Grey and white matter abnormalities are associated with impaired spatial working memory ability in first-episode schizophrenia

Schizophrenia Research 115 (2009) 163–172

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Schizophrenia Research j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / s c h r e s

Grey and white matter abnormalities are associated with impaired spatial working memory ability in first-episode schizophrenia Luca Cocchi a,⁎, Mark Walterfang a, Renée Testa a, Stephen J. Wood a, Marc L. Seal a, John Suckling b, Tsutomu Takahashi a,c, Tina-Marie Proffitt d, Warrick J. Brewer d, Christopher Adamson a,e, Bridget Soulsby a, Dennis Velakoulis a, Patrick D. McGorry d, Christos Pantelis a a b c d e

Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne & Melbourne Health, Melbourne, Australia Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom Department of Neuropsychiatry, University of Toyama, Toyama, Japan ORYGEN Research Centre, The University of Melbourne & Melbourne Health Developmental and Functional Brain Imaging, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, Australia

a r t i c l e

i n f o

Article history: Received 2 February 2009 Received in revised form 3 August 2009 Accepted 7 September 2009 Available online 17 October 2009 Keywords: Voxel-based morphometry Working memory Schizophrenia Grey matter White matter First-episode psychosis Cognition

a b s t r a c t Spatial working memory (SWM) dysfunction has been suggested as a trait marker of schizophrenia and implicates a diffuse network involving prefrontal, temporal and parietal cortices. However, structural abnormalities in both grey and white matter in relation to SWM deficits are largely unexplored. The current magnetic resonance imaging (MRI) study examined this relationship in a sample of young first-episode schizophrenia (FES) patients using a whole-brain voxel-based method. SWM ability of 21 FES patients and 41 comparable controls was assessed by the CANTAB SWM task. Using an automated morphometric analysis of brain MRI scans, we assessed the relationship between SWM abilities and both grey matter volume and white matter density in both groups. Our findings demonstrated the different directionality of the association between SWM errors and grey matter volume in left frontal regions and white matter tracts connecting these regions with temporal and occipital areas between FES patients and controls. This suggests that the substrate underpinning the normal variability in SWM function in healthy individuals may be abnormal in FES, and that the normal neurodevelopmental processes that drive the development of SWM networks are disrupted in schizophrenia. © 2009 Elsevier B.V. All rights reserved.

1. Introduction Impairment of spatial working memory (SWM) is considered a core cognitive dysfunction in schizophrenia (Park and Holzman, 1992; Pantelis et al., 1997; Pantelis and Maruff, 2002; Piskulic et al., 2007), and may be a trait marker for the illness (Wood et al., 2003; Seidman et al., 2006; Smith et al., 2006). SWM deficits in schizophrenia were originally thought

⁎ Corresponding author. Melbourne Neuropsychiatry Centre, The University of Melbourne, c/o National Neuroscience Facility, 161 Barry Street, Carlton South 3053, Australia. Tel.: +61 3 8344 1861; fax: +61 3 9348 0469. E-mail address: [email protected] (L. Cocchi). URL: http://www.psychiatry.unimelb.edu.au/mnc/ (L. Cocchi). 0920-9964/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.schres.2009.09.009

to relate to prefrontal abnormalities (Goldman-Rakic, 1994; Goldman-Rakic and Selemon, 1997), but recent functional magnetic resonance imaging (fMRI) (Meyer-Lindenberg et al., 2005; Ragland et al., 2007) and diffusion tensor imaging (DTI) (Karlsgodt et al., 2008) findings suggest that abnormalities in fronto-parietal and fronto-temporal networks may underlie impairments in SWM ability in schizophrenia. Most evidence from neuroimaging studies suggests that schizophrenia is associated with subtle but widespread morphologic brain changes, predominantly in the frontal, temporolimbic and paralimbic regions (Shenton et al., 2001; Fornito et al., 2009). However, more recent findings have implicated changes in parietal regions in the pathophysiology of the illness (Paulus et al., 2002; Kim et al., 2003; Danckert et al., 2004; Zhou et al., 2007). Similar findings have been

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demonstrated in first-episode schizophrenia (FES) patients (Whitford et al., 2005), suggesting that these deficits are present at illness onset, with evidence that they progress over the initial years of the disorder (Pantelis et al., 2003; Whitford et al., 2006; Sun et al., 2008). Increasing evidence also points to a role for white matter abnormalities in schizophrenia (Davis et al., 2003; Walterfang et al., 2006) and it has been suggested that progressive grey matter reductions following the illness progression are related to changes in white matter (Friedman et al., 2008; Peters et al., 2008). Only a few recent voxel-based morphometry (VBM) studies, which allow for the investigation of the distribution of regional structural changes of both grey and white matter components within the whole brain, have investigated the relationship between white and grey matter abnormalities in schizophrenia (Douaud et al., 2007; Miyata et al., 2009; Spoletini et al., 2009). These studies suggest that changes in the two tissue compartments are related, and may be due to the same pathological processes. MRI studies that have compared schizophrenia patients to healthy controls have demonstrated a series of brain regions and networks disrupted by the illness (Glahn et al., 2008; Fornito et al., 2009). A number of VBM analyses have explored the relationship between these brain changes and clinical features of the illness, such as level of psychotic symptoms (e.g. Shapleske et al., 2002; Yoshihara et al., 2008; Lui et al., 2009) or neuropsychological deficits (Antonova et al., 2005; Spoletini et al., 2009), however, the structural underpinnings of SWM ability in schizophrenia have not been welldocumented. In their recent study combining Tract-Based Spatial Statistics (TBSS) and VBM analyses, Spoletini et al. (2009) demonstrated that verbal working memory deficits in chronic schizophrenia were associated with grey matter abnormalities in frontal regions as well as reduced frontoparietal connectivity. However, this study failed to find morphologic changes specifically related to visuo-spatial working memory dysfunctions, possibly due to confounding factors associated with analyzing chronic patients, such as the effects of chronic medication and the impact of relapses. The study of FES populations could reduce the influence of such confounding factors, but no VBM study has attempted to determine the structural underpinnings of SWM ability in young FES patients. In the present study, we sought to characterize the relationships between grey and white matter structure and SWM performance in young FES patients and healthy controls, and to compare the relationships in these two groups. Based on previous work (Wood et al., 2003; Smith et al., 2006; Cocchi et al., 2009), we predicted that FES would exhibit poorer SWM performance compared to controls, and that these SWM deficits would be related to grey and white matter abnormalities in the brain regions involving fronto-parietal and fronto-temporal networks. 2. Material and methods 2.1. Subjects A total of 21 FES patients and 41 comparable control subjects were included in the study. All participants were selected from a larger database at the Melbourne Neuropsy-

