Linear and non-linear associations of symptom dimensions and cognitive function in first-onset psychosis

Schizophrenia Research 140 (2012) 221–231

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Linear and non-linear associations of symptom dimensions and cognitive function in first-onset psychosis Eugenia Kravariti a,⁎, 1, Manuela Russo a, 1, Evangelos Vassos a, Kevin Morgan a, 2, Paul Fearon a, 3, Jolanta W. Zanelli a, Arsime Demjaha a, Julia M. Lappin a, Elias Tsakanikos b, 4, Paola Dazzan a, Craig Morgan a, Gillian A. Doody c, Glynn Harrison d, Peter B. Jones e, Robin M. Murray a, Abraham Reichenberg a a NIHR Biomedical Research Centre for Mental Health, South London and Maudsley NHS Foundation Trust and Institute of Psychiatry, King's College London, De Crespigny Park, London SE5 8AF, UK b ESTIA Centre/Health Service and Population Research, South London and Maudsley NHS Foundation Trust and Institute of Psychiatry, King's College London, 66 Snowfields, London SE1 3SS, UK c Community Health Sciences, Queen's Medical Centre, Institute of Clinical Research, University of Nottingham, Nottingham NG7 2UH, UK d Academic Unit of Psychiatry, Cotham House, University of Bristol, Bristol BS6 6JL, UK e Department of Psychiatry, University of Cambridge, Box 189, Addenbrooke's Hospital, Cambridge CB2 2QQ, UK

a r t i c l e

i n f o

Article history: Received 20 September 2011 Received in revised form 9 May 2012 Accepted 5 June 2012 Available online 4 July 2012 Keywords: Population based First onset psychosis Affective Non affective Symptom dimensions Cognition

a b s t r a c t Background: Associations between symptom dimensions and cognition have been mainly studied in nonaffective psychosis. The present study investigated whether previously reported associations between cognition and four symptom dimensions (reality distortion, negative symptoms, disorganisation and depression) in nonaffective psychosis generalise to a wider spectrum of psychoses. It also extended the research focus to mania, a less studied symptom dimension. Methods: Linear and non-linear (quadratic, curvilinear or inverted-U-shaped) associations between cognition and the above five symptom dimensions were examined in a population-based cohort of 166 patients with first-onset psychosis using regression analyses. Results: Negative symptoms showed statistically significant linear associations with IQ and processing speed, and a significant curvilinear association with verbal memory/learning. Significant quadratic associations emerged between mania and processing speed and mania and executive function. The contributions of mania and negative symptoms to processing speed were independent of each other. The findings did not differ between affective and non-affective psychoses, and survived correction for multiple testing. Conclusions: Mania and negative symptoms are associated with distinct patterns of cerebral dysfunction in firstonset psychosis. A novel finding is that mania relates to cognitive performance by a complex response function (inverted-U-shaped relationship). The associations of negative symptoms with cognition include both linear and quadratic elements, suggesting that this dimension is not a unitary concept. These findings cut across affective and non-affective psychoses, suggesting that different diagnostic entities within the psychosis spectrum lie on a neurobiological continuum. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Individuals with the same diagnosis within the psychosis spectrum often vary considerably in clinical characteristics (Jablensky, 2006; Joyce and Roiser, 2007; Stroup, 2007). At the same time, ⁎ Corresponding author. Tel.: + 44 20 784 80 331; fax: + 44 20 784 80 287. E-mail address: [email protected] (E. Kravariti). 1 Contributed equally to this manuscript (Joint First Authors). 2 Present address: Department of Psychology, University of Westminster, 309 Regent Street, London W1B 2UW, UK. 3 Present address: Department of Psychiatry, St. Patricks University Hospital and Trinity College, University of Dublin, James St., Dublin 8, Ireland. 4 Present address: Department of Psychology, Roehampton University, Holybourne Avenue, London SW15 4JD, UK. 0920-9964/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.schres.2012.06.008

different diagnostic categories show overlapping psychopathology, indistinct clinical boundaries and shared etiological factors (Squires and Saederup, 1991; Murray et al., 2004; Kaymaz and Van Os, 2009). Attempts to reconcile the heterogeneity within, and overlap across, psychoses have considered dimensional (e.g. symptom) approaches to classification as a useful adjunct or alternative to categorical (e.g. diagnostic) representations. Exploratory factor analyses in schizophrenia and, more recently, in the full spectrum of psychoses, have identified a discrete number of psychopathological dimensions (e.g. psychomotor poverty, disorganisation, reality distortion, mania, depression) (Liddle, 1987; Dikeos et al., 2006; Demjaha et al., 2009). These have been reported to provide more meaningful information than diagnostic categories in relation to clinically and neurobiologically significant characteristics, including disease course/outcome, likely

