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Original Article Neuropsychological symptom dimensions in bipolar disorder and schizophrenia Controversy exists over whether bipolar disorder (BPD) and schizophrenia (SZ) are best character- ized as separate disorders or along a continuum (1). The classical position assumes a categorical view based on Kraepelin’s proposition from more than a century ago (2); it considers differences between psychotic symptoms across diagnoses as qualitatively different. Current diagnostic systems such as DSM (3) and ICD-10 (4) operationalized this view, and try to separate bipolar illness (excluding recurrent major depression, which Kraepelin had grouped with manic depression) and SZ in a categorical fashion by requiring the presence or absence of certain symptoms for the purpose of diagnosis. However, since symptoms Czobor P, Jaeger J, Berns SM, Gonzalez C, Loftus S. Neuropsychological symptom dimensions in bipolar disorder and schizophrenia. Bipolar Disord 2007: 9: 71–92. ª Blackwell Munksgaard, 2007 Background: While neurocognitive (NC) impairments have been well documented in schizophrenia (SZ), there is limited data as to whether similar impairments are present in other persistent mental illnesses. Recent data indicate that NC impairments may be manifested in bipolar disorder (BPD) and that they persist across disease states, including euthymia. An important question is whether a comparable structure of NC impairments is present in the 2 diagnostic groups. Objective: In a previous factor analytic study, we identified 6 factors to describe the basic underlying structure of neuropsychological (NP) functioning in SZ: Attention, Working Memory, Learning, Verbal Knowledge, Non-Verbal Functions, Ideational Fluency. The goal of this study was to investigate whether this factor structure is generalizable for BPD. Methods: The BPD sample included patients (n ¼ 155) from an ongoing longitudinal study evaluating BPD at the time of hospitalization for relapse and at multiple time points over the following 2 years. The SZ sample included patients (n ¼ 250) from a 3-year study. For the current examination the baseline NP evaluations were selected for both samples. Results: Exploratory and confirmatory factor analyses in the BPD sample yielded factors similar to those identified in the SZ sample. The coefficients of congruence ranged between 0.66–0.90 for the individual factors, indicating a good overall correspondence between the factor structures in the 2 diagnostic groups. Analysis of covariance (ANCOVA) analysis with education level, full scale-IQ, gender and ethnicity as covariates indicated that SZ patients had markedly worse performance on the Attention and Non-Verbal Functioning factors compared to the BPD patients. Conclusions: Together, these data suggest that while the same underlying factor structure describes NP functioning in both groups, the profile of impairments appears to vary with the diagnosis. Pa ´l Czobor a,b , Judith Jaeger c,d , Stefanie M Berns c , Cristina Gonzalez c and Shay Loftus c a DOV Pharmaceutical Inc., Hackensack, NJ, b Nathan Kline Institute for Psychiatric Research, Orangeburg, c The Center for Neuropsychiatric Outcome and Rehabilitation Research, The Zucker Hillside Hospital, North Shore Long Island Jewish Health System, Glen Oaks, d Department of Psychiatry and Behavioral Sciences, Albert Einstein College of Medicine, Bronx, NY, USA Key words: bipolar disorder – commonality in factor structure – neuropsychological symptom dimensions – schizophrenia Received 1 July 2005, revised and accepted for publication 17 August 2006 Corresponding author: Judith Jaeger, PhD, MPA, AstraZeneca Pharmaceutical Company, FOC W2-651, 1800 Concovel Plaza, Wilmington, DE 19803, USA. Fax: +1 302 886 4803. e-mail: [email protected] The authors of this paper do not have any commercial associations that might pose a conflict of interest in connection with this manu- script. Bipolar Disorders 2007: 9: 71–92 Copyright ª Blackwell Munksgaard 2007 BIPOLAR DISORDERS 71
Transcript

Original Article

Neuropsychological symptom dimensions inbipolar disorder and schizophrenia

Controversy exists over whether bipolar disorder(BPD) and schizophrenia (SZ) are best character-ized as separate disorders or along a continuum(1). The classical position assumes a categoricalview based on Kraepelin’s proposition from more

than a century ago (2); it considers differencesbetween psychotic symptoms across diagnoses asqualitatively different. Current diagnostic systemssuch as DSM (3) and ICD-10 (4) operationalizedthis view, and try to separate bipolar illness(excluding recurrent major depression, whichKraepelin had grouped with manic depression)and SZ in a categorical fashion by requiring thepresence or absence of certain symptoms for thepurpose of diagnosis. However, since symptoms

Czobor P, Jaeger J, Berns SM, Gonzalez C, Loftus S.Neuropsychological symptom dimensions in bipolar disorder andschizophrenia.Bipolar Disord 2007: 9: 71–92. ª Blackwell Munksgaard, 2007

Background: While neurocognitive (NC) impairments have been welldocumented in schizophrenia (SZ), there is limited data as to whethersimilar impairments are present in other persistent mental illnesses.Recent data indicate that NC impairments may be manifested in bipolardisorder (BPD) and that they persist across disease states, includingeuthymia. An important question is whether a comparable structure ofNC impairments is present in the 2 diagnostic groups.

Objective: In a previous factor analytic study, we identified 6 factors todescribe the basic underlying structure of neuropsychological (NP)functioning in SZ: Attention, Working Memory, Learning, VerbalKnowledge, Non-Verbal Functions, Ideational Fluency. The goal of thisstudy was to investigate whether this factor structure is generalizable forBPD.

Methods: The BPD sample included patients (n ¼ 155) from anongoing longitudinal study evaluating BPD at the time of hospitalizationfor relapse and at multiple time points over the following 2 years. The SZsample included patients (n ¼ 250) from a 3-year study. For the currentexamination the baseline NP evaluations were selected for both samples.

Results: Exploratory and confirmatory factor analyses in the BPDsample yielded factors similar to those identified in the SZ sample. Thecoefficients of congruence ranged between 0.66–0.90 for the individualfactors, indicating a good overall correspondence between the factorstructures in the 2 diagnostic groups. Analysis of covariance (ANCOVA)analysis with education level, full scale-IQ, gender and ethnicity ascovariates indicated that SZ patients had markedly worse performanceon the Attention and Non-Verbal Functioning factors compared to theBPD patients.

Conclusions: Together, these data suggest that while the sameunderlying factor structure describes NP functioning in both groups, theprofile of impairments appears to vary with the diagnosis.

Pal Czobora,b, Judith Jaegerc,d,Stefanie M Bernsc, CristinaGonzalezc and Shay Loftusc

aDOV Pharmaceutical Inc., Hackensack, NJ,bNathan Kline Institute for Psychiatric Research,

Orangeburg, cThe Center for Neuropsychiatric

Outcome and Rehabilitation Research, The Zucker

Hillside Hospital, North Shore Long Island Jewish

Health System, Glen Oaks, dDepartment of

Psychiatry and Behavioral Sciences, Albert Einstein

College of Medicine, Bronx, NY, USA

Key words: bipolar disorder – commonality in

factor structure – neuropsychological symptom

dimensions – schizophrenia

Received 1 July 2005, revised and accepted for

publication 17 August 2006

Corresponding author: Judith Jaeger, PhD, MPA,

AstraZeneca Pharmaceutical Company, FOC

W2-651, 1800 Concovel Plaza, Wilmington, DE

19803, USA. Fax: +1 302 886 4803.

e-mail: [email protected]

The authors of this paper do not have any commercial associations

that might pose a conflict of interest in connection with this manu-

script.

