web viewword count: 5, 956 (inc. footnotes) ... include the posterior-anterior shift in aging model...

BRAIN COACTIVITY: PROSPECTIVE MEMORY, APOE AND AGE Candidate no. 87533

Differences in brain coativity in prospective memory associated with varying APOE allele

combinations and age: An event-related fMRI experiment.

Candidate Number: 87533

Supervisor: Jennifer Rusted

Word Count: 5, 956 (inc. footnotes)

Psychology BSc

School of Psychology, University of Sussex

May 2013

Acknowledgements

This study was funded by a BBSRC grant to Jennifer Rusted (BB/H000518/1) who

supervised this project. Thanks to Torsten Ruest for the data collection and Simon Evans for

study design and image preprocessing. The writer of this report was responsible for the PPI

analysis – with guidance from Simon Evans – and completion of this manuscript.

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Abstract

The apolipoprotein (APOE) ε4 allele is theorised to cause accelerated cognitive aging

because of its antagonistic pleiotropic properties: the allele correlates with increased

cognitive performance in young adulthood and earlier onset of AD in older adulthood. To

better understand how APOE affects cognitive aging, the current study investigated

differences in brain coactivation as a function of genotype (ε4+s vs. ε4-s) and age (middle

age vs. younger adult) in addition to trial-type (ongoing trials vs. prospective memory (PM)

trials). The study primarily focused on middle-aged participants who completed a

computerised-PM task whilst in the fMRI scanner. Secondarily, a younger cohort was added

to examine the genotype-specific effects of age on coactivation. The current study predicted

there would be an increase in bilateral frontal coactivation as a function of age in ε4+s based

on theories of normal cognitive aging and the antagonistic pleiotropic theory. The most

significant finding was the genotype-specific effect of age: increased bilateral coactivation

with the lInfFrontal (seed) region for middle-aged ε4+s compared to younger ε4+s when

masked by the contrast, middle-aged ε4-s > younger ε4-s. For future research, it would be

interesting to conduct a longitudinal study that focused on coactivation differences as a

function of age, genotype and AD-conversion to better understand how coactivation

differences predict AD.

Keywords: APOE ε4, antagonistic pleiotropy, prospective memory, coactivation

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1. Introduction

1.1 APOE ε4, The Brain and Alzheimer’s Disease

Since the early 1980s—when beta-amyloid (Aβ) plaques and neurofibrillary tangles

were first linked to the Alzheimer’s Disease (AD) pathology— there has been an ongoing

debate regarding what causes AD (Selkoe, 2011). The conversation continues because no

genetic factor, biomarker or environmental factor has been unequivocally linked to the cause

of AD.

In the mid-1990s, it was discovered that harbouring at least one apolipoprotein

(APOE) ε4 allele, located on chromosome 19, increased the risk of AD and negatively

correlated with the age of onset (Selkoe, 2011). In support of this discovery, Corder et al.

(1993) found that 64% of patients within a sporadic AD sample and 80% within a familial

sample had at least one ε4 allele, whilst only 14% of the general population have the allele

(Bertram, McQueen, Mullin, Blacker, & Tanzi, 2007). Therefore, though harbouring the ε4

allele is not the direct cause of AD, the allele’s presence is correlated with AD1.

The ε4 allele’s overrepresentation in the AD population has correlated with the

presence of amyloid beta (Aβ) plaques (Fillipinni, 2011; Mann, 1991), neuronal atrophy

(Buttini et al., 1999), and a loss of cortical choline acetyltransferase (ChAT) activity in key

areas of the brain (Poirier et al., 1995). In terms of ε4’s relation to Aβ plaques, Aβ deposition

is one of the first visible signs of the pathological process of AD, and APOE ε4 is known to

inhibit the normal clearing process of Aβ plaques in the brain (Fillipinni, 2011; Mann, 1991).

In addition, Buttini et al. (1999) discovered that mice harbouring at least one ε4 allele

experienced increased dendritic and synaptic loss with age (i.e., increased cerebral atrophy).

1.2. APOE ε4 as an Antagonistic Pleiotropic 1 There are three types of APOE alleles: ε2, ε3, and ε4. Whilst ε4 is overrepresented in

the AD population, ε2 is underrepresented, ie., ε2 may protect against the onset of AD.

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Curiously, on average, younger adults with at least one ε4 allele have higher IQ scores

and achieve higher levels of education than their ε3 and ε2 counterparts (Hubacek et al.,

2001; Yu, Lin, Chen, Hong, & Tsai, 2000). More specifically, young ε4+s (participants with

at least one ε4 allele) have been found to have a domain-specific advantage on frontal tasks

(decision making, prospective memory performance, and verbal fluency) (Marchant, King,

Tabet & Rusted, 2010). In turn, APOE ε4 is considered antagonistic pleiotropic (i.e., a gene

which has different effects on evolutionary fitness at different ages): APOE ε4 is correlated

with increased cognitive performance in younger adults and decreased cognitive performance

in older adults (Marchant et al.,2010).

