Neurocognitive signs in prodromal Huntington disease.

Neurocognitive Signs in Prodromal Huntington Disease

Julie C. StoutMonash University

Jane S. PaulsenUniversity of Iowa

Sarah QuellerIndiana University

Andrea C. SolomonUniversity of Alabama at Birmingham

Kathryn B. Whitlock and J. Colin CampbellIndiana University

Noelle CarlozziKessler Foundation Research Center, West Orange, NJ

Kevin Duff, Leigh J. Beglinger, andDouglas R. Langbehn

University of Iowa

Shannon A. JohnsonDalhousie University

Kevin M. BiglanUniversity of Rochester

Elizabeth H. AylwardUniversity of Washington

The PREDICT-HD Investigators and Coordinators of the Huntington Study Group

Objective: PREDICT-HD is a large-scale international study of people with the Huntington disease (HD) CAG-repeat expansion who are not yet diagnosed with HD. The objective of this study was to determine the stage in theHD prodrome at which cognitive differences from CAG-normal controls can be reliably detected. Method: For eachof 738 HD CAG-expanded participants, we computed estimated years to clinical diagnosis and probability ofdiagnosis in 5 years based on age and CAG-repeat expansion number (Langbehn, Brinkman, Falush, Paulsen, &Hayden, 2004). We then stratified the sample into groups: NEAR, estimated to be �9 years; MID, between 9 and 15years; and FAR, �15 years. The control sample included 168 CAG-normal participants. Nineteen cognitive taskswere used to assess attention, working memory, psychomotor functions, episodic memory, language, recognition offacial emotion, sensory–perceptual functions, and executive functions. Results: Compared with the controls, theNEAR group showed significantly poorer performance on nearly all of the cognitive tests and the MID groupon about half of the cognitive tests (p � .05, Cohen’s d NEAR as large as �1.17, MID as large as �0.61).One test even revealed significantly poorer performance in the FAR group (Cohen’s d � �0.26). Individualtasks accounted for 0.2% to 9.7% of the variance in estimated proximity to diagnosis. Overall, the cognitivebattery accounted for 34% of the variance; in comparison, the Unified Huntington’s Disease Rating Scalemotor score accounted for 11.7%. Conclusions: Neurocognitive tests are robust clinical indicators of thedisease process prior to reaching criteria for motor diagnosis of HD.

Keywords: cognitive assessment, presymptomatic, neuropsychology, psychomotor, prediagnosis

Supplemental materials: http://dx.doi.org/10.1037/a0020937.supp

Huntington disease (HD) is a progressive, fatal, autosomal dom-inant neurodegenerative disease that primarily affects movement,cognition, and psychiatric functions. Diagnosis of HD is based on

the presence of unequivocal motor signs of HD, in conjunctionwith a positive genetic test for the HD CAG expansion or aconfirmed family history of HD. Most people with the HD gene

This article was published Online First October 4, 2010.Julie C. Stout, School of Psychology, Psychiatry, and Psychological

Medicine, Monash University; Jane S. Paulsen, The Roy J. and LucilleA. Carver College of Medicine, University of Iowa; Sarah Queller,Kathryn B. Whitlock, and J. Colin Campbell, Department of Psycho-logical and Brain Sciences, Indiana University; Andrea C. Solomon,Department of Neurology, University of Alabama at Birmingham;Noelle Carlozzi, Kessler Foundation Research Center, West Orange,NJ; Kevin Duff, Leigh J. Beglinger, and Douglas R. Langbehn, Depart-ment of Psychiatry, University of Iowa; Shannon A. Johnson, Depart-ment of Psychology, Dalhousie University; Kevin M. Biglan, ClinicalTrials Coordination Center, University of Rochester; Elizabeth H. Ayl-ward, Department of Radiology, University of Washington; and ThePREDICT-HD Investigators and Coordinators of the Huntington StudyGroup. The full list of PREDICT-HD Investigators, Coordinators, Mo-

tor Raters, and Cognitive Raters who took part in this study can befound at http://dx.doi.org/10.1037/a0020937.supp

Elizabeth H. Aylward is now at Seattle Children’s Hospital in Seattle,WA. J. Colin Campbell is now at School of Psychology, Psychiatry andPsychological Medicine, Monash University. Kevin Duff is now at De-partment of Neurology, University of Utah.

This work was supported by the National Institutes of Health, National Instituteof Neurological Disorders and Stroke [NS40068], and CHDI Foundation, Inc. Wethank the HD participants who volunteer their time to assist in clinical research;without their commitment, progress in HD trials would not be possible.

Correspondence concerning this article should be addressed to Jane S.Paulsen, University of Iowa Carver College of Medicine, Iowa City, IA52242-1000. E-mail: [email protected]; and Julie C. Stout, MonashUniversity, School of Psychology, Psychiatry, and Psychological Medicine,Victoria 3800, Australia. E-mail: [email protected]

Neuropsychology © 2010 American Psychological Association2011, Vol. 25, No. 1, 1–14 0894-4105/10/$12.00 DOI: 10.1037/a0020937

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appear healthy throughout their youth and early adulthood, andthen gradually develop signs and symptoms of HD, often leadingto a diagnosis in middle age. The age of diagnosis varies inaccordance with the number of CAG repeats on the expandedallele, although there is also substantial individual variability notaccounted for by this genetic factor (Andrew et al., 1993; Gusella,MacDonald, Ambrose, & Duyao, 1993). A growing community ofresearchers is directing efforts at finding treatments to delay onsetor slow the progression of early pathological changes in an attemptto reduce the tremendous personal and social costs of HD. Findingeffective therapeutic or preventive treatments for HD dependscritically on the ability to reliably and sensitively measure clinicalsigns of disease.

Cognitive measures have excellent potential both for identifyingindividuals beginning to show subtle signs prior to the diagnosis ofHD who might be suitable for clinical trials and as sensitiveoutcome measures in HD trials. Cognition is an important targetfor therapeutic trials because even subtle cognitive changes canaffect functional abilities such as work performance, driving, andfinancial management. Cognition is actively studied in the HDprodrome, with more than 150 empirical reports of neurocognitivefunction published since the genetic mutation for HD was identi-fied in 1993. In early studies, the evidence of cognitive dysfunctionwas inconsistent. But more recent, adequately powered studieshave revealed that people with the HD prodrome have poorerperformance on measures of attention, working memory, process-ing speed, psychomotor functions, episodic memory, emotion pro-cessing, sensory–perceptual functions, and executive functions(Johnson et al., 2007; Kirkwood et al., 2000; Paulsen, Hayden, etal., 2006; Paulsen et al., 2001, 2008; Pirogovsky et al., 2007; e.g.,Stout et al., 2007).

The use of cognitive measures in clinical trials requires a sub-stantial knowledge base, indicating when in the HD prodromecognitive changes can be reliably detected and which measuresshow adequate sensitivity. The PREDICT-HD study was designedto address these questions. PREDICT-HD is a 32-site internationalstudy of potential clinical and biological markers of HD in indi-viduals with the CAG expansion for HD, but who did not meetcriteria based on motor impairment for diagnosis at the time ofenrollment. The PREDICT-HD cohort is large, with more than 700nondiagnosed CAG-expanded (prodromal) participants. For eachparticipant, age-conditioned estimates of time to onset of motordisease were derived using a survival analysis regression equationbased on CAG repeat length (Langbehn, Brinkman, Falush,Paulsen, & Hayden, 2004). Estimated time to motor disease onsetin the sample varies from 5 to 25 years. The size and diversity of thissample make it possible to stratify participants on the basis of theirestimated proximities to diagnosis, allowing an estimation of when, inthe extended period leading up to HD diagnosis, cognitive signs canbe detected, and which cognitive measures appear to have the mostpromise for detecting change in longitudinal studies.

We report findings based on three stratified prodromal HDgroups, a far from diagnosis group (FAR) estimated to be morethan 15 years from diagnosis; a middle group, estimated to bebetween 9 and 15 years from diagnosis (MID); and a near diag-nosis group, estimated to be less than 9 years from diagnosis(NEAR). For each of these groups compared with controls, wecomputed effect sizes for individual cognitive measures, allowinga direct comparison of all the tasks in our cognitive assessment.