Bipolar Disorders 2007: 9: 71–92Copyright ª Blackwell Munksgaard 2007

BIPOLAR DISORDERS

71

may overlap, sometimes for extended periods, thedifferential diagnosis of BPD and SZ frequentlyposes a problem in clinical practice. In response tothis, an alternative, dimensional view is ofteninvoked in contrast to the prevailing categoricalapproach, which posits that BPD and SZ do notrepresent a discrete illness entity. For example,Crow proposed that psychosis might vary along acontinuum, extending from unipolar affective dis-order through bipolar affective disorder and schiz-oaffective disorder to typical SZ (1, 5).Recently, the dimensional view has gained favor

in a rapidly growing literature emphasizing sharedabnormalities that cut across the current diagnosticdivide. For example, shared morphometric find-ings, such as enlarged ventricles (6), and whitematter volume reductions in the left frontal andtemporoparietal regions were found in both disor-ders (7). Furthermore, common cellular andmolecular patterns were observed, including adecrease in cell density in the GABAergic inter-neurons in SZ as well as in BPD (8). At theintracellular level, both diagnostic groups showedabnormalities in intracellular molecules (e.g.,PSD95) that provide a physical link betweenmultiple neurotransmitter systems (including theglutamatergic and dopaminergic systems) whichare potentially involved in the neurobiology of SZand affective disorders (9). Since these studies donot systematically exclude cases that are diagnos-tically challenging (e.g., share substantial featuresof both disorders) findings of shared pathophysi-ology may be confounded by the incorrect classi-fication of cases.Recent studies have also reported apparent

overlap in the genetic susceptibility between BPDand SZ. For example, family studies show asubstantial degree of familial co-aggregationbetween bipolar illness and SZ (10). Moreover,systematic whole genome linkage studies raised thepossibility of some common chromosomal regionsshared by BPD and SZ, although various meta-analyses yielded inconsistent results with regard tothe strength of the evidence for each of thepotential candidate regions (11–13). Additionally,in candidate gene studies, specific genes have beenidentified in which variation appears to confer therisk to both BPD and SZ (with the strongestevidence shown for G72/G30, in the 13q candidateregion, but common susceptibility was raised forexample for BDNF, COMT, DISC1, neuregulin 1,and dysbindin) (11, 14). In addition, results fromthe first diagnostically unrestricted twin studyindicate that the common shared additive geneticvariance is substantially higher for mania and SZ(49% and 68%, respectively) than the diagnosis-

specific additive genetic variance (19% for maniaand 33% for SZ) (15). Similar to studies examiningpathophysiology, studies of genetic susceptibilityfor the most part suffer from design challenges thatbias against findings that would distinguish thegroups as Kraepelin had proposed (e.g., thedifficulty of blinding the co-twin’s diagnosis duringthe diagnostic process, the practice of includingcases with overlapping features which increases thechance of diagnostic error and the exclusion ofrecurrent major depression from the bipolargroup).To address the question of disease boundaries,

there is growing interest in identifying moreprecisely defined quantitative traits, which wouldrepresent more direct �downstream� biologicalconsequences of genes than the symptoms. Suchtraits, or endophenotypes could serve as an alter-native (or complement) to the categorical diseasephenotypes, and potentially underlie a more accu-rate diagnostic classification. Based on their herit-ability and the fact that they can be measuredobjectively and reliably, certain domains of neuro-cognitive (NC) performance have been consideredas candidate endophenotypes in major mentaldisorders including BPD and SZ.In the case of SZ, general NC deficits and deficits

in various specific tasks indexing broader cognitivedomains have been demonstrated, particularly intasks of Attention, Long-Term Memory, WorkingMemory, and Executive Functioning (16). Withregard to BPD, in the earlier literature, a commonmisconception was that, in contrast to SZ, bipolaraffective disorder is not associated with generalcognitive impairment independent of illnessepisodes, or in the premorbid state (6). However,newer literature challenged this view, and con-verging evidence suggests that persons with BPDexhibit persistent cognitive impairment across arange of tasks of Attention, Memory and Execu-tive Function during remission (17–21). Further-more, cognitive dysfunctions seem to be present inBPD patients not only during acute symptomexacerbation but both in prodromic and residualphases (14).Some of the authors concluded that particularly

poor performance on tests of Verbal Memory wasconsistently found as a characteristic of BPD (17,22). Glahn et al. (23) recently suggested thatVerbal Learning and Memory and ExecutiveFunction/Working Memory may represent themost salient endophenotypic components ofneurocognition in BPD because these domainsappear heritable, co-segregated within families,associated with the disease, and impaired duringperiods of symptom remission.

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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 review

of 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 (Repeatable

Battery 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 ¼ 0.65)obtained a significant difference between thegroups. The difference in terms of attention func-

tioning did not reach significance (effect size ¼0.33). 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 compared

NC 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 forthe 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 interrelationshipsamong 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,

Neuropsychological symptom dimensions

73

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 factorstructure 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 NC

variables 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 patientsenrolled (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 principalpractical 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 PANSSraters had demonstrated interrater reliability com-pared to an expert (ICC ‡ 0.80).For the present analyses, the final dataset from

this 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 ¼ 185; 74%) or schizoaffective disorder(n ¼ 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., nofurther changesweremade 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 0–7 for questionable and 8–15 for mildmania and, for the Hamilton Depression RatingScale (HAM-D; 17 items) (31), 0–6 for notdepressed and 7–17 mildly depressed, we foundthat the majority (approximately 54%) of thesample 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 ¼ 17) of the subjects in the

primary dataset (n ¼ 155) evidenced symptoms on

Czobor et al.

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Delusions involving �Replacement of Will� (Delu-sions of Control, Thought Insertion, ThoughtDeletion, Thought Broadcasting) and Hallucina-tions, reflecting the overlapping boundaries ofBPD with the SZ spectrum in terms of symptompresentation. In secondary analyses, we investi-gated whether the inclusion of these subjects in thesample had an impact on the principal results.

Comparison of the 2 samples

The demographic characteristics of the bipolar(n ¼ 155) and SZ (n ¼ 250) samples are shown inTable 1.As Table 1 shows, the 2 groups were essentially

identical in terms of age, onset of illness, and age atwhich they received the first psychiatric treatment.The groups, however, were significantly different(p < 0.05) in their ethnicity and gender distribu-tions. In particular, a significantly higher propor-tion of patients from the white ethnic group werepresent in the bipolar as compared to the SZsample. Furthermore, as expected on the basis of

demographic prevalence data, the proportion offemale patients was higher in the bipolar ascompared to SZ group. In addition, the bipolarsample demonstrated a significantly higher fullscale-IQ and more years of education, although theformer difference was quite modest (3.7 points infull scale-IQ). The 2 groups evidenced mild levelsof symptom severity as shown by the respectivepsychometric ratings in each group, CARS-M (30)and the HAM-D scale (31) for the bipolar patients;the PANSS positive and negative symptom sub-scale for the schizophrenics (Table 1).In the bipolar sample, at the time of the current

analyses, medication data were available for a totalof 142 patients (91.6% of 155). The distribution(%) of the most common treatments was thefollowing: lithium (69.0%), anticonvulsants(67.3%), neuroleptics (typical and atypical neuro-leptics combined: 65.5%), valproic acid (60.6%),antidepressants (38.0%), benzodiazepines (22.4%),and anxiolytics (18.3%).Overall, the analysis of the medication data

indicated that all patients received polypharmacyin the bipolar sample. In the SZ sample, whilepolypharmacy was common, the overwhelmingmajority of the patients (93% of the sample) weretaking at least 1 neuroleptic medication at baseline.The distribution of atypical and typical agents inthe sample was 68% and 32%, respectively. Inaddition to the neuroleptics, in the SZ sample,many patients were taking another class ofpsychotropic medication as well including moodstabilizers, anxiolytics, and antidepressants.

