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How age-related changes in the locus coeruleus affect selective attention and memory
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How age-related changes in the locus coeruleus affect selective attention and memory
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Content
How age-related changes in the locus coeruleus affect selective attention and memory
by
Kelly Anne Durbin
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
PSYCHOLOGY
August 2021
Copyright 2021 Kelly Anne Durbin
ii
Dedication
To my parents, thank you for the unconditional love and support. I will forever be grateful for
everything that you have done for me. I could have never come this far without you. And, to my
father, thank you for always believing in me and my educational and intellectual abilities. It has
been an honor to follow in your footsteps and carry on the Lehigh legacy.
iii
Acknowledgements
First and foremost, I would like to thank my advisor and dissertation chair, Mara Mather.
You are a brilliant researcher, mentor, and role model. I am so incredibly grateful and honored
that I could be a graduate student in your lab. You helped inspire me to become a better scientist,
researcher, and writer. Thank you for all of your guidance, support, and patience over the past six
years and I really hope to collaborate with you for years to come.
I would also like to thank the rest of my dissertation committee, Daniel Nation, Duke
Han, and Gerald Davidson. I really appreciate your support, patience, and willingness to serve on
my committee. Your knowledge, insightful feedback, and work in your own respective fields has
been an inspiration for me throughout graduate school.
In addition, I want to extend my deepest gratitude to my clinical advisors and mentors
who have been so supportive and encouraging, especially Robert Bilder, Kathleen Tingus, Dale
Sherman, Patricia Walshaw, Daniel Nation, and Carol McCleary. It has been so incredibly
valuable to learn from your expertise.
I also would like to express my sincerest appreciation for everyone in the Emotion and
Cognition Lab, especially Ringo Huang. Thank you for being there for all of this data collection
and for having such calm, patient energy that kept me sane when I felt completely stressed and
overwhelmed at the DNI. Without you, this dissertation would not have been possible. Thank
you to Shelby Bachman for her assistance with the neuroimaging analyses. I also want to thank
Dave Clewett, who has been such a professional inspiration and also one of my closest friends.
You are absolutely brilliant. Thank you for all the laughs over the years. I am so excited to start
this next chapter at UCLA with you.
iv
Most importantly, I would like to thank all of the friends that I have made at USC who
have loved and supported me unconditionally throughout graduate school, especially Sarah
Stoycos, Alyssa Morris, Sarah Malamut, and Mona Khaled. I would have never survived this
journey without you ladies. You all believed in me way more than I ever believed in myself, both
personally and professionally, and I am so incredibly lucky to have such amazing friends. Also,
thank you to my clinical cohort. I feel like we survived a battle together and have an
indescribable bond having gone through this process with each other. Graduate school would
have never been the same without you all and I will forever be grateful for all the laughs,
memories, and times we spent commiserating together. Thank you for being such great friends to
me throughout this journey. Lastly, I would like to thank Manfred Tejerina for helping to push
me over the finish line. Thank you for always being there for me, cheering me up, encouraging
me, and giving me push when I needed one.
v
Table of Contents
Dedication ........................................................................................................................... ii
Acknowledgements ............................................................................................................. iii
List of Tables ...................................................................................................................... viii
List of Figures ..................................................................................................................... x
Abstract ............................................................................................................................... xi
Chapter 1. Introduction ....................................................................................................... 1
1.1 In Vivo Measurements of the LC .................................................................... 1
1.2 The Role of the LC in Cognition .................................................................... 2
1.3 LC Structural Changes in Neurodegenerative Disease ................................... 4
1.4 Changes in the LC in Healthy Aging .............................................................. 6
1.5 The Role of the LC in Cognitive Aging ......................................................... 8
1.5.1 Attention and Executive Functioning .............................................. 8
1.5.2 Memory ............................................................................................ 10
1.6 The Present Study ........................................................................................... 13
Chapter 2. Methods ............................................................................................................. 15
2.1 Participants .....................................................................................................
15
2.2 Experimental Design ......................................................................................
17
2.3 Materials .........................................................................................................
18
2.3.1 Pattern Separation Task ...................................................................
18
2.3.2 Selective Attention Task ..................................................................
20
2.3.3 Oddball Task ....................................................................................
22
2.3.4 Emotional Stroop Task ....................................................................
24
2.3.5 Sustained Attention to Response Task ............................................
25
2.4 Procedure ........................................................................................................
26
2.4.1 Session 1 ..........................................................................................
26
2.4.1.1 Pattern Separation Task ....................................................
27
2.4.1.2 Selective Attention Task ...................................................
28
2.4.1.3 MRI Data Acquisition .......................................................
29
2.4.1.4 LC Signal Intensity ...........................................................
30
2.4.2 Session 2 ..........................................................................................
33
vi
2.4.2.1 Oddball Task .....................................................................
33
2.4.2.2 Picture Rating Task ...........................................................
35
2.4.2.3 Emotional Stroop Task .....................................................
35
2.4.2.4 Sustained Attention to Response Task .............................
36
2.4.2.5 Questionnaires ..................................................................
36
Chapter 3. Results ...............................................................................................................
37
3.1 LC Contrast Ratios among Participants ..........................................................
37
3.2 Session 1 .........................................................................................................
37
3.2.1 Pattern Separation Task ...................................................................
37
3.2.1.1 Behavioral Results ............................................................
37
3.2.1.2 LC Contrast Ratios and Behavioral Task Performance ....
41
3.2.2 Selective Attention Task ..................................................................
43
3.2.2.1 Behavioral Results ............................................................
43
3.2.2.2 LC Contrast Ratios and Behavioral Task Performance ....
45
3.3 Session 2 .........................................................................................................
47
3.3.1 Oddball Task ....................................................................................
47
3.3.1.1 Behavioral Results ............................................................
47
3.3.1.2 LC Contrast Ratios and Behavioral Task Performance ....
49
3.3.2 Emotional Stroop .............................................................................
51
3.3.2.1 Behavioral Results ............................................................
51
3.3.2.2 LC Contrast Ratios and Behavioral Task Performance ....
54
3.3.3 Sustained Attention to Response Task ............................................
56
3.3.3.1 Behavioral Results ............................................................
56
3.3.3.2 LC Contrast Ratios and Behavioral Task Performance ....
57
Chapter 4. Discussion .........................................................................................................
58
4.1 Memory ...........................................................................................................
60
4.1.1 Pattern Separation ............................................................................
60
4.1.2 Selective Memory under Arousal ....................................................
63
4.2 Attention and Executive Functioning .............................................................
64
4.2.1 Selective Attention ...........................................................................
64
4.2.2 Sustained Attention ..........................................................................
67
4.2.3 Cognitive Control of Emotional Information ..................................
68
4.3 Conclusions and Clinical Significance ...........................................................
70
Footnotes .............................................................................................................................
72
vii
References ...........................................................................................................................
73
Supplemental Materials ......................................................................................................
90
viii
List of Tables
Table 1. Demographic and self-report mood measures by age group ................................
16
Table 2. Mean valence and arousal ratings for negative and neutral oddball scenes as a
function of age ....................................................................................................................
24
Table 3. Proportions of responses to repetitions, lures, and new items by age group ........ 40
Table 4. Results of multiple linear regression analyses between LC contrast and
memory performance taking into account hemispheric differences ...................................
42
Table 5. Results of multiple linear regression analyses between LC contrast and
memory performance taking into account differences along the rostrocaudal axis ...........
43
Table 6. Mean proportion of targets accurately detected as a function of age, cognitive
load, and the presence of irrelevant checkerboard stimuli .................................................
45
Table 7. Results of multiple linear regression analyses between LC contrast ratios
within each hemisphere and the mean proportion of targets detected ................................
46
Table 8. Results of multiple linear regression analyses between LC contrast ratios along
the rostrocaudal axis and the mean proportion of targets detected .....................................
47
Table 9. Recall memory during the oddball task as a function of age and oddball
valence ................................................................................................................................
49
Table 10. Results of multiple linear regression analyses between LC contrast and
oddball memory performance taking into account hemispheric differences ......................
50
Table 11. Results of multiple linear regression analyses between LC contrast and
oddball memory performance taking into account differences along the rostrocaudal
axis ......................................................................................................................................
51
Table 12. Mean response times (ms) on correct trials during the Emotional Stroop task
as a function of age, valence, and arousal level ..................................................................
53
Table 13. Mean proportion of errors during the Emotional Stroop task as a function of
age, valence, and arousal level ...........................................................................................
53
ix
Table 14. Results of multiple linear regression analyses between LC contrast ratios
within each hemisphere and mean response times on correct trials ...................................
54
Table 15. Results of multiple linear regression analyses between LC contrast ratios
along the rostrocaudal axis and mean response times on correct trials ..............................
55
Table 16. Results of multiple linear regression analyses between LC contrast ratios and
errors on the SART task taking into account hemispheric differences ..............................
57
Table 17. Results of multiple linear regression analyses between LC contrast ratios and
errors on the SART task taking into account differences along the rostrocaudal axis .......
58
x
List of Figures
Figure 1. Schema of experimental design .......................................................................... 18
Figure 2. Example stimulus pairs used in the pattern separation task ................................ 20
Figure 3. Targets in the (A) low cognitive load and (B) high cognitive load conditions... 21
Figure 4. Stimuli from the target detection task were accompanied with either (A) no
task-irrelevant checkerboards present, (B) flashing bilateral checkerboard patterns with
greater visual contrast on the left, or (C) flashing bilateral checkerboard patterns with
greater visual contrast on the right ......................................................................................
22
Figure 5. An example of a negative-neutral matched pair designated as oddball stimuli... 23
Figure 6. Schematic depiction of trials during the SART task ........................................... 26
Figure 7. Schematic representation of the encoding and test phases used in the pattern
separation task ....................................................................................................................
28
Figure 8. Example trials for a block in the high cognitive load condition without any
task-irrelevant checkerboard patterns (A) and a block in the low cognitive load
condition with flashing bilateral checkerboards that had greater visual contrast in the left
hemisphere (B) ....................................................................................................................
29
Figure 9. Raw image of LC signal from a neuromelanin-sensitive T1-weighted MRI
sequence ..............................................................................................................................
32
Figure 10. Depictions of the left and right LC meta masks and the reference mask in the
dorsal pontine tegmentum ...................................................................................................
33
Figure 11. An example trial from the oddball task ............................................................ 34
Figure 12. Proportion of responses during the pattern separation task .............................. 41
xi
Abstract
Evidence suggests that structural and functional deficits in the LC-NE system, either from
advancing age or disease pathology, are associated with impaired cognition, including select
aspects of attention, memory, and executive functioning. Yet, very few studies have examined
the association between this system and cognitive functioning in healthy older adults. In the
present study, we examined the relationship between the integrity of the LC-NE system and
performance on five separate cognitive tasks in healthy younger and older adults. To quantify the
integrity of the LC, we used high-resolution, neuromelanin-sensitive, fast spin-echo (FSE) T1-
weighted MRI sequences and a pre-existing LC meta mask to compute four separate
measurements of LC signal intensity (i.e., left, right, caudal, rostral). We found that older adults
with higher LC contrast, specifically in the left LC, were less likely to engage in pattern
completion (i.e., mistakenly reinstating an existing mental representation when presented with a
similar stimulus). Results also demonstrated that higher contrast in the caudal portion of the LC
in older adults was associated with better selective attention and inhibition of task-irrelevant
information under high cognitive demand. Lastly, our findings showed that older adults with
higher contrast in the rostral LC exerted greater cognitive control when presented with negative,
low arousing words on an Emotional Stroop task. Taken together, our results provide novel
evidence indicating that the integrity of the LC-NE system is related to select cognitive abilities
in healthy older adults. Furthermore, our findings suggest that these differences may depend on
hemispheric differences and variations in signal intensity within specific regions of the LC,
underscoring the importance of taking into account LC topography when investigating how the
LC-NE system affects cognitive performance across the lifespan.
1
Introduction
The locus coeruleus (LC) consists of two small, bilateral nuclei in the pons region of the
brainstem that are responsible for producing and distributing norepinephrine (NE) widely
throughout the central nervous system (Berridge & Waterhouse, 2003). These two nuclei, located
in the left and right hemispheres of the brain, are roughly symmetrical and contain a similar
number of neurons (German et al., 1988; Vijayashankar & Brody, 1979). The LC has a
topographical organization in which axons projecting to the forebrain (e.g., hippocampus,
prefrontal cortex) typically arise from the dorsal area of the LC, whereas neurons within the
ventral region of the LC primarily send projections to the cerebellum and spinal cord (Schwarz
& Luo, 2015). There is also a topographic organization along the rostrocaudal axis of the LC in
which the highest number of neurons are located within the caudal region of the LC and the cell
numbers gradually decrease in the rostral direction (German et al., 1988). Researchers have also
found hemispheric differences in which LC signal intensity is higher in the left than the right
hemisphere (Bachman et al., 2021). Evidence suggests that the efferent projections to the various
cortical regions of the brain typically arise from the caudal region of the ipsilateral LC
(Waterhouse, Lin, Burne, & Woodward, 1983). Critically, these cortical areas are innervated by
long unmyelinated and weakly myelinated axons, making LC neurons particularly vulnerable to
toxins and capable of spreading disease pathology that is associated with specific
neurodegenerative disorders, such as Alzheimer’s disease, to other regions of the brain (Janitzky,
2020; Mather & Harley, 2016).
1.1 In Vivo Measurements of the LC
Historically, the LC has been challenging to observe using traditional neuroimaging
methods given its small size (i.e., approximately 10,000 – 15,000 nuclei in humans) and close
2
proximity to the fourth ventricle (Berridge & Waterhouse, 2003). However, in recent years,
advancements in magnetic resonance imaging (MRI) methodologies have allowed investigators
to map the LC so that its structure and function can be visualized in human neuroimaging studies
(e.g., Clewett et al., 2016; Keren, Lozar, Harris, Morgan, & Eckert, 2009; Sasaki et al., 2006;
Shibata et al., 2006; for a discussion, see Betts et al., 2019). The LC contains neuromelanin, a
dark, insoluble pigment that is synthesized from oxidized catecholamines, and therefore a waste
by-product of NE metabolism. Consequently, by using neuromelanin-sensitive T1-weighted MRI
sequences, researchers have been able to easily visualize the LC (for example, see Figure 9) and
use LC signal intensity as an estimate for the structural integrity of the LC (Clewett et al., 2016;
Sasaki et al., 2006; Shibata et al., 2006; Takahashi et al., 2015). This LC signal intensity (also
referred to as LC contrast) is quantified by using a contrast-to-noise ratio that compares the
neuromelanin signal in the LC to the signal intensity from an adjacent region within the dorsal
pontine tegmentum (Clewett et al., 2016; Keren et al., 2009; Sasaki et al., 2006; Shibata et al.,
2006). These novel in vivo neuroimaging methods allow researchers to investigate how the
integrity of the LC is related to cognitive performance in healthy individuals and those with
neurodegenerative disease.
1.2 The Role of the LC in Cognition
Given that the LC is the sole source of NE to the hippocampus and prefrontal cortex
(Berridge & Waterhouse, 2003; Sara, 2009), it is believed that the LC-NE system modulates
numerous cognitive abilities, including attention, memory, and executive functioning (Aston-
Jones & Cohen, 2005; Mather, 2020; Mather, Clewett, Sakaki, & Harley, 2016; Sara, 2009,
2015). More specifically, axons from the LC innervate multiple areas within the frontal cortex,
including the medial prefrontal, orbitofrontal, and anterior cingulate cortices (Chandler, Gao, &
3
Waterhouse, 2014), implicating the LC-NE system for a number of higher-order cognitive
functions including selective attention, cognitive control, inhibition, working memory, and
cognitive flexibility. In fact, blocking NE receptors in the prefrontal cortex elicits behaviors that
are comparable to attention-deficit/hyperactivity disorder, such as increased distractibility and
impulsivity (Arnsten & Li, 2005). The LC-NE system also plays a fundamental role in various
aspects of memory (e.g., encoding, consolidation, retrieval) and facilitates both synaptic
plasticity and long-term potentiation via its projections to hippocampal subfields (Neuman &
Harley, 1983; Sara, 2009, 2015).