Marchant et al. (2010) speculated that younger ε4+s’ increased cognitive performance

caused accelerated cognitive aging/earlier age-related decline in older ε4+s’, which may be

an additional explanation for the allele’s overrepresentation in the AD population. The

antagonistic pleiotropy theory is supported by the metabolic function of APOE ε4 discussed

earlier: high Aβ levels correlate with increased neuronal and synaptic activity in younger

adults and reduced functional brain connectivity in healthy older adults (Wei, 2010).

1.3. The Effects of APOE ε4 on Cognition and Cognitive Aging

Today, because APOE ε4 is the second leading risk factor for developing AD, after

age, there has been motivation to determine how ε4 affects specific cognitive processes

affected by AD and cognitive aging (i.e., prospective memory, visual attention, and memory

encoding) throughout the lifespan (Rocchi, Pellegrini, Siciliano and Murri, 2003).

Determining how the ε4 allele affects cognitive processes throughout the lifespan will help

researchers more accurately understand how the ε4 allele facilitates cognitive aging and the

development of AD in old age.

The ε4 allele appears to impede visual attention in middle-age. Greenwood,

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Sunderland, Friz and Prasuraman (2000) discovered that the effect of cue-validity (i.e., the

cost of having an invalid cue) on reaction time is greatest for middle-age ε4+ participants

compared to ε4-s (participants with homozygous ε3). Thus, the researchers concluded that the

possession of an ε4 allele in middle-age is associated with changes in attention processing

similar to that of someone with mild-AD (Greenwood et al., 2000).

In terms of how ε4 affects memory (i.e., encoding) with age, Filippini (2011)

discovered that aging was associated with decreased brain activity in ε4+s and increased

brain activity in ε4-s. Furthermore, the over-activity of brain function initially found in young

ε4+s was found to be disproportionately reduced with age even before the onset of

measurable memory impairment (Filippini, 2011; Mondadori et al., 2006). A caveat in

Filippini’s (2011) study is the broader age-range in the “older adults” group (50-78: 28 years)

compared to the “younger adults” group (20-35: 15 years). Thus, in light of current age-

related theories of compensation and dedifferentiation (to be discussed in the next section), it

is possible that that the large “older adult” age-range diluted a point of increased activation in

ε4+s that occurred earlier on in their lifespan (i.e., middle-age) than non-carriers (Marchant et

al., 2010).

1.4. Theories of Normal Cognitive Aging

The three most prominent theories of normal cognitive aging include the Posterior-

Anterior Shift in Aging model (PASA), the dedifferentiation model of cognitive aging, and

the Hemispheric Asymmetry Reduction in Older Adults model (HAROLD). These all involve

forms of increased brain activation with age as compensation for neuronal loss and/or an

inability to recruit specialised brain regions (Cabeza, 2001; Davis, Dennis, Daselaar, Fleck &

Cabeza, 2008; Park & Reuter-Lorenz, 2009).

1.4.1. The Posterior-Anterior Shift in Aging Model (PASA). The PASA model

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contends that cognitive aging is associated with a reduction in posterior activity (e.g., in the

occipital lobe) and an increase in anterior activity (e.g., in the frontal lobe) as a compensatory

mechanism of cognitive aging (Davis et al., 2008; Grady et al., 1994). Parker and Reuter-

Lorenz (2009) argued that this increase in frontal activation, as a function of age, is a

compensatory mechanism in response to neuronal changes caused by declining neural

structure and function.

1.4.2. The Dedifferentiation Model. In opposition to PASA—and all other

compensatory models— the dedifferentiation model suggests that when older adults carry out

certain cognitive processes, they engage more generalized neuronal processes as a

consequence of cognitive aging (Han, Bangen, & Bondi, 2009). In contrast to Han et al.

(2009), Cabeza (2001) argued that compensation and dedifferentiation theories are not

necessarily incompatible because dedifferentiation (combining neuronal pathways) can be

thought to compensate for cognitive decline associated with cognitive aging.

1.4.3. The Hemispheric Asymmetry Reduction in Older Adults Model

(HAROLD). An example of how compensation and dedifferentiation models can be

compatible is found in the HAROLD model (Cabeza, 2001). This model suggests that brain

activity tends to be less lateralized in older adults compared to younger adults during memory

tasks. Thus, the HAROLD effect can be explained by compensation models (i.e.,

bihemispheric involvement may help counteract age-related neurocognitive decline) and

dedifferentiation models alike (i.e., the loss of lateralisation reflects a difficulty in recruiting

specialized neural mechanisms).

1.5. Prospective Memory

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The current work specifically focused on how genotype and age affected prospective

memory (PM)—an individual’s ability to create, rehearse and carry out an intended action—

because of its relevance to everyday life and independent living (Burgess, Gonen-Yaacovi, &

Volle, 2011; Burgess, Scott & Frith, 2003; Marchant et al., 2010; Rusted, Ruest & Gray

2011). In fact, a decline in PM is one of the first major complaints older adults with memory

loss experience (Luo & Craik 2008). In addition, prospective memory is frontally mediated

and thus useful for determining the extent to which harbouring an APOE ε4 allele accelerates

age-related processes, which would be evidence for accelerated cognitive aging.

