I The Brain Basis of Executive Dysfunction in Older People Living with HIV

I

The Brain Basis of Executive Dysfunction in Older People Living with HIV:

Insights from Behavioral and EEG Responses during the Simon Task

Chien-Ming Chen

Integrated Program in Neuroscience

McGill University, Montreal

August 2017

A thesis submitted to McGill University in partial fulfillment of the requirements

of the degree of Master of Science

© Chien-Ming Chen 2017

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Table of Contents

ABSTRACT .............................................................................................................................IV

RÉSUMÉ .................................................................................................................................VI

ACKNOWLEDGMENTS ....................................................................................................VIII

PREFACE: CONTRIBUTION OF THE STUDENT ....................................................... IX

1. INTRODUCTION............................................................................................................. 1

1.1 How does HIV infection affect cognitive function? ..................................................... 1

1.2 How does HIV affect the brain? ................................................................................... 3

1.2.1 Evidence from structural brain imaging ........................................................... 3

1.2.2 Evidence from functional brain imaging .......................................................... 4

1.2.3 Evidence from electroencephalography ............................................................ 6

1.3 What are the causes of cognitive and brain dysfunction in HIV? ................................ 9

1.3.1 Direct effects of HIV infection on the brain ...................................................... 9

1.3.2 Cardiovascular effects in older HIV+ populations ......................................... 10

1.4 The Simon task as a probe for executive dysfunction ................................................ 12

1.5 Specific aims and hypothesis ..................................................................................... 15

2. METHODS ...................................................................................................................... 18

2.1 Participants ................................................................................................................ 18

2.2 Procedures ................................................................................................................. 19

2.3 Cognitive Assessment ................................................................................................. 21

2.4 Simon task .................................................................................................................. 21

2.5 Analysis ...................................................................................................................... 23

2.5.1 Behavioral Analysis ........................................................................................ 23

2.5.2 Distributional Analysis ................................................................................... 23

2.5.3 EEG Recording and Analyses ......................................................................... 24

2.5.4 Statistical Analysis .......................................................................................... 25

3. RESULTS ......................................................................................................................... 28

3.1 Sample Characteristics .............................................................................................. 28

3.2 Behavioral results ...................................................................................................... 30

III

3.2.1 Relationship of Simon task performance and BCAM score ............................ 30

3.2.2 Distributional analyses ................................................................................... 34

3.2.3 Relationship of Simon task performance and nadir CD4 cell count .............. 39

3.2.4 Relationship of Simon task performance and CVD risk ................................. 42

3.3 ERP results ................................................................................................................. 44

3.3.1 Relationship of Simon task ERP and BCAM score ......................................... 47

3.3.3 Relationship of Simon task ERP and CVD risk............................................... 54

4.1 Executive impairment reflects generalized slowing of processing ............................ 58

4.1.1 Simon task ERP relationship with overall cognitive ability............................ 59

4.2 Contributors to executive dysfunction in HIV ............................................................ 60

4.2.1 HIV infection severity ..................................................................................... 60

4.2.2 CVD risk.......................................................................................................... 62

4.3 Strengths and Limitations .......................................................................................... 65

4.4 Conclusion ................................................................................................................. 67

REFERENCES ....................................................................................................................... 68

IV

ABSTRACT

Executive dysfunction can occur in people with human immunodeficiency virus (HIV),

even with well-controlled infection. The brain basis of this impairment, and its relationship

with other aspects of cognitive dysfunction remain unclear. The underlying pathophysiology

is also unknown, with potential contributions from direct HIV infection, comorbidities

common in those with HIV, and aging effects as people live longer with the virus. Here, we

assessed executive function with the Simon task, collecting behavioral and EEG data in 84

older people living with HIV, treated with combination antiretroviral therapy and without

frank dementia, drawn from the Positive Brain Health Now cohort. We asked whether poorer

performance reflected impulsive responding or impaired control. We also tested whether

these measures related to overall cognitive ability, measured by a brief neuropsychological

battery, and to clinical variables, including age, HIV infection severity and cardiovascular

risk. We found that poor performers on the Simon task showed a general processing slowing

pattern, and that performance correlated with global cognitive ability, arguing for diffuse

brain injury rather than localized cortical or sub-cortical dysfunction. Poor performers also

had smaller amplitude event-related potentials (ERP). The severity of initial HIV infection or

current HIV control did not predict Simon task impairment, but those with more

cardiovascular risk factors performed more poorly and showed smaller amplitude ERP. This

study supports the hypothesis that executive dysfunction in older people with systemically-

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controlled HIV infection is one facet of diffuse brain dysfunction. This relates more to age

and other cardiovascular risk factors than to ongoing HIV effects in these cART-treated

patients, arguing that preventing or treating cognitive dysfunction will require shifting the

focus to comorbidities with a negative impact on the brain.

VI

RÉSUMÉ

Les personnes vivant avec le virus de l’immunodéficience humaine (VIH) peuvent souffrir

une atteinte aux fonctions exécutives, même lorsque l’infection est bien contrôlée. Cependant,

les fondements neuronaux de ce trouble ainsi que sa relation aux autres aspects du

disfonctionnement cognitif demeurent inconnus. On ignore également la pathophysiologie de

ces troubles exécutifs, qui est potentiellement attribuable à l’infection au VIH en tant que telle,

à certaines comorbidités communément rapportées chez le gens vivant avec le VIH, et au

vieillissement, puisque les patients vivent de plus en plus longtemps avec le virus. Ici, nous

utilisons la tâche de Simon pour évaluer les fonctions cognitives de 84 participants vivants avec

le VIH sous multi-thérapie antirétrovirale issus de la cohorte « Pour un cerveau en santé», alors

que leur activité cérébrale est mesurée par électroencéphalographie. Nous cherchions d’abord

à savoir si une faible performance reflète une plus grande impulsivité de réponse, ou une faible

capacité de contrôle. Nous avons également testé si ces mesures sont liées aux capacités

cognitives générales, mesurées à l’aide d’une brève batterie de tests neuropsychologiques, et à

d’autres variables d’intérêt clinique comprenant l’âge, la sévérité de l’infection au VIH et le

risque cardiovasculaire. Nos résultats montrent que les participants qui ont moins bien

performés à la tâche de Simon présentent un ralentissement généralisé de traitement neuronal,

et que la performance à cette tâche est corrélée avec les capacités cognitives globales, suggérant

la présence de dommages diffus au cerveau plutôt qu’une atteinte corticale ou sous-corticale

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locale. Une faible performance s’accompagne aussi de potentiels évoqués (PE) de plus faible

amplitude. Les résultats obtenus à la tâche de Simon ne sont pas liés à la gravité de l’infection

au VIH initiale ni actuelle, mais les participants avec un risque cardiovasculaire plus élevée ont

obtenu de moins bonnes performances ainsi que des PE de plus faible amplitude. Cette étude

supporte l’hypothèse selon laquelle les troubles de fonctions exécutives chez les gens vivants

avec le VIH constituent un aspect d’une atteinte diffuse au cerveau, qui se rapporte davantage

au risque cardiovasculaire et à l’âge qu’à l’infection au VIH en tant que telle chez ces patients

traités par multi-thérapie antirétrovirale. Ceci suggère que la prévention et le traitement des

troubles cognitifs devront être orientés vers certaines comorbidités de la maladie ayant un

impact négatif sur le cerveau.

VIII

ACKNOWLEDGMENTS

This project was supported by a CIHR Team Grant (TCO-125272) and the CIHR

Canadian HIV Trials Network (CTN 273). I thank the study participants for their

commitment. I would also like to thank the members of the lab: Ana, Christine and Marcus

for their contributions to this project, and Gabriel for kindly translating the thesis abstract,

and other lab mates, pass and present, Gloria, Mattias, Avi, Alison for exchanges of

knowledge and good times. Finally, I thank my supervisor, Lesley K Fellows.

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PREFACE: CONTRIBUTION OF THE STUDENT

Chien-Ming Chen (Thesis candidate): I contributed to conducting the experiment and to

refining and carrying out the behavioral and EEG data analysis. I was involved in collecting

the primary behavioral and EEG data. I contributed to developing the EEG data processing

pipeline, and implemented it in my dataset. I processed the event-related potential data and

carried out the regression analyses. I was also responsible for the literature review, for

refining the research questions, and for writing this thesis.

Lesley K Fellows (Supervisor): Dr. Fellows is the Principal Investigator on the Positive Brain

Health Now project. She designed the experiment, together with other investigators in the

Brain Health Now team. She supervised my research, providing input into details of study

design and implementation, analysis and interpretation of the data. She reviewed this thesis,

and provided critical feedback in terms of scientific content and writing style.

Ana Lucia Fernandez Cruz (PhD student): Ana worked together with me to conduct the

experiment, including the primary data collection and the EEG processing.

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Christine Déry (Lab coordinator): Christine provided help in recruitment of the participants

and in data collection.

Brain Health Now Team: This project was a sub-study of a large cohort study, the Positive

Brain Health Now project. Demographic and clinical information were collected within the

larger project by research assistants at the clinical study sites, and made available to me as

needed for my regression analyses.

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1. INTRODUCTION

With the development of combination antiretroviral treatment (cART), people with

human immunodeficiency virus (HIV) infection now have a life expectancy that is near

normal (Kirk & Goetz, 2009). However, up to 50 percent of patients show some degree of

cognitive impairment, as measured by neuropsychological tests, even when plasma viral load

is fully suppressed (Heaton et al., 2010). This impairment is usually mild (i.e. dementia is

rare now), but can have functional impact: for example, studies have found that patients with

poorer performance in executive tasks also have everyday function impairment (Heaton et al.,

2004) and poorer quality of life (Scott et al., 2011; Tozzi et al., 2003). Given the prevalence

and impact of cognitive impairment, it is important to better understand the causes.

Suppressing HIV in plasma, alone, may not be sufficient to address this problem in

chronically-infected patients.

1.1 How does HIV infection affect cognitive function?

There are different ideas about whether brain dysfunction in HIV reflects a generalized

pathological process, or is predominately due to dysfunction of specific structures (i.e.

subcortical injury to the basal ganglia or thalamus) or brain circuits (i.e. fronto-striatal loops).

The causes of this dysfunction are also unclear, as I will discuss further below.

A first step in understanding causes is to establish whether cognitive dysfunction

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follows specific, localizable patterns. There is evidence that various cognition domains are

affected in people living with HIV, including prominent trouble with executive function, for

example as tested by working memory or verbal fluency tasks, and with related processes

such as selective and sustained attention (see Grant, 2008 and Woods, et al., 2009 for review).

Early work prior to the advent of cART, when HIV was a nearly universally fatal

infection with dementia as a frequent feature, described HIV-associated dementia as a

‘subcortical’ or ‘fronto-subcortical’ dementia (Navia, Jordan, & Price, 1986). This was

supported by neuropathological studies suggesting that the virus may particularly affect basal

ganglia structures adjacent to the ventricles, as HIV may cross into the brain from the

cerebrospinal fluid (Aylward et al., 1993; Berger & Nath, 1997).

