APOE4 carriers and APOE3 homozygotes, except for increased BBB permeability in the
caudate nucleus and minor leaks in the frontal cortex and corpus callosum in cognitively normal
APOE4 carriers (Figure 2.2).

Figure 2.1 BBB breakdown in the HC and PHG in APOE4 carriers increases with cognitive
impairment, independently of CSF Aβ and tau status
105
a, b, Maps of BBB permeability transfer constant (K trans) generated by DCE-MRI (a) in the HC of APOE3
homozygotes (APOE3) and APOE4 carriers (APOE4) with CDR scores of 0 or 0.5 (b). FA, flip angle; T1w,
T1 weighted. c, d, BBB K trans in the HC (c) and PHG (d) in individuals with CDR 0 bearing APOE3 (black,
n = 128) or APOE4 (red, n = 68) and with CDR 0.5 bearing APOE3 (black, n = 14) or APOE4 (red, n =
25). e, f, K trans in the HC (e) and PHG (f) in APOE4 carriers with CDR 0 who were Aβ 1–42 negative (Aβ−;
n = 37) or positive (Aβ+; n = 16), or with CDR 0.5 who were Aβ− (n = 7) or Aβ+ (n = 10). g, h, K trans in
the HC (g) and PHG (h) in APOE4 carriers with CDR 0 who were pTau− (n = 42) or pTau+ (n = 10), and
with CDR 0.5 who were pTau− (n = 13) or pTau+ (n = 5). i, HC (blue) and PHG (orange) overlaid on a 3D
template. j, k, Volumes of the HC (j) and PHG (k) in individuals with CDR 0 bearing APOE3 (n = 124) or
APOE4 (n = 75) and with CDR 0.5 bearing APOE3 (n = 13) or APOE4 (n = 20). l, m, K trans (estimated
marginal means ± s.e.m. from ANCOVA models corrected for age, sex, education, CSF Aβ 1–42 and pTau
status, and HC and PHG volumes) in the HC (l) and PHG (m) in individuals with CDR 0 bearing APOE3
(black, HC n = 125; PHG n = 128) or APOE4 (red, HC and PHG n = 68) and with CDR 0.5 bearing APOE3
(black, HC n = 12; PHG n = 14) or APOE4 (red, HC n = 20; PHG n = 25). c–h, j, k, Continuous line,
median; dotted line, interquartile range (IQR). Significance by ANCOVA for main effects and post hoc
comparisons controlling for age, sex, and education. All ANCOVA omnibus tests remained significant at
false discovery rate (FDR) threshold of 0.05.

APOE Genotype APOE3 APOE4 APOE3 APOE4
Clinical Dementia Rating (CDR) 0 0 0.5 0.5
No. of participants 130 76 14 25
Age at MRI, years, Mean (SD) 69.9 (7.9) 67.3 (8.7) 73.8 (8.3) 69.4 (8.7)
Female, % 62.3 57.9 42.9 56
Education, years, Mean (SD) 16.6 (2.7) 16.7 (2.0) 16.4 (2.5) 17.1 (2.1)
Cognitive domain impairment, No. 0, 1, 2+ 78, 17, 2 38, 16, 4 6, 2, 6 6, 8, 9
Vascular risk factors, No. 0-1, 2+ 77, 53 38, 38 8, 6 10, 15
Table 2.1 APOE3 and APOE4 participants studied for regional blood-brain barrier permeability
changes by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

106

Figure 2.2 Regional BBB Ktrans constant in eight additional brain regions in APOE4 carriers and non-
carriers (APOE3) with CDR status 0 and 0.5
DCE-MRI BBB permeability, Ktrans constant, in the inferior temporal gyrus (ITG, a), superior frontal gyrus
(SFG, b), caudate nucleus (CN, c), thalamus (Thal, d), striatum (Str, e), subcortical watershed normal-
appearing white matter (Subcort. WS NAWM, f), corpus callosum (CC, g), and internal capsule (IC, h) in
CDR 0 APOE3 (black, n=128) and APOE4 (red, n=68) carriers, CDR 0.5 APOE3 (black, n=14) and APOE4
(red, n=25) carriers. Violin plot continuous lines indicate median values and dotted lines indicate
interquartile range. Significance by ANCOVAs for main effects and post-hoc comparisons controlling for
age, sex, and education.

These findings held when cognitive dysfunction was evaluated by neuropsychological
performance (Figure 2.3 and Figure 2.4). The volumes of the HC and PHG decreased with
cognitive impairment in APOE4 but not APOE3 carriers (Figure 2.1i–k). The breakdown of the
BBB in the HC and PHG in APOE4 carriers, but not APOE3 homozygotes, remained a highly
significant predictor of cognitive impairment after we statistically controlled for age, sex,
education, CSF Aβ and pTau status, and HC and PHG volumes, as shown by the estimated
marginal means from the ANCOVA models (Figure 2.1l, m), and confirmed by logistic regression
107
models. The BBB dysfunction (Figure 2.1c, d, l, m) preceded brain atrophy (Figure 2.1j, k) and
was independent of systemic vascular risk factors (Figure 2.5).

Figure 2.3 BBB breakdown in the hippocampus and parahippocampal gyrus in APOE4 carriers
increases with cognitive domain impairment
(a,b) DCE-MRI BBB permeability, Ktrans constant, in the hippocampus (HC, a) and parahippocampal gyrus
(PHG, b) in individuals with 0 cognitive domain impaired that are APOE3 (black, n=70) and APOE4 (red,
n=40) carriers, 1 cognitive domain impaired that are APOE3 (black, n=18) and APOE4 (red, n=21) carriers,
and 2+ cognitive domains impaired that are APOE3 (black, n=7) and APOE4 (red, n=12) carriers. (c,d)
Ktrans (estimated marginal means ± SEM from ANCOVA models corrected for age, sex, education, CSF
Aβ 1-42 and pTau status, and HC and PHG volumes) in the HC (c) and PHG (d) in individuals with 0
cognitive domain impaired that are APOE3 (black, n=70) and APOE4 (red, n=40) carriers, 1 cognitive
domain impaired that are APOE3 (black, n=18) and APOE4 (red, n=21) carriers, and 2+ cognitive domains
impaired that are APOE3 (black, n=7) and APOE4 (red, n=12) carriers. Panels a and b: Violin plot
continuous lines indicate median values and dotted lines indicate interquartile range. Significance by
ANCOVA for main effects and post-hoc comparisons controlling for age, sex, and education. All
ANCOVA omnibus tests remained significant at false discovery rate threshold of 0.05.
108

Figure 2.4 Regional BBB Ktrans constant in eight additional brain regions in APOE4 carriers and non-
carriers (APOE3) with different degree of cognitive domain impairment
DCE-MRI BBB permeability, Ktrans constant, in the inferior temporal gyrus (ITG, a), superior frontal gyrus
(SFG, b), caudate nucleus (CN, c), thalamus (Thal, d), striatum (Str, e), subcortical watershed normal-
appearing white matter (Subcort. WS NAWM, f), corpus callosum (CC, g), and internal capsule (IC, h) in
individuals with 0 cognitive domain impaired that are APOE3 (black, n=70) and APOE4 (red, n=40)
carriers, 1 cognitive domain impaired that are APOE3 (black, n=18) and APOE4 (red, n=21) carriers, and
2+ cognitive domains impaired that are APOE3 (black, n=7) and APOE4 (red, n=12) carriers. Violin plot
continuous lines indicate median values and dotted lines indicate interquartile range. Significance tests from
ANCOVAs for main effects and post-hoc comparisons controlling for age, sex, and education.
109

Figure 2.5 Regional BBB Ktrans constant in all studied brain regions in APOE4 carriers and non-
carriers (APOE3) in relation to vascular risk factors
DCE-MRI BBB permeability, Ktrans constant, in the hippocampus (HC, a), parahippocampal gyrus (PHG,
b), inferior temporal gyrus (ITG, c), superior frontal gyrus (SFG, d), caudate nucleus (CN, e), thalamus
(Thal, f), striatum (Str, g), subcortical watershed normal-appearing white matter (Subcort. WS NAWM, h),
corpus callosum (CC, i), and internal capsule (IC, j) in APOE3 (black, n=80) and APOE4 (red, n=42)
carriers with 0-1 vascular risk factors (VRFs), and APOE3 (black, n=58) and APOE4 (red, n=51) carriers
with 2+ VRFs. Violin plot continuous lines indicate median values and dotted lines indicate interquartile
range. Significance by ANCOVAs for main effects and post-hoc comparisons controlling for age, sex, and
education (ns=non-significant).

