neural correlates of cognitive fatigue in multiple sclerosis using functional mri

ences 270 (2008) 28–39www.elsevier.com/locate/jns

Journal of the Neurological Sci

Neural correlates of cognitive fatigue in multiple sclerosisusing functional MRI

John DeLuca a,b,⁎, Helen M. Genova b, Frank G. Hillary c, Glenn Wylie b

a Graduate School of Biomedical Sciences, and Department of Physical Medicine and Rehabilitation,University of Medicine and Dentistry of New Jersey, NJ, USA

b Kessler Medical Rehabilitation Research and Education Center, NJ, USAc Department of Psychology, Pennsylvania State University, PA, USA

Received 25 July 2007; received in revised form 16 January 2008; accepted 18 January 2008Available online 11 March 2008

Abstract

Although fatigue is one of the major symptoms of persons with multiple sclerosis (MS), the behavioral and neural correlates are poorlyunderstood. The present study utilized a novel approach to cognitive fatigue examining objective behavioral performance whilesimultaneously monitoring brain activity using fMRI. Fifteen persons with MS and 15 healthy controls were given 4 trials of a behavioraltask assessing processing speed (mSDMT) during fMRI acquisition. It was hypothesized that individuals with MS would show an abnormalpattern of activity across time in specific brain areas previously hypothesized to subserve fatigue [Chaudhuri A, Behan PO. Fatigue andbasal ganglia. J Neurol Sci 2000;179:34–42]. Specifically, it was hypothesized that persons with MS would show a greater increase incerebral activation across time during behavioral performance than that seen in healthy controls, which was interpreted as fatigue. Nodifference in performance accuracy on the mSDMTwas observed, although the MS group was significantly slower than controls. Behavioralalterations indicative of fatigue in the MS group were associated with increased activation in the basal ganglia, frontal areas includingsuperior, medial, middle and inferior regions, parietal regions (precuneus and cuneus), thalamus and the occipital lobes. These data providedirect support for the Chaudhuri and Behan model of “central” fatigue which hypothesizes a specific role of the “non-motor” functions of thebasal ganglia.© 2008 Elsevier B.V. All rights reserved.

Keywords: Multiple sclerosis; Fatigue; Cognitive fatigue; fMRI; Basal ganglia

1. Introduction

Multiple sclerosis is a chronic neurological disorder lead-ing to multifocal demyelination and axonal damage [2]. Fa-tigue is the most common symptom, reported in over 90% ofpersons with MS [3] and is reported as the worst symptom inover two-thirds of persons with MS [4,5]. Fatigue is alsoassociated with a high degree of functional disability, with

⁎ Corresponding author. Kessler Medical Rehabilitation Research andEducation Center, Neuropsychology and Neuroscience laboratory, 300Executive Drive, Suite 10, West Orange, NJ 07052, United States. Tel.: +1973 530 3600; fax: +1 973 736 7880.

E-mail address: [email protected] (J. DeLuca).

0022-510X/$ - see front matter © 2008 Elsevier B.V. All rights reserved.doi:10.1016/j.jns.2008.01.018

more than 50% of persons with MS reporting fatigue as theirmost disabling symptom [3]. Disability includes reducedenergy and endurance [6], altered mood and ability to cope[7], decreased overall quality of life [8] and fatigue can be themain feature of a relapse[9]. Fatigue is often described asmultifactorial in nature, and is frequently divided into var-ious components such as peripheral vs. central fatigue [1] orphysical vs. cognitive or mental, which are themselves oftendifficult to define or operationalize (see [10]).

The assessment of fatigue has been plagued by difficultiesin its conceptualization and definition for over 100 years [10].Fatigue is typically assessed using various self-report ques-tionnaires which either define fatigue as a singular construct ormultidimensional (e.g., physical, cognitive fatigue) in nature

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http://dx.doi.org/10.1016/j.jns.2008.01.018

29J. DeLuca et al. / Journal of the Neurological Sciences 270 (2008) 28–39

[4,11]. For instance, theMultiple Sclerosis Council for ClinicalPracticeGuidelines published a consensus definition of fatiguefor MS patients [12] which defined fatigue as a “subjectivelack of physical and/or mental energy that is perceived by theindividual or caregiver to interfere with usual and desired ac-tivities” (p. 2). However, a major impediment in the under-standing of fatigue lies in the fact that for over 100 years,research has shown little to no relationship between self-reportand actual, objective measurements of fatigue in a variety ofclinical populations (see [10] for a review). For instance, inMS, subjective ratings of fatigue show little or no relationshipto disease characteristics such as lesion load, disease duration,disease course (e.g., relapsing–remitting vs. progressive), orfunctional impairment as measured by the Expanded Dis-ability Status Scale (EDSS) [4,13].