However, these patients can also show impairments in other domains, including

psychomotor slowing (poor performance of motor tasks) and sensory-perceptual impairments

(see Grant, 2008 for review). This has led to an alternative idea of “processing slowing” as a

common underlying cause for these difficulties, especially as they are often detected in

speeded tasks. One meta-analysis of 11 studies, focusing on reaction time (RT) performance

of people living with HIV showed that, on average, patients meeting diagnostic criteria for

AIDS were 22% slower than healthy people across all the examined RT tasks (Hardy &

Hinkin, 2002). It is notable that the putatively fronto-striatal deficits are most often detected

in speeded tests e.g. of working memory or attention (see Plessis et al., 2014 and Woods et

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al., 2009 for review). Also of note, a similar explanation has been proposed to explain the

cognitive changes seen in healthy aging (Salthouse, 1996). As we will see below, it has been

suggested that people with HIV infection may be experiencing accelerated aging.

1.2 How does HIV affect the brain?

1.2.1 Evidence from structural brain imaging

The evidence from neuroimaging studies carried out in the cART era argues that fronto-

striatal dysfunction is a specific case of more general impairment in brain network function in

HIV. For example, a recent study found that global brain atrophy (ie. the total volume of grey

and white matter) was associated with motor function and information processing in a sample

of 95 people living with HIV. The greater the atrophy score, the worse the task performance

(Janssen et al., 2015). Likewise, diffusion tensor imaging (DTI) studies have found that white

matter integrity is lower across the whole brain in HIV+ individuals including in those treated

with cART compared to healthy controls (Chen et al., 2009; Su, Caan, et al., 2016; Thurnher

et al., 2005). However, these studies have involved small samples, and did not relate the DTI

changes to cognition.

Studies using magnetic resonance imaging (MRI) to measure volumes of specific brain

regions have revealed that HIV patients have smaller volumes in many cortical (e.g. medial

frontal and posterior cortices) and subcortical (e.g. basal ganglia) regions, as well as of the

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white matter, compared to HIV- groups (reviewed in Masters & Ances, 2014). Individual

studies report specific regional effects, but these are not consistent across studies (reviewed in

Ances & Hammoud, 2014). Many studies have involved small samples, and have used

different structural MRI analysis methods, likely explaining some of this variability.

1.2.2 Evidence from functional brain imaging

There have been only a few studies using functional MRI (fMRI) to study executive

function in HIV. These studies have probed the possibility of fronto-striatal dysfunction, but

results are mixed. A meta-analysis was conducted involving 105 HIV+ and 102 healthy

controls in six studies (three using nonverbal attention tasks, one a letter N-back task, one

mental rotation and one semantic sequencing task) drawing on similar information processing

steps (selective attention to visual stimuli, retaining and manipulating relevant information).

They found the blood oxygen level-dependent (BOLD) signal was increased in left inferior

frontal gyrus and the left caudate in those with HIV infection. The authors proposed that

HIV-related inflammation reduces neural efficiency and results in compensatory neuronal

activation to meet task demands (Plessis et al., 2014). A second recent review of fMRI in

older people with HIV included 15 studies. Four studies focused on attention using a visual

attention task (moving ball paradigm), finding hyperactivation in the attention network (right

prefrontal and cingulate cortex) in HIV+ compare to healthy controls. Three studies used a

sequential number task to test working memory, finding an increase in activation in lateral

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prefrontal cortex and parietal regions in the HIV+ group compared to healthy controls. Two

studies investigated memory using encoding and recall tasks, both reported differences in

hippocampal and prefrontal regions in the HIV+ compared to the healthy group during

encoding, but one study found increased activation and the other reported reduced activation

in these areas during recognition (Hakkers et al., 2017).

Overall, these fMRI studies suggest a hyperactivation pattern in HIV+ compared to

healthy control groups, arguing for enhanced activity to offset underlying brain dysfunction.

However, Melrose et al. (2008) showed hypoactivation of left dorsolateral prefrontal cortex,

left caudate and bilateral ventral prefrontal cortex in 11 HIV+ participants compared to 11

healthy controls carrying out a picture sequencing task (Melrose et al., 2008), and Plessis et

al. (2015) found less activation in putamen in 18 cART-naïve HIV patients compared to 17

healthy controls during a stop-signal paradigm (Plessis et al., 2015).

These studies highlight a general challenge for interpreting task-based fMRI in clinical

samples. On the one hand, BOLD signal increases might reflect compensatory strategies, i.e.

engaging more of the brain to accomplish the task in the face of pathology (Cabeza,

Anderson, Locantore, & McIntosh, 2002). On the other, pathology might lead to decreased

BOLD signal, i.e. if there is loss of volume of a given brain region (Cabeza, 2002).

Nevertheless, the fMRI studies reviewed provide some support for the fronto-striatal

dysfunction hypothesis of executive dysfunction in HIV. Whether this is specific to fronto-

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striatal networks, or reflects a more general dysfunction remains unclear, as the fronto-striatal

localization in these studies using task-based fMRI is to be expected, given the tasks

involved.

FMRI BOLD signal can also provide information about how brain regional activity

covaries across brain regions, whether at rest or during task performance, using functional

connectivity analyses. Only a few studies have used this approach in HIV. One study found

that HIV+ patients showed lower resting-state functional connectivity between nodes within

and between the default mode network, cognitive control network and dorsal attention

network (Thomas et al., 2013). Other studies found lower resting-state functional

connectivity in cortico-striatal, including fronto-striatal regions(Ipser et al., 2015; Ortega,

Brier, & Ances, 2015). Thus, lower functional connectivity has been found in fronto-striatal

networks, but also in other brain networks in people with HIV.

1.2.3 Evidence from electroencephalography

Electroencephalography (EEG) is another method to study brain function. The excellent

temporal resolution of EEG could be particularly informative in testing the processing speed

account, but little work has been done in HIV using this method (see Fernández-Cruz &

Fellows, 2017, for a comprehensive review). As the present study focuses on event-related

potentials (ERP) in HIV, I will review that literature in HIV briefly here.

Studies of ERP in HIV to date have mainly assessed attention, measured with auditory

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oddball tasks, focusing specifically on the P3 ERP that is thought to reflect the ability to

discriminate stimuli (i.e. standard from oddball stimuli) in this task. Three studies found that,

compared to HIV- controls, people with HIV infection showed smaller P3 amplitude and

longer latency (Chao, Lindgren, Flenniken, & Weiner, 2004; Polich et al., 2000; Tartar et al.,

2004). Additionally, Polich et al. (2000) found that the P3 latency positively correlated with

current viral load, suggesting it reflected on-going viral or inflammatory effects. However,

the sample sizes were very small (n = 15 per group in Chao et al.’s study, 15 per group in

Polich et al.’s study and total n = 34 in Tartar et al.’s study) and the antiretroviral treatment

status of the patient groups was not reported in either of these studies, both of which predated

the widespread use of cART.

Two studies used a similar oddball paradigm but with visual rather than auditory

stimuli. They also found longer P3 latency and smaller P3 amplitude in HIV+ compare to

healthy controls (Bauer, 2011; Bauer & Shanley, 2006). The sample sizes were larger than in

the auditory oddball studies (total n = 170 in the first study and 165 in the second study). One

of these studies also reported that P3 latency was longer in the HIV+ individuals with higher

body mass index (Bauer, 2011). This result suggested that cardiovascular risk might

contribute to brain dysfunction in HIV; i.e. that injury might not be due (or only due) to direct

effects of HIV on neurons, but indirectly via ischemic injury from cerebrovascular

dysfunction (discussed further below). However, they did not relate ERP measures to other

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cardiovascular risk factors.

Two ERP studies focused on executive function. In a pre-cART era study, Nielsen-

Bohlman et al (2003) used a lexical decision task to test semantic processing and found that

those with HIV showed smaller N400 amplitude at electrodes Cz and Pz compared to healthy

controls. Furthermore, the N400 amplitude was associated with attention score measured with

a cognitive battery (Nielsen-Bohlman et al., 2003). Another study used a Stroop color-word

interference task to measure cognitive control in those with HIV. That study also examined

the effects of a family history of substance abuse. A decreased P3 amplitude was found in the

HIV+ group compared to healthy controls, in the absence of a family history of substance

abuse (Bauer, 2008).

Two recent EEG studies used emotion-attention tasks in women with HIV. McIntosh et

al. (2015) focused on attention to emotional stimuli, and found that HIV+ women showed

larger P2 amplitude and smaller late positive potential (LPP) then healthy controls,

suggesting early attention bias to negative stimuli and disrupted cognitive reappraisal of

emotion processing, which can be considered an element of executive function (McIntosh,

Tartar, Widmayer, & Rosselli, 2015). Tartar et al. (2014) used an affective priming paradigm

to study whether affective changes can alter cognitive processes. They found that the LPP,

reflecting attentional processing to emotionally-charged visual stimuli, showed reduced

amplitude compared to controls (Tartar et al., 2014).

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Together, these results suggest that ERP may capture relevant aspects of brain

dysfunction in people with HIV. It seems that those with HIV infection tend to have a

decreased amplitude of ERP indexing attention, working memory, or cognitive control.

However, given the small samples, heterogeneity in HIV treatment status, and variable tasks,

a clear picture of the ERP patterns and their relation to HIV variables and overall cognitive

function has yet to emerge.

1.3 What are the causes of cognitive and brain dysfunction in HIV?

Clarifying the patterns of brain dysfunction in people with HIV may shed light on the

underlying pathophysiology. Several causes are currently proposed. A recent review article

reported that common risk factors for cognitive impairment in HIV include older age, low

education, cardiovascular disease, depression, substance abuse and direct effects of HIV

infection on the brain (Tedaldi, Minniti, & Fischer, 2015).

1.3.1 Direct effects of HIV infection on the brain

There is evidence for direct effects of HIV infection on the brain in the pre-cART era.

For example, inflammation may cause astrocytosis and dysmyelination, which could in turn

cause cognitive impairment (see Merrill & Chen, 1991). Cytokines circulating in the

peripheral blood as a result of chronic infection may also lead to brain atrophy and cognitive

dysfunction (Cartier, Hartley, Dubois-Dauphin, & Krause, 2005). Lower nadir CD4 counts

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(indexing the worst condition of the immune system during HIV infection, typically before

treatment is initiated) have been associated with more cognitive dysfunction (Ellis et al.,

2011; Mccombe, Vivithanaporn, Gill, & Power, 2013; Muñoz-Moreno et al., 2008) and worse

cortical atrophy as measured by brain imaging (Ances & Hammoud, 2014). This suggests

that brain injury occurs at the time of intial, untreated infection. While further injury might be

arrested or slowed with cART, the pre-treatment brain changes might not be reversible.