1.5.1. Prospective Memory and the Brain. Burgess et al. (2011) concluded that the

rostral prefrontal cortex (rPFC), especially the lateral rostral PFC, Brodmann Area 10 (BA

10), plays a superordinate role during the many stages of PM. Thus, the researcher proposed

the Gateway Hypothesis of Rostral PFC, which suggests that the main purpose of the rPFC is

to control differences between attending to independent thought (inner mental life) and

attending to the external world (stimulus-oriented attention). In addition, there has recently

been evidence for a fronto-parietal role in PM which links into Burgess’s Gateway

Hypothesis. More specifically, the fronto-parietal hypothesis suggests that parietal regions

project to frontal areas to complete the more executive tasks associated with PM (Rusted et

al., 2011; Simons et al., 2006).

1.5.2. Event-Based Prospective Memory (EBPM). There are two types of PM tasks

that can be tested in the laboratory and that are susceptible to age-related decline: The event-

based PM task (EBPM) and the time-based PM task (TBPM) (Luo & Craik, 2008; Henry,

MacLeod, Phillips &, Crawford, 2004). This study focused on the EBPM task—wherein

participants are asked to perform an action in response to an environmental cue. The specific

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EBPM task used in this study was embedded in a computer-based card-sort task (i.e., the

ongoing task) first developed by Rusted, Sawyer, Jones, Trawley and Marchant (2009).

Rusted et al. (2009) defined the task as attention-demanding and contended that PM detection

engages general attention processes in addition to those under the control of the central

executive system.

1.6. The Current Study

The current study looked at the effect of trial-type (PM trials vs. ongoing trials),

genotype (ε4+s vs. ε4-s) and age (younger adults vs. middle-age adults) on coactivation in the

brain (based on the results of an fMRI analysis) whilst participants completed Rusted et al.’s

(2009) card-sort task in the scanner. Differences in coactivation as a function of the

independent variables were measured by a Psychophysiological Interaction (PPI) Analysis.

Three seed regions were chosen which have been consistently implicated in PM: BA 10, the

Left Frontal Cortex and the Right Inferior Parietal Cortex (Burgess et al., 2011; Rusted et al.,

2011).

The two major goals of this study were to find out (1) how differences in coactivation

as a function of trial-type relate to current theories of PM and (2) how differences in

coactivation as a function of genotype and genotype-specific age differences relate to current

theories of cognitive aging.

In terms of cognitive aging theories, the results of our study will help us to better

understand how harboring an ε4 allele links to theories of normal cognitive aging and the

development of mild cognitive impairment (MCI) and/or mild AD. In contrast to Fillipinni

(2011), this study focuses on middle-age (50-65) because it is concerned with neuronal

changes as a function of age and genotype that specifically precede the onset of age-related

memory decline. Additionally, the middle-age cohort is an under-researched group compared

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to older adults.

Previous research has already suggested that for certain memory tasks, middle-aged

adults with an APOE ε4 allele demonstrated increased bilateral activation compared to non-

carriers (Bondi et al., 2008; Bondi et al., 2006). This supports the HAROLD model and

Marchant et al.’s (2010) contention that harbouring an ε4 allele is related to accelerated

cognitive-aging (Cabeza, 2001).

1.6.1 Aims and Predictions. The primary aim of the current research was to look at

the effect of trial-type and genotype on brain coactivity as measured by the Blood Oxygen

Level Dependent (BOLD) response – an indirect measure of brain activity— in the middle-

aged cohort and secondarily to include the younger cohort in order to examine genotype-

specific age differences. Data from the younger adults have also been analysed elsewhere.

Based on past research, this study predicted that there would be differences in

coactivation as a function of trial-type and genotype. In terms of trial-type, we predicted that

BA 10 would coactivate more with other frontal regions during the PM trials compared to the

ongoing trials. In support of the fronto-parietal hypothesis, we predicted that more fronto-

parietal coactivation would occur during the PM trials compared to the ongoing trials. In

terms of genotype, based on the PASA and HAROLD models of cognitive aging and the

possibility that harbouring an ε4 allele accelerates cognitive aging, we hypothesised that there

would be an increase in bilateral frontal coactivation in middle-aged ε4+s compared to

middle-aged ε4-s.

In addition, this study predicted that there would be coactivation differences as a

function of genotype-specific age differences. More specifically, we predicted that the

increase in bilateral frontal coactivation as a function of age would be more significant in

ε4+s compared to ε4-s because of ε4s role as an antagonistic pleiotropic.

