Bipolar esquizofrenia neuropsicologia


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Bipolar esquizofrenia neuropsicologia

  1. 1. Copyright ª Blackwell Munksgaard 2007Bipolar Disorders 2007: 9: 71–92 BIPOLAR DISORDERSOriginal ArticleNeuropsychological symptom dimensions inbipolar disorder and schizophrenia Czobor P, Jaeger J, Berns SM, Gonzalez C, Loftus S. Pa Czobora,b, Judith Jaegerc,d, ´l Neuropsychological symptom dimensions in bipolar disorder and Stefanie M Bernsc, Cristina schizophrenia. Gonzalezc and Shay Loftusc Bipolar Disord 2007: 9: 71–92. ª Blackwell Munksgaard, 2007 a DOV Pharmaceutical Inc., Hackensack, NJ, b Nathan Kline Institute for Psychiatric Research, Background: While neurocognitive (NC) impairments have been well Orangeburg, cThe Center for Neuropsychiatric documented in schizophrenia (SZ), there is limited data as to whether Outcome and Rehabilitation Research, The Zucker similar impairments are present in other persistent mental illnesses. Hillside Hospital, North Shore Long Island Jewish Recent data indicate that NC impairments may be manifested in bipolar Health System, Glen Oaks, dDepartment of disorder (BPD) and that they persist across disease states, including Psychiatry and Behavioral Sciences, Albert Einstein euthymia. An important question is whether a comparable structure of College of Medicine, Bronx, NY, USA 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 Key words: bipolar disorder – commonality in coefficients of congruence ranged between 0.66–0.90 for the individual factor structure – neuropsychological symptom factors, indicating a good overall correspondence between the factor dimensions – schizophrenia structures in the 2 diagnostic groups. Analysis of covariance (ANCOVA) analysis with education level, full scale-IQ, gender and ethnicity as Received 1 July 2005, revised and accepted for covariates indicated that SZ patients had markedly worse performance publication 17 August 2006 on the Attention and Non-Verbal Functioning factors compared to the BPD patients. Corresponding author: Judith Jaeger, PhD, MPA, AstraZeneca Pharmaceutical Company, FOC Conclusions: Together, these data suggest that while the same W2-651, 1800 Concovel Plaza, Wilmington, DE underlying factor structure describes NP functioning in both groups, the 19803, USA. Fax: +1 302 886 4803. profile of impairments appears to vary with the diagnosis. e-mail: jaeger.ju@gmail.comControversy exists over whether bipolar disorder than a century ago (2); it considers differences(BPD) and schizophrenia (SZ) are best character- between psychotic symptoms across diagnoses asized as separate disorders or along a continuum qualitatively different. Current diagnostic systems(1). The classical position assumes a categorical such as DSM (3) and ICD-10 (4) operationalizedview based on Kraepelin’s proposition from more this view, and try to separate bipolar illness (excluding recurrent major depression, which Kraepelin had grouped with manic depression)The authors of this paper do not have any commercial associations and SZ in a categorical fashion by requiring thethat might pose a conflict of interest in connection with this manu- presence or absence of certain symptoms for thescript. purpose of diagnosis. However, since symptoms 71
  2. 2. Czobor et al.may overlap, sometimes for extended periods, the specific additive genetic variance (19% for maniadifferential diagnosis of BPD and SZ frequently and 33% for SZ) (15). Similar to studies examiningposes a problem in clinical practice. In response to pathophysiology, studies of genetic susceptibilitythis, an alternative, dimensional view is often for the most part suffer from design challenges thatinvoked in contrast to the prevailing categorical bias against findings that would distinguish theapproach, which posits that BPD and SZ do not groups as Kraepelin had proposed (e.g., therepresent a discrete illness entity. For example, difficulty of blinding the co-twin’s diagnosis duringCrow proposed that psychosis might vary along a the diagnostic process, the practice of includingcontinuum, extending from unipolar affective dis- cases with overlapping features which increases theorder through bipolar affective disorder and schiz- chance of diagnostic error and the exclusion ofoaffective disorder to typical SZ (1, 5). recurrent major depression from the bipolar Recently, the dimensional view has gained favor group).in a rapidly growing literature emphasizing shared To address the question of disease boundaries,abnormalities that cut across the current diagnostic there is growing interest in identifying moredivide. For example, shared morphometric find- precisely defined quantitative traits, which wouldings, such as enlarged ventricles (6), and white represent more direct ÔdownstreamÕ biologicalmatter volume reductions in the left frontal and consequences of genes than the symptoms. Suchtemporoparietal regions were found in both disor- traits, or endophenotypes could serve as an alter-ders (7). Furthermore, common cellular and native (or complement) to the categorical diseasemolecular patterns were observed, including a phenotypes, and potentially underlie a more accu-decrease in cell density in the GABAergic inter- rate diagnostic classification. Based on their herit-neurons in SZ as well as in BPD (8). At the ability and the fact that they can be measuredintracellular level, both diagnostic groups showed objectively and reliably, certain domains of neuro-abnormalities in intracellular molecules (e.g., cognitive (NC) performance have been consideredPSD95) that provide a physical link between as candidate endophenotypes in major mentalmultiple neurotransmitter systems (including the disorders including BPD and SZ.glutamatergic and dopaminergic systems) which In the case of SZ, general NC deficits and deficitsare potentially involved in the neurobiology of SZ in various specific tasks indexing broader cognitiveand affective disorders (9). Since these studies do domains have been demonstrated, particularly innot systematically exclude cases that are diagnos- tasks of Attention, Long-Term Memory, Workingtically challenging (e.g., share substantial features Memory, and Executive Functioning (16). Withof both disorders) findings of shared pathophysi- regard to BPD, in the earlier literature, a commonology may be confounded by the incorrect classi- misconception was that, in contrast to SZ, bipolarfication of cases. affective disorder is not associated with general Recent studies have also reported apparent cognitive impairment independent of illnessoverlap in the genetic susceptibility between BPD episodes, or in the premorbid state (6). However,and SZ. For example, family studies show a newer literature challenged this view, and con-substantial degree of familial co-aggregation verging evidence suggests that persons with BPDbetween bipolar illness and SZ (10). Moreover, exhibit persistent cognitive impairment across asystematic whole genome linkage studies raised the range of tasks of Attention, Memory and Execu-possibility