Measures

Psychopathology. Psychometric assessments ofsymptom severity in each study were conductedat baseline and each of the follow-up visits includ-ing neuropsychological testing. The rating instru-ments in each study were specific to the populationtargeted in that study. In Study 1, which focusedon patients with SZ and schizoaffective disorder,the principal measures of psychopathology werethe PANSS and the Brief Psychiatric Rating Scale(BPRS) (32). In Study 2, which focused on patientswith BPD, the principal measures of psychopa-thology were the CARS-M (30) and the HAM-D(31). The raters for each of these rating instrumentsin our study had demonstrated interrater reliabilitycompared to an expert (ICC > 0.80).

Neurocognitive performance. The NC battery wasdesigned to examine functional domains previouslyconsidered important by virtue of their demon-strated impairment in people with major mental

Table 1. Descriptive and demographic characteristics in the bipolar and theschizophrenia (reference) sample

Characteristics

Bipolarsample(n ¼ 155a)

Schizophreniasample(n ¼ 250a,b)

Mean (SD) Mean (SD)Age 35.4 (10.9) 36.3 (9.1)Onset of illness 19.1 (8.4) 19.1 (6.5)Age first treated 21.2 (8.8) 20.6 (6.8)Education 14.1c (2.4) 12.0c (2.5)Full scale-IQ 86.4c (11.9) 82.7c (10.3)CARS-Md/PANSS POSe 13.0 (8.9) 18.9 (5.5)HAM-Dd/PANSS NEGe 10.6 (6.4) 20.1 (5.8)Gender, n (%)

Male 67 (43.2f) 156 (62.4f)Female 88 (56.8) 94 (37.6)

Race, n (%)White 113 (72.9f) 99 (39.6f)Black 29 (18.7) 106 (42.4)Hispanic 7 (4.5) 28 (11.2)Other 6 (3.9) 17 (6.8)

aSample size may vary due to missing data.bDiagnostic distribution: schizophrenia ¼ 74% (n ¼ 185) versusschizoaffective disorder 26% (n ¼ 65).cSignificant mean difference (p < 0.05) between the two sam-ples (ANOVA).dIn the bipolar sample, symptom severity was indexed by thetotal score on the Clinician-Administered Rating Scale for Mania(CARS-M) and the Hamilton Rating Scale for Depression (HAM-D; 17-item version), respectively.eIn the schizophrenia sample, symptom severity was indexed bythe total score on the positive (POS) and negative symptom(NEG) subscale of the Positive and Negative Symptom Scale(PANSS), respectively.fSignificant difference in proportions (p < 0.05) between the twosamples (chi-square test).

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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 previously

trained 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 ofoverlap 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<0.05 for these factors). These variables were theVisual Memory Span Forward [Wechsler MemoryScale-Revised (WMS-R); included in the Attentionfactor based on Study 1]; Wechsler Adult Intelli-gence Scale-Revised (WAIS-R) Information (in-cluded in the �Verbal Knowledge� factor); and theWAIS-R Object Assembly variables (included inthe �Non-Verbal Functions� factor).At the time of the previous publication, Study 1

was ongoing and data were available only from asubset of 156 subjects. By the time of the currentanalyses, the data were available from the entire SZsample; thus, we used all available data for thecurrent study of the replicability of the NC factorstructure across the 2 diagnostic samples.

Conceptual framework of the statistical analyses

NC test batteries typically yield a large number ofvariables, hence a fundamental goal in NC

research is dimensionality reduction – to find asuitable representation of such multivariate data(i.e., to identify, based on the pattern of relation-ships among the observed variables, a relativelylow number of basic underlying dimensions thatprovide the most efficient description of the vari-ation in the data). This goal, in general, can beachieved by various multivariate techniques,including factor and principal component analyses(PCA), which view the observed variables asmanifestations of some underlying, latent set offactors (dimensions).However, when applied to NC data, traditional

multivariate methods, including PCA run intoserious difficulties because of the extremely highnumber of variables in the data relative to thenumber of observations. Even if the geometricproperties of PCA remain valid, and numericaltechniques yield stable results, the covariancematrix on which the analysis is carried out issometimes a poor estimate of the real populationcovariance. Thus, the analysis under these condi-tions fails to provide a robust, generalizablesolution.To deal with this problem, in our previous study

to identify the basic NC dimensions in patientswith SZ, a 2-stage procedure was designed toimplement the PCA in a stratified way. Briefly, inStage 1, the neuropsychological variables weredivided into blocks based on a priori knowledgeabout their observed associations. The 10 a prioriblocks comprised Sustained Vigilance, Short-TermMemory Capacity/Span, Working Memory, SetShifting/Cognitive Flexibility, Ideational Fluency,Verbal Learning, Non-Verbal Learning, VerbalKnowledge, Non-Verbal Reasoning/ProblemSolving, and Motor Functioning. In Stage 2, thevariables in each block were subjected to factor(principal component) analysis to identify the basicunderlying NC constructs (factors) that explainedmost of the variation within such a block ofvariables.The factor analysis was based on the principal

component method, and the PROMAX rotation(33) was applied in order to obtain a conceptuallyinterpretable simple structure. The PROMAXrotation is an oblique rotation technique whichallows for correlation between factors. Since thereare conceptual as well as clinical reasons topresume a substantial correlation between the NCfactors, this technique provides a more realisticrepresentation of the data than the orthogonalsolution which assumes independence. Furtherdetails of our procedures are described elsewhere.We note here, however, that a technique called�block principal component analysis� (BPCA) has

Table 2. Neuropsychological tests used in the present study

Neuropsychological tests

Wechsler Adult Intelligence Scale-Revised (WAIS-R) (57)Wechsler Memory Scale Revised (WMS-R) (58)Letter Number Span (46)Complex Ideational Material (47)Concentration Endurance Test (D2) (48)Stroop Test (49)Wisconsin Card Sorting Test (128-card manual version) (50)Trail Making Test (A&B) (51)Controlled Oral Word Association Test (COWAT) (52)Animal Naming Test (51)Ruff Figural Fluency Test (53)Grooved Pegboard Test (54)Finger Tapping Test (55)Edinburgh Handedness Inventory (56)

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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 previous

study (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 Thurstone’s 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 tostatistically 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, thepsychometric 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

Neurocognitivefactor

Neurocognitive measure includedin factor

Attention D2 – letters minus errorsStroop - words onlyStroop - color onlyTrails AWMS-R Visual Memory Span Forwarda

WAIS-R Digit symbolWorking memory D2 fluctuation

WAIS-R Digit span forwardLNS, number correctLNS, longestWAIS-R ArithmeticWAIS-R Digit Span BackwardWMS-R Log Mem Immed