This allowed a determination of which tasks are most adverselyaffected during the HD prodrome and an upper limit estimate ofhow early in the prodromal phase significant effects can be de-tected (Paulsen et al., 2008). In addition, we examined the asso-ciations between cognitive findings and results of the clinicalmotor examination, which also reveal subtle signs prior to diag-nosis (Biglan et al., 2009). Thus, this study provides a compre-hensive depiction of the cognitive prodrome for HD.

Method

Participants

Data from 906 participants in the PREDICT-HD study, includ-ing 738 prodromal HD participants and 168 control participants,were included in these analyses. These data were collected at 32sites in the United States, Canada, Australia, Germany, Spain, andthe United Kingdom from 2002 to 2008. Consent was obtainedaccording to the Declaration of Helsinki and study protocol wasapproved by the Institutional Review Board at The University ofIowa as well as institutional review boards at each participatinginstitution.

All participants had a family history of HD and prior voluntary,independent genetic testing for the HD CAG expansion; prodromalHD participants had the expansion (�36 CAG repeats) and controlparticipants did not (�36 CAG repeats). Exclusion criteria in-cluded clinical evidence of unstable medical or psychiatric illness,alcohol or drug abuse within the previous year, learning disabilityor mental retardation requiring special education, history of othercentral nervous system disease or events such as seizures or headtrauma, pacemaker or metallic implants, age younger than 18years, prescription of antipsychotic medications within the past 6months, and use of phenothiazine-derivative antiemetic medica-tions more than 3 times per month. Other prescribed, over-the-counter, and natural remedies were not restricted.

Genetic Status

Confirmatory DNA testing was completed using blood drawn atthe baseline visit. CAG-repeat length for each participant wasdetermined using a polymerase chain reaction method (Warner,Barron, & Brock, 1993).

Motor Examination

Participants were evaluated using a standardized neurological ex-amination, the Unified Huntington’s Disease Rating Scale (UHDRS;Huntington Study Group, 1996), which includes 31 items assess-ing chorea, bradykinesia, rigidity, dystonia, and oculomotor func-tion. Item scores range from 0 (no impairment) to 4 (impairment),and all item scores are summed to create total motor scores (seeTable 1). In addition to the motor scores, the motor examiner alsoassigned a rating indicating the level of the examiner’s confidencethat any observed motor signs were a manifestation of HD. Con-fidence level ratings ranged from 0 (normal, no abnormalities) to 4(motor abnormalities unequivocal signs of HD; �99% confi-dence). Only participants with diagnostic confidence level ratings�4 are included in the current report.

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Proximity to Clinical Diagnosis

For each participant, we computed estimated years to diagnosis(as defined by the presence of unequivocal motor signs of HD) andthe probability of diagnosis in 5 years on the basis of the partici-pant’s age and the number of CAG repeats on the expanded allele(Langbehn et al., 2004; Langbehn, Hayden, & Paulsen, 2009;Paulsen et al., 2008). Given previous findings indicating thatneurocognitive symptoms are more prominent in individuals withthe HD genetic mutation who are estimated to be closer to diag-nosis (Paulsen et al., 2001, 2008; Stout et al., 2007), we stratifiedour prodromal HD sample into three groups to allow a focusedexamination of neurocognitive differences in the HD prodrome:NEAR to diagnosis (�9 years), MID (9 to 15 years), and FARfrom diagnosis (�15 years). The groups represent approximateterciles of CAG-expanded participants, rounded to the nearestestimated year to diagnosis.

Demographics

Approximately two thirds of the PREDICT-HD study partici-pants were women; however, female overrepresentation is com-mon in observational studies of HD, and the ratio of women to mendid not differ across the NEAR, MID, FAR, and control groups( p � .09). For prodromal HD participants, those participants whowere estimated to be nearer to the clinical diagnostic thresholdwere older, on average, than participants farther from their esti-mated age of diagnosis ( p � .0001). This relationship was antic-ipated because age and CAG length interact to determine estimatedyears to diagnosis. The control group was older than the MIDgroup and younger than the NEAR group. Education levels did notdiffer significantly by group ( p � .13; see Table 1).

Estimate of Premorbid Intellectual Functioning

Participants in the United States, Canada, and Australia com-pleted the American National Adult Reading Test (ANART;Grober & Sliwinski, 1991; Schwartz & Saffran, 1987) and partic-ipants in the United Kingdom completed the National Adult Read-ing Test (NART-2; Nelson & Willison, 1991). These tests assesspronunciation of nonphonetic, low-frequency English words such

as prelate and chamois and are used to retrospectively estimate anindividual’s verbal premorbid intelligence. Such tests are used tocontrol for individual differences in intelligence that are unrelatedto illness. The Word Accentuation Test (WAT), used in Spain,assesses pronunciation of low-frequency Spanish words, absentwritten accents to guide pronunciation (Del Ser, Gonzalez-Mon-talvo, Martinez-Espinosa, Delgado-Villapalos, & Bermejo, 1997).German participants completed the Wortschatztest (WST; Schmidt& Metzler, 1992), which involves discriminating written Germanwords from nonword alternatives. An estimated premorbid verbalIQ was calculated based on raw scores. For the NART-2, ANART,and WST, look-up tables from test manuals allowed conversionfrom raw errors to estimated verbal IQ. We were unable to identifystandards for conversion to verbal IQ for the WAT, so we esti-mated IQ by standardizing the participant’s raw WAT scores to themean and standard deviation of estimated verbal IQs for all otherparticipants in the study. This is admittedly a rough method forestimating verbal IQ from the WAT; however, it affected only 1%of our sample and we chose to use this rough estimate overdropping the Spanish speakers from our analyses. Estimated pre-morbid verbal IQ did not differ across the NEAR, MID, FAR, andcontrol groups ( p � .49).

Neurocognitive Assessment

Neurocognitive performance was assessed using a comprehen-sive battery of neuropsychological and cognitive tasks designed tomaximize sensitivity to frontostriatal neural circuitry. Tasks wereselected to assess a broad range of cognitive functions that areknown to be affected in the early stages of HD. The PREDICT-HDneurocognitive battery alternates between a longer battery admin-istered in odd-numbered years (Years 1, 3, 5, etc.) and a shorterbattery administered in even-numbered years (Years 2, 4, 6). Wereport cross-sectional baseline data corresponding to the initialadministration of each task, most of which occurred at Visit 1, butthat also included several tasks administered for the first time atVisit 2 (see Table 2).

The cognitive assessment included a total of 19 separate tasks,each of which we report here. Because each of the tasks yieldedseveral variables, we employed a data reduction strategy to select

Table 1Participant Characterization

Characteristic NEAR MID FAR Controls p

Visit 2 sample sizes 202 268 268 168Gender 118F, 84M 166F, 102M 181F, 87M 116F, 52M .09Age (years) 44.5 � 9.8 342.4 � 9.9 37.0 � 8.0 43.9 � 11.1 �.0001Education (years) 14.1 � 2.7 14.3 � 2.7 14.5 � 2.5 14.7 � 2.7 .13Estimated IQ 111.8 � 8.2 112.0 � 8.2 112.3 � 7.2 112.9 � 7.4 .49CAG repeat length 44.3 � 2.9 42.6 � 2.1 41.1 � 1.6 19.8 � 3.5 �.0001UHDRS motor score 7.4 � 6.5 4.8 � 4.8 3.5 � 3.8 2.5 � 3.1 �.0001

Note. NEAR � estimated to be � 9 years to Huntington disease (HD) diagnosis; MID � between 9 and 15years to HD diagnosis; FAR � � 15 years to HD diagnosis; F � female; M � male; UHDRS � UnifiedHuntington’s Disease Rating Scale. Values shown are means � standard deviations except gender and samplesizes, which are counts. Scheffe tests indicate the following post hoc findings: (a) For age, the FAR group wasyounger than all other groups, and there were no other pairwise differences; (b) for CAG lengths, all pairwisecomparisons are significant; (c) for UHDRS motor scores, all pairwise comparisons are significantly differentexcept controls compared with the FAR group.