of some common chromosomal regions tive Function during remission (17–21). Further-shared by BPD and SZ, although various meta- more, cognitive dysfunctions seem to be present inanalyses yielded inconsistent results with regard to BPD patients not only during acute symptomthe strength of the evidence for each of the exacerbation but both in prodromic and residualpotential candidate regions (11–13). Additionally, phases (14).in candidate gene studies, specific genes have been Some of the authors concluded that particularlyidentified in which variation appears to confer the poor performance on tests of Verbal Memory wasrisk to both BPD and SZ (with the strongest consistently found as a characteristic of BPD (17,evidence shown for G72/G30, in the 13q candidate 22). Glahn et al. (23) recently suggested thatregion, but common susceptibility was raised for Verbal Learning and Memory and Executiveexample for BDNF, COMT, DISC1, neuregulin 1, Function/Working Memory may represent theand dysbindin) (11, 14). In addition, results from most salient endophenotypic components ofthe first diagnostically unrestricted twin study neurocognition in BPD because these domainsindicate that the common shared additive genetic appear heritable, co-segregated within families,variance is substantially higher for mania and SZ associated with the disease, and impaired during(49% and 68%, respectively) than the diagnosis- periods of symptom remission.72
  3. 3. Neuropsychological symptom dimensions An important theoretical question regarding NC tioning did not reach significance (effect size ¼functions as potential candidate endophenotypes is 0.33). However, it is difficult to evaluate thetheir diagnostic specificity. A recently conducted validity of these results since it is conceivable thatmeta-analysis of all comparative studies indicated the group differences were confounded by thethat patients with BPD generally perform better extent to which the NC domains representedthan patients with SZ, but the distribution of effect different underlying constructs (factors) acrosssizes revealed a large degree of heterogeneity (24). diagnoses.In particular, this investigation compared NC In general, the above literature that comparedperformance in patients with BPD and SZ in 11 NC in patients with BPD and SZ had certainNC domains. The 11 domains comprised: Verbal limitations. The majority of studies used only aFluency, Verbal Working Memory, Executive relatively small set of tasks, and the composition ofControl, Visual Memory Delayed, Mental Speed, tasks was vastly different across studies. ThisVerbal Memory Immediate, IQ, Verbal Memory makes the comparisons difficult, and limits theDelayed, Concept Formation, Visual Memory interpretability of the findings since the variousImmediate, and Fine Motor Skills. The meta- components of the NC profiles across diagnosesanalysis (24) showed significantly worse perfor- were assembled from data derived from differentmance in the patients with SZ in 9 out of 11 studies. A potential research strategy to overcomecognitive domains. The only areas in which this problem and to compare patterns of NCperformance of the 2 patient groups were not deficits in BPD and SZ is to administer a compre-statistically significant were delayed Visual Mem- hensive neuropsychological (NP) battery consistingory and Fine Motor Skills. of several measures tapping into each of several Another recently published meta-analytic review putative NC domains. However, those studies thatof the literature (16), defined only 4 major NC investigated multiple areas simultaneously, focuseddomains, which included IQ, Attention (Sustained, on a different number of domains, and appliedSelective), Memory, and Executive Functions different definitions. Since component measures(Cognitive Flexibility, Working Memory, Verbal were arbitrarily selected, the domainsÕ (construct)Fluency). This review concluded that BPD patients validity may not generalize to different samples, orexhibit extensive cognitive abnormalities with a within the same sample over time. The 2 largepattern of deficits that is not unique to this disease. recent meta-analyses published only a few monthsThe study by Seidman et al. (22) focused specif- apart from each other (16, 24; see above), consid-ically on a comparison of profiles of NC abnor- ered 11 and 4 domains, respectively, whereas themalities between BP and SZ in 8 domains, study by Seidman et al. (22) defined 8 domains forincluding Verbal Ability, Visuo-Spatial Ability, the comparison of respective NP profiles.Abstraction/Executive, Verbal/Declarative Mem- To our knowledge, no empirical evidence hasory, Perceptual-Motor Functions, Mental Control, been shown to demonstrate that the variousand Sustained Attention/Vigilance. Similar to the definitions of the underlying NC domains wereabove 2 meta-analyses, this study concluded that valid in a particular diagnostic group, and gener-while the level of impairments was higher in alizable across diagnoses. Obtaining such evidencepatients with SZ, the profile shape did not differ is a logical prerequisite of further group compar-between BPD and SZ. Overall, Abstraction, Mem- isons, and as stated by Horn and McArdle (26,ory, Perceptual-Motor Functions, and Vigilance p. 117) without such evidence, Ôthe basis forshowed the largest impairments in both groups, drawing scientific inference is severely lackingÕ.with a higher level of impairment in patients with Factor analysis provides 1 way to obtain thisSZ in this study (22). evidence based on the analysis of interrelationships Using a standardized test battery (Repeatable among various NC measures. Surprisingly, despiteBattery for the Assessment of Neuropsychological the fact that a substantial research effort has beenStatus; RBANS), Hobart et al. (25) showed that spent to demonstrate that BPD and SZ sharepatients with SZ were more impaired than patients specific domains of psychopathology in terms ofwith BPD in terms of general functioning [medium factor analytic structure, as far as we know, noeffect size (0.55) for the total score], and that previous studies compared the NC factor structureamong 5 NC domains including Visuospatial/ derived from the same instrument in both bipolarConstructional, Language, Attention, Delayed and schizophrenic patients. In our previous factorMemory and the Immediate Memory only the analysis of patients with SZ, on the basis of thelatter (Immediate Memory, effect size ¼ 0.65) analysis of a comprehensive NC test battery, weobtained a significant difference between the derived 6 clearly identifiable factors that had goodgroups. The difference in terms of attention func- psychometric properties with excellent construct, 73