There is also some evidence that direct effects of HIV on the brain may continue

despite cART; several antiretrovirals may not cross the blood-brain barrier, potentially

allowing viral “escape” in the central nervous system. For example, detectable HIV RNA in

cerebrospinal fluid was associated with less total white matter measured with structural MRI

in the CNS HIV Antiretroviral Therapy Effects Research (CHARTER) study which including

251 treated HIV+ individuals between 23 and 67 years old (Jernigan et al., 2011).

1.3.2 Cardiovascular effects in older HIV+ populations

Systemic inflammatory effects of HIV infection can affect blood vessel health, leading

to ischemia and small vessel dysfunction (Merrill & Chen, 1991). Cardiovascular disease

(CVD) risk factors, such as hypertension, diabetes and smoking may exacerbate vascular

dysfunction through the same or synergistic mechanisms (Baker & Duprez, 2010; Beeri,

Ravona-Springer, Silverman, & Haroutunian, 2009). With cART rendering HIV a chronic

disease, and given the higher rates of standard cardiovascular risk factors in HIV (due to

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lifestyle factors, and side effects of cART on metabolic processes leading to dyslipidemia)

(Martin-Iguacel, Llibre, & Friis-Moller, 2015), recent studies have begun to focus on the

effects of age and cardiovascular disease as relevant to overal health, as well as to brain

health, in the HIV+ population.

Folley et al (2010) found that cerebrovascular risk, measured by self-reported

questionnaire, was related to slower processing speed even after controlling for age (Foley et

al., 2010). With 292 participants across four countries, Jawaid et al. (2011) showed that prior

cardiovascular disease (CVD) was associated with neurocognitive impairment in middle-aged

(mean age 40 y) people with HIV, 90% of whom were taking cART (Jawaid et al., 2011).

Another study found that poorer neuropsychological performance in older HIV patients

(mean age = 55 y) was related to the presence of CVD risk factors, with poorer performance

in those with multiple risk factors (Nakamato et al., 2011).

CVD risk factors may be associated with sub-clinical small vessel brain ischemia,

indicated by white matter hyperintensities (WMH) (Debette et al., 2011). Indeed, recent

studies in HIV found that greater WMH volume was related to cardiovascular risk and global

cognitive impairment measured by neuropsychological tasks in older HIV+ patients (Su, et

al., 2016; Watson et al., 2017).

There is evidence that CVD risk factors and aging also affect cognition in otherwise

healthy (HIV-) people (for reviews, see Gorelick et al., 2011; Harada, Natelson, & Triebel,

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2013). The speed mediation hypothesis (Salthouse, 1996), suggests that processing speed

slows with age, so that older people need more time to execute cognitive tasks. Processing

speed declines with age, as does attention and visuospatial ability (Harada, Natelson, &

Triebel, 2013; Salthouse, 2013). Age also alters the brain structurally and functionally, as

measured by various brain imaging methods (see review by Damoiseaux, 2017).

This raises a general problem in understanding HIV-associated cognitive impairment:

how much of it is just age and accumulating “regular” comorbidity, and how much is special

to HIV? Do age and HIV have additive effects on the brain, or could there be synergistic

effects (i.e. an interaction—so-called accelerated aging) (Valcour, Paul, Neuhaus, & Shikuma,

2011; Wendelken & Valcour, 2012)?

1.4 The Simon task as a probe for executive dysfunction

Given that executive dysfunction is prominent in HIV, executive tasks should be useful

probes to investigate underlying brain mechanisms and the cause of any dysfunction. While

there are a variety of ways to decompose executive function, most agree that executive

function includes the ability to monitor ongoing performance and suppress inappropriate

responses (Miyake et al., 2000). The latter may be of particular interest in HIV, given the

literature specifically implicating frontal-striatal circuits in response inhibition (Aron et al.,

2007; Ridderinkhof et al., 2011). The Simon task, a task that induces response conflict, thus

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requiring inhibitory control, has the additional advantage that it has been related to a detailed

cognitive model, the so-called dual-route model (Ridderinkhof, 2002). According to this

model, in the Simon task the direct route contributes to response activation based on the

salience of the stimuli, reflecting bottom-up processing. The deliberate route contributes

activation based on task instruction, i.e. engaging top-down processing. Finally, selective

suppression is engaged when conflict occurs, reflecting inhibitory control.

Specific behavioral measures are thought to index these processes. For example,

activation of the direct route is indexed by fast responses, which occur before there is time to

build up selective suppression and the activation of the deliberate route. This leads to an

incorrect response in incongruent trials. On the other hand, in slow RT trials, selective

inhibition has time to be engaged and neutralize the incorrect response. Therefore, the

reduction of interference in later responses reflects the efficiency of cognitive control

(Ridderinkhof, 2002).

Specific EEG measures and brain regions have also been linked to these processes. The

N2 component, a negative wave peaking 200 ms after stimulus onset with frontocentral

distribution, is believed to be associated with conflict detection and monitoring (Folstein &

van Petten, 2008). Also, it is correlated with greater activation of dorsal anterior cingulate

cortex in incongruent trials measured with fMRI (Carter & van Veen, 2007; Mathalon,

Whitfield, & Ford, 2003). The P3 component is a broad positive wave, usually peaking 300

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ms after stimulus onset. It is believed to be related to allocation of attentional resources and

the efficiency of detecting and evaluating target stimuli (Polich, 2007, 2012). The P3

amplitude is smaller and latency is longer for incongruent compared to congruent trials in the

Simon task, suggesting that more attentional resources (and longer stimulus processing times)

are needed to resolve response conflict (Melara, Wang, Vu, & Proctor, 2008).

The Simon task has been used with and without EEG to assess executive function in

healthy populations. For example, it has been used to study aging effects (van der Lubbe &

Verleger, 2002) and the effects of bilingualism on cognitive control (Bialystok, Craik, Klein,

& Viswanathan, 2004; Kousaie & Phillips, 2012). It has also been applied in clinical

populations, showing differences between healthy controls and people in the prodromal stage

of Alzheimer`s disease (Cespon, Galdo-Alvarez, & Diaz, 2013), hepatic encephalopathy

(Schiff et al., 2014), attention-deficit hyperactivity disorder (Mullane, Corkum, Klein, &

McLaughlin, 2009) and Parkinson’s disease (Schmiedt-Fehr, Schwendemann, Herrmann, &

Basar-Eroglu, 2007). Of note, the specific behavioral patterns differ in some of these

populations, when considered within the dual-route model. For example, those with attention-

deficit disorder show enhanced response activation (Ridderinkhof, Scheres, Oosterlaan, &

Sergeant, 2005), while those with Parkinson’s disease show a selective deficit in the

engagement of cognitive control (Wylie, Ridderinkhof, Bashore, & van den Wildenberg,

2010).

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1.5 Specific aims and hypothesis

This study examined executive function in people over age 35 with chronic, cART-

treated HIV infection. The sample was drawn from a longitudinal cohort study of brain health

in older people living with HIV in Canada. Participants in two sub-studies of the effects of

cognitive training or exercise on cognition underwent behavioral assessment and EEG at

baseline and after these interventions. Here we report on the pre-intervention baseline

assessment. We administered the Simon task, assessing response conflict detection and

cognitive control, to probe executive function, and examined both behavioral and ERP

measures. We asked if behavioral and ERP measures relate to overall cognitive ability, as

assessed by a brief, more general set of computerized cognitive tests, and whether behavioral

or ERP measures from the Simon task were related to indicators of current or past HIV

severity, or to cardiovascular risk.

The specific aims of this study were:

1. To provide evidence that Simon task performance and ERP indices of conflict

detection and cognitive control relate to overall cognitive performance (assessed

by a battery of cognitive tests) in older people with HIV treated with cART.

2. To provide evidence that executive dysfunction measured with the Simon task

reflects impaired control, rather than enhanced impulsivity, in this sample.

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3. To explore the extent to which Simon task performance and EEG measures

relate to the severity of current or past HIV infection (nadir CD4, current

detectable plasma HIV) and to cardiovascular risk.

The ability to resolve response conflict in the Simon task depends on a frontostriatal

network, which is amongst the networks compromised in older HIV+ individuals. Therefore,

we hypothesized that Simon task behavioral and ERP measures would relate to overall

cognitive ability in older people with HIV infection.

Second, we hypothesized that weaker performance on the Simon task in this population

is due to reduced efficiency of cognitive control reflecting compromise of executive fronto-

striatal networks, rather than more impulsive responding related to a shift in response

threshold (as is seen, for example, in attention-deficit disorder). Thus, we predicted that those

with higher cognitive ability would be more efficient to solve response conflict (smaller

Simon effect) compared to those with lower cognitive ability, whereas the tendency towards

impulsive responding would be similar.

Finally, we hypothesized that both HIV infection severity and cardiovascular risk would

relate to Simon task performance and EEG metrics. In line with previous studies suggesting

that nadir CD4 cell count relates to cognitive performance and to loss of volume in cortex and

striatum, we predicted that those with a nadir CD4 cell count less than 200 cells/mL would

have poorer Simon task performance compared to those without this evidence of past severe

17

immunosuppression. Given existing reports that cardiovascular risk is related to slower

processing speed in people with HIV infection, we predicted that those with higher

cardiovascular risk, as indicated by Framingham risk score, would have poorer Simon task

performance.

18

2. METHODS

2.1 Participants

Eight-four HIV+ individuals participated in this study. All were drawn from two sub-

studies of the Positive Brain Health Now study, a longitudinal study of brain health in older

people living with HIV recruited at 5 sites across Canada (two in Montreal, two in Ontario,

one in Vancouver) (Mayo, Brouillette, & Fellows, 2016). Both sub-studies were testing non-

pharmacological interventions to improve cognition: computerized cognitive training, or

exercise (68 from cognitive training, 16 from exercise training). The present experiment

reports data from the baseline assessment for these two sub-studies.

The inclusion criteria for the main Brain Health Now study were: 1) age ≥ 35 y, 2)

HIV+ for a least 1 y, 3) able to communicate adequately in either French or English, 4) able

to give written informed consent. The exclusion criteria were: 1) dementia as defined by

Memorial Sloan Kettering (MSK) rating stage 3 or more- cognitive component only, 2)

concern about capacity to consent, 3) life expectancy of < 3 years or other personal factor

limiting the ability to participate in follow-up, 4) non-HIV-related neurological disorder

likely to affect cognition, 5) known active central nervous system opportunistic infection or

hepatitis C requiring interferon (IFN) treatment during the follow-up period, 6) psychotic

disorder, 7) current substance dependence or abuse (as per Diagnostic and Statistical Manual

of Mental Disorders, 4th Edition criteria) within the past 12 months (Mayo et al., 2016).