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E. Kravariti et al. / Schizophrenia Research 140 (2012) 221–231

response to treatment (Van Os et al., 1999; Allardyce et al., 2007) and cognitive performance (Dixon et al., 2004; Simonsen et al., 2009). Despite similar symptom dimensions emerging in factor analytic studies of patients with affective- and those with non-affective psychoses (Peralta et al., 1997; Lindenmayer et al., 2008; Smith et al., 2009), studies exploring relationships between symptom dimensions and neuropsychological performance have mainly focused on non-affective psychoses, especially schizophrenia. A recent meta-analysis of this literature reported modest, statistically significant, and partly dissociable correlations of negative symptoms and disorganisation with neuropsychological performance, but no significant associations of the positive and depressive symptom clusters with cognition (Dominguez et al., 2009). Compared to disorganisation, negative symptoms yielded a significantly stronger correlation with verbal fluency, and significantly less robust associations with reasoning/problem solving and attention/vigilance. The two dimensions did not differ in their strength of correlation with IQ, executive control, speed of processing, verbal working memory, and verbal/visual learning (Dominguez et al., 2009). The latter systematic review identified only four studies exploring the association of cognitive performance with the manic/excitement dimension and excluded the corresponding data as being too limited for an informative synthesis. The present study addressed the hypothesis that findings from non-affective psychosis in relation to symptom dimensions and neuropsychological performance (specifically, the partly dissociable, significant associations of negative symptoms and disorganisation with cognition, and the non-significant associations of reality distortion and depression with cognition) (Dominguez et al., 2009), replicate in a population-based cohort of patients with first-onset psychoses including both non-affective and affective categories. A further aim was to extend previous findings by exploring associations between neurocognition and manic symptoms. Although exploring non-linear (quadratic, curvilinear or invertedU-shaped) associations between psychopathology and cognition was not among the aims of the study, in line with strong statistical evidence of nonlinear processes in brain dysfunction in schizophrenia (Breakspear, 2006), our main analysis suggested potential deviations from linearity for some associations. It was therefore important to follow this indicative finding with post-hoc analyses, particularly in the light of evidence that many relationships in behavioural and social sciences do not follow a straight line. Nonlinear curve fitting is often required in the analysis of biological, biochemical and pharmacological data (Breakspear, 2006), but is less commonly applied to cognition and symptom dimensions. An earlier study of recent-onset schizophrenia reported quadratic associations between negative symptoms and several neuropsychological measures (Van der Does et al., 1993), further underlining the importance of exploring non-linear patterns in our data. 2. Method

2.2. The analytic cohort The analytic cohort comprised 166 ÆSOP cases with consensus ICD10 diagnoses of schizophrenia (F20; N = 64), schizoaffective disorder (F25; N = 8), bipolar disorder/mania (F30.2/F31.2/F31.5; N = 31), depressive psychosis (F32.3/F33.3; N = 27) or other psychotic disorders, including persistent delusional, acute and transient, other nonorganic, and unspecified nonorganic psychotic disorders (F22/F23/F28/F29; N = 36). All patients had Item Group Checklist (IGC) ratings on the Schedules for Clinical Assessment in Neuropsychiatry (SCAN) (World Health Organization, 1994), Wechsler Adult Intelligence Scale-Revised (WAIS-R) (Wechsler, 1981) Full-Scale IQ≥ 70, one or more measurements on the ÆSOP neuropsychological battery, and a good command of English. To satisfy the latter criterion, participants were required to be native speakers of English or migrants to the UK by age 11 (i.e. have completed at least their secondary education in the UK). Due to being selected for having no learning disability and for being proficient in English (which are standard requirements for neuropsychological testing), as expected, the study sample differed significantly in IQ (t= 4.12, d.f.= 190, P b 0.001) and ethnicity (χ2 = 20.97, P b 0.001) from the remaining ÆSOP cases (of the 370 patients with IGC ratings who were excluded from the current study, 288–318 had available demographic and clinical data, and 26 had available IQ data). The study sample also scored lower on reality distortion compared to the remaining ÆSOP cases (t= −2.13, d.f.= 482, P = 0.033). The two groups did not differ significantly in gender (χ2 = 0.001, P = 0.980), education (χ2 = 4.916, P = .086), diagnostic breakdown (χ2 = 8.108, P = 0.088), age at testing (t= 1.030, d.f. = 482, P = 0.304), age at illness onset (t= 1.102, d.f.= 466, P = 0.271), duration of untreated psychosis (t = 0.263, d.f. = 468, P = 0.793), or dimension scores for mania (t = − 0.327, d.f. = 482, P = 0.744), negative symptoms (t = − 1.220, d.f. = 482, P = 0.223), depression (t = − 0.977, d.f. = 482, P = 0.329) and disorganisation (t = 0.987, d.f. = 482, P = 0.324). 2.3. Assessment of socio-demographic and clinical characteristics Data on age, gender, ethnicity and education were collected by interviews with the participants using the Medical Research Council Sociodemographic Schedule (Mallett, 1997). Clinical data were collected using the SCAN (World Health Organization, 1994). This incorporates the Present State Examination (PSE) Version 10, which was used to elicit symptom-related data at presentation. Ratings on the SCAN are based on clinical interview, case note review and information from informants (e.g. relatives or health professionals). ICD10 diagnoses were determined using the SCAN data on the basis of consensus meetings involving one of the principal investigators and other members of the research team. The kappa scores for independent diagnostic ratings ranged from 1.0 for psychosis as a category to 0.6–0.8 for individual diagnoses. The participants' demographic, diagnostic and medication characteristics are presented in Table 1.

2.1. The ÆSOP study 2.4. Symptom dimensions The data were derived from the baseline population-based ÆSOP (Aetiology and Ethnicity in Schizophrenia and Other Psychoses) study, which identified all cases aged 16–64 years with first-onset psychoses (ICD-10 codes F10-F29 and F30-F33 in ICD-10) (World Health Organization, 1992) presenting to specialist mental health services in tightly defined catchment areas in South-east London, Nottingham and Bristol in September 1997–August 2000. Exclusion criteria were previous contact with health services for psychosis, organic causes of psychosis, and transient psychotic symptoms due to acute intoxication. The study further included a random sample of community controls, and was approved by local research ethics committees. Participants gave written informed consent to participate. A detailed overview of the ÆSOP study has been published elsewhere (Fearon et al., 2006; Morgan et al., 2006).