  4. 4. Czobor et al.divergent and predictive validity, and stability over received a comprehensive NC test battery andtime in a longitudinal study (factors included Positive and Negative Symptom Scale (PANSS)Attention, Working Memory, Learning, Verbal (29) ratings at baseline (used for the present report)Knowledge, Non-Verbal Functions, and Ideational and again after 6, 18 and 36 months (not includedFluency). The principal objective of the current in this report). Staff administering NC tests werestudy was to extend this research further, by previously trained and observed in test batteryinvestigating whether the same underlying factor administration to assure uniformity. The PANSSstructure of NC functions that characterized patients raters had demonstrated interrater reliability com-with SZ would generalize to patients with BPD. pared to an expert (ICC ‡ 0.80). For the present analyses, the final dataset from this study was used; subjects were included in theMethods analyses if they had completed the baseline NCThe data for the research reported here were assessment. Baseline NC testing was conductedcollected in 2 longitudinal clinical studies inves- whenever possible when patients were optimallytigating predictive and concurrent associations stabilized after hospitalization for the indexbetween neurocognitive performance and disability episode. A total of 250 patients, with the diagnosisin life (psychosocial) functioning (LF) in individ- of SZ (n ¼ 185; 74%) or schizoaffective disorderuals with serious mental illnesses [see companion (n ¼ 65; 26%) were enrolled in the study.paper (27) in this issue for further details of thisresearch]. The 2 studies represented subsequent Study 2: Bipolar sample. The subjects for thephases of the research project. The goal of the first analyses that we report here are consenting patients(Study 1: ÔSchizophrenia StudyÕ) was to test the from an ongoing 24-month study investigatinglongitudinal relationship between NC deficits and predictive and concurrent associations betweenlife functioning (disability) in patients with SZ NC deficits and disability in life functioning inor schizoaffective disorder; the aim of the second individuals with BPD. The objective of this natu-(Study 2: ÔBipolar StudyÕ) was to investigate the ralistic longitudinal study is to evaluate approxi-above relationship in patients with BPD. mately 200 individuals aged 18 to 54 years with Both studies collected a large number of NC BPD [diagnosed using SCID (3)] at the time ofvariables and aimed to conduct factor analyses for hospitalization for relapse and at multiple timethe purpose of data (dimensionality) reduction. points over the following 24 months. For theThis aim was previously accomplished in the first present analyses, an interim dataset from thisstudy in a subset comprised of the first 156 patients ongoing study was cleaned and frozen (i.e., noenrolled (see below for further details). The core further changes were made in the database); subjectsresults, including details concerning the NC factors from this database were included in the analyses, ifthat were identified, have been published (28). they had completed the baseline NC assessment.Since the principal purpose of Study 2 was similar Baseline NC data from a total of 155 subjects wereto that of Study 1, and dimensionality reduction used for the purpose of the current investigation.was an important tool to achieve a reduction in Using cut-off scores for the Clinician-Adminis-Type I error arising from multiple repeated testing tered Rating Scale for Mania (CARS-M; 15 items)of individual variables, an essential question was (30) of 0–7 for questionable and 8–15 for mildwhether the same factor structure that we found in mania and, for the Hamilton Depression Ratingthe SZ sample is applicable to the bipolar sample. Scale (HAM-D; 17 items) (31), 0–6 for notHence, the question of generalizability of the NC depressed and 7–17 mildly depressed, we foundfactors across diagnoses served as a principal that the majority (approximately 54%) of thepractical motivating problem for the current sample had no or mild symptoms on both scales.investigation. Approximately 30% had moderate to high mania with no or low depressive symptoms, and, con- versely, approximately 11% of the sample hadSubjects moderate to high depression with no or mild maniaStudy 1: Schizophrenia sample. Subjects were con- at the time of neurocognitive testing. Approxi-senting patients in a 3-year study of SZ and mately 5% of the sample had active mixed symp-schizoaffective disorder [diagnosed using the Struc- tomatology at the time of testing (e.g., moderate ortured Clinical Interview for DSM-IV (SCID)] greater symptoms on both mania and depressionwhich involved repeated neurocognitive testing. rating scales).Subjects were enrolled within 6 months of symp- Altogether, 11% (n ¼ 17) of the subjects in thetom exacerbation requiring hospitalization, and primary dataset (n ¼ 155) evidenced symptoms on74
  5. 5. Neuropsychological symptom dimensionsDelusions involving ÔReplacement of WillÕ (Delu- demographic prevalence data, the proportion ofsions of Control, Thought Insertion, Thought female patients was higher in the bipolar asDeletion, Thought Broadcasting) and Hallucina- compared to SZ group. In addition, the bipolartions, reflecting the overlapping boundaries of sample demonstrated a significantly higher fullBPD with the SZ spectrum in terms of symptom scale-IQ and more years of education, although thepresentation. In secondary analyses, we investi- former difference was quite modest (3.7 points ingated whether the inclusion of these subjects in the full scale-IQ). The 2 groups evidenced mild levelssample had an impact on the principal results. of symptom severity as shown by the respective psychometric ratings in each group, CARS-M (30) and the HAM-D scale (31) for the bipolar patients;Comparison of the 2 samples the PANSS positive and negative symptom sub-The demographic characteristics of the bipolar scale for the schizophrenics (Table 1).(n ¼ 155) and SZ (n ¼ 250) samples are shown in In the bipolar sample, at the time of the currentTable 1. analyses, medication data were available for a total As Table 1 shows, the 2 groups were essentially of 142 patients (91.6% of 155). The distributionidentical in terms of age, onset of illness, and age at (%) of the most common treatments was thewhich they received the first psychiatric treatment. following: lithium (69.0%), anticonvulsantsThe groups, however, were significantly different (67.3%), neuroleptics (typical and atypical neuro-(p < 0.05) in their ethnicity and gender distribu- leptics combined: 65.5%), valproic acid (60.6%),tions. In particular, a significantly higher propor- antidepressants (38.0%), benzodiazepines (22.4%),tion of patients from the white ethnic group were and anxiolytics (18.3%).present in the bipolar as compared to the SZ Overall, the analysis of the medication datasample. Furthermore, as expected on the basis of indicated that all patients received polypharmacy in the bipolar sample. In the SZ sample, whileTable 1. Descriptive and demographic characteristics in the bipolar and the polypharmacy was common, the overwhelmingschizophrenia (reference) sample majority of the patients (93% of the sample) were Bipolar Schizophrenia taking at least 1 neuroleptic medication at baseline. sample sample The distribution of atypical and typical agents inCharacteristics (n ¼ 155a) (n ¼ 250a,b) the sample was 68% and 32%, respectively. In Mean (SD) Mean (SD) addition to the neuroleptics, in the SZ sample,Age 35.4 (10.9) 36.3 (9.1) many patients were taking another class ofOnset of illness 19.1 (8.4) 19.1 (6.5) psychotropic medication as well including moodAge first treated 21.2 (8.8) 20.6 (6.8) stabilizers, anxiolytics, and antidepressants.