19

The additional inclusion criteria for the two sub-studies were: 1) evidence of cognitive

deficits (performance in the lower half of the distribution of the whole Positive Brain Health

Now sample on a short neuropsychological task battery, the Brief Cognitive Ability

Assessment (B-CAM)), 2) able to have convenient daily access to the Internet to participate

in the computerized training, or able to participate in a three-times a week in-gym exercise

program for the exercise study 3) stable medical condition, 4) have been on a stable highly

active anti-retroviral therapy (CART) regimen for > 6 months, 5) have not had a change in

medications that could potentially interfere with cognition in the past 4 months.

Participants in both sub-studies were followed at one of the two Montreal study sites

(MUHC Immunodeficiency Clinic, or Clinique médicale l’Actuel). A sample of 60

participants meeting these criteria was planned for the cognitive training sub-study, and 30

for the exercise sub-study. Finally, an additional 20 participants were recruited with higher

cognitive ability, i.e. from the upper half of the BCAM performance distribution, who

otherwise met criteria for the cognitive training sub-study, to better reflect the characteristics

of the sample as a whole, for the purposes of the present study, and a companion MRI study.

These 20 were offered the cognitive training intervention.

2.2 Procedures

For the Brain Health Now project, research assistants at each study site were

20

responsible for recruitment. The patient lists in the participating HIV clinics were pre-

screened to identify potentially eligible patients. If the patient met inclusion criteria, the

research assistant then approached the patient to explain the details of the study. After

informed consent, demographic and self-report data were collected. Selected questions and

clinical data were repeated on follow-up visits every 9 months. The BCAM was administered

by the research assistant at each visit (Mayo et al., 2016). The demographic and clinical data

for the current study were drawn from the nearest visit before the baseline assessment for

cognitive training and exercise training sub-studies.

Participants in the Brain Health Now study who met inclusion criteria and who agreed

to participate in the cognitive training and exercise sub-studies were invited to complete EEG

recordings and additional behavioral tests in our lab at baseline and after the 8 to 12-week

intervention was completed. All participants provided written informed consent. The protocol

was approved by the McGill University Health Centre Research Ethics Board.

Participants carried out the task battery while seated in a comfortable chair in a dimly

light, soundproof room. The task battery included the auditory oddball task, a feedback task,

the Simon task and 5 minutes of resting-state EEG recording. Here, we report on the Simon

task. The participant could rest between tasks if desired. The total experiment took about one

hour. The sequence of the tasks was randomly assigned except that the resting state recording

was always last.

21

2.3 Cognitive Assessment

A custom computerized cognitive battery, called the Brief Cognitive Ability Measure

(BCAM) was used to measure overall cognitive ability. This computerized test battery

includes cognitive tests and three self-report items (questions about cognitive performance

drawn from the Perceived Deficits Questionnaire (Sullivan, Edgley & Dehoux, 1990)). The

cognitive tests assessed performance in different domains, including processing speed

(simple reaction time task), memory (verbal recall task), working memory (shape 2-back

task), spatial working memory (Corsi blocks forward and backward) and executive function

(Flanker task, Trail-Making Task-B, phonemic verbal fluency, Tower of London). The final

selection of items and their scoring was based on Rasch analysis, a data-driven approach that

yields a single, ruler-like measurement reflecting global cognitive ability conceived of as a

unidimensional construct, suited to assess the range of cognitive ability expected in this

population (Brouillette et al., 2015; Koski et al., 2011).

2.4 Simon task

The Simon task was programmed using E-prime software (www.pstnet.com;

Psychology Software Tools, Inc.). The stimuli were squares and diamonds presented on a

black background either to the left or right of a fixation cross. Participants were required to

press the right button to respond to the square stimulus and the left to the diamond stimulus,

or vice versa, counter-balanced across the group. The stimuli were presented for 250 ms

22

followed by a fixation cross shown for a random duration between 1000-1500 ms (see Figure

1). Participants were instructed to respond quickly and accurately.

Before the experiment, the participant read a standard set of written instructions, and

performed 16 practice trials. The experiment consisted of 380 trials, half congruent and half

incongruent, presented in random order. On congruent trials, the stimulus that requires a right

button press (for example) appears on the right side of the screen, while on incongruent trials,

it appears on the left side, provoking a response conflict (i.e. between the spatial location-

triggered response mapping and the instructed stimulus-response mapping). The experiment

was divided into 4 blocks, each taking about 4 minutes, with the opportunity to rest between

blocks if desired.

Figure 1. Simon task showing the screen and response options. The stimuli were present

for 250ms with an inter-trial interval between 1000-1500 ms. The blue circle indicates an

example of a congruent trial and the pink circle indicates an example of an incongruent

trial.

23

2.5 Analysis

2.5.1 Behavioral Analysis

The reaction time (RT) and accuracy for both conditions, and the Simon effect

(incongruent RT - congruent RT and incongruent accuracy - congruent accuracy) were

averaged across left- and right-hand responses for each participant. The first two trials in each

block were considered as warm-up trials and excluded from the analysis. Trials with

premature responses (< 150 ms) or slower than three SD from the mean for each condition

were removed without replacement. This amounted to a mean of 4.0 (3.6) % of trials. Error

trials were also excluded from the RT and EEG analyses.

2.5.2 Distributional Analysis

Distributional analysis was also carried out for each participant followed procedures

similar to those in Ridderinkhof et al. (2005). RTs were ranked from fastest to slowest and

separated into four even quartiles, separately for incongruent and congruent trials. Mean RT

and accuracy rates were then calculated for each quartile. Accuracy rates were then plotted

against the average RT for each quartile to generate the conditional accuracy function (CAF)

plot. The mean Simon effect (RT) was computed for each quartile and plotted against the

average RT for each quartile, producing a so-called delta plot. Slopes were computed between

the Simon effect of each quartile (the data points of Quartile 1 and 2, Quartile 2 and 3,

Quartile 3 and 4).

24

2.5.3 EEG Recording and Analyses

EEG was recorded with a 256-channel HydroCel Geodesic Sensor Net (Electrical

Geodesics, Inc., Eugene, OR). Electrode impedance levels were kept below 50 kΩ. A

sampling rate of 1,000 Hz was applied and the Cz channel was used as a reference. Only

correct trials were included in the EEG analysis. The pre-processing steps followed the order

recommended by Luck (2014).

Brainstorm software was used for all ERP analyses (Tadel, Baillet, Mosher, Pantazis,

&Leahy, 2011) (http://neuroimage.usc.edu/brainstorm). Seventy-eight channels located over

the neck and cheek tended to be contaminated by muscle artifact and were excluded a priori.

The data were then band-pass filtered offline between 0.1 – 30 Hz and downsampled to 500

Hz. The data were then re-referenced to the average of the left and right mastoid. Eye

movement artifacts were identified using the automatic artifact algorithm in Brainstorm,

using the default parameters (frequency band as 1.5 to 15 Hz, the threshold as 2 times the SD,

the minimum duration between two events was 800ms). The detected blinks were then

removed using the Signal-Space Projection (SSP) algorithm in Brainstorm. Epochs extended

from -200 to 900 ms relative to the onset of the visual stimuli and were baseline-corrected

based on the average activity from − 200 to 0 msec. Epochs in which the activity exceeded ±

100 μV were excluded. A minimum of 30 artifact-free epochs was required in each condition

(congruent and incongruent) for a participant to be included in further analyses. The average

http://neuroimage.usc.edu/brainstorm

25

number of trials meeting these criteria was 109 (SD 34, range 42 to 174) and 102 (SD 38,

range 37 to 176) in the congruent and incongruent condition respectively, after preprocessing.

The ERP analysis began with defining the time windows of interest: N2 was set at 200

to 300 ms and P3 was set at 300 to 550 ms base on the grand average. For defining the

electrode clusters of interest, the Brainstorm toolbox was used to identify the difference

between high and low BCAM groups at the single channel level. The electrodes with

significant differences within the time windows after false discovery rate (FDR) correction

were selected as the clusters for each ERP component. Key analyses were also carried out

using single electrodes conventionally reported for the N2 (E021) and P3 (E101). Results

were similar to those obtained with the cluster approach, and are not reported further.

ERP amplitude was defined as the mean amplitude within the time window and within

the cluster, and the ERP latency was defined as the time of the most positive or negative peak

in each time window for each of the components (Luck, 2014). ANOVA was used to test for

the effects of trial type on ERP components in the Simon task.

2.5.4 Statistical Analysis

Statistical analyses were performed using IBM SPSS Statistics for Windows, version

22. ANOVA was used to test for the effects of trial type on RT and accuracy in the Simon

task. Multiple linear regression analysis was conducted to assess the influence of BCAM

score, age and education on Simon task performance (congruent and incongruent RT,

26

accuracy, and Simon effect (RT difference)) and on EEG measures.

The relationship between cognitive ability and distributional analysis metrics was

assessed by comparing CAF and delta plots for higher and lower BCAM performers (median

split on the BCAM score). This analysis focused on the first time segment for the CAF plot,

and the last two time segments for the delta plot, as in previous work (Forstmann, van den

Wildenberg, & Ridderinkhof, 2008; Ridderinkhof et al., 2005). A two-way, mixed design

ANOVA, with BCAM group as a between-subject factor and trial type as a within-subject

factor, was applied to test the difference in the first segment of CAF slope between groups.

The same approach was used to test for a group difference in the delta plot.

ANOVA was used to test for the effects of trial type on ERP components in the Simon

task. Multiple linear regression analysis was conducted to test the effects of BCAM score,

age and education on Simon task performance and EEG measures.

Two regression analyses were carried out to explore the relationship between HIV

infection severity and cardiovascular risk and Simon task behavior and EEG measures. To

examine the effect of severity of initial HIV infection, participants were separated into two

groups based on their nadir CD4 cell counts. The cutoff was 200 cells/mL, which is the level

that defines AIDS (i.e. severe immunosuppression). Multiple linear regression was conducted

to test the effects of low and high nadir CD4 count groups, age and education on Simon task

performance and EEG measures.

27

To test the effect of cardiovascular risk, multiple linear regression was conducted with

cardiovascular risk (Framingham Risk Score) and education as independent variables and

Simon task performance and EEG measures as dependent variables. Alpha was set at 0.05 for

all analyses. No experiment-wise correction for multiple comparisons was imposed in this

exploratory study.

28

3. RESULTS

3.1 Sample Characteristics

Eight-four HIV+ individuals completed this study. Four participants were excluded

from further analysis because of poor Simon task performance (accuracy in either condition

less than 60%). Therefore, 80 HIV+ individuals (8 women; mean age 55 (SD 7) y) were

included in the behavioral analysis. Seventeen further participants were excluded from the

ERP analysis because of poor EEG signal (fewer than 30 artifact-free epochs, see Methods).

Therefore, 63 HIV+ individuals (4 women) were included in the ERP analysis.

