Neuropsychological symptom dimensions in bipolar disorder and schizophrenia

8/12/2019 Neuropsychological symptom dimensions in bipolar disorder and schizophrenia

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An important theoretical question regarding NCfunctions as potential candidate endophenotypes istheir diagnostic specificity. A recently conductedmeta-analysis of all comparative studies indicatedthat patients with BPD generally perform betterthan patients with SZ, but the distribution of effectsizes revealed a large degree of heterogeneity (24).

In particular, this investigation compared NCperformance in patients with BPD and SZ in 11NC domains. The 11 domains comprised: VerbalFluency, Verbal Working Memory, ExecutiveControl, Visual Memory Delayed, Mental Speed,Verbal Memory Immediate, IQ, Verbal MemoryDelayed, Concept Formation, Visual MemoryImmediate, and Fine Motor Skills. The meta-analysis (24) showed significantly worse perfor-mance in the patients with SZ in 9 out of 11cognitive domains. The only areas in whichperformance of the 2 patient groups were notstatistically significant were delayed Visual Mem-ory and Fine Motor Skills.

Another recently published meta-analytic reviewof the literature (16), defined only 4 major NCdomains, which included IQ, Attention (Sustained,Selective), Memory, and Executive Functions(Cognitive Flexibility, Working Memory, VerbalFluency). This review concluded that BPD patientsexhibit extensive cognitive abnormalities with apattern of deficits that is not unique to this disease.The study by Seidman et al. (22) focused specif-ically on a comparison of profiles of NC abnor-malities between BP and SZ in 8 domains,

including Verbal Ability, Visuo-Spatial Ability,Abstraction/Executive, Verbal/Declarative Mem-ory, Perceptual-Motor Functions, Mental Control,and Sustained Attention/Vigilance. Similar to theabove 2 meta-analyses, this study concluded thatwhile the level of impairments was higher inpatients with SZ, the profile shape did not differbetween BPD and SZ. Overall, Abstraction, Mem-ory, Perceptual-Motor Functions, and Vigilanceshowed the largest impairments in both groups,with a higher level of impairment in patients withSZ in this study (22).

Using a standardized test battery (RepeatableBattery for the Assessment of NeuropsychologicalStatus; RBANS), Hobart et al. (25) showed thatpatients with SZ were more impaired than patientswith BPD in terms of general functioning [mediumeffect size (0.55) for the total score], and thatamong 5 NC domains including Visuospatial/Constructional, Language, Attention, DelayedMemory and the Immediate Memory only thelatter (Immediate Memory, effect size 0.65)obtained a significant difference between thegroups. The difference in terms of attention func-

tioning did not reach significance (effect size 0.33). However, it is difficult to evaluate thevalidity of these results since it is conceivable thatthe group differences were confounded by theextent to which the NC domains representeddifferent underlying constructs (factors) acrossdiagnoses.

In general, the above literature that comparedNC in patients with BPD and SZ had certainlimitations. The majority of studies used only arelatively small set of tasks, and the composition oftasks was vastly different across studies. Thismakes the comparisons difficult, and limits theinterpretability of the findings since the variouscomponents of the NC profiles across diagnoseswere assembled from data derived from differentstudies. A potential research strategy to overcomethis problem and to compare patterns of NCdeficits in BPD and SZ is to administer a compre-hensive neuropsychological (NP) battery consistingof several measures tapping into each of severalputative NC domains. However, those studies thatinvestigated multiple areas simultaneously, focusedon a different number of domains, and applieddifferent definitions. Since component measureswere arbitrarily selected, the domains (construct)validity may not generalize to different samples, orwithin the same sample over time. The 2 largerecent meta-analyses published only a few monthsapart from each other (16, 24; see above), consid-ered 11 and 4 domains, respectively, whereas thestudy by Seidman et al. (22) defined 8 domains for

the comparison of respective NP profiles.To our knowledge, no empirical evidence has

been shown to demonstrate that the variousdefinitions of the underlying NC domains werevalid in a particular diagnostic group, and gener-alizable across diagnoses. Obtaining such evidenceis a logical prerequisite of further group compar-isons, and as stated by Horn and McArdle (26,p. 117) without such evidence, the basis fordrawing scientific inference is severely lacking.Factor analysis provides 1 way to obtain thisevidence based on the analysis of interrelationships

among various NC measures. Surprisingly, despitethe fact that a substantial research effort has beenspent to demonstrate that BPD and SZ sharespecific domains of psychopathology in terms offactor analytic structure, as far as we know, noprevious studies compared the NC factor structurederived from the same instrument in both bipolarand schizophrenic patients. In our previous factoranalysis of patients with SZ, on the basis of theanalysis of a comprehensive NC test battery, wederived 6 clearly identifiable factors that had goodpsychometric properties with excellent construct,

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divergent and predictive validity, and stability overtime in a longitudinal study (factors includedAttention, Working Memory, Learning, VerbalKnowledge, Non-Verbal Functions, and IdeationalFluency). The principal objective of the currentstudy was to extend this research further, byinvestigating whether the same underlying factor

structure of NC functions that characterized patientswith SZ would generalize to patients with BPD.

Methods

The data for the research reported here werecollected in 2 longitudinal clinical studies inves-tigating predictive and concurrent associationsbetween neurocognitive performance and disabilityin life (psychosocial) functioning (LF) in individ-uals with serious mental illnesses [see companionpaper (27) in this issue for further details of thisresearch]. The 2 studies represented subsequentphases of the research project. The goal of the first(Study 1: Schizophrenia Study) was to test thelongitudinal relationship between NC deficits andlife functioning (disability) in patients with SZor schizoaffective disorder; the aim of the second(Study 2: Bipolar Study) was to investigate theabove relationship in patients with BPD.

Both studies collected a large number of NCvariables and aimed to conduct factor analyses forthe purpose of data (dimensionality) reduction.This aim was previously accomplished in the firststudy in a subset comprised of the first 156 patients

enrolled (see below for further details). The coreresults, including details concerning the NC factorsthat were identified, have been published (28).Since the principal purpose of Study 2 was similarto that of Study 1, and dimensionality reductionwas an important tool to achieve a reduction inType I error arising from multiple repeated testingof individual variables, an essential question waswhether the same factor structure that we found inthe SZ sample is applicable to the bipolar sample.Hence, the question of generalizability of the NCfactors across diagnoses served as a principal

practical motivating problem for the currentinvestigation.

Subjects

Study 1: Schizophrenia sample. Subjects were con-senting patients in a 3-year study of SZ andschizoaffective disorder [diagnosed using the Struc-tured Clinical Interview for DSM-IV (SCID)]which involved repeated neurocognitive testing.Subjects were enrolled within 6 months of symp-tom exacerbation requiring hospitalization, and

received a comprehensive NC test battery andPositive and Negative Symptom Scale (PANSS)(29) ratings at baseline (used for the present report)and again after 6, 18 and 36 months (not includedin this report). Staff administering NC tests werepreviously trained and observed in test batteryadministration to assure uniformity. The PANSS

raters had demonstrated interrater reliability com-pared to an expert (ICC 0.80).

For the present analyses, the final dataset fromthis study was used; subjects were included in theanalyses if they had completed the baseline NCassessment. Baseline NC testing was conductedwhenever possible when patients were optimallystabilized after hospitalization for the indexepisode. A total of 250 patients, with the diagnosisof SZ (n 185; 74%) or schizoaffective disorder(n 65; 26%) were enrolled in the study.