chiatry Centre. Patients that had a MRI scan, completed the SWM task and were right handed were included in the study. Subjects were screened for comorbid medical and psychiatric conditions by clinical assessment and physical and neurological examination. Patients were excluded if there was poor (uncorrectable) eyesight, history of head injury with loss of consciousness, electro-convulsive therapy within the preceding 3 months, epilepsy, other neurological disorder, a significant medical condition considered to affect cognitive performance, or DSM-III-R criteria of alcohol or substance abuse or dependence. All participants with a premorbid and current IQ below 70 as assessed by Wechsler Adult Intelligence Scale—Revised (WAIS-R IQ; Wechsler, 1981) were also excluded. Subjects were assessed using four subtests of the WAIS-R to obtain an estimate of the current full scale IQ (FSIQ; Kaufman, 1990). Patients and control subjects were assessed with the National Adult Reading Test (NART; Nelson and Willison, 1991) to provide an estimate of premorbid cognitive functioning. This was converted to the WAIS-R IQ (Wechsler, 1981) using standard tables. For those subjects who scored fewer than 10 correct words on the NART, the Schonell Graded Word Reading Test (Schonell, 1942) was administered to provide a more accurate assessment of premorbid IQ. 2.1.1. First-episode schizophrenia patients Twenty-one FES patients were recruited from the ORYGEN Youth Health — Clinical Program, Melbourne, Australia. Study inclusion criteria were: (1) age of onset between 15 and 29 years; and (2) currently psychotic as reflected by the presence of at least 1 symptom (either delusions, hallucinations, disorder of thinking and/or speech other than simple acceleration or retardation, and disorganised, bizarre, or markedly inappropriate behaviour). All patients had a diagnosis of schizophreniform psychosis at the time of neuropsychological assessment and a diagnosis of established schizophrenia was subsequently confirmed for nine patients. Follow-up diagnoses were unknown for twelve FES patients. Diagnoses were based on one or more of the following: a structured clinical interview using the Positive and Negative Syndrome Scale (PANSS, Kay et al., 1987), together with chart review using the Diagnostic and Statistical Manual of Mental Disorders (DSM) criteria, and the Structured Clinical Interview for DSMIV Axis I Disorders (SCID-R or SCID-I; Spitzer et al., 1992). Eleven patients received atypical antipsychotic drugs (chlorpromazine equivalents 140.9 mg/day ± 99.5, Woods, 2003) and eight received typical (chlorpromazine equivalents: 177 mg/day ± 108.76) medication. Data were not available for two patients. 2.1.2. Healthy control subjects A single group of normal control participants was recruited, selected to be matched in age and gender and comparable as closely as possible to the patient group for years of completed education, and IQ (see Table 1). The control group was recruited by advertisements, or by approaching ancillary hospital staff and their families. Control subjects were recruited from similar socio-demographic areas to patients; those with a personal or family (immediate or extended) history of psychiatric illness were also excluded from the analyses.

L. Cocchi et al. / Schizophrenia Research 115 (2009) 163–172 Table 1 Demographic and clinical characteristics of control subjects and first-episode schizophrenia patients. Characteristics

Gender Female

Age (years) Education (years) PM-IQ (controls, n = 40) FS-IQ (controls, n = 39, FES = 20) PANSS global (FES, n = 12) PANSS negative (FES, n = 13) PANSS positive (FES, n = 13) Duration of the illness (years)

Controls (n = 41)

FES (n = 21)

N

%

N

%

Statistic

9

21.9

5

23.8

p = 0.868

Mean

SD

Mean

SD

22.5 13.0 101.7 110.7

6.7 1.5 10.3 9.0

21.6 11.4 95.3 96.9

3.2 1.1 9.5 9.7

83.0 19.4 22.9 0.1

18.4 6.6 5.8 0.17

p = 0.458 p b 0.001 p = 0.022 p b 0.001

Note. FES = first-episode schizophrenia, SD = standard deviation, PM-IQ = premorbid IQ, FS-IQ = current full scale IQ, PANSS = Positive and Negative Symptom Scale.

2.2. Spatial working memory measure Subjects were assessed using the spatial working memory task from the Cambridge Neuropsychological Test Automated Battery (CANTAB) (Owen et al., 1990; Robbins et al., 1994; Sahakian and Owen, 1992). This self-ordered task required participants to ‘search’ through a number of boxes on the computer screen in order to locate tokens hidden inside. The key instruction was that once a token had been located inside a particular box, it would not appear there again during that particular trial. The measure of interest from this task, relevant to the present study, was the total number of ‘between-search’ errors (BSEs). These errors are committed when a subject returned to search a box in which a token had already been found during a previous searching sequence, and are indicative of a SWM failure. 2.3. Structural MRI image acquisition All participants underwent scanning with a 1.5-Tesla scanner (GE Signa Horizon; General Electric Medical Systems, Milwaukee, Wisconsin) at the Royal Melbourne Hospital. Head movement was minimized by foam padding and straps across the forehead and chin. A 3-dimensional volumetric spoiled gradient recalled echo in the steady state sequence generated 124 contiguous, 1.5-mm coronal slices. Imaging parameters were time-to-echo, 3.3 ms; time-to-repetition, 14.3 ms; flip angle, 30°; matrix size, 256 × 256; field of view, 24 × 24 cm matrix; voxel dimensions, 0.937 × 0.937 × 1.5 mm, respectively. 2.4. Structural MRI data analysis 2.4.1. Image pre-processing Voxel-based morphometry (VBM) analysis (including preprocessing) was performed using Intel based workstations running Debian Linux 4.0. Images were pre-processed using FSL (http://www.fmrib.ox.ac.uk/fsl/) and SPM5 (Wellcome Department of Cognitive Neurology, ION, London: http://www.fil.ion.