Research has also established that this system is critical for the formation of emotional
memories through its interactions with neuromodulators and hormones in the amygdala and
hippocampus (Berridge & Waterhouse, 2003; Sara, 2009, 2015). For example, there is greater
connectivity between the LC and the amygdala when retrieving information that was encoded in
an emotional, relative to a neutral, context (Sterpenich et al., 2006). An increase of NE can even
improve the detection of emotionally arousing stimuli (De Martino, Strange, & Dolan, 2007).
Additionally, by receiving signals from the LC, activation in the frontal areas responsible for
attention and executive functioning becomes amplified under heightened arousal and when the
cognitive demands of a task have increased (Mather et al., 2016; Raizada & Poldrack, 2008; Sara
& Bouret, 2012).
Activation of the LC-NE system in response to high cognitive demands and increased
emotional arousal can occur from both bottom-up (e.g., threat, emotional salience) and top-down
influences (e.g., goal-relevance; for a discussion, see Mather, 2020). This increase in sympathetic
arousal then facilitates selective attention and memory by enhancing one’s focus on high-priority
(e.g., goal-relevant, emotionally salient) information while simultaneously suppressing the
4
perception and memory of less relevant stimuli (e.g., Lee, Greening, & Mather, 2015; Lee,
Sakaki, Cheng, Velasco, & Mather, 2014; Sakaki, Fryer, & Mather, 2014; Sutherland & Mather,
2012; for a review, see Mather & Sutherland, 2011). The LC-NE system likely promotes
selective attention and memory under arousal by creating a hotspot in which local glutamate and
NE release create a positive feedback loop that amplifies the activity of neurons responsible for
transmitting high-priority information and suppresses neuronal activity for low-priority
information (Mather et al., 2016).
Supporting this notion, a recent study found greater activity in both the LC and the
parahippocampal cortex, a brain region involved in processing and remembering visual scenes
(e.g., landscapes), when participants encoded task-relevant scenes under arousal (Clewett,
Huang, Velasco, Lee, & Mather, 2018). Specifically, Clewett et al. (2018) found that when
younger adults were under the threat of monetary loss, they showed enhanced memory for goal-
relevant scenes and a trend towards greater suppression of task-irrelevant objects. In addition,
individuals with higher neuromelanin signal intensity were more likely to engage the LC when
encoding goal-relevant information under arousal. Consequently, any structural or functional
deficits in the LC-NE system, either from advancing age or disease pathology, should be
associated with an impaired ability to selectively attend to and remember high-priority
information, especially under threatening and emotionally arousing circumstances.
1.3 LC Structural Changes in Neurodegenerative Disease
Researchers have suggested that the locus coeruleus is the most probable site where
Alzheimer’s disease pathology first begins to manifest (Braak & Del Tredici, 2012, 2015)
Subsequently, tau pathology spreads to the medial temporal lobe via projections from the LC to
the transentorhinal cortex (Braak, Thal, Ghebremedhin, & Del Tredici, 2011; Stratmann et al.,
5
2016). These findings suggest that disease pathology within the LC could contribute to the
initiation and progression of Alzheimer’s disease (for a review and discussion, see Weinshenker,
2018). Changes in the LC are also observed in other neurodegenerative conditions, such as
Parkinson’s disease, frontotemporal dementia, Huntington’s disease, multiple system atrophy,
and progressive supranuclear palsy (Braak et al., 2003; Holland, Robbins, & Rowe, 2021).
Postmortem studies have also found significant reductions in both the volume and the
number of neurons in the LC among individuals with Alzheimer’s and Parkinson’s disease
(German et al., 1992; Kelly et al., 2017; Theofilas et al., 2017; Zarow, Lyness, Mortimer, &
Chui, 2003; for a meta-analysis, see Lyness, Zarow, & Chui, 2003). Specifically, the rostral
region of the LC seems to be particularly affected in Alzheimer’s disease, whereas there is
widespread degeneration of the LC in Parkinson’s disease (Weinshenker, 2018). Other
neurodegenerative diseases, such as dementia with Lewy bodies and frontotemporal dementia,
have also shown profound degeneration and neuronal loss in the LC (Brunnström, Friberg,
Lindberg, & Englund, 2011; Irwin et al., 2016; Szot et al., 2006; for a recent review, see
Holland, Robbins, & Rowe, 2021). One study also found that the volume of the LC was smaller
among individuals with mild cognitive impairment due to Alzheimer’s disease as well as in
healthy older adults who later developed Alzheimer’s disease (Dutt, Li, Mather, & Nation,
2020).
Research has also shown that LC neuromelanin signal intensity is significantly lower in
patients with Parkinson’s disease, suggesting a loss of neuromelanin-containing neurons in the
LC among individuals with this disease (Li et al., 2019; Liu et al., 2017; Sasaki et al., 2006;
Sulzer et al., 2018). Evidence has also revealed that LC contrast was reduced in individuals with
Alzheimer’s disease and mild cognitive impairment (Takahashi et al., 2015). Likewise, Olivieri
6
et al. (2019) found a significant decrease in LC signal intensity among amnestic patients who had
suspected non-Alzheimer pathophysiology as well as in individuals with Alzheimer’s disease,
relative to healthy controls. Furthermore, there was a positive correlation between LC signal
intensity and episodic memory in this sample of amnestic patients, suggesting that the integrity
of the LC may be related to the cognitive impairments often observed in individuals with
neurodegenerative disease.
Supporting this notion, a number of studies have shown a link between cognitive deficits
and LC structural integrity in those diagnosed with Alzheimer’s disease. For instance, in a
sample that included patients with amnestic mild cognitive impairment and Alzheimer’s disease,
a decrease in LC neurons was associated with worse performance on tests of memory, perceptual
speed, and visuospatial ability as well as declined performance on measures of global cognitive
functioning (Kelly et al., 2017). Similarly, research has discovered that smaller LC volume
among individuals with mild cognitive impairment was related to poorer performance on
neuropsychological tests of attention and executive functioning (Dutt, Li, Mather, & Nation,
2021). Another study revealed that Parkinson’s disease patients with lower LC contrast had
worse performance on a set-shifting task (i.e., Trail Making Test), suggesting that degradation of
the LC-NE system may correspond to some of the cognitive deficits observed in Parkinson’s
disease (Li et al., 2019). Researchers have also suggested that pathological changes in this
system could play a role in the impulsivity, another aspect of executive functioning, that is
observed in those with frontotemporal dementia (Passamonti, Lansdall, & Rowe, 2018). Taken
together, these findings underscore the clinical significance of identifying the structural and
functional changes in the LC that can occur at the earliest stages of neurodegenerative disease.
1.4 Changes in the LC in Healthy Aging
7
Research has shown that the volume of the LC diminishes in older age and the number of
LC neurons can decrease by up to 40-50% over the course of healthy aging (Chan-Palay & Asan,
1989; Lohr & Jeste, 1988; Manaye et al., 1995; Vijayashankar & Brody, 1979). However, studies
that have used more modern techniques, such as unbiased estimation procedures, or taken into
account neurofibrillary tangles across the entire brain have not found an age-related loss of LC
neurons (Mather & Harley, 2016). Investigators have discovered that age-related cell loss in the
LC occurs at different rates along the rostrocaudal extent of the LC, such that there is greater cell
loss within the rostral, relative to the caudal, region of the LC (Chan-Palay & Asan, 1989;
Manaye et al., 1995). However, recent work proposes that the volume of the LC is not affected
by aging per se, but rather by the specific disease pathology (e.g., hyperphosphorylated tau) that
can occur in older age (Theofilas et al., 2017; cf. Mouton, Pakkenberg, Gundersen, & Price,
1994; Ohm, Busch, & Bohl, 1997).
Several studies have found that the neuromelanin concentration in the LC is higher in
healthy older adults relative to younger adults (Clewett et al., 2016; Zecca et al., 2004).
However, it appears that neuromelanin concentration accumulates in a nonlinear fashion across
the adult lifespan, such that LC contrast increases until middle-to-late adulthood and then
gradually decreases in older age (Manaye et al., 1995; Mann, 1983; Shibata et al., 2006). There
are also age-related differences in LC neuromelanin signal intensity along the rostrocaudal axis
(Bachman et al., 2021; Dahl et al., 2019). Specifically, Bachman et al. (2021) identified a
significant interaction between the two age groups in which older adults showed greater LC
contrast in the caudal region of the LC and marginally lower contrast in the rostral area of LC
relative to younger adults. Furthermore, these investigators showed that LC contrast was
positively associated with cortical thickness in the frontal, parietal, and occipital cortices,
8
including regions that correspond to the frontoparietal network (Bachman et al., 2021). Thus,
variations in LC signal intensity in later life could be related to individual differences for specific
cognitive abilities, such as sustained attention and working memory.
1.5 The Role of the LC in Cognitive Aging
Researchers have postulated that the LC-NE system is closely linked to cognitive
functioning in later life (for a review and discussion, see Mather & Harley, 2016). Interestingly,
the cognitive abilities that tend to show the greatest declines in older age are dependent on the
LC-NE system, including various aspects of attention, memory, and executive functioning
(Mather & Harley, 2016). In fact, NE may have a neuroprotective role that mediates cognitive
reserve and reduces the risk of neurodegenerative disease (Robertson, 2013). Yet, despite its
clinical implications, relatively little research has been conducted on the integrity of the LC-NE
system and cognitive performance in healthy older adults. In one longitudinal study, Wilson et
al. (2013) found that greater neuronal density in the LC was associated with a slower rate of
cognitive decline, which was operationalized using a composite measure derived from 19
cognitive tests assessing memory, visuospatial ability, and perceptual speed. In addition, Clewett
et al. (2016) showed that verbal intelligence was positively associated with LC signal intensity in
older adult participants. By using a composite score comprised of educational background,
occupational attainment, and verbal intelligence, they also discovered that older adults with
higher LC signal intensity had greater cognitive reserve (Clewett et al., 2016). Together, these
studies provide direct evidence for an association between the integrity of the LC and cognitive
functioning in older adulthood.
1.5.1 Attention and Executive Functioning
9
Research has established that selective attention and the ability to inhibit distracting
information decreases across the lifespan (Braver & West, 2008; Gazzaley et al., 2008;
Gazzaley, Cooney, Rissman, & D’Esposito, 2005; Hasher & Zacks, 1988; Kim, Hasher, &
Zacks, 2007). Even under conditions of threat, when selective attention and memory may be
particularly advantageous, older adults have difficulty suppressing memory for task-irrelevant
stimuli (Durbin, Clewett, Huang, & Mather, 2018; Gallant, Durbin, & Mather, 2020). Indeed, a
recent neuroimaging study found that older adults showed greater activation for both low- and
high-priority stimuli under arousal, suggesting that arousal amplifies information
indiscriminately in older age (Lee et al., 2018).
The LC modulates selective attention under heightened arousal by interacting with the
attentional regions of the brain, including the frontoparietal and salience networks (Lee et al.,
2018; Lee, Kim, Katz, & Mather, 2020; Mather, Clewett, Sakaki, & Harley, 2016; Sara &
Bouret, 2012). Therefore, one possible explanation for age-related deficits in selective attention
and inhibition may be a disruption in the communication between the LC and these frontal
systems. Supporting this notion, researchers have discovered that older adults had reduced
functional connectivity between the LC and the frontoparietal network, relative to younger adults
(Lee et al., 2018). In addition, Lee et al. (2020) found that older adults also exhibited reduced
connectivity between the LC and the salience network, which could negatively affect older
adults’ ability to prioritize incoming information. That is, these observed age differences in
functional connectivity could explain why older adults are more prone to distraction and have
greater difficulty inhibiting task-irrelevant information.
Expanding upon this notion, another study investigated the role of the LC-NE system on
selective attention in younger and older adults using an auditory selective attention task that
10
presented fear-conditioned stimuli, allowing the investigators to examine arousal levels on trial-
by-trial basis (Dahl, Mather, Sander, & Werkle-Bergner, 2020). By creating a single composite
of LC-NE responsiveness using EEG and pupil dilation, an indirect measure of arousal-mediated
NE release and LC activity (Joshi, Li, Kalwani, & Gold, 2016; Murphy, O’Connell, O’Sullivan,
Robertson, & Balsters, 2014), they showed that older age was associated with lower
responsiveness of the LC-NE system. Furthermore, in both age groups, greater LC-NE
responsiveness was linked to better selective attention. Despite these findings, few MRI studies,
if any, have examined how variability in the LC-NE system is related to younger and older
adults’ ability to selectively attend to high-priority information under arousal.
1.5.2 Memory
Disruptions in the ability to selectively attend to task-relevant information while
simultaneously inhibiting distracting or irrelevant stimuli has negative downstream consequences
for older adults’ memory (Biss, Campbell, & Hasher, 2013; Campbell, Grady, Ng, & Hasher,
2012). Thus, it is plausible that degradation of the LC-NE system also plays a role in age-related
memory impairments. Indeed, recent evidence has emerged establishing a link between the LC
and memory performance in older adulthood (Dahl et al., 2019; Hämmerer et al., 2018; Jacobs et
al., 2015). For example, greater connectivity between the left LC and the left parahippocampal
gyrus in healthy older adults was associated with better encoding and recall on a verbal learning
task (Jacobs et al., 2015).
Preliminary evidence has also found a correlation between lower LC neuromelanin signal
intensity and worse verbal memory performance in older adults (Solders et al., 2020). Likewise,
other research has found that individual differences in LC intensity corresponded to older adults’
verbal memory performance, which was assessed using the Rey Auditory Verbal Learning Test
11
(RAVLT; Dahl et al., 2019). Moreover, older adults with higher contrast in the rostral LC, which
projects to the hippocampus, exhibited better memory performance. Similarly, another study
found that higher diffusivity in both the rostral and caudal regions of the LC was correlated with
better recall on the RAVLT in older, but not younger, adults (Langley, Hussain, Huddleston,
Bennett, & Hu, 2021). Thus, it is possible that LC signal intensity is associated with performance
on memory tasks that are susceptible to age-related deficits.
One aspect of memory that is particularly sensitive to age-related changes is called
“pattern separation,” which refers to the ability to differentiate similar experiences into two
distinct, non-overlapping memory representations (McClelland, McNaughton, & O’Reilly, 1995;
O’Reilly & Norman, 2002). In contrast, “pattern completion” refers to the memory error in
which the presentation of a similar stimulus reinstates an existing mental representation so that
these two representations are no longer distinguishable. Prior research has observed age-related
deficits in pattern separation performance, such that this ability shows a linear decline with
advancing age (Stark, Yassa, Lacy, & Stark, 2013; Toner, Pirogovsky, Kirwan, & Gilbert, 2009).
Instead, older adults are more likely to engage in pattern completion (Stark, Yassa, & Stark,
2010; Yassa et al., 2011). Impaired pattern separation ability has also been documented in
neurodegenerative diseases, including individuals diagnosed with amnestic mild cognitive
impairment (Stark et al., 2013; Yassa et al., 2010).
Neuroimaging studies have found that pattern separation is associated with activation in
the dentate gyrus and CA3 subregions of the hippocampus, whereas pattern completion is linked
to activity in the CA1 hippocampal subfield (Bakker, Kirwan, Miller, & Stark, 2008; Kirwan &
Stark, 2007; Lacy, Yassa, Stark, Muftuler, & Stark, 2011; Leutgeb, Leutgeb, Moser, & Moser,
2007; Yassa et al., 2011; for a review, see Yassa & Stark, 2011). Given that the dentate gyrus
12
receives dense projections from the LC (for a discussion, see Harley, 2007) and NE activation
enhances pattern separation ability (Segal, Stark, Kattan, Stark, & Yassa, 2012), it is possible
that differences in the integrity of the LC-NE system correspond to pattern separation ability in
older adulthood (Mather & Harley, 2006). However, to date, no studies have examined whether
individual differences in the structural and functional integrity of the LC are related to older
adults’ pattern separation performance.
As discussed above, the LC is particularly involved in emotional memory, which is also
susceptible to age group differences (e.g., Charles, Mather, & Carstensen, 2003). Thus, it is
possible that the age-related changes in emotional memory are associated with variability in the
LC-NE system. Studies have demonstrated that there is a positive relationship between LC signal
intensity and negative emotional memory in older, but not younger, adults (Liu et al., 2020).