3NEUROCOGNITION IN PRODROMAL HD

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Table 2Effect Sizes (Cohen’s d) in the NEAR, MID, and FAR Groups

Task and variable

d

NEAR MID FAR

Benton Facial Recognition Test: Total correct �0.42��a �0.39�� 0.13Category learning task: Rule-based

Maximum % correct in a block �0.12 �0.11 �0.07% blocks completed �0.50��a �0.32 �0.20

Category learning task: NonverbalizableMaximum % correct in a block �0.45��a �0.25 �0.21% blocks completed �0.11 �0.09 �0.02

Dynamic emotion recognition task: Number correct (negative emotions only) �0.84��a �0.41�� 0.05Emotion recognition task: Number correct (negative emotions only) �1.10��a �0.61�� 0.26�

Hopkins Verbal Learning Test—RevisedTotal learning (Trials 1–3) �0.95��a �0.48�� 0.18Delayed recall �0.75�� 0.34�� 0.08Delayed recognition discriminability �0.77�� 0.41�� 0.22

n-back task: 1-back discriminability indexesFoils �0.57��a �0.27 �0.14Lures �0.44�� 0.20 0.02

n-back task: 2-back discriminability indexesFoils �0.56�� 0.13 �0.07Lures �0.64��a �0.18 �0.16

Phonemic verbal fluency: Total correct �0.51��a �0.29� �0.09Self-timed finger tapping taskb

Dominant index finger: Mean of ITIs 0.09 0.07 0.09Dominant index finger: SD of ITIs �1.06�� 0.50�� 0.10Alternating thumbs: Mean of ITIs 0.09 0.12 0.07Alternating thumbs: SD of ITIs �1.17��a �0.61�� 0.24

Cued movement sequence task: Low cue leveld

Mean �0.69�� 0.23 �0.02SD �0.72��a �0.25� 0.04

Cued movement sequence task: Medium cue leveld

Mean �0.41�� 0.12 0.16SD �0.775��a �0.181 0.075

Cued movement sequence task: High cue leveld

Mean �0.21 �0.18 0.03SD �0.48��a �0.26� �0.05

Serial response time task: Learningb

Learning effect (B5 – B4) 0.05a �0.14 0.02Interference effect (B6 – B4) 0.02 0.01 �0.11

Simple response time taskb

Mean �0.77��a �0.40�� 0.10SD �0.59�� 0.29� 0.04

Two-choice response time taskb

Mean �0.80��a �0.43�� 0.02SD �0.39�� 0.33�� 0.03

Speeded tapping taskb

Dominant index finger: Mean of ITIs �0.77�� 0.38�� 0.14Dominant index finger: SD of ITIs �0.74�� 0.43�� 0.10Nondominant index finger: Mean of ITIs �1.14��a �0.39�� 0.10Nondominant index finger: SD of ITIs �0.82�� 0.61�� 0.19Alternating thumbs: Mean of ITIs �0.75�� 0.35�� 0.09Alternating thumbs: SD of ITIs �0.94�� 0.55�� 0.12

Stroop color: Total correct �0.75��a �0.39�� 0.11Stroop word: Total correct �0.66��a �0.27� 0.03Stroop interference: Total correct �0.62��a �0.32�� 0.03Symbol Digit Modalities Test: Total correct �0.96��a �0.49�� 0.05Tower 3 task

Mean number of moves (4 trials) �0.24a �0.08 0.11Learning (T4 – T1) 0.02 �0.03 �0.02

Tower 4 taskMean number of moves (4 trials) �0.36��a �0.11 �0.15Learning (T4 – T1) �0.02 �0.09 �0.07

Trail Making Test A: Time to completion (s) �0.60��a �0.22 0.00Trail Making Test B: Time to completion (s) �0.80��a �0.33�� 0.10University of Pennsylvania Smell Identification Test: Total correct �1.04��a �0.36�� 0.12

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variables to include in this report. This data reduction strategyincluded (a) for standardized clinical tests, selection of the variableknown to have the best sensitivity and measurement characteristics(i.e., for Trails, number of seconds instead of number of errors);(b) for tests with multiple conceptually distinct measures, a vari-able that represented each component (i.e., for finger tapping tests,one variable indicating the speed of tapping and another indicatingtapping variability); and (c) where necessary, using statisticalmethods to exclude variables having little evidence of sensitivity.This latter approach involved computing results on all summaryvariables for a given computerized test. Using this approach forthe 19 tasks that each yielded several variables, we identified a setof 51 variables, with at least one variable from each of the 19original tasks. These 51 variables were used in our main presen-tation of effect sizes. Then, for the final set of analyses thatexamined the relationship between cognitive findings and theclinical motor examination, we reduced this number further whilestill maintaining at least one variable from each of the 19 tasks.From the total of 51 variables above, we reduced the set to 29variables, one from each conceptually distinct task component(e.g., one each for Trail A and Trail B) or difficulty level in thecognitive battery (e.g., tower tasks in the three-disk and four-diskversions).

All testing was conducted in the native language spoken at thestudy site. Thirty of the 32 sites were English speaking, and therewas one site in Germany and another in Spain. Translation of thecognitive assessment battery, which was developed first in En-glish, consisted of translations of all stimulus materials and in-structions used with study participants. Translation of these mate-rials generally occurred in three stages. First, translations werecompleted by a local translator involved in HD research who wasalso fluent in English. Next, a second translator, either local to thesite or a native speaker working with the study team and familiarwith cognitive assessment, checked the original translations andidentified any elements for which alternative translations could bewarranted. Third, discussion ensued between the two translators,and when necessary, included one of the authors (JCS or SAJ) toarrive at a consensus for the wording thought to best capture thenuances in the original English version. Translations of some testshad additional considerations, and these are noted where relevantwithin the description of those tests below.

Two computerized tower tasks, similar to Saint-Cyr, Taylor, andLang (1988), were used to assess planning and reasoning. For the

first task, Tower 3, participants viewed three vertical pegs. On onepeg was a stack of three disks of increasing sizes, with the largeston the bottom. Participants attempted to relocate the stack, inexactly the same configuration, to a different peg, following tworules: Only the topmost disk could be moved, and larger diskscould not be placed on smaller disks. Participants also completedTower 4, a three-peg task that used four disks and the same rulesas described above. Tower 3 and Tower 4 each included fouridentical trials; overall performance was assessed separately forTowers 3 and 4 by computing the mean number of moves per-formed across the four trials within each task. Learning was alsoassessed as the difference in the number of moves from the first tothe fourth trial for each task.

A computerized serial response time task (Willingham, Nissen,& Bullemer, 1989) was used to assess implicit learning of a motorsequence. Throughout the task, asterisks were presented serially inone of four locations. When an asterisk appeared, participantsresponded by pressing one of four buttons located on an externalresponse device. Response buttons were aligned with the fourscreen positions where the asterisks could appear. Participantsresponded using index and middle fingers from both hands, keep-ing their fingers positioned above the four buttons throughout thetask. For the first four blocks, asterisks appeared serially in a fixed12-asterisk sequence, which was repeated 8 times to allow learn-ing. In a fifth (interference) block, asterisks appeared in the fourlocations in random order. Finally, in the sixth block, asterisksagain appeared in the previously presented repeating sequence toascertain whether the random block affected any learning gainsthat had been achieved by the fourth block. Participants were neverinformed that it was a repeating sequence. Learning was assessedas the difference in response time between Block 5 (randomsequence) and Block 4 (fourth repetition of the same sequence).The impact of the random block (interference effect) was assessedby the difference between Blocks 6 and 4.

To examine rule-based learning of categories, we tested partic-ipants using the computerized category learning task, for which thedecision rule can be verbalized (Ashby & Maddox, 2005). Partic-ipants attempted to assign visual stimuli to either Category A or Bon the basis of the width and orientation of darker and lighterbands inside a circular frame. They were not told how to categorizethe stimuli, but were given corrective feedback after each trial tofacilitate learning. In the verbalizable category learning task, Cat-egories A and B were differentiated by a simple rule such that

Table 2 (continued)

Task and variable

d

NEAR MID FAR

WAIS–III Letter–Number Sequencing subtest: Total score �0.51��a �0.43�� 0.19WASI Matrix Reasoning subtestc: Raw score �0.24a �0.08 0.01WASI Vocabulary subtestc: Raw score �0.47��a �0.180 �0.13Unified Huntington’s Disease Rating Scale: Motor score �1.10�� 0.50�� 0.26�

Note. NEAR � estimated to be �9 years to Huntington disease (HD) diagnosis; MID � between 9 and 15 years to HD diagnosis; FAR � �15 yearsto HD diagnosis; ITI � intertap interval measured in milliseconds; B � block; WAIS–III � Wechsler Adult Intelligence Scale—Third Edition; WASI �Wechsler Abbreviated Scale of Intelligence.a The 29 variables included in models assessing neurocognitive plus motor. b Effect sizes for means and standard deviations of response times. c Tasksfirst administered at Visit 2. d Effect sizes for means and standard deviations of response times for accurately completed trials measured in milliseconds.� p � .05. �� p � .01. �� p � .001 for Dunnett’s test of mean differences in performance for each prodromal HD group compared with controls.