Based on a factor analytic study by the ÆSOP Study Group (Demjaha et al., 2009), patients were rated on five symptoms dimensions: Mania (6 IGC items: ‘heightened subjective functioning’, ‘expansive mood’, ‘expansive delusions and hallucinations’, ‘rapid subjective tempo’, ‘over-activity’, ‘socially embarrassing behaviour’), Reality Distortion (6 IGC items: ‘non-affective auditory hallucinations’, ‘non-specific auditory hallucinations’, ‘experience of disordered form of thoughts’, ‘delusions of reference’, ‘bizarre delusions and interpretations’, ‘delusions of persecution’), Negative Symptoms (4 IGC items: ‘nonverbal communication’, ‘poverty of speech’, ‘flat and incongruous affect’, ‘motor retardation’), Depressive Symptoms (3 IGC items: ‘special features of depressed mood’, ‘depressed mood’, ‘depressive delusions and hallucinations’) and Disorganisation (2 IGC items: ‘incoherent

E. Kravariti et al. / Schizophrenia Research 140 (2012) 221–231

223

Table 1 Demographic, diagnostic, medication and symptom characteristics of patients with first-onset psychoses (n = 166). Patients with first onset psychoses

Gender Male Female Ethnicity Caucasian Caribbean/African Asian Other Completed education School Further Higher ICD-10 Disorder Schizophrenia Schizoaffective Bipolar/mania Depressive psychosis Other psychosis Medication a Antipsychotic Mood Stabilising Antidepressant Antiparkinsonian None

Age at testing Age at illness onset DUP (weeks) Antipsychotic dose a Dimension score b Mania Reality distortion Negative symptoms Depression Disorganisation

Total sample (n = 166)

Non-affective psychoses (n = 100)

Affective psychoses (n = 66)

Non-affective vs. affective psychoses

N

N

N

Χ2

d.f.

P

5.57

1

0.02

%

%

%

94 72

56.6 43.4

64 36

64.0 36.0

30 36

45.5 54.6

105 45 7 9

63.3 27.1 4.2 5.4

59 32 1 8

59.0 32.0 1.0 8.0

38 18 5 5

57.6 27.3 7.6 7.6

106 34 26

63.9 20.5 15.7

71 17 11

71.7 17.2 11.1

34 16 15

52.3 24.6 23.1

64 8 31 27 36

38.6 4.8 18.7 16.3 21.7

64 0 0 0 36

64.0 0.0 0.0 0.0 36.0

0 8 31 27 0

0.0 12.1 47.0 40.9 0.0

52 8 23 10 8

71.2 11.0 31.5 13.7 11.0

33 0 14 5 7

75.0 0.0 31.8 11.4 15.9

19 7 8 5 4

65.5 24.1 27.6 17.2 13.8

0.77 1 Fisher's Exact 0.20 1 Fisher's Exact Fisher's Exact

0.38 0.001 0.65 0.51 1.00

Mean

SD

Mean

SD

Mean

SD

t

d.f.

P

29.99 29.27 57.81 205.50

10.72 10.50 160.12 159.44

28.84 28.06 61.04 188.82

10.10 9.68 126.93 99.30

31.83 31.08 52.59 234.47

11.36 11.46 200.79 230.44

1.78 1.80 0.33 0.82

164 160 160 21.91

0.08 0.07 0.74 0.42

1.63 3.34 1.34 1.31 0.66

2.44 2.66 1.81 1.75 0.91

0.73 3.61 1.31 0.91 0.82

1.10 2.88 1.78 1.48 1.01

3.00 2.92 1.38 1.91 0.41

3.17 2.25 1.87 1.95 0.68

5.60 1.72 0.24 3.54 3.14

75.41 159.28 164 112.91 163.92

b 0.001 0.09 0.81 0.001 0.002

Fisher's Exact

0.19

6.93

2

0.03

n/a

n/a

n/a

a Information on medication is reported from a sub-sample of 44 cases with non-affective psychoses and 29 cases with affective psychoses, all from London (73 patients, comprising 44% of the total patient sample); Nottingham and Bristol did not record detailed information on medication. b Each dimension was rated by summing up the scores of the individual Item Group Checklist (IGC) items under that dimension. Scores for individual IGC items ranged from 0 (below threshold) to 1 (moderate) to 2 (severe) depending on the frequency and severity of symptoms. Total dimension scores ranged from 0 to 11 for mania and reality distortion, from 0 to 8 for negative symptoms, from 0 to 6 for depression and from 0 to 4 for disorganisation.

speech’, ‘emotional turmoil’). Only items with loadings of at least 0.4 were used to construct the five dimensions, which accounted for 48% of the total variance in symptoms (Mania 15%; Reality Distortion 11%; Negative Symptoms 10%; Depressive Symptoms 7%; Disorganisation 5%) (Demjaha et al., 2009). Each dimension was rated by summing up the scores of the individual IGC items under that dimension. Scores for individual IGC items ranged from 0 (below threshold) to 1 (moderate) to 2 (severe) depending on the frequency and severity of symptoms. 2.5. Neuropsychological assessment Five neuropsychological domains were evaluated: ‘Full-scale IQ’ was derived from the WAIS-R Vocabulary, Comprehension, Digit Symbol and Block Design subtests; ‘Verbal Memory/Learning’ was assessed using the Rey Auditory Verbal Learning Test (Trials 1–5, 6 and 7) (Spreen and Strauss, 1991); ‘Visual Memory’ was examined using Visual Reproduction (immediate recall) of the Wechsler Memory Scale–Revised (Wechsler, 1987); ‘Executive Function/Working Memory’ was evaluated using Trail Making-Part B (Reitan, 1958), Letter-Number Span (Gold et al., 1997) and Raven's Coloured Progressive Matrices-Sets AB and B (Raven, 1995); and ‘Processing Speed’ was measured using Trail Making Test-Part A (Reitan, 1958) and WAIS-R Digit Symbol.

