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Age-related differences in amygdala subregion functional connectivity with Ventromedial Prefrontal Cortex and Dorsolateral Prefrontal Cortex
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Age-related differences in amygdala subregion functional connectivity with Ventromedial Prefrontal Cortex and Dorsolateral Prefrontal Cortex
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Content
Age-related differences in amygdala subregion functional connectivity with Ventromedial
Prefrontal Cortex and Dorsolateral Prefrontal Cortex
by
Kalekirstos Alemu, B.S.
A Thesis Presented to the
FACULTY OF THE USC DORNSIFE COLLEGE OF LETTERS, ARTS AND SCEINCES
AND LEONARD DAVIS SCHOOL OF GERONTOLOGY
In Partial Fulfillment of the
Requirements for the Degree
MASTER OF SCIENCE
(PSYCHOLOGY)
December 2024
Copyright 2024 Kalekirstos Alemu
ii
TABLE OF CONTENTS
List of Tables .................................................................................................................................iii
List of Figures................................................................................................................................ iv
Tables.............................................................................................................................................. v
Figures............................................................................................................................................ ix
Abstract........................................................................................................................................ xiv
Chapter One: Introduction .............................................................................................................. 1
Chapter Two: Method ..................................................................................................................... 8
Chapter Three: Results.................................................................................................................. 12
Chapter Four: Discussion.............................................................................................................. 15
References..................................................................................................................................... 20
iii
List of Tables
Table 1. Participant Demographics................................................................................................. v
Table 2. Amygdala Subregions....................................................................................................... v
Table 3. vmPFC Subregion Regions of Interest (ROI) and Corresponding Montreal Neurological
Institute (MNI) Coordinates........................................................................................................... vi
Table 4. dlPFC Subregion Regions of Interest (ROI) and Corresponding Montreal Neurological
Institute (MNI) Coordinates........................................................................................................... vi
Table 5. Three-Way Mixed Effects ANOVA of Amygdala Subregion Functional Connectivity
with Pregenual and Subgenual PFC............................................................................................... vi
Table 6. Post Hoc Comparisons of vmPFC Subregion Functional Connectivity by Age Group
Interaction Effect .......................................................................................................................... vii
Table 7. Two-Way Mixed Effects ANOVA of Left Amygdala Subregion Functional
Connectivity with Pregenual and Subgenual PFC........................................................................ vii
Table 8. Post Hoc Comparisons Left Amygdala Subregions to vmPFC Subregion Functional
Connectivity by Age Group Interaction Effect............................................................................. vii
Table 9. Two-Way Mixed Effects ANOVA of Right Amygdala Subregion Functional
Connectivity with Pregenual and Subgenual PFC........................................................................ vii
Table 10. Three-Way Mixed Effects ANOVA of Amygdala Subregion Functional Connectivity
with Right and Left dlPFC......................................................................................................... vviii
iv
List of Figures
Figure 1. Amygdala subregions adopted from Klein-Flügge et al. (2022)................................... iix
Figure 2. Pregenual and Subgenual PFC ROIs............................................................................. iix
Figure 3. Bilateral dlPFC ROIs....................................................................................................... x
Figure 4. Pregenual and subgenual PFC functional connectivity with amygdala subregions across
both age groups (YA and OA)....................................................................................................... xi
Figure 5. Age-related difference in amygdala subregion functional connectivity with pregenual
and subgenual PFC ....................................................................................................................... xii
Figure 6. Functional connectivity of pregenual and subgenual PFC to the left amygdala
subregions.................................................................................................................................... xiii
v
Tables
Table 1. Participant Demographics
OA (n = 60) YA (n =113) Statistic p-values
Gender 18 male 57 male X
2 (1) = 6.669 0.010
Handedness 5 left 7 left X
2 (1) = 0.278 0.598
Mean (SD) Range Mean
(SD) Range
Age in years 65.03
(6.66) 55-80 22.8 (2.81) 18-31 t(171) = -58.42 < 0.001