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) MeasuresHAM-Dd/PANSS NEGe 10.6 (6.4) 20.1 (5.8)Gender, n (%) Psychopathology. Psychometric assessments of Male 67 (43.2f) 156 (62.4f) symptom severity in each study were conducted Female 88 (56.8) 94 (37.6)Race, n (%) at baseline and each of the follow-up visits includ- White 113 (72.9f) 99 (39.6f) ing neuropsychological testing. The rating instru- Black 29 (18.7) 106 (42.4) ments in each study were specific to the population Hispanic 7 (4.5) 28 (11.2) targeted in that study. In Study 1, which focused Other 6 (3.9) 17 (6.8) on patients with SZ and schizoaffective disorder,a Sample size may vary due to missing data. the principal measures of psychopathology wereb Diagnostic distribution: schizophrenia ¼ 74% (n ¼ 185) versus the PANSS and the Brief Psychiatric Rating Scaleschizoaffective disorder 26% (n ¼ 65). (BPRS) (32). In Study 2, which focused on patientsc Significant mean difference (p < 0.05) between the two sam- with BPD, the principal measures of psychopa-ples (ANOVA).d thology were the CARS-M (30) and the HAM-D In the bipolar sample, symptom severity was indexed by thetotal score on the Clinician-Administered Rating Scale for Mania (31). The raters for each of these rating instruments(CARS-M) and the Hamilton Rating Scale for Depression (HAM- in our study had demonstrated interrater reliabilityD; 17-item version), respectively. compared to an expert (ICC > 0.80).e In the schizophrenia sample, symptom severity was indexed bythe total score on the positive (POS) and negative symptom Neurocognitive performance. The NC battery was(NEG) subscale of the Positive and Negative Symptom Scale(PANSS), respectively. designed to examine functional domains previouslyf Significant difference in proportions (p < 0.05) between the two considered important by virtue of their demon-samples (chi-square test). strated impairment in people with major mental 75
  6. 6. Czobor et al.Table 2. Neuropsychological tests used in the present study research is dimensionality reduction – to find aNeuropsychological tests suitable representation of such multivariate data (i.e., to identify, based on the pattern of relation-Wechsler Adult Intelligence Scale-Revised (WAIS-R) (57) ships among the observed variables, a relativelyWechsler Memory Scale Revised (WMS-R) (58) low number of basic underlying dimensions thatLetter Number Span (46)Complex Ideational Material (47) provide the most efficient description of the vari-Concentration Endurance Test (D2) (48) ation in the data). This goal, in general, can beStroop Test (49) achieved by various multivariate techniques,Wisconsin Card Sorting Test (128-card manual version) (50) including factor and principal component analysesTrail Making Test (A&B) (51) (PCA), which view the observed variables asControlled Oral Word Association Test (COWAT) (52)Animal Naming Test (51) manifestations of some underlying, latent set ofRuff Figural Fluency Test (53) factors (dimensions).Grooved Pegboard Test (54) However, when applied to NC data, traditionalFinger Tapping Test (55) multivariate methods, including PCA run intoEdinburgh Handedness Inventory (56) serious difficulties because of the extremely high number of variables in the data relative to thedisorder and their relations to functional outcomes. number of observations. Even if the geometricIt includes 14 tests focused on measures of General properties of PCA remain valid, and numericalAbility, Attention, Working Memory, Verbal techniques yield stable results, the covarianceKnowledge, Learning, Non-Verbal Functions, Ide- matrix on which the analysis is carried out isational Fluency, Executive Functions, and Motor sometimes a poor estimate of the real populationSkills (Table 2). The specific tests used have been covariance. Thus, the analysis under these condi-previously described by us and others; thus, we tions fails to provide a robust, generalizableprovide only a brief description in the Appendix. solution. Staff administering NP tests were previously To deal with this problem, in our previous studytrained and observed in test battery administration to identify the basic NC dimensions in patientsto assure uniformity. As mentioned above, the with SZ, a 2-stage procedure was designed tosame neuropsychological test battery was admin- implement the PCA in a stratified way. Briefly, inistered in both studies; however, we note that 3 of Stage 1, the neuropsychological variables werethe variables were not obtained in the bipolar study divided into blocks based on a priori knowledgedue to the fact that our preliminary analyses about their observed associations. The 10 a prioriindicated that they displayed a high degree of blocks comprised Sustained Vigilance, Short-Termoverlap with variables in their respective factors, Memory Capacity/Span, Working Memory, Setand that the omission of these variables had Shifting/Cognitive Flexibility, Ideational Fluency,essentially no impact on the internal consistency Verbal Learning, Non-Verbal Learning, Verbalof these factors (change in Cronbach alpha was Knowledge, Non-Verbal Reasoning/Problem<0.05 for these factors). These variables were the Solving, and Motor Functioning. In Stage 2, theVisual Memory Span Forward [Wechsler Memory variables in each block were subjected to factorScale-Revised (WMS-R); included in the Attention (principal component) analysis to identify the basicfactor based on Study 1]; Wechsler Adult Intelli- underlying NC constructs (factors) that explainedgence Scale-Revised (WAIS-R) Information (in- most of the variation within such a block ofcluded in the ÔVerbal KnowledgeÕ factor); and the variables.WAIS-R Object Assembly variables (included in The factor analysis was based on the principalthe ÔNon-Verbal FunctionsÕ factor). component method, and the PROMAX rotation At the time of the previous publication, Study 1 (33) was applied in order to obtain a conceptuallywas ongoing and data were available only from a interpretable simple structure. The PROMAXsubset of 156 subjects. By the time of the current rotation is an oblique rotation technique whichanalyses, the data were available from the entire SZ allows for correlation between factors. Since theresample; thus, we used all available data for the are conceptual as well as clinical reasons tocurrent study of the replicability of the NC factor presume a substantial correlation between the NCstructure across the 2 diagnostic samples. factors, this technique provides a more realistic representation of the data than the orthogonal solution which assumes independence. FurtherConceptual framework of the statistical analyses details of our procedures are described elsewhere.NC test batteries typically yield a large number of We note here, however, that a technique calledvariables, hence a fundamental goal in NC Ôblock principal component analysisÕ (BPCA) has76