Despite the extensive use of subjective ratings to measurefatigue, a wide array of techniques have also been developedto objectively measure physical or physiological manifesta-tions of fatigue (c.f., [14]), although – as pointed out above –these measures typically do not correlate with subjectivereports of fatigue [14]. In contrast, few techniques have beendeveloped to objectively measure mental or cognitive fa-tigue. One reason for this is that cognitive fatigue is as yetnot well defined or understood. Hence, cognitive fatigue hasbeen conceptualized in at least two different ways. First,cognitive fatigue is often described as decreased perfor-mance over a prolonged period of time, such as during thecourse of a work day. Unfortunately, there is little support forthis contention among clinical populations [15]. A secondway to view cognitive fatigue is as decreased performanceduring acute but sustained mental effort [16]. This latterconception of fatigue is similar to that typically thought of inthe motor fatigue literature where fatigue has been defined asa failure to maintain a required force or output of powerduring sustained or repeated muscle contraction [17]. Thereis indeed some support for this second conceptualization ofcognitive fatigue in clinical populations. Several recent stud-ies have shown a significant decline in performance duringthe second half vs. the first half of a sustained cognitive taskin MS subjects relative to controls [16,18]. This model ofcognitive fatigue offers an objective method for the assess-ment of cognitive fatigue. Given the support in behavioralstudies of MS that fatigue effects have been shown duringsustained mental effort, the current study will utilize a modelof sustained mental effort to investigate fatigue using func-tional MRI.

Despite the elusive nature of objectively measuring cog-nitive fatigue in clinical populations, interest in the potentialmechanisms responsible for fatigue in MS using neuroima-ging techniques has been increasing. Studies using structuralimaging techniques have been primarily negative, showinglittle to no relationship between subjective ratings of fatigueand conventional measures of MRI including brain volume,lesion load and brain atrophy [19–23], although a positiverelationship was found with MRI lesion burden by Colomboet al. [24]. In contrast, functional neuroimaging studies have

been somewhat more successful using fMRI [20], PET [25],and various neurophysiological techniques [26–28]. How-ever, all of these studies assessed fatigue subjectively. Thereare no functional imaging studies to date which haveexamined cognitive fatigue by examining fatigue effectsduring sustained mental effort in persons with MS. Thecurrent study is the first to examine the effects of sustainedmental effort on brain activation over time in MS.

The effects of sustained mental effort have been inves-tigated in healthy controls and typically brain activity de-creases during performance. Decreases over time could bedue to a number of factors including practice effects [29,30]or a switch from controlled to automatic processing [31], orpriming [32], or habituation [33]. However the temporal ef-fects of sustained mental processing in persons with MShave not been examined using fMRI. There is a great deal ofresearch to support the idea that clinical populations, such aspersons with MS, show generalized increased activationthroughout the brain compared to HCs, and this increasedactivation is often hypothesized to be due to increased effort[34]. We propose that this increased effort is linked to cog-nitive fatigue during a task of sustained mental effort.

The purpose of the present study was to examine theeffects of sustained mental effort over time on cerebral acti-vation, which we predicted would cause cognitive fatigue. Itwas hypothesized that MS subjects would show evidence ofcognitive fatigue compared to healthy controls and that thiswould manifest as a greater increase in functional cerebralactivity across time, in specific brain areas previously hy-pothesized to underlie fatigue [1], namely the basal ganglia,frontal lobes and the thalamus.

Critical to the current study is the fact that, to date, onlyCook et al. [35] have demonstrated a link between subjectivefatigue and brain activity using a cognitive paradigm. Theyused fMRI to investigate the neural systems associated withfatigue in a patient group known to suffer from fatigue(Chronic Fatigue Syndrome or CSF). CFS subjects reportedincreases in fatigue over time after performing a demandingcognitive task (the mPASAT). Furthermore, subjective re-ports of fatigue after task performance correlated with brainactivity in several areas including the cerebellum, the cin-gulate gyrus, superior temporal gyrus, inferior frontal gyrusand superior parietal cortex. While this correlation provides acritical, and hitherto elusive, link between subjective andobjective measures of fatigue, Cook et al. [35] did not ex-amine brain activity over time. That is, their analysis lookedat brain activity collapsed across all of the runs of themPASAT. Because Cook et al. [35] correlated collapsedmPASAT performance with fatigue measures assessed afterand before, but not during performance, the correlationalrelationship may not be causative, but due to some otherfactor, such as cognitive impairment in the CFS group com-pared to controls. What is needed is an examination ofaltered cerebral activity with actual changes in cognitiveperformance over time. This study represents an initial in-vestigation into this issue.

Fig. 1. An example of the stimuli. A ‘match’ is depicted.

30 J. DeLuca et al. / Journal of the Neurological Sciences 270 (2008) 28–39

The approach used in the present study is to examinechanges in brain activity over time (i.e., with repeated ad-ministration of a cognitive task). While we interpret thecerebral changes as “cognitive fatigue”, we recognize thatothers have interpreted increases in cerebral activation in MSrelative to HC samples as ‘compensation’ related to increasedtask ‘effort’. It should be recognized that in these studies,“compensation” is simply an interpretation – a hypothesis –which has not been proven, and has recently been challenged[36]. Nonetheless, given that our approach has been in a largepart theory driven [1], the interpretation of cognitive fatigueappears equally viable at this stage of inquiry.