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2. Methods

2.1 Participants

98 “younger” adults and 93 “middle-age” adults were recruited via SONA (The

University of Sussex’s online participant-recruitment service) or local adverts. Before

participating, all participants provided written consent in accordance with the ethics

procedures set out by the University of Sussex Schools of Psychology and the Life Sciences

Research Ethics Committee. All volunteers were informed that they could withdraw from the

study at any point and that all data would be kept anonymous and confidential. Volunteers

were excluded on the basis of untreated high blood pressure, cardiac pathology, a history of

psychiatric or neurological illness, pregnancy and presence of metallic implants including

bridges or braces, or tattoos above the shoulder – making them unsuitable for the fMRI

scanner. In addition, participants were excluded on the basis of smoking behaviour; this is

because the final two sessions (out of three) of the study involved receiving a nicotine or

placebo nasal spray. The sessions involving nicotine nasal spray are not analysed here.

Volunteers’ medical and psychiatric histories were assessed by self-report

questionnaires. Blood pressure and BMI were measured by the experimenter prior to the first

session. In order to determine participants’ APOE genotypes, cheek swab DNA-samples were

collected from every participant. KBiosciences then carried out the DNA analysis and

determined APOE genotypes. Participants with at least one APOE ε2 allele were excluded

from the study because research has shown that harbouring the ε2 allele might improve

cognition, which would therefore reduce the reliability of the results (Bloss, Delis, Salmon &

Bondi, 2010). After the selection procedure, 20 middle-age adults and 20 younger adults

were identified as “ε4-“ (ε3 homozygous) and 21 middle-age adults and 20 younger adults

were identified as “ε4+” (either ε4 homozygous or ε4/ ε3 heterozygous). The final sample of

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middle-age adults contained 18 males and 23 females between the ages of 43-57 (M = 49.95

years, SD = 4.2). The final sample of younger adults contained 13 males and 27 females

between the ages of 19 and 24 (M = 20.18 years, SD = 1.88). Participants that dropped out of

the study prior to completion were excluded from the analysis. The study was conducted

under double blind procedures (i.e., neither the participants nor the researchers knew the

genotype group allocation) – a triangulation process prevented participants and researchers

from determining participant genotypes.

2.2 Experimental task

The computerised PM card-sorting task was first developed by Rusted et al. (2009) as

a laboratory-based measure of event-related PM made of everyday stimuli. The task was

created using MATLAB and involved two parts: the ongoing task and the PM task, which is

not explained to the participant until after he/she was familiarised with the ongoing task. The

ongoing task involved sorting computerised images of 52 regular playing cards. Using a 4

button-box in their right hand, volunteers were instructed to press button 1 for HEART cards,

button 2 for SPADE cards (‘sort’ trials), and to make no response to CLUBS or DIAMONDS

(‘withhold’ trials). Volunteers were also instructed to respond as quickly as possible. Before

entering the fMRI scanner, participants were given the opportunity to practice the ongoing

task and are introduced to the PM task. For the PM task, participants were asked to press

button 3 for any occurrences of the number 7 card independent of suit. Whilst in the scanner,

participants performed the ongoing task as practiced outside of the scanner but with the

additional PM instruction.

During the card-sorting task, card faces were displayed for 1s, after which the card

back was displayed for 2s plus a variable jitter between .1 and 1s (M = .5 s, SD = .24 s).

During each of the 3 scanning-sessions, volunteers sorted 8 decks of cards with the PM

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intention over a total period of about 15 minutes. Per session, there was a total of 416 trials

(32 were PM trials, 192 were sort trials and 192 were withhold trials). In order to maximise

the estimate-ability of each event-type, ensure a delayed onset of PM-events, and ensure

minimum separation between PM-events, card order was pseudo-randomised (Friston,

Phillips, Chawla, &, Buchel, 1999). This involved randomising the task within the following

parameters: (1) never having a PM card occur in the first 7 cards of the entire sequence, (2)

always having at least 3 intervening cards between PM events and (3) having 1 PM trial (out

of 4 PM trials per deck) occur once per quarter-deck (13 cards). Accuracy and reaction times

for ongoing and PM trials were recorded for another behavioural analysis (not reported here).

2.3. Design

This study was a mixed-subject design. The first independent variable was trial-type

(two levels; repeated-measures): PM trials and ongoing card-sort trials. The second

independent variable was genotype (two levels; independent-measures): ε4-s and ε4+s.

Lastly, the third independent variable was age (two levels; independent-measures): middle-

age adults and younger adults. The dependent variable was activation (measured by the

BOLD response) that covaried with brain activity in the seed region during PM trials

(compared to ongoing card-sort trials) and as a function of the other independent variables.

Coactivation was measured by PPI analyses that included three main seed regions: lateral

rostral PFC (BA 10), the Left Frontal Cortex (which included both the Left Superior Frontal

Cortex (lSupFrontal) and the Left Inferior Frontal Cortex (lInfFrontal)) and the Right Inferior

Parietal Cortex (rInfParietal).