110
Because both Aβ and tau can lead to blood vessel abnormalities and BBB breakdown
1,350,351
,
we studied whether BBB disruption in APOE4 carriers was downstream from amyloid and tau
accumulation in a subset of 74 and 96 participants, respectively (Table 2.2 and Table 2.3). Voxel-
based analysis by positron emission tomography (PET) indicated a substantially higher
accumulation of amyloid in the orbital frontal cortex (OFC) in cognitively normal APOE4 carriers
compared to APOE3 homozygotes, as reported
347
, but did not detect accumulation of tau tracer in
either APOE4 or APOE3 carriers. To determine how BBB permeability relates to accumulation of
amyloid and tau, we selected 5-mm-thick coronal slices in regions of interest that included the HC
and PHG (where BBB disruption is seen first in APOE4 carriers compared to APOE3 homozygotes
(Figure 2.1b,d,e)), the OFC (where amyloid accumulation develops initially in APOE4 carriers),
and the inferior temporal gyrus (ITG; a region that is affected early by tau pathology
348
).
Brain uptake of amyloid and tau tracers (after correction for the choroid plexus off-target
binding for tau tracer; see Positron emission tomography and analysis) and indicated no difference
between APOE4 and APOE3 carriers in the HC, although uptake of both tracers was modestly
increased compared to the background values in cerebellum (Figure 2.6a, b). The BBB was
disrupted in the HC in APOE4 carriers compared to APOE3 homozygotes (Figure 2.6c), consistent
with our findings in the larger cohort (Figure 2.1b, c). There was no difference in amyloid and tau
accumulation in the PHG between APOE4 carriers and APOE3 homozygotes, despite BBB
disruption in APOE4 carriers (Figure 2.6d–f). Amyloid accumulation in the OFC was higher in
cognitively normal APOE4 carriers than in APOE3 carriers (Figure 2.6g, h), but there was no
difference in BBB integrity (Figure 2.6g, i). In the ITG, there were no differences in tau
accumulation or BBB integrity between APOE4 and APOE3 carriers (Figure 2.6j–l). Together,
these data suggest that BBB disruption in the HC and PHG in APOE4 carriers is independent of
111
AD pathology, and that BBB breakdown in APOE4 carriers starts in the medial temporal lobe,
which is responsible for memory encoding and other cognitive functions.

APOE Genotype APOE3 APOE4
Clinical Dementia Rating (CDR) 0 0
No. of participants 45 29
No. of participants (FBB, FBP) 5, 40 9, 20
Age at amyloid PET, years, Mean (SD) 68.4 (7.5) 65.7 (8.8)
Female, % 73.3 65.5
Education, years, Mean (SD) 16.7 (2.7) 16.5 (2.1)
Cognitive domain impairment, No. 0, 1, 2+ 24, 4, 0 17, 4, 1
Vascular risk factors, No. 0-1, 2+ 23, 22 19, 10
Table 2.2 APOE3 and APOE4 participants studied for regional amyloid brain accumulation by PET
and blood-brain barrier permeability changes by DCE-MRI
FBB, participants who received 18F-Florbetaben; FBP, participants who received 18F-Florbetapir.

APOE Genotype APOE3 APOE4
Clinical Dementia Rating (CDR) 0 0
No. of participants 60 37
Age at tau PET, years, Mean (SD) 68.7 (7.9) 64.0 (8.4)
Female, % 66.6 37.8
Education, years, Mean (SD) 16.5 (2.7) 16.6 (2.1)
Cognitive domain impairment, No. 0, 1, 2+ 27, 5, 1 15, 6, 1
Vascular risk factors, No. 0-1, 2+ 36, 24 23, 14
Table 2.3 APOE3 and APOE4 participants studied for regional tau brain accumulation by PET and
blood-brain barrier permeability changes by DCE-MRI
112

Figure 2.6 Blood-brain barrier breakdown in APOE4 carriers is independent of amyloid and tau
accumulation in the brain
All studies were performed in individuals with CDR score 0. a, Representative superimposed left HC
amyloid PET (top), tau PET (middle), and BBB K trans maps (bottom) from APOE3 (left) and APOE4 (right)
carriers. b, c, Amyloid and tau tracer uptake (b) and BBB K trans (c) in HC of APOE3 (n = 45, 60, and 65)
and APOE4 (n = 29, 37, and 31) carriers. d, Representative superimposed left PHG amyloid PET (top), tau
PET (middle), and BBB K trans maps (bottom) from APOE3 (left) and APOE4 (right) carriers. e, f, Amyloid
and tau tracer uptake (e) and BBB Ktr ans (f) in PHG of APOE3 (n = 45, 60, and 65) and APOE4 (n = 29,
37, and 31) carriers. g, Representative superimposed left medial OFC amyloid PET (top) and BBB K trans
maps (bottom) from APOE3 (left) and APOE4 (right) carriers. h, i, Amyloid tracer uptake (h) and BBB
K trans (i) in OFC of APOE3 (n = 45 and 44) and APOE4 (n = 29 and 23) carriers. j, Representative
superimposed left ITG tau PET (top) and BBB K trans maps (bottom) from APOE3 (left) and APOE4 (right)
carriers. k, l, Tau tracer uptake (l) and BBB K trans (l) in ITG of APOE3 (n = 60 and 59) and APOE4 (n = 37
and 28) carriers. b, c, e, f, h, i, k, l, Continuous lines, median; dotted lines, IQR. BBB K trans was determined
in all participants (see Table 2.2 and Table 2.3) who received either both amyloid and tau tracers (n = 58),
only amyloid tracer (n = 9) or only tau tracer (n = 29). Significance by ANCOVA for group comparisons
controlling for age, sex, and education, and two-tailed t-tests for comparison of PET values to standardized
uptake value ratios (SUVR) = 1.