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width exceeding a criterion belonged to one category and widthless than that criterion belonged to the other category. Stimuli werepresented in blocks of 50 trials. Participants completed the task byachieving 92% accuracy in any given block, or the task continuedfor a maximum of six blocks (300 trials) if the accuracy criterionwas not met. We used two measures of performance: percentagecorrect in the block with the highest accuracy, and percentage ofblocks completed to reach the accuracy criterion.

To examine associative learning of categories, participants com-pleted a version of the computerized category learning task inwhich the decision rule is nonverbalizable (Ashby & Maddox,2005). The task works similarly to the rule-based category learningtask except that (a) the optimal decision rule requires integratinginformation from both width and orientation in a manner that couldnot be described verbally, (b) the accuracy criterion was 80%, and(c) there were 10 possible blocks. The accuracy criterion wasreduced and number of blocks increased because of the greaterlevel of difficulty of this task; however, the same two performancemeasures as assessed in the rule-based version of the task werecollected: percentage correct in the block with the highest accu-racy, and percentage of blocks completed to reach the accuracycriterion.

A computerized emotion recognition task (Johnson et al., 2007)was used to assess facial emotion recognition. Participants viewedphotographs of faces expressing one of six emotions or a neutralexpression (Ekman & Friesen, 1976). They then selected theemotion label that identified the expression from a multiple-choiceresponse set that included the words fear, disgust, happy, sad,surprise, anger, and neutral. There were 10 stimuli per emotion.The dependent variable was the total number correct using only thenegative emotions: anger, disgust, fear, and sadness (maximum �40). We opted to include only the negative emotion trials in thetotal score based on our earlier work in a subset of this sample,which suggested a relative deficit in recognition of negative emo-tions (Johnson et al., 2007).

In a separate task, dynamic emotion recognition, we examinedemotion recognition using a set of facial stimuli with and withoutsimulated movement. Again, participants chose from a multiple-choice response set including anger, disgust, fear, happy, sad, andsurprise (Spencer-Smith et al., 2001). For the trials without sim-ulated movement, an emotional expression of moderate intensitywas presented for 1 s. For the trials with simulated movement, aemotional expression of mild intensity was presented for 500 ms,followed by an emotional expression of moderate intensity foranother 500 ms. Again, only the four negative emotions wereincluded in the total score (maximum � 40).

A computerized n-back task (Kirchner, 1958) was used to assessverbal working memory. Participants viewed a series of letters, 3 sapart, and were asked to judge whether the current letter matchedthe previous letter (1-back condition) or the letter presented 2 back(2-back condition). The 1-back and 2-back conditions were pre-sented separately in 100 randomly ordered trials. Participantsresponded to every trial using the external response device, select-ing either MATCH or NONMATCH responses. Trials were ofthree types: matches, nonmatch foils, and nonmatch lures. Foilswere unambiguous nonmatches. Lures, more easily confused withmatches, were defined as trials in which the current trial’s letterwas presented near the n-back position. For the 1-back condition,lures were in the 2-back position, and for the 2-back condition,

lures were in either the 1-back or 3-back position. Discriminabilityindexes, comparing the ability to discriminate either lures frommatches or foils from matches, were computed separately for the1-back and 2-back conditions.

Precision of movement timing was examined in a self-timedfinger tapping task, which used a response box interfaced to thecomputer (Hinton et al., 2007; Paulsen et al., 2004). Participantslistened to a tone repeated at 1.8 Hz, and when ready, begantapping along with the tone. The tone then continued for 11 moretaps. After the tone stopped, participants were to continue tapping,at the same rate, until 31 taps had occurred without the pacingtone, at which point the trial ended. This sequence was repeated forfive trials. Means and standard deviations of intertap intervals overthe five trials are reported for two conditions: dominant hand indexfinger and thumbs in alternation.

The speeded tapping task, which is another computerized taskwith the response box, requires participants to tap as quickly aspossible for five 10-s trials. Results are reported as the mean andstandard deviation of the intertap intervals, separately for the indexfingers on each hand, as well as for the tapping of the thumbs, inalternation, on two adjacent response buttons.

A computerized cued movement sequence task, based on Geor-giou, Bradshaw, Phillips, Chiu, and Bradshaw (1995), was used toassess the impact of advance cuing on response times. Filled bluecircles, arranged in 12 vertical pairs, were displayed along thebottom of a touch screen. At the left side a single circle indicateda start location and was illuminated (i.e., changed from blue towhite) to initiate the trial. Trials proceeded from left to right, withone circle in each vertical pair illuminating at a time. Participantswere asked to press each illuminated circle in order. Three condi-tions provided varied levels of advance information. In the low cuelevel condition, when the finger was lifted from the illuminatedcircle, a circle in the next column illuminated to indicate the nextresponse location. In the medium cue level condition, the cueregarding the next response location occurred slightly earlier; assoon as the finger pressed the illuminated circle, a circle in theadjacent pair illuminated simultaneously. The high cue level con-dition was similar to the medium cue level in that as soon as thefinger pressed the illuminated circle, a circle in the adjacent pairilluminated simultaneously. In addition, however, as the partici-pant’s finger lifted, a circle two pairs over from the current pairalso illuminated, and the illuminated circle in the adjacent pairextinguished. Up to 28 attempts were allowed to complete eithereight (low and medium cue level conditions) or 16 (high cue levelcondition) error-free trials. We report the means and standarddeviations of the response times within accurate trials for each cuelevel condition.

Simple and two-choice response times (RTs) were examinedusing the computer and a response device fitted with a single startbutton at the bottom and two adjacent response buttons at the top.Participants initiated trials by placing the dominant index finger onthe start button. For simple RT, a single hollow circle appeared onthe computer screen, then filled in green between 0 and 3.2 s later.Participants responded to the filled circle by pressing the right-sided response button as quickly as possible. The two-choice RTcondition was similar except that two adjacent hollow circlesappeared. One of the circles then filled in green between 0and 3.2 s later, and the participant pressed the corresponding

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response button. For both simple and two-choice RT, we reportmeans and standard deviations of response times.

The Letter–Number Sequencing subtest of the Wechsler AdultIntelligence Scale—Third Edition (Wechsler, 1997) was used toassess auditory–verbal working memory. The examiner read aseries of numbers and letters, in mixed order. Participants at-tempted to repeat back the numbers first in numerical order,followed by the letters in alphabetical order. Three trials werepresented at each set length (a total of two to eight letters andnumbers), with increasing set sizes across the task. The dependentmeasure was the number of correct trials (maximum � 21).

The Benton Facial Recognition Test (Benton & Hamsher, 1978;Benton, Hamsher, Varney, & Spreen, 1983) assesses visuopercep-tual processing and face recognition abilities. Participants viewedan image of a face (frontal view, with either the full face, a partial[3/4] face, or in darkened conditions) and then selected, from thesix alternatives in the multiple-choice response set, the face thatmatched the target. We report total correct (maximum � 27).

The University of Pennsylvania Smell Identification Test(UPSIT; Doty, Shaman, Kimmelman, & Dann, 1984; Sensonics,Inc., Haddon Heights, NJ) assesses olfactory recognition. For theGerman and Spanish sites, translated versions of the test were sourcedfrom the manufacturer. Participants “scratched and sniffed” odorpatches and attempted to identify the odors from a multiple-choiceresponse set. We report total correct (maximum � 40).