With the exception of Full-Scale IQ (which is a standard score based on population-based normative data, as described in the WAIS-R manual), normative standards for the neuropsychological measurements were created by regressing age, gender, ethnicity and education on each of the neuropsychological variables in 177 healthy ÆSOP controls (Zanelli et al., 2010), and then creating standard (i.e., Z) scores from the regression-adjusted (residual) scores. A similar procedure was applied to the patient sample (Z-scores were created using the Mean ± SD of the controls' residual scores). Where appropriate, Z-scores were averaged across tests to give a single score per cognitive domain. 2.6. Data analysis Regression analyses carried out in the programme STATA v.10.0 for Windows (StataCorp, 2007) showed no significant interactions between the effects of ‘type of psychosis’ (‘affective’ vs. ‘nonaffective’) and symptom dimensions on neurocognitive function (all P values > 0.05), with the exception of an interaction between depression and type of psychosis in relation to IQ (P b 0.05) (see Results). As mentioned above, unlike the procedure followed in relation to the remaining cognitive domains, the covariate effects of gender, age, ethnicity and education were not regressed out of IQ. After

224 Table 2 Cognitive scores

E. Kravariti et al. / Schizophrenia Research 140 (2012) 221–231

a

in patients with first-onset psychoses (n = 166). Patients with first onset psychoses Total sample (n = 166)

Full-Scale IQ b Verbal memory/learning c RAVLT Trials 1–5 d RAVLT Trial 6 d RAVLT Trial 7d Visual memory e Executive function/working memory Trail Making-Part B f Letter-Number Span d Raven's CPM-Set AB d Raven's CPM-Set B d Processing Speed Trail Making-Part A f WAIS-R Digit Symbol d

Non-affective psychoses (n = 100)

Affective psychoses (n = 66)

Non-affective vs. affective psychoses

Mean

SD

Mean

SD

Mean

SD

t

d.f.

P

91.55 − 0.64 − 0.80 − 0.55 − 0.57 − 0.43 − 0.67 − 0.55 − 0.62 − 0.65 − 0.79 − 0.70 − 0.66 − 0.74

16.33 1.06 1.19 1.14 1.08 1.01 0.98 1.38 1.07 1.48 1.50 0.98 1.26 0.94

89.86 − 0.67 − 0.81 − 0.58 − 0.60 − 0.36 − 0.66 − 0.45 − 0.66 − 0.57 − 0.83 − 0.73 − 0.69 − 0.77

15.74 1.07 1.15 1.19 1.09 0.96 0.97 1.46 1.08 1.35 1.54 1.01 1.32 0.96

94.12 − 0.60 − 0.78 − 0.49 − 0.53 − 0.56 − 0.69 − 0.70 − 0.55 − 0.80 − 0.73 − 0.64 − 0.62 − 0.69

16.98 1.05 1.25 1.05 1.07 1.08 1.00 1.23 1.07 1.67 1.44 0.93 1.17 0.91

1.65 0.37 0.14 0.47 0.37 − 1.20 − 0.13 − 1.09 0.61 − 0.94 0.39 0.53 0.33 0.51

164 149 149 149 145 145 156 147 150 151 151 158 152 155

0.10 0.71 0.89 0.64 0.71 0.23 0.89 0.28 0.54 0.35 0.70 0.60 0.74 0.61

Abbreviations: CPM: Coloured Progressive Matrices; RAVLT, Rey Auditory Verbal Learning Test; WAIS-R, Wechsler Adult Intelligence Scale-Revised. a With the exception of Full-Scale IQ, all scores are age-, gender-, ethnicity- and education-regressed (residual) raw scores, which were Z-transformed using the mean (SD) scores of 177 ÆSOP controls (Zanelli et al., 2010). Full-Scale IQ is a standard score based on normative data, as described in the Wechsler Adult Intelligence Scale-Revised (WAIS-R) manual (Wechsler, 1981). The mean scores on the cognitive domains of Verbal Memory/Learning, Executive Function/Working Memory and Processing Speed represent averages across the individual tests that are subsumed under each of the respective cognitive domains. b Based on a short form of the WAIS-R (Wechsler, 1981) including Vocabulary, Comprehension, Block Design and Digit Symbol. The sums of scaled scores for the Verbal and Performance subtests were prorated by multiplying the sum of the Vocabulary and Comprehension scaled scores by 6/2 and the sum of the Block Design and Digit Symbol scaled scores by 5/2, respectively. The two prorated sums were summed up before obtaining Full-Scale IQ using the tables in the WAIS-R manual (Wechsler, 1981). c Based on the number of items recalled correctly in Trials 1–5 (assessing immediate free recall and learning), Trial 6 (assessing short-delay free recall) and Trial 7 (assessing long-delay free recall) of the Rey Auditory Verbal Learning Test. d Total number of correct responses was assessed. e Based on the total accuracy score on the immediate recall trial of Visual Reproduction of the Wechsler Memory Scale-Revised (Wechsler, 1987), which involves drawing three geometric designs from memory. f Time (seconds) taken to complete the task was assessed.