113
In humans with AD and animal models, elevated levels of soluble platelet-derived growth
factor receptor-β (sPDGFRβ) in the CSF indicate that pericyte injury is linked to BBB
breakdown
22,23,63
and cognitive dysfunction
23,63
. Using a median split for visual display of the CSF
sPDGFRβ baseline levels from 350 participants, we stratified all participants into two groups, with
low CSF sPDGFRβ levels (0–600 ng ml
−1
) and high sPDGFRβ levels (600–2,000 ng ml
−1
). In 146
APOE4 carriers and APOE3 homozygotes who were evaluated by cognitive exams at 2-year
intervals up to 4.5 years from baseline, participants with higher baseline CSF sPDGFRβ exhibited
accelerated cognitive decline on a global mental status exam and global cognitive composite z-
scores, which remained significant after controlling for CSF Aβ and tau status. When stratified by
APOE status, higher baseline CSF sPDGFRβ levels in APOE4 carriers predicted cognitive decline
after controlling for CSF Aβ and pTau status, but did not predict decline in APOE3 homozygotes.
The increase in CSF sPDGFRβ with cognitive impairment was also found on cross-sectional
CDR analysis in APOE4 carriers but not APOE3 homozygotes. Increased levels of sPDGFRβ in
the CSF of APOE4 carriers correlated with increases in BBB permeability in the HC and PHG,
and elevated levels of molecular biomarkers of BBB breakdown including albumin CSF/plasma
quotient, and CSF fibrinogen and plasminogen.
Next, we focused on the proinflammatory cyclophilin A–matrix metalloproteinase-9 (CypA–
MMP9) pathway. When activated by brain capillary pericytes in APOE4 (but not APOE3) knock-
in mice, this pathway leads to MMP9-mediated breakdown of the BBB, which in turn induces
neuronal stress related to leaked blood-derived neurotoxic proteins followed by neuronal
dysfunction and loss of synaptic proteins
68
. Brain tissue analysis has also shown higher activation
of the CypA–MMP9 pathway in degenerating brain capillary pericytes in APOE4 carriers than in
APOE3 homozygotes16. In our cohort, APOE4 carriers, but not APOE3 homozygotes, developed
114
an increase in CypA CSF levels with cognitive impairment, which correlated with elevated CSF
sPDGFRβ. APOE4 carriers, but not APOE3 homozygotes, also developed elevated MMP9 in the
CSF with cognitive impairment, which correlated with elevated CSF CypA levels, suggesting that
activation of the CypA–MMP9 pathway in APOE4 carriers correlates with pericyte injury, as
shown in animal models
68
. There were no differences in glia or in inflammatory or endothelial cell
injury CSF biomarkers between cognitively impaired and unimpaired APOE4 and APOE3
participants, but there was an increase in neuron-specific enolase (NSE) with cognitive impairment
in APOE4 carriers, confirming neuronal stress and consistent with atrophy of the HC and PHG.
Studies in APOE knock-in mice and mouse pericytes have shown that apoE3, but not apoE4,
transcriptionally inhibits CypA via low-density lipoprotein receptor-related protein 1, which in
turn transcriptionally inhibits MMP919. Consistent with the mouse data, pericytes derived from
APOE4 (ε4/ε4) human induced pluripotent stem cells (iPSCs) had substantially higher levels of
CypA and secreted MMP9 than those derived from APOE3 (ε3/ε3) cells, suggesting that apoE
may control the CypA–MMP9 pathway in human pericytes in an isoform-specific manner, as in
mouse models
68
.
In APOE4 carriers, CSF Aβ1–42 was reduced and CSF pTau levels were increased with
cognitive impairment, compared to APOE3 homozygotes, as reported
28
; this difference remained
significant after controlling for CSF sPDGFRβ levels. Together, these findings support the idea
that the Aβ and tau pathways operate independently of the BBB breakdown pathway during the
early stages of cognitive impairment in APOE4 carriers.

115
Discussion
We have shown that BBB breakdown contributes to cognitive decline in APOE4 carriers
independent of AD pathology; that high baseline CSF levels of sPDGFRβ can predict future
cognitive decline in APOE4 carriers; and that APOE4, but not APOE3, activates the CypA–MMP9
pathway in the CSF, which may lead to accelerated BBB breakdown and thereby cause neuronal
and synaptic dysfunction
68
. As blockade of the CypA–MMP9 pathway in APOE4 knock-in mice
restores BBB integrity and subsequently normalizes neuronal and synaptic function
68
, it is possible
that CypA inhibitors (some of which have been used in humans for non-neurological
applications
69
) might also suppress the CypA pathway in cerebral blood vessels in APOE4 carriers.
This should improve cerebrovascular integrity, and reduce the associated neuronal and synaptic
deficits, thereby slowing cognitive impairment.
Acknowledgement
I would like to acknowledge the contributions of several co-authors in this study who helped
with different aspects of data generation and analyses. Specifically:
1) Dr. Daniel Nation (Associate Professor in Psychology at University of California Irvine)
who run the statistical models and helped me determine the participants’ cognitive
domains impairment.
2) Dr. Axel Montagne (Chancellor's Fellow & UK DRI Group Leader at The University of
Edinburgh), with whom I analyzed the DCE-MRI scans to determine the participants’
regional Ktrans BBB permeability.
3) Dr. Ararat Chakhoyan (Postdoctoral Research Associate at Zilkha Neurogenetic Institute
at USC), who processed the PET data.
116
4) Dr. Abhay Sagare (Assistant Professor of Research in Physiology & Neuroscience at
USC), Dr. Melanie Sweeney (Global Operations at Amgen), and the PPG Biomarker Core,
who performed the CSF biomarker analyses and the APOE genotyping.
5) Everyone involved with recruiting and enrolling participants at USC ADRC (including
HMRI) and Washington University Knight ADRC.
This study was supported by the National Institutes of Health (NIH) grant nos. 5P01AG052350
(Zlokovic/Toga) and 5P50AG005142 (Chui), the Alzheimer’s Association strategic grant no.
509279 (Zlokovic/Toga), Cure Alzheimer’s Fund, and the Foundation Leducq Transatlantic
Network of Excellence for the Study of Perivascular Spaces in Small Vessel Disease reference no.
16 CVD 05 (Zlokovic/Wardlaw). Enrollment of participants into the Washington University
Knight ADRC is supported by NIH grant nos. P50AG05681 (Morris), P01AG03991 (Morris), and
P01AG026276 (Morris). Enrollment of participants into the USC ADRC is supported by NIH
grant no. 5P50AG005142 (Chui).

117
Chapter 3:
APOE4 cell-specific mechanisms underlying cerebrovascular
disorder, neuronal and synaptic dysfunction, and cognitive deficits
in mice
Adapted from:
Barisano G*, Wilkinson B*, Nikolakopoulou AM*, Gilliam W*, et al., Under review

Introduction
Apolipoprotein E4 (APOE4), the main susceptibility gene for Alzheimer’s disease
314–317,352
,
exerts cerebrovascular toxicity
353
leading to blood-brain barrier (BBB) breakdown in
humans
33,35,318
and APOE4 transgenic mice
68,354–356
. Moreover, BBB dysfunction predicts
cognitive decline in APOE4 human carriers
33
. However, the comprehensive large-scale analysis
of cell-specific mechanisms underlying APOE4 cerebrovascular disorder and how it relates to
neuronal disorder is lacking. Using single-nucleus RNA-sequencing, here we show that APOE4
compared to APOE3 leads to an early disruption of the BBB transcriptome in 2-3-month-old
APOE4 knock-in mice
357
. This includes genes regulating cell junctions, cytoskeleton, and
translation in brain endothelium, and transcription and RNA-splicing suggestive of DNA damage
in pericytes. Changes in BBB signaling mechanisms paralleled an early, progressive BBB
breakdown and loss of pericytes starting at 2-3 months of age. The BBB breakdown preceded loss
of neurites and behavioral deficits that developed 2-5 months later, and was associated with
astrocyte and microglia responses protecting BBB integrity. Thus, disruption of the BBB cell-
118
specific signaling mechanisms could be an initial central contributor to APOE4-mediated neuronal
disorder, and possibly a target to correct APOE4-related cognitive deficits.