Likewise, Hämmerer et al. (2018) showed that a decrease in the structural integrity of the LC
was related to worse memory for negative emotional events in older adults. Yet, this relationship
was not observed in younger adults. Further support for the link between the LC-NE system and
emotional memory comes from an animal study that found that when NE levels were increased,
the emotional memory impairments observed in aged rats were eliminated (Luo et al., 2015).
Although these findings provide a basis for understanding the role of the LC-NE system
on emotional memory in later life, no studies have directly examined whether variability in LC
signal intensity in younger and older adults corresponds to selective memory under increased
emotional arousal. One memory paradigm that could test this unanswered question is the
“oddball” task. In this paradigm, a perceptually salient or emotionally arousing image (i.e., the
“oddball”) is displayed within a series of less salient or neutral images. Memory for the items
that immediately precede and follow an oddball stimulus (i.e., “oddball – 1” and “oddball + 1”
13
items, respectively) are worse if the oddball is emotionally arousing, relative to neutral (Knight
& Mather, 2009; Sakaki, Fryer, & Mather, 2014). As discussed earlier, the ability to inhibit
memory for low-priority information in response to heightened emotional arousal is believed to
be modulated by the LC-NE system (Mather et al., 2016). Supporting this theory, researchers
have shown that neural responses to oddball stimuli and the retrograde amnesia for items
preceding emotional oddballs are in fact mediated by NE projections (Hurlemann et al., 2005;
Strange & Dolan, 2007; Strange, Hurlemann, & Dolan, 2003). Thus, the oddball paradigm could
provide valuable insight into how the LC-NE system modulates selective memory under arousal
in both younger and older adults.
1.6 The Present Study
As outlined above, age-related changes in the structural and functional integrity of the
LC-NE system likely contribute to the cognitive declines observed in older adults. However,
very few studies have examined the association between the structural integrity of the LC,
quantified using LC neuromelanin signal intensity, and cognitive performance in healthy older
adults.
In the present study, younger and older adults were asked to complete five cognitive tasks
over two separate sessions. During the first session, participants completed a pattern separation
task and a modified version of a selective attention task that manipulated cognitive demand
(Schwartz et al., 2005) while receiving an MRI scan. In the second session, younger and older
adults were asked to complete three behavioral tasks. First, an oddball task was used to assess
selective memory under conditions of increased emotional arousal. Then, the Sustained Attention
to Response Task was administered to evaluate sustained attention and vigilance. Lastly,
participants’ cognitive control of emotional information was examined using an emotional
14
Stroop task. To investigate if variability in the integrity of the LC-NE system corresponded to
cognitive performance, multiple linear regression analyses were performed to determine if
younger and older adults’ performance on these five cognitive tasks was associated with LC
signal intensity. Overall, we hypothesized that higher LC contrast would be associated with
enhanced cognitive performance, specifically better pattern separation ability, less interference
from task-irrelevant stimuli, greater inhibition of low-priority information, better sustained
attention, and superior cognitive control of emotional information. Given that prior research has
found an association between LC contrast and memory performance in older, but not younger,
adults (Hämmerer et al., 2018; Langley et al., 2021; Liu et al., 2020), we also reasoned that these
correlations may only be observed in older adult participants.
In addition, since there is evidence suggesting that LC neuromelanin concentration
changes across the lifespan (Clewett et al., 2016; Shibata et al., 2006), we also examined if there
were age group differences in LC signal intensity. Based on prior research (e.g., Clewett et al.,
2016), one possibility is that older adults will exhibit higher LC signal intensity relative to
younger adults. On the other hand, other evidence has shown that LC neuromelanin
concentration follows an inverted, U-shape pattern in which LC signal intensity increases until
middle age and then decreases in older adulthood (Manaye et al., 1995; Mann, 1983; Shibata et
al., 2006). Thus, it also plausible that LC contrast will not differ between younger and older
adults.
The current study quantified LC signal intensity (i.e., LC contrast) as a marker of LC
integrity using high-resolution, neuromelanin-sensitive, fast spin-echo (FSE) T1-weighted MRI
sequences and a pre-existing LC meta mask (Dahl et al., 2021; Keren et al., 2009). Given that
age-related differences in LC neuromelanin signal intensity have been detected in the rostral and
15
caudal regions of the LC (Bachman et al., 2021; Dahl et al., 2019) and hemispheric differences
have also been observed (e.g., Bachman et al., 2021; Jacobs et al., 2015), we examined the
relationship between the LC and cognitive performance using four separate measurements of LC
signal intensity (i.e., left, right, caudal, rostral). We anticipated that the association between LC
signal intensity and cognitive performance in older adults may only be observed within a specific
hemisphere or region of the LC, suggesting that certain areas of the LC may be particularly
responsible for the cognitive deficits seen in healthy aging and neurodegenerative disease.
Method
2.1 Participants
Thirty-five younger adults (ages 18–26 years; M = 20.7; SD = 2.2; 22 females) and 35
older adults (ages 60-84 years; M = 70.1; SD = 6.4; 25 females) participated in the study. One
additional older adult was excluded for disclosing information during the study that violated
recruitment criteria. This sample size was chosen based on a power analysis for detecting
moderate-to-large effect sizes and is comparable to prior studies examining an association
between LC integrity and cognitive performance in younger and older adults (e.g., Dahl et al.,
2020; Langley et al., 2021; Lee et al., 2018). All participants had normal or corrected-to-normal
vision, denied any history of neurological or psychiatric illness, and were not taking any beta-
blockers or noradrenergic drugs (e.g., serotonin-norepinephrine reuptake inhibitor). To better
characterize participants, we administered several questionnaires that assessed a variety of
factors including education, subjective health and stress, depression, anxiety, and physical
activity (see Table 1 for the means and relevant statistics for demographic and questionnaire data
by age group). As shown in Table 1, older adults had more years of education than younger
adults, who were current undergraduate students. In addition, older adults reported greater
16
positive affect and lower levels of both depressive symptomatology and trait anxiety than
younger adults did. However, these age differences are consistent with prior research in which
older adults have reported lower levels of anxiety and negative affect and better emotional well-
being at baseline than younger adults (Carstensen, Pasupathi, Mayr, & Nesselroade, 2000;
Carstensen et al., 2011; Phillips & Allen, 2004). Older adults also reported experiencing a greater
number of cognitive failures (e.g., memory lapses, absent-mindedness) in their everyday life than
younger adults did. All participants provided written informed consent in accordance with a
protocol approved by the University of Southern California’s Institutional Review Board and
received a monetary stipend.
Table 1. Demographic and self-report mood measures by age group
Younger adults
Older adults
M
SD
M
SD
df
t
d
Years of education
14.7
1.9
16.8
2.1
68
4.38
***
1.05
Subjective health rating
7.1
1.2
7.4
1.1
68
0.94
0.26
Subjective stress level
3.7
1.9
3.4
2.1
68
0.75
0.15
WTAR
44.9
3.4
44.5
6.6
51.53
0.31
0.08
CFQ
37.3
14.2
30.7
11.3
66
2.13
*
0.51
STAI – State Anxiety
32.2
9.6
28.7
8.2
66
1.61
0.39
STAI – Trait Anxiety
38.1
10.7
30.8
8.1
66
3.17
**
0.77
PANAS – Positive Affect
24.3
6.7
33.1
7.9
66
4.97
***
1.20
PANAS – Negative Affect
12.6
2.8
13.1
5.7
49.79
0.42
0.11
BDI-II
8.2
7.2
5.0
3.0
42.47
2.37
*
0.58
Note. Subjective health was rated on a scale from 1 (very poor) to 9 (excellent). Subjective
ratings for today’s stress level were rated on a scale from 1 (very low) to 9 (very high). WTAR =
17
Wechsler Test of Adult Reading; CFQ = Cognitive Failures Questionnaire; STAI = State-Trait
Anxiety Inventory; PANAS = Positive and Negative Affect Schedule; BDI-II = Beck Depression
Inventory-II. Degrees of freedom (df) vary due to several participants’ failure to complete all
questionnaires and because Levene’s test for equality of variances indicated that the variance
between age groups was not always equal. In those instances, the reported t statistic and p value
are when equal variances were not assumed.
*
p < .05;
**
p < .01;
***
p < .001.
2.2 Experimental Design
With regard to the overall experimental procedure, participants completed the study
across two separate sessions (see Figure 1 for a schematic diagram of the overall procedure). The
attrition rate was very low; however, two younger adults (one male, one female) dropped out of
the study after the first session for unknown reasons and did not complete the second session.
The average number of days in between the two sessions did not differ between younger (M =
10.15, SD = 9.38) and older adults (M = 13.43, SD = 25.48), t(66) = 0.70, p = .49, d = 0.17. In
the first session, which lasted 2.5 hours, participants underwent an MRI scan and completed a
pattern separation task and a selective attention task while inside the scanner. The second session
lasted approximately 1.5 hours and participants were asked to complete several computer-based
cognitive tasks and paper-and-pencil questionnaires. Participants were debriefed about the study
at the end of the second session.
18
Figure 1. Schema of experimental design.
Note. MPRAGE = magnetization-prepared rapid acquisition gradient-echo; EPI = echo-planar
imaging; FSE = fast-spin echo; SART = Sustained Attention to Response Task.
2.3 Materials
2.3.1 Pattern Separation Task
Stimuli consisted of 216 color photographs of common everyday objects that were
acquired through various sources, including the Internet and prior research studies (Goh et al.,
2004, 2007). Within this stimulus set, half of the objects were visually and conceptually similar
19
to another object in the set, resulting in 108 similar stimulus pairs (see Figure 2 for examples).
These stimulus pairs were randomly divided into three sets of 36 object pairs. Two sets were
viewed during the encoding phase and the third set was designated as the “new” items during the
memory test. For the two encoding sets, one set was assigned to be the “repetition” set in which
participants would view the exact same object during the test phase and the other encoding set
was designated as the “lure” set in which the similarly matched lure item would be presented
during the memory test instead of the object that was shown during the encoding phase.
Accordingly, each participant saw 72 objects during encoding and 108 objects during the test
phase (36 objects previously seen during encoding, 36 “lure” items that were similar to objects
seen during encoding, and 36 “new” objects). The assignment of sets as “repetitions,” “lures,”
and “new” items was counterbalanced across participants.
Object pictures were placed onto a gray background using Adobe Photoshop. To control
for the influence of image luminance on pupil diameter, we normed the luminance levels across
all stimuli using a modified version of the SHINE toolbox in MATLAB (Willenbockel et al.,
2010). Then, each RGB channel was modified for each image using SHINE so that the global
luminance of that channel fell within the set average. Once each picture was normed for
luminance, pictures were centered onto a gray background and this same background was used
for all object pictures.
20
Figure 2. Example stimulus pairs used in the pattern separation task.
2.3.2 Selective Attention Task
The selective attention task was a modified version of the visual load experiment
described in Schwartz et al. (2005). Stimuli used in this target detection task consisted of t-
shaped items that varied in both color (red, yellow, green, blue, purple) and orientation (upright,
inverted). These t-shaped stimuli were displayed one at a time in the center of the screen and
participants pressed a button whenever they detected one of the predetermined targets. To
manipulate the amount of attentional and cognitive effort required to complete the task, there
were two cognitive load conditions. In the low cognitive load condition, the target was any red
“t”, regardless of its orientation (Figure 3A). Targets in the high cognitive load condition were an
upright green “t” and an inverted yellow “t” (Figure 3B).
21
Figure 3. Targets in the (A) low cognitive load and (B) high cognitive load conditions.
While this target detection task was displayed in the center of the screen, bilateral
checkerboard patterns that were entirely irrelevant to the task flashed in the periphery. One third
of the trials were shown to participants without any checkerboard patterns present (Figure 4A).
For the remaining trials, half were presented with a bilateral checkerboard that had greater visual
contrast on the left side (Figure 4B) and the other half were accompanied by a bilateral
checkerboard with greater visual contrast on the right side (Figure 4C). The task consisted of 36
blocks (18 low-load blocks, 18 high-load blocks) and the three checkerboard conditions occurred
an equal number of times within each cognitive load condition. For example, in the low
cognitive load condition, there were six blocks without any task-irrelevant checkerboard patterns
present, six blocks accompanied by the bilateral left-contrast checkerboards, and six blocks with
the bilateral right-contrast checkerboards. Blocks were presented in a randomized order for each
participant. Within each block, there were 13 trials (3 targets, 10 non-targets). The non-target
stimuli for each block were randomly chosen and the trial order within each block was
pseudorandomized so that targets did not appear consecutively. One additional high cognitive
22
load block without any checkerboard patterns displayed was presented at the beginning of the
task as a practice block and was excluded from the analyses.
Figure 4. Stimuli from the target detection task were accompanied with either (A) no task-
irrelevant checkerboards present, (B) flashing bilateral checkerboard patterns with greater visual
contrast on the left, or (C) flashing bilateral checkerboard patterns with greater visual contrast on
the right.
2.3.3 Oddball Task
Stimuli consisted of 168 neutral object pictures selected from the Internet and 24 matched
pairs of scene images that were obtained from a prior study (Mather & Nesmith, 2008). For each
pair, one highly arousing, negative scene was matched with a neutral scene that was similar in
appearance, visual complexity, and verbal label (see Figure 5 for an example). All negative
pictures were taken from the International Affective Picture System (IAPS; Lang, Bradley, &
Cuthbert, 2008).
1
Ratings of valence and arousal for the negative and neutral scenes did not
differ between younger and older adults (all ps > .09; see Table 2 for ratings by age group). All
stimuli were centered onto a gray background and a word label that generally described each
picture appeared above the image in lowercase letters. These verbal labels were used to assist
participants in recalling the images without having to provide an extensive description of each
23
picture. All stimuli were luminance normed to control for the influence of image luminance on
pupil diameter. To do this, the same procedure was used as described above for the pattern
separation stimuli. To further increase the perceptual salience of the oddball scenes, a red border
was applied to each scene image.
Figure 5. An example of a negative-neutral matched pair designated as oddball stimuli
Neutral object pictures were randomly divided into 24 encoding trials. The matched pairs
of scene images were divided into two sets of 12 negative and 12 neutral images. Using the first
set, one scene image was randomly selected to be the perceptual oddball during each encoding
trial, so that half of the encoding trials included a negative oddball and the remaining half
contained a neutral oddball. Another encoding list was created in which the perceptual oddball
was replaced with its negative or neutral match from the second set. These encoding lists (i.e.,
the presentation of either a negative or neutral oddball on a given trial) were counterbalanced
across participants. To reduce primacy and recency effects, the oddball stimulus only appeared in
the third, fourth, fifth, or sixth position of each list.
24
Table 2. Mean valence and arousal ratings for negative and neutral oddball scenes as a function
of age.
Younger adults
Older adults
Valence
Negative
2.01 (0.59)
2.43 (1.44)
Neutral
5.42 (0.51)
5.43 (0.87)
Arousal
Negative
7.46 (1.30)
7.37 (1.70)
Neutral
4.16 (1.23)
4.74 (1.51)
Note. Subjective ratings for all 24 negative and 24 neutral oddballs were provided using a 1 (very
low arousal) to 9 (very high arousal) scale for arousal and a 9-point scale for valence where 1
was “very negative,” 5 was “neutral,” and 9 was “very positive.” These picture ratings were
completed immediately after the oddball task. Standard deviations are given in parentheses.
2.3.4 Emotional Stroop Task
Stimuli consisted of 135 words (45 positive, 45 negative, and 45 neutral words) that were
selected from the Affective Norms for English Words database (ANEW; Bradley & Lang, 1999).