Years of
Education 16.61 (2.4) 12-25 16 (2.1) 12-24 t(171) = -1.74 0.084
Race N % N %
Asian 10 16.7 78 69
White 36 60 19 16.8
African American 10 16.7 4 3.5
Mixed 3 0.05 2 1.8
Bi-racial -- -- 1 0.009
Other -- -- 7 6.2
Not Stated 1 1.7 2 1.8
Ethnicity N % N %
Non-Hispanic 54 90 97 85.8
Hispanic 5 8.3 7 7.1
Not Stated 1 1.7 9 8
Table 2. Amygdala Subregions
Amygdala Subregion Abbreviatio
n
#
Voxels
Central Nucleus Ce 62
Cortical Nuclei CoN 133
Basal BaL 74
Auxiliary basal/basomedial AB/BM 104
Lateral (intermediate part) LaI 84
Lateral (dorsal part) LaD 86
Lateral (ventral part) containing aspects of
basolateral
LaV/BL 104
Note: Amygdala subregions adopted from (Klein-Flügge et al., 2022)
vi
Table 3. vmPFC Subregion Regions of Interest (ROI) and Corresponding Montreal Neurological
Institute (MNI) Coordinates
ROI x y z
Pregenual PFC -2 46 -10
Subgenual PFC 0 26 -12
Note: Pregenual and subgenual PFC ROIs derived from peak coordinates reported by Tashjian
et al. (2021)
Abbreviation: PFC: prefrontal cortex
Table 4. dlPFC Subregion Regions of Interest (ROI) and Corresponding Montreal Neurological
Institute (MNI) Coordinates
ROI x y z
Right dlPFC 40 32 30
Left dlPFC -36 44 20
Note. Left and right dlPFC ROIs derived from coordinates reported in a working memory
meta-analysis (Owen et al., 2005)
Abbreviation: ROI: region of interest; dlPFC: dorsolateral prefrontal cortex
Table 5. Three-Way Mixed Effects ANOVA of Amygdala Subregion Functional Connectivity
with Pregenual and Subgenual PFC
Source df MS F p Effect Size
vmPFC Subregions 1 11.5 371.5 <0.001 0.520
vmPFC Subregions x Age 1 0.15 4.92 0.028 0.011
Amygdala Subregions 6 2.18 213.3 <0.001 0.452
Amygdala Subregions x Age 6 0.012 1.15 0.334 <0.001
Amygdala Laterality 1 0.043 2.98 0.086 0.004
Amygdala Laterality x Age 1 0.021 1.47 0.227 <0.001
vmPFC Subregions x Amygdala Subregions 6 2.248 269.47 <0.001 0.492
vmPFC Subregions x Amygdala Subregions x
Age
6 0.01 1.25 0.278 <0.001
vmPFC Subregions x Amygdala Laterality 1 0.8 6.49 0.012 0.009
vmPFC Subregions x Amygdala Laterality x Age 1 0.069 5.59 0.019 0.008
Amygdala Subregions x Amygdala Laterality 6 0.013 1.65 0.131 0.002
Amygdala Subregions x Amygdala Laterality x
Age
6 0.005 0.67 0.671 <0.001
vmPFC Subregions x Amygdala Subregions x
Amygdala Laterality
6 0.017 2.71 0.013 0.006
vmPFC Subregions x Amygdala Subregions x
Amygdala Laterality x Age
6 0.007 1.01 0.415 <0.001
vii
Note. Effect size = ω
2
Abbreviation: vmPFC: ventromedial prefrontal cortex; MS: mean squares
Table 6. Post Hoc Comparisons of vmPFC Subregion Functional Connectivity by Age Group
Interaction Effect
YA OA
vmPFC Subregion M SE 95% CI M SE 95% CI pbonf
Pregenual PFC -0.097 0.006 [-0.108, -0.086] -0.094 0.008 [-0.109, -0.079] >0.99
Subgenual PFC 0.017 0.003 [0.012, 0.023] -0.003 0.004 [-0.011, 0.004] 0.036
Note. vmPFC: ventromedial prefrontal cortex; PFC: prefrontal cortex; M: means; SE: standard errors; CI:
confidence intervals
Table 7. Two-Way Mixed Effects ANOVA of Left Amygdala Subregion Functional
Connectivity with Pregenual and Subgenual PFC
Source df MS F p Effect Size
vmPFC Subregions 1 6.735 297.09 <0.001 0.444
vmPFC Subregions x Age 1 0.213 9.38 0.003 0.022
Amygdala Subregions 6 1.167 129.63 <0.001 0.335
Amygdala Subregions x Age 6 0.009 1.01 0.419 <0.001
vmPFC Subregions x Amygdala Subregions 6 1.201 154.63 <0.001 0.364
vmPFC Subregions x Amygdala Subregions x
Age
6 0.009 1.17 0.32 <0.001
Note. Effect size = ω
2
Abbreviation: vmPFC: ventromedial prefrontal cortex; MS: mean squares
Table 8. Post Hoc Comparisons Left Amygdala Subregions to vmPFC Subregion Functional
Connectivity by Age Group Interaction Effect
YA OA
M SE 95% CI M SE 95% CI pbonf
Pregenual PFC -0.11 0.007 [-0.124, -0.097] -0.095 0.009 [-0.114, -0.076] 0.108
Subgenual PFC 0.02 0.004 [0.013, 0.028] -0.004 0.005 [-0.014, 0.006] 0.022
Note. vmPFC: ventromedial prefrontal cortex; PFC: prefrontal cortex; M: means; SE: standard errors; CI:
confidence intervals
Table 9. Two-Way Mixed Effects ANOVA of Right Amygdala Subregion Functional
Connectivity with Pregenual and Subgenual PFC
Source df MS F p Effect Size
vmPFC Subregions 1 4.82 234.87 <0.001 0.414
vmPFC Subregions x Age 1 0.008 0.397 0.530 <0.001
Amygdala Subregions 6 1.029 110.79 <0.001 0.323
viii
Amygdala Subregions x Age 6 0.008 0.87 0.516 <0.001
vmPFC Subregions x Amygdala Subregions 6 1.064 151.57 <0.001 0.376
vmPFC Subregions x Amygdala Subregions x
Age
6 0.008 1.124 0.346 <0.001
Note. Effect size = ω
2
Abbreviation: vmPFC: ventromedial prefrontal cortex; MS: mean squares
Table 10. Three-Way Mixed Effects ANOVA of Amygdala Subregion Functional Connectivity
with Right and Left dlPFC
Source df MS F p Effect Size
Bilateral dlPFC 1 0.009 0.51 0.476 <0.001
Bilateral dlPFC x Age 1 <0.001 0.03 0.873 <0.001
Amygdala Subregions 6 0.011 1.9 0.078 0.003
Amygdala Subregions x Age 6 0.005 0.87 0.519 <0.001
Amygdala Laterality 1 0.038 3.59 0.060 0.005
Amygdala Laterality x Age 1 0.003 0.29 0.588 <0.001
Bilateral dlPFC x Amygdala Subregions 6 0.004 0.87 0.518 <0.001
Bilateral dlPFC x Amygdala Subregions x Age 6 0.002 0.336 0.918 <0.001
Bilateral dlPFC x Amygdala Laterality 1 0.119 15.17 <0.001 0.023
Bilateral dlPFC x Amygdala Laterality x Age 1 0.013 1.7 0.194 0.001
Amygdala Subregions x Amygdala Laterality 6 0.007 1.61 0.141 0.002
Amygdala Subregions x Amygdala Laterality x
Age
6 0.002 0.57 0.752 <0.001
Bilateral dlPFC x Amygdala Subregions x
Amygdala Laterality
6 0.001 0.39 0.884 <0.001
Bilateral dlPFC x Amygdala Subregions x
Amygdala Laterality x Age
6 0.004 1.12 0.351 <0.001
Note. Effect size = ω
2
Abbreviation: dlPFC: dorsolateral prefrontal cortex; MS: mean squares
ix
Figure 1. Amygdala subregions adopted from Klein-Flügge et al. (2022).