Methods
Mice
Human APOE3 and APOE4 knock-in (KI)
flox/flox
mice, E3F and E4F, respectively, in which
the human apoE coding region is surrounded by loxP sites, were generated as recently described
357

and produced by the Cure Alzheimer’s Fund. All mice in the study were maintained on C57BL/6J
background. Both male and female mice were used. For RNAseq analysis 4 mice per group at 2-
3- and 9-12 months-of age were used for each genotype. For MRI analysis 8 mice per group at 2-
3-, 4-6- and 9-12 months of age were used for each genotype and time point. For tissue analysis,
5 mice per group at 2-3-, 4-6- and 9-12 months of age were used for each genotype and time point.
For behavior studies, 16 mice per group at 4-6- and 6-8 months of age were used for each genotype
and time point. All procedures were approved by the Institutional Animal Care and Use Committee
at the University of Southern California with National Institutes of Health guidelines. All
experiments were blinded; the operators responsible for experimental procedure and data analysis
were blinded and unaware of group allocation throughout the experiment.
Transcardial perfusion and tissue collection
Animals were anesthetized intraperitoneally with 200 mg/kg ketamine and 20 mg/kg xylazine.
For brain nuclear isolation and tissue collection, mice were transcardially perfused with cold 1X
phosphate buffer saline (PBS), pH = 7.4. The brain was collected and the brainstem and cerebellum
were removed. For isolation of nuclei, the right cortical mantle was separated from the right
hemisphere after the removal of the hippocampus and the visible white matter, and subsequently
119
flash frozen in liquid nitrogen. The left hemisphere from same animals was placed in optimal
cutting temperature compound (OCT) and used for histological analysis.
Isolation of nuclei from frozen cortical mantle
Nuclei were isolated as previously described
358
. Briefly, flash frozen cortical mantles were
homogenized in a Dounce homogenizer in Lysis Buffer (10 mM Tris-HCl, pH 7.5, 10 mM NaCl,
3 mM MgCl2, and 0.1% Nonidet P40 Substitute in Nuclease-Free Water). After 15 minutes
incubation, the suspension was filtered through a 30-μm Pluriselect cell strainer and centrifuged
at 500g for 5 min at 4 °C to pellet the nuclei. Nuclei were washed and filtered twice through a 40-
μm Falcon cell strainer with a nuclei wash (2% bovine serum albumin in sterile PBS with 0.2 U/μl
of RNase Inhibitor (Protector)). Nuclei were again pelleted by spinning the sample at 500g for 5
minutes at 4 °C. Nuclei pellets were resuspended in 500 μl nuclei wash and 900 µl 1.8 M sucrose.
In order to further separate the nuclei from myelin and other debris, the nuclei solution was layered
on top of 500 μl 1.8 M sucrose and centrifuged at 13,000g for 45 minutes at 4 °C. The pellet with
nuclei was resuspended in nuclei wash at approximately 1000 nuclei per μl and filtered through a
40-μm FlowMi Cell Strainer.
Single-nucleus RNA sequencing
Single nuclei isolated from mouse cortical mantles were loaded onto the Chromium platform
from 10X Genomics for droplet-based library preparation. The Chromium 3’ Reagent Kits v3 was
used to capture RNA molecules for amplification. As a quality control step, the libraries were first
sequenced using an Illumina MiSeq sequencer to examine sample multiplexing and mapping rate
to the reference mouse genome. Production sequencing runs were then carried out on Illumina
HiSeq platform to acquire more than 400 million read pairs per sample.
120
Processing data. To process fastq raw data, a customized pre-mRNA GRCm38 reference
database was created, which included human APOE transgene sequence. Alignment and gene
quantification was then performed using Cellranger v3.1.0 with default parameters and 64 CPU
threads for parallel processing. From aligned bam files, APOE3 and APOE4 genotypes were first
examined. For downstream secondary analysis, gene count matrices from all the samples were
combined before applying a cutoff of 500-7,500 genes and percent of mitochondrial genes less
than 5%. The filtered gene count matrix included 170,235 single nuclei with a median number of
2,260 genes per nucleus, similar to what has been recently reported
358
.
Clustering and annotation of mouse brain cell types. Gene counts were normalized and scaled
to regress out total unique molecular identifiers (UMI) counts per barcode using Seurat v4.0.1. The
first 30 principal components from principal component analysis (PCA) were used to find
neighbors with Findneighbors function before cell clustering with FindClusters function
(resolution = 0.02). Uniform Manifold Approximation and Projection (UMAP) dimensionality
reduction was performed using RunUMAP function with uwot-learn selected for the parameter
umap.method.
A color-coded UMAP plot was generated to visualize ten different cell clusters (Figure 3.1a).
Expression pattern of cell-type specific marker genes was visualized in a dot plot (Figure 3.1b) to
annotate cell clusters. Guided by known cell-type marker gene expression pattern, a total of six
distinct cell types were classified (Figure 3.1b and Figure 3.1c) for all the nuclei, including:
49.84% excitatory neurons (Slc17a7, Satb2), 28.83% inhibitory neurons (Gad1, Gad2), 10.54%
oligodendrocytes/OPC (Mbp, Plp1, Cspg4, Vcan, Pdgfra), 6.36% astrocytes (Slc1a2, Slc1a3,
Gja1, Aqp4), 2.51% vascular cells (Flt1, Pecam1, Cldn5, Vtn, Pdgfrb) and 1.92% microglia
(Inpp5d, C1qa, Csf1r, Hexb). Cell-type specific marker genes were called using FindMarkers
121
function with the parameters only.pos=TRUE and test.use = “MAST”. Other parameters were the
default. Genes with Bonferroni correction adjusted p-value < 0.05 were considered as marker
genes.
In silico sorting of endothelial cells and pericytes from vascular group. To separate
endothelial cells (EC) and pericytes (PC) within the vascular cell group, cell-type specific gene
expression data in PC, aSMC, microglia, astrocytes, capillary EC and arteriolar EC from a
published study of a molecular atlas of cell types in brain vasculature
6
was used to run k-means
clustering for the mouse vascular group marker genes, and the resulting six gene groups were
plotted in a clustered heatmap (color-scale representing the z-score across cell types) (Figure 3.1d).
Within 1820 vascular group marker genes, 327 and 139 genes were categorized as EC and PC
markers, respectively. These two marker gene lists were subject to AddModuleScore function to
calculate EC and PC scores for each vascular nucleus for in silico sorting of EC nuclei (EC score
>0 and PC score <0) and PC nuclei (PC score >0 and EC score <0). Within a total of 4276 vascular
nuclei, 1250 and 2072 were annotated as EC and PC, respectively. After in silico sorting, cell
identity of EC and PC was further confirmed by the expression pattern of known EC and PC
specific markers (Figure 3.2c).
122

Figure 3.1 snRNA-sequencing analysis
a, Uniform manifold approximation and projection (UMAP) space representing 10 distinct clusters obtained
via unsupervised clustering analysis. b, Dotplot reporting cell-type-specific markers used to define the
clusters. c, Proportion of nuclei included in each cluster. d, Heatmap showing average expression values of
vascular cluster signature genes in selected vascular-associated cell types, including pericytes (PC), arterial
smooth muscle cells (aSMC), capillary endothelial cells (capilEC), and arterial endothelial cells (aEC), as
well as in microglia (MG) and astrocytes (AC) according to the mouse brain vascular atlas
6
. Nuclei included
in vascular signature groups 1 (violet) and 5 (cyan) were defined as endothelial cells (EC) and pericytes
(PC) respectively. Data are from 16 mice.