Demographic and clinical information is provided in Table 1, and Simon task

performance is summarized in Table 2. All participants were taking cART; 75 had

undetectable plasma HIV RNA copies (i.e. < 50 copies, the desired range), 4 had HIV RNA

copies ranging from 100-250, and one had over 1500 HIV RNA copies.

29

Table 1.

Demographic and clinical variables for the whole sample and the subset with ERP data

sufficient for analysis (mean (SD) or count) Full sample (n = 80) ERP sample (n = 63)

Demographics

Age (years) 55 (7) 54 (7)

Education (count)

not college-educated 25 20

some college education 54 43

education not reported 1 1

Sex (count)

male 72 59

female 8 4

BCAM score 20 (4) 21 (4)

HIV infection indicators

Nadir CD4 count (count)

< 200 cells/mL 49 41

200-500 cells/mL 22 18

> 500 cells/mL 7 4

Nadir CD4 count not reported 1 1

Mean HIV infection duration (y) 18 (7) 18 (7)

Cardiovascular Risk Factors

Systolic blood pressure (mmHg) 125 (13) 126 (13)

Total cholesterol (mmol/L) 4.8 (.09) 4.7 (0.9)

HDL (mmol/L) 1.2 (0.4) 1.2 (0.3)

Smoker (count) 20 15

Diabetes (count) 8 4

Treated hypertension (count) 15 12

Framingham Risk Score (10 y

risk of CVD, %)

14 (8) 14 (8)

30

3.2 Behavioral results

A one-way repeated ANOVA with trial type as within factor shows that the congruent

mean RT is faster than the incongruent mean RT, F(1,79) = 115, p < .001 and the congruent

accuracy rate is higher than the incongruent accuracy rate, F(1,79) = 52, p < .001 in the full

sample (Table 2).

Table 2.

Simon task behavioral measures (mean (SD)) for the full sample and the subset with

ERP data sufficient for analysis. Incongruent trials were slower and less accurate in

both samples (p < 0.001). Full sample (n = 80) ERP sample (n = 63)

Congruent (CG)

RT (ms) 423 (71) 411 (65)

Accuracy (%) 97 (2.4) 97 (2.3)

Incongruent (IG)

RT (ms) 471 (59) 463 (50)

Accuracy (%) 92 (6.5) 92 (6.9)

Simon effect (IG-CG)

RT (ms) 49 (41) 52 (44)

Accuracy (%) -4.5 (5.6) -4.9 (5.9)

3.2.1 Relationship of Simon task performance and BCAM score

A multiple linear regression was calculated to predict Simon task performance

(congruent RTs and accuracy rate, incongruent RTs and accuracy rate, and Simon effect RT

and accuracy rate) based on BCAM score, age, sex and education level. A significant

regression equation was found for congruent and incongruent RTs (F(4,74) = 4.60, p < .001,

31

F(4,74) = 7.22, p < .0001), with an R2 of 0.199 and 0.281 respectively. No significant

regression equation was found for other Simon task performance metrics, Fs(4,74) < .62, ps

< . 65. The findings are shown in Table 3.

Table 3.

Simon task RT related to BCAM, age, sex and education (N = 80).

Predicting

variables

Predictor

variables b Beta t p sr2 R2

Adj

R2

Congruent RT BCAM -3.72 -.218 -1.89 .063 .038

Age (decade) 14.53 .146 1.28 .204 .018

Sex 67.50 .273 2.57 .012 .071

Education -2.63 .017 -.16 .870 < .001 .199** .156

Incongruent RT BCAM -4.58 -.324 -2.97 .004

Age (decade) 12.63 .154 1.42 .159 .085

Sex 51.04 .250 2.48 .016 .020

Education -7.42 -.059 -.59 .557 .060 .281*** .242

** p <.01. *** p <.001.

The predicted congruent RT is equal to 416.48 – 3.72 (BCAM) + 14.53 (age) – 2.63

(education level) + 67.50 (sex), where BCAM is measured as a continuous score, age is

measured in decades, education is coded as 1 = less than college, 2 = at least some college,

and sex is coded as 0 = men, 1 = women. The same conventions will apply for all subsequent

regressions. Only sex was a significant predictor of congruent RT (p < .05), while there was a

trend for BCAM (p = .06). The congruent RT in men was 68 ms faster than in women.

The predicted incongruent RTs is equal to 502.76 – 4.58 (BCAM) + 12.63 (age) – 7.42

32

(education level) + 51.04 (sex). Only BCAM and sex were significant predictors of

incongruent RT (p < .05). The incongruent RT was 4.58 ms lower for each unit increment in

BCAM score and the incongruent RT in men was 51 ms faster than in women.

Sex explained variance in Simon task performance in both congruent and incongruent

RTs. However, there were only 8 women in this sample, insufficient to reliably adjust for sex

effects. Therefore, for subsequent analyses, we focused on men only. The data are presented

separately for women to provide a preliminary, qualitative view of whether the relationships

identified in men likely also hold in women.

In men, a multiple regression was conducted to predict Simon task performance based

on BCAM score, age and education level. A significant regression equation was found for

incongruent RTs (F(3,68) = 3.9, p < .05) with an R2 of 0.12. No significant regression

equation was found for other Simon task performance metrics, Fs(3,68) < 1.7, ps < .17. The

findings are shown in Table 4.

The predicted incongruent RT is equal to 494.00 – 3.88 (BCAM) + 10.93 (age) – 5.29

(education level). Only BCAM was a significant predictor of incongruent RT. The

incongruent RT was 3.9 ms lower for each unit increment in BCAM score. Plots of the

relationship between Simon task RT and BCAM are shown in Figure 2.

33

Table 4.

Simon task RT related to BCAM, age and education (N = 72).

Predicting

variables

Predictor


Adj

R2

Congruent RT BCAM -3.01 -.183 -1.46 .149 .029

Age (decade) 12.70 .139 1.10 .277 .016

Education 1.85 .013 .13 .914 <.001 .071 .030

Incongruent RT BCAM -3.88 -.294 -2.44 .017 .075

Age (decade) 10.93 .148 1.22 .226 .019

Education -5.29 -.046 -.40 .690 .002 .147 * .109

* p <.05.

Figure 2. Congruent RT (left) and Incongruent RT (right) as a function of BCAM score.

Large panels show results in men. Small panels (inset) show the relationships in women.

The blue line indicates the simple regression line and the gray shading indicates the 95%

confidence interval.

34

3.2.2 Distributional analyses

To address the second aim regarding whether poorer Simon performance was related to

weaker inhibitory control or greater impulsivity, we split the sample (men only) into two

groups based on the median BCAM score. The distribution of BCAM scores is shown in

Figure 3. The demographic profiles of these groups are shown in Table 5, and Simon

performance is provided in Table 6. Since the higher BCAM group is younger than the lower

BCAM group, age was included as a covariate in subsequent analyses.

Figure 3. Distribution of BCAM scores. Red dashed line indicates the median score for

men.

35

Table 5.

Demographic and clinical variables of high and low BCAM groups (men only)

High BCAM

(n = 40)

Low BCAM

(n = 32) p-value

Demographics

Age (years) 53(6) 57(9) <.05

Education 1.0




HIV-related variables

Nadir CD4 count (count) .23

< 200 cells/mL 27 17

200-500 cells/mL 11 13

> 500 cells/mL 3 2

Nadir CD4 count not reported 1 1

Mean HIV infection duration (y) 17 (7) 19 (8) .28


Systolic blood pressure (mmHg) 126 (12) 125 (13) .77

Total cholesterol (mmol/L) 4.9 (1.0) 4.4 (0.7) <.05

HDL (mmol/L) 1.1 (0.3) 1.3 (0.4) <.05

Smoker (count) 12 5 .18

Diabetes (count) 3 4 .69

Treated hypertension (count) 5 10 .08

Framingham Risk Score (10 y risk

of CVD, %) 15 (9) 14 (7) .69

36

Table 6.

Simon task variables for high and low BCAM groups (men only)

High BCAM

(n = 40)

Low BCAM

(n = 32) p-value

Congruent (CG)

RT (ms) 399 (57) 434 (73) .024

Accuracy (%) 97 (3) 97 (2) .236

Incongruent (IG)

RT (ms) 447 (44) 486 (58) .002

Accuracy (%) 92 (5.9) 92 (7.3) .882


RT(ms) 49 (38) 52 (46) .745

Accuracy (%) -4.2 (5.4) -5.1 (6.1) .493

37

A conditional accuracy function plot was constructed to assess whether the speed-

accuracy tradeoff was systematically changed in those with lower cognitive ability: The RT

distribution in each group was divided into 4 equal bins, shown on the x-axis, and the mean

accuracy of the trials for that RT quartile is shown on the y-axis (Figure 4). This allows

visualization of the relationship between speed and accuracy. This plot shows a similar

pattern in both groups, arguing against the possibility that poor Simon task performance

reflected a tendency to impulsive responding. A two-way, mixed design ANCOVA, with

BCAM group as a between-subject factor, trial type as a within-subject factor and age as a

covariate, confirmed that there was no significant difference between groups, F(1,68) = 1.17,

p = .28.

Figure 4. Conditional accuracy functions by condition and BCAM group. Red color

indicates high BCAM group and the blue color indicates low BCAM group. Circles

indicate congruent trials and triangles indicate incongruent trials. For both groups,

errors occur at the fastest RTs (first quartile), and the pattern of speed-accuracy trade-

off is similar. Error bars show standard error.

38

The RT Delta plot is shown in Figure 5. The x-axis shows RT by quartile and the y-axis

shows the Simon effect, i.e. the RT difference between incongruent and congruent trials, for

the trials falling in each of four RT quartiles. This plot shows how cognitive control is

increasingly effective when RT is longer. The color indicates the BCAM groups and the

shape indicates the trial type. A two-way mixed design ANCOVA with BCAM group as a

between-subject factor, the last two RT quartiles as a within-subject factor and age as a

covariate showed no significant difference between groups in the engagement of cognitive

control as a function of time (F(1,68) = 0.64, p = .43)

Figure 5. RT delta plots for BCAM groups. Red color indicates high BCAM group,

and blue color indicates low BCAM group. Error bars show standard error.

39

3.2.3 Relationship of Simon task performance and nadir CD4 cell count

One of the participants was excluded because of an extreme nadir CD4 count (1588),

well above the upper limit of normal in healthy people. The remaining 71 participants were

separated into two groups based on nadir CD4 cell count with 200 cells/mL as the cutoff. The

demographic profiles of these two groups are shown in Table 7 and Simon task performance

is shown in Table 8.

A multiple regression was calculated to predict Simon task performance based on nadir

CD4 counts groups, age, and education level. However no significant regression equation

was found Fs(3,67) < 1.7, ps > .18. The findings are shown in Table 9. Scatter plots of Simon

task RT and nadir CD4 counts are provided in Figure 6.