Names of nuclei: Ce: Central nucleus, CoN: cortical nuclei, B: Basal, AB/BM: auxiliary basal/basomedial,
LaV/BL: lateral (ventral part) containing aspects of basolateral, LaI: lateral (intermediate part), LaD: lateral
(dorsal part).
Figure 2. Pregenual and Subgenual PFC ROIs.
Five-millimeter spherical ROIs were centered at peak coordinates reported by Tashjian et al. (2021). MNI
coordinates: A. Pregenual PFC: x = -2, y = 46, z = -10; B. Subgenual PFC: x = 0, y = 26, z = -12.
Figures
A
B
x
Figure 3. Bilateral dlPFC ROIs.
Five-millimeter spherical ROIs were centered at peak coordinates reported by Owen et al. (2005). MNI
coordinates: A. Right dlPFC: x = 40, y = 32, z = 30; B. Left dlPFC: x = -36, y = 44, z = 20.
B
A
xi
***
−1.0
−0.5
0.0
0.5
Pregenual PFC Subgenual PFC
vmPFC Subregion
Functional Connectivity
AvmPFC
PvmPFC
Amygdala Functional Connectivity with Pregenual and Subgenual PFC Across Age Groups
Figure 4. Pregenual and subgenual PFC functional connectivity with amygdala subregions across both
age groups (YA and OA).
A. There was significantly greater functional connectivity between amygdala subregions and
subgenual PFC compared to pregenual PFC, p < 0.001
B. Right panel: relationship displayed on violin plots.
A B
xii
NS. ***
Pregenual PFC Subgenual PFC
OA YA OA YA
−1.0
−0.5
0.0
0.5
Age Group
Functional Connectivity
Amygdala Functional Connectivity with Pregenual and Subgenual PFC
Figure 5. Age-related difference in amygdala subregion functional connectivity with pregenual and
subgenual PFC.
Greater functional connectivity between amygdala subregions and subgenual PFC in YA than in OA, p
= 0.028.
A. Depicts pregenual and subgenual PFC functional connectivity in OA.
B. Depicts pregenual and subgenual PFC functional connectivity in YA.
C. Relationship displayed on violin plots.
A B
C
xiii
Figure 6. Functional connectivity of pregenual and subgenual PFC to the left amygdala subregions.
Greater functional connectivity between left amygdala subregions and subgenual PFC than pregenual
PFC in both YA and OA. Subgenual functional connectivity with left amygdala subregions was
greater in YA than OA, p = 0.022.
xiv
Abstract
Despite physical and cognitive decline, older adults (OA) show a positivity effect in
attention and memory in which they prioritize positive information over negative information
compared to young adults (YA). Relatedly, a key brain structure implicated in processing and
modulating response to emotionally relevant stimuli, the amygdala serves as a hub for neural
networks involved in emotion processing. Moreover, along with showing biases toward positive
emotions in memory and attention, OA rely on more implicit and automatic emotion regulation
strategies that are less cognitively demanding. Previous research examining neural mechanisms
underlying different emotion regulation strategies have implicated the ventromedial prefrontal
cortex (vmPFC) and dorsolateral prefrontal cortex (dlPFC) in implicit-automatic and explicitcontrolled emotion regulation strategies, respectively. In addition, detailed analyses of the
vmPFC have revealed that the anterior (pregenual PFC) and posterior (subgenual PFC) portions
are differentially involved in response to positive and negative emotional stimuli, respectively.
The majority of studies investigating the positivity effect and age-related differences in amygdala
functional connectivity have predominantly employed task-based paradigms. Therefore, the
present study investigated age-related differences in resting-state functional connectivity patterns
between seven amygdala subregions and pregenual and subgenual PFC, as well as the dlPFC in
YA (n = 113; ages 18-31) and OA (n = 60; ages 65-80). Results revealed stronger functional
connectivity between amygdala subregions and subgenual PFC in YA than in OA. Relative to
the subgenual PFC, connectivity in the pregenual PFC is maintained in old age. We did not
observe age-related differences in amygdala functional connectivity with the dlPFC
1
Chapter One: Introduction
According to the United States Census Bureau, people aged 65 and older reached a
population of 55.8 million accounting for nearly 17% of the entire United States (U.S.)
population in 2020 (Vespa et al., 2020). This population is projected to outnumber the number of
children for the first time by 2035 and is expected to account for 23% of the total population by
the year 2060 (Vespa et al., 2020). Therefore, gaining a thorough and comprehensive
understanding of age-related changes is important for the promotion of healthy aging,
development of effective interventions, and health policies that will adequately meet the needs of
this population.
Although decline across various cognitive domains is a common feature of aging, even in
the absence of neuropathology, this alone does not provide a complete picture of the
psychological changes that occur with aging. Notably, exciting research within the last two
decades has consistently and reliably shown that, despite cognitive decline, overall emotional
well-being remains intact or even shows improvements with age (Brummer et al., 2014;
Henderson et al., 1998; Krasucki et al., 1998; Wolitzky-Taylor et al., 2010). A considerable
number of behavioral studies have reported that, relative to younger adults (YA), older adults
(OA) prioritize and show preference to positive information over negative information in
attention and memory (Mather & Carstensen, 2005). This age-by-valence interaction was coined
as the positivity effect by Mather and Carstensen (2005).