2. Method

2.1. Participants

The current study included thirty participants: 15healthy controls (HCs), 6 of whom were women and whoranged in age from 22 to 53 years (M=35.4, SD=10.1),without any reported medical disabilities; and 15 partici-pants with clinically definite MS [37], 10 of whom werewomen and who ranged in age from 24 to 55 (M=40.8,SD=7.4). The MS group consisted of 12 subjects with arelapsing–remitting course and 3 with primary progressivecourse of the disease. The average time since diagnosis ofMS was 6.4 years (SD=4.9). The HC group had an averageof 15.8 years of education (SD=2.3) and the MS group hadan average of 15.6 years of education (SD=1.9). There wasno statistically significant difference between the twogroups in age (F(1, 28)=2.89, p=0.1), or in years ofeducation (F(1,28)b1), or in gender proportions (X2(1)=0.133p=0.72).

All MS participants were free of corticosteroid use, andnone had experienced an exacerbation within the one monthprior to the study. Neurologic disability was not assessed. Allsubjects were interviewed before beginning the study toensure they met the following criteria: no previous admissionto an alcohol/drug treatment program, no previously diag-nosed neurological disorder (e.g. stroke, seizure disorder),and no reported Axis I disorder.

Consistent with the policy of the University HeightsCenter for Advanced Imaging at the University of Medicineand Dentistry of New Jersey, subjects were also excluded ifthey had any metal in their bodies (e.g. cranial metal im-plants, cochlear implants, pace-makers), determined by ametal screening form and metal detector, or if they werepregnant, determined by a urine pregnancy test.

2.2. General procedure

Prior to final enrollment, all subjects provided informedconsent (approved by the Institutional Review Boards ofKessler Medical Research Rehabilitation and EducationCenter and the University of Medicine and Dentistry of NewJersey), and all experimental procedures complied with

HIPAA standards. The study lasted approximately 3 h, andall participants received $50 for participation.

On the same day as the MRI scanning, a battery of neu-ropsychological tests was administered to each participant.This battery assessed common cognitive functions known tobe impaired in individuals with MS, such as processingspeed, working memory, and new learning. Processing speedtasks included the oral version of the Symbol Digit ModalitiesTest (SDMT) [38], Trail-Making Test (TMT) A and B [39].Letter and Symbol Cancellation Tasks [40], and PacedAuditory and Serial Addition Task (PASAT) [41]. Generalintelligence was assessed with the Wide Range AchievementTest 3, Reading Subtest [42] (WRAT-3) andMatrix reasoning[43] (WAIS-III). Working memory was assessed with theDigit Span subtest of the WAIS-III [43] (WAIS-III). Verbalmemory was assessed using the Hopkins Verbal LearningTask [44] (HVLT).

2.3. Behavioral task

The task performed during the fMRI acquisition was amodified version of the Symbol Digit Modalities Task(mSDMT) [38], which has been modified for usage in fMRI[45] (see Fig. 1). While lying supine in the scanner, thesubjects viewed a panel of 9-paired stimulus boxes projectedonto a screen. The boxes in the upper row each contained asymbol and the boxes in the lower row each contained a digit(1–9). Below the panel of boxes were two paired “probe”boxes containing a digit and a symbol. The subject was re-quired to determine if the probe pair matched any of thestimulus boxes above and then respond “match” or “nomatch”by making a right or left thumb key-press, respectively.

The array of exemplar stimuli and the probe were pre-sented simultaneously, and remained on the screen for 6 s.Subjects were instructed to respond (match or mis-match) asquickly and as accurately as possible. In order to minimizelearning or practice effects, the 9 symbol–digit exemplarschanged with every presentation such that the pairing ofsymbols to digits randomly varied across trials. Over thecourse of 4 trials, 256 trials were presented. The inter-stimulus interval randomly varied between 0, 4, 8 or 12 s.Participants' response times and accuracy were recorded.

2.4. Magnetic resonance imaging procedure

Neuroimaging was performed at UMDNJ on the SiemensAllegra 3 TMRI. Sagittal T1-weighted images were obtained


before fMRI. Prior to acquiring the functional data, high-resolution (in-plane resolution=0.859 mm2), whole-brainaxial T1-weighted conventional spin-echo images wereobtained (TR/TE=450/14 ms, 32 contiguous 5 mm slices,256×256 matrix, FOV=24 cm, NEX=1). Functionalimaging consisted of multislice gradient echo, T2⁎-weightedimages acquired with echoplanar imaging (EPI) methods(TE=60 ms; TR=2000 ms; FOV=24 cm; flip angle=90°;slice thickness=5 mm contiguous), with an in-plane resolu-tion of 3.75mm2, in a 64×64matrix. Thirty-two axial imageswere acquired, which covered the entire brain. In order toprovide an additional set of T2-weighted structural images, aset of co-planar T2-weighted EPI structural images withidentical parameters were also obtained.