Learning WMS-R – Verbal Pair IWMS-R – Verbal Pair IIWMS-R – Visual Pair IWMS-R – Visual Pair II

Verbal knowledge WAIS-R – VocabularyWAIS-R – Informationa

WAIS-R – ComprehensionWAIS-R – Similarities

Non-verbal functions WAIS-R – Block DesignWAIS-R – Object Assemblya

WAIS-R – Picture CompletionWAIS-R – Picture Arrangement

Ideational fluency WCST Number of Perseverative ErrorsRuff Figural Fluency Unique DesignsCOWATAnimal Naming

D2 ¼ Concentration Endurance Test; Stroop ¼ Stroop Color-Word Interference Test; Trails ¼ Trailmaking Test; LNS ¼ LetterNumber Span Test; Log Mem Immed ¼ Logical Memory(immediate recall); WCST ¼ Wisconsin Card Sorting Test;COWAT ¼ Controlled Oral Word Association Test.aVariables not available in the bipolar sample included:Wechsler Memory Scale Revised (WMS-R) Visual Memory SpanForward; Wechsler Adult Intelligence Scale-Revised (WAIS-R)Information; and the WAIS-R Object Assembly.

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derived a null-model likelihood by positing anunstructured homogeneous correlation matrix forthe empirical data across the 2 diagnostic groups.Second, we relaxed the homogeneity condition(posited a heterogeneous correlation matrix bydiagnostic group) and examined whether theresulting improvement in the likelihood reachedstatistical significance. Test of improvement inmodel fit was based on chi-square statistics.

Step 2: Exploratory factor analyses. A failure toreject the null-hypothesis with regard to thehomogeneity of the correlation matrices acrossthe 2 diagnostic groups may be a reflection of lowstatistical power. Thus, in view of the fact that wehad a relatively small sample size, it is possible thatthe 2 groups have certain systematic differenceswhich would not result in the rejection of thenull-hypothesis in our study. For example, it isconceivable that the number of interpretablefactors is different in the 2 samples, or that mostbut not all of the factors are replicable (i.e., partialversus full factorial invariance). Therefore, beforewe proceeded with the CFA, we performed EFA toinvestigate whether the theoretically-postulatedfactor structure derived from the SZ samplerepresents an adequate representation of thepattern of observed associations among a groupof variables in the BPD sample. More specifically,in these preliminary analyses, we investigatedwhether model improvements were necessary interms of the number of factors that need to beretained for further analyses, and in terms of thefactor structure of the individual factors based onthe distribution of salient and non-salient loadings.Similar to our previous study, we used the principalcomponent method for factor extraction. ThePROMAX rotation was applied in order to derivea simple structure to facilitate the interpretation. Inorder to examine the dimensionality in an EFA, weused the Kaiser–Guttman eigenvalue >1 criterion(37) and Cattell’s Scree plot (38). Items wereallocated to factors according to their highestloading; the threshold loading of 0.5 was chosen toindicate saliency.

Step 3: Confirmatory factor analyses. The relation-ship between the observed variables and thehypothesized underlying constructs can be investi-gated by CFA. The CFA techniques used in thisinvestigation set a priori definitions of the factorstructure (measurement model) based on the find-ings from the SZ sample and based on ourpreliminary EFA findings in the BPD sample. Inthe structural part of the CFA models, 2 theoret-ically possible alternatives were tested against each

other. In model 1, the basic assumption was thatthe 6 NC factors represent 6 distinct constructswith no relationship (correlation) between them. Inmodel 2, all factors were considered interrelatedconstructs and a correlation was therefore allowedbetween any of the 6 factors. In the CFA, estimatesof loadings of the individual neuropsychologicalitems were obtained for their hypothesized factors.Values of t-statistics were used to test whether theindividual items were significantly related to theirspecific factors.The Root Mean Square Error of Approximation

(RMSEA) and the Goodness of Fit Index (GFI)were used to assess model fit for the entire CFAmodel. The RMSEA indicates the fit of the modelto the covariance matrix (or correlation matrix, asin our study). It represents the square root of theaverage amount that the sample covariances differfrom their estimates derived on the basis of theposited factor model. As a guideline, RMSEAvalues below 0.1 are generally considered toindicate an adequate fit, whereas values of <0.05represent a close fit. For GFI, values above 0.90are considered as an indication of an adequatemodel fit.

Step 4: Generalizability across samples. As de-scribed above, following Thurstone (35), the mostbasic conceptualization of a construct is thepattern of non-zero and zero loadings, not theparticular magnitude of the non-zero loadings. Inthis theoretical framework, in order to establishwhether a construct can be conceptualized in thesame way across diagnoses, the requirement is thatthe same pattern of (zero and non-zero) factorloadings is found in the individual groups. For thisreason, in a multi-group CFA no cross-sampleconstraints are imposed on the magnitude of thesalient factor loadings; the non-salient loadingsare (implicitly) specified to be equal (i.e., zero).Therefore, whereas the CFA addresses theconfigurational invariance of factors acrosssamples, it does not indicate the extent of similarity(generalizability), since it does not take theparticular magnitude of the loadings into account.For the current study, confirmatory Procrustes

rotation (39) was applied to investigate the extentof similarity (generalizability) between the SZ andthe BPD samples (maximum congruence). Thisconfirmatory procedure rotates empirically ex-tracted principal components to a theoreticallyspecified target matrix of factor loadings to max-imize their similarity. The theoretical factor-load-ing matrix specifies the number of components tobe fitted and the factor-loading pattern of the testitems. Unlike the CFA method, the Procrustes

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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 visualand/or auditory hallucinations or delusions andrates whether �attention is too easily drawn tounimportant or irrelevant external stimuli�) and�Disordered 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 overallseverity 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 ¼ 5130.5 (df ¼ 350, p ¼0.0001), whereas the heterogeneous correlationmodel resulted in chi-square ¼ 5330.5 (df ¼ 701,p ¼ 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 Kaiser–Guttmaneigenvalue criterion (i.e., eigenvalue > 1 forfactors retained for further analyses) and onCattell’s scree-plot criterion based on the break-point of the curve. Together, the 6 factorsexplained approximately 68.0% of the totalvariance 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 similar

to what we found in the expanded sample ofschizophrenic patients that we used for the purposeof the current analyses [n ¼ 250, including thesubsample of patients used for our previous

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analyses (n ¼ 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 varianceacross 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 that

focused 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 notbe 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 priori definitions of the factor structure based on

Table 4. Descriptive statistics for individual neurocognitive measures

Neurocognitive measure

Bipolar sample (n ¼ 155a) Schizophrenia sample (n ¼ 250a)