Figure 6. Congruent RT (left) and Incongruent RT (right) as a function of nadir CD4 cell

count. The blue line indicates the simple regression and the gray shading indicates 95%

confidence interval.

40

Table 7.

Demographic and clinical variables of high and low nadir CD4 counts groups

High nadir CD4 counts

(>= 200 cells/ml)

(n = 27)

Low nadir CD4 counts

(< 200 cells/ml)

(n = 44)

p-

value

Demographic

Age (years) 56 (9) 54(6) .24

Education 1.0




BCAM 20.2 (4.7) 21.1 (3.7) .40

HIV-related variables

Mean HIV infection duration (y) 15 (8) 20 (7) .002


Systolic blood pressure (mmHg) 124 (12) 126 (13) .46

Total cholesterol (mmol/L) 4.9 (1.0) 4.5 (0.8) .10

HDL (mmol/L) 1.3 (0.4) 1.1 (0.3) .02

Smoker (count) 4 12 .26

Diabetes (count) 3 4 1.0

Treated hypertension (count) 11 4 .002

Framingham Risk Score (10 y

risk of CVD, %) 15 (8) 14 (9) .96

41

Table 8.

Simon task variables for high and low nadir CD4 count groups

High nadir CD4 counts

(>= 200 cells/ml)

(n = 27)

Low nadir CD4 counts

(< 200 cells/ml)

(n = 44)

p-value

Congruent (CG)

RT (ms) 424 (65) 409 (68) .381

Accuracy (%) 97 (2.6) 97 (2.4) .904

Incongruent (IG)

RT (ms) 471 (60) 461 (50) .464

Accuracy (%) 92 (6.4) 92 (6.7) .955


RT(ms) 47 (42) 52 (42) .647

Accuracy (%) -4.6 (6.1) -4.5 (5.5) .907

Table 9.

The relationship between Simon task RT and nadir CD4 count group, age and education (N =

71).

Predicting

variables

Predictor


Adj

R2

Congruent RT Nadir CD4

group

10.85 .079 .66 .515 .006

Age (decade) 17.35 .189 1.55 .126 .034

Education .98 .007 .056 .955 <.001 .046 .003

Incongruent RT Nadir CD4

group

6.184 .056 .47 .640 .003

Age (decade) 17.65 .239 1.99 .051 .055

Education -5.99 -.052 -.43 .666 .003 .070 .029

42

3.2.4 Relationship of Simon task performance and CVD risk

The distribution of the Framingham 10-year CVD risk (%) is shown in Figure 7 (A).

The distribution is not normal (W(71) = 0.95, p < .01), even after excluding the one obvious

outlier (CVD risk = 56%). Therefore, arcsine transformation was applied. The transformed

data follow a normal distribution (W(71) = 0.98, p > .05; Figure 7B).

Figure 7. Distribution of Framingham 10-year cardiovascular risk. Left panel shows the

distribution in men (n = 72). Right panel shows the distribution of arcsine transformed data

after excluding the outlier (n = 71).

A multiple regression was calculated to predict Simon task performance based on

Framingham 10-year cardiovascular risk (arcsine transformed) and education. Age was not

included, as it is amongst the variables used to calculate the cardiovascular risk. A significant

regression equation was found for incongruent RTs (F(2,68) = 4.78, p < .05) with an R2 of

0.123. The findings are shown in Table 10.

The predicted incongruent RT is equal to 411.52 + 182.49 (transformed CVD) – 8.47

43

(education level), where CVD is the Framingham 10-year cardiovascular risk (arcsine

transformed). Only cardiovascular risk was a significant predictor of incongruent RT (p

<.01). The incongruent RT was 183 ms faster for each unit increment in arcsine transformed

cardiovascular risk.

Table 10.

Simon task RT related to CVD and education (N = 71).

Predicting

variables

Predictor


Adj

R2

Congruent RT CVD 144.03 .214 1.80 .077 .045

Education -2.7 -0.19 -.16 .877 <.001 .047 0.19

Incongruent RT CVD 182.49 .336 2.94 .004 .112

Education -8.47 -.072 -.63 .531 .005 .123* .097

* p <.05.

Scatter plots of the relationship between Simon task performance and cardiovascular

risk are shown in Figure 8.

Figure 8. Congruent RT (left) and Incongruent RT (right) as a function of 10-year

cardiovascular risk. The blue line indicates the simple regression and the gray shading

indicates the 95% confidence interval.

44

3.3 ERP results

Seventeen participants were excluded from the EEG analysis due to incomplete data or

poor technical quality of the EEG recording. Demographic and clinical information for the

remaining 63 participants are provided in Table 1. Simon task performance is shown in Table

2. The four women were excluded, leaving 59 men for analysis.

The Brainstorm toolbox was used to identify the clusters of interest. The incongruent

ERP signal was averaged across the pre-defined time window for each ERP component.

Multiple t tests were applied to each channel to identify the cluster with the greatest ERP

signal difference between high and low BCAM groups. False discovery rate correction was

conducted for multiple comparisons with alpha level set at .05. For the N2 component, no

significant difference was found even with uncorrected multiple comparisons. Therefore, the

two channels (E015 and E023) with the highest t value (-1.54 and -1.66 respectively) were

selected as the N2 cluster. For P3, three channels (E110, E119, and E128) were significant

after correction, and selected as the P3 cluster. The topography of the t map is shown in

Figure 9.

45

Figure 9. Selection of the channels for subsequent analysis. T value map of the ERP

differences between high and low BCAM groups on incongruent trials. This image shows only

the channels that carried signal that met criteria for analysis (artefact-free) in all participants.

The left panel shows the N2, the right panel shows the P3. Yellow dots indicate the selected

channels for N2 and P3 clusters, i.e. the most positive or negative t value of the contrast. The

blue color indicates negative t value in the left panel and t values less than 1 in the right panel.

The red color indicates positive t values in left panel and t values greater than 2 in the right

panel.

The ERP values for frontal (N2) and posterior (P3) clusters are shown in Table 11. A

repeated ANOVA with trial type as within factor revealed that there was no significant

difference between congruent and incongruent trials in N2 and P3 amplitude or latency,

Fs(1,58) < 2.19, ps > .14. Figure 10 shows the grand average waveform at each cluster in the

congruent and incongruent conditions collapsed across BCAM group.

46

Table 11.

Simon task ERPs (mean [SD])

Frontal cluster Posterior cluster

Amplitude Latency Amplitude Latency

N2

Congruent 0.79 (1.7) 251 (33) －－

Incongruent 0.70 (1.7) 253 (31) －－

P3

Congruent －－ 2.78 (2.2) 414 (80)

Incongruent －－ 2.71 (2.2) 425 (80)

Note: amplitude and latency are presented in uV and msec respectively; all values mean (SD).

Figure 10. Stimulus-locked grand average waveforms for the Simon task collapsed across

BCAM group. The waveform at the frontal cluster and posterior cluster are shown in the left

panel and right panel, respectively. The green line indicates the congruent condition and the red

line indicates the incongruent condition.

47

3.3.1 Relationship of Simon task ERP and BCAM score

Focusing on men only, a multiple regression was conducted to predict Simon task ERP

(N2 and P3 amplitude and latency) based on BCAM score, age, and education. A significant

regression equation was found for N2 amplitude in both trial types (F(3,55) = 3.98, p < .05

and F(3,55) = 4.42, p < .01 for congruent and incongruent trials) with an R2 = .178 and R2

= .194 respectively. A significant regression equation was found for P3 amplitude in both trial

types as well (F(3,55) = 6.00, p < .01 and F(3,55) = 7.64, p < .001 for congruent and

incongruent trials) with an R2 = .246 and R2 = .294 respectively. No significant regression

equation was found for N2 and P3 latency, Fs (3,55) < 2.13, ps > .11. (see Table 12 for

details).

For N2, the predicted congruent N2 amplitude is equal to -0.07 – 0.06 (BCAM) + 0.58

(age) – 0.60 (education level). There were no significant predictors explaining congruent N2

amplitude (ps >.186), although there was a trend for age (p = .054).

The predicted incongruent N2 amplitude is equal to -0.58 – 0.06 (BCAM) + 0.64 (age)

– 0.53 (education level). Only age was a significant predictor of incongruent N2 amplitude (p

< .05). The incongruent N2 amplitude increased 0.64 uV for each decade of age.

For P3, the predicted congruent P3 amplitude is equal to -1.49 + 0.25 (BCAM) + 0.20

(age) – 1.24 (education level). Only BCAM and education level were significant predictors of

congruent P3 amplitude (p < .001 and p < .05 respectively). The congruent P3 amplitude was

48

0.25 uV greater for each unit increment in BCAM score, and was 1.24 uV smaller for those

with college education.

The predicted incongruent P3 amplitude is equal to -2.87 + 0.29 (BCAM) + 0.28 (age)

– 1.18 (education level). Only BCAM and education level were significant predictors of

congruent P3 amplitude (p < .001 and p < .05 respectively). The incongruent P3 amplitude

was 0.29 uV higher for each unit increment in BCAM score, and was 1.18 uV smaller for

those with college education.

49

Table 12.

Simon task ERPs related to BCAM, age and education (N = 59).

Predicting

variables

Predictor


Adj

R2

Congruent

N2 BCAM -.062 -.156 -1.18 .242 .021

amplitude Age (decade) .58 .262 1.97 .054 .058

Education -.60 -.168 -1.34 .186 .027 .178* .133

N2 BCAM 2.04 .266 1.87 .067 .057

latency Age (decade) -6.02 -.135 -.98 .330 .016

Education -5.40 -.075 -.58 .562 .005 .104 .056

P3 BCAM .25 .481 3.81 <.001 .199

Amplitude Age (decade) .20 .069 .55 .587 .004

Education -1.24 -.266 -2.21 .032 .067 .246** .205

P3 BCAM -.165 -.009 -.061 .952 <.001

latency Age (decade) 16.45 .156 1.08 .286 .020

Education 15.48 .092 .67 .506 .008 .027 -.026

Incongruent

N2 BCAM -.063 -.159 -1.22 .227 .022


Education -.53 -.151 -1.22 .229 .022 .194** .150

N2 BCAM .17 .022 .17 .877 <.001

latency Age (decade) -8.73 -.209 -1.46 .149 .037

Education -5.41 -.081 -.60 .552 .006 .046 -.006

P3 BCAM .29 .545 4.47 <.001 .256

Amplitude Age (decade) .28 .094 .76 .450 .007

Education -1.18 -.250 -2.14 .037 .059 .294*** .256

P3 BCAM 2.65 .139 .97 .335 .017


Education 7.79 .046 .34 .738 .002 .036 -.016

* p <.05. ** p < .01. *** p < .001.