co-varying for education (which differed significantly between affective and non-affective psychoses), this interaction was no longer statistically significant (P>0.10) (see Results). Therefore, the relationship between each symptom dimension and each neuropsychological domain was assessed in the total patient sample using univariate regression analyses. Scatter-plots suggested potential deviations from linearity for some associations. Therefore, in a second step, we expanded each regression model (y= a + b1 * x) with a quadratic term (y = a + b1*x+ b2*x2). When a statistically significant (P b 0.05) non-linear association was

detected, the Likelihood-Ratio (LR) test was performed to examine if the non-linear model was statistically significantly better fitting than the linear model (the non-linear model, having more parameters, will always fit at least as well as the linear model. Whether it fits significantly better and should thus be preferred is determined by deriving the probability or P‐value of the observed difference D between the two models when the null hypothesis is true). Due to the exploratory nature of our analysis and the nonindependence of the neurocognitive domains, we used the False

Non-Affective Psychoses

Affective Psychoses

4 3.5

Symptom severity

3 2.5 2 1.5 1 0.5 0 Mania***

Reality Distortion Negative Symptoms

Depression***

Disorganization**

**/***

Patients with non-affective psychoses differed from those with affective psychoses at the 0.01/0.001 level of statistical significance

Fig. 1. Symptom dimension scores in patients with non-affective (n = 100) and affective (n = 66) psychoses.

E. Kravariti et al. / Schizophrenia Research 140 (2012) 221–231

Non-Affective Psychoses

225

Affective Psychoses

0

Z scores

-0.2

-0.4

-0.6

-0.8

Verbal Memory/Learning

uc tio n Tra il M ak ing -Pa rt B Le tte r-N um be rS pa Ra n ve n's CP MSe tA B Ra ve n's CP MSe tB Tra il M ak ing -Pa rt A WA IS R Di git Sy mb ol

Vis ua lR ep rod

Tri a

l7

l6 RA VL T

TT ria RA VL

RA VL T

Tri a

ls 1-5

-1

Visual Executive Function/Working Memory Processing Speed Memory

Fig. 2. Z scores in 10 neurocognitive tests in patients with non-affective (n = 100) and affective (n = 66) psychoses.

Discovery Rate (FDR) method to control for multiple comparisons (Benjamini and Hochberg, 1995). The FDR reflects the proportion of expected false positives in a set of significant results. The FDR-adjusted P‐values are called q-values. The FDR procedure was carried out separately for the linear- and quadratic-regression P‐values (5 neurocognitive domains by 5 symptom factors gave rise to 25 P‐values for the linear regression models and 25 P‐values for the quadratic regression models) using the bootstrap method and the QVALUE software (Storey and Tibshirani, 2003. Results with P‐valuesb 0.05 and q-valuesb 0.1 were retained as significant. A detailed description of the FDR method can be found in Curran-Everett (2000), Ling et al. (2009) and Strimmer (2008). 3. Results The participants' demographic, diagnostic, medication, symptom and cognitive characteristics, as well as the results of the statistical comparisons between the affective and non-affective categories are presented in Tables 1–2. Figs. 1–2 display the mean symptom dimension and cognitive subtest scores in the affective and non-affective patient groups. The effects of the symptom dimension by group (affective vs. non-affective) interactions on the cognitive domains are presented in Table 3. The results of the linear and non-linear regression models for the associations between symptom dimensions and cognitive domains in the full patient sample are presented in Table 4 and in Figs. 3–4. The affective and non-affective patient groups differed significantly in gender (P b 0.05), level of completed education (P b 0.05), mood stabilisers (P = 0.001), mania (P b 0.001), depression (P = 0.001) and disorganisation (P b 0.01) scores (Table 1, Fig. 1), but in none of the cognitive scores (Table 2, Fig. 2). There was an isolated statistically significant group by depression interaction in relation to Full-Scale IQ (P b 0.05), which disappeared after co-varying for education (Table 3). Statistically significant, both linear and non-linear, associations were detected in relation to mania and executive function/working memory, negative symptoms and Full-Scale IQ, negative symptoms and verbal memory/learning, and negative symptoms and processing speed (Table 4). In addition, a statistically significant non-linear association was detected in relation to mania and processing speed (Table 4). The

q-values indicated low probability (b10%) that these findings were false positives, with the exception of the linear association between mania and executive function/working memory (q-value = 0.100). The Likelihood Ratio test showed that the quadratic model provided a statistically significantly better fit than the linear model in relation to mania and executive function/working memory, mania and processing speed, and negative symptoms and verbal memory/learning (Table 4, Figs. 3–4). As both negative symptoms and mania were associated with processing speed, we further examined whether these associations were independent of each other. A multivariate regression model including both negative symptoms (as a linear term) and manic symptoms (as a non-linear term, co-varying for the linear term) showed that both dimensions were independently associated with processing speed (negative symptoms: t = −2.71, P = 0.008; manic symptoms: t = −2.85, P = 0.005). Together, the two dimensions explained 10.6% of the variance in processing speed. In addition, the quadratic associations between mania and each of the processing speed and executive function domains remained significant (P = 0.002–0.050) after covarying for both reality distortion and disorganisation (the three clusters frequently co-exist in the same patient). In line with published data on the epidemiology of schizophrenia (Howard et al., 2000), 25 (15.4%) patients of our epidemiologically ascertained sample had late onset psychoses (> 40 years). After excluding these cases from the analysis, mania and negative symptoms (but no other symptom dimension) showed statistically significant associations with the same cognitive domains as in our main analysis. However, the quadratic model was significantly better fitting only in relation to mania and processing speed (P = 0.007) (and showed a non-significant trend towards a better fit in relation to mania and executive function/working memory; P = 0.057). 4. Discussion Our study examined linear and non-linear associations between five symptom dimensions and five cognitive domains in an epidemiologically

226

Table 3 Results of the regression models examining statistical interactions between each symptom dimension and type of psychosis (non-affective vs. affective) in relation to 5 cognitive domains in patients with first-onset psychoses (n = 166).