Study 2: Bipolar sample. The subjects for theanalyses that we report here are consenting patientsfrom an ongoing 24-month study investigatingpredictive and concurrent associations betweenNC deficits and disability in life functioning inindividuals with BPD. The objective of this natu-ralistic longitudinal study is to evaluate approxi-mately 200 individuals aged 18 to 54 years withBPD [diagnosed using SCID (3)] at the time ofhospitalization for relapse and at multiple timepoints over the following 24 months. For thepresent analyses, an interim dataset from thisongoing study was cleaned and frozen (i.e., no

further changes were made in the database); subjectsfrom this database were included in the analyses, ifthey had completed the baseline NC assessment.Baseline NC data from a total of 155 subjects wereused for the purpose of the current investigation.

Using cut-off scores for the Clinician-Adminis-tered Rating Scale for Mania (CARS-M; 15 items)(30) of 07 for questionable and 815 for mildmania and, for the Hamilton Depression RatingScale (HAM-D; 17 items) (31), 06 for notdepressed and 717 mildly depressed, we foundthat the majority (approximately 54%) of the

sample had no or mild symptoms on both scales.Approximately 30% had moderate to high maniawith no or low depressive symptoms, and, con-versely, approximately 11% of the sample hadmoderate to high depression with no or mild maniaat the time of neurocognitive testing. Approxi-mately 5% of the sample had active mixed symp-tomatology at the time of testing (e.g., moderate orgreater symptoms on both mania and depressionrating scales).

Altogether, 11% (n 17) of the subjects in theprimary dataset (n 155) evidenced symptoms on

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disorder and their relations to functional outcomes.It includes 14 tests focused on measures of GeneralAbility, Attention, Working Memory, VerbalKnowledge, Learning, Non-Verbal Functions, Ide-ational Fluency, Executive Functions, and MotorSkills (Table 2). The specific tests used have beenpreviously described by us and others; thus, weprovide only a brief description in the Appendix.

Staff administering NP tests were previouslytrained and observed in test battery administrationto assure uniformity. As mentioned above, thesame neuropsychological test battery was admin-istered in both studies; however, we note that 3 ofthe variables were not obtained in the bipolar studydue to the fact that our preliminary analysesindicated that they displayed a high degree of

overlap with variables in their respective factors,and that the omission of these variables hadessentially no impact on the internal consistencyof these factors (change in Cronbach alpha was


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been described recently in the literature (34), whichanalogous to the 2-stage procedure employed inour study, relies on variable stratification. Usingmultivariate statistical theory, it has been demon-strated that BPCA is as efficient as ordinaryprincipal component analysis for dimensionalityreduction (34).

Based on the above approach, in our previousstudy (28), 6 factors were extracted as having goodconstruct, divergent and predictive validity, andstability over time over an 18-month period ofobservation. The 6 factors were Attention, Work-ing Memory, Learning, Verbal knowledge, Non-Verbal functions, and Ideational Fluency (Table 3).An additional 5 NC measures, which have beenwidely studied in SZ, could not be reliably com-bined with any of these factors or with eachanother, indicating the need to examine themseparately. These include: Wisconsin Card Sorting

Test Perseverative Errors, Stroop Interference,Trails B-Trails A/Trails A, Grooved PegboardPreferred plus Non-Preferred Hand, Finger Tap-ping Preferred plus Non-Preferred Hand.

Statistical analyses

For the purpose of the current investigation,generalizability was considered as factorial invar-iance, i.e., constancy in the structure of theunderlying NC constructs across diagnoses (BPDversus SZ). The concept of factorial invariance wasbased on Thurstones notion of simple structure(35), which states that the pattern of salient (non-zero) and non-salient (zero or near-zero) loadingsdefines the structure of a psychometric construct.In terms of factorial invariance, the principle ofsimple structure entails configurational invariance;items comprising the same construct are expectedto exhibit the same configuration of salient andnon-salient factor loadings across the 2 diagnosticgroups.

The analyses were conducted in multiple steps.First, the homogeneity of the correlation matricesacross the 2 diagnostic samples was tested. Second,the empirical data from the bipolar sample weresubjected to unrestricted exploratory factor analy-sis (EFA) to examine whether model modificationswere necessary in terms of the number of the factorsand item composition of the underlying constructsderived in the SZ sample. Third, confirmatoryfactor analyses (CFA) (33) were conducted to

statistically test the configurational invariance ofthe hypothesized factor structure, i.e., to examinewhether the items have the same relationship to thesame underlying factor as posited on the basis ofthe earlier analyses in the SZ sample. Fourth, sincethe CFA addresses the configurational invarianceof factors across samples but does not directlyinvestigate the extent of similarity, a factoranalysis with confirmatory Procrustes rotationwas performed to examine the extent of similaritybetween the BPD and SZ samples with regard toeach of the individual factors. Finally, in Step 5, the

psychometric properties (reliability and constructvalidity) of the NC factors derived in the bipolarsample were examined.

Step 1: Homogeneity of correlation matrices. InStep 1, we tested the null-hypothesis of no-differ-ence in the correlation matrices between the BPDand the SZ sample. The analysis was based on thelikelihood ratio approach, using nested hierarchi-cal models of the data as implemented by the SASPROC MIXED procedure (36). In particular,using the maximum likelihood estimation, first we

Table 3. Six neurocognitive factors derived from the schizophrenia sample

Neurocognitive

factor

Neurocognitive measure included

in factor

Attention D2 letters minus errors

Stroop - words only

Stroop - color only

Trails A

WMS-R Visual Memory Span Forwarda

WAIS-R Digit symbol

Working memory D2 fluctuation

WAIS-R Digit span forward

LNS, number correct

LNS, longest

WAIS-R ArithmeticWAIS-R Digit Span Backward

WMS-R Log Mem Immed

Learning WMS-R Verbal Pair I

WMS-R Verbal Pair II

WMS-R Visual Pair I

WMS-R Visual Pair II

Verbal knowledge WAIS-R Vocabulary

WAIS-R Informationa

WAIS-R Comprehension

WAIS-R Similarities

Non-verbal functions WAIS-R Block Design

WAIS-R Object Assemblya

WAIS-R Picture Completion

WAIS-R Picture Arrangement

Ideational fluency WCST Number of Perseverative ErrorsRuff Figural Fluency Unique Designs

COWAT

Animal Naming

D2 Concentration Endurance Test; Stroop Stroop Color-

Word Interference Test; Trails Trailmaking Test; LNS Letter

Number Span Test; Log Mem Immed Logical Memory

(immediate recall); WCST Wisconsin Card Sorting Test;

COWAT Controlled Oral Word Association Test.aVariables not available in the bipolar sample included:

Wechsler Memory Scale Revised (WMS-R) Visual Memory Span

Forward; Wechsler Adult Intelligence Scale-Revised (WAIS-R)

Information; and the WAIS-R Object Assembly.


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approach estimates loadings for all items (includ-ing items that are considered non-salient). Themodel fit was evaluated by the coefficient ofcongruence (CC) (38), normed between +1 and)1. Values of CC of 0.80 and above are consideredto indicate sufficient similarity between the em-pirically Procrustes-rotated and theoretically pos-

tulated factors. The sampling variation of the CCwas estimated using the bootstrap/resamplingapproach (40). In order to do this, we firstrandomly selected 1,000 samples with replacementfrom the original database; then, each of thesesamples, whose size was identical to the size oforiginal dataset, was subjected to factor analysiswith Procrustes rotation.