Analysis of gene differential expression. DEG analysis was performed using FindMarkers
function with min.pct=0.01, logfc.threshold=0.1, test.use=’poisson’. Lists of mouse DEGs were
generated by filtering all genes with Bonferroni correction adjusted p-value < 0.05. Unless noted,
all plots were generated using R scripts. Functional categories were determined via manual
0
6
2
3
5
1
7
4
9
8
0
1
2
3
4
5
6
7
8
9
a
UMAP
UMAP
0
1
2
3
4
5
6
7
8
9
APOE
Pdgfrb
Vtn
Cldn5
Pecam1
Flt1
Cspg4
Vcan
Pdgfra
Inpp5d
C1qa
Csf1r
Hexb
Aqp4
Gja1
Slc1a3
Slc1a2
Mbp
Plp1
Gad2
Gad1
Satb2
Slc17a7
Identity
% Expressed
25
50
75
−1
0
1
2
Expression
b
Excitatory neurons 49.84%
Inhibitory neurons 28.83%
Oligodendrocytes/OPC 10.54%
Astrocytes 6.36%
Vascular 2.51%
Microglia 1.92%
c
Betsholtz cell−specific gene expression
PC
aSMC
MG
AC
capilEC
aEC
group
Group
Vascular signature 1
Vascular signature 2
Vascular signature 3
Vascular signature 4
Vascular signature 5
Vascular signature 6
−2
−1
0
1
2
d
123
curation by using the reviewed and manually annotated records available in the UniProt
Knowledgebase for genes encoding proteins with known function, as we previously reported for
protein interaction analysis
359,360
. Fisher’s exact test was used to calculate statistical significance
of overlapping gene counts in EC from E4F vs. E3F mice with published brain EC transcriptome
module in mouse models with BBB dysfunction including stroke, epilepsy, TBI and EAE
361
.
Magnetic Resonance Imaging (MRI)
As we described previously
15,80
, magnetic resonance imaging (MRI) scans were performed
using our MR Solutions 7T PET MR system (bore size ~24-mm, up to 600 mT.m
−1
maximum
gradient) and a 20-mm internal diameter quadrature bird cage mouse head coil. Briefly, mice were
anesthetized by 1–1.2% isoflurane in air. Respiration rate (80.0 ± 10.0 breaths per min) and body
temperature (36.5 ± 0.5 °C) were monitored during the experiments. The sequences were collected
in the following order: T2-weighted imaging (2D-fast spin echo (FSE), TR/TE (time
repetition/time echo) 4,000/26 ms, 32 slices, slice thickness 300 μm, in-plane resolution 100 x 70
μm
2
) to obtain structural images followed by a dynamic contrast-enhanced (DCE) protocol for the
brain vessel permeability assessment. Total imaging time was approximately 30 min per mouse.
As previously described
80
, the DCE MRI imaging protocol was performed coronally within
the dorsal hippocampus region, and included measurement of pre-contrast T1 values using a
variable flip angle (VFA) fast low angle shot (FLASH) sequence (FA (flip angle) = 5, 10, 15, 30,
and 45°, TE 3 ms, slice thickness 1 mm, in-plane resolution 60 x 120 μm
2
), followed by a dynamic
series of 180 T1-weighted images with identical geometry and a temporal resolution of 5.1 s
(FLASH, TR/TE = 20/3 ms, flip angle 15°, slice thickness 1 mm, in-plane resolution 60 x 120
μm2). A bolus dose (140 μl) of 0.5 mmol.kg
−1
Gd-DTPA (gadolinium diethylenetriamine
124
pentaacetic acid (Gd-DPTA) diluted in saline 1:6) was injected into the tail vein at a rate of 600
μL.min
−1
using a power injector. DCE images were collected within 15 min of the injection.
MRI Post-Processing Analysis of BBB permeability to gadolinium. T1 relaxation times were
estimated using the VFA method, prior to Gd-DTPA injection, with a series of FLASH images
with varying FA and constant TR and TE as previously described
22,23,33,80
.
Ktrans Mapping. We determined the BBB permeability transfer constant, Ktrans, to
intravenously injected gadolinium-based contrast agent in the dorsal hippocampus and primary
somatosensory cortex as we previously reported in mice
15,80
and humans
22,23,33,80
uisng the post-
processing Patlak analysis
22,23,33
. We determined the arterial input function (AIF) in each mouse
from the common carotid artery, as previously reported
15,80
.
The present Patlak analysis requires that the tracer's diffusion (Gd-DTPA) across the capillary
vessel wall remains unidirectional during the acquisition time. The total tracer concentration in the
brain tissue, Ctissue (t), can be described as a function of the vascular concentration CAIF (t), the
intravascular blood volume vp, and a transfer constant Ktrans that represents the flow from the
intravascular to the extravascular space using the equation below.

Post-processing of the collected DCE-MRI data was performed using in-house DCE
processing software (Rocketship) implemented in Matlab R2019b version
332
.
Statistical analysis
Sample sizes were calculated using nQUERY assuming a two-sided alpha-level of 0.05, 80%
power, and homogeneous variances for the 2 samples to be compared, with the means and common
standard deviation for different parameters predicted from published data and our previous
studies
15,80,362–364
. Data are presented as mean ± S.E.M. as indicated in the figure legends. For
0
() ( ) ()
t
tissue trans AIF p AIF Ct K C d C t tt n =+×
ò
125
multiple comparisons, Bartlett’s test for equal variances was used to determine the variances
between the multiple groups and one-way analysis of variance (ANOVA) followed by
Bonferroni’s post hoc test was used to test statistical significance, using GraphPad Prism 8.3.1
software. Data were tested for normality using the Shapiro-Wilk test. For parametric comparison
between two groups, F test was conducted to determine the similarity in the variances between the
groups that are statistically compared, and statistical significance was analyzed by Student’s t-test.
A P value of less than 0.05 was considered statistically significant. Additional statistical analysis
specific for DEG are described in the corresponding section of the methods.