Word valence was based on normative ratings from the ANEW database using a 9-point Likert
scale: positive (M = 7.73, SD = 0.59, range = 6.17 – 8.64), negative (M = 2.27, SD = 0.59, range
= 1.25 – 3.91), and neutral words (M = 5.01, SD = 0.50, range = 4.05 – 5.86). Using a median
split, positive and negative words were categorized into low and high arousal based on normative
ratings from the ANEW database using a 9-point Likert scale. By definition, neutral words are
low in arousal, so neutral words were only included as a control condition to reduce habituation
to emotional stimuli. Given that this task focused on the comparison of emotional items,
responses to neutral words will not be included in the analyses. Along the dimension of arousal,
25
positive (M = 6.36, SD = 0.68) and negative words (M = 6.35, SD = 0.73) were equated, t(88) =
0.08, p = .94, and neutral words (M = 4.49, SD = 0.69) were significantly less arousing than both
positive, t(88) = 12.93, p < .001, and negative words, t(88) = 12.41, p < .001. Positive, negative,
and neutral words were equated for word length, number of syllables, frequency, and part of
speech based on scores provided by the Medical Research Council (MRC) Psycholinguistic
Database (Coltheart, 1981; all ps > .06). Words were divided into three blocks of 45 words with
an equal number of words from each valence category. The presentation order for words within
each block were pseudorandomized so that no more than two words of the same valence
category were presented in a row. Words were presented in one of four colors: red, green, blue,
and pink. The color assignment for each word was pseudorandomized so that each color was
presented an equal number of times within each valence category. Four lists were created so that
each word rotated through each of the four possible color options, counterbalanced across
participants. See Supplementary Table 1 for the list of words used in this task.
2.3.5 Sustained Attention to Response Task
Stimuli for the Sustained Attention to Response Task (SART) were based off the stimuli
and procedure described by Robertson et al. (1997). In this task, stimuli consisted of single-digit
numbers ranging from 1 to 9 that were displayed in one of five font sizes (Symbol font; 48 pt, 72
pt, 94 pt, 100 pt, 120 pt). The number 3 was identified as the target stimulus (i.e., No-Go trial)
and all other numbers were allocated as the non-target stimuli (i.e., Go trials). During the
experimental task, there were 225 trials in which each of the nine digits was displayed 25 times.
Thus, target stimuli were presented approximately 11% of the time in a pre-fixed, quasi-random
order. After each number was presented, a mask was displayed, which consisted of an “X”
26
surrounded by a circle. All of the numbers and the mask were displayed in white font that was
presented on a black background (see Figure 6 for example trials).
Figure 6. Schematic depiction of trials during the SART task
2.4 Procedure
2.4.1 Session 1
Participants were verbally instructed on the pattern separation and selective attention
tasks outside of the scanner and were given an opportunity to practice each task. Instructions
were clarified as necessary, and participants could repeat the practice trials if needed. For the
pattern separation task, stimuli presented during the practice trials were separate items that were
not used during the actual experimental task in the scanner. During the fMRI tasks, participants’
pupil diameter was measured using eye-tracking technology and physiological measurements of
cardiac rate and respiration were collected using a pulse oximeter and respiration belt,
respectively. However, this data was obtained as part of a larger dataset and will not be discussed
further.
27
2.4.1.1 Pattern Separation Task. During the intentional encoding phase, participants
were presented with a series of 72 object pictures displayed one at a time for 2000 ms, followed
by a 2000, 3000, or 4000 ms jittered interstimulus interval. Half of the objects would be
presented again during the memory test, whereas for the remaining half, a similar lure would be
presented during the memory test instead. The presentation order of the object pictures was
randomized for each participant. For each object during encoding, participants decided whether
or not they believed that the item would fit inside a standard shoebox. Roughly half of the items
in each object set (i.e., “repetition,” “lure,” and “new” items) could fit inside a shoebox. This
task was used to help ensure that participants attended to each object picture during encoding.
Responses were collected using fiber-optic response pads and participants indicated “yes” by
pressing the button under their right index finger or “no” by pressing the button under their right
middle finger. Participants were instructed to pay close attention to the objects because their
memory for the objects would be tested later.
Immediately after the encoding task, participants completed a three-alternative forced-
choice memory test in which 108 objects were randomly presented one at a time for 3000 ms,
followed by a 1000 or 2000 ms jittered interstimulus interval. For each object, participants were
instructed to decide if the object was the exact same object that was displayed during the
encoding phase, if it was similar to an object shown during encoding but not exactly the same, or
if it was a new object. Participants made their responses on the two fiber-optic response pads by
choosing “same” or “similar” with their right index and middle finger, respectively, or “new” by
pressing the button under their left index finger. Both the encoding and test phases took place
while participants were inside the MRI scanner (see Figure 7 for example trials for both phases).
28
Figure 7. Schematic representation of the encoding and test phases used in the pattern separation
task.
2.4.1.2 Selective Attention Task. Immediately after the pattern separation task,
participants completed two runs of a selective attention task, each run was approximately 8.5
minutes and consisted of the 36 task blocks and one additional practice block that was presented
at the beginning of each run. Prior to each block, a prompt displaying the targets for the
upcoming block appeared on the screen for 2000 ms followed by a fixation cross displayed for
2000 ms. The t-shaped stimuli within each block were presented for 750 ms and the
checkerboard patterns flickered at a rate of 8 Hz. Participants were instructed to press the button
under their right index finger whenever they detected one of the predetermined targets for that
block. See Figure 8 for a schematic depiction of the procedure.
29
Figure 8. Example trials for a block in the high cognitive load condition without any task-
irrelevant checkerboard patterns (A) and a block in the low cognitive load condition with
flashing bilateral checkerboards that had greater visual contrast in the left hemisphere (B).
2.4.1.3 MRI Data Acquisition. All neuroimaging data was acquired at the University of
Southern California’s Dana and David Dornsife Cognitive Neuroimaging Center using a 3T
Siemens PRISMA scanner equipped with a 32-channel head coil. Neuroanatomical images were
collected for each participant using a high-resolution T1-weighted magnetization-prepared rapid
acquisition gradient-echo (MPRAGE) sequence (176 sagittal slices; repetition time [TR] = 2300
ms; echo time [TE] = 2.26 ms; inversion time [TI] = 1060 ms; flip angle = 9°; field of view
30
[FOV] = 256 mm; bandwidth = 200 Hz/Px; in-plane resolution = 1 mm
2
; slice thickness = 1 mm;
acquisition time: 4 minutes and 44 seconds). Functional images for the pattern separation task
were acquired using two T2*-weighted multiband echo planar imaging (EPI) sequences (88
interleaved slices; TR/TE = 1000 ms/7.6 ms; flip angle = 52°; FOV = 128 mm; 1.7 mm
3
voxel
resolution; multiband acceleration factor = 8; acquisition time: 6 minutes and 12 seconds for the
encoding sequence and 8 minutes and 12 seconds for the test sequence). Functional images for
the selective attention task were acquired using two T2*-weighted EPI sequences (41 interleaved
slices; TR/TE = 2000 ms/25 ms; flip angle = 90°; FOV = 64 mm; 3.0 mm
3
voxel resolution;
acquisition time: 8 minutes and 48 seconds). Of note, functional data obtained during the pattern
separation and selective attention tasks were part of a larger dataset and will not be discussed
further. After acquiring all functional images, we collected a neuromelanin sensitive-weighted
MRI image using a T1-weighted fast-spin echo (FSE) sequence (11 axial slices; TR/TE = 750
ms/12 ms; flip angle = 120°; FOV = 512 mm; bandwidth = 285 Hz/Px; in-plane resolution = 0.75
mm
2
; slice thickness = 2.5 mm; acquisition time: 1 minute and 53 seconds).
2.4.1.4 LC Signal Intensity. To obtain LC signal intensity contrast ratios for each
individual, we used the semi-automated procedure outlined by Dahl et al. (2019). Prior to this
procedure, the whole-brain (T1 MPRAGE) and brainstem (T1 FSE) scans were upsampled to
double the image resolution, allowing for improved visualization of the LC by increasing the
matrix size (Betts et al., 2017; Dahl et al., 2019). These individual upsampled whole-brain scans
were then coregistered to a template space and then averaged across participants to create a
group whole-brain template using Advanced Normalization Tools (ANTs) v2.1 (Avants, Epstein,
Grossman, & Gee, 2008). Next, each participant’s upsampled brainstem scan was aligned and
coregistered to the whole-brain template, which was then averaged across participants to create a
31
group brainstem template. This group brainstem template was then coregistered to the group
whole-brain template. Lastly, the upsampled brainstem scans and the two group templates were
transformed into standard Montreal Neurological Institute (MNI) coordinate space (i.e., 0.5 mm
3
MNI152 space) to allow for comparisons with prior research (e.g., Betts et al., 2017; Dahl et al.,
2019, 2021; Keren et al., 2009).
Next, in standard space, the upsampled brainstem scans were masked to select a highly
probable LC volume of interest using the high-confidence LC meta mask described by Dahl et al.
(2021) (see Figure 10). This LC meta mask used a meta-analytic approach that aggregated
previously published LC masks (Betts et al., 2017; Dahl et al., 2019; Keren et al., 2009; Liu et
al., 2019; Tona et al., 2017; Ye et al., 2021) to improve the validity and reliability of detecting
the LC. This LC meta mask forms a volume of interest that is 14 mm along the rostrocaudal axis
and has an overall volume of 77 mm
3
(i.e., 592 voxels). The individual brainstem scans were also
masked to a reference region of interest in the dorsal pontine tegmentum (DPT) using the
approach described by Dahl et al. (2021) (see Figure 10). This method yields a reference mask
that is 4 x 4 mm in plane and has a constant distance of 8.5 mm from the LC meta mask along
the rostrocaudal axis. Using these masked scans, an automated procedure identified voxels
within each hemisphere of the brain that reflected the peak, maximum intensity within the LC,
across slices in the z-axis. This same method was applied to identify the peak intensity in the
DPT reference region, across slices in the z-axis. These peak intensity values were then used to
calculate the LC contrast values for each individual, separately for the left and right hemisphere,
using the following formula:
LC contrast value = (peak (LC) – peak (Ref)) / peak (Ref)
32
Finally, we calculated contrast values for the rostral and caudal regions of the LC for
each individual. To do this, we obtained a contrast value for each slice along the z-axis using the
formula above. These contrast ratios were then averaged across hemispheres within each slice.
The contrast values for the z-slices corresponding to the caudal region of the LC (z = 87-95)
were then averaged for each individual to yield a single caudal LC value. Likewise, values for
each slice within the rostral region of the LC (z = 101-104) were averaged to create a single
rostral LC value for each participant. These four contrast values (left, right, caudal, rostral) were
obtained to determine if individual differences in LC signal intensity correlated with cognitive
task performance taking into account hemispheric differences and variations along the
rostrocaudal axis. We chose these two different approaches to obtain LC contrast values (i.e.,
peak values within each hemisphere vs. averaging across caudal/rostral slices) since, to date, it is
not known which methodology is a better reflection of LC structural integrity and its relation to
cognitive performance. Data for three older adults could not be obtained; one older adult asked to
be taken out of the scanner prior to the T1-weighted FSE sequence and two older adults had
significant motion artifact overlapping the LC and pons.
Figure 9. Raw image of LC signal from a neuromelanin-sensitive T1-weighted MRI sequence
Note. R = Right; L = Left.
33
Figure 10. Depictions of the left and right LC meta masks and the reference mask in the dorsal
pontine tegmentum
2.4.2 Session 2
2.4.2.1 Oddball Task. Prior to the task, verbal instructions and practice trials were
administered with stimuli that differed from the objects and scenes used in the experimental task.
If necessary, instructions were clarified and practice trials were repeated. To ensure that each
participant’s pupil diameter and eye gaze were accurately being recorded during the oddball task,
participants first completed a pupil calibration task that required participants to focus on a small
circle that appeared in various locations on the computer screen. Analysis of the pupillometry
data was collected as part of a larger dataset that is beyond the scope of the present study and
will not be discussed further.
After this calibration task, participants completed 24 encoding trials, each containing
seven object pictures (i.e., non-oddball stimuli) and one negative or neutral oddball scene (for an
example trial, see Figure 11). At the beginning of each trial, a fixation cross appeared for 1000
ms, which cued participants that the next encoding trial was about to begin. Afterwards, pictures
(1200 x 1200 pixels) were displayed one at a time in the center of the computer screen for 2000
ms followed by a 1000 ms interstimulus interval. Participants were instructed to focus on the
34
pictures because their memory for the pictures would be tested immediately after each trial. After
each encoding trial, participants were shown four math equations to prevent further encoding of
the stimuli and to reduce ceiling effects. For each math equation (e.g., “42 – 20 = 23”),
participants had to decide if the statement was “true” or “false.” Participants responded by
pressing “1” on the numeric keypad if the math equation was true and “2” if it was false. Each
math problem was displayed on the screen for five seconds. Immediately after this distracter
task, a fixation cross was displayed on the screen and participants were instructed to recall aloud
as many pictures as they could remember from the previous encoding trial. This recall task was
self-paced and participants’ verbal responses were typed into the computer by an experimenter.
Figure 11. An example trial from the oddball task. Half of the trials included a negative oddball
from the matched pair and the remaining half of the trials included in the neutral oddball. The
presentation of either the negative or neutral oddball on a given trial was counterbalanced across
participants. The “oddball – 1” item was the object that immediately preceded the oddball
stimulus, whereas the “oddball + 1” item was the object presented immediately after the oddball
stimulus was displayed.
35
2.4.2.2 Picture Rating Task. Immediately after the oddball task, participants were
presented with a picture rating task that instructed participants to provide valence and arousal
ratings for all 48 oddball scenes (24 negative oddballs, 24 neutral oddballs). As described earlier,
these oddball scenes consisted of the 24 negative-neutral matched pairs, which were divided into
two encoding lists. Thus, within each valence category (i.e., negative, neutral), half of the
oddballs in the picture rating task had been shown to participants during the oddball task and the
remaining half were the unused oddballs from the matched pair. Oddball scenes (360 x 360
pixels) were presented one at a time in the center of the computer screen and a rating scale was
displayed beneath the picture. For each picture, participants rated the level of arousal on a 1
(very low) to 9 (very high) scale. Participants were instructed that “low arousal means that the
picture elicits calm or bored feelings,” whereas “high arousal means that the picture elicits strong
emotions, such as excitement, anger, disgust, or amazement.” Then, the exact same picture was
displayed again and participants rated the valence of the picture on a 9-point scale where 1 was
“very negative,” 5 was “neutral,” and 9 was “very positive.” All ratings were self-paced.
2.4.2.3 Emotional Stroop Task. Participants were seated in front of a laptop computer
equipped with an external keyboard and were instructed to view a series of words and identify
the font color of each word as quickly and accurately as possible. Participants made their
responses for blue, red, green, and pink on the external keyboard by pressing the ‘d’, ‘f’, ‘j’, and
‘k’ keys, respectively. Colored stickers were placed onto the keys to cue participants as to the
color that each key represented. Prior to starting the task, participants completed a practice block
of 45 trials. Instead of emotional words, practice trials consisted of “XXXXXX” displayed on the
screen in Arial, 60pt font. These practice items gave participants an opportunity to familiarize
themselves to the task and to which key corresponded to each color. Immediately after the
36
practice trials, the main task was administered, which consisted of three blocks of 45 words each
(15 words from each valence category). Words (Arial, 60pt font) were displayed on a gray
background using E-Prime 2.0 software (Psychology Software Tools, Pittsburgh, PA), followed
by a 150 ms interstimulus interval. Participants were instructed to keep their index and middle
fingers on the keys at all times to reduce variability in the amount of time spent getting their
fingers to the keyboard to make their response. The task was self-paced and the screen would not
advance until the correct color had been identified. Participants could take breaks in between
blocks, if needed.
2.4.2.4 Sustained Attention to Response Task. Using the stimuli described above (see
Materials), each number was displayed one at a time on the screen for 250 ms, followed by a
mask presented for 900 ms. Participants were instructed to press the spacebar as quickly as
possible whenever they see a number, except for the number 3, appear on the screen. They were
told to not press any key when the number 3 was displayed on the screen. The experimental task,
consisting of 225 trials presented in a continuous order lasted 4.3 minutes. Prior to these
experimental trials, an additional 18 numbers (including two targets) were presented, which were
considered practice trials and were not included in the analyses.
2.4.2.5 Questionnaires. After the SART task, the Wechsler Test of Adult Reading
(Wechsler, 2001) was administered, which requires participants to pronounce a list of 50 words
that have unusual or irregular grapheme-to-phoneme translations. Then, participants completed
several paper-and-pencil questionnaires that included the Positive and Negative Affect Schedule
(Watson, Clark, & Tellegen, 1988), Beck Depression Inventory-II (Beck, Brown, & Steer, 1996),
State-Trait Anxiety Inventory (Spielberger, Gorsuch, Lushene, Vagg, & Jacobs, 1983), Cognitive
Failures Questionnaire (Broadbent, Cooper, FitzGerald, & Parkes, 1982), Rapid Assessment of
37
Physical Activity (Topolski et al., 2006), and a modified version of the Lifetime Physical
Activity Questionnaire (Chasan-Taber et al., 2002). After completing the questionnaires,
participants were debriefed about the study.