2.4. Procedure

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The participants that were selected to participate in this three-session study provided

informed consent in accordance with University of Sussex Schools of Psychology and the

Life Sciences Research Ethics Committee. During the first session, participants completed

various pen and paper tasks (e.g., the National Adult Reading Test) and provided their family

histories (i.e., prevalence of dementia and mental illness) for baseline measures that will not

be discussed in this paper. In addition, participants were given the opportunity to practice the

computer card-sort task outside of the scanner without the PM trials. During the second and

third sessions, participants were again given the opportunity to practice the card-sort task

outside of the scanner without the PM trials. Then, all of the participants self-administered

either a nicotinic nasal spray or placebo (NB half were given nicotinic nasal spray during

session 2 and the other half during session 3). In addition to genotype, the type of nasal spray

administered also followed double-blind procedures. 18-20 minutes after administering the

nasal spray, participants performed the card-sort task with the PM trials inside of the fMRI

scanner. Only data from the placebo sessions were analysed in this paper. It is important to

note, performance and reaction time did not differ significantly between the two final

sessions; thus, although half of the results discussed were from the third rather than the

second session, there was no evidence of practice effects. After the final session, participants

were debriefed and compensated for their time.

2.5 fMRI recording and analysis

fMRI datasets sensitive to BOLD contrast were acquired at 1.5 T (Siemens Avanto).

The BOLD responses acquired were from the overcompensation phase of the hemodynamic

response function— the point when blood flow increases to compensate for oxygen loss after

neuronal activity occurs (Ward, 2010). Thus, although the fMRI analysis provided high

spatial accuracy, when neuronal activity actually occurred was a few seconds after the

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BOLD. To minimise signal artefacts originating from the sinuses, axial slices were tilted 30

from inter-commissural plane. Thirty-six 3 mm slices (0.75 mm inter-slice gap) were

acquired with an in-plane resolution of 3 mm × 3 mm (TR = 3300 ms per volume, TE = 50

ms). Images were pre-processed using SPM8 (Ashburner et al. 2012). Raw T2 volumes were

spatially realigned and unwarped, spatially normalised to standard space and smoothed (8

mm kernel).

2.5.1 The PPI Analysis. A Psychophysiological Interaction Analysis (PPI) was used

to measure changes in coactivation (as measured by the BOLD response) between a “seed

region” and all other regions of the brain as a result of the study’s independent variables

(O’Reilly et al., 2012). A “seed region” was defined as a 5 mm (radius) sphere centred on the

coordinates of peak activation. Peak activations were taken from a previous event-related

fMRI analysis of the dataset. Three seed regions were chosen that have been consistently

implicated in PM: the lateral rostral PFC (BA 10), the Left Frontal Cortex (lSupFrontal and

the lInfFrontal) and the Right Inferior Parietal Cortex (rInfParietal) (Burgess et al., 2011;

Rusted et al., 2011). The first step of the PPI analysis involved calculating the PPI regressor.

The PPI regressor is a product of the BOLD response (the physiological factor) and task

condition of interest (the psychological factor; i.e., PM trials contrasted against ongoing

trials) at the seed region. The regressor therefore contains information from the time-course

of neuronal activity at the seed region specific to the task condition of interest. The second

step of the analysis involved including the PPI regressor in a new design matrix. This design

matrix also included regressors for the physiological and psychological factors. Including

these factors is important so as to account for the main effects of task and physiological

correlation. Thus, we could be confident that coactivation attributed to the PPI regressor was

specific to the product of the physiological and psychological factors. Only trials where

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correct responses were made were included in the PPI analysis. In order to determine the

effect of genotype on coactivity (for middle-age adults only), genotype (ε4+s and ε4-s) was

entered into a second-level full factorial model. In order to genotype-specific effect of age on

coactivity, genotype (ε4+s and ε4-s) and age (middle-age and younger adults) were entered

into a separate model.

The majority of analyses are presented at a threshold uncorrected for family-wise

error at p < .001. The exception was the computation of genotype-specific age differences: in

order to identify the genotype-specific effects of cognitive aging, exclusive masks were

applied at a threshold uncorrected for family-wise error at p < .05. The contrasts compared

young adults and middle-aged adults for each genotype. The contrasts were subsequently

masked against each other in order to identify age-related changes exclusive to each

genotype. A 50-voxel threshold was also applied. Regions of interest were defined using the

Wake Forest University PickAtlas. Anatomical localisation of clusters was performed using

the Talairach Daemon (University of Texas, USA), and the anatomy toolbox for SPM

(Eickhoff et al, 2005).

3. Results

3.1. Seed Region: BA 10

3.1.1. Average Effect of Condition in the Middle-age Cohort

Figure 1. BA 10 (seed) average effect of condition indicated by increased coactivation with

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the right and left caudate nucleus during PM trials.

Significantly increased brain coactivity was found in the right and left caudate

nucleus, when BA 10 was the seed region, during PM trials compared to ongoing trials

(cluster k = 506, Peak MNI: x = 12, y = 14, z = 3, p < .001 unc.). Thus, there was an average

effect of condition on BA 10 coactivity.