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ucl.ac.uk/spm) software. Pre-processing steps inherent to the VBM analysis were based on the study by Good et al. (Good et al., 2002, see also Good et al., 2001) and for each step of processing and analysis, the authors carefully selected what was considered to be the most methodologically and statistically robust method. Pre-processing steps specifically related to this study are described below. 2.4.2. Grey and white matter tissue maps and customised templates Grey and white matter tissue maps were created from the following pre-processing steps: a) Removal of non-brain tissue. This process was performed in FSL using the Brain Extraction Tool (BET), parameters adjusted as required to optimize brain extraction, and results visually inspected; b) Segmentation of grey and white matter. This was performed using FSL's FMRIB's Automated Segmentation Tool (FAST), with visual inspection of results; c) Group template creation. A template created from the brain-extracted images of the study participants (patients and controls) was averaged and normalized using SPM5; d) Image spatial normalization to group templates. Each grey and white matter tissue map was then spatially normalized to this analysis specific template using SPM5; e) Modulation. To preserve the original volume in each grey matter voxel after normalization, modulation was performed using SPM5; f) Smoothing of images. Smoothing using a 12-mm, full-width half-maximum (FWHM) kernel was undertaken in SPM5; g) Creation of grey and white matter templates. Finally, a sample of 83 patients and controls (from the Melbourne Neuropsychiatry Centre database) smoothed grey and white matter tissue maps was averaged to create corresponding grey and white matter templates specific to the statistical analysis of MRI data. Modulation was performed on grey matter voxels to preserve volume, whereas white matter voxels were not modulated and thus provided information on white matter density rather than volume. White matter density is thought more likely to be altered in schizophrenia, given previous studies showing reductions in white matter density in schizophrenia subjects (McIntosh et al., 2008; Tanskanen et al., 2008; Wolf et al., 2008). 2.4.3. Statistical analysis of MRI data Statistical analyses were conducted using CamBa (version 1.2.0, http://www-bmu.psychiatry.cam.ac.uk/software/ publications/), an evolution of the BAMM software used in similar studies (e.g. Tanskanen et al., 2008; Walterfang et al., 2008). Statistical testing was via a cluster-based permutation inference method, which has been described in detail elsewhere (Brammer et al., 1997; Bullmore et al., 1999, 2001; Suckling and Bullmore, 2004; Suckling et al., 2006). In brief, at each intracerebral voxel in template space a general linear model was regressed onto the values of grey matter volume or white matter density separately to estimate the between-group effect sizes and their standard errors, generating an observed F-map (i.e. difference divided by its standard error). Following Π permutations of group membership, maps of F under the simulated nullhypothesis of no group difference were also estimated.

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Probabilistic thresholding of the observed F-map was then performed in two-stages: voxel- and cluster-level. All estimates at all intra-cerebral voxels from the Π permuted F-maps were pooled to sample the two-tailed, null-distribution. For positive and negative values separately, all voxels less than the critical value at the 2.5% level, CVvox, were set to zero. This procedure resulted in sets of Cl voxel clusters in the observed, l=0, and permuted, l=...Π, F-maps. For any given map, the clusters c =...Cl, were each comprised of Vc voxels, spatially contiguous (nearest and next-nearest neighbours) in threedimensions. Cluster-level statistics, Λc, were then computed as the sum of suprathreshold voxel statistics for all clusters in all maps as: Vc



Λc = ∑ ðFm −CVvox Þ: m=1

Those values obtained from the median permuted F-maps were pooled to sample the null-distribution of clusters with Λ c N 0. Critical values for the cluster-level statistics, CVclu, were calculated for the number of type I errors, η, expected under the null-hypothesis. This is related to the corresponding two-tailed p-value by: p=

Π η ; where Ξ = ∑ Cl Ξ l=1

where Ξ is the total number of clusters in all permuted F-maps, and was typically ~11,000 (p ≈ 9.0 × 10− 5 for η = 1). CVclu was used as a threshold for the observed clusters with surviving regions displayed as a colour overlay on template slices. One-way between-group comparison analysis was completed by a second analysis used to estimate alterations in grey matter volume and white matter density in brain regions showing an interaction between SWM BSEs and group. At each intra-cerebral voxel in standard space a general linear model was regressed onto the grey matter volumes and white matter density values. This two-way factorial design has main effects of group (FES and controls) and SWM errors (continuous values) as well as the interaction between group × SWM errors. F-maps were estimated for the observed values and under the null-hypothesis following appropriate permutation of factors (Suckling and Bullmore, 2004; Suckling et al., 2006). Statistical inference then proceeded as described above. The mean values of grey matter volume or white matter density were extracted for each participant from within the system of regions demonstrating group × SWM BSE interactions in the analysis of the corresponding tissue type. These data were subsequently tested for correlations with SWM BSEs using SPSS (SPSS 16; SPSS Inc, Chicago, Illinois, USA). Significant grey matter clusters were anatomically localised using both Talairach and Tournoux atlas implemented in CamBa. Tracts in the white matter cluster were identified using the ICBM DTI-81 and the white matter tractography atlas (http://cmrm.med.jhmi.edu/). 2.4.4. Statistical analysis of behavioural, clinical and demographic data Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS 16; SPSS Inc, Chicago, Illinois, USA). Independent-sample t-tests were used to ex-

amine between-group differences in age, IQ, educational level and for the number of SWM BSEs. Cohen's d was used to estimate effect size. Pearson's correlations were used to explore correlations between SWM BSEs and: years of education, IQ, age, total PANSS positive scores, total PANSS negative and total PANSS general scores. 3. Results 3.1. Demographics and clinical variables Controls and FES were matched for age (t(59.887) = 0.748, p = 0.458) and gender (X2 = 0.027, df = 1, phi = 0.021, p = 0.868), but controls underwent more years of formal education (t(59) = 4.519, p b 0.001) and had a higher premorbid and current IQ [premorbid IQ (PM-IQ) t(59) = 2.350, p = 0.022; current IQ (FS-IQ) t(57) = 5.427, p b 0.001]. Premorbid IQ was used as covariate of interest for SWM BSEs because this measure may be considered as independent of illness. SWM BSEs were not significantly correlated with PM-IQ and years of formal education, in both groups (p N 0.05). In the FES, there were no significant correlations between SWM BSEs and PANSS symptom scores (general, r = −0.05, Table 2 First-episode schizophrenia patients vs controls (between group comparison FES N controls for all regions). Grey matter Cluster 1, size 14,695 voxels Max. −3.703290 at 42.00,−64.00,−38.00 mm in the right cerebellum Crus 1 Hippocampus R (86 voxels) Parahippocampal L (70 voxels) Parahippocampal R (152 voxels) Amygdala L (186 voxels) Lingual R (393 voxels) Occipital Sup L (102 voxels) Occipital Inf R (89 voxels) Fusiform R (1102 voxels) Postcentral L (382 voxels) Temporal Inf R (85 voxels) Cerebellum Crus1 L (16 voxels) Cerebellum Crus1 R (1584 voxels) Cerebellum Crus2 L (1446 voxels) Cerebellum Crus2 R (719 voxels) Cerebellum 3 L (594 voxels) Cerebellum 3 R (13 voxels) Cerebellum 4 5 L (2 voxels) Cerebellum 4 5 R (541 voxels) Cerebellum 6 L (263 voxels) Cerebellum 6 R (1413 voxels) Cerebellum 7b L (1152 voxels) Cerebellum 7b R (214 voxels) Cerebellum 8 L (174 voxels) Cerebellum 8 R (603 voxels) Cerebellum 9 L (1046 voxels) Cerebellum 9 R (9 voxels) Cerebellum 10 L (86 voxels) Cerebellum 10 R (3 voxels) Vermis 1 2 (14 voxels) Vermis 3 (10 voxels) Vermis 4 5 (12 voxels) Vermis 6 (221 voxels) Vermis 7 (286 voxels) Vermis 8 (95 voxels) Vermis 9 (173 voxels) Vermis 10 (41 voxels) Vermis 10 (39 voxels)