Results
3.1 LC Contrast Ratios among Participants
Contrast ratios did not differ between younger and older adults in the left, t(65) = 0.47, p
= .639, d = 0.11, or right LC, t(65) = 0.13, p = .894, d = 0.03. Likewise, contrast values for the
caudal, t(55.75) = 0.21, p = .837, d = 0.05, and rostral LC, t(65) = 0.74, p = .460, d = 0.18, were
comparable between the two age groups. Consistent with findings from Bachman et al. (2021),
contrast ratios were higher in the left than right LC across participants, t(66) = 6.32, p < .001, d =
0.77. Follow-up analyses confirmed that this hemispheric difference was observed in both
younger, t(34) = 4.44, p < .001, d = 0.75, and older adults, t(31) = 4.48, p < .001, d = 0.79.
Across participants, contrast ratios between the caudal and the rostral LC did not differ, t(66) =
1.19, p = .239, d = 0.15.
3.2 Session 1
3.2.1 Pattern Separation Task
3.2.1.1 Behavioral Results. First, we evaluated participants’ responses during the
encoding phase to confirm that both age groups attended to and responded to a similar number of
items during encoding. Response rates did not differ between younger (M = .98, SD = .07) and
older adults (M = .95, SD = .13), t(63) = 1.29, p = .203, d = 0.29.
2
Next, for each individual, we
calculated the proportion of responses during the test phase (i.e., “same,” “similar,” and “new”)
for each item type (i.e., repetitions, lures, and new items). Data for three older adults was
excluded; one older adult had difficulty pressing the buttons on the response pad and two older
38
adults had poor comprehension and understanding of the task (e.g., responded “new” for every
item). Therefore, our final sample size for the pattern separation analyses consisted of 35
younger adults and 32 older adults. In addition, three older adults responded in a way that made
it clear that they used different buttons on the response pad to make their responses than the
buttons assigned to represent “same,” “similar,” and “new” responses. We recoded their
responses to reflect their intended response. Notably, the overall pattern of results did not change
when their original responses were used or when these individuals were excluded from the
analyses.
To evaluate responses during the memory task, we first conducted a 2 (Age: younger,
older) x 3 (Item Type: repetition, lure, new) x 3 (Response: same, similar, new) mixed analysis
of variance (ANOVA) on participants’ raw response rates (see Table 3 for means). There was a
significant main effect of Response, F(2, 130) = 18.85, p < .001, hp
2
= .23, with participants
providing fewer “similar” responses than “same”, t(66) = 5.33, p < .001, mean difference = 0.12,
95% CI [0.07, 0.16], d = 0.65, or “new” responses, t(66) = -4.39, p < .001, mean difference = -
0.09, 95% CI [-0.13, -0.05], d = -0.54. Response rates for “same” and “new” responses did not
significantly differ, t(66) = 1.46, p = .149, mean difference = 0.03, 95% CI [-0.01, 0.07], d =
0.18. A significant Item Type x Response interaction, F(4, 260) = 595.96, p < .001, hp
2
= .90,
indicated that participants’ responses differed depending on the item type (e.g., more likely to
respond “new” to new items than to lures or repetitions). A significant Age x Response
interaction, F(2, 130) = 3.43, p = .035, hp
2
= .05, was qualified by an Age x Item Type x
Response interaction, F(4, 260) = 7.65, p < .001, hp
2
= .11. To assess the effects of age on
participants’ responses, we conducted follow-up Age x Item Type ANOVAs for each response
type. We found a significant Age x Item Type interaction for “same”, F(2, 130) = 4.48, p = .013,
39
hp
2
= .06, “similar”, F(2, 130) = 10.96, p < .001, hp
2
= .14, and “new” responses, F(2, 130) =
6.55, p = .002, hp
2
= .09. Follow-up t-tests showed that older adults were more likely than
younger adults to respond “same” to lures, t(65) = -2.79, p = .007, mean difference = -0.11, 95%
CI [-0.18, -0.03], d = -0.68, and new items, t(38.79) = -3.62, p = .001, mean difference = -0.05,
95% CI [-0.08, -0.02], d = -0.90. In line with our prediction, younger adults responded “similar”
to lures more often than older adults did, t(65) = 3.78, p < .001, mean difference = 0.17, 95% CI
[0.08, 0.26], d = 0.92. The number of “similar” responses for repetitions, t(65) = -0.15, p = .880,
mean difference = -0.004, 95% CI [-0.06, 0.05], d = -0.04, and new items, t(55.44) = 0.01, p =
.991, mean difference = 0.0003, 95% CI [-0.05, 0.05], d = 0.003, did not differ between age
groups. The significant Age x Item Type interaction for “new” responses emerged because
younger adults were marginally more likely to respond “new” to new items than older adults
were, t(49.05) = 1.77, p = .083, mean difference = 0.05, 95% CI [-0.01, 0.12], d = 0.44, whereas
older adults were marginally more likely than younger adults to respond “new” to lures, t(65) = -
1.88, p = .065, mean difference = -0.07, 95% CI [-0.14, 0.004], d = -0.46. For repetitions, the
number of “new” responses did not differ between age groups, t(65) = -0.27, p = .790, mean
difference = -0.01, 95% CI [-0.05, 0.04], d = -0.07.
Next, we calculated several corrected scores since the proportion of raw response rates do
not take into account any possible response biases that may have occurred. First, we calculated a
corrected recognition score by subtracting the proportion of “same” responses for new items
from the proportion of “same” responses for repetitions [p(“same”|repetitions) – p(“same”|new
items)] (see Figure 12A). Results revealed that recognition for repeated items did not
significantly differ between younger (M = .76, SD = .14) and older adults (M = .70, SD = .18),
t(65) = 1.64, p = .106, mean difference = 0.06, 95% CI [-0.01, 0.14], d = 0.40. Next, we
40
calculated a corrected pattern separation score [p(“similar”|lures) – p(“similar”|new items)]. As
Figure 12B illustrates, younger adults (M = .43, SD = .19) demonstrated better pattern separation
ability by responding “similar” to lures significantly more often than older adults did (M = .26,
SD = .18), t(65) = 3.78, p < .001, mean difference = 0.17, 95% CI [0.08, 0.26], d = 0.92. We then
calculated a corrected pattern completion score [p(“same”|lures) – p(“same”|new items)] (see
Figure 12C). Although numerically, older adults (M = .31, SD = .15) were more likely to
mistakenly respond that a lure item was the “same” item than younger adults were (M = .26, SD
= .12), this trend did not reach significance, t(65) = -1.59, p = .117, mean difference = -0.05,
95% CI [-0.12, 0.01], d = -0.39. Lastly, we calculated a corrected new score [p(“new”|new items)
– p(“new”|lures)]. As Figure 12D indicates, younger adults (M = .69, SD = .15) were able to
successfully identify new items more often than older adults did (M = .57, SD = .17), t(65) =
3.04, p = .003, mean difference = 0.12, 95% CI [0.04, 0.20], d = 0.74.
Table 3. Proportions of responses to repetitions, lures, and new items by age group
Younger adults
Older adults
Repetitions
“Same”
0.78 (0.14)
0.77 (0.18)
“Similar”
0.14 (0.09)
0.15 (0.14)
“New”
0.08 (0.10)
0.09 (0.08)
Lures
“Same”
0.28 (0.13)
0.38 (0.18)
“Similar”
0.58 (0.18)
0.41 (0.19)
“New”
0.14 (0.14)
0.21 (0.15)
New Items
“Same”
0.02 (0.03)
0.07 (0.08)
41
“Similar”
0.15 (0.09)
0.15 (0.12)
“New”
0.83 (0.09)
0.78 (0.15)
Note. Standard deviations are given in parentheses.
Figure 12. Proportion of responses during the pattern separation task
Note. Error bars are the standard error of that bar’s mean.
3.2.1.2 LC contrast ratios and behavioral task performance. To determine if LC
signal intensity was associated with pattern separation ability, multiple linear regression analyses
42
were conducted to investigate the effects of LC contrast ratios on the corrected pattern separation
and pattern completion scores calculated above, separately for each age group. First, we
constructed a regression model that was sensitive to hemispheric differences using the left and
right LC as predictors of memory performance (for complete results, see Table 4). Results
indicated that LC contrast explained a significant amount of variance in older adults’ pattern
completion scores, F(2, 27) = 3.721, p = .037, R
2
= .216, adjusted R
2
= .158. Examination of the
individual predictors revealed that older adults with higher contrast in the left LC were
significantly less likely to engage in pattern completion (i.e., mistakenly respond that a lure item
was the same item that they had seen earlier), b = -1.056, p = .036.
We also constructed a second regression model to assess if differences along the
rostrocaudal axis of the LC was related to pattern separation ability (see Table 5). In both age
groups, neither the caudal nor rostral LC was significantly associated with memory performance
(all ps > .10).
Table 4. Results of multiple linear regression analyses between LC contrast and memory
performance taking into account hemispheric differences
Predictor b SE 95% CI t p
(A) Corrected Pattern Separation Scores
Younger adults, F(2, 32) = .173, p = .842, R
2
= .011, adjusted R
2
= -.051
Left .419 .748 -1.105, 1.943 .560 .579
Right -.350 .689 -1.752, 1.053 -.508 .615
Older adults, F(2, 27) = 1.259, p = .300, R
2
= .085, adjusted R
2
= .018
Left .498 .641 -.818, 1.814 .776 .444
Right .369 .611 -.884, 1.622 .604 .551
(B) Corrected Pattern Completion Scores
Younger adults, F(2, 32) = 2.296, p = .117, R
2
= .125, adjusted R
2
= .071
Left -.710 .454 -1.635, .215 -1.564 .128
Right .892 .418 .041, 1.744 2.136 .040
43
Older adults, F(2, 27) = 3.721, p = .037, R
2
= .216, adjusted R
2
= .158
Left -1.056 .479 -2.039, -.074 -2.206 .036
Right .050 .456 -.885, .986 .110 .913
Note. SE = standard error; CI = confidence interval.
Table 5. Results of multiple linear regression analyses between LC contrast and memory
performance taking into account differences along the rostrocaudal axis
Predictor b SE 95% CI t p
(A) Corrected Pattern Separation Scores
Younger adults, F(2, 32) = .133, p = .876, R
2
= .008, adjusted R
2
= -.054
Caudal .206 .833 -1.490, 1.902 .247 .806
Rostral -.324 .632 -1.612, .963 -.513 .611
Older adults, F(2, 27) = 2.251, p = .125, R
2
= .143, adjusted R
2
= .079
Caudal .686 .683 -.716, 2.087 1.004 .325
Rostral .623 .843 -1.107, 2.354 .739 .466
(B) Corrected Pattern Completion Scores
Younger adults, F(2, 32) = .410, p = .667, R
2
= .025, adjusted R
2
= -.036
Caudal .404 .533 -.681, 1.490 .759 .454
Rostral .069 .405 -.755, .893 .171 .865
Older adults, F(2, 27) = .617, p = .547, R
2
= .044, adjusted R
2
= -.027
Caudal -.254 .582 -1.448, .940 -.436 .666
Rostral -.343 .719 -1.818, 1.131 -.478 .636
Note. SE = standard error; CI = confidence interval.
3.2.2 Selective Attention Task
3.2.2.1 Behavioral Results. Data for two older adult participants was excluded because
one participant had difficulty pressing buttons on the respond pad and their data was not
recorded and another participant had poor understanding of the task instructions since they only
responded during the high cognitive load condition. Thus, the final sample size used in the
analyses for the selective attention task consisted of 35 younger adults and 33 older adults. To
44
investigate the effects of age, cognitive load, and the presence of irrelevant checkerboard stimuli
on a target detection task, we conducted a 2 (Age: younger, older) x 2 (Load: low, high) x 2
(Checkerboard: none, bilateral) mixed ANOVA on the mean hit rates (see Table 6 for means).
There was a main effect of Age, F(1, 66) = 49.14, p < .001, hp
2
= .43; younger adults (M = .89,
SD = .08) had overall higher hit rates than older adults (M = .67, SD = .17). A significant main of
effect of Load, F(1, 66) = 145.91, p < .001, hp
2
= .69, showed that participants were better at
detecting the targets in the low cognitive load condition (M = .87, SD = .16) relative to the high
load condition (M = .69, SD = .20). There was also a significant Age x Load interaction, F(1, 66)
= 8.13, p = .006, hp
2
= .11. Follow-up t-tests indicated that the age difference in target detection
performance was greater in the high cognitive load condition, t(48.93) = 7.08, p < .001, mean
difference = 0.26, 95% CI [0.19, 0.34], d = 1.73, relative to the low cognitive load condition,
t(38.55) = 5.34, p < .001, mean difference = 0.18, 95% CI [0.11, 0.25], d = 1.31. A main effect of
Checkerboard, F(1, 66) = 8.45, p = .005, hp
2
= .11, was superceded by both a marginal Load x
Checkerboard interaction, F(1, 66) = 3.80, p = .056, hp
2
= .05, and a marginal Age x
Checkerboard interaction, F(1, 66) = 2.93, p = .092, hp
2
= .04.
Follow-up t-tests showed that the presence of bilateral checkerboards improved
performance in the low cognitive load condition, t(67) = 3.15, p = .002, mean difference = 0.04,
95% CI [0.01, 0.06], d = 0.38, but did not impact performance in the high cognitive load
condition, t(67) = 0.44, p = .664, mean difference = 0.005, 95% CI [-0.02, 0.03], d = 0.05. In
addition, older adults actually performed better when the irrelevant checkerboards were present
relative to when they were not displayed, t(32) = 2.59, p = .014, mean difference = 0.03, 95% CI
[0.01, 0.06], d = 0.45, whereas younger adults’ ability to detect targets did not differ between
45
checkerboard conditions, t(34) = 1.02, p = .315, mean difference = 0.01, 95% CI [-0.01, 0.02], d
= 0.17. The three-way interaction was not significant, F(1, 66) = 0.45, p = .500, hp
2
= .01.
Table 6. Mean proportion of targets accurately detected as a function of age, cognitive load, and
the presence of irrelevant checkerboard stimuli
Younger adults
Older adults
Low cognitive load
No checkerboards
.94 (.08)
.74 (.21)
Bilateral checkerboards
.96 (.06)
.80 (.18)
High cognitive load
No checkerboards
.82 (.12)
.55 (.21)
Bilateral checkerboards
.82 (.11)
.56 (.19)
Note. Standard deviations are given in parentheses.
3.2.2.2 LC contrast ratios and behavioral task performance. To evaluate the
relationship between LC signal intensity and selective attention under arousal, multiple linear
regressions were performed in each age group to investigate the effect of LC contrast ratios on
the mean proportion of targets accurately detected within each load condition while
checkerboard patterns were flashing in the periphery. We performed two separate regression
models that took into account hemispheric differences and variations in the topographical
organization of the LC and across the lifespan. In the first model, both younger and older adults
showed no association between the left or right LC and the ability to selectively attend to task-
relevant stimuli under varying cognitive loads (all ps > .10; Table 7). In contrast, our second
model demonstrated that LC contrast along the rostrocaudal axis was associated with selective
attention (for complete results, see Table 8). In older adults, higher caudal LC contrast was
46
associated with better target detection during the high cognitive load condition when distracting
checkerboard patterns were being displayed, b = 1.936, p = .008 (Table 8, part B).
Additionally, there was a trend indicating that the variance in older adults’ ability to
detect targets in the low load condition was also related to LC contrast along the rostrocaudal
axis, F(2, 27) = 2.986, p = .067, R
2
= .181, adjusted R
2
= .120 (Table 8, part A). Further
examination of the individual predictors showed that, when these distracting checkerboards were
present, older adults with higher caudal LC contrast were better at detecting targets in the low
load condition, b = 1.605, p = .034. However, older adults with higher contrast in the rostral
region of the LC were actually worse at target detection in this condition, b = -1.986, p = .035.