Results
To begin unravelling the effects of APOE4 gene on cell-specific signaling mechanisms at the
BBB and in brain, we performed single-nucleus RNA-sequencing (snRNA-seq) of the cortex of
APOE3 and APOE4 knock-in (KI)
flox/flox
mice
357
, i.e., E3F and E4F, respectively (Figure 3.2a;
Figure 3.1a-b). As reported in the mouse brain
358
, we identified clusters of excitatory and inhibitory
neurons, oligodendrocytes, astrocytes, microglia and vascular cells (Figure 3.2b; Figure 3.1c). To
study changes at the BBB, we focused on endothelial cells (EC) that form a tightly-sealed
continuous monolayer in vivo
2
and pericytes (PC), the BBB-associated mural cells that maintain
BBB integrity
11,14,15,365
. To separate EC from PC in the vascular cluster, we used in silico single-
cell RNAseq-guided analysis from the molecular atlas of the mouse brain vasculature
6
(Figure
3.1d; Figure 3.2c; see Single-nucleus RNA sequencing). To differentiate between early and later
changes, we performed snRNA-seq analysis in 2-3- and 9-12-month-old E4F and E3F mice.
In EC, we identified 208 and 435 differentially expressed genes (DEGs) in 2-3- and 9-12-
month-old E4F compared to E3F mice, respectively, of which 182 and 382 DEGs respectively,
126
encoded proteins with known function according to the UniProt Knowledgebase. In both young
and older E4F mice, most of those DEGs were upregulated, i.e., 178/182 and 380/382, respectively
(Figure 3.2d), and 141 DEGs overlapped in both age groups (Figure 3.2e). The overlapping DEGs
encoded proteins that regulate BBB integrity including adhesion proteins such as cadherins and
proto-cadherins (Cdh13, Cdh18, Pcdh7, Pcdh9, Pcdh15, and Pcdh17), contactins (Cntn4, Cntn5,
Cntnap2) and catenins (Ctnna2, Ctnna3)
2
, solute transporters, cytoskeletal proteins, and genes
controlling expression of proteases causing BBB breakdown, such as matrix-metalloproteinase 3
and the α-secretase disintegrin metalloproteinase 10 (ADAM10)
2
(Figure 3.2f-h). Overall, these
data suggest that a core component of APOE4 EC transcriptome dysfunction is present at a young
age indicating an early dysregulation of the underlying BBB functions.
In PC, 51/54 unique identified DEGs were upregulated in young E4F compared to E3F mice
of which 45/47 were with a known function (Figure 3.2i). In contrast, only 33/150 identified DEGs
with known function were upregulated in 9-12-month-old E4F mice (Figure 3.2i), and most DEGs
(117/150) were downregulated (Figure 3.2i). This includes genes encoding cell adhesion proteins
(Figure 3.2k-l), and the tight junction protein 1 (TJP1) encoding zonula occludens-1 (ZO-1), that
is critical for BBB integrity
2
. Genes involved in transcriptional regulation, e.g., Ebf1, Egr1, Glis3
and St18, were upregulated in young, but downregulated in older E4F mice. This age-dependent
reversal in PC gene expression has been observed for a number of DEGs that were initially
upregulated in young E4F mice (Figure 3.2j) suggesting a feedback control that was not observed
in EC, therefore contributing to a dissimilar response of EC and PC to the APOE4 gene.
We performed Fisher’s exact test to determine if there is an overlap between brain EC
transcriptome changes in 9-12-month-old E4F vs. E3F mice and a published EC transcriptome
module in mouse models of stroke, traumatic brain injury (TBI), epilepsy, and experimental
127
allergic encephalitis (EAE)
361
that all develop BBB dysfunction (Figure 3.2m). This analysis
revealed modest or no overlap between upregulated EC genes in E4F mice and mice with acute,
subacute and chronic stroke, TBI and epilepsy, and/or acute and subacute EAE. There was some
overlap between 42 upregulated EC genes in a chronic model of EAE and E4F mice, including
cell adhesion molecules, solute transporters, cytoskeletal and the extracellular matrix proteins. The
EC transcriptome in the studied disease models also identified downregulated DEGs
361
, which was
not case in E4F mice. Together these data suggest that EC transcriptome in E4F mice is not shared
for the most part with other mouse disease models with BBB dysfunction, and is rather specific
for the APOE4 gene. Moreover, these data indicate that APOE4 leads to pericyte dysfunction at
multiple levels including RNA-splicing suggestive of DNA damage, transcription, cell
differentiation, structure and motility, which in turn can further contribute to loss of BBB integrity,
as pericytes critically maintain the EC barrier
11,14,15,365
.

128

Figure 3.2 APOE4 disrupts the blood-brain barrier transcriptome
a, Schematic of nuclei isolation and sampling workflow from mouse cortex for snRNA-sequencing. See
Methods for details. b, Uniform manifold approximation and projection (UMAP) space representing 6
distinct clusters obtained via unsupervised clustering analysis and subsequent definition of each cluster
based on cell-type-specific cell markers. c, Dotplot reporting average expression of the cell-specific markers
of endothelial cells (EC) and pericytes (PC) in the vascular cluster by in silico sorting. d, Volcano plot
showing differentially expressed genes (DEGs) in EC in 2-3-month-old (red) and 9-12-month-old (blue)
E4F compared to E3F mice. e, Plot comparing the average log 2-fold change of DEGs in EC in 2-3-month-
old (y-axis) and 9-12-month-old (x-axis) E4F compared to E3F mice (n=141 DEGs with known function
according to the UniProt Knowledgebase out of 158 total). f-h, Bar charts reporting the number of DEGs
in EC encoding proteins with known function in each functional class in 2-3-month-old (f) and 9-12-month-
old (g) E4F compared to E3F mice, and the overlapping DEGs in both age groups of E4F compared to E3F
mice (h). i, Volcano plot showing DEGs in PC in 2-3-month-old (red) and 9-12-month-old (blue) E4F
compared to E3F mice. j, Plot comparing the average log 2-fold change of DEGs in PC in 2-3-month-old (y-
axis) and 9-12-month-old (x-axis) E4F compared to E3F mice (n=25 DEGs). k, l, Bar charts reporting the
number of DEGs in PC encoding proteins with known function in each functional class in 2-3-month-old
(k) and 9-12-month-old (l) E4F compared to E3F mice. All data in b-l are from 4 mice per group. m,
Heatmap showing overlap between DEGs in EC from 9-12-month-old E4F compared to E3F mice
(columns of the heatmap) and DEGs in EC from the published mouse models of acute, subacute, and
chronic experimental allergic encephalitis (EAE), epilepsy, stroke and traumatic brain injury (TBI)
361
(rows
of the heatmap). Color scale represents –log 10P value. Significance by Fisher’s exact test.
129
To functionally validate disruption of the BBB signaling mechanisms, we studied BBB
integrity by dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). The DCE-
MRI
15,80
indicated an early BBB breakdown in the hippocampus (Figure 3.3a-b) and cortex (Figure
3.3c) in 2-3-month-old E4F compared to E3F mice, and progressing over 9-12 months. This was
confirmed by elevated cerebrospinal fluid (CSF) biomarkers of BBB breakdown: CSF:plasma
albumin quotient
2,33
, CSF fibrinogen
23
, and pericyte injury marker soluble platelet-derived growth
factor receptor-b (sPDGFRb)
23,33
. Tissue analysis demonstrating accumulation of peri-capillary
fibrinogen deposits and loss of capillary coverage by pericytes further confirmed BBB disruption.
These functional data paralleled our cell-specific molecular findings of disrupted BBB functions
in EC and PC.

Figure 3.3 Blood-brain barrier breakdown in E4F mice
a, b, c The BBB permeability K trans maps in the hippocampus in 2-month-old E3F and E4F mice by DCE-
MRI (a), and the K trans values in the hippocampus (Hipp, b) and the cortex (Ctx, c) in 2-3-, 4-6- and 9-12-
month-old E3F and E4F mice.