An overview of the fMRI task procedures was explainedto participants outside of the scanner in order to familiarizethem with the task. Prior to entering the scanner, subjectswere administered five practice trials of the mSDMT task. Ifan error was made, the subjects were given another fivetrials. This continued until the subject achieved correct per-formance on all 5 trails. Most subjects reached this criterionwith only one administration.

2.5. Data analysis

2.5.1. Cognitive fatigueCognitive fatigue was operationally defined as an

increase in cerebral activity across time (as indexed by theBOLD response). It was hypothesized that participants withMS would show a greater increase in cerebral activity on themSDMT across time compared to HCs. This is based onfMRI data with various clinical populations with repeatedadministration of a task [46]. In contrast, based on a con-siderable number of imaging studies which show decreasedfunctional cerebral activity with repeated presentation of atask [29,30,47,48], it was expected that HCs would showdecreases in activation over time. However, in order to avoidbeing overly restrictive, we kept our hypothesis as broad aspossible; positing only that participant's with MS wouldshow a greater increase over time, compared to HCs.

Because cognitive fatigue is poorly conceptualized, thepresent study developed two conceptual constructs for cog-nitive fatigue, based on published literature to date [15]:within-run fatigue and across-run fatigue. In assessing with-in-run fatigue, we evaluated cognitive performance withineach run, based on work showing that performance decreases(i.e., cognitive fatigue) from the first to the second half of acognitive task [15]. In assessing across-run fatigue, we ex-amined performance across four successive runs of themSDMT, and thus looked at fatigue across a longer time-scale. These operational definitions of cognitive fatigue re-sulted in an empirically-driven expectation about the patternof activation both within a run (within-run fatigue) and acrossruns (across-run fatigue). For within-run fatigue, we expectedan interaction between Run-half (1st half vs. 2nd half of eachrun) and Group (MS vs. HC) such that the MS group would

show a greater increase in activity from the 1st to the 2nd halfof the run than that seen in HCs. For across-run fatigue, weexpected an interaction between Run (1st, 2nd, 3rd, 4th) andGroup such that the MS group would show a greater increasein activity from one run to the next (the 1st to the 2nd, the 2ndto the 3rd, etc.) than HCs showed. We refer to these expectedinteractions as “fatigue interactions” (FI).

The preprocessing of the fMRI data was done in twosteps. After the first nine volumes of the time-series fromeach of the four trials were removed (in order to control forsaturation effects), SPM2 [49] was used. The raw time-seriesdata were realigned to one another, coregistered to the T1,normalized into standard steriotactic space (the standardtemplate from the Montreal Neurological Institute, MNI, wasused), and smoothed with an 8×8×10 mm Gaussian kernel.

The preprocessing was finished using the AFNI softwaresuite [50]. The time-series data was detrended, and all voxelsoutside the brain were excluded from analysis. The rawintensity values were then scaled to percent signal change.The hemodynamic response was modeled by a standardhemodynamic response function, and this was coded into thedesign matrix as a regressor. The events from each of the fourruns were sorted by run (Runs 1, 2, 3, 4) and by run-half (1stvs. 2nd), resulting in eight regressors (Run 1, 1st half; Run 1,2nd half; Run 2, 1st half; etc.). For each event, two regressorswere included in the model: the first was of unit amplitude,and modeled the trial itself; the amplitude of the second wasproportional to the RT on each trial (in order to ensure thatthe second regressor was orthogonal to the first, the mean RTfor each subject was subtracted from the RT on each trial).This second regressor was included to account for anyvariance that was specifically associated with task perfor-mance (RT). Contrasts were specified using the GeneralLinear Model, and a voxel-level probability threshold ofpb0.05, corrected for multiple comparisons, was used. Thecorrection for multiple comparisons was achieved by usinga voxel cluster-level threshold that was determined usingthe AlphaSim program, with Monte Carlo simulations. Thenumber of voxels in the cluster-level correction depended onthe number of voxels in the mask (see below), and the amountof spatial correlation in the simulation was determined byestimating the spatial correlation in the residual variance ofthe statistical tests.