Table 7. Results of multiple linear regression analyses between LC contrast ratios within each
hemisphere and the mean proportion of targets detected
Predictor b SE 95% CI t p
(A) Low Cognitive Load – Bilateral Checkerboards
Younger adults, F(2, 32) = 1.826, p = .177, R
2
= .102, adjusted R
2
= .046
Left -.384 .224 -.840, .072 -1.717 .096
Right .110 .206 -.310, .529 .533 .598
Older adults, F(2, 27) = .144, p = .867, R
2
= .011, adjusted R
2
= -.063
Left -.353 .726 -1.842, 1.136 -.496 .631
Right .084 .673 -1.296, 1.465 .125 .901
(B) High Cognitive Load – Bilateral Checkerboards
Younger adults, F(2, 32) = .256, p = .776, R
2
= .016, adjusted R
2
= -.046
Left -.235 .412 -1.074, .605 -.570 .573
Right .023 .379 -.749, .796 .062 .951
Older adults, F(2, 27) = .404, p = .671, R
2
= .029, adjusted R
2
= -.043
Left -.443 .695 -1.869, .983 -.638 .529
Right .576 .644 -.747, 1.898 .893 .380
Note. SE = standard error; CI = confidence interval.
47
Table 8. Results of multiple linear regression analyses between LC contrast ratios along the
rostrocaudal axis and the mean proportion of targets detected
Predictor b SE 95% CI t p
(A) Low Cognitive Load – Bilateral Checkerboards
Younger adults, F(2, 32) = 1.560, p = .226, R
2
= .089, adjusted R
2
= .032
Caudal -.280 .251 -.790, .231 -1.115 .273
Rostral -.161 .190 -.548, .227 -.845 .404
Older adults, F(2, 27) = 2.986, p = .067, R
2
= .181, adjusted R
2
= .120
Caudal 1.605 .719 .131, 3.079 2.234 .034
Rostral -1.986 .896 -3.824, -.148 -2.217 .035
(B) High Cognitive Load – Bilateral Checkerboards
Younger adults, F(2, 32) = 1.507, p = .237, R
2
= .086, adjusted R
2
= .029
Caudal -.702 .441 -1.601, .198 -1.589 .122
Rostral -.015 .335 -.698, .668 -.044 .965
Older adults, F(2, 27) = 4.225, p = .025, R
2
= .238, adjusted R
2
= .182
Caudal 1.936 .670 .561, 3.311 2.890 .008
Rostral -1.392 .835 -3.106, .322 -1.667 .107
Note. SE = standard error; CI = confidence interval.
3.3 Session 2
3.3.1 Oddball Task
3.3.1.1 Behavioral Results. Experimenters transcribed participants’ verbal responses
during the recall memory task and these transcripts were independently coded by two research
assistants who were blind to the hypotheses. For each trial, coders determined how many items
were recalled, if the oddball scene was recalled, and whether the object that immediately
preceded (i.e., “oddball – 1”) or followed (i.e., “oddball + 1”) the oddball was recalled (see Table
9 for means). Overall agreement between the two coders was 87.7%, indicating adequate inter-
rater reliability, and any disagreements between the coders were resolved by a third coder. Data
48
was missing from one older adult participant who declined to participate in the task due to the
violent and grotesque nature of the negative oddball scenes.
We first assessed younger and older adults’ overall recall memory on negative and
neutral trials during the oddball task. A 2 (Age: younger, older) x 2 (Emotion: negative, neutral)
mixed ANOVA conducted on the average number of items recalled per trial revealed only a
main effect of Age, F(1, 65) = 46.58, p < .001, hp
2
= .42, with younger adults (M = 4.75, SD =
0.89) remembering an overall greater number of items per trial than older adults (M = 3.33, SD =
0.82). We then examined whether the proportion of negative and neutral oddballs recalled
differed between age groups by conducting a 2 (Age: younger, older) x 2 (Emotion: negative,
neutral) mixed ANOVA. Again, a main effect of Age was observed, F(1, 65) = 32.00, p < .001,
hp
2
= .33, with younger adults remembering a significantly greater number of oddballs,
regardless of valence, than older adults did. There was also a main effect of Valence, F(1, 65) =
13.36, p = .001, hp
2
= .17, indicating that participants had better recall memory for negative
oddballs (M = 77, SD = .21) than neutral oddballs (M = 70, SD = .21). The two-way interaction
was not significant, F(1, 65) = 0.09, p = .767, hp
2
= .001.
Next, we examined the proportion of “oddball – 1” objects recalled as a function of age
and emotion using a 2 (Age: younger, older) x 2 (Emotion: negative, neutral) mixed ANOVA.
Results only revealed a main effect of Age, F(1, 65) = 36.10, p < .001, hp
2
= .36, with younger
adults recalling more “oddball – 1” objects than older adults did. The main effect of Emotion and
the two-way interaction were not significant (both ps > .20). A 2 (Age: younger, older) x 2
(Emotion: negative, neutral) mixed ANOVA was conducted on the proportion of “oddball + 1”
objects recalled. Once again, there was a main effect of Age, F(1, 65) = 21.03, p < .001, hp
2
=
49
.24, such that recall of “oddball + 1” objects was higher for younger than older adults. Neither
the main effect of Emotion nor the two-way interaction was significant (both ps > .82).
Table 9. Recall memory during the oddball task as a function of age and oddball valence
Younger adults
Older adults
Item Recall
Negative
4.78 (0.93)
3.37 (0.86)
Neutral
4.73 (0.93)
3.29 (0.86)
Oddball Recall
Negative
0.89 (0.12)
0.66 (0.22)
Neutral
0.81 (0.14)
0.60 (0.21)
“Oddball – 1” Recall
Negative
0.54 (0.19)
0.30 (0.17)
Neutral
0.50 (0.20)
0.29 (0.15)
“Oddball + 1” Recall
Negative
0.45 (0.17)
0.29 (0.16)
Neutral
0.46 (0.18)
0.29 (0.17)
Note. Item recall refers to the average number of items recalled per trial. The remaining values
represent the average proportion that was recalled across trials. Standard deviations are given in
parentheses.
3.3.1.2 LC contrast ratios and behavioral task performance. To assess if there was
an association between LC signal intensity and selective memory under heightened emotional
arousal, multiple linear regression analyses were conducted in each age group using LC contrast
ratios as predictors and recall memory for oddball scenes, “oddball – 1” objects, and “oddball +
1” objects on negative trials as the dependent variables. We constructed two separate regression
50
models; one that took into account hemispheric differences and the other that was sensitive to
differences along the rostrocaudal extent of the LC. In the first model, there were no associations
between the left or right LC and selective memory under arousal in either age group (all ps > .10;
see Table 10 for results). Likewise, as Table 11 indicates, LC contrast ratios along the
rostrocaudal axis were not significantly associated with memory during the oddball task (all ps >
.10).
Table 10. Results of multiple linear regression analyses between LC contrast and oddball
memory performance taking into account hemispheric differences
Predictor b SE 95% CI t p
(A) Oddball Recall
Younger adults, F(2, 30) = 1.241, p = .303, R
2
= .076, adjusted R
2
= .015
Left .618 .466 -.334, 1.570 1.326 .195
Right -.121 .429 -.997, .755 -.283 .779
Older adults, F(2, 28) = 1.313, p = .285, R
2
= .086, adjusted R
2
= .020
Left .728 .698 -.702, 2.157 1.042 .306
Right .220 .707 -1.227, 1.668 .312 .757
(B) “Oddball – 1” Recall
Younger adults, F(2, 30) = .179, p = .837, R
2
= .012, adjusted R
2
= -.054
Left .249 .758 -1.299, 1.796 .328 .745
Right -.411 .697 -1.835, 1.013 -.589 .560
Older adults, F(2, 28) = .504, p = .609, R
2
= .035, adjusted R
2
= -.034
Left .454 .571 -.716, 1.624 .794 .434
Right -.565 .578 -1.750, .620 -.977 .337
(C) “Oddball + 1” Recall
Younger adults, F(2, 30) = .292, p = .749, R
2
= .019, adjusted R
2
= -.046
Left .484 .697 -.939, 1.907 .694 .493
Right -.154 .641 -1.463, 1.156 -.240 .812
Older adults, F(2, 28) = .477, p = .626, R
2
= .033, adjusted R
2
= -.036
Left .090 .571 -1.080, 1.260 .158 .876
Right .377 .578 -.808, 1.561 .651 .520
Note. SE = standard error; CI = confidence interval.
51
Table 11. Results of multiple linear regression analyses between LC contrast and oddball
memory performance taking into account differences along the rostrocaudal axis
Predictor b SE 95% CI t p
(A) Oddball Recall
Younger adults, F(2, 30) = 2.023, p = .150, R
2
= .119, adjusted R
2
= .060
Caudal .785 .499 -.233, 1.804 1.575 .126
Rostral .233 383 -.548, 1.015 .609 .547
Older adults, F(2, 28) = .749, p = .482, R
2
= .051, adjusted R
2
= -.017
Caudal -.100 .781 -1.701, 1.501 -.128 .899
Rostral .959 .912 -.908, 2.826 1.052 .302
(B) “Oddball – 1” Recall
Younger adults, F(2, 30) = .875, p = .427, R
2
= .055, adjusted R
2
= -.008
Caudal -1.066 .812 -2.724, .592 -1.313 .199
Rostral .385 .623 -.888, 1.657 .617 .542
Older adults, F(2, 28) = .189, p = .829, R
2
= .013, adjusted R
2
= -.057
Caudal -.324 .635 -1.624, .977 -.510 .614
Rostral .429 .740 -1.087, 1.946 .580 .567
(C) “Oddball + 1” Recall
Younger adults, F(2, 30) = .916, p = .411, R
2
= .058, adjusted R
2
= -.005
Caudal -.574 .749 -2.103, .955 -.767 .449
Rostral .755 .574 -.418, 1.928 1.315 .198
Older adults, F(2, 28) = .761, p = .476, R
2
= .052, adjusted R
2
= -.016
Caudal .143 .621 -1.130, 1.416 .231 .819
Rostral .604 .725 -.881, 2.089 .833 .412
Note. SE = standard error; CI = confidence interval.
3.3.2 Emotional Stroop Task
3.3.2.1 Behavioral Results. For each participant, incorrect responses and response
times (RTs) that were 2.5 SD above the mean on correct trials were excluded. To investigate the
effects of age and emotion on RTs, a 2 (Age: younger, older) x 2 (Valence: positive, negative) x
2 (Arousal: low, high) mixed ANOVA was conducted on the mean RTs of correct responses (see
52
Table 12 for means). A main effect of Age, F(1, 66) = 88.55, p < .001, hp
2
= .57, revealed that
older adults (M = 999.5, SD = 150.9) were overall slower to respond than younger adults (M =
692.3, SD = 116.0). There was also a main effect of Arousal, F(1, 66) = 20.78, p < .001, hp
2
=
.24, that was qualified by a significant Valence x Arousal interaction, F(1, 66) = 5.44, p = .023,
hp
2
= .08. Follow-up t-tests showed that both age groups were slower to respond to highly
arousing negative words (M = 873.3, SD = 230.8) than negative words that were low in arousal
(M = 831.4, SD = 202.9), t(67) = 4.86, p < .001, mean difference = 41.86, 95% CI [24.65, 59.07],
d = 0.59. RTs for high arousal positive words (M = 853.0, SD = 210.7) were numerically higher
than low arousal positive words (M = 838.7, SD = 196.1), but this trend was not significant, t(67)
= 1.70, p = .095, mean difference = 14.23, 95% CI [-2.52, 30.98], d = 0.21. In addition, there was
no difference in RTs between low arousal positive and low arousal negative words, t(67) = 1.07,
p = .290, mean difference = 7.32, 95% CI [-6.37, 21.00], d = 0.13; however, participants were
slower to respond to highly arousing negative words than highly arousing positive words, t(67) =
2.10, p = .040, mean difference = 20.31, 95% CI [0.98, 39.65], d = 0.25. There was also a
significant Age x Valence interaction, F(1, 66) = 4.57, p = .036, hp
2
= .07; post-hoc t-tests
showed that RTs for positive and negative words did not differ in younger adults (Mpositive =
692.9, SD = 113.6; Mnegative = 686.7, SD = 119.4), t(32) = 0.88, p = .384, mean difference = 6.25,
95% CI [-8.18, 20.67], d = 0.15, whereas older adults were slower to respond to negative words
(M = 1009.1, SD = 159.3) relative to positive words (M = 990.4, SD = 152.0), t(34) = 2.01, p =
.052, mean difference = 18.69, 95% CI [-0.17, 37.55], d = 0.34. The Age x Arousal interaction
was marginally significant, F(1, 66) = 3.22, p = .077, hp
2
= .05; the main effect of Valence and
the three-way interaction were not significant (both ps ³ .30).
53
Finally, we calculated the mean proportion of errors for each individual and conducted on
a 2 (Age: younger, older) x 2 (Valence: positive, negative) x 2 (Arousal: low, high) mixed
ANOVA on these error rates (see Table 13 for means). Results only revealed a main effect of
Age, F(1, 66) = 12.58, p = .001, hp
2
= .16; younger adults (M = .07, SD = .05) made significantly
more errors than older adults (M = .03, SD = .04). No other main effects or interactions were
significant (all ps > .19).
Table 12. Mean response times (ms) on correct trials during the Emotional Stroop task as a
function of age, valence, and arousal level.
Younger adults
Older adults
Positive
Low arousal
688.3 (109.1)
980.6 (147.8)
High arousal
697.4 (123.6)
999.6 (165.7)
Negative
Low arousal
674.2 (116.1)
979.7 (147.8)
High arousal
698.7 (128.1)
1037.9 (178.9)
Note. Standard deviations are given in parentheses.
Table 13. Mean proportion of errors during the Emotional Stroop task as a function of age,
valence, and arousal level.
Younger adults
Older adults
Positive
Low arousal
.06 (.08)
.03 (.05)
High arousal
.07 (.07)
.03 (.05)
Negative
54
Low arousal
.07 (.06)
.03 (.05)
High arousal
.08 (.09)
.03 (.03)
Note. Standard deviations are given in parentheses.
3.3.2.2 LC contrast ratios and behavioral task performance. To examine the
relationship between LC intensity and cognitive control for emotional information, multiple
linear regressions were performed with LC contrast ratios as predictors of response times on
correct trials during the emotional Stroop task. First, we constructed a regression model that was
sensitive to hemispheric differences with the left and right LC as predictors of response times
(see Table 14). This model demonstrated that LC contrast ratios were not associated with
younger and older adults’ response times during the emotional Stroop task. A second model was
performed that assessed if LC contrast ratios along the rostrocaudal axis was related to response
times (see Table 15). In this model, results indicated that LC contrast explained a marginally
significant proportion of the variance in older adults’ response times for negative low arousal
words, F(2, 29) = 3.083, p = .061, R
2
= .175, adjusted R
2
= .118. Further inspection of the
individual predictors revealed that, in older adults, higher rostral LC contrast was associated with
faster response times on trials displaying low arousal negative words, b = -1407.332, p = .025.
Table 14. Results of multiple linear regression analyses between LC contrast ratios within each
hemisphere and mean response times on correct trials
Predictor b SE 95% CI t p
(A) Positive Low Arousal Words
Younger adults, F(2, 30) = .512, p = .605, R
2
= .033, adjusted R
2
= -.031
Left -282.242 430.332 -1161.097, 596.612 -.656 .517
Right -45.838 396.032 -856.643, 762.967 -.116 .909
55
Older adults, F(2, 29) = .085, p = .919, R
2
= .006, adjusted R
2
= -.063
Left -53.629 531.586 -1140.845, 1033.587 -.101 .920
Right -135.854 526.328 -1212.316, 940.607 -.258 .798
(B) Positive High Arousal Words
Younger adults, F(2, 30) = .219, p = .804, R
2
= .014, adjusted R
2
= -.051
Left -169.025 531.974 -1255.461, 917.411 -.318 .753
Right -101.775 489.573 -1101.615, 898.066 -.208 .837
Older adults, F(2, 29) = .190, p = .828, R
2
= .013, adjusted R
2
= -.055
Left 344.318 591.890 -866.234, 1554.869 .582 .565
Right -300.561 586.036 -1499.138, 898.016 -.513 .612
(C) Negative Low Arousal Words
Younger adults, F(2, 30) = .781, p = .467, R
2
= .050, adjusted R
2
= -.014
Left -344.182 461.627 -1287.564, 599.199 -.745 .462
Right -95.777 425.109 -963.965, 772.411 -.225 .823
Older adults, F(2, 29) = .427, p = .657, R
2
= .029, adjusted R
2
= -.038
Left -1.842 508.228 -1041.286, 1037.602 -.004 .997
Right -369.585 503.201 -1398.747, 659.577 -.734 .469
(D) Negative High Arousal Words
Younger adults, F(2, 30) = .213, p = .809, R
2
= .014, adjusted R
2
= -.052
Left -310.072 515.057 -1361.958, 741.814 -.602 .552
Right 282.330 474.004 -685.715, 1250.374 .596 .556
Older adults, F(2, 29) = .450, p = .642, R
2
= .030, adjusted R
2
= -.037
Left 577.789 634.407 -719.719, 1875.297 .911 .370
Right -477.510 628.132 -1762.183, 807.164 -.760 .453
Note. SE = standard error; CI = confidence interval.