Step 5: Reliability, construct validity. Scale (fac-torial) reliability was examined through the inter-nal consistency reliability. Internal consistency foreach of the 6 NC factors was determined by the useof Cronbach alpha (41). External (criterion-re-lated) validity of the NC factors derived in thebipolar sample was investigated through the con-vergent, discriminant and concurrent validity. Inparticular, in order to establish convergent validity,we examined the degree to which the NC factorsyielded convergent information with other, exter-nal measures that they would theoretically beexpected to be similar to. For the purpose of theanalyses reported here, 2 of the items of the CARS-M, including Distractibility (Item 6, whichexcludes distractibility due to intrusions of visual

and/or auditory hallucinations or delusions andrates whether attention is too easily drawn tounimportant or irrelevant external stimuli) andDisordered Thinking (Item 11) were investigated.Since, apart from such selected items, NC func-tioning and psychopathology may represent sepa-rate dimensions, for discriminant validity, weexamined the degree to which the 6 NC factorsoverlapped with psychometric ratings of clinicalsymptoms. In particular, discriminant validity wasexamined via bivariate correlations between thecomponents of the NC factors and the overall

severity score of clinical symptoms, indexing maniaand depression, respectively. To examine concur-rent validity we assessed the ability of the 6 NCfactors to distinguish between the 2 diagnosticgroups.

Results

Demographic and basic descriptive data at baseline

Descriptive neuropsychological data on all indi-vidual NC variables of interest are shown in

Table 4. Comparison of the 2 groups on theindividual measures indicated a significantly betterperformance in the BPD as compared to the SZsample for 15 of 30 measures (corrected formultiple testing using the Hochberg procedure),although the magnitude of the difference wasgenerally modest.

Homogeneity of correlation matrices

The null-hypothesis of no-difference between thecorrelation matrices from the BPD and the SZsample was tested by the likelihood ratio test. Inparticular, first we derived the null-model likeli-hood by positing an unstructured, homogeneouscorrelation matrix across the 2 diagnostic groups.Second, the homogeneity condition was relaxed(i.e., a heterogeneous correlation matrix wasposited across the 2 groups), and we examinedwhether the resulting improvement in the model-likelihood over the null-model likelihood reachedstatistical significance. The null-model likelihoodindicated chi-square 5130.5 (df 350, p 0.0001), whereas the heterogeneous correlationmodel resulted in chi-square 5330.5 (df 701,p 0.0001). The likelihood ratio chi-squarestatistic for the improvement in model fit didnot reach statistical significance (p > 0.1), indi-cating that the homogeneous correlation structureprovides adequate fit to the data across the 2diagnostic groups.

Exploratory factor analysis

Overall, similar to our published findings in the SZsample, results of the exploratory factor analysis(principal component method with PROMAXrotation) in the bipolar sample indicated 6factors based on both the KaiserGuttmaneigenvalue criterion (i.e., eigenvalue > 1 forfactors retained for further analyses) and onCattells scree-plot criterion based on the break-point of the curve. Together, the 6 factorsexplained approximately 68.0% of the total

variance in the neuropsychological dataset inthe bipolar sample. The distribution of theamount of variance explained across the 6 factorswas: Working Memory (12.6%), Attention(12.5%), Verbal Knowledge (12.0%), Non-VerbalFunctions (11.6%), Ideational Fluency (11.1%),and Learning (9.2%).

These results in the bipolar sample were similarto what we found in the expanded sample ofschizophrenic patients that we used for the purposeof the current analyses [n 250, including thesubsample of patients used for our previous


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analyses (n 156)]. In particular, the 6-factorsolution in the SZ sample explained 67.8% of thevariance. Furthermore, the individual factorsexplained a similar amount of variance in the SZas in the BPD sample, with the exception of theideational fluency factor which was associated witha smaller amount of explained variance in the SZsample. The distribution of explained variance

across the 6 factors in the SZ sample was:Attention (15.0%), Working Memory (12.5%),Verbal Knowledge (11.7%), Non-Verbal Func-tions (11.5%), Learning (10.7%) and IdeationalFluency (3.4%).

In addition to the above EFA analyses thatfocused on the same set of variables that weincluded in our previous analyses in the SZ sample,similar to our published study, we exploredwhether a separate motor factor can be derivedin the BPD sample. For the purpose of thisinvestigation, we added the 4 motor measures

(Table 4, last 4 rows) to the set of NC variablesthat we used above, and repeated the exploratoryfactor analysis that we performed for the morelimited set of measures that did not include themotor variables. Similar to our previous analyses,the results indicated that the motor variables didnot load on any of the 6 basic NC factors describedabove. In addition, a single motor factor could not

be derived. Instead, based on the 4 variables thatwe used for the analysis 2 independent smallfactors (containing 2 related variables only)emerged, 1 for motor speed (Finger TappingPreferred and Non-Preferred hand, respectively)and 1 for dexterity (Grooved Pegboard Preferredand Non-Preferred hand, respectively).

Confirmatory factor analysis

As mentioned in the methods, the CFA analysis seta prioridefinitions of the factor structure based on

Table 4. Descriptive statistics for individual neurocognitive measures

Neurocognitive measure

Bipolar sample (n 155a) Schizophrenia sample (n 250a)

Mean (SD) Q1Q3b Mean (SD) Q1Q3b

D2 letters minus errors 358.5c (98.5) 297429 321.2c (96.7) 251395

Stroopwords only 89.6c (17.5) 76.5102.0 79.1c (18.5) 68.091.0

Stroopcolors only 59.7c (13.8) 49.069.0 53.7c (14.7) 43.064.0

Trail Making A Time 43.7c

(19.3) 31.052.0 51.0c

(22.9) 34.061.0WAIS-R Digit Symbol Raw 44.3c (13.6) 34.555.0 38.8c (12.6) 30.046.0

D2 Fluctuations 16.2 (7.0) 12.020.0 15.7 (7.2) 10.019.0

WMS-R Digit Span Forward 7.3 (2.1) 6.09.0 7.1 (2.0) 6.08.0

LNS Total Correct 12.0c (4.1) 10.015.0 10.5c (4.1) 8.013.0

LNS Longest Item Passed 4.7 (1.1) 4.05.0 4.4 (1.3) 3.05.0

WAIS-R Arithmetic Raw 8.9c (3.4) 6.011.0 7.8c (3.4) 5.010.0

WMS-R Digit Span Backward 5.8 (2.4) 4.07.0 5.2 (2.0) 4.06.0

WMS-R Log Mem Immed 19.9c (8.0) 13.025.0 16.1c (7.1) 11.021.0

Ruff Figural Fluency Unique Designs 66.8 (24.9) 46.582.0 60.2 (21.0) 45.073.0

COWAT Total Correct 33.7 (12.4) 24.043.0 31.7 (11.4) 24.039.0

Animal Naming Total Correct 18.9c (6.8) 15.022.0 16.5c (5.8) 13.020.0

WAIS-R Vocabulary Raw 40.2c (12.7) 30.049.0 34.1c (14.9) 21.045.0

WAIS-R Comprehension Raw 15.9c (5.6) 11.020.0 13.9c (5.7) 9.018.0

WAIS-R Similarities Raw 16.1 (4.7) 13.019.0 15.3 (5.4) 12.019.5

WAIS-R Block Design Raw 22.6 (10.5) 15.029.0 19.7 (9.7) 12.025.0WAIS-R Picture Completion Raw 11.7 (3.9) 9.015.0 11.3 (4.1) 9.014.0