  7. 7. Neuropsychological symptom dimensionsbeen described recently in the literature (34), which Test Perseverative Errors, Stroop Interference,analogous to the 2-stage procedure employed in Trails B-Trails A/Trails A, Grooved Pegboardour study, relies on variable stratification. Using Preferred plus Non-Preferred Hand, Finger Tap-multivariate statistical theory, it has been demon- ping Preferred plus Non-Preferred Hand.strated that BPCA is as efficient as ordinaryprincipal component analysis for dimensionality Statistical analysesreduction (34). Based on the above approach, in our previous For the purpose of the current investigation,study (28), 6 factors were extracted as having good generalizability was considered as factorial invar-construct, divergent and predictive validity, and iance, i.e., constancy in the structure of thestability over time over an 18-month period of underlying NC constructs across diagnoses (BPDobservation. The 6 factors were Attention, Work- versus SZ). The concept of factorial invariance wasing Memory, Learning, Verbal knowledge, Non- based on Thurstone’s notion of simple structureVerbal functions, and Ideational Fluency (Table 3). (35), which states that the pattern of salient (non-An additional 5 NC measures, which have been zero) and non-salient (zero or near-zero) loadingswidely studied in SZ, could not be reliably com- defines the structure of a psychometric construct.bined with any of these factors or with each In terms of factorial invariance, the principle ofanother, indicating the need to examine them simple structure entails configurational invariance;separately. These include: Wisconsin Card Sorting items comprising the same construct are expected to exhibit the same configuration of salient andTable 3. Six neurocognitive factors derived from the schizophrenia sample non-salient factor loadings across the 2 diagnosticNeurocognitive Neurocognitive measure included groups.factor in factor The analyses were conducted in multiple steps.Attention D2 – letters minus errors First, the homogeneity of the correlation matrices Stroop - words only across the 2 diagnostic samples was tested. Second, Stroop - color only the empirical data from the bipolar sample were Trails A subjected to unrestricted exploratory factor analy- WMS-R Visual Memory Span Forwarda sis (EFA) to examine whether model modifications WAIS-R Digit symbolWorking memory D2 fluctuation were necessary in terms of the number of the factors WAIS-R Digit span forward and item composition of the underlying constructs LNS, number correct derived in the SZ sample. Third, confirmatory LNS, longest factor analyses (CFA) (33) were conducted to WAIS-R Arithmetic statistically test the configurational invariance of WAIS-R Digit Span Backward WMS-R Log Mem Immed the hypothesized factor structure, i.e., to examineLearning WMS-R – Verbal Pair I whether the items have the same relationship to the WMS-R – Verbal Pair II same underlying factor as posited on the basis of WMS-R – Visual Pair I the earlier analyses in the SZ sample. Fourth, since WMS-R – Visual Pair II the CFA addresses the configurational invarianceVerbal knowledge WAIS-R – Vocabulary WAIS-R – Informationa of factors across samples but does not directly WAIS-R – Comprehension investigate the extent of similarity, a factor WAIS-R – Similarities analysis with confirmatory Procrustes rotationNon-verbal functions WAIS-R – Block Design was performed to examine the extent of similarity WAIS-R – Object Assemblya between the BPD and SZ samples with regard to WAIS-R – Picture Completion WAIS-R – Picture Arrangement each of the individual factors. Finally, in Step 5, theIdeational fluency WCST Number of Perseverative Errors psychometric properties (reliability and construct Ruff Figural Fluency Unique Designs validity) of the NC factors derived in the bipolar COWAT sample were examined. Animal NamingD2 ¼ Concentration Endurance Test; Stroop ¼ Stroop Color- Step 1: Homogeneity of correlation matrices. InWord Interference Test; Trails ¼ Trailmaking Test; LNS ¼ Letter Step 1, we tested the null-hypothesis of no-differ-Number Span Test; Log Mem Immed ¼ Logical Memory ence in the correlation matrices between the BPD(immediate recall); WCST ¼ Wisconsin Card Sorting Test; and the SZ sample. The analysis was based on theCOWAT ¼ Controlled Oral Word Association Test.a likelihood ratio approach, using nested hierarchi- Variables not available in the bipolar sample included:Wechsler Memory Scale Revised (WMS-R) Visual Memory Span cal models of the data as implemented by the SASForward; Wechsler Adult Intelligence Scale-Revised (WAIS-R) PROC MIXED procedure (36). In particular,Information; and the WAIS-R Object Assembly. using the maximum likelihood estimation, first we 77
  8. 8. Czobor et al.derived a null-model likelihood by positing an other. In model 1, the basic assumption was thatunstructured homogeneous correlation matrix for the 6 NC factors represent 6 distinct constructsthe empirical data across the 2 diagnostic groups. with no relationship (correlation) between them. InSecond, we relaxed the homogeneity condition model 2, all factors were considered interrelated(posited a heterogeneous correlation matrix by constructs and a correlation was therefore alloweddiagnostic group) and examined whether the between any of the 6 factors. In the CFA, estimatesresulting improvement in the likelihood reached of loadings of the individual neuropsychologicalstatistical significance. Test of improvement in items were obtained for their hypothesized factors.model fit was based on chi-square statistics. Values of t-statistics were used to test whether the individual items were significantly related to theirStep 2: Exploratory factor analyses. A failure to specific factors.reject the null-hypothesis with regard to the The Root Mean Square Error of Approximationhomogeneity of the correlation matrices across (RMSEA) and the Goodness of Fit Index (GFI)the 2 diagnostic groups may be a reflection of low were used to assess model fit for the entire CFAstatistical power. Thus, in view of the fact that we model. The RMSEA indicates the fit of the modelhad a relatively small sample size, it is possible that to the covariance matrix (or correlation matrix, asthe 2 groups have certain systematic differences in our study). It represents the square root of thewhich would not result in the rejection of the average amount that the sample covariances differnull-hypothesis in our study. For example, it is from their estimates derived on the basis of theconceivable that the number of interpretable posited factor model. As a guideline, RMSEAfactors is different in the 2 samples, or that most values below 0.1 are generally considered tobut not all of the factors are replicable (i.e., partial indicate an adequate fit, whereas values of <0.05versus