Mean (SD) Q1–Q3b Mean (SD) Q1–Q3b

D2 – letters minus errors 358.5c (98.5) 297–429 321.2c (96.7) 251–395Stroop–words only 89.6c (17.5) 76.5–102.0 79.1c (18.5) 68.0–91.0Stroop–colors only 59.7c (13.8) 49.0–69.0 53.7c (14.7) 43.0–64.0Trail Making A Time 43.7c (19.3) 31.0–52.0 51.0c (22.9) 34.0–61.0WAIS-R Digit Symbol Raw 44.3c (13.6) 34.5–55.0 38.8c (12.6) 30.0–46.0D2 Fluctuations 16.2 (7.0) 12.0–20.0 15.7 (7.2) 10.0–19.0WMS-R Digit Span Forward 7.3 (2.1) 6.0–9.0 7.1 (2.0) 6.0–8.0LNS Total Correct 12.0c (4.1) 10.0–15.0 10.5c (4.1) 8.0–13.0LNS Longest Item Passed 4.7 (1.1) 4.0–5.0 4.4 (1.3) 3.0–5.0WAIS-R Arithmetic Raw 8.9c (3.4) 6.0–11.0 7.8c (3.4) 5.0–10.0WMS-R Digit Span Backward 5.8 (2.4) 4.0–7.0 5.2 (2.0) 4.0–6.0WMS-R Log Mem Immed 19.9c (8.0) 13.0–25.0 16.1c (7.1) 11.0–21.0Ruff Figural Fluency Unique Designs 66.8 (24.9) 46.5–82.0 60.2 (21.0) 45.0–73.0COWAT Total Correct 33.7 (12.4) 24.0–43.0 31.7 (11.4) 24.0–39.0Animal Naming Total Correct 18.9c (6.8) 15.0–22.0 16.5c (5.8) 13.0–20.0WAIS-R Vocabulary Raw 40.2c (12.7) 30.0–49.0 34.1c (14.9) 21.0–45.0WAIS-R Comprehension Raw 15.9c (5.6) 11.0–20.0 13.9c (5.7) 9.0–18.0WAIS-R Similarities Raw 16.1 (4.7) 13.0–19.0 15.3 (5.4) 12.0–19.5WAIS-R Block Design Raw 22.6 (10.5) 15.0–29.0 19.7 (9.7) 12.0–25.0WAIS-R Picture Completion Raw 11.7 (3.9) 9.0–15.0 11.3 (4.1) 9.0–14.0WAIS-R Picture Arrangement Raw 8.6 (4.5) 5.0–12.0 7.4 (4.4) 4.0–10.0WMS-R Verbal Paired Association I 16.2 (5.0) 13.0–20.0 15.5 (4.7) 13.0–19.0WMS-R Verbal Paired Association II 6.6 (1.6) 6.0–8.0 6.5 (1.6) 6.0–8.0WMS-R Visual Paired Association I 12.0c (5.0) 8.0–17.0 10.1c (4.6) 7.0–14.0WMS-R Visual Paired Association II 4.8 (1.7) 4.0–6.0 4.5 (1.7) 3.0–6.0WCST Number of Perseverative Errors 21.0c (16.9) 7.0–33.0 31.2c (22.8) 16.0–38.0Finger Tapping Preferred 47.5c (9.8) 41.0–53.6 42.6c (9.9) 36.0–50.3Finger Tapping Non-Preferred 43.6c (8.9) 38.1–49.5 39.4c (9.4) 33.3–46.0Grooved Pegboard Preferred 99.0 (37.1) 73.5–114.5 111.1 (62.4) 77.0–119.0Grooved Pegboard Non-Preferred 116.7 (53.2) 80.0–136.0 125.4 (69.0) 90.0–133.0

D2 ¼ Concentration Endurance Test; Stroop ¼ Stroop Color-Word Interference Test; LNS ¼ Letter Number Span Test; Log MemImmed ¼ Logical Memory (immediate recall); WAIS-R ¼ Wechsler Adult Intelligence Scale-Revised; WMS-R ¼ Wechsler MemoryScale-Revised; COWAT ¼ Controlled Oral Word Association Test.aSample size may vary due to missing data.bQ1–Q3 ¼ Interquartile range.cSignificant mean difference (p < 0.05, with Hochberg’s 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 fortheir hypothesized factors only; loadings outsidethe underlying construct were not estimated(restricted to be 0).Results of the CFA analysis indicated that the

correlated 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 ¼ 164.4, df ¼ 15, p < 0.0001;SZ sample: chi-square ¼ 663.3, df ¼ 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 ¼ 0.69;SZ sample GFI ¼ 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 for

Model 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 <0.45) in both samples.Since these findings suggested low indicator

reliability for these variables with respect to theirunderlying construct (Working Memory, for bothD2 Fluctuations and LMI), the above 2 variableswere omitted from our final CFA model. The CFAresults based on this model indicated an improve-ment in the model fit indices. In the BPD sample,the GFI and the RMSA were 0.72 and 0.086respectively; in the SZ sample, the analogousvalues were 0.84 (GFI) and 0.064 (RMSA),respectively. Although the GFI indices failed toreach the recommended threshold, our final factormodel was based on the restricted set of variables

Table 5. Confirmatory factor analysis estimates of factor loadings

Factor Neurocognitive measure

Bipolar sample Schizophrenia sample

Loading (SE) t-statistic* Loading (SE) t-statistic*

Attention D2 – letters minus errors 0.69 (0.11) 6.19 0.75 (0.06) 12.18Stroop-words only 0.58 (0.12) 4.98 0.78 (0.06) 12.88Stroop-colors only 0.70 (0.11) 5.95 0.81 (0.06) 13.69Trail Making A Time 0.69 (0.11) 6.24 0.65 (0.06) 10.21WAIS-R Digit Symbol Raw 0.79 (0.11) 7.41 0.75 (0.06) 12.31

Working memory D2 Fluctuations 0.34 (0.12) 2.83 0.23 (0.07) 3.26WMS-R Digit Span Forward 0.63 (0.11) 5.74 0.59 (0.06) 9.16LNS Total Correct 0.95 (0.09) 10.58 0.95 (0.05) 18.48LNS Longest Item Passed 0.87 (0.10) 9.00 0.93 (0.05) 17.85WAIS-R Arithmetic Raw 0.52 (0.12) 4.53 0.95 (0.06) 10.36WMS-R Digit Span Backward 0.65 (0.11) 5.86 0.63 (0.06) 10.03LMI 0.40 (0.12) 3.37 0.41 (0.07) 6.14

Ideational fluency Ruff Figural Fluency Unique Designs 0.80 (0.11) 7.32 0.75 (0.07) 10.02COWAT Total Correct 0.56 (0.12) 4.70 0.76 (0.07) 9.74Animal Naming Total Correct 0.66 (0.11) 5.78 0.84 (0.05) 7.96

Verbal knowledge WAIS-R Vocabulary Raw 0.86 (0.11) 7.74 0.85 (0.06) 14.43WAIS-R Comprehension Raw 0.68 (0.12) 5.78 0.81 (0.06) 13.58WAIS-R Similarities Raw 0.65 (0.12) 5.52 0.80 (0.06) 13.31

Non-verbal functions WAIS-R Block Design Raw 0.70 (0.11) 6.18 0.79 (0.06) 12.62WAIS-R Picture Completion Raw 0.64 (0.12) 5.51 0.72 (0.06) 11.24WAIS-R Picture Arrangement Raw 0.73 (0.11) 6.43 0.74 (0.06) 11.64

Learning WMS-R Verbal Paired Association I 0.61 (0.12) 5.19 0.75 (0.06) 11.87WMS-R Verbal Paired Association II 0.76 (0.11) 6.97 0.74 (0.06) 11.58WMS-R Visual Paired Association I 0.78 (0.11) 7.18 0.74 (0.06) 11.63WMS-R Visual Paired Association II 0.68 (0.11) 5.95 0.72 (0.06) 11.15

D2 ¼ Concentration Endurance Test; Stroop ¼ Stroop Color-Word Interference Test; LNS ¼ Letter Number Span Test; LMI ¼ LogicalMemory (immediate recall); WAIS-R ¼ Wechsler Adult Intelligence Scale-Revised; WMS-R ¼ Wechsler Memory Scale Revised;COWAT ¼ Controlled Oral Word Association Test.*p < 0.05 for all values in the column.