Despite age-related physical and cognitive deterioration, as well as an increase in the
frequency and severity of health problems, this phenomenon, reflecting an age-related
developmental trend from negative-to-positive attendance and/or recall of information, could
potentially help explain the increase in self-reported emotional well-being among OA
2
(Isaacowitz, 2022). For instance, even though being disproportionately affected by the
Coronavirus Disease 2019 (COVID-19), several studies reported lower prevalence of anxiety and
depression and higher emotional well-being among OA compared to YA (Bruine de Bruin, 2021;
Joseph, 2022; Wolfe & Isaacowitz, 2022). In addition, the implications of poor emotional wellbeing on morbidity and mortality have been well-reported; however, it is important to not
underestimate the bidirectionality of this relationship (Cintron & Ong, 2024; DeSteno et al.,
2013; for a review, see Kiecolt-Glaser et al., 2002; Ostir et al., 2001). This further underscores
the importance of understanding the underlying mechanisms of the positivity effect and
identifying a subgroup of OA who do not fit this emotional profile.
While the literature regarding whether improvements in emotion regulation skills in old
age contribute to enhancements in affective well-being can be mixed and confusing (see e.g.,
Allen & Windsor, 2019; Isaacowitz, 2022; Mather, 2016), research indicates that OA may rely
on less cognitively demanding emotion regulation strategies to down-regulate negative emotions
and modify their affective state (Allen & Windsor, 2019; Blanchard-Fields et al., 2004; Scheibe
et al., 2015; Whitmoyer et al., 2024). For instance, cognitive reappraisal, a form of emotion
regulation strategy that involves changing one’s interpretation of emotion-eliciting situation to
reduce its emotional impact (Gross & John, 2003), requires more cognitive resources than
acceptance (deliberate attempt to not regulate emotions) and distraction (shifting focus away
from an emotional stimulus) (Opitz et al., 2012). In a systematic review, Allen and Windsor
(2019) found that OA showed greater tendency to use acceptance than YA in situations with
moderate emotional intensity or anxiety provoking situations. This suggests that OA may rely
and effectively employ these (acceptance and distraction) passive and less cognitively
3
demanding emotion regulation strategies (Charles et al., 2009; Luong & Charles, 2014) to
compensate for age-related declines in lateral prefrontal regions (Mather, 2016).
Relatedly, studies have also demonstrated that the positivity effect can be attenuated
under certain conditions (Knight et al., 2007; Mather & Knight, 2005; Urry & Gross, 2010).
Mather and Knight (2005) manipulated attention (using an auditory distractor) during the
encoding of both neutral and emotional images to examine the influence of limited cognitive
resources on the positivity effect. Their findings revealed that the positivity effect was preserved
when OA were allowed to direct their full attention during the image encoding process.
However, the positivity bias for positive images completely disappeared (and even reversed)
when their attentional capacity was constrained. In another study, Knight et al. (2007) used eye
tracking to assess visual attention to emotional stimuli. Similar to the Mather and Knight (2005)
study, attention was manipulated using an auditory distractor. Similarly, their findings showed
that OA allocated less visual attention to negative stimuli during the full attention condition than
YA. However, when distracted, the positivity bias was eliminated. These studies indicate that
when cognitive resources are depleted, OA may not be able to modulate their emotional goals
effectively.
A key brain region that plays an important role in processing and modulating response to
emotionally relevant stimuli is the amygdala. The amygdala is a subcortical structure that serves
as a focal point for neural networks involved in emotion processing, social behavior, and social
cognition (Medina et al., 2017). In addition, the amygdala has been implicated in a multitude of
emotional and cognitive processes such as implicit emotional learning memory, emotional
modulation of memory, emotional influences on attention and perception, emotion and social
behavior, and emotion inhibition and regulation (for a review see, Phelps & LeDoux, 2005).
4
Furthermore, pioneering animal, lesion, and postmortem human studies have established that the
amygdala is not a single uniform structure, but rather a complex structure comprising structurally
and functionally distinct nuclei. While there are ongoing discussions regarding the precise nuclei
that constitute the amygdaloid complex, the majority of studies employing cytoarchitectonic,
myeloarchitectonic, and chemoarchitectonic mapping techniques have consistently segmented
the structure into three distinct subdivision: the laterobasal nuclei group, the superficial nuclei
group, and the centromedial nuclei group (Amunts et al., 2005).
Given the involvement of the amygdala in cognitive and emotional processes, which may
underlie psychological disorders (Aggleton, 1993; Andrewes & Jenkins, 2019; Drevets, 2003),
significant efforts have been made over the last two decades to characterize the anatomical and
functional properties of the human amygdala using in vivo mapping techniques. Dissecting the
amygdala into its constituent subregions using in vivo techniques provides the basis for a
nuanced understanding of each nucleus and their role in emotional well-being and emotion
regulation. This approach to examining the amygdala provides insights that may not be apparent
when studying the amygdala as a single homogenous structure. For example, human magnetic
resonance imaging (MRI) studies have identified functional properties of amygdala subregion
that are consistent to those observed in animal models (Ball et al., 2007; Roy et al., 2009). This
degree of detailed examination will enable us to explore how amygdala subregion functional
properties change as a function of the normal aging process.
While findings from studies examining age-related amygdala structural changes have
been somewhat inconsistent (Aghamohammadi‐Sereshki et al., 2018; Jernigan et al., 2001; Kurth
et al., 2019; Malykhin et al., 2008; Walhovd et al., 2005), functional MRI (fMRI) studies have
reported age-related differences in amygdala activity and functional connectivity. Task-based
5
studies probing amygdala activation in response to emotionally negative and aversive stimuli
have consistently reported reduced amygdala activity in OA compared to YA (Erk et al., 2008;
Fischer et al., 2005; Gunning-Dixon et al., 2003; Mather et al., 2004; P. L. St. Jacques et al.,
2009; Tessitore et al., 2005). Moreover, age-related changes in amygdala functional connectivity
with prefrontal cortex areas implicated in emotion regulation and processing have also been
reported. For instance, St. Jacques et al., (2010) found that OA showed increased functional
connectivity between the amygdala and ventral anterior cingulate cortex during the processing of
negatively valenced pictures. In another study, Ford et al., (2014) reported that OA showed
increased activity in prefrontal regions during the retrieval of negative images. These findings
suggest greater involvement of prefrontal regions to modulate amygdala activity in response to
negative stimuli among OA.