WAIS-R Picture Arrangement Raw 8.6 (4.5) 5.012.0 7.4 (4.4) 4.010.0

WMS-R Verbal Paired Association I 16.2 (5.0) 13.020.0 15.5 (4.7) 13.019.0

WMS-R Verbal Paired Association II 6.6 (1.6) 6.08.0 6.5 (1.6) 6.08.0

WMS-R Visual Paired Association I 12.0c (5.0) 8.017.0 10.1c (4.6) 7.014.0

WMS-R Visual Paired Association II 4.8 (1.7) 4.06.0 4.5 (1.7) 3.06.0

WCST Number of Perseverative Errors 21.0c (16.9) 7.033.0 31.2c (22.8) 16.038.0

Finger Tapping Preferred 47.5c (9.8) 41.053.6 42.6c (9.9) 36.050.3

Finger Tapping Non-Preferred 43.6c (8.9) 38.149.5 39.4c (9.4) 33.346.0

Grooved Pegboard Preferred 99.0 (37.1) 73.5114.5 111.1 (62.4) 77.0119.0

Grooved Pegboard Non-Preferred 116.7 (53.2) 80.0136.0 125.4 (69.0) 90.0133.0

D2 Concentration Endurance Test; Stroop Stroop Color-Word Interference Test; LNS Letter Number Span Test; Log Mem

Immed Logical Memory (immediate recall); WAIS-R Wechsler Adult Intelligence Scale-Revised; WMS-R Wechsler Memory

Scale-Revised; COWAT Controlled Oral Word Association Test.aSample size may vary due to missing data.bQ1Q3 Interquartile range.cSignificant mean difference (p < 0.05, with Hochbergs adjustment for multiple testing) between the 2 samples (ANOVA).

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our earlier findings from the SZ sample. Inparticular, the CFA assumed a simple structure:observed NC variables were allowed to assume anon-zero estimate only for 1 of the 6 underlyingconstructs, for which they were considered asindicators. In other words, estimates of loadingsof the individual NC variables were obtained for

their hypothesized factors only; loadings outsidethe underlying construct were not estimated(restricted to be 0).

Results of the CFA analysis indicated that thecorrelated factor model (Model 2) which allowedcorrelations between the 6 underlying factorsprovided a significantly better fit to the data thanthe independent factor model (Model 1) (BPDsample: chi-square 164.4, df 15, p < 0.0001;SZ sample: chi-square 663.3, df 15,p < 0.0001). Indices of overall model fit showedthat GFI did not reach the recommended level ineither of the 2 samples (BPD sample GFI 0.69;SZ sample GFI 0.82); the RMSA values were0.094 and 0.074 in the BPD and the SZ samples,respectively.

Table 5 displays the estimated factor loadings forModel 2 (correlated factors) based on the CFA

analysis conducted in the BPD and in the SZsamples, respectively. As Table 5 shows, the resultswere similar in both samples, suggesting configura-tional invariance across the 2 samples. In partic-ular, the estimated loading coefficients reachedstatistical significance for each of the indicators(observed NC variables) for each of the hypothe-

sized factors in both samples. We note, however,that for 2 of the variables [Concentration Endur-ance Test (D2) Fluctuations and Logical memory immediate recall (LMI)] the coefficients were low(loading estimate


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(i.e., not including D2 Fluctuations and LMI) sincethis set provided a closer fit to the empirical data.

Procrustes matching

As described in the Methods, confirmatory Pro-crustes rotation was applied to investigate the

extent of congruence between the factor structuresderived in the bipolar and the SZ sample. Thismethod is suitable for maximizing the similaritybetween a matrix of factor loadings and anassumed underlying structure by means of the-ory-based expectations as targets. Unlike the CFA,the Procrustes approach estimates for each factorthe loadings for all variables used in the analysis(including items that are considered non-salient fora particular factor). For the purpose of the currentstudy, the Procrustes analysis used the theoreticallypostulated target structure based on the factorstructure derived in the final factor model from theCFA analyses. Similar to our previous analysis, thefactor analysis was based on the principal compo-nent method, and the PROMAX approach wasused to allow for correlation among the 6 NCfactors.

Table 6 displays the estimated coefficients ofcongruence between the corresponding factor pairsfrom the BPD and the SZ samples, respectively. Asshown in Table 6, for 5 of the 6 factors includingAttention, Working Memory, Verbal Knowledge,Non-Verbal Functions, and Learning, there was ahigh level of similarity between the set of loadings

derived in the BPD and the SZ samples, respec-tively. For 1 of the factors (Ideational Fluency), thecongruence was moderate.

The factor loading estimates yielded by theProcrustes analysis are depicted in Figs 16 foreach of the 6 NC factors, respectively. Consistentwith coefficient of congruence estimates, Figs 16indicate a good correspondence between the set of

loadings derived in the BPD and the SZ samples,respectively, for all factors except for IdeationalFluency. An inspection of Fig. 3 indicates that thisrelative lack of congruence for this factor is due tothe fact that, in the BPD sample, only 2 of theconstituting items whereas in the SZ sample all 3 ofthe items reached saliency (in particular, in the

bipolar sample, the loading for the Ruff FiguralFluency Unique Designs was close to zero).

As mentioned before, approximately 26% of thesample in the Schizophrenia Studywas diagnosedwith schizoaffective disorder, and 11% in theBipolar Study evidenced some symptoms ofDelusions or Hallucinations. Inclusion of thesesubjects in the analyses increased diagnostic het-erogeneity and phenomenological overlap acrossdiagnoses, which may have served as a majorcontributing factor to the similarity of the factorstructures across diagnoses. To investigate thispossibility further, in additional secondary analy-ses, we excluded the aforementioned subjects, andrecomputed the coefficient of congruence for thefactor structure across diagnoses. Results indicatedthat the 6 NC factors were replicable with the morehomogeneous samples; the values of CC remainedalmost unchanged between the 2 diagnostic sam-ples (Attention 0.863, Working Memory 0.805, Ideational Fluency 0.601, Verbal Knowl-edge 0.797, Non-Verbal Functions 0.821 andLearning 0.890).

Reliability, validity

Construct reliability. Table 7displaysthe Cronbachalpha estimate (measuring internal consistency) foreach factor in each of the 2 samples. As Table 7shows, the internal consistency for the individualfactors was generally good, with the exception ofthe Ideational Fluency factor for which theinternal consistency estimate in each sample wasonly of moderatemagnitude. Overall, no meaningfuldifferences were observed between the 2 samples interms of construct reliability of the 6 NC factors.

Convergent validity. For convergent validity, weexamined the degree to which the NC factorsprovided convergent information with measuresthat they would theoretically be expected to beoverlapping. The analyses focused on 2 items of theCARS-M, including Distractibility (Item 6) andDisordered Thinking (Item 11). In particular,association between the above 2 items (i.e., Dis-tractibility, Disordered Thinking) and the 6 NCfactors, respectively, was examined by logisticregression analysis. Results of the logistic regres-sions analyses are shown in Table 8.

Table 6. Coefficient of congruence (CC) between factors derived in the

bipolar and the schizophrenia samplea

FactorObservedCC value

95% Confidence

limitsb

Lower Upper

Attention 0.883 0.787 0.979

Working memory 0.878 0.794 0.962

Ideational fluency 0.658 0.467 0.850

Verbal knowledge 0.818 0.704 0.932

Non-verbal functions 0.837 0.675 0.999

Learning 0.903 0.813 0.993

aFactor analysis was based on the PROMAX method using

Procrustes rotation.bBootstrap/resampling estimates, based on 1,000 samples

drawn randomly from the original observed dataset.

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As Table 8 indicates, the clinical rating ofDistractibility was associated with poorer func-tioning on the Attention and Non-Verbal Func-tions factors (and to a lesser extent on Learning).As expected, the largest effect size was observed forthe association with the Attention factor. Disor-dered Thinking had a more general relationshipwith NC functioning, as indexed by the NCfactors. In particular, a statistically significantassociation was observed for 5 of the 6 factorsincluding Attention, Working Memory, IdeationalFluency, Verbal Knowledge, Non-Verbal Func-

tions. The association did not reach significance forLearning.