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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 theextent 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 of

congruence 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 loadingsderived 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 the

Procrustes analysis are depicted in Figs 1–6 foreach of the 6 NC factors, respectively. Consistentwith coefficient of congruence estimates, Figs 1–6indicate 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 thebipolar sample, the loading for the Ruff FiguralFluency Unique Designs was close to zero).As mentioned before, approximately 26% of the

sample in the �Schizophrenia Study� was diagnosedwith schizoaffective disorder, and 11% in the�Bipolar 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 ¼ 0.863, Working Memory ¼0.805, Ideational Fluency ¼ 0.601, Verbal Knowl-edge ¼ 0.797, Non-Verbal Functions ¼ 0.821 andLearning ¼ 0.890).

Reliability, validity

Construct reliability. Table 7displays theCronbachalpha 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 ofmoderatemagnitude.Overall, nomeaningfuldifferences 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) and�Disordered 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 thebipolar and the schizophrenia samplea

FactorObservedCC value

95% Confidencelimitsb

Lower Upper

Attention 0.883 0.787 0.979Working memory 0.878 0.794 0.962Ideational fluency 0.658 0.467 0.850Verbal knowledge 0.818 0.704 0.932Non-verbal functions 0.837 0.675 0.999Learning 0.903 0.813 0.993

aFactor analysis was based on the PROMAX method usingProcrustes rotation.bBootstrap/resampling estimates, based on 1,000 samplesdrawn 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.

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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 tothe 6 factors identified on the basis of previous study (28).D2 ¼ Concentration Endurance Test; Stroop ¼ Stroop Color-Word Interference Test; LNS ¼ Letter Number Span Test;COWAT ¼ Controlled Oral Word Association Test; WAIS ¼ 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 ¼ 148, r ¼)0.20, p ¼ 0.017) and the Non-Verbal Knowledgefactors (n ¼ 148, r ¼ )0.16, p ¼ 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 BPD

sample 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

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

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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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diagnostic groups, in additional ANCOVA anal-yses, these variables were introduced in order totest the possibility of a differential relationship.None of the analyses indicated a significant effect(i.e., p > 0.10 in all analyses for the main effector for the interaction of the above variables withthe diagnosis).

Discussion

To our knowledge, this is the first study thatcompared the factor structure across diagnosesincluding SZ and BPD. The results, taken together,indicated that the factor structure is generalizableacross the 2 diagnostic samples. In particular, thecoefficient of congruence between the individual

factors derived from the 2 samples was high, andthe CFA showed that the items loaded on thefactors that were theoretically stipulated on thebasis of measurement model based on our prior SZstudy. Furthermore, the congruence between thefactor structures remained essentially unchangedwith the more homogeneous samples (i.e., afterexcluding subjects who had schizoaffective disorderin Study 1; or evidenced DSM-IV symptoms ofdelusions and hallucinations in Study 2).It is noteworthy that similar factor structures

emerged in spite of sample differences on somedemographic characteristics and IQ. Specifically,the bipolar sample had a higher proportion offemales and white ethnicity than the SZ sample, aswell as more years of education and higher full

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Fig. 6. Learning: comparison of factor loadings obtained in the bipolar and schizophrenia samples. See Fig. 1 for completedescription and abbreviations.

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Fig. 5. Non-verbal functions: comparison of factor loadings obtained in the bipolar and schizophrenia samples. See Fig. 1 forcomplete description and abbreviations.

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scale-IQ (although full scale-IQwas 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 structure

across 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 Kaiser–Guttman eigenvalue >1criterion and Cattell’s 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 intheNCdataset in theBPDsample. 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 errorsStroop-words onlyStroop-color onlyTrails AWAIS-R Digit symbol

0.83 0.86

Working memory WAIS-R Digit Span ForwardLNS, number correctLNS, longestWAIS-R ArithmeticWAIS-R Digit Span Backward

0.83 0.87

Ideational fluency Ruff Figural Fluency Unique DesignsCOWATAnimal Naming

0.65 0.65

Verbal knowledge WAIS-R – VocabularyWAIS-R – ComprehensionWAIS-R – Similarities

0.80 0.86

Non-verbal functions WAIS-R – Block DesignWAIS-R – Object AssemblyWAIS-R – Picture CompletionWAIS-R – Picture Arrangement

0.70 0.80

Learning WMS-R – Verbal Pair IWMS-R – Verbal Pair IIWMS-R – Visual Pair IWMS-R – Visual Pair II

0.80 0.82

D2 ¼ Concentration Endurance Test; Stroop ¼ Stroop Color-Word Interference Test; LNS ¼ Letter Number Span Test; COWAT ¼Controlled Oral Word Association Test; WAIS-R ¼ Wechsler Adult Intelligence Scale-Revised; WMS-R ¼ Wechsler Memory Scale-Revised.

Table 8. Criterion-related validity: bipolar sample (n ¼ 155)a

Characteristic

Distractibility Disordered thinking

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

Attention 1.6 (1.0–2.5) 4.2 (0.040) 2.0 (1.2–3.3) 7.8 (0.0054)Working memory 1.4 (0.9–2.1) 2.3 (0.13) 1.8 (1.2–2.9) 6.5 (0.011)Ideational fluency 1.4 (0.9–2.0) 2.8 (0.10) 1.8 (1.2–2.8) 8.5 (0.0035)Verbal knowledge 1.4 (0.9–2.1) 2.4 (0.12) 1.9 (1.2–3.0) 7.6 (0.006)Non-verbal functions 1.5 (1.0–2.3) 4.0 (0.046) 1.8 (1.2–2.9) 7.6 (0.0058)Learning 1.4 (0.9–2.2) 2.0 (0.087) 1.3 (0.8–2.0) 1.4 (0.24)

aSample size may vary due to missing data.bOR ¼ odds ratio statistics, indicating the odds ratio increase for higher symptom severity for each SD unit of decrease in functioning ona 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 10–15%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,the coefficient of congruencebased on theProcrustesanalysis showedonly amoderate agreement 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 factorstructures 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 that

the 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 werelow (loading estimate <0.45) in both samples.Since these findings suggested low indicatorreliability for these variables with respect to theirunderlying construct, they were omitted from ourfinal model. In addition, the motor variables werenot included in final CFA model because, similarto our previous findings, the current results failedto provide empirical support for combining motorspeed and dexterity measures into a single motorfactor (28). Instead, the analyses yielded 2 inde-pendent small factors, 1 for motor speed (FingerTapping Preferred and Non-Preferred hand,

respectively) and 1 for dexterity (Grooved Peg-board Preferred and Non-Preferred hand, respec-tively). Consequently, in future studies, the set of 6NC factors needs to be supplemented by additionalmeasures if these abilities are of specific interest(e.g., motor variables, Wisconsin Card Sorting).In addition to generalizability across diagnoses,

the set of 6 NC factors showed favorable psycho-metric properties in both samples. More specifi-cally, the internal consistency for the individualfactors was generally good (typically 0.80 orabove), with the exception of the Ideational Flu-ency factor for which the internal consistencyestimate in each sample was of moderate magni-tude (0.65 in each sample). The internal con-sistency was similar across the samples, with onlyminor differences between the 2 diagnostic groups.With regard to external validators, the NC