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p = 0.876; positive, r = −0.237, p = 0.436; negative, r = 0.133, p = 0.665), or illness duration (r = 0.030, p = 0.897). 3.2. Spatial working memory FES were significantly impaired in SWM performance compared to controls (BSEs average: FES, 22.4 ± 15.16; controls, 11.6 ± 8.9; t(27.345) = 3.005; d = 0.9 p = 0.006). 3.3. Structural MRI results 3.3.1. Between-group comparisons of grey and white matter FES patients showed increased grey matter volume compared to controls in one extensive cluster (t(60) = 3.121, d = 0.81, p = 0.003) including occipital regions (bilaterally), the right fusiform gyrus, the left amygdala and the cerebellum (see Table 2 for details). A diffuse decrease in white matter density in four extensive clusters including the corticospinal tract, inferior frontooccipital fasciculus, inferior longitudinal fasciculus, anterior thalamic radiation and the right forceps major was found in FES patients compared to controls (see red regions in Fig. 1). In contrast, increased white matter density (blue regions in Fig. 1) was evidenced in FES compared to controls in the terminal part of the left forceps major, inferior longitudinal fasciculus and inferior fronto-occipital fasciculus (left hemisphere). 3.3.2. Relationship between grey and white matter changes and SWM BSEs An initial analysis was carried out to determine whether the regions of significant difference identified in the global grey and white matter comparisons were related to impairment in SWM performance. These analyses showed no significant correla-

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tions in either the grey matter cluster (patients, r = −0.358, p = 0.111; controls, r = −0.103, p = 0.523) or the white matter clusters (Pearson's correlations, r b 0.3, p N 0.1). We then examined the differential relationship between SWM BSEs and brain structure across the entire brain. There was a significant interaction by group in the association between SWM BSEs and grey matter volume in a specific cluster mainly involving left prefrontal regions (Table 3 and Fig. 2). A comparison of grey matter volume in the SWM interaction cluster demonstrated that the two groups had similar volumes (t(60) = 0.906, d = 0.24, p = 0.368). Post hoc correlation analysis (see Fig. 2) suggested that in patients, SWM BSEs were not significantly correlated to grey matter volume changes (r = 0.197, p = 0.391). In contrast, controls showed a strong negative correlation between SWM BSEs and grey matter volume (r = − 0.547, p b 0.001). There was a significant interaction by group for the relationship between SWM BSEs and white matter density in one spatially extensive cluster (see Fig. 3). This cluster included the posterior part of the uncinate fasciculus and extended posteriorly into the inferior fronto-occipital fasciculus, bilaterally. The anterior thalamic radiation, the superior longitudinal fasciculus (bilaterally) and the forceps minor were also included in this cluster. Independent t-tests showed no significant difference in white matter density between patients and controls (t(60) = 1.623, p = 0.110). However, supplementary effect size analysis suggested a moderate increase of white matter density in patients (d = 0.42). Post hoc correlation analysis (see Fig. 3) showed that in this cluster a loss of white matter density was related to a decrease in SWM BSEs in controls (r = 0.489; p b 0.001). No significant correlation between SWM BSEs and white matter

Fig. 1. Red areas denote regions where FES patients display decreased white matter density compared to control subjects. Blue regions indicate increased white matter density in FES compared to controls. The left side of the panel represents the right side of the brain. The Z coordinate for each axial slice in the standard space of Talairach and Tounoux is given in mm. In this analysis we used a cluster-wise probability of alpha error of less than 1% (i.e. less than one false positive test in the whole map). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

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Table 3 First-episode schizophrenia patients vs controls (interaction of group by SWM errors). Grey matter Cluster 1, size 4129 voxels Max. −3.837460 at −62.00, 24.00, 10.00 mm in the left inferior orbitofrontal cortex Frontal Sup L (75 voxels) Frontal Sup Orb L (51 voxels) Frontal Mid Orb L (1104 voxels) Frontal Inf Oper L (3 voxels) Frontal Inf Tri L (471 voxels) Frontal Inf Orb L (1115 voxels) Rolandic Oper L (31 voxels) Putamen L (244 voxels) Pallidum L (333 voxels) Thalamus L (121 voxels)

density was found in patients (r = −0.278, p = 0.223), but the direction of the relationship was reversed (i.e. a decrease in white matter density seems related to increased SWM BSEs, see Fig. 3). 4. Discussion To our knowledge this is the first study that has examined the relationship between both grey and white matter structure and SWM function in young FES patients and control participants. We have shown that the normal relationships between structure and function are lost in the disorder, but that the regions where these relationships break down are not the same as those regions which differentiate patients and controls more generally. Our findings tend to implicate a dissociation between the development of executive function and the development of brain structure in this population. The main purpose of this study was to characterize specific abnormalities in the neuronal network supporting SWM abilities in an initial illness phase, by adopting a whole-brain approach. Our results are consistent with the hypothesis that SWM impairments appear early in the course of schizophrenia (Cocchi et al., 2009; Reilly et al., 2006; Mathes et al., 2005). SWM deficits have also been demonstrated in relatives of patients with schizophrenia (Park and Holzman, 1992) and high-risk populations (Smith et al., 2006; Wood et al., 2003), suggesting that abnormalities in neural networks related to SWM dysfunction are a cardinal trait marker of schizophrenia and may be present throughout the illness course (reviewed by Pantelis et al., 2009). Therefore, the characterisation of the underlying anatomical substrate for SWM impairments present at the onset of illness may allow for the identification of individuals “at-risk” for schizophrenia prior to illness onset, and facilitate targeted interventions at the earliest phase of illness. A significant interaction between group and SWM errors was identified in specific grey matter regions overlapping with neural networks that have demonstrated abnormalities in established schizophrenia (i.e. left frontal regions; Glahn et al., 2008). Left frontal regions are known to be involved in SWM processes (reviewed by Zimmer, 2008). Given the role of frontal grey matter changes (e.g., smaller left orbitofrontal regions; Mitelman et al., 2005; Pantelis et al., 2003; Hazlett et al., 2008) as potential markers of illness and outcome of psychosis, our findings might represent the neural substrate