Discriminant validity. For discriminant validity,we investigated the degree to which the 6 NCfactors overlapped with psychometric ratings. Inparticular, discriminant validity was examined viabivariate correlations between the neurocognitivefactors and the overall severity score of clinicalsymptoms, indexing mania (total score on theCARS-M scale) and depression (total score onHAM-D scale, 17-item version), respectively.

D2Lett.-

Error

Stroop,

Words

Stroop,

Colors

TrailsA,

Time

DigitSymbol

DigitSp.

Forw.

LNS,

Correct

LNS,

Longest

Arithmetic

DigitSp.

Back.

RuffUniq.Des.

COWATTotal

Anim.

Naming

WAISVocab.

WAISCompr.

WAISSimilar.

WAISBlockD.

WAISPict.Cp.

WAISPict.Arr.

Verb.

PairedI

Verb.

PairedII

VisualPairedI

VisualPairedlI

Factorloadings

Working memory factor

1.00

0.50

0.00

Bipolar

SCH/SCA

Fig. 2. Working memory: comparison of factor loadings obtained in the bipolar and schizophrenia samples. See Fig. 1 for completedescription and abbreviations.

Attention factor

Factorloadings

1.00

0.50

0.00

Bipolar

SCH/SCA

D2Lett.-

Error

Stroop,

Words

Stroop,

Colors

TrailsA,

Time

DigitSymbol

DigitSp.

Forw.

LNS,

Correct

LNS,

Longest

Arithmetic

DigitSp.

Back.

RuffUniq.Des.

COWATTotal

Anim.

Naming

WAISVocab.

WAISCompr.

WAISSimilar.

WAISBlockD.

WAISPict.Cp.

WAISPict.Arr.

Verb.

PairedI

Verb.

PairedII

VisualPairedI

VisualPairedlI

Fig. 1. Attention: comparison of factor loadings obtained in the bipolar and schizophrenia samples. The factor analysis was based onthe principal component method applying Procrustes rotation. Factors from the 2 samples were matched (paired) on the basis of theircongruence. On the horizontal axis, individual neuropsychological variables entering the factor analysis were grouped according to

the 6 factors identified on the basis of previous study (28).D2 Concentration Endurance Test; Stroop Stroop Color-Word Interference Test; LNS Letter Number Span Test;COWAT Controlled Oral Word Association Test; WAIS Wechsler Adult Intelligence Scale.


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Results of these analyses revealed no statisticallysignificant association between the total score onthe HAM-D scale and any of the 6 NC factors.Analyses of the total score on the CARS-M scaleindicated 2 significant, but modest associationsincluding the Working Memory (n 148, r )0.20, p 0.017) and the Non-Verbal Knowledgefactors (n 148, r )0.16, p 0.047), respectively.

Concurrent validity. To examine concurrent valid-ity we assessed the ability of the 6 NC factors todistinguish between the 2 diagnostic groups. Theanalyses were based on the analysis of covariance(ANCOVA) model using the NC factors asdependent variables, with a separate analysis

performed for each of the factors. Diagnosticgroup served as an independent variable in theANCOVA analysis; full-scale IQ, education, gen-der and ethnicity were used as covariates. Resultsof the comparisons between the 2 diagnosticgroups are summarized in Table 9.

As shown in Table 9, patients in the BPDsample displayed a significantly better functioningon each of the NC factors than patients in the SZsample. However, after adjustment for the covar-iates, a significant group difference was detect-able only on the Attention and Non-VerbalFunctions factors. Since age, onset of illness,and the age at first treatment may have adifferential impact on NC functioning in the 2

D2Lett.-

Error

Stroop,

Words

Stroop,

Colors

TrailsA,

Time

DigitSymbol

DigitSp.

Forw.

LNS,

Correct

LNS,

Longest

Arithmetic

DigitSp.

Back.

RuffUniq.Des.

COWATTotal

Anim.

Naming

WAISVocab.

WAISCompr.

WAISSimilar.

WAISBlockD.

WAISPict.Cp.

WAISPict.Arr.

Verb.

PairedI

Verb.

PairedII

VisualPairedI

VisualPairedlI

Bipolar

SCH/SCA

Factorloadings

Verbal knowledge actor

1.00

0.50

0.00

Fig. 4. Verbal knowledge: comparison of factor loadings obtained in the bipolar and schizophrenia samples. See Fig. 1 for completedescription and abbreviations.

D2Lett.-

Error

Stroop,

Words

Stroop,

Colors

TrailsA,

Time

DigitSymbol

DigitSp.

Forw.

LNS,

Correct

LNS,

Longest

Arithmetic

DigitSp.

Back.

RuffUniq.Des.

COWATTotal

Anim.

Naming

WAISVocab.

WAISCompr.

WAISSimilar.

WAISBlockD.

WAISPict.Cp.

WAISPict.Arr.

Verb.

PairedI

Verb.

PairedII

VisualPairedI

VisualPairedlI

Bipolar

SCH/SCA

Factorload

ings

Ideational fluency factor

1.00

0.50

0.00

Fig. 3. Ideational fluency: comparison of factor loadings obtained in the bipolar and schizophrenia samples. See Fig. 1 for completedescription and abbreviations.

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scale-IQ (although full scale-IQ was generally low inboth samples). However, in contrast to the abovevariables, the 2 diagnostic groups were almostidentical in terms of age and age at onset of illness.This is consistent with the fact that SZ and bipolarillness share a number of characteristics, includingtheir onset starting in early adult life (42).

The factor analyses yielded a similar structureacross diagnoses both in terms of the number offactors and configurational invariance (salience ofthe loadings). With respect to the number of factors,

the exploratory factor analysis indicated 6 factorsbased on the KaiserGuttman eigenvalue >1criterion and Cattells scree plot. The total amountof variance explained by the 6 NC factors in the 2samples, respectively, was essentially identical. Spe-cifically, the 6 factors, together, explained approxi-mately 68.0% and 67.8% of the total variance inthe NC dataset in the BPD sample. In addition to thetotal variance explained, the distribution of theexplained variance across the individual NC factorswas also similar in the 2 samples.

Table 7. Internal consistency reliability (Cronbach alpha) and item composition of each neurocognitive factor

Neurocognitive factor Neurocognitive measure included in factor

Standardized alpha

Bipolar sample Schizophrenia sample

Attention D2 letters minus errors

Stroop-words only

Stroop-color only

Trails AWAIS-R Digit symbol

0.83 0.86

Working memory WAIS-R Digit Span Forward

LNS, number correct

LNS, longest

WAIS-R Arithmetic

WAIS-R Digit Span Backward

0.83 0.87

Ideational fluency Ruff Figural Fluency Unique Designs

COWAT

Animal Naming

0.65 0.65

Verbal knowledge WAIS-R Vocabulary

WAIS-R Comprehension

WAIS-R Similarities

0.80 0.86

Non-verbal functions WAIS-R Block Design

WAIS-R Object Assembly

WAIS-R Picture CompletionWAIS-R Picture Arrangement

0.70 0.80

Learning WMS-R Verbal Pair I

WMS-R Verbal Pair II

WMS-R Visual Pair I

WMS-R Visual Pair II

0.80 0.82

D2 Concentration Endurance Test; Stroop Stroop Color-Word Interference Test; LNS Letter Number Span Test; COWAT

Controlled Oral Word Association Test; WAIS-R Wechsler Adult Intelligence Scale-Revised; WMS-R Wechsler Memory Scale-

Revised.