The Stroop task (Stroop, 1935) assesses processing speed andexecutive functions. Participants rapidly named colors (color-nam-ing condition), read color names (word-reading condition), andfinally named colors while inhibiting the dominant reading re-sponse (e.g., correct response to the word blue printed in red ink is“red”; interference condition). We report total correct separatelyfor each of the three conditions.

The Symbol Digit Modalities Test (Smith, 1991) assesses psy-chomotor speed and working memory. Participants were presentedwith a key at the top of the page, which paired the digits 1through 9 with unique symbols, such as X and �. Below the key,they were presented with the symbols, arranged in rows, and theywere required to write below each symbol the corresponding digit.We report the number of correct matches completed in 90 s.

The Trail Making Test (Reitan, 1958) assesses psychomotorspeed and executive functions. For Trail A, participants connectedcircles containing numbers in ascending numerical order. In TrailB, participants connected circles containing numbers and letters byalternating between numbers and letters in ascending order (e.g.,1-A-2-B, etc.). We report time to completion for each condition.

Phonemic verbal fluency (Benton et al., 1983) asked participantsto say as many words as possible beginning with a specified letterwithin 60 s. In the English language, two forms of the task (B, W,R and L, D, T) were randomly assigned by site. For the Germansite, P, K, and T were used, and for the Spanish site F, A, and S.The selection of these three letters for the non-English sites weremade in conjunction with neuropsychologists native to the region.We report the sum of correct words across three trials.

The Hopkins Verbal Learning Test—Revised (HVLT–R;Brandt & Benedict, 2001) assesses verbal episodic memory.Forms 2 and 4 were used and counterbalanced across all sites,including non-English sites. German and Spanish forms weredeveloped and tested in small pilot studies locally at the site usingsamples of approximately 30 healthy controls. For the HVLT–

R, 12 words were presented and recalled over three trials tomeasure learning. In addition, the words were recalled after a20-min delay, which was followed by a recognition trial. A subsetof these data was also reported in greater detail by Solomon et al.(2007).

The Matrix Reasoning subtest of the Wechsler AbbreviatedScale of Intelligence (WASI; Wechsler, 1999) assesses abstractnonverbal reasoning. For each item, the participant examined amatrix of images that was missing one component and selected theresponse option that best completed the matrix. We analyzed thetotal correct (maximum � 35 or 32 for participants ages 18–44and 45–79, respectively). This test was not administered at theGerman or Spanish site.

The Vocabulary subtest of the WASI (Wechsler, 1999) assessesvocabulary knowledge by asking participants to define a series ofwords of increasing difficulty. Each item was scored as 0, 1, or 2points on the basis of the completeness of the definition. We reporttotal correct (maximum � 42). This test was not administered atthe German or Spanish site.

Statistical Analyses

Descriptive statistics on the cognitive measures indicated thatvariability measures from the following timed tests were positivelyskewed: self-timed tapping, cued movement sequencing, simpleRT, two-choice RT, and speeded tapping. For these measures, wecomputed reciprocals to improve their normality. Also, we com-puted arcsine transformations of the discriminability indexes fromthe n-back task to improve their normality. The analyses describedbelow used a standard set of covariates consisting of gender, age,estimated verbal IQ, years of education, and bivariate (yes/no)indications of self-reported typing and musical expertise. The lattertwo covariates were intended to control for expertise in fine motorabilities that could add disease-irrelevant noise to our psychomotortask measures; to keep a consistent method of analysis across allmeasures, we included the typing and musical expertise measuresin the covariate set for all analyses.

Primary analyses: Sensitivity of cognitive measures in pro-dromal HD NEAR, MID, and FAR from diagnosis.

Effect size estimates. To identify the most sensitive cognitivemeasures, we computed effect sizes to produce a common metric,the number of standard deviations of difference between prodro-mal HD group and control group performance. We then assessedeffect sizes for the NEAR, MID, and FAR groups to determinewhen in the prodromal timeline specific cognitive measures be-come sensitive. Effect sizes were computed on the basis of sepa-rate univariate linear regression equations for each neurocognitivevariable, adjusting for the standard set of covariates. From theseregression models, we derived least squares means for each group,and we estimated performance variability pooled across all fourgroups (root mean square error). These values were then used tocalculate effect sizes (Cohen’s d; Cohen, 1988) for each prodromalHD group (NEAR, MID, FAR) compared with controls:

Effect SizeprHD group �MeanprHD group � Meancontrol

Pooled Standard Deviation.

We recoded effect size estimates so that poorer performance ofthe prodromal HD groups compared with controls was indicated


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by negatively signed effect sizes and superior performance of theprodromal HD group compared with controls was indicated bypositively signed effect sizes. In total, we computed 51 effect sizecomparisons for each of the prodromal HD groups, allowing us tobuild a reasonably comprehensive view of all variables in the testbattery that we thought had the potential to be sensitive.

Detection of significant group effects. We used Dunnett’s testto determine the statistical significance of differences betweeneach prodromal HD group and controls for each cognitive variable.Because the effect sizes are simply the differences between themean of the prodromal HD and control groups divided by anestimate of variability, Dunnett’s test is also conceptually equiv-alent to testing whether effect sizes differ from zero.

Secondary analyses: Sensitivity of cognitive measures andUHDRS motor scores. We investigated whether cognitive mea-sures tap unique variance in proximity to diagnosis that is notcaptured by the routinely administered UHDRS motor exam. Welimited these analyses from 51 to 29 variables, one from eachconceptually distinct task component in the cognitive battery (e.g.,Trails A and Trails B). From each of the 29 task components, weselected the variable with the largest effect size in the NEARgroup. We then computed separate univariate linear regressions foreach of these variables, predicting probability of HD diagnosiswithin 5 years, after controlling for UHDRS motor score and thestandard set of covariates. Finally, we examined the extent towhich the full set of cognitive measures, in aggregate, was asso-ciated with estimated proximity to diagnosis. To address this, wecomputed three additional univariate linear regression models inwhich we predicted the probability of diagnosis within 5 yearsbased on UHDRS total motor score alone (Model 1); based on thefull set of neurocognitive measures as listed in Table 3, excludingUHDRS motor score (Model 2); and based on the full set ofneurocognitive measures in addition to UHDRS motor score(Model 3). In each case, the models included our standard set ofcovariates. For these models, the relative contributions of themotor and neurocognitive assessments were based on the percent-age of variance explained relative to covariates alone (partial r2

and partial F test) and on the total variance explained by the modelincluding covariates (adjusted R2).

Results

Dunnett’s tests revealed that the NEAR group performed morepoorly than controls on 40 of the 51 cognitive variables (16 ofthe 19 tasks), with the largest effect size being �1.17 (see Table 2for effect size estimates and Table 3 for least squares means andconfidence intervals). The only tests that were not sensitive werethe three-disk tower task, serial response time task, and the WASIMatrix Reasoning subtest. The MID group performed more poorlythan controls on 28 of the 51 cognitive variables (13 of 19 tasks),with the largest effect size being �0.61. The FAR group showedthe least impairment, with significantly worse performance thancontrols on only one task (the emotion recognition task), with aneffect size of �0.26. An important finding was that several of thelargest effect sizes for neurocognitive tasks (maximum Cohen’sd � �1.17 in NEAR group) were of similar magnitude to theeffect size for the UHDRS motor score (Cohen’s d � �1.10 inNEAR group). Tasks showing the greatest sensitivity (largest

effect sizes) were reasonably consistent across the NEAR, MID,and FAR groups and included speeded and self-timed tappingmeasures and recognition of negative facial emotions. It is worthnoting that the prodromal HD groups did not perform significantlybetter than the control group on any of the 51 cognitive variables.