One particular prefrontal cortex region that is implicated in affective regulation, through
its relationship with the amygdala, is the ventromedial prefrontal cortex (vmPFC; for a review
see, Hiser & Koenigs, 2018). Animal models, utilizing rodent and non-human primates, have
demonstrated that stimulating the vmPFC modulates fear response through the inhibition of the
amygdala and that this relationship is underlined by direct projections from the vmPFC to
inhibitory interneurons within the amygdala (Hiser & Koenigs, 2018). This relationship has also
been validated through in vivo functional neuroimaging techniques in humans (McLaughlin et
al., 2015; Motzkin et al., 2015; for a review, see Nashiro et al., 2017; Silvers et al., 2017). In
humans, individuals with bilateral vmPFC lesions showed elevated amygdala response to
aversive stimuli, suggesting vmPFC dysfunction results in disinhibition of amygdala activity
(Motzkin et al., 2015). Regarding OA, Urry et al., (2006) demonstrated increased functional
connectivity between the amygdala and vmPFC during effortful affective regulation. In another
6
study, Ford and Kensinger (2017) found that OA showed increased recruitment of the vmPFC
during negative relative to positive retrieval of events.
A related line of research examining neural mechanisms underlying different emotion
regulation strategies has strongly implicated the vmPFC in supporting implicit-automatic
emotion regulation strategies (for a review, see Braunstein et al., 2017). In contrast, other regions
within the prefrontal cortex such as the dorsolateral prefrontal cortex (dlPFC), ventrolateral
prefrontal cortex (vlPFC), dorsal anterior cingulate cortex (dACC), and dorsal medial prefrontal
cortex (dmPFC) support explicit-automatic emotion regulation strategies (Braunstein et al.,
2017). Furthermore, more nuanced examinations of the vmPFC have revealed differential
involvement of vmPFC subregions in response to positive and negative emotional stimuli.
Specifically, the anterior vmPFC (referred to as pregenual PFC hereinafter) and posterior vmPFC
(referred to as subgenual PFC hereinafter) show differential responses to positive and negative
affective stimuli, respectively (Chang et al., 2021; Lindquist et al., 2016; Mather, in press; Yang
et al., 2020). A meta-analysis across 397 functional neuroimaging studies found that the
pregenual PFC reliably responded to positive than negative stimuli (Lindquist et al., 2016).
Another study utilizing a naturalistic paradigm found that activity in the pregenual PFC was
associated with subjective positive affect (i.e., hope, pride) whereas subgenual PFC was
associated with negative affect (i.e., sadness, fear; Chang et al., 2021). Using meta-analytic
coactivation from Neurosynth, (Tashjian et al., 2021) found that activity in pregenual and
subgenual PFC is associated with sense of safety and threat, respectively. Taken together, these
studies suggest that the vmPFC plays a critical role in emotion regulation and that different
subregions may be involved in different affective processes.
7
The majority of studies investigating the positivity effect and age-related differences in
amygdala functional connectivity have predominantly employed task-based paradigms.
Furthermore, studies utilizing resting-state fMRI have primarily focused on amygdala functional
connectivity in childhood, adolescence, and middle adulthood (Gee et al., 2013; Roy et al., 2009;
Xiao et al., 2018). In order to address this gap, we utilized resting-state functional connectivity to
examine age-related changes in connectivity patterns between amygdala subregions and
pregenual and subgenual PFC, as well as the dlPFC in YA and OA. We utilized seven amygdala
subregions as defined by Klein-Flügge et al. (2022), extracted from high-quality resting-state
functional data from the human connectome project (HCP). We predicted that, relative to YA,
OA would exhibit increased functional connectivity between amygdala subregions and pregenual
PFC while this relationship would be reversed for the subgenual PFC. In addition, given that the
dlPFC is implicated in explicit and effortful emotion regulation strategies, we hypothesize
reduced functional connectivity between amygdala subregions and bilateral dlPFC in OA relative
to YA.
8
Chapter Two: Method
Participants
The present study utilized a subset of data from a larger study that investigated the effects
of heart rate variability (HRV) on emotional health (Heart Rate Variability and Emotion
Regulation (HRV-ER); ClinicalTrials.gov, Identifier: 03458910). The dataset comprised preintervention resting-state fMRI scans from 113 YA (aged 18-31; mean age: 22.8; 57 male) and
60 OA (aged 55-80; mean age: 65.03; 18 male). The overall sample was composed of the
following racial identities: 50.9% Asian, 31.8% White, 8.1% African American, 4% Other, 2.9%
Mixed, 0.6% Biracial, 1.7% not stated. See
Table 1 for a detailed summary of participant demographics. Participants were recruited
via the University of Southern California (USC) Healthy Minds community subject pool (a USC
online bulletin board), Facebook, and flyers between January 2018 and March 2020. Individuals
with significant medical, neurological, or psychiatric conditions, as well as those taking
psychotropic medications were screened and excluded. However, individuals who had been
taking antidepressants or anti-anxiety medications and/or attending psychotherapy for at least
three months, without anticipated changes, were not excluded. In addition, since HRVbiofeedback training was an important variable for the larger aim of the HRV-ER study,
individuals with conditions that may hinder HRV-biofeedback training, such as coronary artery
disease, angina, and those with cardiac pacemakers, as well as individuals who were engaged in
relaxation, biofeedback or breathing practice were also excluded. Further, OA who scored below
16 on the TELE (Gatz et al., 1995), a telephone dementia screening protocol, were excluded for
potential dementia. The study protocol was approved by the Institutional Review Board (IRB) of
USC. All participants were compensated for their participation.