Table 8. Criterion-related validity: bipolar sample (n 155)a

Characteristic

Distractibility Disordered thinking

ORb Chi-square (pc) ORb Chi-square (p)c

Attention 1.6 (1.02.5) 4.2 (0.040) 2.0 (1.23.3) 7.8 (0.0054)

Working memory 1.4 (0.92.1) 2.3 (0.13) 1.8 (1.22.9) 6.5 (0.011)

Ideational fluency 1.4 (0.92.0) 2.8 (0.10) 1.8 (1.22.8) 8.5 (0.0035)

Verbal knowledge 1.4 (0.92.1) 2.4 (0.12) 1.9 (1.23.0) 7.6 (0.006)

Non-verbal functions 1.5 (1.02.3) 4.0 (0.046) 1.8 (1.22.9) 7.6 (0.0058)

Learning 1.4 (0.92.2) 2.0 (0.087) 1.3 (0.82.0) 1.4 (0.24)

aSample size may vary due to missing data.bOR odds ratio statistics, indicating the odds ratio increase for higher symptom severity for each SD unit of decrease in functioning on

a particular neurocognitive factor.cBased on logistic regression analysis with symptom severity (Disordered Thinking, Distractibility) as a dependent variable and neu-

rocognitive factor as an independent variable.

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In general, the individual factors accounted forapproximately 1015%of the variance in each of thesamples, with the exception of the IdeationalFluency which explained a substantially smalleramount of the variance in the BPD (3.4%) than inthe SZ sample (11.1%). Consistent with this finding,thecoefficient of congruence based on theProcrustesanalysisshowed onlya moderateagreement betweenthe 2 samples for the Ideational Fluency factor, incontrast to the high level agreement observed for allother factors. As shown in Fig. 3, the relativelylower congruence for this factor is due to the factthat in the bipolar sample only 2 of the 3 constitutingitems reached saliency (whereas in SZ sample all 3 ofthese items provided high loadings on the factor).Since all subjects in this study received medication(typically polypharmacy; see above), it is conceiv-able that the high degree of similarity across factor

structures in the 2 samples was to due to medicationeffects. However, while this possibility cannot beexcluded, we think that this explanation is unlikelysince the distribution of treatments in the 2 diag-nostic groups showed marked differences in thecurrent investigation.

Overall, whereas the CFA results indicated thatthe estimated loading coefficients obtained statisti-cal significance for each of the indicators (observedNC variables) for each of the hypothesized factorsthat they were considered part of, for 2 variables(D2 Fluctuations and LMI) the coefficients were

low (loading estimate


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factors (r )0.16), respectively. Furthermore,there was no significant association between theHAM-D total score and any of the 6 NC factors.These results are consistent with the notion thatclinical symptoms and NC functioning constituteindependent dimensions.

In the current investigation, concurrent validity

was supported by the finding that the NC factorsdistinguished the 2 diagnostic groups. Overall, theresults showed a general difference among the 2groups: without an adjustment for the covariates,patients in the BPD sample displayed significantlybetter functioning on each of the NC factors thanpatients in the SZ sample. Thus, these results, atface value, are consistent with the view thatpatients with BPD suffer less severe cognitiveimpairments than do patients with SZ (22, 43).

However, we note that after an adjustment forthe observed group differences in the covariates, asignificant difference between the 2 samples wasdetectable only on the Attention and Non-VerbalFunctions factors. Because it can be argued thatdifferences in IQ and education may be a conse-quence of the illness and therefore the adjustmentfor these covariates is hard to justify in a study ofNC differences, we repeated the analyses bycontrolling for the demographic variables, butnot for IQ and education. The results indicatedthat in addition to Attention and Non-VerbalFunctions, the difference in Ideational Fluencyreached significance. Thus, together, these findingsindicate a specific profile of difference, instead of a

general difference in the overall NC functioningbetween the 2 diagnostic groups.

Nonetheless, it should be noted that in terms ofstatistical effect size (Cohens d) the differencebetween the 2 diagnoses was relatively modest. Inparticular, the effect sizes, after adjusting for thecovariates, fell in the moderate range for theAttention and Non-Verbal Functions (0.41 and0.38, respectively); for factors that failed to obtainstatistical significance the effect size was small (0.2for Ideational Fluency, and 0.7 as opposed to>1, the KaiserGuttman rule that was adopted forour study).

Third, the investigation of the generalizability ofthe factor structure was based on cross-sectionaldata; such data have the potential to confound state

and trait effects. Since the factor structure maychangeovertime,theanalysisoflongitudinalchangesin NC functioning and their impact on the factorstructure in the BPD sample is essential. However,we note that the theoretical factor structure that wetested in this study was derived based on both cross-sectional and longitudinal approaches, using datafrom an on-going longitudinal study of SZ. Finally,the analyses were conducted in bipolar patientsonly; additional studies should therefore address theissue of broader diagnostic generalizability (e.g.,with regard to major depressive disorder).

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Conclusion

Together, the results of this study indicate that whilethe same underlying factor structure describes NCfunctioning in both diagnostic groups, the profile ofimpairments mayvary with thediagnosis. The groupcomparisons revealed differences between patients

with BPD and SZ in the neurocognitive domains ofAttention and Non-Verbal Functions, which mayindicate that NC factors operate in a different way inthe 2 illnesses. The large degree of overlap betweenthe respective distributions of NC variables acrossdiagnoses can be interpreted as reflection of adimensional rather than a categorical transitionbetween the 2 diagnoses. It may be underlied byshared genetic susceptibility, although alternativeexplanations are conceivable including (but notrestricted to) iatrogenic effects due to medicationand confounding factors such as drug and alcoholabuse. Overall, the finding of similar factor structureis consistent with the hypothesis that the samecognitive processes are involved in both diseaseentities; however, the nature of these processesappears to be different in the 2 disorders.

Acknowledgements

The authors are grateful for the valuable advice and support

provided by Dr Samuel Gershon, Dr Anil Malhotra, and

Estelle Douglas, as well as the diligence in data collection and

quality management provided by Drs Sara Davis-Conway,

Scott Greisberg, Rebecca Iannuzzo, Pradeep Nagachandran

and Sarah Uzelac and by Mr Sherif Abdelmessih, Ms Claudia

Salazar, Ms Marilyn Mejia, Ms Pam DeRosse, Ms PriyaMatneja and Ms Donna OShea. The authors owe enormous

gratitude to the study participants who give generously of their

time and without whose efforts and patience this work would

never be possible. Funding Source: NIMH R01 MH 60904,

Stanley Medical Research Institute.

References

1. Crow TJ. The continuum of psychosis and its genetic

origins. The sixty-fifth Maudsley lecture. Br J Psychiatry

1990; 156: 788797.

2. Kraepelin E. Psychiatrie. Ein Lehrbuch fu r Studirende und

Aerzte, 6th edn. Leipzig: JA Barth, 1899.

3. American Psychiatric Association. Diagnostic and Statis-tical Manual for Mental Disorders, 4th edn. Washington,

DC: American Psychiatric Press, 1994.

4. WHO. The ICD10 Classification of Mental and Beha-

vioural Disorders. Diagnostic Criteria for Research. Gen-

eva: World Health Organization, 1993.

5. Crow TJ. A continuum of psychosis, one human gene, and

not much else the case for homogeneity. Schizophr Res

1995; 17: 135145.

6. Bearden CE, Hoffman KM, Cannon TD. The neuropsy-

chology and neuroanatomy of bipolar affective disorder: a

critical review. Bipolar Disord 2001; 3: 106150.