factors provided convergent information withmeasures that they would theoretically be expectedto be overlapping. Logistic regression analysesindicated that the CARS-M Distractibility ratingwas related to poorer functioning on the Attentionand Non-Verbal Functions factors, with the largesteffect size observed for the association with theAttention factor. Disordered Thinking had a moregeneral relationship with neurocognitive function-ing; a significant association was present for 5 ofthe 6 factors including Attention, Working Mem-ory, Ideational Fluency, Verbal Knowledge, Non-Verbal Functions. Overall, these results revealed aremarkable consistency between the NC factorsand the clinicians� observations.The fact that there was only a minimal overlap

between the 6 NC factors and the overall severityon symptoms of mania and depression, respective-ly, indicates that these factors have good discrimi-nant validity as compared to clinical symptoms.The CARS-M total score showed only 2 statisti-cally significant (p < 0.05), but clinically modestassociations, 1 with the Working Memory (r ¼)0.20) and with the Non-Verbal Knowledge

Table 9. Neurocognitive factor scores in the bipolar and the schizophrenia samples

Neurocognitive measure Bipolar sampleMean (SD)

Schizophrenia sampleMean (SD)

Group differenceF (p)a

Attention 0.45 (0.75) )0.01 (0.82) 7.8 (0.006)Working Memory 0.27 (0.79) )0.03 (0.81) 0.1 (0.80)Ideational Fluency 0.27 (0.92) )0.03 (0.75) 2.2 (0.14)Verbal Knowledge 0.33 (0.74) )0.03 (0.87) 0.0 (0.94)Non-Verbal Functions 0.27 (0.81) )0.02 (0.85) 6.7 (0.01)Learning 0.26 (0.85) )0.01 (0.80) 1.2 (0.28)

ap-values are based on the analysis of covariance model and were adjusted for full-scale IQ, years of education, gender and ethnicity ascovariates; a significant group difference between groups was observed on each factor without the adjustment for the covariates. Highermean values indicate better functioning. Since the schizophrenia sample was used as a reference, means in this sample were essentiallyidentical to zero.

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factors (r ¼ )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 for

the 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 ageneral difference in the overall NC functioningbetween the 2 diagnostic groups.Nonetheless, it should be noted that in terms of

statistical effect size (Cohen’s 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 <0.15 for the 3remaining factors after correcting for the covari-ates). Effect sizes of this magnitude are associatedwith a large degree of overlap in terms of theunderlying distributions. Specifically, an effect sizeof approximately 0.4 (Attention) indicates a 73%overlap for the factor score distributions betweenthe 2 diagnostic groups, whereas an effect size ofapproximately 0.20 (Ideational Fluency) shows an85% overlap. Such a large degree of overlap can beindicative of a dimensional rather than a categor-ical transition between the 2 diagnoses, and may beexplained by shared genetic susceptibility, and/orcommon underlying neurobiological substrates.

However, in addition to the possibility that NCfunctions serve as common endophenotypic mark-ers across diagnoses, alternative explanations areconceivable [e.g., iatrogenic effects such as use ofmedication; confounding factors such as drugand alcohol abuse; NC impairment (e.g., impair-ment in attention) represent non-specific of braindysfunction; imprecision of diagnoses]. Futurestudies need to investigate these possibilitiesfurther.

Limitations

There were a number of limitations in this study.First, despite the large sample size, the number ofobservations for the multivariate analyses wasrelatively small, and therefore sampling variancemay have affected the results.Second, whereas the CFA showed high loadings

for the individual NC variables with respect totheir hypothesized factors, the overall fit indices inthe CFA analysis did not demonstrate a close fitfor the entire model. The lack of close fit in thecurrent investigation is consistent with the fact thatapproximately 1/3 the total variance of theobserved empirical variables remained unaccount-ed for by the factors (i.e., the 6 factors togetherexplained 68% of the variance).While the aforementioned proportion of

explained variance appears low, we note that thisproportion (68%) is higher or comparable to thosedescribed for widely used psychometric scales, suchas the BPRS (44) and PANSS (45). This value isalso comparable with the amount of variance(74%) explained by 6 factors in the analyses ofthe Repeatable Battery for the Assessment ofNeuropsychological Status (RBANS) (25) (despitethat the factors in the analyses of the RBANS wereretained if their eigenvalue was >0.7 as opposed to>1, the Kaiser–Guttman rule that was adopted forour study).Third, the investigation of the generalizability of

the factor structure was based on cross-sectionaldata; such data have the potential to confound stateand 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 ofimpairmentsmayvarywith the diagnosis. The groupcomparisons revealed differences between patientswith BPD and SZ in the neurocognitive domains ofAttention and Non-Verbal Functions, which mayindicate thatNC 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 supportprovided by Dr Samuel Gershon, Dr Anil Malhotra, andEstelle Douglas, as well as the diligence in data collection andquality management provided by Drs Sara Davis-Conway,Scott Greisberg, Rebecca Iannuzzo, Pradeep Nagachandranand Sarah Uzelac and by Mr Sherif Abdelmessih, Ms ClaudiaSalazar, Ms Marilyn Mejia, Ms Pam DeRosse, Ms PriyaMatneja and Ms Donna O’Shea. The authors owe enormousgratitude to the study participants who give generously of theirtime and without whose efforts and patience this work wouldnever be possible. Funding Source: NIMH R01 MH 60904,Stanley Medical Research Institute.

References

1. Crow TJ. The continuum of psychosis and its geneticorigins. The sixty-fifth Maudsley lecture. Br J Psychiatry1990; 156: 788–797.

2. Kraepelin E. Psychiatrie. Ein Lehrbuch fur Studirende undAerzte, 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, andnot much else – the case for homogeneity. Schizophr Res1995; 17: 135–145.

6. Bearden CE, Hoffman KM, Cannon TD. The neuropsy-chology and neuroanatomy of bipolar affective disorder: acritical review. Bipolar Disord 2001; 3: 106–150.

7. McDonald C, Bullmore ET, Sham PC et al. Association ofgenetic risks for schizophrenia and bipolar disorder with

specific and generic brain structural endophenotypes. ArchGen Psychiatry 2004; 61: 974–984.

8. Woo TU, Walsh JP, Benes FM. Density of glutamic aciddecarboxylase 67 messenger RNA-containing neurons thatexpress the N-methyl-D-aspartate receptor subunit NR2Ain the anterior cingulate cortex in schizophrenia andbipolar disorder. Arch Gen Psychiatry 2004; 61: 649–657.

9. Clinton SM, Meador-Woodruff JH. Abnormalities of theNMDA receptor and associated intracellular molecules inthe thalamus in schizophrenia and bipolar disorder.Neuropsychopharmacology 2004; 29: 1353–1362.

10. Craddock N, O’Donovan MC, Owen MJ. Genes forschizophrenia and bipolar disorder? Implications for psy-chiatric nosology. Schizophr Bull 2006; 32: 9–16.

11. Craddock N, O’Donovan MC, Owen MJ. The genetics ofschizophrenia and bipolar disorder: dissecting psychosis. JMed Genet 2005; 42: 193–204.