50

The grand average ERP of both conditions in high and low BCAM groups are shown in

Figure 11. An ANCOVA with BCAM group as between factor and age as a covariate revealed

that P3 amplitude is significantly larger in both congruent and incongruent trials in the high

BCAM group compared to the low BCAM group, F(2,56) = 4.38, p < .05 and F(2,56) = 5.61,

p < .01 respectively.

Figure 11. Grand average waveforms of high and low BCAM group.

The green line indicates the high BCAM group while the red line indicates the low BCAM group.

The upper row shows congruent ERP and the lower row shows incongruent ERP. The left panel

shows the N2 waveform at the frontal cluster in both groups and the right panel shows the P3

waveform at the posterior cluster in both groups. The yellow area indicates the time window of the

ERP component. N = 35 in higher BCAM group and N = 24 in lower BCAM group.

* p <.05. ** p < .01.

51

3.3.2 Relationship of Simon task ERP and nadir CD4 cell count

A multiple regression was conducted to predict Simon task ERPs (N2 and P3 amplitude

and latency) based on nadir CD4 count (groups split at 200 cell/mL), age, and education. A

significant regression equation was found for N2 amplitude in both trial types (F(3,55) =

3.63, p < .05 and F(3,55) = 3.88, p < .01 for congruent and incongruent trials) with an R2

= .165 and R2 = .175 respectively. No significant regression equation was found for P3

amplitude, or N2 or P3 latency, Fs(3,55) < 1.19, ps > .32. (see Table 13 for detail).

The predicted congruent N2 amplitude is equal to -1.62 – 0.31 (nadir CD4 count group)

+ 0.72 (age) – 0.64 (education level), where nadir CD4 count group is coded as 1 = below

200 cell/mL, 2 = above or equal 200 cell/mL. Only age was a significant predictor of

congruent N2 amplitude (p < .05). The congruent N2 amplitude was 0.78 uV higher for each

decade increment in age.

The predicted incongruent N2 amplitude is equal to -2.30 – 0.16 (nadir CD4 count

group) + 0.77 (age) – 0.59 (education level). Only age was a significant predictor of

incongruent N2 amplitude (p < .01). The incongruent N2 amplitude was 0.77 uV higher for

each decade increment in age.

52

Table 13.

Simon task ERPs related to nadir CD4 count, age and education (N = 59).

Predicting

variables

Predictor


Adj

R2

Congruent

N2 Nadir CD4 group -.31 -.090 -.73 .471 .008


Education -.64 -.182 -1.44 .155 .031 .165* .120

N2 Nadir CD4 group -7.37 -.108 -.82 .416 .011

latency Age (decade) -9.3 -.210 -1.56 .124 .042

Education -3.31 -.047 -.35 .728 .002 .059 .008

P3 Nadir CD4 group -.36 -.080 -.61 .547 .006

Amplitude Age (decade) -.24 -.083 -.62 .540 .007

Education -1.00 -.214 -1.60 .117 .044 .053 .002

P3 Nadir CD4 group -21.27 -.134 -1.01 .319 .016


Education 14.64 .089 .66 .514 .009 .041 -.012

Incongruent

N2 Nadir CD4 group -.16 -.047 -.381 .704 .002


Education -.59 -.166 -1.33 .190 .026 .175* .130

N2 Nadir CD4 group 7.96 .124 .940 .351 .015

latency Age (decade) -.964 -.231 -1.717 .092 .050

Education -5.51 -.083 -.618 .539 .007 .061 .010

P3 Nadir CD4 group -.341 -.075 -.561 .577 .005

Amplitude Age (decade) -.24 -.081 -.600 .551 .006

Education -.906 -.191 -1.418 .162 .035 .044 -.008

P3 Nadir CD4 group -28.51 -.179 -1.353 .182 .057


Education 13.89 .084 .626 .534 .004 .052 <.001

* p <.05

53

The grand average incongruent ERP for both groups is shown in Figure 12. An

ANCOVA with nadir CD4 cell count group as between factor and age as a covariate revealed

that there was no significant difference between the groups in any of the ERPs components,

Fs(2,56) < 1.15, ps > .224.

Figure 12.

Grand average waveforms of high and low nadir CD4 cell count groups in the incongruent

condition. The blue line indicates the high nadir CD4 group while the red line indicates the low

nadir CD4 group. The upper row shows congruent ERP and the lower row shows incongruent ERP.

The left panel shows the N2 waveform at the frontal cluster in both groups and the right panel

shows the P3 waveform at the posterior cluster in both groups. The yellow area indicates the time

window of the ERP component. N = 22 in high nadir CD4 cell count group and N = 37 in low nadir

CD4 count group.

54

3.3.3 Relationship of Simon task ERP and CVD risk

A multiple regression was conducted to predict Simon task ERPs (N2 and P3 amplitude

and latency) based on arcsine transformed Framingham 10-year CVD risk (arcsine

transformed) and education. A significant regression equation was found for N2 amplitude in

congruent and incongruent trials (F(2,55) = 5.45, p < .01 and F(2,55) = 4.26, p < .05) with an

R2 = .165 and R2 = .134 respectively. No significant regression equation was found for P3

amplitude, N2 or P3 latency, Fs(2,55) < 1.36, ps > .27. (see Table 14 for detail).

The predicted congruent N2 amplitude is equal to -.03 + 5.56 (transformed CVD risk) -

0.70 (education level), where CVD is the Framingham 10-year cardiovascular risk (arcsine

transformed) and education is coded as 1 = less than college, 2 = at least some college. Only

transformed CVD was a significant predictor of congruent N2 amplitude (p < .05). The

congruent N2 amplitude was 5.56 uV higher for each unit increase in arcsine transformed

CVD risk score.

The predicted incongruent N2 amplitude is equal to 0.20 + 4.71 (transformed CVD

risk) – 0.70 (education level). Only transformed CVD risk score was a significant predictor of

incongruent N2 amplitude (p < .05). The incongruent N2 amplitude was 4.71 uV higher for

each unit increase in arcsine transformed CVD risk score.

55

Table 14.

Simon task ERPs related to CVD risk (transformed) and education (N = 58).

Predicting

variables

Predictor


Adj

R2

Congruent

N2 CVD risk 5.56 .327 2.62 .011 .104

amplitude Education -.70 -.195 -1.56 .124 .037 .165** .135

N2 CVD risk -15.49 -.046 -.34 .739 .002

latency Education -1.65 -.023 -.17 .867 <.001 .002 -.034

P3 CVD risk 1.43 .064 .48 .634 .004

amplitude Education -.86 -.182 -1.36 .180 .032 .041 .006

P3 CVD risk -118.45 -.147 -1.09 .281 .021

latency Education 9.14 .053 .40 .694 .003 .027 -.009

Incongruent

N2 CVD risk 4.71 .279 2.19 .033 .076

amplitude Education -.70 -.197 -1.55 .128 .038 .134* .103

N2 CVD risk -67.21 -.212 -1.59 .118 .044

latency Education -6.29 -.093 -.70 .487 .008 .047 .013

P3 CVD risk 2.97 .131 .98 .331 .017

amplitude Education -.70 -.145 -1.09 .282 .020 .044 .010

P3 CVD risk -57.32 -.070 -.52 .607 .005

latency Education 6.51 .038 .28 .783 .001 .007 -.029

* p <.05. ** p < .01.

56

The grand average ERP for both conditions and higher and lower cardiovascular risk

groups are shown in Figure 13. An ANOVA with cardiovascular risk as between factor

revealed that that N2 amplitude is significantly smaller in both congruent and incongruent

trials in the lower CVD risk group compared to the higher CVD risk group, F(1,57) = 27.72,

p < .01 and F(1,57) = 25.10, p < .01 respectively.

Figure 13.

Grand average waveforms of high and low CVD risk group in incongruent condition.

The brown line indicates the high CVD risk group while the yellow line indicates the low

CVD risk group. The upper row shows congruent ERP and the lower row shows incongruent

ERP. The left panel shows the N2 waveform at the frontal cluster in both groups and the right

panel shows the P3 waveform at the posterior cluster in both groups. The yellow area

indicates the time window of the ERP component. * p <.05. ** p < .01.

57

4. DISCUSSION

The goal of the present thesis was to determine if Simon task performance and EEG

signals during the task in older people living with HIV related to overall cognitive function,

and to assess whether impairment on this task was due to poor cognitive control or enhanced

impulsivity. We also explored whether nadir CD4 count and cardiovascular risk were

associated with executive dysfunction as indexed by the Simon task and the associated ERP.

As hypothesized, the incongruent RT in the Simon task was explained by overall

cognitive function measured by BCAM score. Weaker performers were simply overall

slower, showing neither excess impulsivity nor selectively weaker cognitive control. Current

viral control did not explain variance in performance, but the large majority of the sample had

complete viral suppression. Past severity of HIV infection as reflected in nadir CD4 cell

count also did not relate to Simon task performance. A linear relationship was found between

cardiovascular risk and incongruent RT, although this relationship was not significant after

controlling for education level.

The Simon task ERP analysis showed that the P3 amplitude in both congruent and

incongruent trials was significantly associated with BCAM score and education level. The

regression equation that included nadir CD4 count, as well as BCAM score, age, and

education level as the predictors explained variance in the N2 amplitude but not the P3

amplitude. However, only age, and not nadir CD4, was a significant predictor of N2

amplitude. Finally, the regression equation with CVD and education level as the predictors

58

explained N2 amplitude in both congruent and incongruent ERPs, with CVD risk a

significant predictor of N2 amplitude in both conditions. These findings will be discussed in

more detail, in turn.

4.1 Executive impairment reflects generalized slowing of processing

Poorer overall cognitive ability as assessed by BCAM score was associated with longer

RT in both congruent and incongruent Simon task conditions, rather than specifically

associated with the Simon effect (typically considered a more specific indicator of cognitive

control). This argues for a general slowing of processing in those men with HIV with poorer

Simon task performance, rather than specific impairment of cognitive control. The

distributional analyses also support this idea. This is consistent with a diffuse underlying

brain pathology, rather than dyfunction in a particular cortical or subcortical region or

network. Such an explanation aligns with the finding that Simon task performance relates to

overall cognitive ability. Of note, the cognitive ability measure (BCAM) includes some timed

tasks, but also untimed measures of episodic memory, working memory span, and self-

reported cognitive symptoms, so is not tapping speed-of-processing alone.

The overall slowing finding also agrees with recent imaging work that is increasingly

arguing for a generalized pathological process in people with HIV, with evidence for diffuse

white matter injury and diffuse or multifocal white and gray matter atrophy affecting cortical

59

and subcortical regions. For example, using DTI, Tate et al. (2010) found that fractional

anisotropy (FA, an index of white matter integrity) was associated with performance in

simple motor tapping task and attention switching task (computer adaptation of Trail Making

Test B) in a middle-aged HIV+ group. The lower the white matter integrity, the longer the

time to complete the switching task (Tate et al., 2010). Janssen et al. (2015) found that whole

gray and white matter volumes were associated with information processing, motor function

and cognitive task performance in a group of 95 HIV+ individuals (mean age = 45) on cART

(Janssen et al., 2015).