9
fMRI Data Acquisition
MRI data were collected at the USC Dana and David Dornsife Neuroimaging Center on a
3T Siemens MAGNETOM Trio scanner (Siemens, Erlangen, Germany) using a 32-channel head
array coil. A magnetization prepared rapid acquisition gradient echo (MPRAGE) sequence was
used to acquire T1-weighted 3D structural images (repetition time [TR]: 2,300 ms; echo time
[TE]: 2.26ms; slice thickness: 1.0 mm; flip angle: 9°; field of view [FOV]: 256 mm, voxel size:
1.0 x 1.0 x 1.0 mm; volumes: 175; duration: 4 min 44 s). Resting-state fMRI scan was obtained
using multi-echo planar imaging sequence. A 7-minute scan consisting of 175 slices was
acquired (TR: 2400 ms, TE: 18, 35, 53 ms; slice thickness: 3.0 mm; flip angle 75°; FOV: 240
mm; voxel size: 3.0 x 3.0 x 3.0). Participants were instructed to rest, breathe normally, and look
at a central white cross on a black screen.
fMRI Data Preprocessing
The pipeline used for preprocessing includes the multi-echo independent component
analysis (ME-ICA). This pipeline is efficient in differentiating Blood Oxygenated Level
Dependent (BOLD) signal from non-BOLD artifacts such as physiological signal and motion as
they show different relationship with echo-time (Kundu et al., 2013; Nashiro et al., 2023).
Regions of Interest (ROI) Selection
Individual amygdala masks were obtained from a publicly available data shared by KleinFlügge et al. (2022; OSF repository: https://doi.org/10.17605/OSF.IO/EGM2R). They used highresolution resting-state data from the Human Connectome Project (HCP) to parcellate constituent
amygdala subregions. To identify subregions, they used hierarchical clustering on similarities of
whole brain functional connectivity patterns between different amygdala voxels. This yielded
10
seven bilateral subregions that resemble established histological subregions of the amygdala. See
Table 2 and Figure 1 for amygdala subregions.
Given that the pregenual and subgenual PFC regions have been shown to respond
differentially to positive and negative stimuli, respectively, we defined spherical ROI around
peak coordinates reported by Tashjian et al. (2021). They identified pregenual and subgenual
subregions of the PFC based on meta-analytic coactivation from Neurosynth from studies
reporting responses to safety and threat. Five-millimeter spherical ROIs were centered at
corresponding Montreal Neurological Institute (MNI) coordinates (pregenual PFC: x = -2, y =
46, z = -10 and subgenual PFC: x = 0, y = 26, z = -12). See
Table 3 and Figure 2 for ROI locations. Bilateral dlPFC was defined based on a previous
meta-analysis of N-back working memory tasks (right dlPFC: x = 40, y = 32, z = 30 and left
dlPFC: x = -36, y = 44, z = 20; Owen et al., 2005). See Table 4 and Figure 3 for ROI locations.
Resting-State Functional Connectivity Analysis
Functional connectivity was analyzed using FMRIB Software Library (FSL) version
6.0.3 (Jenkinson et al., 2012). We used FSL FLIRT to linearly align each subject’s preprocessed
data to their brain-extracted structural image and the standard MNI 2-mm brain. Time series was
extracted from each amygdala mask. For each participant, a multiple regression analysis was
performed in FSL FEAT with nine regressors, including the time series for each amygdala mask,
signal from white matter, signal from cerebrospinal fluid and six motion parameters. The
individual amygdalae were registered to the standard MNI 2-mm brain using FSL FLIRT with
trilinear interpolation followed by a threshold of 0.25 and binarize operation with fslmaths to
11
keep the masks similar size. From each participant’s respective pregenual PFC, subgenual PFC,
left dlPFC, and right dlPFC connectivity map, we extracted the mean beta values from the seven
bilateral amygdala subregions.
12
Chapter Three: Results
ROI Analysis: The Effects of Age on Amygdala Functional Connectivity with Pregenual
and Subgenual PFC
To examine the effect of age on functional connectivity between amygdala subregions
and pregenual and subgenual vmPFC, we performed a three-way mixed ANOVA. Age group
(YA, OA) was included as a between-subject factor and vmPFC (subgenual, pregenual) and
amygdala subregions (central nucleus – Ce, cortical nuclei - CoN, auxiliary basal/basomedial -
Bal, lateral (intermediate part) - LaI, lateral (dorsal part) - LaD, lateral (ventral part) containing
aspects of basolateral - LaV/BL) as within-subject factors. We did not find a significant main
effect for age group, F(1,171) = 2.85, p = 0.093, ω
2 = 0.005. There was a significant main effect
for vmPFC subregion functional connectivity to amygdala subregions across all participants,
F(1,171) = 371.53, p < 0.001, ω
2 = 0.52, with subgenual PFC showing stronger connectivity to
amygdala subregions; Figure 4. There was a significant vmPFC subregion x age group
interaction effect on the functional connectivity between amygdala subregions to vmPFC
F(1,171) = 4.92, p = 0.028, ω
2 = 0.011. Post hoc testing using Bonferroni correction revealed
that subgenual PFC functional connectivity with amygdala subregions was stronger for YA than
OA (p = 0.036);
Table 6 and Figure 5. There was no significant age-related difference in pregenual PFC
functional connectivity to amygdala subregions (p > 0.99);
Table 6 and Figure 5.