12. Badner JA, Gershon ES. Meta-analysis of whole-genomelinkage scans of bipolar disorder and schizophrenia. MolPsychiatry 2002; 7: 405–411.

13. Segurado R, Detera-Wadleigh SD, Levinson DF et al.Genome scan meta-analysis of schizophrenia and bipolardisorder, part III: bipolar disorder. Am J Hum Genet 2003;73: 49–62.

14. Maier W, Zobel A, Wagner M. Schizophrenia and bipolardisorder: differences and overlaps. Curr Opin Psychiatry2006; 19: 165–170.

15. Cardno AG, Rijsdijk FV, Sham PC et al. A twin study ofgenetic relationships between psychotic symptoms. Am JPsychiatry 2002; 159: 539–545.

16. Daban C, Martinez-Aran A, Torrent C et al. Specificity ofcognitive deficits in bipolar disorder versus schizophrenia. Asystematic review. Psychother Psychosom 2006; 75: 72–84.

17. Johnson MH, Magaro PA. Effects of mood and severity onmemory processes in depression and mania. Psychol Bull1987; 101: 28–40.

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: 32–40.

19. van Gorp WG, Altshuler L, Theberge DC et al. Cognitiveimpairment in euthymic bipolar patients with and withoutprior alcohol dependence. A preliminary study. Arch GenPsychiatry 1998; 55: 41–46.

20. Tabares-Seisdedos R, Balanza-Martinez V, Salazar-Fraile Jet al. Specific executive/attentional deficits in patients withschizophrenia or bipolar disorderwho have a positive familyhistory of psychosis. J Psychiatr Res 2003; 37: 479–486.

21. Martinez-Aran A, Vieta E, Colom F et al. Neuropsycho-logical performance in depressed and euthymic bipolarpatients. Neuropsychobiology 2002; 46 (Suppl. 1): 16–21.

22. Seidman LJ, Kremen WS, Koren D et al. A comparativeprofile analysis of neuropsychological functioning inpatients with schizophrenia and bipolar psychoses. Schiz-ophr Res 2002; 53: 31–44.

23. Glahn DC, Bearden CE, Niendam TA et al. The feasibilityof neuropsychological endophenotypes in the search forgenes associated with bipolar affective disorder. BipolarDisord 2004; 6: 171–182.

24. KrabbendamL,ArtsB, vanOs J et al. Cognitive functioningin patients with schizophrenia and bipolar disorder: aquantitative review. Schizophr Res 2005; 80: 137–149.

25. Hobart MP, Goldberg R, Bartko JJ et al. Repeatablebattery for the assessment of neuropsychological status asa screening test in schizophrenia, II: convergent/discrimi-nant validity and diagnostic group comparisons. Am JPsychiatry 1999; 156: 1951–1957.

Neuropsychological symptom dimensions

89

26. Horn JL, McArdle JJ. A practical and theoretical guide tomeasurement invariance in aging research. Exp Aging Res1992; 18: 117–144.

27. Jaeger J, Berns S, Loftus S, Gonzalez C, Czobar P.Neurocognitive test performance predicts functionalrecovery from acute exacerbation leading to hospitaliza-tion in bipolar disorder. Bipolar Disord 2007; 9: 93–102.

28. Jaeger J, Czobor P, Berns SM. Basic neuropsychologicaldimensions in schizophrenia. SchizophrRes2003;65: 105–116.

29. Kay SR, Fiszbein A, Opler LA. The positive and negativesyndrome scale (PANSS) for schizophrenia. Schizophr Bull1987; 13: 261–276.

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; 36:124–134.

31. Hamilton M. A rating scale for depression. J NeurolNeurosurg Psychiatry 1960; 23: 56–62.

32. Overall J. The Brief Psychiatric Rating Scale in psycho-pharmacology research. In: Pichot P ed. PsychologicalMeasurements in Psychopharmacology. Basel: Karger,1974: 67–78.

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 dataclassification. Stat Med 2002; 21: 3465–3474.

35. Thurstone LL. Multiple-Factor Analysis. Chicago, IL:University of Chicago Press, 1947.

36. Littel RC, Milliken GA, Stroup WW et al. SAS System forMixed Model. Cary, NC: SAS Institute, Inc., 2002.

37. Kaiser HF. The application of electronic computers tofactor analysis. Educ Psychol Meas 1960; 20: 141–151.

38. Cattel R. The Scientific Use of Factor Analysis. New York:Plenum, 1978.

39. Hurley JR, Cattel RB. The Procrustes Program: producingdirect rotation to test a hypothesized factor structure.Behav Sci 1962; 7: 258–262.

40. Efron B, Gong G. A leisurely look at the bootstrap, thejackknife, and cross-validation. Am Stat 1983; 37: 36–48.

41. Cronbach L. Coefficient alpha and the internal structure oftests. Psychometrika 1951; 16: 297–334.

42. Leboyer M, Henry C, Paillere-Martinot ML et al. Age atonset in bipolar affective disorders: a review. BipolarDisord 2005; 7: 111–118.

43. Goldberg TE. Some fairly obvious distinctions betweenschizophrenia and bipolar disorder. Schizophr Res 1999;39: 127–132.

44. Czobor P, Volavka J. Dimensions of the Brief PsychiatricRating Scale: an examination of stability during halo-peridol treatment. Compr Psychiatry 1996; 37: 205–215.

45. Lindenmayer JP, Grochowski S, Hyman RB. Five factormodel of schizophrenia: replication across samples. Schiz-ophr Res 1995; 14: 229–234.

46. Gold JM,CarpenterC,RandolphC et al. Auditoryworkingmemory and Wisconsin Card Sorting Test performance inschizophrenia. Arch Gen Psychiatry 1997; 54: 159–165.

47. Goodglass H, Kaplan E. The Assessment of Aphasia andRelated 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 performanceacross the adult life span. J Clin Exp Neuropsychol 1997;19: 405–420.

50. Heaton RK, Chelune GJ, Talley JL et al. Wisconsin CardSorting 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 ExaminationManual. 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 rightfrontal lobe lesions. Arch ClinNeuropsychol 1994; 9: 41–55.

54. Matthews CG, Love H. Instructions Manual for the AdultNeuropsychology Test Battery. Madison, WI: Universityof Wisconsin Medical School, 1964.

55. Reitan R, Davidson L. Clinical Neuropsychology: CurrentStatus and Applications. New York: Hemisphere, 1974.

56. OldfieldRC.The assessment and analysis of handedness: theEdinburgh Inventory. Neuropsychologia 1971; 9: 97–113.

57. Wechsler D. Wechsler Adult Intelligence Scale-RevisedManual. New York: Psychological Corporation, HarcourtBrace Jovanovich, Inc., 1981.

58. Wechsler D. Wechsler Memory Scale-Revised Manual. SanAntonio: Psychological Corporation, 1987.

Appendix: Neuropsychological tests used in the

battery

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

The goal of this scale is to provide an overallevaluation of intellectual functioning. The scale iscomposedof 11 subtests, 6 verbal and 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 previousfactor 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 namecolors as quickly as possible. The number of correctresponses within a 60-s trial was used as themeasureof interest. The test consists of 3 conditions:presenting color names in black ink (labeled as�words�) and presenting a block of x�s 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 �green� printed 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 manualversion) (50)

This test has been extensively described in theliterature 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 BostonDiagnostic 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 timeconstraints. 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.

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