4.1.1 Simon task ERP relationship with overall cognitive ability

We found that lower overall cognitive function as assessed by the BCAM score was

related to smaller P3 amplitude in the Simon task, but N2 amplitude and P3 latency were not.

It is possible that the P3 latency was contaminated by the motor response, which occurs at

about the same time as the P3, making the latency a less reliable measure. The N2 may occur

too early to fully reflect processing speed impairment. Examples of this can be found in the

healthy aging literature: Using the Attention Network Test, William et al (2016) found that

healthy older adults (age 60-76) had similar early ERP components (N1, P1) but reduced

amplitudes of later components such as the P3, compared to healthy younger adults (age 19-

29) (Williams et al., 2016).

To our knowledge, this is the first ERP study of the P3 in an executive task in HIV,

60

relating the P3 to cognitive function. P3 amplitude is thought to reflect engagement of

attentional resources in this task. As the present study did not include an HIV- group, we

cannot distinguish between reduced attentional resources in those with cognitive impairment,

or enhanced (i.e. compensatory) attentional engagement in those with better cognitive ability.

However, reduced engagement seems the more likely explanation for the pattern of P3

amplitude variation observed here.

Previous ERP studies in HIV focused on the P3 component in a simpler task, the

auditory oddball task. That work found that HIV+ individuals had smaller P3 amplitude and

longer latency as compared to healthy controls (Chao et al., 2004; Polich, 2000; Tartar et al.,

2004). Future work aiming at defining EEG biomarkers should compare the reliability,

sensitivity and specificity of the P3 in these two tasks in relation to cognitive performance, or,

perhaps more importantly, to predicting decline in cognitive performance over time.

4.2 Contributors to executive dysfunction in HIV

4.2.1 HIV infection severity

No relationship was found here between indicators of HIV infection severity and Simon

task performance or ERP. Previous studies have shown a relationship between HIV variables

and performance on neuropsychological tests. Heaton et al (2010) found that history of low

nadir CD4 count was a strong predictor of cognitive impairment among participants without

61

severe comorbidities in the CHARTER study (Heaton et al., 2010). Likewise, Ellis et al

(2011) found that higher nadir CD4 cell count (less severe immunosuppression) was

associated with lower odds of neuropsychological impairment.

One possible reason for the absence of a detectable effect of nadir CD4 count here is

that the sample size was not large enough. With only 80 participants available for behavioral

analysis, and even fewer for the ERP analysis (n = 63), we were powered to detect effect

sizes larger than those reported in the studies comparing HIV+ to HIV- groups cited above,

which involved large samples of several hundred subjects. (For example, the effect size in the

Heaton et al study was small: 0.19).

There is no HIV literature on the relationship of ERP components and nadir CD4

counts. However, ERP differences between HIV+ and HIV- control groups tend to have a

moderate effect size. For example, a study using the auditory oddball paradigm compared the

P3 component between 23 HIV+ and 11 healthy controls, yielding a moderate effect size

(0.43 for P3 amplitude) (Tartar et al., 2004), more plausibly detectable in our study. Thus, the

null findings here for nadir CD4 effects on ERP measures are more compelling than the

absence of a detectable relationship with Simon task performance.

The relationship between nadir CD4 and cognitive dysfunction may be larger in people

studied closer to the acute phase of HIV infection. Focusing on acute and early HIV infection

(mean duration of infection: 2.1 months, N=34), Rujvi et al (2016) found that higher global

62

neurobehavioral dysfunction was associated with lower nadir CD4 counts, slower

information processing speed, and lower daily life function (Rujvi et al., 2016). In patients

with very long disease duration (and therefore also with accumulating effects of age and other

comorbidities), the impact of initial HIV infection severity may be less important. Our

sample, by design, included patients with at least one year of HIV infection, and the mean

duration of infection was 18 years, which is much longer than previous studies. The

CHARTER study did not report the duration of HIV infection, but the mean cART treatment

duration of their participants was 11 months (IQR = 4-27 months) (Heaton et al., 2010) and

the HIV infection duration in the Ellis et al (2011) study was 2 years.

4.2.2 CVD risk

In contrast to the null findings with respect to HIV variables, we observed a

relationship between CVD risk and Simon task performance. CVD risk was associated with

longer incongruent RT. This could be consistent with a contribution of CVD to processing

slowing and, in turn, cognitive control ability. Other studies have found that cerebrovascular

risk was associated with slower processing speed after accounting for age in HIV (Foley et

al., 2010). Becker et al. (2009) found that subclinical CVD, measured by coronary artery

calcium and carotid artery intima-media thickness, was related to psychomotor speed and

memory test performance in HIV (mean age = 50) (Becker, Kingsley, Mullen, Cohen, &

Sacktor, 2009). In addition to slow processing speed, studies also found that CVD was related

63

to neurocognitive impairment in middle age (43 year old) and older (55 year old) people with

HIV (Jawaid et al., 2011; Nakamato et al., 2011).

Previous studies have also shown that CVD risk is associated with executive task

performance in otherwise healthy populations. Nishtala et al (2014) found that CVD risk was

related to executive function in the Framingham Offspring Study cohort (n = 5,124) (Nishtala

et al., 2014). Jefferson et al (2015) found that in healthy older people (> 70 y.), increasing

CVD risk was related to worse cognition as indexed by Mini-Mental State Examination

score, information processing speed (Digit Symbol and Trail Making Test A), and executive

function tests (Trail Making Test B, category naming) (Jefferson et al., 2015).

On the other hand, the relationship between CVD and Simon task performance here

was no longer significant when education was included as a covariate. Thus, this apparent

effect may have more to do with demographic or socioeconomic factors that are associated

both with cognitive performance and the presence of CVD risk factors.

However, CVD risk in the present sample of older HIV+ patients was also related to N2

amplitude, although not P3 amplitude, after controlling for education. The higher the CVD

risk, the less negative the N2 amplitude in both congruent and incongruent conditions. As the

N2 is associated with conflict detection and monitoring (Folstein & van Petten, 2008), this

result suggests that those with high CVD risk had weaker conflict detection abilities. This

ERP result also argues that CVD-related cognitive dysfunction affects earlier processing

64

stages (i.e. stimulus detection). This result is consistent with the previous literature studying

ERP effects of CVD. For example, Cicconetti et al. (2000) measured ERP during the auditory

oddball task in elderly hypertension patients. They found that N2 latency was longer

compared to that seen in healthy controls, but there was no significant difference in P3

latency. In addition, they found longer N2 latency was associated with higher systolic blood

pressure (Cicconetti et al., 2000). Using the same paradigm, van Harten et al (2006) studied

patients with vascular cognitive impairment caused by subcortical ischemic vascular disease.

Similarly, they found patients had longer N2 latency compared to age-matched controls,

whereas the latencies of the P3 were not significantly different (van Harten et al., 2006). Ours

is the first study examining the effects of CVD risk on ERP in HIV. Our results suggest that

CVD risk factors change early perceptual processing in older HIV+ men taking cART, in a

pattern similar to that seen (at an older age) in HIV- individuals with higher CVD risk.

Although age is an important contributor to CVD risk, the different relationships we observed

here between age and cognition (related to P3) and CVD risk (N2) suggest we are not simply

observing a common effect of aging, alone. It will be important to follow-up this finding with

neuroimaging of cerebrovascular ischemic injury, to more directly trace the pathophysiology

underlying this observation.

65

4.3 Strengths and Limitations

This study examined a highly relevant population with HIV infection: older people

without frank dementia, most of whom had excellent systemic viral control. These people

pose a clinical challenge: many have mildly impaired cognitive performance, and it is not

clear how best to detect this cognitive impairment, nor how to treat it. We found that the

Simon task is a relevant probe of cognition in this population, correlated with a more

extensive battery which also included a few self-report questions, arguing for real world

relevance. However, additional work should address in more detail whether Simon task

performance predicts clinically-relevant outcomes, such as everyday function.

EEG might provide a useful biomarker for cognitive impairment in HIV: Here, we

found that the P3 in the Simon task is promising in this regard. However, again, more work is

needed to establish whether it might be a useful biomarker. Finally, we identify CVD risk as

relevant for understanding cognition and EEG changes in this population, for the first time.

The N2 in the Simon task could be a candidate biomarker for deleterious effects of CVD on

the brain in HIV.

The sample we studied has been richly characterized in other ways (Mayo, Brouillette,

& Fellows, 2016). We focused on CVD comorbidity here, because of its emerging importance

in the field of neuro-HIV and because it may be treated with medication or lifestyle changes.

However, other common additional comorbidities, such as depression, illicit drug use or

66

alcohol use may also explain some of the variance in cognitive performance and EEG

measures. In principle, the impact of these additional comorbidities could also be examined,

although in practice power limitations make this a challenge in the current sample. Studying a

healthy control group with similar demographic characteristics could also provide further

insights, particularly with respect to the contributions of age.

Although this is amongst the largest studies using ERP in people with HIV, statistical

power was limited. Post hoc power calculations show that we could have detected moderate-

to-large effects. This can help in planning future studies. In addition to statistical power

considerations, specific participant characteristics are important to consider. Only people who

were able and willing to participate in the training intervention sub-studies were tested. These

patients might not be representative of the entire older HIV+ population, particularly with

respect to motivation. Some patients were also excluded because they did not have time or

could not access the Internet to complete 8 weeks of computer based cognitive training. This

should be kept in mind in considering generalizability of the findings. Importantly, we

identified an effect of gender on cognitive performance, but we had too few women in the

sample to study this properly. Future work should recruit more women, to better understand

the basis of this apparent difference due to gender. The results presented here may not

generalize to women living with HIV, although many of the patterns seemed similar or even

stronger in inspecting the data from the women who did participate.

67

4.4 Conclusion

The present work studied the relationship between overall cognitive function and

Simon task performance and ERP within an older, virally-suppressed HIV+ group, and

explored the potential mechanisms underlying cognitive and brain dysfunction. Our findings

argue for a processing slowing account of cognitive variation in older men with HIV, likely

related to diffuse brain changes. The Simon task is relevant as a probe of overall cognitive

performance in this sample, and the related P3 ERP could be studied further as a biomarker

for cognitive decline. We add to emerging evidence that the severity of HIV infection indexed

by nadir CD4 is less relevant to cognitive difficulties in these older people with multiple

comorbidities than in the phase of acute infection in younger people. CVD risk, on the other

hand, may be important. This finding should be pursued, as CVD risk can be modified, and

people with HIV tend to have higher baseline CVD risk for a variety of reasons.

68

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I The Brain Basis of Executive Dysfunction in Older People Living with HIV

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