Figure 2. BA 10 (seed) average effect of condition indicated by a significant increase in

coactivation with the right cuneus during PM trials.

In addition, there was significantly increased brain coactivity in the right cuneus with

the BA 10 region (the seed region) during PM trials compared to ongoing trials (cluster k =

109, Peak MNI: x = 13, y = 78, z = 31, p < .001 unc.).

Figure 3. BA 10 (seed) average effect of condition indicated by a significant increase in

coactivation with the left anterior cingulate gyrus during PM trials.

There was significantly increased brain coactivity in the left anterior cingulate with

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the BA 10 region (the seed region) during PM trials compared to ongoing trials (see Figure 3)

(cluster k = 79, Peak MNI: x = 0, y = 32, z = 17, p < .001, unc.).

3.1.2. Main Effect Genotype in the Middle-age Cohort. Since there was no

significantly increased or decreased BA 10 coactivation in ε4+s compared to ε4-s, no main

effect of genotype on BA 10 coactivation was observed.

3.2. Seed Region: Left Superior Frontal Cortex (lSupFrontal)

3.2.1. Average Effect of Condition in the Middle-age Cohort

Figure 4. lSupFrontal (seed) average effect of condition indicated by a significant increase in

coactivation with the left postcentral gyrus during PM trials.

A significant difference in brain coactivity was found in the left postcentral gyrus

with the lSupFrontal region (seed region) during PM trials compared to ongoing trials (cluster

k = 71, Peak MNI: x = -50, y = -16, z = 52, p < .001, unc). Thus, there was a significant

average effect of condition on lSupFrontal (seed region) coactivity.

3.2.2. Main Effect of Genotype in the Middle-age Cohort

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Figure 5. lSupFrontal (seed) main effect of genotype indicated by a significant difference in

coactivation with the right superior frontal gyrus as a function of genotype.

There was a significant increase in brain coactivity in the right superior frontal gyrus

with the lSupFrontal region (the seed region) for ε4+s compared to ε4-s (see Figure 5)

(cluster k = 51, Peak MNI: x = 14, y = 54, z = 36, p < .001, unc.). Thus, there was a main

effect of genotype on lSupFrontal coactivity.

Figure 6. Parameter estimates for the main effect

of genotype on lSupFrontal (seed) coactivation with the right superior frontal gyrus. The red

bars represent 90% confidence intervals.

The above contrast (Figure 6) indicates increased coactivation in the right superior

frontal region with the lSupFrontal region (the seed region) for ε4+s compared to ε4-s.

Genotypeε4+

Contrast estimates at [14, 54, 36]

ε4-

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Figure 7. lSupFrontal (seed) main effect of genotype (ε4+s vs. ε4-s) indicated by a significant

difference in coactivation with areas 3a and 4p as a function of genotype.

There was a significant increase in coactivation in areas 3a and 4p with the

lSupFrontal region (the seed region) for ε4+s compared to ε4-s (see Figure 7) (cluster k = 57,

Peak MNI = x= 43, y= -9, z= 34, p < .001, unc.).

Figure 8. Parameter estimates for the main effect of genotype on

lSupFrontal (seed) coactivation with areas 3a and 4p in ε4+s compared to ε4-s. The red bars

represent 90% confidence intervals.

The above contrast (Figure 8) indicates increased coactivation in area 3a and 4p with

the lSupFrontal region (the seed region) for ε4+s compared to ε4-s .

ε4+ε4-Genotype

Contrast estimates at [43, -9, 34]]

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3.3. Seed Region: Right Inferior Parietal Cortex (rInfParietal)

3.3.1. Average Effect of Condition and Main Effect of Genotype in the Middle-

age Cohort. No main effect of condition (PM vs. ongoing) or genotype was observed; thus,

there was no significant increase or decrease in rInfParietal coactivation during the PM trials

compared to the ongoing trials. Also, there was no significant increased or decreased

rInfParietal coactivation in ε4+s compared to ε4-s.

3.3.2. Genotype-specific Age Difference. In addition to no main effect of genotype,

there were no genotype-specific age differences. Thus, there was no significant increase or

decrease in rInfParietal coactivation as a function age (middle-age vs. younger adults) for a

specific genotype (ε4-s vs. ε4+s).

3.4. Seed Region: Left Inferior Frontal Cortex (lInfFrontal)

3.4.1. Age x Genotype Interaction (including the younger and middle-age cohort)

Figure 9. lInfFrontal (seed) genotype-specific coactivity difference as a function of age with

the right and left superior medial gyrus.

Figure 9 indicates that there was a significant genotype-specific age difference in

lInfFrontal (seed) coactivation with the right and left superior medial gyrus. (cluster k = 95,

Peak MNI = x= 8, y= 25, z= 46, p < .001).