full factorial invariance). Therefore, before represent a close fit. For GFI, values above 0.90we proceeded with the CFA, we performed EFA to are considered as an indication of an adequateinvestigate whether the theoretically-postulated model fit.factor structure derived from the SZ samplerepresents an adequate representation of the Step 4: Generalizability across samples. As de-pattern of observed associations among a group scribed above, following Thurstone (35), the mostof variables in the BPD sample. More specifically, basic conceptualization of a construct is thein these preliminary analyses, we investigated pattern of non-zero and zero loadings, not thewhether model improvements were necessary in particular magnitude of the non-zero loadings. Interms of the number of factors that need to be this theoretical framework, in order to establishretained for further analyses, and in terms of the whether a construct can be conceptualized in thefactor structure of the individual factors based on same way across diagnoses, the requirement is thatthe distribution of salient and non-salient loadings. the same pattern of (zero and non-zero) factorSimilar to our previous study, we used the principal loadings is found in the individual groups. For thiscomponent method for factor extraction. The reason, in a multi-group CFA no cross-samplePROMAX rotation was applied in order to derive constraints are imposed on the magnitude of thea simple structure to facilitate the interpretation. In salient factor loadings; the non-salient loadingsorder to examine the dimensionality in an EFA, we are (implicitly) specified to be equal (i.e., zero).used the Kaiser–Guttman eigenvalue >1 criterion Therefore, whereas the CFA addresses the(37) and Cattell’s Scree plot (38). Items were configurational invariance of factors acrossallocated to factors according to their highest samples, it does not indicate the extent of similarityloading; the threshold loading of 0.5 was chosen to (generalizability), since it does not take theindicate saliency. particular magnitude of the loadings into account. For the current study, confirmatory ProcrustesStep 3: Confirmatory factor analyses. The relation- rotation (39) was applied to investigate the extentship between the observed variables and the of similarity (generalizability) between the SZ andhypothesized underlying constructs can be investi- the BPD samples (maximum congruence). Thisgated by CFA. The CFA techniques used in this confirmatory procedure rotates empirically ex-investigation set a priori definitions of the factor tracted principal components to a theoreticallystructure (measurement model) based on the find- specified target matrix of factor loadings to max-ings from the SZ sample and based on our imize their similarity. The theoretical factor-load-preliminary EFA findings in the BPD sample. In ing matrix specifies the number of components tothe structural part of the CFA models, 2 theoret- be fitted and the factor-loading pattern of the testically possible alternatives were tested against each items. Unlike the CFA method, the Procrustes78
  9. 9. Neuropsychological symptom dimensionsapproach estimates loadings for all items (includ- Table 4. Comparison of the 2 groups on theing items that are considered non-salient). The individual measures indicated a significantly bettermodel fit was evaluated by the coefficient of performance in the BPD as compared to the SZcongruence (CC) (38), normed between +1 and sample for 15 of 30 measures (corrected for)1. Values of CC of 0.80 and above are considered multiple testing using the Hochberg procedure),to indicate sufficient similarity between the em- although the magnitude of the difference waspirically Procrustes-rotated and theoretically pos- generally modest.tulated factors. The sampling variation of the CCwas estimated using the bootstrap/resampling Homogeneity of correlation matricesapproach (40). In order to do this, we firstrandomly selected 1,000 samples with replacement The null-hypothesis of no-difference between thefrom the original database; then, each of these correlation matrices from the BPD and the SZsamples, whose size was identical to the size of sample was tested by the likelihood ratio test. Inoriginal dataset, was subjected to factor analysis particular, first we derived the null-model likeli-with Procrustes rotation. hood by positing an unstructured, homogeneous correlation matrix across the 2 diagnostic groups.Step 5: Reliability, construct validity. Scale (fac- Second, the homogeneity condition was relaxedtorial) reliability was examined through the inter- (i.e., a heterogeneous correlation matrix wasnal consistency reliability. Internal consistency for posited across the 2 groups), and we examinedeach of the 6 NC factors was determined by the use whether the resulting improvement in the model-of Cronbach alpha (41). External (criterion-re- likelihood over the null-model likelihood reachedlated) validity of the NC factors derived in the statistical significance. The null-model likelihoodbipolar sample was investigated through the con- indicated chi-square ¼ 5130.5 (df ¼ 350, p ¼vergent, discriminant and concurrent validity. In 0.0001), whereas the heterogeneous correlationparticular, in order to establish convergent validity, model resulted in chi-square ¼ 5330.5 (df ¼ 701,we examined the degree to which the NC factors p ¼ 0.0001). The likelihood ratio chi-squareyielded convergent information with other, exter- statistic for the improvement in model fit didnal measures that they would theoretically be not reach statistical significance (p > 0.1), indi-expected to be similar to. For the purpose of the cating that the homogeneous correlation structureanalyses reported here, 2 of the items of the CARS- provides adequate fit to the data across the 2M, including ÔDistractibilityÕ (Item 6, which diagnostic groups.excludes distractibility due to intrusions of visualand/or auditory hallucinations or delusions and Exploratory factor analysisrates whether Ôattention is too easily drawn tounimportant or irrelevant external stimuliÕ) and Overall, similar to our published findings in the SZÔDisordered ThinkingÕ (Item 11) were investigated. sample, results of the exploratory factor analysisSince, apart from such selected items, NC func- (principal component method with PROMAXtioning and psychopathology may represent sepa- rotation) in the bipolar sample indicated 6rate dimensions, for discriminant validity, we factors based on both the Kaiser–Guttmanexamined the degree to which the 6 NC factors eigenvalue criterion (i.e., eigenvalue > 1 foroverlapped with psychometric ratings of clinical factors retained for further analyses) and onsymptoms. In particular, discriminant validity was Cattell’s scree-plot criterion based on the break-examined via bivariate correlations between the point of the curve. Together, the 6 factorscomponents of the NC factors and the overall explained approximately 68.0% of the totalseverity score of clinical symptoms, indexing mania variance in the neuropsychological dataset inand depression, respectively. To examine concur- the bipolar sample. The distribution of therent validity we assessed the ability of the 6 NC amount of variance explained across the 6 factorsfactors to distinguish between the 2 diagnostic was: Working Memory (12.6%), Attentiongroups. (12.5%), Verbal Knowledge (12.0%), Non-Verbal Functions (11.6%), Ideational Fluency (11.1%), and Learning (9.2%).Results These results in the bipolar sample were similar to what we found in the expanded sample ofDemographic and basic descriptive data at baseline schizophrenic patients that we used for the purposeDescriptive neuropsychological data on all indi- of the current analyses [n ¼ 250, including thevidual NC variables of interest are shown in subsample of patients used for our previous 79