of the predictive nature of SWM performances demonstrated in psychotic disorders (Wood et al., 2003; Smith et al., 2006; Cocchi et al., 2009). The present findings also suggest that abnormalities of the white matter tracts connecting these grey matter regions are also closely associated with impaired SWM abilities in schizophrenia. High SWM BSEs in control subjects were related to decreased grey matter volume in the left frontal regions, basal ganglia and thalamus (see Fig. 2). These correlations may relate to variations in maturation or development of the anatomical substrate subserving SWM function, such as grey matter pruning (see Shaw et al., 2006; Shaw et al., 2008). Intriguingly, FES patients showed no significant correlation between SWM BSEs and grey matter volume — indeed, the correlation trend was opposite to that seen in controls. Recent functional magnetic resonance imaging (fMRI) investigations suggest abnormal connectivity between the frontal cortex and the temporal (Meyer-Lindenberg et al., 2001; MeyerLindenberg et al., 2005; Mitelman et al., 2005; Karlsgodt et al., 2008) and parietal (Chua et al., 2007; Seok et al., 2007) regions in schizophrenia. These connections play a critical role for SWM abilities (e.g. Todd and Marois, 2004; Rissman et al., 2008) and our results suggest that structural and functional abnormalities in the left frontal grey matter early in the course of schizophrenia may be related to morphological changes in fronto-temporal and fronto-occipital white matter tracts. In line with this hypothesis, changes in white matter structure that co-occur with grey matter changes have been suggested to be compensatory in psychotic disorders (Bartzokis et al., 2003; Walterfang et al., 2006), and increases in density in cortico-cortical association fibres occurring in concert with grey matter reductions in the pre-psychotic phase have been postulated to reflect this process (Walterfang et al., 2008a). Effect size analysis suggests a moderate increase in white matter density in regions involved in SWM ability and that the relationship between brain structure and SWM function in FES which occurs in the opposite direction suggests a failure of this hypothetical compensatory mechanism, leading to the suggested anatomical and functional disconnectivity in schizophrenia (Friston and Frith, 1995; Stephan et al., 2006). Although more work is needed to confirm this hypothesis, our results encourage further longitudinal study investigating the relation between grey and white matter changes at different illness phases and its relevance for cognitive functions and outcome. In this study we found a grey matter volume increase in occipital regions, right fusiform gyrus, left amygdala and cerebellum in FES patients. Increased grey matter volume in frontal and occipital regions and cerebellum has previously been reported in some FES samples (Whitford et al., 2006). However, reduced grey matter volume in FES in cerebellum, frontal, parietal, temporolimbic and paralimbic regions has been demonstrated in other studies (e.g. Bottmer et al., 2005; Pantelis et al., 2007), whereas other VBM studies have failed to find significant differences in grey matter volume in these regions (see Fornito et al., 2009). These inconclusive results, which may reflect differences in patient selection, stability of first-episode diagnoses and neuroimaging analysis methodology, underline the need for further studies exploring grey matter volumetric changes in FES patients that adopt a whole-brain approach (linking white and grey matter

L. Cocchi et al. / Schizophrenia Research 115 (2009) 163–172

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Fig. 2. Blue areas denote regions where there are significant interactions by group for the relationship between SWM errors and grey matter volume. Post hoc correlations within the significant interaction cluster are shown in the scatterplot. The left side of the panel represents the right side of the brain. The Z coordinate for each axial slice in the standard space of Talairach and Tounoux is given in mm. In this analysis we used a cluster-wise probability of alpha error of less than 1% (i.e. less than one false positive test in the whole map). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

changes) and following FES patients through to established illness or recovery. FES patients showed diffuse reductions in white matter density in long association tracts arising from frontal cortical regions, compared to control subjects. Our white matter findings occurred almost entirely in tracts connecting frontal cortical to subcortical and other association cortical regions, suggesting a critical role of early prefrontal changes (e.g. Sun et al., 2008) in the establishment of the diffuse morphological abnormalities in established schizophrenia (e.g. Pantelis et al., 2007). Several methodological limits need to be considered. The sample size of the clinical group is relatively small, which may result in an under-representation of significant grey and white matter changes. Furthermore, definitive diagnosis was known for only nine patients and we cannot exclude that some patients did not develop an established schizophrenia. Premorbid IQ was unrelated to SWM BSEs, grey matter

volume and white matter density in both groups. Analyses of current IQ showed a lack of overlap between patients and controls, which restricted our ability to control the influence of this variable on the results. In this study we adopted a stringent statistical analysis (i.e. interaction between SWM errors and group with a conservative threshold of less than one false positive test per map). This approach increases the likelihood of type II errors, but allows for a focus on the most significant (and thus, potentially abnormal) brain regions related to SWM functioning in an early stage of schizophrenia. The spatial normalization step used in the analysis may in contrast increase the likelihood of type I errors, producing apparent between-group differences that may be related to differential morphological heterogeneity within groups; however, in general the results obtained using VBM are consistent with manual, region of interest findings that account for inter-individual differences in sulcal and gyral