In certain models with BBB dysfunction, BBB leaks precede and/or lead to neuronal
dysfunction, as shown in pericyte-deficient mice
14,15,80
, mice haploinsufficient in GLUT1 EC
glucose transporter
16
, and mice with loss of EC Major Facilitator Superfamily Domain Containing
2A transporter for essential omega 3 fatty acids
17
, and/or lipoprotein receptor
363
. To determine
b c
e d
PSD95
7 mo E4F vs. E3F
0
1
a
E3F
E4F
2
K
trans
(x10
-3
min
-1
)
Hipp
0 .0
0 .5
1 .0
1 .5
2-3 mo
4-6 mo
9-12 mo
E3F
E4F
Hipp
K
trans
(x10
-3
min
-1
)
P=10
-3
P=3x10
-5
P=5x10
-6
PSD95
2-3 mo E4F vs E3F
Ctx
E3F
E4F
2-3 mo
4-6 mo
9-12 mo
K
trans
(x10
-3
min
-1
)
0 .0
0 .5
1 .0
1 .5
P=10
-4
P=2x10
-3
P=3x10
-3
130
whether early BBB dysfunction in E4F mice affects neuronal function, first we studied neurite
density and neuron counts. We found no changes in neurite density in 2-3-month-old E4F
compared to E3F mice at the time when BBB breakdown was observed. However, E4F mice
developed neurite loss by 4-6 months of age, which was not accompanied by neuron loss. Loss of
neurites after BBB breakdown could be related to accumulation of neurotoxic fibrinogen which
has been shown to inhibit neurite outgrowth in neuronal cultures
366
and in vivo in pericyte-deficient
mice
39
.
In support of our finding showing neurite loss, we found several dysregulated DEGs in
neurons involved in the organization of the cytoskeleton and synaptic plasticity in 9-12-month-old
compared to 2-3-month-old E4F mice, which were not found in E3F mice (Figure 3.4). For
example, in excitatory neurons, Klhl1, an actin-organizing protein which modulates neurite
outgrowth
367
, was downregulated, whereas Arhgef25 and Spata13, guanine nucleotide exchange
factors involved in morphogenesis of dendritic spine, axon growth, and synapse
formation
368
, Brsk2, regulating polarization of cortical neurons and axonogenesis via
phosphorylation of microtubule-stabilizing protein MAPT/TAU
369
, Mapk8ip1, a regulator of the
c-Jun N-terminal kinase signaling promoting axonal growth
370
, Mdga1, involved in the
maintenance of inhibitory synapses
371
, Prrt1, required for synapse development and plasticity
372
,
and Pou3f2, a transcription factor which regulates synaptic function via neurotrophin-3, were all
upregulated. In inhibitory neurons, Lrfn1 and Lrrtm2 involved in the regulation and maintenance
of synapses
373
, Ngf, activating Rac1 and neurite formation
374
, Pdlim5, which interacts with the
post-synaptic densities (PSD)-95-binding protein SPAR causing dendritic spine shrinkage
375
, and
isoaspartyl peptidase/L-asparaginase, which regulates production of the inhibitory
neurotransmitter L-aspartate, were all upregulated. Together, these results may suggest a
131
compensatory response of neurons to neurite loss likely caused by blood-derived neurotoxic
fibrinogen
80,366
, that accumulates in brains of E4F mice.

Figure 3.4 DEG analysis in neurons of E4F and E3F mice comparing 9-12-month-old versus 2-3-
month-old mice
a, Volcano plot showing the differentially expressed genes (DEGs) identified in excitatory neurons
of E3F (red) and E4F (green) mice at 9-12- versus 2-3-month-old. b, Plots comparing the average log 2-fold
change of the overlapping DEGs identified in excitatory neurons of both E3F (x-axis) and E4F (y-axis)
mice (9-12-month-old versus 2-3-month-old mice). c, Bar charts reporting the number of DEGs encoding
for proteins with known function in each functional class, as exclusively identified in excitatory neurons
of 9-12- versus 2-3-month-old E4F mice only (134 DEGs), but not in 9-12- versus 2-3-month-old E3F mice.
d, Volcano plot showing the DEGs identified in inhibitory neurons of E3F (red) and E4F (green) mice at 9-
12- versus 2-3-month-old. e, Plots comparing the average log 2-fold change of the overlapping DEGs
identified in inhibitory neurons of both E3F (x-axis) and E4F (y-axis) mice (9-12- versus 2-3-month-old
mice). f, Bar charts reporting the number of DEGs encoding for proteins with known function in each
functional class, as exclusively identified in inhibitory neurons of 9-12- versus 2-3-month-old E4F mice
only (153 DEGs), but not in 9-12- versus 2-3-month-old E3F mice. All data are from 4 mice per group.

We observed behavioral changes in E4F mice at 6-8 months of age, as we show by novel
object location and recognition, nesting, and burrowing tests. No changes in behavior were
observed at an earlier stage in 4-6-month-old E4F mice.
100
200
300
2 1 0 1 2
log 2 (fold change)
log (adjusted P value)
E3F
E4F
2
1
0
1
2
2 1 0 1 2
log 2 (fold change) E3F
log 2 (fold change) E4F
100
200
300
2 1 0 1 2
log 2 (fold change)
log (adjusted P value)
E3F
E4F
EC DEGs, E4F vs E3F
2
1
0
1
2
2 1 0 1 2
log 2 (fold change) E3F
log 2 (fold change) E4F
Adapter
Antioxidant
ECM
Neuropeptides metabolism
Protein phosphatase
Signaling
Development
Apoptosis / Anti proliferative
Cell Adhesion
Endocytosis
Scaffold
Exocytosis
GTP Signaling
mytochondrial enzyme
Protein Kinase
Receptor
Cell Survival/Proliferation
Protein Degradation
Lipid metabolism
RNA Binding
Solute Transporter
Synaptic plasticity
Metabolic Enzyme
Cytoskeletal Dynamics
DNA Binding
0 5 10 15 20
Frequency
DEGs in Inhibitory Neurons
9 12 mo vs 2 3 mo in E4F
Endocytosis
Immune response
Scaffold
Antioxidant
Development
GTP Signaling
mytochondrial enzyme
Protein phosphatase
Receptor
Signaling
Apoptosis / Anti proliferative
Cell Adhesion
Exocytosis
Adapter
Cell Survival/Proliferation
ECM
Lipid metabolism
Protein Kinase
Protein Degradation
Solute Transporter
Synaptic plasticity
RNA Binding
Metabolic Enzyme
Cytoskeletal Dynamics
DNA Binding
0 5 10 15
Frequency
DEGs in Excitatory Neurons
9 12 mo vs 2 3 mo in E4F
a b
d e
UP
DOWN
UP
DOWN
c
f
Excitatory Neuron DEGs,
9-12- vs 2-3-month-old
Inhibitory Neuron DEGs,
9-12- vs 2-3-month-old
132
Although we did not find changes in astrocyte numbers in E4F compared to E3F mice at either
age, the snRNA-seq analysis in 9-12-month-old compared to 2-3-month-old mice indicated 311
upregulated DEGs in E4F mice, but not in E3F mice (Figure 3.5a). This included genes encoding
metabolic enzymes such as SERPINB6, an inhibitor of neurotoxic thrombin that accumulates in
brain after BBB breakdown
2,14
, protein S, a cofactor to activated protein C (APC) that prevents
BBB breakdown
376
, SIRTUIN 2, a protein deacetylase that downregulates VEGF possibly
protecting from VEGF-induced BBB breakdown
377
, as well as Chuk and Nfkbia, NF-kB inhibitors,
possibly suppressing the proinflammatory NF-kB pathway linked to BBB breakdown
68
. Overall,
these data suggest that astrocytes probably tend to mount a response to protect BBB integrity.
We also did not find changes in microglia numbers in E4F compared to E3F mice, but
the snRNA-seq analysis of microglia in 9-12- compared to 2-3-month-old mice indicated 259
DEGs dysregulated in E4F mice, but not E3F mice, most of which were upregulated, 250/259
(Figure 3.5b). The upregulated DEGs included Il18 (Interleukin-18) that attenuates BBB
disruption
378,379
and genes modulating anti-inflammatory BBB-protective TGFβ signaling
pathway (Smad2, Smad3 and Smad7)
55,380
, likely suggesting a vasculoprotective response. These
findings were consistent with data showing that microglia migrate rapidly to the sites of capillary
wall lesions to seal and repair damaged BBB, which requires G-protein coupled purinergic
receptor P2RY12
381,382
that was also upregulated in microglia in E4F mice. Additionally, we found
several upregulated kinases involved in cell motility and migration such as HCK, PAK2, PKN1,
SGK1 and STK10, and upregulated genes modulating TYROBP pathways (Maf, Fkbp15,
Plek and Creb3l2) reflecting impaired microglia homeostatic state
383,384
. How these findings relate
to BBB breakdown and whether these changes reflect mainly response of capillary-associated
microglia
385
remains unknown. A group of DEGs were involved in negative regulation of
133
microglia apoptosis
386
and in neuron projection development (Gak, Mylip, Prag1, Ptpn9 and
Rhoa), and synaptic formation and transmission (Camk1, Dnm2, Lrrc4c, Lrrtm4). Overall, these
data likely suggest a protective microglia response counteracting BBB damage and neurite loss.