Verbal Full-Scale IQ D.F.

P

F

D.F.

Visual Memory P

F

D.F.

Function/Working Memory P

F

D.F.

P

Processing Speed F

D.F.

P

Mania Linear

1.19

1,162

0.277

0.02

1,147

0.893

2.34

1,143

0.128

0.05

1,154

0.818

0.16

1,156

0.694

Non-Linear

1.09

1,161

0.298

0.52

1,146

0.473

3.26

1,142

0.074

1.32

1,153

0.252

0.37

1,155

0.546

Reality Distortion Linear

0.72

1,162

0.396

0.22

1,147

0.642

0.71

1,143

0.400

0.17

1,154

0.682

0.34

1,156

0.559

Non-Linear

0.22

1,162

0.636

0.25

1,147

0.620

0.18

1,143

0.669

0.12

1,154

0.725

0.25

1,156

0.620

Negative Symptoms Linear

0.04

1,162

0.851

0.74

1,147

0.391

2.39

1,143

0.124

0.84

1,154

0.361

2.73

1,156

0.101

Non-Linear

0.00

1,161

0.955

0.29

1,146

0.591

0.68

1,142

0.412

0.35

1,153

0.558

1.46

1,155

0.228

Depression Linear

6.58

1,162

0.011

0.21

1,147

0.647

3.03

1,143

0.084

3.65

1,154

0.058

2.81

1,156

0.096

Non-Linear

6.06

1,161

0.015

0.03

1,146

0.874

3.50

1,142

0.064

2.70

1,153

0.102

1.12

1,155

0.292

After co-varying for education † Linear

0.66

1,158

0.418

Non-Linear

0.86

1,157

0.356

Disorganisation Linear

2.19

1,162

0.140

1.72

1,147

0.192

1.53

1,143

0.218

2.17

1,154

0.142

0.00

1,156

0.975

Non-Linear

0.75

1,161

0.389

2.48

1,146

0.118

0.73

1,142

0.394

1.66

1,153

0.200

0.15

1,155

0.695

† Unlike the procedure followed in relation to the remaining cognitive domains, the covariate effects of gender, age, ethnicity and education were not regressed out of Full-Scale IQ, raising the possibility that the significant Group by Depression interaction in relation to Full-Scale IQ was due to demographic differences between the affective and non-affective categories of psychosis. Statistically significant P value (<0.05).

E. Kravariti et al. / Schizophrenia Research 140 (2012) 221–231

F

Executive

Memory/Learning

Table 4 Linear and non-linear associations between 5 symptom dimensions and 5 cognitive domains in patients with first-onset psychoses (n = 166).

Verbal Full-Scale IQ R

2

P

Executive

Memory/Learning Q†

2

R

P

0.003 0.004

0.48 6 0.720

Visual Memory Q†

2

R

P

Function/Working Memory Q†

2

R

P

Q†

Processing Speed R2

P

Q†

Mania 0.009 0.009

0.215 0.464

0.003 0.014

0.512 0.362

0.034 0.058

0.020 0.010

0.100 0.071

LR χ2(1)=3.90, P=0.048*

Linear Vs. Non-Linear ‡

0.015 0.064

0.124 0.006

0.058

LR χ2(1)=8.21, P=0.004**

Reality Distortion Linear

0.000

0.851

0.007

0.312

0.001

0.717

0.000

0.939

0.000

0.887

0.002

0.851

0.008

0.548

0.003

0.791

0.003

0.773

0.001

0.901

Linear

0.048

0.005

0.040

0.050

0.006

0.040

0.000

0.932

0.009

0.229

0.052

0.004

0.040

Non-Linear

0.049

0.017

0.073

0.074

0.004

0.058

0.005

0.715

0.010

0.457

0.052

0.015

0.073

Non -Linear Linear Vs. Non-Linear ‡ Negative Symptoms

Linear Vs. Non-Linear ‡

LRχ2(1)=0.17, P=0.677

LR χ2(1)=3.87, P=0.049*

LR χ2(1)=0.01, P=0.928

Depression Linear

0.004

0.426

0.000

0.865

0.006

0.373

0.004

0.420

0.000

0.868

Non-Linear

0.004

0.722

0.000

0.985

0.012

0.424

0.011

0.417

0 .003

0.788

Linear Vs. Non-Linear ‡ Disorganisation Linear

0.010

0.199

0.022

0.068

0.003

0.501

0.000

0.845

0.010

0.213

Non-Linear

0.011

0.398

0.022

0.186

0.011

0.439

0.000

0.967

0.014

0.343

E. Kravariti et al. / Schizophrenia Research 140 (2012) 221–231

Linear Non -Linear

Linear Vs. Non-Linear ‡ † Q values are reported only for significant P values (<0.05). ‡ When a statistically significant (P<0.05) non-linear association was detected, the Likelihood-Ratio (LR) test was performed to examine if the non-linear model was statistically significantly better fitting than the linear model (the nonlinear model, having more parameters, will always fit at least as well as the linear model. Whether it fits significantly better and should thus be preferred is determined by deriving the probability or P-value of the observed difference D between the two models when the null hypothesis is true). The value is statistically significant (P value<0.05) or indicates low probability of a false positive finding (Q value<0.1). */** The P value indicates that the non-linear model is statistically significantly better fitting than the linear model at the 0.05/0.01 level of statistical significance.