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Fig. 3. Red indicates regions where there are significant interactions by group for the relationship between SWM errors and white matter density. Post hoc correlations within the significant interaction cluster are shown in the scatterplot. The left side of the panel represents the right side of the brain. The Z coordinate for each axial slice in the standard space of Talairach and Tounoux is given in mm. In this analysis we used a cluster-wise probability of alpha error of less than 1% (i.e. less than one false positive test in the whole map). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

anatomy (Fornito et al., 2009). Additionally, in spite of the very short duration of antipsychotic treatment, the effects of medication cannot be confidently excluded. Finally, the neurobiological correlates of grey matter volume differences between groups remain unclear and further studies are needed to clarify the cause(s) of these differences (see Fornito et al., 2009). Our findings do however provide evidence suggesting that the directionality of the association between SWM errors and grey matter volume in left frontal regions and in tracts connecting these regions with temporal and occipital areas is different in FES patients compared to controls. This suggests that the substrate underpinning the normal variability in SWM function in healthy individuals may be abnormal in FES patients, and that the normal neurodevelopmental processes that drive the development of SWM networks are disrupted in schizophrenia. Role of funding source The funding sources had no role in the study design, in the collection, analysis and interpretation of data, in the writing of the report, and in the decision to submit the paper for publication. Contributors Luca Cocchi, Mark Walterfang, Renée Testa, Stephen J. Wood, Bridget Soulsby, Dennis Velakoulis, and Christos Pantelis designed the study and wrote

the protocol. Luca Cocchi managed the literature searches, conducted the statistical analyses, and wrote the first draft of the manuscript. Patrick McGorry, Tina-Marie Proffitt, and Warrick J. Brewer made major contributions to the data set. Tsutomu Takahashi, Stephen J. Wood, John Suckling, Marc Seal, Christopher Adamson, and Bridget Soulsby helped in the data analysis and manuscript writing. All authors contributed to and have approved the final manuscript. Conflict of interest All authors report no competing interests. Acknowledgements The Swiss National Foundation for the Scientific Research supported this study (LC) PBLAB-119622 and PBLAB3-119622; SJW was supported by a NHMRC Clinical Career Development Award (359223) (SJW 578) and MW was supported by a Pfizer NSR grant and by Melbourne Health. This study was also supported by NHMRC Program Grants (566529 579 and 350241) and NHMRC Project Grants (145627, 145737, 981112, 970598, and 970599).

References Antonova, E., Kumari, V., Morris, R., Halari, R., Anilkumar, A., Mehrotra, R., et al., 2005. The relationship of structural alterations to cognitive deficits in schizophrenia: a voxel-based morphometry study. Biol. Psychiatry 58, 457–467. Bartzokis, G., Nuechterlein, K.H., Lu, P.H., Gitlin, M., Rogers, S., Mintz, J., 2003. Dysregulated brain development in adult men with schizophrenia: a magnetic resonance imaging study. Biol. Psychiatry 53, 412–421. Bottmer, C., Bachmann, S., Pantel, J., Essig, M., Amann, M., Schad, L.R., et al., 2005. Reduced cerebellar volume and neurological soft signs in firstepisode schizophrenia. Psychiatry Res. 140, 239–250.

L. Cocchi et al. / Schizophrenia Research 115 (2009) 163–172 Brammer, M.J., Bullmore, E.T., Simmons, A., Williams, S.C., Grasby, P.M., Howard, R.J., et al., 1997. Generic brain activation mapping in functional magnetic resonance imaging: a nonparametric approach. Magn. Reson. Imaging 15, 763–770. Bullmore, E., Long, C., Suckling, J., Fadili, J., Calvert, G., Zelaya, F., et al., 2001. Colored noise and computational inference in neurophysiological (fMRI) time series analysis: resampling methods in time and wavelet domains. Hum. Brain Mapp. 12, 61–78. Bullmore, E.T., Suckling, J., Overmeyer, S., Rabe-Hesketh, S., Taylor, E., Brammer, M.J., 1999. Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain. IEEE Trans. Med. Imaging 18, 32–42. Chua, S.E., Cheung, C., Cheung, V., Tsang, J.T., Chen, E.Y., Wong, J.C., et al., 2007. Cerebral grey, white matter and csf in never-medicated, firstepisode schizophrenia. Schizophr. Res. 89, 12–21. Cocchi, L., Debbané, M., Vianin, P., Bircher, R., Roulin, S., Conus, P., et al., 2009. Encoding dysfunctions in a dynamic–static paradigm for visuospatial working memory in first-episode psychosis patients: a 2-year follow-up study. Early Intervention in Psychiatry 3, 44–51. Danckert, J., Saoud, M., Maruff, P., 2004. Attention, motor control and motor imagery in schizophrenia: implications for the role of the parietal cortex. Schizophr. Res. 70, 241–261. Davis, K.L., Stewart, D.G., Friedman, J.I., Buchsbaum, M., Harvey, P.D., Hof, P.R., et al., 2003. White matter changes in schizophrenia: evidence for myelin-related dysfunction. Arch. Gen. Psychiatry 60, 443–456. Douaud, G., Smith, S., Jenkinson, M., Behrens, T., Johansen-Berg, H., Vickers, J., et al., 2007. Anatomically related grey and white matter abnormalities in adolescent-onset schizophrenia. Brain 130, 2375–2386. Fornito, A., Yucel, M., Patti, J., Wood, S.J., Pantelis, C., 2009. Mapping grey matter reductions in schizophrenia: an anatomical likelihood estimation analysis of voxel-based morphometry studies. Schizophr. Res. 108, 104–113. Friedman, J.I., Tang, C., Carpenter, D., Buchsbaum, M., Schmeidler, J., Flanagan, L., et al., 2008. Diffusion tensor imaging findings in first-episode and chronic schizophrenia patients. Am. J. Psychiatry 165, 1024–1032. Friston, K.J., Frith, C.D., 1995. Schizophrenia: a disconnection syndrome? Clin. Neurosci. 3, 89–97. Glahn, D.C., Laird, A.R., Ellison-Wright, I., Thelen, S.M., Robinson, J.L., Lancaster, J.L., et al., 2008. Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. Biol. Psychiatry 64, 774–781. Goldman-Rakic, P.S., 1994. Working memory dysfunction in schizophrenia. J. Neuropsychiatry Clin. Neurosci. 6, 348–357. Goldman-Rakic, P.S., Selemon, L.D., 1997. Functional and anatomical aspects of prefrontal pathology in schizophrenia. Schizophr. Bull. 23, 437–458. Good, C.D., Ashburner, J., Frackowiak, R.S., 2001. Computational neuroanatomy: new perspectives for neuroradiology. Rev. Neurol. (Paris) 157, 797–806. Good, C.D., Scahill, R.I., Fox, N.C., Ashburner, J., Friston, K.J., Chan, D., et al., 2002. Automatic differentiation of anatomical patterns in the human brain: validation with studies of degenerative dementias. Neuroimage 17, 29–46. Hazlett, E.A., Buchsbaum, M.S., Haznedar, M.M., Newmark, R., Goldstein, K.E., Zelmanova, Y., et al., 2008. Cortical gray and white matter volume in unmedicated schizotypal and schizophrenia patients. Schizophr. Res. 101, 111–123. Karlsgodt, K.H., van Erp, T.G., Poldrack, R.A., Bearden, C.E., Nuechterlein, K.H., Cannon, T.D., 2008. Diffusion tensor imaging of the superior longitudinal fasciculus and working memory in recent-onset schizophrenia. Biol. Psychiatry 63, 512–518. Kay, S.R., Fiszbein, A., Opler, L.A., 1987. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophr. Bull. 13, 261–276. Kaufman, A.S., 1990. Assessing Adolescent and Adult intelligence. Allyn & Bacon, Boston, USA. Kim, J.J., Kwon, J.S., Park, H.J., Youn, T., Kang, D.H., Kim, M.S., et al., 2003. Functional disconnection between the prefrontal and parietal cortices during working memory processing in schizophrenia: a [15(O)]H2O PET study. Am. J. Psychiatry 160, 919–923. Lui, S., Deng, W., Huang, X., Jiang, L., Ma, X., Chen, H., et al., 2009. Association of cerebral deficits with clinical symptoms in antipsychotic-naive firstepisode schizophrenia: an optimized voxel-based morphometry and resting state functional connectivity study. Am. J. Psychiatry 166, 196–205. Mathes, B., Wood, S.J., Proffitt, T.M., Stuart, G.W., Buchanan, J.A., Velakoulis, D., et al., 2005. Early processing deficits in object working memory in first-episode schizophreniform psychosis and established schizophrenia. Psychol. Med. 35, 1053–1062. McIntosh, A.M., Moorhead, T.W., Job, D., Lymer, G.K., Munoz Maniega, S., McKirdy, J., et al., 2008. The effects of a neuregulin 1 variant on white matter density and integrity. Mol. Psychiatry 13, 1054–1059. Meyer-Lindenberg, A., Poline, J.B., Kohn, P.D., Holt, J.L., Egan, M.F., Weinberger, D.R., et al., 2001. Evidence for abnormal cortical functional connectivity during working memory in schizophrenia. Am. J. Psychiatry 158, 1809–1817.