Figure 3.5 DEG analysis in astrocytes and microglia of 9-12-month-old versus 2-3-month-old E4F
mice.
a, Bar charts reporting the number of DEGs encoding for proteins with known function in each functional
class, as exclusively identified in astrocytes (n=234 DEGs) of 9-12-month-old versus 2-3-month-old
E4F mice only, but not in 9-12- vs. 2-3-month-old E3F mice. b, Bar charts reporting the number of DEGs
encoding for proteins with known function in each functional class, as exclusively identified in microglia
(n=221 DEGs) of 9-12-month-old versus 2-3-month-old E4F mice only, but not in 9-12- vs. 2-3-month-old
E3F mice. All data are from 4 mice per group.

Other
Phosphodiesterase
Transcription regulator
Development
ECM
Signaling
GTP Signaling
Lipid metabolism
Scaffold
Protein Kinase
Exocytosis
mytochondrial enzyme
Apoptosis / Anti−proliferative
Endocytosis
Cell Survival/Proliferation
Protein Degradation
Cell Adhesion
Synaptic plasticity
RNA Binding
Solute Transporter
DNA Binding
Immune response
Cytoskeletal Dynamics
Metabolic Enzyme
0 10 20 30 40
Frequency
DEGs in Astrocytes
9−12−mo vs 2−3−mo in

E4F
UP
DOWN
Apoptosis / Anti−proliferative
Lipid metabolism
Neuropeptides metabolism
Other
Signaling
Development
ECM
Exocytosis
mytochondrial enzyme
Receptor
Scaffold
Cell Adhesion
Protein phosphatase
Solute Transporter
Adapter
Synaptic plasticity
Transcription regulator
Protein Kinase
Endocytosis
Cell Survival/Proliferation
RNA Binding
GTP Signaling
Immune response
Metabolic Enzyme
Protein Degradation
Cytoskeletal Dynamics
DNA Binding
0 10 20
Frequency
DEGs in Microglia
9−12−mo vs 2−3−mo in E4F
a
b
UP
DOWN
134
Discussion
In summary, we show that APOE4 disrupts the BBB signaling mechanisms in EC and PC in
a cell-type-specific fashion, which precedes neurite loss, synaptic dysfunction, and behavioral
deficits. Because the BBB leaks lead to brain accumulation of blood-derived neurotoxic proteins
such as thrombin, plasminogen, iron-containing proteins
14,68
, fibrinogen
80,387
and/or albumin
55
,
these findings raise a possibility that disruption of the BBB cell-specific signaling mechanisms
could be an initial central contributor to APOE4-mediated neuronal disorder, and possibly a major
target to correct APOE4-related cognitive deficits. Whether targeting disrupted PPIs at the BBB
with biologics such as APC, which elicits a large scale protective gene expression profile in
dysfunctional EC
376
and a barrier-protective phosphoproteome EC profile
388
, and/or whether
targeting the key dysregulated pathways in EC, such as TJP1, with EC-specific gene delivery
363

can restore the BBB integrity and/or slow down synaptic and neuronal deficits remains to be
determined. Future studies in APOE KI
flox/flox
mice
357
crossed with astrocyte-, pericyte-, and/or
vascular smooth muscle cell-specific specific Cre lines should also be able to address how
neurovascular APOE derived from different cell-specific sources regulates the BBB signaling
mechanisms and neuronal and synaptic function in mice.

135
Acknowledgement
I would like to acknowledge the contributions of all the co-authors in this study who helped
with different aspects of data generation and analyses. Specifically:
1) William Gilliam, who helped me with processing the mouse brain samples for single-
nuclei RNA sequencing experiments, performing the isolation of nuclei, and preparing the
libraries.
2) Dr. Mikko Huuskonen for helping with the MRI data acquisition and analysis.
3) Dr. Angeliki Nikolakopoulou and Dr. Yaoming Wang for performing the histological
analysis and the behavioral experiments.
4) Dr. Justin Ichida and his lab members, in particular Shu-Ting Hung (Michelle), for
supporting us in preparing the libraries.
5) Dr. Fan Gao for processing the sequencing data.
This study was supported by the National Institutes of Health (NIH) grant nos.
5P01AG052350 (Zlokovic/Toga), R01NS034467, R01AG023084, R01AG039452,
R01NS100459, R01NS117827, and P30AG06653, in addition to Alzheimer’s Association (VCID-
17-509279), Cure Alzheimer’s Fund, and the Foundation Leducq Transatlantic Network of
Excellence for the Study of Perivascular Spaces in Small Vessel Disease reference no. 16CVD05.

136
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Abstract (if available)

Abstract In this dissertation, I investigated the brain vasculature, and specifically the blood-brain barrier (BBB) and the perivascular spaces (PVS), in humans using Magnetic Resonance Imaging (MRI) and in pre-clinical models using single-nuclei RNA-sequencing.
The first chapter includes a description of the anatomy and physiology of the BBB and the PVS, as well as a literature review about the pathological changes affecting these vascular components in neurological disorders. I also reported the results of my original studies of PVS in healthy young adults and in spaceflight: these studies demonstrate that multiple factors influence the visibility of PVS on MRI, including body mass index, time of day, and genetics, and constitute a resource for researchers and clinicians interested in the quantitative analysis of PVS.
In the second chapter, I analyzed in humans the relationship between BBB permeability and the E4 variant of apolipoprotein E (APOE4), the main susceptibility gene for Alzheimer’s disease. We found that cognitively unimpaired individuals bearing APOE4 have higher BBB permeability in the hippocampus and parahippocampal gyrus compared with APOE3 homozygous participants. This breakdown is more severe in patients with mild cognitive impairment and is not related to amyloid-β or tau pathology measured in cerebrospinal fluid or by positron emission tomography. These results suggest that BBB breakdown occurs early in the pathogenesis of Alzheimer ‘s disease and is independent of amyloid plaques and tau tangles.
Finally, in the last chapter, I investigated the molecular mechanisms underlying APOE4 cerebrovascular disorder using single-nuclei RNA-sequencing in human APOE3 and APOE4 knock-in mice. We found dysregulation of multiple genes induced by human APOE4, including genes regulating cell junctions and cytoskeleton in brain endothelium, and transcription and RNA- splicing suggestive of DNA damage in pericytes.

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Asset Metadata

Creator Barisano, Giuseppe (author)

Core Title The role of vascular dysfunction in cognitive impairment

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Neuroscience

Degree Conferral Date 2022-08

Publication Date 07/23/2022

Defense Date 06/13/2022

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag APOE,blood-brain barrier,cerebrovasculature,dementia,MRI,neuroimaging,OAI-PMH Harvest,perivascular spaces,single-nuclei RNA-sequencing

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Toga, Arthur W. (committee chair), Ichida, Justin (committee member), Mack, William (committee member), Zlokovic, Berislav V. (committee member)

Creator Email gbarisan@usc.edu,giuseppe.barisano91@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC111374350

Unique identifier UC111374350

Legacy Identifier etd-BarisanoGi-10941

Document Type Dissertation

Format application/pdf (imt)

Rights Barisano, Giuseppe

Type texts

Source 20220728-usctheses-batch-962 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

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