Table 15. Results of multiple linear regression analyses between LC contrast ratios along the
rostrocaudal axis and mean response times on correct trials
Predictor b SE 95% CI t p
(A) Positive Low Arousal Words
Younger adults, F(2, 30) = 1.775, p = .187, R
2
= .106, adjusted R
2
= .046
Caudal -39.731 453.466 -965.833, 886.371 -.088 .934
Rostral -601.009 347.924 -1311.564, 109.546 -1.727 .094
56
Older adults, F(2, 29) = .962, p = .394, R
2
= .062, adjusted R
2
= -.002
Caudal 74.345 559.953 -1070.887, 1219.576 .133 .895
Rostral -795.202 656.822 -2138.553, 548.149 -1.211 .236
(B) Positive High Arousal Words
Younger adults, F(2, 30) = 1.174, p = .323, R
2
= .073, adjusted R
2
= .011
Caudal 339.949 565.479 -814.912, 1494.811 .601 .552
Rostral -644.257 433.866 -1550.329, 221.814 -1.531 .136
Older adults, F(2, 29) = .710, p = .500, R
2
= .047, adjusted R
2
= -.019
Caudal 497.860 630.867 -792.408, 1788.129 .789 .436
Rostral -875.764 740.004 -2389.243, 637.715 -1.183 .246
(C) Negative Low Arousal Words
Younger adults, F(2, 30) = 2.193, p = .129, R
2
= .128, adjusted R
2
= .069
Caudal 141.997 484.964 -848.432, 1132.426 .293 .772
Rostral -759.759 372.090 -1519.669, .151 -2.042 .050
Older adults, F(2, 29) = 3.083, p = .061, R
2
= .175, adjusted R
2
= .118
Caudal 363.651 507.867 -675.053, 1402.355 .716 .480
Rostral -1407.332 595.725 -2625.726, -188.937 -2.362 .025
(D) Negative High Arousal Words
Younger adults, F(2, 30) = 1.318, p = .283, R
2
= .081, adjusted R
2
= .020
Caudal 645.464 544.960 -467.492, 1758.420 1.184 .246
Rostral -610.293 418.122 -1464.213, 243.626 -1.460 .155
Older adults, F(2, 29) = .623, p = .543, R
2
= .041, adjusted R
2
= -.025
Caudal 376.849 684.090 -1022.272, 1775.970 .551 .586
Rostral -892.405 802.434 -2533.567, .748.758 -1.112 .275
Note. SE = standard error; CI = confidence interval.
3.3.3 Sustained Attention to Response Task
3.3.3.1 Behavioral Results. Data was missing from one younger adult participant due to
technical difficulties. For each participant, the proportion of inaccurate responses on No-Go trials
(i.e., target stimuli) was calculated. These commission errors reflect trials in which participants
were unable to inhibit their response. The proportion of commission errors did not differ between
younger (M = .38, SD = .22) and older adults (M = .41, SD = .20), t(65) = 0.50, p = .617. The
57
proportion of omission errors (i.e., no response on a Go trial) for non-target stimuli was also
calculated for each participant. Omission errors for younger (M = .08, SD = .06) and older adults
(M = .06, SD = .06) did not significantly differ, t(65) = 1.43, p = .156.
3.3.3.2 LC contrast ratios and behavioral task performance. To evaluate if there was
a relationship between LC signal intensity and one’s ability to sustain attention, multiple linear
regressions were conducted in each age group using the LC contrast ratios as predictors of the
proportion of omission and commission errors on the SART task. The first regression model took
into account possible hemispheric differences in LC contrast (Table 16), whereas the second
model was performed to take into account any contrast differences along the rostrocaudal axis
(Table 17). For both age groups, LC contrast ratios were not associated with the proportion of
inaccurate responses on this sustained attention task (all ps > .10).
Table 16. Results of multiple linear regression analyses between LC contrast ratios and errors on
the SART task taking into account hemispheric differences
Predictor b SE 95% CI t p
(A) Omission Errors
Younger adults, F(2, 29) = .437, p = .650, R
2
= .029, adjusted R
2
= -.038
Left .002 .228 -.463, .468 .010 .992
Right .142 .203 -.274, .557 .696 .492
Older adults, F(2, 29) = .664, p = .523, R
2
= .044, adjusted R
2
= -.022
Left .212 .195 -.187, .610 1.088 .286
Right -.068 .193 -.463, .326 -.354 .726
(B) Commission Errors
Younger adults, F(2, 39) = .458, p = .637, R
2
= .031, adjusted R
2
= -.036
Left -.532 .887 -2.346, 1.283 -.599 .554
Right .758 .793 -.863, 2.379 .957 .347
Older adults, F(2, 29) = .607, p = .552, R
2
= .040, adjusted R
2
= -.026
Left .748 .679 -.641, 2.138 .748 .679
58
Right -.467 .673 -1.843, .909 -.467 .673
Note. SE = standard error; CI = confidence interval.
Table 17. Results of multiple linear regression analyses between LC contrast ratios and errors on
the SART task taking into account differences along the rostrocaudal axis
Predictor b SE 95% CI t p
(A) Omission Errors
Younger adults, F(2, 29) = .321, p = .728, R
2
= .022, adjusted R
2
= -.046
Caudal .087 .249 -.423, .597 .350 .729
Rostral .103 .186 -.278, .484 .553 .584
Older adults, F(2, 29) = .363, p = ..699, R
2
= .024, adjusted R
2
= -.043
Caudal .161 .213 -.275, .598 .755 .456
Rostral -.027 .250 -.539, .485 -.106 .916
(B) Commission Errors
Younger adults, F(2, 29) = .025, p = .976, R
2
= .002, adjusted R
2
= -.067
Caudal .092 .983 -1.918, 2.101 .093 .926
Rostral .115 .735 -1.388, 1.618 .157 .876
Older adults, F(2, 29) = .703, p = .503, R
2
= .046, adjusted R
2
= -.020
Caudal -.599 .735 -2.102, .903 -.816 .421
Rostral 1.009 .862 -.753, 2.772 1.172 .251
Note. SE = standard error; CI = confidence interval.
Discussion
Evidence suggests that structural and functional deficits in the LC-NE system, either
from advancing age or disease pathology, are associated with impaired cognitive abilities,
including attention, memory, and executive functioning (Aston-Jones & Cohen, 2005; Chandler
et al., 2014; Mather, 2020; Mather & Harley, 2016; Sara, 2009, 2015). In addition, prior research
has shown hemispheric differences (e.g., Jacobs et al., 2015) as well as variations along the
rostrocaudal axis of the LC in older adults (Bachman et al., 2021; Chan-Palay & Asan, 1989;
Dahl et al., 2019; Manaye et al., 1995), suggesting that the association between the LC-NE
59
system and cognitive aging may be related to the topographic organization of the LC. In the
present study, we used high-resolution, neuromelanin-sensitive, fast spin-echo (FSE) T1-
weighted MRI sequences and a pre-existing LC meta mask (Dahl et al., 2021) to collect four
separate measurements of LC signal intensity (i.e., left, right, caudal, rostral) to examine the
association between the integrity of the LC-NE system and cognitive performance in a sample of
healthy younger and older adult participants.
First, we examined if there were age group differences in LC neuromelanin concentration
using these four measurements of LC signal intensity. Results indicated that LC contrast did not
differ between younger and older adults in the left, right, caudal, or rostral LC. Although prior
research has found higher LC contrast in older than younger adults (Clewett et al., 2016; Zecca et
al., 2004), our findings are largely consistent with the notion that neuromelanin concentration
accumulates in a nonlinear fashion across the lifespan, such that LC contrast increases until
middle age and then gradually decreases in older adulthood (Manaye et al., 1995; Mann, 1983;
Shibata et al., 2006). However, investigators have detected age-related changes in LC signal
intensity along the rostrocaudal extent of the LC (Bachman et al., 2021; Dahl et al., 2019).
Specifically, evidence revealed that older adults have greater neuromelanin signal intensity in the
caudal portion of the LC and marginally less LC contrast in the rostral region of the LC, relative
to younger adults (Bachman et al., 2021). One possibility as to why we did not observe age
differences in neuromelanin signal intensity within the caudal or rostral regions of the LC may
be related to our smaller sample size. Both studies that found age group differences in LC
contrast along the rostrocaudal axis had over 200 older adult participants (Bachman et al., 2021;
Dahl et al., 2019), whereas our final sample for that analysis consisted of 33 older adults. Thus, it
is possible that these age group differences only emerge in larger sample sizes.
60
Novel to this study, we sought to determine whether the integrity of the LC-NE system is
related to cognitive functioning across the lifespan. To do this, we examined whether
neuromelanin signal intensity within the left and right LC as well as in the caudal and rostral
regions of the LC were associated with younger and older adults’ performance on five separate
cognitive tasks that are highly susceptible to age-related changes.
4.1 Memory
4.1.1 Pattern Separation
Prior research examining age differences in pattern separation has shown that the ability
to separate two similar mental representations declines in older age (Stark et al., 2013; Toner et
al., 2009). Instead, older adults are prone to pattern completion in which the presentation of a
similar stimulus reinstates an existing mental representation (Stark et al., 2010; Yassa et al.,
2011). Replicating prior research, when we examined participants’ raw response rates, younger
adults had better pattern separation ability than older adults. Furthermore, our results showed that
older adults were more likely than younger adults to mistakenly respond that a lure item was the
same object that they had seen during encoding, indicating a greater propensity towards pattern
completion. However, after controlling for possible response biases, the finding that older adults
were more likely to engage in pattern completion was no longer significant. In contrast, the
corrected scores still showed that younger adults had better pattern separation ability than older
adults. Thus, while our findings demonstrated that older adults had worse pattern separation
ability than younger adults, we could not definitively conclude that older adults were more apt to
engage in pattern completion.
We then examined if the integrity of the LC-NE system was related to pattern separation
performance in younger and older adults. We found that older adults with higher contrast in the
61
left LC were significantly less likely to engage in pattern completion. These novel findings
provide direct evidence to suggest that the integrity of the LC-NE system is intrinsically related
to older adults’ pattern separation ability, an aspect of memory that is susceptible to age-related
impairments (Stark et al., 2013; Toner et al., 2009). These results are also consistent with prior
evidence that has found a correlation between lower LC neuromelanin signal intensity and worse
verbal memory in older adults (Dahl et al., 2019; Solders et al., 2020). Furthermore, our
observation that the left LC was related to older adults’ pattern separation ability coincides nicely
with evidence showing that greater connectivity between the left LC and the left
parahippocampal gyrus in healthy older adults is associated with better encoding and recall of
verbal information (Jacobs et al., 2015). Taken together, our results support and extend previous
findings by demonstrating that variations in LC signal intensity, particularly in the left LC, are
associated with older adults’ memory abilities. In addition, the fact that we observed an
association in the left, but not right, LC is consistent with prior literature suggesting that there are
hemispheric differences in LC signal intensity, with higher LC contrast values observed in the
left hemisphere (Bachman et al., 2021), a finding that was also observed in the current study.
As discussed previously, efferent projections to the various cortical regions of the brain
typically arise from the caudal region of the ipsilateral LC (Waterhouse, Lin, Burne, &
Woodward, 1983). These cortical areas are innervated by unmyelinated and weakly myelinated
axons, which are capable of spreading disease pathology to other brain regions (Janitzky, 2020;
Mather & Harley, 2016). Thus, it is possible that these early cognitive changes (i.e., disrupted
pattern separation ability) could reflect the types of functional deficits that can occur from
disease pathology in the LC before it spreads to the medial temporal lobe and transentorhinal
cortex (Braak et al., 2011; Stratmann et al., 2016). Future work would benefit from examining
62
longitudinal data to determine if older adults with lower LC contrast and worse pattern
separation ability later develop amnestic mild cognitive impairment or Alzheimer’s disease.
We also found that LC contrast ratios were not associated with younger adults’
performance during this pattern separation task. This observation is consistent with prior
research showing an association between LC contrast and memory performance in older, but not
younger, adults (Hämmerer et al., 2018; Langley et al., 2021; Liu et al., 2020). Why would LC
contrast only be related to older adults’ memory performance? One possibility is that these
individual differences observed in older adults are reflective of specific disease pathology (e.g.,
hyperphosphorylated tau) that can accumulate throughout the lifespan and is therefore higher and
more variable in older age (Theofilas et al., 2017). That is, the relationship between
neuromelanin concentration in the LC and memory performance is a function of disease
pathology, which has not yet developed in younger adults, rather than age per se. Future research
is needed to determine if these age group differences occur because of the aging process or
whether they are more reflective of neuropathological changes in the LC.
As discussed earlier, pattern separation is associated with activation in the dentate gyrus
and CA3 subregions of the hippocampus (Bakker et al., 2008; Kirwan & Stark, 2007; Lacy et al.,
2011; Leutgeb et al., 2007). Given that the dentate gyrus (DG) receives dense projections from
the LC (Harley, 2007), it is likely that LC integrity is related to activation within these regions
during pattern separation. Future work in our lab will investigate if individual differences in LC
contrast are associated with activation in the CA3/DG subfields of the hippocampus, specifically
on trials that involve pattern separation (i.e., lures during the memory task). Given prior research
showing that healthy older adults with worse pattern separation abilities show hyperactivity in
CA3/DG hippocampal subregions (Yassa et al., 2011), we hypothesize that there will be an
63
inverse relationship in older adults, such that older adults who show greater activation in the
CA3/DG subfields will have lower LC contrast. In addition, we also plan to examine the
functional connectivity between the LC and activity within the CA3/DG subregions on trials
requiring pattern separation (i.e., lures) in both younger and older adults.
4.1.2 Selective Memory under Arousal
To assess whether the integrity of the LC was related to selective memory under
increased arousal, we administered an emotional oddball task. Prior to examining the association
with LC contrast, we first reviewed participants’ behavioral performance. Replicating previous
research showing that arousal increases the processing of highly salient stimuli in both younger
and older adults (Lee et al., 2018), both age groups had better recall for negative than neutral
oddballs. However, in contrast to prior work (e.g., Hurlemann et al., 2005; Knight & Mather,
2009; Sakaki et al., 2014), memory for the items immediately preceding and following the
oddball stimulus (i.e., “oddball – 1” and “oddball + 1” items, respectively) was not lower when
the oddball was negative, as compared to neutral, in either age group. There are a number of
possibilities that could explain why we did not observe memory suppression for the items
immediately surrounding the oddball stimulus under conditions of increased arousal. First, there
were several methodological differences in our study. For example, Sakaki et al. (2014) used
recognition memory tests to assess memory performance, whereas we assessed free recall.
Although Knight and Mather (2009) also evaluated immediate free recall in a paradigm that was
very similar to ours and observed retrograde amnesia under heightened arousal, they asked
participants to write down as many as items that they could remember, whereas we asked
participants to recall items aloud to the examiner. A second, more likely, explanation is that our
distracter task was too emotionally arousing and interfered with the oddball task. In our distracter
64
task, we presented four complex math problems for five seconds each before the screen
automatically advanced to the next math problem. A number of participants reported that they
found the distracter task to be stressful and were more concerned about their math performance
than their performance on the memory task. Although Knight and Mather (2009) also presented
math problems during the distracter task, their participants were given 25 seconds and it is
unclear if these math problems were self-paced. Additionally, their math problems may have
been less complex and did not require as much cognitive effort. Thus, it is possible that by
inadvertently increasing arousal levels during the distracter task, it interfered with our ability to
observe suppression of low-priority items as a function of oddball valence. Future research
would benefit from directly comparing how arousing versus non-arousing distracter tasks affect
selective memory under arousal during these oddball tasks.