In order to assess the effects of Group (MS vs. HC) and ofWithin-run fatigue (1st half of run vs. 2nd half), a 3-factor,mixed between- and within-subjects ANOVAwas used, withSubject as a random factor (using the 3dANOVA3 programin AFNI). In order to assess the effects of Group and Across-run fatigue (Runs 1, 2, 3, 4), a second 3-factor, mixedbetween- and within-subjects ANOVAwas used (again, withSubject as a random factor). Finally, in order to assess theinteraction of Long-term and Within-run fatigue and Group,a third 3-factor ANOVA was used in which Subject wasagain a random factor, Group was the between-subjectsfactor, and the difference between the first and second half ofeach run (the first half of Run 1 minus the second half, the

Table 1Neuropsychological performance scores by participants group

Domainassessed

MS Healthycontrols

t

Speed ofinformationprocessing

PASAT 121.4 (32.3) 151.8 (26.3) −2.9⁎⁎Cancel H 89.9 (17.7) 70.8 (14.1) 3.4⁎⁎

SDMT 50.1 (8.9) 60.3 (11.1) −2.9⁎⁎TMT (b–a) 38.9 (16.8) 31.2 (17.2) 1.3

Generalintelligence

WRAT 49.8 (4.9) 51.5 (3.7) −1.14Matrix reasoning 15.6 (5.8) 18.4 (6.0) −1.3

Memory HVLT 28.2 (4.8) 28.5 (2.8) −0.242Working

memoryDigit Span 9.1 (1.7) 9.1 (2.4) −0.075Forward Digit SpanBackward

6.9 (2.1) 7.8 (2.3) −1.3

⁎⁎pb0.01.


first half of Run 2 minus the second half, etc.) was the thirdfactor.

2.5.2. Cognitive fatigue interactions and ROI analysisData analysis proceeded in three steps: full-brain analysis,

fatigue-interaction (FI) analysis and FI+ROI analysis.Because very little imaging work has been done on fatigue,we began with a full-brain analysis in which any interactioninvolving Group was investigated. Monte Carlo simulationsrevealed that the cluster threshold necessary to correctfor multiple comparisons in this analysis was 231 contiguousvoxels.

In the second step, we constrained the analysis by in-vestigating only those areas in which the mean data showedthe fatigue interaction (a larger increase across time for theMS group relative to HCs). An FI map was calculated forboth short-term and across-run fatigue, and for the interac-tion of short- and long-term fatigue. Monte Carlo simulationsshowed that the cluster threshold necessary to correct formultiple comparisons for within-run fatigue was 79contiguous voxels, the threshold for across-run fatigue was

Fig. 2. Response time as a function of within- and across-run fatigue for both

21 contiguous voxels, and the threshold for the interaction ofshort- and long-term fatigue was 17 contiguous voxels.

Finally, we incorporated what is known about the neuralarchitecture of fatigue into our analysis, based on a model offatigue outlined by Chaudhuri and Behan [1]. Here, we useda region of interest (ROI) analysis in addition to the FIanalysis. These ROIs were based on specific regions outlinedby Chaudhuri and Behan [1] to be critically involved infatigue. The ROIs included were the middle and superiorfrontal cortices (including dorso-lateral prefrontal cortex),the cingulate cortices, the parietal cortices, the basal gangliaand the thalamus (in all cases, bilaterally). The cluster thres-holds were 39, 11 and 11 for short-term, long-term and theinteraction of short- and long-term, respectively.

3. Results

3.1. Neuropsychological tests

Of all the domains assessed (i.e., processing speed,workingmemory, new learning), reliable differences were onlyobserved on measures of processing speed between the twogroups, withMS participants performing slower thanHCs (seeTable 1). This is in agreement with previouswork showing thatprocessing speed is one of the primary deficits in MS [51,52].

3.2. Behavior

The response time (RT) data was analyzed with a 4×2×2mixed-design ANOVA (see Fig. 2). The within-subjectsfactors were across-run fatigue (ACROSS-F: Runs 1–4) andwithin-run fatigue (WITHIN-F: first half of each run vs. thesecond half) and the between-subjects factor was Group (MSvs. HC). Each of the main effects were reliable: ACROSS-F(F(3,26)=24.57, pb0.0001); WITHIN-F (F(1,28)=25.71,pb0.0001); Group (F(1,28)=4.23, pb0.05). The effect of

healthy controls (HC; circles) and individuals with MS (MS; triangles).


ACROSS-F resulted from subjects responding with shorterRTs as the experiment progressed (trial 1 to 4). The effect ofWITHIN-F resulted from subjects responding with shorterRTs in the second half of each block, relative to the first half.The effect of Group resulted from MS subjects respondingwith longer latencies than HCs. The effect of group wasmodulated by ACROSS-F, as indicated by a reliable inter-action (F(3,26)=3.21, pb0.05), and was due to the HCgroup showing a shallower acceleration curve across thetrials than the MS group. Additionally, ACROSS-F inter-acted with WITHIN-F (F(3,26)=7.44, p=0.001). This wasdue to the effect of WITHIN-F (shorter RTs in the secondhalf of a trial than the first half) reversing in trial 3. The onlyother interaction to approach conventional levels of signif-icance was between WITHIN-F and Group (F(1,28)=3.95,p=0.057), and was due to the MS group showing a largerWITHIN-F effect than the HC group.

No group differences in accuracy of performance on themSDMT were observed with all subjects performed a nearceiling in accuracy (MS: 98%; HC: 96%).