Subtle motor changes are often evident in the HD prodrome, andbecause these changes are intrinsically related to clinical diagnosis,UHDRS motor scores account for significant variability in esti-mates of proximity to diagnosis. We investigated whether cogni-tive variables account for additional variability, independent ofUHDRS motor scores. To address this question, we selected asubset of 29 cognitive variables (from the 51 total variables) thatshowed the largest effect sizes in the NEAR group, and that wereeither different tasks or conceptually distinct measures from withina given task. We found that, after controlling for the UHDRSmotor score (as well as our standard set of covariates), probabilityof diagnosis in 5 years was significantly associated with 17 of 29neurocognitive variables (see Table 4). It is interesting that tests ofmotor/psychomotor function yielded the largest partial R2s evenafter accounting for UHDRS motor score. For instance, the largestpartial R2s were observed for the speeded tapping task, F(1,328) � 35.28, p � .001, and timed tapping task, F(1,328) � 32.66, p � .001), which accounted for 9.7% and 9.1%,respectively (or 14.5% when examined in combination), of thevariance in estimated proximity to diagnosis. Sensory/perceptualtasks also accounted for variance over and above the motor score,with the UPSIT explaining 7.4% and emotion recognition explain-ing 5.5% of the variance in estimated proximity to diagnosis, F(1,328) � 36.17, p � .001, and F(2, 327) � 9.50, p � .001,respectively. Additional findings indicated that the Stroop test(overall, for the three variables) accounted for 3.8% of the vari-ance, F(3, 326) � 4.40, p � .005, with individual conditions asfollows: 3.4%, color naming, F(1, 328) � 11.50, p � .001; 1.9%,word reading, F(1, 328) � 6.34, p � .012; and 2.6%, interference,F(1, 328) � 8.74, p � .003.

We also examined the variance accounted for by the neurocog-nitive battery as a whole. The neurocognitive battery and theUHDRS motor score, together, accounted for 34.4% of the vari-ance in probability of diagnosis within 5 years (partial R2), F(30,300) � 5.24, p � .001; full model with covariates, adjusted R2 �.42, F(36, 300) � 7.75, p � .0001. When considered in theabsence of information from the UHDRS motor examination (i.e.,after removing the UHDRS motor score from the equation), theneurocognitive battery accounted for 34.0% of the variance inprobability of diagnosis within 5 years (partial R2), F(29,301) � 5.35, p � .0001; full model including covariates, adjustedR2 � .42, F(35, 301) � 7.90, p � .0001, whereas the motor scorealone predicted 11.7% of the variance (partial R2), F(1,329) � 43.55, p � .001; full model including covariates, adjustedR2 � .29, F(7, 329) � 20.38, p � .0001.

Discussion

This study demonstrates conclusively that with sufficient samplesizes and a neurocognitive assessment battery designed to maxi-mize sensitivity to HD, neurocognitive signs of the disease aredetectable prior to clinical diagnosis. Our results also show thatthese cognitive changes occur independent of motor signs (detect-

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Table 3Least Squares Means and 95% Confidence Intervals

Task and variable

Group

NEAR MID FAR Control

M CI M CI M CI M CI

Benton Facial Recognition Test: Total correct 22.2 [21.9, 22.5] 22.2 [22.0, 22.5] 22.8 [22.5, 23.0] 23.0 [22.7, 23.3]Category learning task: Rule-based

Maximum % correct in a block 95.9 [95.1, 96.7] 96.0 [95.2, 96.7] 96.2 [95.4, 97.0] 96.5 [95.4, 97.7]% blocks completed 33.1 [30.3, 35.8] 30.2 [27.6, 32.8] 28.2 [25.6, 30.9] 25.0 [21.2, 28.9]

Category learning task: NonverbalizableMaximum % correct in a block 76.9 [75.5, 78.2] 78.4 [77.2, 79.7] 78.7 [77.4, 80.0] 80.3 [78.5, 82.1]% blocks completed 72.6 [67.8, 77.4] 72.0 [67.5, 76.4] 70.1 [65.5, 74.8] 69.6 [63.0, 76.1]

Dynamic emotion recognition task: Number correct(negative emotions only) 10.5 [9.7, 11.2] 12.6 [12.0, 13.2] 14.3 [13.7, 15.0] 14.6 [13.8, 15.3]

Emotion recognition task: Number correct(negative emotions only) 22.6 [21.8, 23.4] 25.1 [24.5, 25.8] 26.9 [26.3, 27.5] 28.2 [27.4, 29.0]

Hopkins Verbal Learning Test—RevisedTotal learning (Trials 1–3) 23.9 [23.2, 24.6] 26.0 [25.4, 26.5] 27.3 [26.8, 27.9] 28.1 [27.4, 28.8]Delayed recall 8.5 [8.2, 8.8] 9.4 [9.1, 9.6] 9.9 [9.6, 10.2] 10.1 [9.7, 10.4]Delayed recognition discriminability 9.9 [9.7, 10.2] 10.5 [10.3, 10.6] 10.7 [10.5, 10.9] 11.0 [10.8, 11.2]

n-back task: 1-back discriminability indexesa

Foils 0.97 [0.97, 0.98] 0.98 [0.98, 0.98] 0.98 [0.98, 0.98] 0.98 [0.98, 0.99]Lures 0.95 [0.94, 0.96] 0.96 [0.95, 0.96] 0.97 [0.96, 0.97] 0.97 [0.96, 0.97]

n-back task: 2-back discriminability indexesa

Foils 0.93 [0.92, 0.93] 0.94 [0.94, 0.95] 0.94 [0.94, 0.95] 0.95 [0.94, 0.96]Lures 0.79 [0.77, 0.81] 0.84 [0.82, 0.85] 0.84 [0.82, 0.86] 0.86 [0.84, 0.88]

Phonemic verbal fluency: Total correct 37.7 [36.2, 39.3] 40.1 [38.8, 41.3] 42.2 [40.9, 43.5] 43.1 [41.4, 44.7]Self-timed finger tapping taskb

Dominant index finger: Mean of ITIs 515 [511, 519] 515 [512, 519] 515 [511, 519] 518 [513, 522]Dominant index finger: SD of ITIs 44 [41, 47] 36 [35, 37] 32 [31, 33] 31 [30, 32]Alternating thumbs: Mean of ITIs 508 [502, 514] 506 [502, 511] 508 [503, 513] 511 [505, 517]Alternating thumbs: SD of ITIs 53 [50, 56] 43 [41, 45] 38 [37, 40] 36 [34, 37]

Cued movement sequence task: Low cue levelc

Mean 611 [601, 622] 579 [570, 587] 564 [555, 573] 563 [552, 574]SD 83 [79, 88] 71 [68, 74] 65 [63, 67] 66 [63, 69]

Cued movement sequence: Medium cue levelc

Mean 537 [526, 548] 516 [507, 525] 496 [487, 506] 508 [496, 519]SD 91 [86, 97] 74 [72, 77] 69 [66, 72] 70 [67, 74]

Cued movement sequence: High cue levelc

Mean 353 [340, 366] 350 [340, 361] 333 [322, 343] 335 [323, 348]SD 89 [82, 98] 79 [75, 85] 72 [68, 76] 70 [66, 76]

Serial response time task: Learningb

Learning effect (B5 – B4) 99 [90, 107] 88 [81, 95] 97 [89, 104] 96 [87, 105]Interference effect (B6 – B4) 12 [6, 18] 12 [7, 17] 17 [12, 23] 13 [7, 19]

Simple response time taskMean 256 [245, 267] 230 [221, 238] 209 [200, 217] 202 [191, 213]SD 57 [51, 64] 46 [43, 50] 38 [36, 41] 39 [36, 43]

Two-choice response time taskb

Mean 295 [284, 306] 268 [260, 277] 236 [227, 245] 238 [226, 249]SD 76 [68, 87] 73 [67, 80] 59 [55, 64] 58 [53, 64]

Speeded tapping taskb

Dominant index finger: Mean of ITIs 234 [228, 241 218 [213, 223] 208 [203, 214] 203 [196, 209Dominant index finger: SD of ITIs 57 [52, 63] 47 [44, 50] 39 [37, 42] 38 [35, 41]Nondominant index finger: Mean of ITIs 280 [273, 287] 245 [239, 251] 231 [225, 237] 227 [220, 234]Nondominant index finger: SD of ITIs 67 [62, 74] 60 [57, 64] 50 [47, 52] 46 [43, 49]Alternating thumbs: Mean of ITIs 202 [194, 210] 180 [174, 187] 166 [159, 173] 162 [153, 170]Alternating thumbs: SD of ITIs 64 [60, 69] 55 [52, 57] 47 [45, 49] 45 [43, 47]