227

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E. Kravariti et al. / Schizophrenia Research 140 (2012) 221–231

Fig. 4. Non-linear associations of Mania with ‘Processing Speed’ and ‘Executive Function/ Working Memory’ in patients with first-onset psychoses (n = 166).

distortion and depressive symptoms showed weak associations with cognition, which were consistently non-significant, corroborating the broad findings by Dominguez et al. (2009). Our study suggests that earlier findings on symptom dimensions and cognitive function in non-affective psychosis have wider applicability to the spectrum of psychoses. Contrary to our hypothesis and the findings by Dominguez et al. (2009), disorganisation failed to elicit significant associations with cognition. 4.1. Negative symptoms and cognition

Fig. 3. Linear and non-linear associations of Negative Symptoms with ‘Full-Scale IQ’, ‘Verbal Memory/Learning’ and ‘Processing Speed’ in patients with first-onset psychoses (n = 166).

ascertained sample of 166 patients with first-onset psychoses, which included both affective and non-affective diagnoses. In line with our hypotheses, negative symptoms showed the strongest and most consistent association with cognition, significantly predicting performance in three of five cognitive domains. These findings are consistent with those reported in a recent meta-analysis by Dominguez et al. (2009). In both investigations, negative symptoms predicted deficits in general intelligence, verbal memory and processing speed, but not in executive control or working memory. Further confirming our hypotheses, reality

Although the associations of negative symptoms with intelligence and processing speed were linear, a significant curvilinear association was detected in relation to verbal memory/learning. This finding is in line with an earlier report of quadratic associations between negative symptoms and several cognitive measures in recent-onset schizophrenia (Van der Does et al., 1993). Although the mechanism underlying such patterns is not known, the authors speculated that mild negative symptoms may reflect withdrawal, while a high negative symptom score is more likely to be indicative of brain pathology (Van der Does et al., 1993). The authors emphasised the distinction between primary negative symptoms and secondary negative symptoms, the latter resulting from depression, neuroleptic medication or the absence of social stimulation (Van der Does et al., 1993). The replication of non-linear associations between negative symptoms and cognition in the present study reinforces the view that this symptom dimension is not a unitary concept (Van der Does et al., 1993).

E. Kravariti et al. / Schizophrenia Research 140 (2012) 221–231

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4.2. Mania and cognition

4.5. Methodological limitations

Mania has been little investigated in cognitive/correlational studies of psychosis to date, and was not examined by Dominguez et al. (2009). However, the ÆSOP Study Group recently demonstrated that, of five symptom dimensions, mania showed the highest number of associations with clinical characteristics and risk indicators (Demjaha et al., 2009). In line with this evidence, mania emerged as the second most informative dimension in the present analysis, explaining variation in two of five cognitive domains, i.e. executive function and processing speed. These associations were independent of those observed in relation to negative symptoms. Our study provides novel evidence that mania relates to neurocognitive performance by a complex response function. The associations of mania with executive function and processing speed were inverted-U-shaped, implying that modest levels of mania are related to better cognitive function compared to minimal or high levels. A possible explanation is that below a critical threshold, the excitability that characterises mania boosts the level of motivation (facilitating engagement with cognitive tasks) and enhances the responsiveness to cognitive stimuli. This explanation is in line with the productivity and potential advantages associated with low levels of mania, countered by the distractibility and impaired decision making seen with high levels. In addition, the reported associations between mania and higher pre-morbid IQ, acute mode of onset, fewer neurological soft signs and shorter duration of untreated psychosis (Cannon et al., 2002; Demjaha et al., 2009; Koenen et al., 2009; MacCabe et al., 2010) could suggest that individuals with manic symptomatology are less compromised neurodevelopmentally, and have better cognitive functioning at baseline (i.e. more cognitive reserve). It would therefore require higher levels of mania for a disruptive effect on cognitive performance to become apparent.

Disorganisation was the least salient factor in the ÆSOP factor analytic study (Demjaha et al., 2009), accounting for less variance in total symptoms (5%) than any other dimension (7%–15%). As the prominence of disorganisation was critically dependent on the SCAN and the number of IGC items entered in the analysis, it is possible that a different clinical schedule might have given rise to a more salient factor and to significant associations with cognition. In addition, due to the different number of items (with satisfactory factor loadings) included in each of the five dimensions, the latter acquired different score ranges. This caveat, albeit unavoidable, may have influenced the correlational analysis. Only patients who met the strict inclusion criteria (e.g. IQ, language, age of immigration) and could cope effectively with the requirements of the neuropsychological assessment (those in sub-acute phases) provided cognitive data to the baseline ÆSOP study. Therefore, only 166 ÆSOP patients of those with IGC ratings (n = 536) were included in the present analysis. Although first-episode samples offer many research advantages (e.g. they are un-confounded by cumulative medication effects), they also pose research challenges compared to chronic samples (e.g. their diagnoses may be less reliable and subject to change, although ‘psychosis’ is generally reliable). Follow-up assessments of the baseline ÆSOP sample are currently under way and are hoped to provide interesting comparative data for future analyses. Due to a lack of non-psychotic affective groups, we could not examine whether our interesting findings pertaining to mania generalise to mania without psychosis. The cognitive tasks used in the present study were not matched for difficulty (for example, letternumber span makes heavier demands on working memory than Coloured Progressive Matrices). They further tap complex mental processes and are likely to load on more than one cognitive domains. This limitation is inherent in neuropsychological research, and was addressed by grouping cognitive tasks according to their selective or prominent, rather than exclusive, properties. The study used an older edition of the Wechsler intelligence series (WAIS-R), which may have slightly over-estimated IQ (but is unlikely to have affected the correlational analysis). The participants were not medication naïve. Medication may influence symptomatology through treatment response or side effects, and can impact on the dimensional structure. Information on medication was only available on 44% of the present patient sample, and did not include length of time on medication. This is a limitation of the study, as medication has known effects on symptoms and cognitive function. It is further important to acknowledge the possibility of multiple disease processes in psychoses, some driving impairments in routine cognitive and information processing and some expressing themselves in psychopathology. Finally, correlations between different symptom dimensions and neuropsychological performance may be differentially confounded. For example, acute mania may interfere with neuropsychological test performance, while negative symptoms may have definitional overlap with some cognitive aspects.