3.3. fMRI

3.3.1. Within-run fatigue vs. GroupThe whole-brain analysis of within-run fatigue (first half

of each run vs. second half) revealed only one interactionthat survived the correction for multiple comparisons: a

Fig. 3. Activity in orbital frontal gyrus. The axial, coronal and sagittal views are sh

medial/orbital frontal activation in BA 11/25 (see Fig. 3 andTable 2). When we focused on the areas that showed afatigue interaction, we found three areas that survived cor-rection for multiple comparisons: the same medial/orbitalfrontal activation, an activation in the inferior parietal lobe(BA 40), and an activation in occipital cortex (BA 19; seeFigs. 3 and 4 and Table 3). Finally, when we restricted ourinvestigation to ROIs that have been previously implicated infatigue, no activations were evident.

3.3.2. Across-run fatigue vs. GroupThe whole-brain analysis of across-run fatigue (Runs 1, 2,

3, 4) revealed interactions in frontal areas including superior,medial, middle and inferior (see Table 2), as well as inter-actions in parietal (precuneus and cuneus), occipital (BA 18)and basal ganglia (caudate, see Figs. 5 and 6). When wefocused the analysis on areas that showed the FI, only two ofthe above remained: the precuneus in the parietal cortex andthe caudate in the basal ganglia (see Table 3 and Figs. 5 and 6).Finally, when we confined the analysis to areas previouslyimplicated in fatigue (FI and ROI), only two areas were re-liable: a region in the superior parietal lobe (BA 7) and thecaudate in the basal ganglia (see Table 4 and Figs. 5 and 6).

3.3.3. Short-term vs. across-run fatigue vs. GroupThe whole-brain analysis of the interaction between

Long- and Short-term fatigue and Group revealed two areas

own for the interaction (inset graph) between group and within-run fatigue.

Table 2Results, whole-brain analysis

BA Left Right Volume

X Y Z X Y Z

Group×within-run fatigueFrontal

Medial/orbitalfrontal gyrus

11/25 −4 31 −19 305

Group×across-run fatigueFrontal

Superior/medialfrontal gyrus

BA 6 0 1 65 241

Middle/superiorfrontal gyrus

8/9 −36 17 47 40 31 35 664/1723

Middle frontalgyrus

BA 10 −32 53 19 349

Inferior/middlefrontal gyrus

45/46 −50 29 17 349

Inferior frontalgyrus

BA 47 −40 25 −25 280

ParietalPrecuneus BA 7 2 −55 59 2266Cuneus/precuneus

BA 31 −24 −81 25 1147

OccipitalLingual gyrus BA 18 4 −85 −21 575

Basal gangliaCaudate/caudatehead

0 11 −9 391

Group×across-×within-run fatigueFrontal

Superior frontalgyrus

BA 9 −18 53 27 1369

Inferior frontalgyrus

BA 45 −58 19 9 404


of reliable differences in frontal cortex (see Table 2): superiorand inferior frontal gyri (BA 9 and 45, respectively). Whenthe analysis was focused to either just the FI or the FI andROI, no areas were found to be active.

4. Discussion

The aim of the present study was to investigate the neuralcorrelates of cognitive fatigue in persons with MS. Ratherthan assessing fatigue subjectively, we examined the cerebralactivation associated with behavioral performance in bothMS and healthy controls over time, which was interpreted asfatigue. Subjects were required to perform a simple cognitivetask (the modified symbol–digit modalities test) repeatedly,across the course of an hour-long scanning session. Asexpected, all subjects showed improved performance acrossthe course of the experiment (the ‘learning curve’), andperformance accuracy did not differ between the MS andcontrol groups, although reaction time was significantlylonger in the MS group. However, because of the knownprominence of fatigue among persons with MS, it wasexpected that continual performance of even this simple task

would result in cognitive fatigue among MS participants, andthat this would be evident in their pattern of brain activity, asindexed by the BOLD response. Thus, it was hypothesizedthat individuals with MS would show an abnormal pattern ofactivity across time in specific brain areas previously hy-pothesized to subserve fatigue [1]. These areas included thebasal ganglia, frontal areas (orbital frontal, middle frontal,and medial frontal), thalamus, and superior parietal areas.Specifically, it was hypothesized that while performing thetask, persons with MS would show a relative increase incerebral activation across time compared to that seen inhealthy controls, which was interpreted as fatigue. This hy-pothesis was investigated by examining both activity changewithin each run and also across successive runs. A three-tiered approach to data analysis was taken. The first was anunconstrained whole-brain analysis, typical of exploratoryfMRI studies. The second approach was a whole-brain anal-ysis which was constrained to only examine a specific anddirectional interaction between group and cognitive fatiguedefined specifically as an increase in activation over timerelative to controls. The third approach was to conduct thisconstrained analysis, but only in regions hypothesized to becritical for “central” fatigue by the Chaudhuri and Behan [1]model. In support of our hypothesis, the first whole-brainanalysis showed the expected interaction (Fatigue×Group) infrontal and parietal areas, as well as occipital areas (within-run fatigue), and in parietal and basal ganglia (across-runfatigue). Interestingly, despite an unconstrained analyticalapproach and a heterogeneous MS population in terms ofdisease course, the areas showing significant activation weremainly those identified by the Chaudhari and Behan [1]model. The occipital activation is not surprising given the factthat the SDMT is a visual task. The results of the second-tieranalysis were very similar to the first, showing areas ofsignificant activation in the areas predicted by the Chaudhariand Behan [1] model. Lastly, when the analysis was con-strained to only those regions implicated in fatigue by themodel, the expected interaction was once again observed,although significant activations were observed only in parietalcortex and basal ganglia, and only for across-run fatigue.