Stroop color: Total correct 72.1 [70.2, 74.0] 76.8 [75.3, 78.4] 80.4 [78.8, 82.1] 81.8 [79.8, 83.8]Stroop word: Total correct 92.5 [90.3, 94.8] 98.5 [96.7, 100.4] 103.1 [101.1, 105.0] 102.6 [100.2, 105.0]Stroop interference: Total correct 41.3 [40.0, 42.6] 43.9 [42.8, 44.9] 47.0 [45.9, 48.1] 46.7 [45.3, 48.1]Symbol Digit Modalities Test: Total correct 44.7 [43.3, 46.2] 49.4 [48.2, 50.6] 53.7 [52.5, 55.0] 54.2 [52.7, 55.8]Tower 3 task

Mean number of moves (4 trials) 9.5 [9.1, 9.8] 9.1 [8.8, 9.4] 8.6 [8.3, 8.9] 8.9 [8.5, 9.3]Learning (T4 – T1) �1.7 [�2.4, �1.1] �1.5 [�2.0, �1.0] �1.6 [�2.1, �1.0] �1.6 [�2.3, �0.9]

(table continues)


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able using a standard clinical motor examination). To our knowl-edge, this is the largest and most comprehensive study ever un-dertaken of neurocognitive signs of HD prior to diagnosis. Ourdata not only lend support to several prior reports of neurocogni-tive effects in the HD prodrome (Johnson et al., 2007; Kirkwoodet al., 2000; Lawrence et al., 1998; Lemiere, Decruyenaere, Evers-Kiebooms, Vandenbussche, & Dom, 2004; Snowden, Craufurd,Thompson, & Neary, 2002; Solomon et al., 2007; Tabrizi et al.,2009), but provide a much more comprehensive and detailedpicture of the cognitive signs of disease prior to diagnosis.

This study included a large cognitive assessment, with 19 dis-tinct tasks that required about 3 hr of assessment, yielding acomprehensive picture of cognition in the HD prodrome. In par-ticular, tests assessing psychomotor performance, emotion recog-nition, and working memory were the most sensitive to prodromalHD neurocognitive effects. Specifically, the speeded finger tap-ping, self-timed finger tapping, emotion recognition, and n-backworking memory tasks had the largest effect sizes near to diagno-sis and accounted for the greatest degree of variance in proximityto diagnosis. These findings are generally consistent with ourunderstanding of the early pathological changes in the basal gan-glia in the HD prodrome (Vonsattel & DiFiglia, 1998; Vonsattel etal., 1985). It is important to note, however, that recent brainimaging studies suggest that brain changes in prodromal HD aremore widespread than previously thought and occur outside of thestriatum. In particular, findings have included reductions in tha-lamic volumes (Harris et al., 1999; Paulsen, Magnotta, et al.,2006); the parietal, occipital, and cerebellar cortices (Beste et al.,2008); bilateral insula and posterior intraparietal sulcus (Kipps etal., 2005); and cortical thinning in the middle and superior frontalregions, the superior parietal region, occipital cortex, and thetemporal cortex (Rosas et al., 2005). Although it may eventually bepossible to link the timing of specific neurocognitive and brainchanges to developmental stages of HD, the nature of these rela-tionships remains unclear at this point.

Our data reveal graded findings across the prediagnosis periodin HD, with neurocognitive effects most consistent and largest inmagnitude in individuals estimated to be relatively closer to diag-nosis (i.e., within 9 years). In addition, significant effects werefound for more than half of the neurocognitive variables in theMID group, which comprised individuals estimated to be be-tween 9 and 15 years from diagnosis. However, we detected lessevidence of cognitive effects in individuals estimated to be farfrom diagnosis, with only one neurocognitive test, the emotionrecognition test, showing a significant effect. This is consistentwith several other reports (Brandt, Shpritz, Codori, Margolis, &Rosenblatt, 2002; Paulsen et al., 2001, 2004; Robins Wahlin,Lundin, & Dear, 2007; Snowden et al., 2002), which indicate thatsymptoms are more difficult to detect in those far from diagnosis.

These findings suggest that the cognitive signs of HD developgradually, perhaps over more than a decade in at least someindividuals with prodromal HD. Our results do not support thepossibility that prodromal HD is associated with cognitive differ-ences throughout the life span (i.e., a trait marker for the HD CAGexpansion), in that we found sparse evidence for cognitive differ-ences from controls in the prodromal HD group that is farthestfrom diagnosis. It remains possible, however, that additional cog-nitive changes may also occur in individuals very far from diag-nosis but that we have not detected them, either because theireffect sizes are below the threshold of detectability in our samplesize, or because we failed to target the affected cognitive functionsin our test battery. One important implication of this study is thata sample not stratified according to proximity to diagnosis mayobscure effects present in those nearer to diagnosis, even if thesample is relatively large, when samples include a large proportionof individuals estimated to be far from diagnosis.

Many previous studies have failed to find differences in neuro-cognitive performance between prodromal HD and control groups(Brandt et al., 2002; Rosas et al., 2006; van der Hiele et al., 2007;Witjes-Ane et al., 2007), which may be attributable to several of

Table 3 (continued)

Task and variable

Group

NEAR MID FAR Control

M CI M CI M CI M CI

Tower 4 taskMean number of moves (4 trials) 27.8 [26.8, 28.7] 26.2 [25.4, 26.9] 26.4 [25.6, 27.2] 25.4 [24.4, 26.4]Learning (T4 – T1) �4.0 [�5.8, �2.2] �3.2 [�4.6, �1.7] �3.5 [�5.0, �2.0] �4.3 [�6.1, �2.4]

Trail Making Test A: Time to completion (s) 31.4 [29.9, 32.9] 27.5 [26.3, 28.8] 25.3 [24.0, 26.6] 25.3 [23.7, 26.8]Trail Making Test B: Time to completion (s) 81.7 [77.2, 86.2] 67.6 [63.9, 71.3] 60.8 [57.0, 64.6] 57.8 [53.1, 62.5]University of Pennsylvania Smell Identification

Test: Total correct 30.7 [30.1, 31.2] 33.2 [32.8, 33.7] 34.1 [33.6, 34.6] 34.5 [34.0, 35.1]WAIS–III Letter–Number Sequencing subtest:

Total score 11.2 [10.8, 11.6] 11.4 [11.1, 11.7] 12.0 [11.7, 12.3] 12.5 [12.1, 12.9]WASI Matrix Reasoning subtest: Raw score 25.3 [24.6, 26.0] 26.0 [25.4, 26.7] 26.4 [25.7, 27.1] 26.4 [25.4, 27.3]WASI Vocabulary subtest: Raw score 61.2 [60.3, 62.1] 62.9 [62.0, 63.7] 63.1 [62.3, 64.0] 63.9 [62.7, 65.1]Unified Huntington’s Disease Rating Scale: Motor

score 7.5 [6.8, 8.2] 4.6 [4.1, 5.2] 3.5 [2.9, 4.1] 2.3 [1.6, 3.0]

Note. NEAR � estimated to be �9 years to Huntington disease (HD) diagnosis; MID � between 9 and 15 years to HD diagnosis; FAR � �15 yearsto HD diagnosis; CI � confidence interval; ITI � intertap interval; B � block; WAIS–III � Wechsler Adult Intelligence Scale—Third Edition; WASI �Wechsler Abbreviated Scale of Intelligence.a Ability to discriminate either lures or foils from matches, whereby 1.0 would indicate perfect discrimination and 0.5 would represent chance. b Responsetimes measured in milliseconds. c Mean response times for accurately completed trials measured in milliseconds.

10 STOUT ET AL.

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the factors that were controlled for in the current study. Theselimitations include small sample sizes, prodromal HD samples farfrom diagnosis of the disease, and the type and range of testsadministered. Indeed, the most sensitive measures in this studytended not to be clinical neuropsychological measures, havingoriginated instead from more experimental cognitive psychologi-cal literature (i.e., self-timed finger tapping, emotion recognition,n-back working memory task).

The size of the PREDICT-HD study, which permitted the use ofstratified groups with substantial sample sizes, made it possible todetect effect sizes that were small or medium in magnitude, incontrast to many previous studies that used sample sizes that wereonly powered to reveal large effect sizes. For example, the sample

size of our MID group (n � 268) allowed us to detect a differencefrom controls as small as 0.37 standard deviations with 90%power.