171

Meyer-Lindenberg, A.S., Olsen, R.K., Kohn, P.D., Brown, T., Egan, M.F., Weinberger, D.R., et al., 2005. Regionally specific disturbance of dorsolateral prefrontal–hippocampal functional connectivity in schizophrenia. Arch. Gen. Psychiatry 62, 379–386. Mitelman, S.A., Buchsbaum, M.S., Brickman, A.M., Shihabuddin, L., 2005. Cortical intercorrelations of frontal area volumes in schizophrenia. Neuroimage 27, 753–770. Miyata, J., Hirao, K., Namiki, C., Fujiwara, H., Shimizu, M., Fukuyama, H., et al., 2009. Reduced white matter integrity correlated with cortico-subcortical gray matter deficits in schizophrenia. Schizophr. Res. 111, 78–85. Nelson, H. and Willison, J., 1991. The Revised National Adult Reading Test (NART) Test Manual (2nd edn). Windsor:NFER: Nelson. Owen, A.M., Downes, J.J., Sahakian, B.J., Polkey, C.E., Robbins, T.W., 1990. Planning and spatial working memory following frontal lobe lesions in man. Neuropsychologia 28, 1021–1034. Pantelis, C., Barnes, T.R., Nelson, H.E., Tanner, S., Weatherley, L., Owen, A.M., et al., 1997. Frontal–striatal cognitive deficits in patients with chronic schizophrenia. Brain 120 (Pt 10), 1823–1843. Pantelis, C., Maruff, P., 2002. The cognitive neuropsychiatric approach to investigating the neurobiology of schizophrenia and other disorders. J. Psychosom. Res. 53, 655–664. Pantelis, C., Velakoulis, D., McGorry, P.D., Wood, S.J., Suckling, J., Phillips, L.J., et al., 2003. Neuroanatomical abnormalities before and after onset of psychosis: a cross-sectional and longitudinal MRI comparison. Lancet 361, 281–288. Pantelis, C., Velakoulis, D., Wood, S.J., Yucel, M., Yung, A.R., Phillips, L.J., et al., 2007. Neuroimaging and emerging psychotic disorders: the Melbourne ultra-high risk studies. Int. Rev. Psychiatry 19, 371–381. Pantelis, C., Yücel, M., Wood, S.J., Brewer, W.J., Fornito, A., Berger, G., Cannon, T., Velakoulis, D., 2009. Neurobiological endophenotypes of psychosis and schizophrenia: are there biological markers of illness onset? In: Jackson, H.J., McGorry, P.D. (Eds.), Recognition and Management of Early Psychosis: a Preventive Approach. Cambridge University Press, Cambridge, UK. Park, S., Holzman, P.S., 1992. Schizophrenics show spatial working memory deficits. Arch. Gen. Psychiatry 49, 975–982. Paulus, M.P., Hozack, N.E., Zauscher, B.E., Frank, L., Brown, G.G., McDowell, J., et al., 2002. Parietal dysfunction is associated with increased outcomerelated decision-making in schizophrenia patients. Biol. Psychiatry 51, 995–1004. Peters, B.D., de Haan, L., Dekker, N., Blaas, J., Becker, H.E., Dingemans, P.M., et al., 2008. White matter fibertracking in first-episode schizophrenia, schizoaffective patients and subjects at ultra-high risk of psychosis. Neuropsychobiology 58, 19–28. Piskulic, D., Olver, J.S., Norman, T.R., Maruff, P., 2007. Behavioural studies of spatial working memory dysfunction in schizophrenia: a quantitative literature review. Psychiatry Res. 150, 111–121. Ragland, J.D., Yoon, J., Minzenberg, M.J., Carter, C.S., 2007. Neuroimaging of cognitive disability in schizophrenia: search for a pathophysiological mechanism. Int. Rev. Psychiatry 19, 417–427. Reilly, J.L., Harris, M.S., Keshavan, M.S., Sweeney, J.A., 2006. Adverse effects of risperidone on spatial working memory in first-episode schizophrenia. Arch. Gen. Psychiatry 63, 1189–1197. Rissman, J., Gazzaley, A., DkEsposito, M., 2008. Dynamic adjustments in prefrontal, hippocampal, and inferior temporal interactions with increasing visual working memory load. Cereb. Cortex 18, 1618–1629. Robbins, T.W., James, M., Owen, A.M., Sahakian, B.J., McInnes, L., Rabbitt, P., 1994. Cambridge Neuropsychological Test Automated Battery (CANTAB): a factor analytic study of a large sample of normal elderly volunteers. Dementia 5, 266–281. Sahakian, B.J., Owen, A.M., 1992. Computerized assessment in neuropsychiatry using CANTAB: discussion paper. J. R. Soc. Med. 85, 399–402. Schonell, F., 1942. Backwardness in the Basic Subjects. Oliver & Boyd, London, UK. Seidman, L.J., Giuliano, A.J., Smith, C.W., Stone, W.S., Glatt, S.J., Meyer, E., et al., 2006. Neuropsychological functioning in adolescents and young adults at genetic risk for schizophrenia and affective psychoses: results from the Harvard and Hillside Adolescent High Risk Studies. Schizophr. Bull. 32, 507–524. Seok, J.H., Park, H.J., Chun, J.W., Lee, S.K., Cho, H.S., Kwon, J.S., et al., 2007. White matter abnormalities associated with auditory hallucinations in schizophrenia: a combined study of voxel-based analyses of diffusion tensor imaging and structural magnetic resonance imaging. Psychiatry Res. 156, 93–104. Shapleske, J., Rossell, S.L., Chitnis, X.A., Suckling, J., Simmons, A., Bullmore, E.T., et al., 2002. A computational morphometric MRI study of schizophrenia: effects of hallucinations. Cereb. Cortex 12, 1331–1341. Shaw, P., Greenstein, D., Lerch, J., Clasen, L., Lenroot, R., Gogtay, N., et al., 2006. Intellectual ability and cortical development in children and adolescents. Nature 440, 676–679. Shaw, P., Kabani, N.J., Lerch, J.P., Eckstrand, K., Lenroot, R., Gogtay, N., et al., 2008. Neurodevelopmental trajectories of the human cerebral cortex. J. Neurosci. 28, 3586–3594.