Next, we examined how LC signal intensity was related to memory performance during
the oddball task. Prior research has shown that neural responses to oddball stimuli and memory
suppression for the items preceding emotional oddballs are mediated by NE projections from the
LC (Hurlemann et al., 2005; Strange et al., 2003; Strange & Dolan, 2007). In our study, we did
not observe an association between LC contrast ratios and oddball recall, “oddball – 1” recall, or
“oddball + 1” recall in either age group. However, the aforementioned methodological
differences (e.g., a highly arousing distracter task) may have interfered with our ability to detect
an association. Future research is still needed to determine if the integrity of the LC-NE system
plays a fundamental role in facilitating selective memory under arousal in healthy older adults.
4.2 Attention and Executive Functioning
4.2.1 Selective Attention
65
Few studies have investigated how the integrity of the LC-NE system is related to
younger and older adults’ ability to selectively attend to high-priority stimuli while
simultaneously inhibiting irrelevant information under arousal. In our study, we used a modified
version of a selective attention task (Schwartz et al., 2005) to evaluate the relationship between
LC signal intensity and selective attention. First, we examined participants’ hit rates during this
target detection task as a function of cognitive load and whether irrelevant, distracting
checkerboard patterns were flashing in the periphery. Supporting prior literature that indicates
that older adults have difficulty inhibiting distracting stimuli, even under conditions of threat and
arousal (Durbin et al., 2018; Gallant et al., 2020; Lee et al., 2018), we found that the age
difference in the ability to accurately detect targets was more pronounced in the high load
compared to the low load condition. Interestingly, our results showed that older adults actually
performed better on this task when the distracting checkerboards were flashing than when they
were not displayed. In contrast, younger adults’ performance did not differ as a function of
whether or not the checkerboards were presented. Why would the presence of irrelevant,
distracting stimuli actually improve older adults’ task performance? One possibility is that the
flashing checkerboards themselves were arousing and increased cognitive demand, since
inhibitory processes were needed to ignore them. As Lee et al. (2018) reported, arousal amplifies
information indiscriminately in older age and increases processing of high-priority stimuli under
arousal. Therefore, it is plausible that the presence of these flashing checkerboard patterns was
particularly stressful and arousing for older adult participants, prompting an enhanced ability to
focus on high-priority, task-relevant stimuli. Future work in our lab will test this hypothesis by
examining younger and older adults’ pupil dilation, an indirect measure of arousal-mediated NE
66
release and LC activity (Murphy et al., 2014; Joshi et al., 2016; Liu et al., 2017), during the
various conditions of this selective attention task.
Next, we evaluated whether LC signal intensity was associated with the mean proportion
of targets detected as a function of age and cognitive load condition. In line with our predictions,
we found that older adults with higher contrast in the caudal LC performed better during the high
cognitive load condition when distracting checkerboards were flashing in the periphery. That is,
when the cognitive demands were increased, thereby increasing arousal levels, older adults who
had higher structural integrity within the caudal region of the LC exhibited better selective
attention. In addition, our regression model revealed a trend suggesting that older adults with
higher caudal LC contrast were also better at detecting targets in the low cognitive load condition
when irrelevant checkerboards were being displayed. These novel findings are consistent with
prior research showing that older adults with greater LC-NE responsiveness, as assessed using a
composite of EEG and pupil dilation, had better selective attention (Dahl et al., 2020). Moreover,
our results extend prior literature by demonstrating for the first time that variability in older
adults’ ability to selectively attend to task-relevant information under arousal may be particularly
related to changes within the caudal region of the LC. To date, no studies have reported a link
between caudal LC contrast and cognitive functioning. Interestingly, prior research has shown
that older adults have greater neuromelanin signal intensity in the caudal region of the LC,
relative to younger adults (Bachman et al., 2021), and that the highest number of neurons are
located in the caudal LC (German et al., 1988). Thus, it is possible that the caudal region of the
LC is particularly involved in NE metabolism and NE-mediated cognitive functioning, including
selective attention.
67
As discussed above, it is believed that the LC modulates selective attention under high
cognitive demand by interacting with the attentional regions of the brain, including the
frontoparietal and salience networks (Lee et al., 2018, 2020; Mather et al., 2016; Sara & Bouret,
2012). Moreover, older adults show reduced functional connectivity between the LC and the
frontoparietal network, relative to younger adults (Lee et al., 2018). Here, we expand on this line
of research by providing evidence to suggest that individual differences in LC signal intensity
could reflect greater disruptions in how the LC communicates with the frontoparietal network.
Future research being conducted in our lab will examine if neuromelanin signal intensity is in
fact correlated with activation of the frontoparietal network during this selective attention task.
Additionally, we hope to assess the functional connectivity between the LC and these frontal
systems to determine if individual differences in the integrity of the LC-NE system is related to
frontal activity. Individual differences in functional connectivity between the LC and frontal
systems could explain why some older adults are more prone to distraction and have greater
difficulty inhibiting task-irrelevant information. If so, longitudinal studies are needed to
determine if these individuals are vulnerable to developing neurodegenerative disease, such as
frontotemporal dementia.
4.2.2 Sustained Attention
To evaluate if there was a relationship between LC signal intensity and sustained
attention, we administered the Sustained Attention to Response Task (SART) to participants.
First, we assessed behavioral performance on this task in both younger and older adults. We
predicted that older adults would perform worse on this task given prior evidence for an age-
related decline in accuracy on the SART task (e.g., Cassarino, Tuohy, & Setti, 2019; McAvinue
et al., 2012). Yet, our results showed that the proportion of omission errors and commission
68
errors did not differ between the two age groups. Although these findings were initially contrary
to our prediction, they are actually consistent with current literature. Specifically, a recent meta-
analysis on age differences in selective attention tasks revealed that older adults are actually
more accurate (i.e., produce fewer errors) on the SART than younger adults (Vallesi, Tronelli,
Lomi, & Pezzetta, 2021). However, these investigators did find that older adults were slower on
the SART than younger adults, consistent with an age-related decline in processing speed.
Additionally, we did not observe an association between LC contrast ratios and accuracy
on this sustained attention in either age group. Based on the recent meta-analysis by Vallesi et al.
(2021), it possible that age group differences will be more likely to emerge when the analyses are
conducted using response times instead of error rates. Although it will be challenging to
disentangle whether any significant differences are due to processing speed or the ability to
sustain attention, future work in our lab will re-analyze this data using response times on go
versus no-go trials as the dependent variables instead of the proportion of omission and
commission errors.
4.2.3 Cognitive Control of Emotional Information
Finally, we administered an emotional Stroop task to assess whether the integrity of the
LC-NE system was related to cognitive control when one is presented with emotional
information. First, we evaluated participants’ response times as a function of age, valence, and
arousal level. Consistent with prior studies using emotional Stroop tasks (e.g., Ashley & Swick,
2009; Kappes & Bermeitinger, 2016), older adults were overall slower to respond than younger
adults and both age groups had higher response times for negative, highly arousing words. When
response times were collapsed across arousal level, response times between positive and negative
words did not differ in younger adults. In contrast, older adults were slower to respond to
69
negative than positive words, indicating that they were more distracted and had worse cognitive
control when negative information was displayed. These findings are consistent with prior
research, which showed that older adults attended to negative words longer than younger adults
did (Kappes & Bermeitinger, 2016). Interestingly, these results are in contrast to the well-
established finding that there is an age-related shift in emotional processing, such that older
adults attend to and remember a greater proportion of positive than negative words, relative to
younger adults (i.e., the positivity effect; Mather & Carstensen, 2005; Reed, Chan, & Mikels,
2014). Future research is needed to identify the underlying mechanisms to explain why older
adults might be more distracted by negative words during emotional Stroop tasks.
Novel to this study, we also examined if LC structural integrity was related to
performance on this emotional Stroop task in both younger and older adults. Results indicated
that older adults with higher contrast in the rostral LC were faster to respond on trials displaying
low arousal negative words. That is, older adults with better LC integrity exhibited greater
cognitive control and were less distracted by the low arousal negative words that were presented
during this task. Prior research examining age-related differences during the emotional Stroop
task actually found that older adults’ increased distractibility, relative to younger adults, was
more pronounced for medium arousal negative words compared to highly arousing negative
words (Kappes & Bermeitinger, 2016). It is possible that both younger and older adults are
similarly distracted by negative words that are high in arousal, an idea that is supported by our
behavioral results. Consequently, age differences in distractibility and attentional biases to
negative words may be more pronounced at lower arousal levels. Supporting this notion, we
found that LC signal intensity was associated with older adults’ response times for low arousal
negative words, but not for negative words that were high in arousal. Overall, these findings
70
suggest that the integrity of the LC-NE system, particularly in the rostral region of the LC, may
mediate older adults’ cognitive control for specific types of emotional information.
4.3 Conclusions and Clinical Significance
There is a growing body of literature that has implicated the LC-NE system in cognitive
aging and neurodegenerative disease (for reviews, see Mather & Harley, 2016; Weinshenker,
2018). Despite the clinical significance, few studies have investigated how the integrity of the
LC is related to cognitive functioning in later life. In the current study, we used LC neuromelanin
signal intensity (i.e., LC contrast) as a marker of the structural integrity of the LC and examined
the relationship between LC contrast and performance on five separate cognitive tasks in
younger and older adults. Furthermore, based on prior observations for hemispheric and
rostrocaudal differences in LC contrast, we computed four separate measurements of LC signal
intensity (i.e., left, right, caudal, rostral). Novel to this study, we found that higher LC contrast in
older adults was related to select cognitive abilities. Specifically, older adults with higher signal
intensity in the left LC were less likely to engage in pattern completion, a memory error that
occurs more likely in late age. In addition, higher contrast in the caudal portion of the LC was
associated with older adults’ selective attention and their ability to inhibit irrelevant stimuli
under high cognitive demand. We also demonstrated that older adults with higher contrast in the
rostral LC were better able to exert cognitive control for specific types of emotional information
(i.e., negative words that were low in arousal). These findings provide novel evidence to suggest
that the integrity of the LC-NE system is associated with certain types of cognitive abilities in
healthy older adults. Moreover, our research demonstrated that this relationship is dependent on
specific regions of the LC, suggesting that variations in older adults’ cognitive functioning may
be related to the topographical organization of the LC and hemispheric differences.
71
Given that changes in the LC are also observed in a number of neurodegenerative
conditions, including Alzheimer’s disease, Parkinson’s disease, frontotemporal dementia,
Huntington’s disease, multiple system atrophy, and progressive supranuclear palsy (Braak et al.,
2003; Holland, Robbins, & Rowe, 2021), it is imperative to understand the structural and
functional changes that occur in this region in both healthy and pathological aging. The current
findings provide insight into the possible cognitive and neural changes that may occur at the very
early stages of neurodegenerative disease. Furthermore, the LC is the most probable site where
Alzheimer’s disease pathology first begins to manifest (Braak & Del Tredici, 2012, 2015). Then,
the disease pathology within the LC (e.g., hyperphosphorylated tau) is believed to contribute to
the initiation and progression of Alzheimer’s disease by spreading disease pathology to the
medial temporal lobe (Braak et al., 2011; Stratmann et al., 2016; Weinshenker, 2018). Thus,
treatment interventions aimed at targeting the LC-NE system before Alzheimer’s disease
pathology spreads to the cortex could help slow the progression of this debilitating disease.
72
Footnotes
1
Negative IAPS images: 2730, 2981, 3005.1, 3069, 3100, 3160, 3168, 3170, 3301, 3400, 3550.1,
6230, 6313, 6570.1, 9250, 9252, 9253, 9265, 9300, 9410, 9570, 9630, 9635.1, 9800.
2
Responses during the encoding phase were not recorded for five older adults.
73
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Supplemental Materials
Supplemental Table 1. Positive, negative, and neutral words from the ANEW database that were
used in the Emotional Stroop Task.
Positive Words
Negative Words
Neutral Words
admired
applause
baby
bake
brave
cash
casino
couple
dazzle
diamond
elated
engaged
exercise
fame
festive
flirt
fun
gift
gold
grin
happy
holiday
honest
humor
joke
joy
kiss
laughter
leader
loved
lucky
miracle
passion
profit
radiant
rescue
riches
romantic
abuse
accident
agony
anger
arrogant
assault
bankrupt
betray
cruel
debt
despise
detest
devil
disaster
enraged
fraud
guilty
hatred
hurt
insane
insult
irritate
killer
lie
lost
madman
murderer
nightmare
poison
pollute
rabies
rage
rape
ridicule
sin
snob
stress
suicide
activate
aggressive
alley
aloof
boxer
bus
cannon
cellar
chair
doctor
excuse
frog
fur
hammer
hide
humble
industry
jug
lazy
limber
manner
modest
mushroom
obscene
passage
rain
rattle
razor
reptile
reverent
rock
rough
scissors
sheltered
ship
shy
solemn
square
91
Note. ANEW = Affective Norms for English Words.
sexy
sunlight
travel
victory
voyage
wealthy
wish
torture
tragedy
trauma
upset
victim
wicked
wounds
storm
stove
swamp
tamper
tank
theory
vanity
Abstract (if available)
Abstract
Evidence suggests that structural and functional deficits in the LC-NE system, either from advancing age or disease pathology, are associated with impaired cognition, including select aspects of attention, memory, and executive functioning. Yet, very few studies have examined the association between this system and cognitive functioning in healthy older adults. In the present study, we examined the relationship between the integrity of the LC-NE system and performance on five separate cognitive tasks in healthy younger and older adults. To quantify the integrity of the LC, we used high-resolution, neuromelanin-sensitive, fast spin-echo (FSE) T1-weighted MRI sequences and a pre-existing LC meta mask to compute four separate measurements of LC signal intensity (i.e., left, right, caudal, rostral). We found that older adults with higher LC contrast, specifically in the left LC, were less likely to engage in pattern completion (i.e., mistakenly reinstating an existing mental representation when presented with a similar stimulus). Results also demonstrated that higher contrast in the caudal portion of the LC in older adults was associated with better selective attention and inhibition of task-irrelevant information under high cognitive demand. Lastly, our findings showed that older adults with higher contrast in the rostral LC exerted greater cognitive control when presented with negative, low arousing words on an Emotional Stroop task. Taken together, our results provide novel evidence indicating that the integrity of the LC-NE system is related to select cognitive abilities in healthy older adults. Furthermore, our findings suggest that these differences may depend on hemispheric differences and variations in signal intensity within specific regions of the LC, underscoring the importance of taking into account LC topography when investigating how the LC-NE system affects cognitive performance across the lifespan.
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Asset Metadata
Creator
Durbin, Kelly Anne
(author)
Core Title
How age-related changes in the locus coeruleus affect selective attention and memory
School
College of Letters, Arts and Sciences
Degree
Doctor of Philosophy
Degree Program
Psychology
Degree Conferral Date
2021-08
Publication Date
07/20/2021
Defense Date
06/09/2021
Publisher
University of Southern California
(original),
University of Southern California. Libraries
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aging,attention,locus coeruleus,memory,neuromelanin,OAI-PMH Harvest
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application/pdf
(imt)
Language
English
Advisor
Mather, Mara (
committee chair
), Davison, Gerald (
committee member
), Han, Duke (
committee member
), Nation, Daniel (
committee member
)
Creator Email
kadurbin@usc.edu,kelly.anne.durbin@gmail.com
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https://doi.org/10.25549/usctheses-oUC15612360
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UC15612360
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etd-DurbinKell-9803
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Source
University of Southern California
(contributing entity),
University of Southern California Dissertations and Theses
(collection)
Access Conditions
The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright. The original signature page accompanying the original submission of the work to the USC Libraries is retained by the USC Libraries and a copy of it may be obtained by authorized requesters contacting the repository e-mail address given.
Repository Name
University of Southern California Digital Library
Repository Location
USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA
Repository Email
cisadmin@lib.usc.edu
Tags
attention
locus coeruleus
memory
neuromelanin