We have shown that cognitive signs are a distinct measurablecharacteristic of the HD prodrome, and not attributable to orwholly redundant with the development of the measured motorfeatures of the disease. Slowed information processing may con-tribute to explaining the effects found in this study; however, aconclusive analysis regarding this issue is elusive given that in-formation processing speed is not a unitary construct (Chiaraval-loti, Christodoulou, Demaree, & DeLuca, 2003), and there is noclear agreement on what neurocognitive test best indicates slowedinformation processing (Drew, Starkey, & Isler, 2009). Nonethe-

Table 4Percentage Variance in Probability of Onset in 5 Years Explained After Controlling for UnifiedHuntington’s Disease Rating Scale Motor Score

Task—Conceptually distinct task component% Variance explained above UHDRS motor score

(Adj. R2 � .29)

Benton Facial Recognition Test: Total correct 0.8Category learning tasks (0.8)

Rule-based: % blocks completed 0.8Nonverbalizable: Maximum % correct in a block 0.6

Emotion tasks: Number correct (negative emotion) (5.5)��

Emotion recognition 1.3�

Dynamic emotion recognition 5.4��

HVLT–R: Total learning (Trials 1–3) 2.9��

n-back tasks (2.0)�

1-back discriminability: Foils 1.02-back discriminability: Foils 1.3�

Phonemic verbal fluency: Total correct 0.4Cued movement sequence task: SD (8.8)��

Low cue level 5.1��

Medium cue level 8.3��

High cue level 1.7�

Serial response time task: Learning effect (B5 – B4) 0.3Simple and two-choice response time task: Mean (2.9)��

Simple response time 0.9Two-choice response time 2.7��

Stroop task: Total correct (3.8)��

Color 3.4��

Word 1.9�

Interference 2.6��

Symbol digit modalities test: Total correct 4.3��

Tapping (14.5)��

Speeded tapping nondominant finger: Mean of ITIs 9.7��

Self-timed tapping alternating thumbs: SD of ITIs 9.1��

Tower tasks: Mean number of moves (4 trials) (0.6)Tower 3 0.6Tower 4 0.2

Trail Making Tests: Time to completion (s) (3.4)��

Trails A 3.1��

Trails B 1.4�

UPSIT: Total correct 7.4��

WAIS–III Letter–Number Sequencing subtest: Total score 0.2WASI Matrix Reasoning subtest: Raw score 0.2WASI Vocabulary subtest: Raw score 1.0

Note. HVLT–R � Hopkins Verbal Learning Test—Revised; ITI � intertap interval; B � block; UPSIT �University of Pennsylvania Smell Identification Test; WAIS–III � Wechsler Adult Intelligence Scale—ThirdEdition; WASI � Wechsler Abbreviated Scale of Intelligence. Parentheses are used for linear regressions thatinvolved multiple variables typically considered in conjunction with each other (e.g., Trials A and B) or a setof variables assessing highly related functions (e.g., speeded and self-timed tapping).� p � .05. �� p � .01.


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less, slowed sensory, perceptual, motor, memory scanning, andother aspects of cognitive processing may have contributed to ourfindings.

Findings of clear cognitive signs in the prodromal period areimportant because they substantiate the fact that HD must beredefined to incorporate the neurocognitive aspects of the disease.A depiction of HD that emphasizes the motor signs fails to dojustice to the experience of people with prodromal HD, who reportexperiencing a whole host of symptoms, including not only motorsymptoms but also cognitive and psychiatric symptoms. Consis-tent with this notion, our group recently reported on a subset of theparticipants studied here that, using cutoffs similar to those appliedin research on mild cognitive impairment, revealed that nearly40% of prodromal HD participants would be classified as havingmild cognitive impairment (see also Duff et al., 2010).

It is also worthy of note that our staging of the prediagnosisperiod did not take into account motor signs, and instead reliedonly on the CAG- and age-based estimates of proximity to diag-nosis. Given the correlation of cognitive signs and geneticallyestimated proximity to diagnosis, after adjusting for motor signs inour data, we would anticipate that even if we had excludedindividuals with subtle preclinical motor signs, evidence of cog-nitive effects would still have been detected. However, this reportdoes not explicitly address whether cognitive signs appear differ-ently in individuals with and without emerging motor features.

Consistent with previous findings in the literature, our resultsadd substantial evidence that differences in cognition emerge inadvance of the clinical diagnosis of HD. An important point,however, is that we and others have demonstrated such effects ingroups, and as such, these findings do not translate into definiteprognoses for individuals with the HD CAG expansion. The aim ofour study was to identify the very early and often subtle changesthat herald the progression toward disease diagnosis, rather than todevelop an algorithm for the diagnosis of a cognitive disorder priorto diagnosis of HD. Also, given that more than half of the statis-tically significant findings in the study relied on tests not typicallyincluded in clinical neuropsychological assessment, we would notexpect these findings to be replicable on individual patients withinthe clinical setting if only standard neuropsychological assessmentstrategies were used. The findings from this study are instructive,however, about how clinical neuropsychological methods might bedeveloped or adapted to enhance sensitivity to cognitive signs thatemerge prior to formal HD diagnosis. It is important to note thatthus far the findings cannot be taken as a clear indication ofclinically significant cognitive impairment prior to disease diag-nosis. The clinical significance of prediagnosis cognitive test per-formance must be evaluated in the context of evidence for its linksto functional impairment in daily activities, a matter that we didnot address in the current analyses.

The ability to study neurocognitive markers prospectively,which will be possible in future years of the PREDICT-HD studyusing actual rather than estimated proximities to diagnosis, willmore precisely define the relationship between neurocognitivefunction and HD diagnosis. Given that neurocognitive signs ac-count for unique variability in estimated proximity to diagnosisbeyond age, CAG length, and motor signs, it is reasonable toexpect that neurocognitive measures will enhance stratification ofindividuals in the HD prodrome. It is important to note, however,for neurocognitive findings to be useful disease markers or to

reveal drug effects, the significance of longitudinal changes mustbe ascertained over the relatively brief intervals used in therapeutictrials. This goal is an essential component of the PREDICT-HDstudy.

Overall, our findings show that with adequate power and sen-sitive assessment tools, neurocognitive signs are measurable ingroups of individuals with the HD prodrome well in advance ofclinical diagnosis of HD. These signs are more marked in individ-uals who are estimated to be relatively closer to the diagnosticthreshold, but may be detectable more than a decade before esti-mated disease diagnosis.

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Received March 8, 2010Revision received June 23, 2010

Accepted June 29, 2010 �

Call for Papers: Special Section on Theory and Data in Categorization:Integrating Computational, Behavioral, and Cognitive Neuroscience Approaches

The Journal of Experimental Psychology: Learning, Memory, and Cognition (JEP:LMC) invitesmanuscripts for a special section on approaches to categorization, to be compiled by guest editorsStephan Lewandowsky and Thomas Palmeri working together with journal Associate EditorMichael Waldmann.

The goal of the special section is to showcase high-quality research that brings togetherbehavioral, computational, mathematical, neuropsychological, and neuroimaging approaches tounderstanding the processes underlying category learning. There has been some divergence betweenapproaches recently, with computational-mathematical models emphasizing the unity of category-learning processes while neuropsychological models emphasize the distinction between multipleunderlying memory systems. We are seeking articles that integrate cognitive neuroscience findingsin designing models or interpreting results, and behavioral studies and modeling results thatconstrain neuroscientific theories of categorization. In addition to empirical papers, focused reviewarticles that highlight the significance of cognitive neuroscience approaches to cognitive theory—and/or the importance of behavioral data and computational models on constraining neuroscienceapproaches—are also appropriate.

The submission deadline is June 1st, 2011. The main text of each manuscript, exclusive offigures, tables, references, or appendixes, should not exceed 35 double-spaced pages (approximately7,500 words). Initial inquiries regarding the special section may be sent to Stephan Lewandowsky([email protected]), Tom Palmeri ([email protected]), orMichael Waldmann ([email protected]).

Papers should be submitted through the regular submission portal for JEP: LMC (http://www.apa.org/pubs/journals/xlm/submission.html) with a cover letter indicating that the paper is tobe considered for the special section. For instructions to authors and other detailed submissioninformation, see the journal Web site at http://www.apa.org/pubs/journals/xlm.

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Neurocognitive signs in prodromal Huntington disease.

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