4.3. Dimensional views of psychosis Our statistical analysis showed no evidence of differential associations between symptom factors and cognitive function in affective and non-affective psychoses. This finding is in line with earlier reports (Kravariti et al., 2005; Simonsen et al., 2009; Smith et al., 2009). For example, in a recent study of 72 individuals with schizophrenia and 25 patients with schizoaffective or bipolar disorders, the two patient groups showed similar dimensions of cognitive function, similar dimensions of psychopathology, and similar relationships between cognition and symptomathology (Smith et al., 2009). These findings suggest that dimensional or hybrid models of psychosis could prove more useful than categorical models in explaining neurocognitive performance.

4.4. Methodological strengths Our study is the first investigation of associations between five symptom dimensions and five cognitive domains in an epidemiologically ascertained cohort of patients with a first episode of affective or non-affective psychosis. It included a broader range of symptom dimensions and diagnostic categories compared to earlier studies. It also explored the comparability of associations in affective and non-affective psychoses, and, importantly, it examined whether a non-linear model offered a more informative account of the explored associations than a linear pattern. Our preliminary findings suggest that this strategy may prove fruitful, and that a uniform focus on linear associations could conceal important relationships between symptom dimensions and cognition. These novel and robust methodological features enabled us to address the aims of the study drawing on a uniquely informative dataset and analysis.

4.6. Implications and conclusions In high agreement with earlier reports (Cameron et al., 2002; Bozikas et al., 2004; Heydebrand et al., 2004; Lucas et al., 2004) and the meta-analysis by Dominguez et al. (2009), the most informative symptom dimensions (in this study: negative symptoms and mania) explained a relatively small proportion of variance in neuropsychological performance (5%–11%). Understanding this replicable finding is a challenge. The issue skirts the notion of cognitive endophenotypes, i.e. the suggestion that some cognitive deficits tap vulnerability to neuropsychiatric disorders, and, as such, are largely dissociated from symptom states (Gottesman and Gould, 2003; Balanzá-Martínez et al., 2008; Burdick et al., 2009; Glahn et al.,

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2010). Notwithstanding this moderating element, our findings indicate that negative symptoms and mania are associated with different patterns of cerebral dysfunction, as reflected in discrete patterns of neuropsychological deficits. In summary, mania relates to cognitive performance by a complex response function (inverted-U-shaped relationship) in epidemiologically ascertained patients with first-onset psychoses. The associations of negative symptoms with cognition include both linear and quadratic elements, suggesting that this dimension is also not a unitary concept. The two dimensions are likely to reflect distinct pathophysiological processes, which appear to cut across affective and non-affective disorders. Such differences can help understand the heterogeneity of psychoses drawing on dimensional rather than categorical (i.e. diagnostic) distinctions. Our findings imply that current diagnostic systems may offer enhanced characterisation of mental disorders by incorporating dimensional specifications, which can critically inform strategies for psychiatric rehabilitation. Role of funding source This work was supported by the Stanley Medical Research Institute, Bethesda, Md, which provided financial support for the conduct of study, collection, management and analysis of data. Evangelos Vassos was supported by a NARSAD Young Investigators Award.

Contributors Eugenia Kravariti and Manuela Russo managed the literature search, contributed to the design and execution of the statistical analysis and wrote the first draft of the article (Joint First Authors). Evangelos Vassos and Abraham Reichenberg contributed to the design and execution of the statistical analysis and edited the final manuscript. All authors contributed to the conceptualization and/or implementation of the study and edited and approved the final manuscript. Conflict of interest Abraham Reichenberg has received speaker's honoraria from AstraZeneca (Greece). Paola Dazzan has received speaker's honoraria and travel support from AstraZeneca, Janssen Pharmaceutica, and Sanofi. Peter B. Jones has served as a consultant to Bristol-Myers Squibb, Eli Lilly, and Otsuka. Robin M. Murray has received speaker's honoraria from AstraZeneca, Janssen Pharmaceutica, Eli Lilly, Bristol-Myers Squibb, and Novartis Pharmaceuticals. The remaining authors report no financial relationships with commercial interests.

Acknowledgements We would like to thank the staff in the mental health services who helped in the case ascertainment and the research subjects. We gratefully acknowledge advice from the late R. E. Kendell, FRCPsych, regarding the design of the study. We wish to acknowledge the contributions of the entire ÆSOP study team, listed online at http:// www.psychiatry.cam.ac.uk/aesop.

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