172

L. Cocchi et al. / Schizophrenia Research 115 (2009) 163–172

Shenton, M.E., Dickey, C.C., Frumin, M., McCarley, R.W., 2001. A review of MRI findings in schizophrenia. Schizophr. Res. 49, 1–52. Smith, C.W., Park, S., Cornblatt, B., 2006. Spatial working memory deficits in adolescents at clinical high risk for schizophrenia. Schizophr. Res. 81, 211–215. Spitzer, R.L., Williams, J.B., Gibbon, M., First, M.B., 1992. The Structured Clinical Interview for DSM-III-R (SCID). I: history, rationale, and description. Arch. Gen. Psychiatry 49, 624–629. Spoletini, I., Cherubini, A., Di Paola, M., Banfi, G., Rusch, N., Martinotti, G., et al., 2009. Reduced fronto-temporal connectivity is associated with frontal gray matter density reduction and neuropsychological deficit in schizophrenia. Schizophr. Res. 108, 57–68. Stephan, K.E., Baldeweg, T., Friston, K.J., 2006. Synaptic plasticity and dysconnection in schizophrenia. Biol. Psychiatry 59, 929–939. Suckling, J., Bullmore, E., 2004. Permutation tests for factorially designed neuroimaging experiments. Hum. Brain Mapp. 22, 193–205. Suckling, J., Davis, M.H., Ooi, C., Wink, A.M., Fadili, J., Salvador, R., et al., 2006. Permutation testing of orthogonal factorial effects in a languageprocessing experiment using fMRI. Hum. Brain Mapp. 27, 425–433. Sun, D., Stuart, G.W., Jenkinson, M., Wood, S.J., McGorry, P.D., Velakoulis, D., van Erp, T.G., Thompson, P.M., Toga, A.W., Smith, D.J., Cannon, T.D., Pantelis, C., 2008. Brain surface contraction mapped in first-episode schizophrenia: a longitudinal magnetic resonance imaging study. Mol. Psychiatry July 8. PMID: 18607377. Electronic publication. Tanskanen, P., Ridler, K., Murray, G.K., Haapea, M., Veijola, J.M., Jaaskelainen, E., et al., 2008. Morphometric brain abnormalities in schizophrenia in a population-based sample: relationship to duration of illness. Schizophr. Bull. Nov. 17. Electronic publication ahead of print. Todd, J.J., Marois, R., 2004. Capacity limit of visual short-term memory in human posterior parietal cortex. Nature 428, 751–754. Walterfang, M., McGuire, P.K., Yung, A.R., Phillips, L.J., Velakoulis, D., Wood, S.J., et al., 2008. White matter volume changes in people who develop psychosis. Br. J. Psychiatry 193, 210–215.

Walterfang, M., Wood, S.J., Velakoulis, D., Pantelis, C., 2006. Neuropathological, neurogenetic and neuroimaging evidence for white matter pathology in schizophrenia. Neurosci. Biobehav. Rev. 30, 918–948. Wechsler, D., 1981. A Manual for the Wechsler Adult Intelligence Scale— Revised. Psychological Corporation, New York, USA. Whitford, T.J., Farrow, T.F., Gomes, L., Brennan, J., Harris, A.W., Williams, L.M., 2005. Grey matter deficits and symptom profile in first episode schizophrenia. Psychiatry Res. 139, 229–238. Whitford, T.J., Grieve, S.M., Farrow, T.F., Gomes, L., Brennan, J., Harris, A.W., et al., 2006. Progressive grey matter atrophy over the first 2–3 years of illness in first-episode schizophrenia: a tensor-based morphometry study. Neuroimage 32, 511–519. Wolf, R.C., Hose, A., Frasch, K., Walter, H., Vasic, N., 2008. Volumetric abnormalities associated with cognitive deficits in patients with schizophrenia. Eur. Psychiatry 23, 541–548. Wood, S.J., Pantelis, C., Proffitt, T., Phillips, L.J., Stuart, G.W., Buchanan, J.A., et al., 2003. Spatial working memory ability is a marker of risk-for-psychosis. Psychol. Med. 33, 1239–1247. Woods, S.W., 2003. Chlorpromazine equivalent doses for the newer atypical antipsychotics. J. Clin. Psychiatry 64, 663–667. Yoshihara, Y., Sugihara, G., Matsumoto, H., Suckling, J., Nishimura, K., Toyoda, T., et al., 2008. Voxel-based structural magnetic resonance imaging (MRI) study of patients with early onset schizophrenia. Ann. Gen. Psychiatry 7, 25. Zhou, S.Y., Suzuki, M., Takahashi, T., Hagino, H., Kawasaki, Y., Matsui, M., et al., 2007. Parietal lobe volume deficits in schizophrenia spectrum disorders. Schizophr. Res. 89, 35–48. Zimmer, H.D., 2008. Visual and spatial working memory: from boxes to networks. Neurosci. Biobehav. Rev. 32, 1373–1395.