There was no significant two-way interaction of individual amygdala subregions x age
group, F(6,1026) = 1.15, p = 0.334, ω
2 < 0.001, indicating that individual amygdala subregion
functional connectivity with the vmPFC regions did not differ between YA and OA. In addition,
13
we did not observe a three-way interaction between vmPFC subregions, amygdala subregions,
and age group F(6,1026) = 1.251, p = 0.278, ω
2 < 0.001, indicating that individual amygdala
subregions did not differ in their functional connectivity pattern with PFC subregions (pregenual
or subgenual) between younger and older adults.
There was also no significant main effect of laterality of amygdala subregions or
interaction effect of laterality of amygdala subregions x age group, indicating that functional
connectivity of left and right amygdala subregions with the vmPFC did not differ overall nor
between YA and OA, Flaterality(1,171) = 2.98, p = 0.09, ω
2 = 0.004; Fage x laterality (1,171) = 1.47, p
= 0.227, ω
2 < 0.001. There was a significant two-way interaction effect of laterality of amygdala
subregions x vmPFC subregion, F(1,171) = 6.49, p = 0.012, ω
2 = 0.009, indicating that amygdala
subregions in the left and right hemisphere differ in their connectivity profile to pregenual and
subgenual PFC. To examine this interaction, we conducted a post hoc test using Bonferroni
correction. Results revealed that there was significantly higher functional connectivity between
pregenual PFC and amygdala subregions in the right hemisphere than amygdala subregions in
the left hemisphere (p = 0.018). There was no significant difference in functional connectivity
between subgenual PFC and amygdala subregions in the left or right hemisphere (p = 0.97).
In addition, there was a significant three-way interaction effect of vmPFC subregion x
laterality of amygdala subregions x age group, F(1,171) = 5.59, p = 0.019, ω2 = 0.008, indicating
that vmPFC subregion connectivity with amygdala differed as function of age group (YA and
OA) and laterality of amygdala subregions (left and right). To examine this interaction closer, we
conducted two two-way mixed ANOVAs by testing the interaction of vmPFC subregion and age
group for left and right amygdala subregions separately. When examining functional
connectivity to the left hemisphere amygdala subregions, we observed a significant interaction
14
effect between vmPFC subregion x age group, FL-vmPFC x age group(1, 171) = 9.38, p = 0.003, ω
2 =
0.022; Table 7. Post hoc analysis using Bonferroni correction revealed that subgenual PFC
functional connectivity with amygdala subregions in the left hemisphere was stronger for YA
than OA (p = 0.022); Table 8 and Figure 6. There was no significant interaction effect in right
hemisphere amygdala subregions FR-vmPFC x age group(1, 171) = 0.397, p = 0.53, ω
2 < 0.001; Table
9. This finding indicates that the significant age x PFC subregion interaction effect observed
above (stronger functional connectivity between amygdala subregions and subgenual PFC in YA
compared to OA) is primarily driven by amygdala subregions in the left than right hemisphere.
For a complete set of results from the three-way mixed ANOVA examining the effect of age on
amygdala subregion functional connectivity with subgenual and pregenual PFC, see Table 5.
ROI Analysis: The Effects of Age on Amygdala Functional Connectivity with Left and
Right dlPFC
We also conducted another three-way mixed ANOVA to examine the effect of age on
functional connectivity between amygdala subregions and bilateral dlPFC. Similarly, age group
was included as a between-subject factor and dlPFC (left and right) and amygdala subregions as
within-subject factors. There were no significant main or interaction effects observed that were
directly related to our main research question. See Table 10 for a complete set of results.
15
Chapter Four: Discussion
While age-related cognitive decline is a common feature of healthy aging, emotional
well-being is commonly observed in old age (Isaacowitz, 2022; Mather, 2016). Gaining a
thorough understanding of the neural mechanisms underlying this phenomenon is important. The
present study examined resting-state functional connectivity between seven bilateral amygdala
subregions, derived from high-resolution resting-state fMRI data (Klein-Flügge et al., 2022) and
prefrontal cortex regions – vmPFC and dlPFC – implicated in affective regulation through their
relationship with the amygdala, within a group of healthy YA and OA. When examining
amygdala functional connectivity to PFC subregions, we found stronger functional connectivity
between amygdala subregions and subgenual PFC in YA than in OA. In addition, this
relationship was driven by amygdala subregions in the left than right hemisphere. Finally,
contrary to our hypothesis, we did not observe age-related differences in amygdala functional
connectivity with the dlPFC. Major findings from the present study are highlighted below.
Greater Functional Connectivity Between Amygdala Subregions and Subgenual PFC in
Younger Adults Compared to Older Adults
Independent of age, resting-state functional connectivity of the amygdala was greater
with the subgenual PFC than with the pregenual portion of the PFC. This finding is not
unexpected given that amygdala subnuclei have been shown to co-activate with the default mode
network (DMN; Elvira et al., 2022), which also includes the PFC as a key node (Uddin et al.,
2008). Furthermore, resting-state functional connectivity may reflect functional coupling
between with regions that have overlapping function (van den Heuvel & Hulshoff Pol, 2010),
therefore, it is understandable that amygdala subregions and subgenual PFC demonstrate
stronger connectivity given that both of these regions show stronger response to negative as
16
opposed to positive stimuli (Bonnet et al., 2015; Chang et al., 2021; Straube et al., 2008;
Tashjian et al., 2021), at least in younger adults. Moreover, previous studies have reported
increased functional coupling between the subgenual PFC and amygdala during the processing of
negative stimuli in task-based fMRI paradigms. A recent meta-analysis using activation
likelihood estimation algorithm found increased activation in response to negative emotions
within the subgenual PFC and left amygdala (Yang et al., 2020).
More importantly and as predicted, the functional connectivity profile of amygdala
subregions to PFC subregions differed across YA and OA. Specifically, we observed stronger
functional connectivity between amygdala subregions and subgenual PFC in YA compared to
OA. This finding provides additional evidence and possible neural mechanism that underlies the
phenomenon of age-related positivity effect observed among OA in behavioral studies. Given
that activity in the subgenual PFC has been associated with the expression of fear and sadness
(Chang et al., 2021), responses to threat (Tashjian et al., 2021), and in response to negative
emotional stimuli (Yang et al., 2020) – also see Mather, in press – our result suggests that the
OA brain may recruit the subgenual PFC less than the YA brain. However, this interpretation
warrants some caution given the lack of behavioral or task data.