7. McDonald C, Bullmore ET, Sham PC et al. Association of

genetic risks for schizophrenia and bipolar disorder with

specific and generic brain structural endophenotypes. Arch

Gen Psychiatry 2004; 61: 974984.

8. Woo TU, Walsh JP, Benes FM. Density of glutamic acid

decarboxylase 67 messenger RNA-containing neurons that

express the N-methyl-D-aspartate receptor subunit NR2A

in the anterior cingulate cortex in schizophrenia and

bipolar disorder. Arch Gen Psychiatry 2004; 61: 649657.

9. Clinton SM, Meador-Woodruff JH. Abnormalities of the

NMDA receptor and associated intracellular molecules in

the thalamus in schizophrenia and bipolar disorder.

Neuropsychopharmacology 2004; 29: 13531362.

10. Craddock N, ODonovan MC, Owen MJ. Genes for

schizophrenia and bipolar disorder? Implications for psy-

chiatric nosology. Schizophr Bull 2006; 32: 916.

11. Craddock N, ODonovan MC, Owen MJ. The genetics of

schizophrenia and bipolar disorder: dissecting psychosis. J

Med Genet 2005; 42: 193204.

12. Badner JA, Gershon ES. Meta-analysis of whole-genome

linkage scans of bipolar disorder and schizophrenia. Mol

Psychiatry 2002; 7: 405411.

13. Segurado R, Detera-Wadleigh SD, Levinson DF et al.

Genome scan meta-analysis of schizophrenia and bipolar

disorder, part III: bipolar disorder. Am J Hum Genet 2003;

73: 4962.

14. Maier W, Zobel A, Wagner M. Schizophrenia and bipolar

disorder: differences and overlaps. Curr Opin Psychiatry

2006; 19: 165170.

15. Cardno AG, Rijsdijk FV, Sham PC et al. A twin study of

genetic relationships between psychotic symptoms. Am J

Psychiatry 2002; 159: 539545.

16. Daban C, Martinez-Aran A, Torrent C et al. Specificity of

cognitive deficits in bipolar disorder versus schizophrenia. A

systematic review. Psychother Psychosom 2006; 75: 7284.

17. Johnson MH, Magaro PA. Effects of mood and severity on

memory processes in depression and mania. Psychol Bull

1987; 101: 2840.

18. Thompson JM, Gallagher P, Hughes JH et al. Neurocog-

nitive impairment in euthymic patients with bipolar affec-

tive disorder. Br J Psychiatry 2005; 186: 3240.

19. van Gorp WG, Altshuler L, Theberge DC et al. Cognitive

impairment in euthymic bipolar patients with and without

prior alcohol dependence. A preliminary study. Arch Gen

Psychiatry 1998; 55: 4146.

20. Tabares-Seisdedos R, Balanza-Martinez V, Salazar-Fraile J

et al. Specific executive/attentional deficits in patients with

schizophrenia or bipolar disorder whohave a positive family

history of psychosis. J Psychiatr Res 2003; 37: 479486.

21. Martinez-Aran A, Vieta E, Colom F et al. Neuropsycho-

logical performance in depressed and euthymic bipolar

patients. Neuropsychobiology 2002; 46 (Suppl. 1): 1621.

22. Seidman LJ, Kremen WS, Koren D et al. A comparative

profile analysis of neuropsychological functioning in

patients with schizophrenia and bipolar psychoses. Schiz-

ophr Res 2002; 53: 3144.23. Glahn DC, Bearden CE, Niendam TA et al. The feasibility

of neuropsychological endophenotypes in the search for

genes associated with bipolar affective disorder. Bipolar

Disord 2004; 6: 171182.

24. Krabbendam L, Arts B, vanOs J et al. Cognitivefunctioning

in patients with schizophrenia and bipolar disorder: a

quantitative review. Schizophr Res 2005; 80: 137149.

25. Hobart MP, Goldberg R, Bartko JJ et al. Repeatable

battery for the assessment of neuropsychological status as

a screening test in schizophrenia, II: convergent/discrimi-

nant validity and diagnostic group comparisons. Am J

Psychiatry 1999; 156: 19511957.


89


20/22

26. Horn JL, McArdle JJ. A practical and theoretical guide to

measurement invariance in aging research. Exp Aging Res

1992; 18: 117144.

27. Jaeger J, Berns S, Loftus S, Gonzalez C, Czobar P.

Neurocognitive test performance predicts functional

recovery from acute exacerbation leading to hospitaliza-

tion in bipolar disorder. Bipolar Disord 2007; 9: 93102.

28. Jaeger J, Czobor P, Berns SM. Basic neuropsychological

dimensions in schizophrenia.SchizophrRes 2003; 65:105116.

29. Kay SR, Fiszbein A, Opler LA. The positive and negative

syndrome scale (PANSS) for schizophrenia. Schizophr Bull

1987; 13: 261276.

30. Altman EG, Hedeker DR, Janicak PG et al. The Clinician-

Administered Rating Scale for Mania (CARS-M): devel-

opment, reliability, and validity. Biol Psychiatry 1994; 36:

124134.

31. Hamilton M. A rating scale for depression. J Neurol

Neurosurg Psychiatry 1960; 23: 5662.

32. Overall J. The Brief Psychiatric Rating Scale in psycho-

pharmacology research. In: Pichot P ed. Psychological

Measurements in Psychopharmacology. Basel: Karger,

1974: 6778.

33. Loehlin JC. Latent Variable Models. Hillsdale, NJ:

Lawrence Erlbaum Associates, 1987.

34. Liu A, Zhang Y, Gehan E et al. Block principal compo-

nent analysis with application to gene microarray data

classification. Stat Med 2002; 21: 34653474.

35. Thurstone LL. Multiple-Factor Analysis. Chicago, IL:

University of Chicago Press, 1947.

36. Littel RC, Milliken GA, Stroup WW et al. SAS System for

Mixed Model. Cary, NC: SAS Institute, Inc., 2002.

37. Kaiser HF. The application of electronic computers to

factor analysis. Educ Psychol Meas 1960; 20: 141151.

38. Cattel R. The Scientific Use of Factor Analysis. New York:

Plenum, 1978.

39. Hurley JR, Cattel RB. The Procrustes Program: producing

direct rotation to test a hypothesized factor structure.

Behav Sci 1962; 7: 258262.

40. Efron B, Gong G. A leisurely look at the bootstrap, the

jackknife, and cross-validation. Am Stat 1983; 37: 3648.

41. Cronbach L. Coefficient alpha and the internal structure of

tests. Psychometrika 1951; 16: 297334.

42. Leboyer M, Henry C, Paillere-Martinot ML et al. Age at

onset in bipolar affective disorders: a review. Bipolar

Disord 2005; 7: 111118.

43. Goldberg TE. Some fairly obvious distinctions between

schizophrenia and bipolar disorder. Schizophr Res 1999;

39: 127132.

44. Czobor P, Volavka J. Dimensions of the Brief Psychiatric

Rating Scale: an examination of stability during halo-

peridol treatment. Compr Psychiatry 1996; 37: 205215.

45. Lindenmayer JP, Grochowski S, Hyman RB. Five factor

model of schizophrenia: replication across samples. Schiz-

ophr Res 1995; 14: 229234.46. Gold JM,Carpenter C, RandolphC et al. Auditoryworking

memory and Wisconsin Card Sorting Test performance in

schizophrenia. Arch Gen Psychiatry 1997; 54: 159165.

47. Goodglass H, Kaplan E. The Assessment of Aphasia and

Related Disorders. Philadelphia, PA: Lea & Febiger, 1983.