Chaudhuri and Behan [1] hypothesize that “central fa-tigue” is a function of the non-motor function of the basalganglia. As such, the analysis in the present study wasfocused primarily on the basal ganglia and striato-corticalconnection with the frontal lobes. These authors suggest that“alterations in the normal flow of sequential activationwithin the basal ganglia system affecting the neural inte-grator and the cortical feedback by the associated loop of thestriato-thalamo-cortical fibers is a possible mechanism ofcentral fatigue …” (p179). The findings of the present studyof altered cerebral activation within the basal ganglia andfrontal lobes during cognitive fatigue lend direct support tothe Chaudhuri and Behan [1] model. The results of thesecond-tier analysis (in which the analysis was constrainedto the ‘fatigue interaction’, but not to the regions proposed byChaudhuri and Behan [1]) indicate that several regions beyond

Fig. 4. Activity in parietal cortex in the interaction (inset graph) between Group and within-run fatigue (blue arrows). The activity in occipital regions can also beseen (red arrow).

Table 3Fatigue-interaction analysis

BA Left Right Volume

X Y Z X Y Z

Group×within-run fatigueFrontal

Medial/orbitalfrontal gyrus

11/25 −4 31 −19 302

ParietalInferior parietallobule

BA 40 44 −39 53 103

OccipitalFusiform gyrus BA 19 50 −67 −17 165

Group×across-run fatigueParietal

Precuneus BA 7 20 −69 45 24Basal ganglia

Caudate/caudatebody

−16 −7 19 58


those proposed by their model are associated with fatigue.Several other studies investigating cognitive impairment inMS have reported similar hyperactivation in these brainregions – specifically, orbital frontal [53] and inferior parietalcortex [54] – while subjects were performing cognitivelydemanding tasks. Therefore, it is possible that the hyperactiva-tion reported in these studies may reflect cognitive fatigue.

While the present study is not the first to investigate theneural correlates of fatigue, it is the first to define cognitivefatigue based on an objective neurophysiologic measure, andto use a theory-driven analytical approach in persons withMS. In contrast to the relatively few previous neurophysio-logical investigations of cognitive fatigue, we defined cogni-tive fatigue as a relative increase in cerebral activation acrosstime based on objective performance. This approach waschosen in part because of the large (and growing) literaturethat have found increased brain activity in patient groups,relative to controls, despite comparable levels of behavioralperformance across groups [35,55–57]. Such studies inter-pret the increased cerebral activity as “compensation” forcognitive problems which somehow allow patients to main-tain adequate behavioral performance. While fatigue wasNOT the aim of many of these prior studies, they nonethelesshypothesized that the altered cerebral activation may rep-resent the extra “effort” (i.e., allocation of more neural re-sources) required to maintain the same level of performance[57,58]. The results of the present study supports this notion

that increased cerebral activation may represent the addi-tional “effort” (i.e., cognitive fatigue) to adequately performbehavioral tasks in persons with MS. It is possible that thisextra effort may not necessarily represent “compensation”,but may actually reflect cognitive fatigue. Unfortunately, wesimply do not know enough about this common finding in

Fig. 5. Activity in parietal cortex in the interaction (inset graph) between Group and across-run fatigue.

Fig. 6. Activity in caudate in the interaction (inset graph) between Group and across-run fatigue.


Table 4Fatigue interaction and ROI analysis

Group×across-run fatigue Left Right Volume

X Y Z X Y Z

ParietalSuperior parietal lobule BA 7 24 −67 45 18

Basal gangliaCaudate/caudate body −16 −7 19 35


the clinical imaging studies to formulate a definitive inter-pretation. For instance, it is possible that while compensationmay indeed be occurring, compensation itself might lead tocognitive fatigue. Equally, cognitive fatigue might requirethe recruitment of additional brain areas to compensate forthe extra ‘effort’ required for continued task performance(i.e., compensation). That is, compensation and fatigue arenot mutually exclusive explanations for increases in activityin MS. Unfortunately, in the current study subjective fatiguewas not assessed, making the distinction between compensa-tion and cognitive fatigue difficult to determine. Futurestudies should incorporate both subjective fatigue ratingsand objective neurophysiological measures in order to dis-tinguish between these two interpretations. While such is-sues of interpretation is the purview of most functionalimaging studies, the approach taken in the current study maypave the way for a host of new studies examining cognitivefatigue in clinical populations.