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Figure 10. Parameter estimates for the genotype-specific effect of age on lInfFrontal (seed)

coactivity with the right and left superior medial gyrus. Red bars indicate 90% confidence

intervals.

The above contrast (Figure 10) indicates increased coactivation of the right and left

superior medial gyrus with the lInfFrontal (seed) region (the seed region) for middle-aged

ε4+s (“Mid ε4+”) compared to younger ε4+s (“Young ε4+”), when masked by the contrast,

middle-aged ε4-s (“Mid ε4-”) > younger ε4-s (“Young ε4-”).

Figure 11. lInfFrontal (seed) genotype-specific coactivity difference as a function of age

with the right superior orbital gyrus.

In addition, there was a significant genotype-specific age difference in lInfFrontal

(seed) coactivation with the right superior orbital gyrus (see Figure 11) (Cluster k = 56, Peak


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MNI = x= 25, y= 41, z= -9, p < .001, unc.).

Figure 12. . Parameter estimates for the genotype-specific effect of age on lInfFrontal (seed)

coactivity with the right superior orbital gyrus. Red bars indicate 90% confidence intervals.

The above contrast (Figure 12) indicates increased coactivation in the right superior

orbital gyrus with the lInfFrontal region (the seed region) for middle-aged ε4+s (“Mid ε4+”)

compared to younger ε4+s (“Young ε4+”), when masked by the contrast, middle-aged ε4-s

(“Mid ε4-”) > younger ε4-s (“Young ε4-”).

4. Discussion

4.1. Summary of Findings

The majority of the results supported the researcher’s predictions. More specifically,

the BA 10 seed region coactivated significantly more with several frontal regions (right and

left caudate nucleus, right cuneus and left anterior cingulate gyrus) for PM vs. ongoing trials.

In addition, the lSupFrontal seed region coactivated significantly more with the left post

central gyrus and right superior frontal gyrus during PM trials compared to ongoing trials. In

addition, there was decreased lateralisation in frontal region coactivation for ε4+s compared

Contrast estimates at [25,41,-9]

Age and Genotype

Young ε4- Mid ε4-Young ε4+ Mid ε4+

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to ε4-s within the middle-aged cohort (i.e. coactivity with the right frontal and parietal gyri).

More specifically, the lSupFrontal seed region coactivated significantly more with the

rSupFrontal gyrus and areas 3a and 4p. Finally, there was a significant increase in frontal

coactivity (when lInfFrontal was the seed region) with the right and left superior medial gyrus

and the right superior orbital gyrus as a function of genotype-specific age differences in the

middle-age ε4+ cohort compared to the younger ε4+s. The one finding that deviated from the

current study’s predictions was the rInfParietal lobe as the seed region: the rInfParietal lobe

did not significantly coactivate with any other brain areas as a function of trial-type, genotype

or a genotype-specific effect of age. Potential explanations for this absence of brain coactivity

will be discussed.

4.2 Analysis of Findings: in light of PM Theories

4.2.1. Seed Region: BA 10. Although there was no main effect of genotype when BA

10 was the seed region, there was a prominent main effect of trial-type. More specifically,

there was increased BA 10 coactivity during PM trials (compared to ongoing trials) in the

right and left caudate nucleus, the right cuneus and the left anterior cingulate. These results

support Burgess’s Gateway Hypothesis, which suggests that BA 10 plays an executive role

during PM trials. It is important to note that BA 10’s increased coactivity with the anterior

cingulate, specifically, is widely supported by past PM imaging research (Burgess et al.,

2011; Hashimoto, Umeda & Kojima, 2011). Hashimoto et al. (2011) found that increased

anterior cingulate activation (or coactivation) during PM tasks was associated with increased

attentiveness. With regards to Rusted et al.’s (2009) card-sorting task, increased vigilance is

reflected in the main effect found for condition.

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4.2.2. Seed Regions: lSupFrontal and rInfParietal. The increased brain coactivity

as a function of trial-type in the left post central gyrus (i.e., a notable parietal region which

makes up the primary somatosensory cortex) when lSupFrontal was the seed region suggests

that other frontal regions, in addition to BA 10, play a central role during PM trials. In

addition, the increased brain coactivity between frontal and parietal regions supports the

frontal-parietal hypothesis, which contends that during a PM task, parietal regions work with

frontal regions in order to carry out the more executive tasks associated with PM. The lack of

increased brain coactivity as a function of trial-type when rInfParietal was the seed region

does not discredit the fronto-parietal hypothesis; rather, the results suggest that frontal-

parietal coactivity during PM may only occur between specific frontal and parietal regions.