  10. 10. Czobor et al.Table 4. Descriptive statistics for individual neurocognitive measures Bipolar sample (n ¼ 155a) Schizophrenia sample (n ¼ 250a)Neurocognitive measure Mean (SD) Q1–Q3b Mean (SD) Q1–Q3bD2 – 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) 90.0–133.0D2 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.a Sample size may vary due to missing data.b Q1–Q3 ¼ Interquartile range.c Significant mean difference (p < 0.05, with Hochberg’s adjustment for multiple testing) between the 2 samples (ANOVA).analyses (n ¼ 156)]. In particular, the 6-factor (Table 4, last 4 rows) to the set of NC variablessolution in the SZ sample explained 67.8% of the that we used above, and repeated the exploratoryvariance. Furthermore, the individual factors factor analysis that we performed for the moreexplained a similar amount of variance in the SZ limited set of measures that did not include theas in the BPD sample, with the exception of the motor variables. Similar to our previous analyses,ideational fluency factor which was associated with the results indicated that the motor variables dida smaller amount of explained variance in the SZ not load on any of the 6 basic NC factors describedsample. The distribution of explained variance above. In addition, a single motor factor could notacross the 6 factors in the SZ sample was: be derived. Instead, based on the 4 variables thatAttention (15.0%), Working Memory (12.5%), we used for the analysis 2 independent smallVerbal Knowledge (11.7%), Non-Verbal Func- factors (containing 2 related variables only)tions (11.5%), Learning (10.7%) and Ideational emerged, 1 for motor speed (Finger TappingFluency (3.4%). Preferred and Non-Preferred hand, respectively) In addition to the above EFA analyses that and 1 for dexterity (Grooved Pegboard Preferredfocused on the same set of variables that we and Non-Preferred hand, respectively).included in our previous analyses in the SZ sample,similar to our published study, we explored Confirmatory factor analysiswhether a separate motor factor can be derivedin the BPD sample. For the purpose of this As mentioned in the methods, the CFA analysis setinvestigation, we added the 4 motor measures a priori definitions of the factor structure based on80
  11. 11. Neuropsychological symptom dimensionsour earlier findings from the SZ sample. In analysis conducted in the BPD and in the SZparticular, the CFA assumed a Ôsimple structureÕ: samples, respectively. As Table 5 shows, the resultsobserved NC variables were allowed to assume a were similar in both samples, suggesting configura-non-zero estimate only for 1 of the 6 underlying tional invariance across the 2 samples. In partic-constructs, for which they were considered as ular, the estimated loading coefficients reachedindicators. In other words, estimates of loadings statistical significance for each of the indicatorsof the individual NC variables were obtained for (observed NC variables) for each of the hypothe-their hypothesized factors only; loadings outside sized factors in both samples. We note, however,the underlying construct were not estimated that for 2 of the variables [Concentration Endur-(restricted to be 0). ance Test (D2) Fluctuations and Logical memory – Results of the CFA analysis indicated that the immediate recall (LMI)] the coefficients were lowcorrelated factor model (Model 2) which allowed (loading estimate <0.45) in both samples.correlations between the 6 underlying factors Since these findings suggested low indicatorprovided a significantly better fit to the data than reliability for these variables with respect to theirthe independent factor model (Model 1) (BPD underlying construct (Working Memory, for bothsample: chi-square ¼ 164.4, df ¼ 15, p < 0.0001; D2 Fluctuations and LMI), the above 2 variablesSZ sample: chi-square ¼ 663.3, df ¼ 15, were omitted from our final CFA model. The CFAp < 0.0001). Indices of overall model fit showed results based on this model indicated an improve-that GFI did not reach the recommended level in ment in the model fit indices. In the BPD sample,either of the 2 samples (BPD sample GFI ¼ 0.69; the GFI and the RMSA were 0.72 and 0.086SZ sample GFI ¼ 0.82); the RMSA values were respectively; in the SZ sample, the analogous0.094 and 0.074 in the BPD and the SZ samples, values were 0.84 (GFI) and 0.064 (RMSA),respectively. respectively. Although the GFI indices failed to Table 5 displays the estimated factor loadings for reach the recommended threshold, our final factorModel 2 (correlated factors) based on the CFA model was based on the restricted set of variablesTable 5. Confirmatory factor analysis estimates of factor loadings Bipolar sample Schizophrenia sampleFactor Neurocognitive measure Loading (SE) t-statistic* Loading (SE) t-statistic*Attention D2 – letters minus errors 0.69 (0.11) 6.19 0.75 (0.06) 12.18 Stroop-words only 0.58 (0.12) 4.98 0.78 (0.06) 12.88 Stroop-colors only 0.70 (0.11) 5.95 0.81 (0.06) 13.69 Trail Making A Time 0.69 (0.11) 6.24 0.65 (0.06) 10.21 WAIS-R Digit Symbol Raw 0.79 (0.11) 7.41 0.75 (0.06) 12.31Working memory D2 Fluctuations 0.34 (0.12) 2.83 0.23 (0.07) 3.26 WMS-R Digit Span Forward 0.63 (0.11) 5.74 0.59 (0.06) 9.16 LNS Total Correct 0.95 (0.09) 10.58 0.95 (0.05) 18.48 LNS Longest Item Passed 0.87 (0.10) 9.00 0.93 (0.05) 17.85 WAIS-R Arithmetic Raw 0.52 (0.12) 4.53 0.95 (0.06) 10.36 WMS-R Digit Span Backward 0.65 (0.11) 5.86 0.63 (0.06) 10.03 LMI 0.40 (0.12) 3.37 0.41 (0.07) 6.14Ideational fluency Ruff Figural Fluency Unique Designs 0.80 (0.11) 7.32 0.75 (0.07) 10.02 COWAT Total Correct 0.56 (0.12) 4.70 0.76 (0.07) 9.74 Animal Naming Total Correct 0.66 (0.11) 5.78 0.84 (0.05) 7.96Verbal knowledge WAIS-R Vocabulary Raw 0.86 (0.11) 7.74 0.85 (0.06) 14.43 WAIS-R Comprehension Raw 0.68 (0.12) 5.78 0.81 (0.06) 13.58 WAIS-R Similarities Raw 0.65 (0.12) 5.52 0.80 (0.06) 13.31Non-verbal functions WAIS-R Block Design Raw 0.70 (0.11) 6.18 0.79 (0.06) 12.62 WAIS-R Picture Completion Raw 0.64 (0.12) 5.51 0.72 (0.06) 11.24 WAIS-R Picture Arrangement Raw 0.73 (0.11) 6.43 0.74 (0.06) 11.64Learning WMS-R Verbal Paired Association I 0.61 (0.12) 5.19 0.75 (0.06) 11.87 WMS-R Verbal Paired Association II 0.76 (0.11) 6.97 0.74 (0.06) 11.58 WMS-R Visual Paired Association I 0.78 (0.11) 7.18 0.74 (0.06) 11.63 WMS-R Visual Paired Association II 0.68 (0.11) 5.95 0.72 (0.06) 11.15D2 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. 81