Another perspective addressed by the current study is thenotion that fatigue must result in decreased performance.This expectation likely stems from motor studies where per-formance reduction is how fatigue is defined. However,many patients complain that while they are often able toadequately perform required activities (e.g., work), the con-sequence of such maintenance is increased fatigue. There arenumerous investigations which show increases in subjectivefatigue with no change in behavioral performance relative tocontrols, across various clinical populations [55–57,59–61]or where behavioral performance actually increases withincreased fatigue [35]. As such, it is time for clinicians andresearchers studying fatigue to accept the fact that fatiguedoes not require diminished performance in order for pa-tients to experience fatigue. Perhaps it is this expectation ofbehavioral decrement as a definition of fatigue, which may atleast in large part, lie at the root of the failure of finding asignificant relationship between subjective and objectivelymeasured fatigue in various populations. Related, the expec-tation that subjective fatigue and objective performancemust correlate may also lead to false impression regardingthe validity of objective measures of fatigue, since they havetraditionally not correlated with the subject assessments.Thus, it may be possible that using cerebral activity as agauge for fatigue may result in improved correlation withsubjective fatigue in persons with MS. That is, while be-havioral performance may not change, what patients may

be experiencing subjectively is the “extra effort” required tomaintain that constant level of behavior. Unfortunately, asubjective measure of fatigue was not included in the presentstudy, so this question remains unanswered in persons withMS, and must await future work. Indeed, although cognitivefatigue is common in MS, not all patients suffer from it, andwe can therefore not be certain that all MS patients in thecurrent sample experienced fatigue or to what extent.However, the recent work in persons with CFS showingthat subjective ratings of fatigue are associated with changesin functional cerebral activity as measured by fMRI duringfatiguing cognitive performance [35] holds promise that thesame will be shown in persons with MS. It is also possiblethat the results observed might be specific to the paradigmused, or other idiosyncratic aspects of our experiment. Thispossibility seems less likely inasmuch as Cook et al. [35]showed similar effects with a different patient population,performing a different task.

While the results of the present study showed increasedcerebral activation over time – here interpreted as cognitivefatigue – some recent neuroimaging studies examining theneural correlates of subjective fatigue have reported deacti-vations. For instance, Filippi et al. [20] examined 15 MSsubjects with subjective fatigue with 14 MS subjects withoutfatigue on a simple motor task using fMRI. MS subjects withfatigue showed significantly lower activation in cortical andsubcortical regions involved in motor planning and execu-tion relative to MS subjects without fatigue. Roelcke et al.[25] compared MS subjects with and without subjectivefatigue using PET scanning. They found that MS subjectswith fatigue showed reduced glucose metabolism particu-larly in the frontal lobes and basal ganglia relative to non-fatigued MS subjects. While we too found effects in thesebroad areas, direct comparisons are difficult because Roelckeet al. [25] report no coordinates for their activations. It isunclear why these studies report hypoactivation in contrast tothe hyperactivation observed in the current study. One majordifference between our study and previous studies is thatours is the first to examine cognitive fatigue during a sus-tained cognitive task using fMRI as a measure of fatigue.Filippi et al. [20] and Roelcke et al. [25] studied fatigueduring a motor task and during rest, respectfully. Therefore,it is quite possible that different neural networks underliemotor fatigue and cognitive fatigue, resulting in differentpatterns of activation. Additionally, another explanationconcerns the way in which the experiments were analyzed. Inthe current study, the effects of fatigue were examined acrosstime (across each run) whereas in other studies, activation isexamined overall. Also, Filippi et al. [20] and Roelcke et al.[25] subdivided their samples into those who reported highfatigue and low fatigue while we did not make this differ-entiation. Lastly, in the only studies we are aware of whichrelated subjective fatigue with fatiguing cognitive perfor-mance during fMRI scanning [57,35], similar to the resultsof the present study, these two studies showed primarily in-creased cerebral activity in persons with CFS. Future studies


will need to examine the differential results of increased vs.decreased cerebral activity during cognitive fatigue.

The current study utilized a unique and innovative ap-proach in that fatigue was assessed by functional brainactivity during actual behavioral performance. Most studiesexamine fatigue through self-report instruments, an approachwhich benefits from ease of obtaining data, but suffers fromdifficulties in interpretation. For example, self-reported fa-tigue most often correlates with degree of psychopathology[62]. In addition, the most consistent finding over 100 yearsof inquiry is that self-reported fatigue does not correlate withobjective measures of fatigue, across various populations(e.g., healthy, MS, TBI, Stroke, heart disease). The pres-ent study's different and unique approach to the study ofcognitive fatigue opens the door for a fresh look at the elusiveconstruct of fatigue which may lead to a better understandingof its neural underpinnings and its behavioral consequences.

Acknowledgments

This study was supported in part by a National MultipleSclerosis Society grant (RG3330A1/3), and by the Henry H.Kessler foundation.

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