4.3. Analysis of Findings: in light of Cognitive aging Theories and AD

4.3.1. Seed Region: lSupFrontal. The left superior frontal cortex (lSupFrontal) was

the only seed region with increased brain coactivity as a function of genotype. More

specifically, there was increased coactivity with the rSupFrontal gyrus and areas 3a and 4p in

ε4+s compared to ε4-s in the middle-aged cohort. This result supports Greenwood et al.’s

(2000) finding that the effect of cue validity was greatest for middle-age ε4+s (compared to

ε4-s)—a function of weaker executive control in middle-age ε4+s. Even though middle-age

ε4+s and ε4-s performed similarly on the card-sorting task —a task that required executive

control to switch attention from ongoing to PM trials— this study supports Greenwood et

al.’s (2000) findings because the increased bilateral brain coactivity in middle-age ε4+s

suggests that they exerted more effort to perform as well as the ε4-s.

4.3.3. Seed Region: lInfFrontal. In order to determine the genotype-specific effect of

age on coactivity in the brain, a final analysis was completed that included younger adults.

24


The analysis concluded that brain coactivity increased as a function of age only for ε4+s.

More specifically, there was increased lInfFrontal coactivation in the right and left superior

medial gyrus and the right superior orbital gyrus in middle-age ε4+s vs. younger ε4+s whilst

no difference between age groups was found for ε4-s. .

In the context of Filippini’s (2011) study, which found that cognitive aging was

associated with decreased brain activity in older ε4+s and increased activity in ε4-s, this study

found a more complex effect. The contrasting findings can be attributed to the fact that

Filippini (2011) included an older cohort that was comprised of middle-age and older adults

whilst the current study only focused on middle-age ε4+s and ε4-s. More specifically, our

findings suggest that after ε4+s period of enhanced cognition during younger adulthood and

before ε4+s significant decrease in cerebral activity during older adulthood, there is a short

period of increased coactivation during middle-age, which mimics the compensation and

dedifferentiation processes of older non-carriers (Parker & Reuter-Lorenz, 2009; Han et al.,

2009).

In the context of the PASA model of cognitive aging, the increased coactivation in

lInfFrontal—a form of compensation in response to neural structure and function—suggests

earlier cognitive aging (Parker & Reuter-Lorenz, 2009). In light of the HAROLD model of

cognitive aging, the decrease in lateralisation and the increase in activation clusters suggest

earlier cognitive aging in ε4+s (Cabeza, 2001). In terms of AD, the decrease in lateralisation

may also be a form of compensation for preclinical declines in PM (Bondi et al., 2006; Han et

al., 2009).

4.3.2. Seed Region: rInfParietal. The lack of increased brain coactivity in middle-age

ε4+s compared to ε4-s when rInfParietal was the seed region does not discredit the theory

that the ε4+ allele causes accelerated cognitive aging. Instead, the finding can attributed to

25


past research that has found that atrophy in the inferior parietal lobe occurred alongside

cognitive decline rather than before it (Jacobs et al., 2011). Thus, increased recruitment is

more necessary in frontal regions as a form of cognitive age-related compensation before the

onset of cognitive-decline.

4.4. Limitations

It is important to note that our data was not corrected for family-wise error (FWE)

because doing so would have eliminated all significant results. Although conservative

parameters were applied (p < .001 and a 50-voxel minimum), Bennett, Wolford & Miller

(2009) have contended that the legitimacy of uncorrected data (i.e., the true likelihood of

false positives) cannot be determined until the results are replicated. In addition, Bennett al.

(2009) noted that within the current model of publication (where for the most part, only

significant results are published) false positives are not easily correctible (i.e., if a group of

researchers fail to reproduce the results of a published study, the null findings would be

difficult to share).

4.5. Future Research

Based on past research, it is possible that the significant increase in frontal

recruitment in middle-age ε4+s (compared to young ε4+s and mid ε4-s) may have been a

function of a reduction in cortical choline acetyltransferase (ChAT) activity in the frontal

cortex. More specifically, the possession of at least one APOE e4 allele has been linked to a

reduction in ChAT activity in the hippocampus (Poirier et al., 1995). In terms of the frontal

cortex and AD specifically, the loss of ChAT was recently found in the superior frontal

cortex of patients with AD (Ikonomovic et al., 2007). Thus, perhaps in future research, the

26


relationship between a reduction in ChAT activity in frontal regions and increased brain

coactivity as a function of genotype and age should be looked at.

In regards to the current study’s focus on the relationship between brain coactivity,

age and genotype, it would be interesting to see whether ε4+s with the greatest coactivation

patterns in middle-age are more likely to experience significant clinical decline (e.g., to MCI

and/or AD) when followed longitudinally.

4.6. Conclusions

In terms of AD research, the most significant finding from this study was that bi-

lateral coactivity in frontal regions increased in middle-age ε4+s compared to young ε4+s,

when masked by ε4-s mids > ε4-s youngs, which suggests that the antagonistic pleiotropic ε4

allele accelerates cognitive aging. Whilst this result verifies previous studies that have found

similar results, how this finding contributes to the AD literature remains unclear (Marchant et

al., 2010). In order to determine how this finding would correspond to the likelihood of

developing AD, researchers would need to conduct a longitudinal study that looked at

coactivation patterns as a function of age, genotype and AD conversion.

27


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6. Appendix I

Ethical approval screen-shot:

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