  12. 12. Czobor et al.(i.e., not including D2 Fluctuations and LMI) since loadings derived in the BPD and the SZ samples,this set provided a closer fit to the empirical data. respectively, for all factors except for Ideational Fluency. An inspection of Fig. 3 indicates that this relative lack of congruence for this factor is due toProcrustes matching the fact that, in the BPD sample, only 2 of theAs described in the Methods, confirmatory Pro- constituting items whereas in the SZ sample all 3 ofcrustes rotation was applied to investigate the the items reached saliency (in particular, in theextent of congruence between the factor structures bipolar sample, the loading for the Ruff Figuralderived in the bipolar and the SZ sample. This Fluency Unique Designs was close to zero).method is suitable for maximizing the similarity As mentioned before, approximately 26% of thebetween a matrix of factor loadings and an sample in the ÔSchizophrenia StudyÕ was diagnosedassumed underlying structure by means of the- with schizoaffective disorder, and 11% in theory-based expectations as targets. Unlike the CFA, ÔBipolar StudyÕ evidenced some symptoms ofthe Procrustes approach estimates for each factor Delusions or Hallucinations. Inclusion of thesethe loadings for all variables used in the analysis subjects in the analyses increased diagnostic het-(including items that are considered non-salient for erogeneity and phenomenological overlap acrossa particular factor). For the purpose of the current diagnoses, which may have served as a majorstudy, the Procrustes analysis used the theoretically contributing factor to the similarity of the factorpostulated target structure based on the factor structures across diagnoses. To investigate thisstructure derived in the final factor model from the possibility further, in additional secondary analy-CFA analyses. Similar to our previous analysis, the ses, we excluded the aforementioned subjects, andfactor analysis was based on the principal compo- recomputed the coefficient of congruence for thenent method, and the PROMAX approach was factor structure across diagnoses. Results indicatedused to allow for correlation among the 6 NC that the 6 NC factors were replicable with the morefactors. homogeneous samples; the values of CC remained Table 6 displays the estimated coefficients of almost unchanged between the 2 diagnostic sam-congruence between the corresponding factor pairs ples (Attention ¼ 0.863, Working Memory ¼from the BPD and the SZ samples, respectively. As 0.805, Ideational Fluency ¼ 0.601, Verbal Knowl-shown in Table 6, for 5 of the 6 factors including edge ¼ 0.797, Non-Verbal Functions ¼ 0.821 andAttention, Working Memory, Verbal Knowledge, Learning ¼ 0.890).Non-Verbal Functions, and Learning, there was ahigh level of similarity between the set of loadings Reliability, validityderived in the BPD and the SZ samples, respec-tively. For 1 of the factors (Ideational Fluency), the Construct reliability. Table 7 displays the Cronbachcongruence was moderate. alpha estimate (measuring internal consistency) for The factor loading estimates yielded by the each factor in each of the 2 samples. As Table 7Procrustes analysis are depicted in Figs 1–6 for shows, the internal consistency for the individualeach of the 6 NC factors, respectively. Consistent factors was generally good, with the exception ofwith coefficient of congruence estimates, Figs 1–6 the Ideational Fluency factor for which theindicate a good correspondence between the set of internal consistency estimate in each sample wasTable 6. Coefficient of congruence (CC) between factors derived in the only of moderate magnitude. Overall, no meaningfulbipolar and the schizophrenia samplea differences were observed between the 2 samples in terms of construct reliability of the 6 NC factors. 95% Confidence limitsb Observed Convergent validity. For convergent validity, weFactor CC value Lower Upper examined the degree to which the NC factors provided convergent information with measuresAttention 0.883 0.787 0.979 that they would theoretically be expected to beWorking memory 0.878 0.794 0.962Ideational fluency 0.658 0.467 0.850 overlapping. The analyses focused on 2 items of theVerbal knowledge 0.818 0.704 0.932 CARS-M, including ÔDistractibilityÕ (Item 6) andNon-verbal functions 0.837 0.675 0.999 ÔDisordered ThinkingÕ (Item 11). In particular,Learning 0.903 0.813 0.993 association between the above 2 items (i.e., Dis-a tractibility, Disordered Thinking) and the 6 NC Factor analysis was based on the PROMAX method usingProcrustes rotation. factors, respectively, was examined by logisticb Bootstrap/resampling estimates, based on 1,000 samples regression analysis. Results of the logistic regres-drawn randomly from the original observed dataset. sions analyses are shown in Table 8.82
  13. 13. Neuropsychological symptom dimensions Attention factor 1.00 Factor loadings 0.50 0.00 D2 Lett.-Error Stroop, Words Stroop, Colors Trails A, Time Digit Symbol Digit Sp. Forw. LNS, Correct LNS, Longest Arithmetic Digit Sp. Back. Ruff Uniq.Des. COWAT Total Anim. Naming WAIS Vocab. WAIS Compr. WAIS Similar. WAIS Block D. WAIS Pict.Cp. WAIS Pict.Arr. Verb. Paired I Verb. Paired II Visual Paired I Visual Paired lI Bipolar SCH/SCAFig. 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. Working memory factor 1.00 0.50 Factor loadings 0.00 D2 Lett.-Error Stroop, Words Stroop, Colors Trails A, Time Digit Symbol Digit Sp. Forw. LNS, Correct LNS, Longest Arithmetic Digit Sp. Back. Ruff Uniq.Des. COWAT Total Anim. Naming WAIS Vocab. WAIS Compr. WAIS Similar. WAIS Block D. WAIS Pict.Cp. WAIS Pict.Arr. Verb. Paired I Verb. Paired II Visual Paired I Visual Paired lI Bipolar SCH/SCAFig. 2. Working memory: comparison of factor loadings obtained in the bipolar and schizophrenia samples. See Fig. 1 for completedescription and abbreviations. As Table 8 indicates, the clinical rating of tions. The association did not reach significance forDistractibility was associated with poorer func- Learning.tioning on the Attention and Non-Verbal Func-tions factors (and to a lesser extent on Learning). Discriminant validity. For discriminant validity,As expected, the largest effect size was observed for we investigated the degree to which the 6 NCthe association with the Attention factor. Disor- factors overlapped with psychometric ratings. Indered Thinking had a more general relationship particular, discriminant validity was examined viawith NC functioning, as indexed by the NC bivariate correlations between the neurocognitivefactors. In particular, a statistically significant factors and the overall severity score of clinicalassociation was observed for 5 of the 6 factors symptoms, indexing mania (total score on theincluding Attention, Working Memory, Ideational CARS-M scale) and depression (total score onFluency, Verbal Knowledge, Non-Verbal Func- HAM-D scale, 17-item version), respectively. 83