48. Brickenkamp R. Concentration-Endurance Test Manual.

Gottingen: Verlag for Psychologie, 1981.

49. Uttl B, Graf P. Color-Word Stroop test performance

across the adult life span. J Clin Exp Neuropsychol 1997;

19: 405420.

50. Heaton RK, Chelune GJ, Talley JL et al. Wisconsin Card

Sorting Test Manual. Odessa, FL: Psychological Assess-

ment Resources, 1993.

51. Lezak M. Neuropsychological Assessment. New York:

Oxford University Press, 1995.

52. Benton A, Hamsher K. Multiphasic Aphasia Examination

Manual. Iowa City, IA: University Of Iowa, 1978.

53. Ruff RM, Allen CC, Farrow CE et al. Figural fluency:

differential impairment in patients with left versus right

frontal lobe lesions. Arch ClinNeuropsychol 1994; 9: 4155.

54. Matthews CG, Love H. Instructions Manual for the Adult

Neuropsychology Test Battery. Madison, WI: University

of Wisconsin Medical School, 1964.

55. Reitan R, Davidson L. Clinical Neuropsychology: Current

Status and Applications. New York: Hemisphere, 1974.

56. Oldfield RC.The assessment andanalysisof handedness: the

Edinburgh Inventory. Neuropsychologia 1971; 9: 97113.

57. Wechsler D. Wechsler Adult Intelligence Scale-Revised

Manual. New York: Psychological Corporation, Harcourt

Brace Jovanovich, Inc., 1981.

58. Wechsler D. Wechsler Memory Scale-Revised Manual. San

Antonio: Psychological Corporation, 1987.

Appendix: Neuropsychological tests used in the

batteryWechsler Adult Intelligence Scale-Revised (WAIS-R) (57)

The goal of this scale is to provide an overallevaluation of intellectual functioning. The scale iscomposed of 11 subtests,6 verbaland 5 performanceoriented, which yield, respectively, the verbal IQ(VIQ), the performance IQ (PIQ), and the full scaleIQ (FSIQ; representing the composite of VIQ andPIQ). The verbal subtests are the Information, DigitSpan (forward and backward tasks), Vocabulary,Arithmetic, Comprehension and Similarities; theperformance subtests are the Picture Completion,

Picture Arrangement, Block Design, Object Assem-bly, and Digit Symbol. The analyses that weconducted for the purpose of this study includedeach of the verbal and performance subtests.

Wechsler Memory Scale Revised (WMS-R) (58)

The WMS-R test investigates various aspects ofmemory functioning, verbal and non-verbal learn-ing and attention. In the current study, the verbaland visual paired associates tasks were included asputative indices of Verbal and Non-Verbal learn-

ing. Logical Memory I (immediate recall) wasincluded as part of the of the Working Memoryfactor; the Visual Memory Span subtest (tappingforward) was used for the Attention factor.

Letter Number Span (46)

In the Letter-Number Span test, the subject isasked to order short sequences of randomlypresented letters and numbers. In order to performthis task, the information needs to be maintained

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over a short delay and transformed. Since the testrequires both memory storage and processing, it isconsidered an index of working memory functions.The current investigation adopted the number ofcorrect trials and the longest sequence as themeasures of interest for the analyses.

Concentration Endurance Test (D2) (48)

The purpose of this test is to assess sustainedattention and visual scanning ability. This paper-and-pencil test is modeled after other cancellationtasks; the subject is asked to detect as many targetletters as possible in a matrix of letters consisting of14 lines. For the purpose of the present study, thetotal score minus errors (letters minus errors) andthe fluctuation (difference between the row with thehighest rate of production and the lowest rate ofproduction) were selected for the analyses.

Trail Making Test (A & B) (51)

Visual motor speed and set shifting were assessedusing the Trail Making Test with 2 parts: A and B.The time to complete each test part (A and B) wasrecorded for each patient, with a maximum of5 min allowed per part. In part A, patients wereasked to connect in sequential order 25 numbersrandomly distributed on a test page. In part B, thetest items included both numbers and letters, andthe sequence connection was numeric-alphabetic inan alternating sequence. Based on our previous

factor analytic study (28), the time to completion inseconds in part A of the test was the principalvariable of interest for this task.

Stroop Test (49)

The Stroop Test is considered a measure of selectiveattention and cognitive flexibility (response inhibi-tion). In the conflict condition, the test requiressubjects to inhibit automatic responses by namingthe color of ink in which color words are presented.Patients are asked to read word names or name

colors as quickly as possible. The number of correctresponses within a 60-s trial was used as the measureof interest. The test consists of 3 conditions:presenting color names in black ink (labeled aswords) and presenting a block ofxs in colored ink,the task being to name the color of each block andfinally a conflict condition in which color names areprinted in text having a different color (e.g., theword greenprinted in red ink). Our previous workshowed that while the conflict condition did notreliably correlate with any of the cognitive factors,

the color and word reading conditions were reliableindices of the Attention factor.

Wisconsin Card Sorting Test (WCST; 128 card manual

version) (50)

This test has been extensively described in the

literature and seems to be cognitively polyfactorial,reflecting Set Shifting, Working Memory Idea-tional Fluency, Abstraction, Hypothesis Testing,and Responsiveness to Feedback. Based on previ-ous literature and our prior factor analyses, theprincipal variable of interest for this study was thenumber of perseverative errors that occurredduring a given trial.

Controlled Oral Word Association Test (COWAT) (52)

Controlled Oral Word Association Test was usedfor the assessment of verbal fluency within phone-mic (letter) constraints. For this task, patients weregiven one letter of the alphabet at a time andinstructed to say aloud as many words beginningwith that letter as they could within 1 min, for atotal of 3 letters in 3 min. The variable of interestfor the current analyses was the total number ofcorrect responses (words provided) for the 3, 1-mintrials.

Animal Naming Test (51)

The Animal Naming Test is part of the Boston

Diagnostic Aphasia Examination. It is a generativenaming task employing semantic constraints. Sub-

jects are instructed to name as many differentanimals as possible in 90 s, and the most produc-tive 60 s are scored.

Ruff Figural Fluency Test (RFFT) (53)

Figural fluency tests have been developed toprovide a non-verbal analogue of the word (verbal)fluency tasks; the RFFT measures the productionof novel designs under both graphical and time

constraints. Ruff et al. (53) suggested that the taskreflects fluid and flexible thinking and the ability tocreate novel responses without repetition.

Grooved Pegboard Test (54)

Fine motor skills including motor speed, visual-motor coordination, and single-hand dexteritywere tested using the Grooved Pegboard Test.Patients were asked to use one hand to put 25 pegsin a 5 by 5 grooved pegboard. The holes of the


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pegboard have slots and the pegs have a key onone side that must be rotated to match the hole inthe board. The number of completed rows, numberof pegs dropped, and time to complete the test wasrecorded for each hand. A maximum of 5 min wasallowed for testing each hand.

Finger Tapping Test (55)

The Finger Tapping Test was adopted as ameasure of motor speed. Subjects tap on a leverfor 5, 10-s trials with their dominant and non-dominant hand. The total number of taps for eachhand was used for statistical analysis.

Complex Ideational Material (47)

Language comprehension was assessed with 8 yes/no questions from the Test of Complex IdeationalMaterial (CIM) from the Boston DiagnosticAphasia Exam (47).

Edinburgh Handedness Inventory (EHI) (56)

The EHI, a standard test of manual dexterity, wasused for determining which hand would be con-sidered preferred hand for motor tests.

Czobor et al.

Documents

Neuropsychological symptom dimensions in bipolar disorder and schizophrenia