We did not observe an age-related overall difference in functional connectivity between
amygdala subregions and the pregenual PFC. This finding suggests that, despite the decreased
functional connectivity in the subgenual PFC, connectivity in the pregenual PFC is maintained in
old age. It lends additional support for task-based fMRI studies that have shown that increased
functional coupling between the pregenual PFC and amygdala in old age is associated with the
positivity effect (Mather, in press; Sakaki et al., 2013).
17
Increased Functional Connectivity Between Subgenual PFC and Left but Not Right
Amygdala Subregions
We found a three-way interaction effect of PFC subregions, laterality of amygdala
subregions, and age group, indicating that vmPFC subregion connectivity with the amygdala
differed as a function of age group and laterality of amygdala subregions. To examine this
interaction effect closer, we conducted a set of exploratory analyses that were not hypothesized a
priori. We found greater functional connectivity between left amygdala subregions and
subgenual PFC than pregenual PFC in both YA and OA. In addition, subgenual functional
connectivity with left amygdala subregions was greater in YA than OA. This relationship was
not observed in the right amygdala subregions. This finding indicates that age-related difference
in subgenual PFC connectivity is primarily driven by amygdala subregions in the left
hemisphere. This finding is relatively consistent with what has been reported in task-based
emotion regulation paradigms. The meta-analysis by Yang et al. (2020) showed that active
downregulation of negative emotions is associated with increased activity in the left but not right
amygdala. Another meta-analysis by Baas et al. (2004) showed left amygdala dominance during
emotion regulation and processing. However, contrasting with our finding, the meta-analysis by
Yang et al. (2020) suggests that when the PFC and amygdala are both involved in active emotion
regulation, subgenual PFC and left but not right amygdala are engaged.
No Age-Related Difference in Functional Connectivity Between Amygdala Subregions and
Bilateral dlPFC
In contrast to our prediction, amygdala functional connectivity to the dlPFC did not vary
between age groups. To our knowledge, this is the first study to examine this relationship
between YA and OA during resting-state fMRI. The lack of age difference in connectivity was
18
somewhat surprising considering that the dlPFC has been strongly implicated in explicitcontrolled emotion regulation strategies (Braunstein et al., 2017) that are less utilized by OA
compared to other less cognitively demanding emotion regulation strategies (Allen & Windsor,
2019; Charles et al., 2009; Luong & Charles, 2014). Given that participants were instructed to
simply rest during the resting-state scan, one possibility is that OA spontaneously engage in
more dlPFC regulation of the amygdala during resting states. Another possibility is that neither
YA nor OA engaged the dlPFC much during rest. Future investigations, utilizing active emotion
regulation tasks, should examine the functional coupling of the dlPFC with amygdala subregions
in both YA and OA.
Limitations
The limitations of the present study are important to note. First, the OA sample was
smaller (60 OA compared to 113 YA) than the YA sample, which warrants cautious
interpretation. The larger HRV-ER study had planned to enroll a total of 208 participants, but
data collection was suspended due to the COVID-19 pandemic. Second, our data only consisted
of resting-state fMRI and therefore may not reflect functional connectivity during task-based
studies. Future studies should examine amygdala subregion functional coupling with PFC
subregions and dlPFC in YA and OA. Finally, the present study lack behavioral study to
supplement the reported neural findings. Thus, we can only infer whether the neural findings are
associated with behavioral outcomes.
Conclusions
To our knowledge, this is the first study to use resting-state fMRI to investigate amygdala
subregion functional connectivity with subregions of the PFC as well as bilateral dlPFC in a
sample of YA and OA. As hypothesized, we found greater age-related decreases in functional
19
connectivity between subgenual PFC and amygdala subregions than in the pregenual PFC or
bilateral dlPFC.
20
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Abstract (if available)
Abstract
Despite physical and cognitive decline, older adults (OA) show a positivity effect in attention and memory in which they prioritize positive information over negative information compared to young adults (YA). Relatedly, a key brain structure implicated in processing and modulating response to emotionally relevant stimuli, the amygdala serves as a hub for neural networks involved in emotion processing. Moreover, along with showing biases toward positive emotions in memory and attention, OA rely on more implicit and automatic emotion regulation strategies that are less cognitively demanding. Previous research examining neural mechanisms underlying different emotion regulation strategies have implicated the ventromedial prefrontal cortex (vmPFC) and dorsolateral prefrontal cortex (dlPFC) in implicit-automatic and explicit-controlled emotion regulation strategies, respectively. In addition, detailed analyses of the vmPFC have revealed that the anterior (pregenual PFC) and posterior (subgenual PFC) portions are differentially involved in response to positive and negative emotional stimuli, respectively. The majority of studies investigating the positivity effect and age-related differences in amygdala functional connectivity have predominantly employed task-based paradigms. Therefore, the present study investigated age-related differences in resting-state functional connectivity patterns between seven amygdala subregions and pregenual and subgenual PFC, as well as the dlPFC in YA (n = 113; ages 18-31) and OA (n = 60; ages 65-80). Results revealed stronger functional connectivity between amygdala subregions and subgenual PFC in YA than in OA. Relative to the subgenual PFC, connectivity in the pregenual PFC is maintained in old age. We did not observe age-related differences in amygdala functional connectivity with the dlPFC.
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Alemu, Kalekirstos
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Age-related differences in amygdala subregion functional connectivity with Ventromedial Prefrontal Cortex and Dorsolateral Prefrontal Cortex
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amygdala
dorsolateral prefrontal cortex
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