White matter abnormalities and neurocognitive deficits associated with the passivity phenomenon in schizophrenia: A diffusion tensor imaging study

Psychiatry Research: Neuroimaging 172 (2009) 121–127

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Psychiatry Research: Neuroimaging 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 / p s yc h r e s n s

White matter abnormalities and neurocognitive deficits associated with the passivity phenomenon in schizophrenia: A diffusion tensor imaging study Kang Sima,⁎, Guo Liang Yangb, Donus Lohc, Lye Yin Poond, Yih Yian Sitohe, Swapna Vermad, Richard Keefef, Simon Collinson g, Siow Ann Chongd, Stephan Heckersh, Wieslaw Nowinskib, Christos Pantelisi a

Department of General Psychiatry, Woodbridge Hospital, Institute of Mental Health, 10, Buangkok View, 539747 Singapore Biomedical Imaging Laboratory, Singapore Biomedical Imaging Consortium, Agency for Science, Technology and Research (ASTAR), Singapore Department of Psychology, Woodbridge Hospital, Institute of Mental Health, Singapore d Early Psychosis Intervention, Institute of Mental Health, Singapore e Department of Neuroradiology, National Neuroscience Institute, Singapore f Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA g Department of Psychology, National University of Singapore, Singapore h Department of Psychiatry, Vanderbilt University, Nashville, TN, USA i Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia b c

a r t i c l e

i n f o

Article history: Received 23 July 2008 Received in revised form 4 February 2009 Accepted 10 February 2009 Keywords: Cortical Subcortical Neural Frontal Thalamus Cingulate

a b s t r a c t The passivity phenomenon is a distressing Schneiderian first rank symptom in patients with schizophrenia. Based on extant data of functional and structural cerebral changes underlying passivity, we sought to examine cerebral white matter integrity in our subjects. We hypothesised that the passivity phenomenon would be associated with white matter changes in specific cortical (frontal, parietal cortices, and cingulate gyrus) and subcortical regions (thalamus and basal ganglia) and correlated with relevant neurocognitive deficits, compared with characteristics in those without the passivity phenomenon. Thirty-six subjects (11 with passivity and 25 without passivity) with schizophrenia were compared with 32 age-, gender- and handednessmatched healthy controls using diffusion tensor imaging. Neuropsychological testing was administered. Patients with passivity were associated with increased fractional anisotropy within the frontal cortex, cingulate gyrus, and basal ganglia and decreased fractional anisotropy within the thalamus when compared with patients without passivity. Within patients with passivity, fractional anisotropy in the frontal cortex correlated with the age of onset of illness and neurocognitive deficits related to attention and executive functioning. The findings suggest distributed involvement of cortical and subcortical regions underlying passivity and support the notion of neural network models underlying specific psychiatric symptoms such as passivity. © 2009 Elsevier Ireland Ltd. All rights reserved.

1. Introduction The passivity phenomenon, in which a patient feels his experiences are controlled by an external agency, was considered one of the first rank symptoms of schizophrenia by Kurt Schneider in the latter half of the last century (Schneider, 1959). Passivity or ‘made’ experiences occur in about 10–20% of patients with schizophrenia and can present in various forms, namely ‘made’ emotions, movements, impulses and somatic passivity (Mellor, 1970). Furthermore, passivity experiences are often distressing threat/control-override symptoms, which are considered as one of the risk factors involved in aggression or hostility (Arboleda-Florez, 1998; Appelbaum et al., 2000). Over the last decade, there has been continued interest in the investigation of and the quest to understand the underlying neural ⁎ Corresponding author. Tel.: +65 63892000; fax: +65 63855900. E-mail address: [email protected] (K. Sim). 0925-4927/$ – see front matter © 2009 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.pscychresns.2009.02.003

substrates and cognitive mechanisms subserving the basic forms of psychopathology found in schizophrenia including the passivity phenomenon (Flashman et al., 2000; Lahti et al., 2001; Hoffman et al., 2003; MacDonald and Paus, 2003; Maruff et al., 2003; Ngan et al., 2003). Various theories have been invoked to explain passivity phenomena such as disordered internal monitoring (Frith and Done, 1989), which can be related to and contributed by abnormalities in the awareness of action or the sense of agency (Blakemore et al., 2003), proprioception (Behrendt, 2004), source monitoring deficits (Keefe et al., 1999; Woodward et al., 2006) or anomalies in motor imagery (Maruff et al., 2003). The ‘alienation model’ seeks to understand the symptom from the standpoint of organic brain lesions producing similar experiences such as the anarchic limb, somatoparaphrenia and phantom limb phenomena, which are associated with lesions involving frontal regions (Feinberg et al., 1992), the cingulate (Mesulam, 1981) and parietal (Leiguarda et al., 1993) regions. More recent models suggest disrupted connectivity between higher control centres and lower motor responses

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(Shallice and Burgess, 1996). Shallice and Burgess (1996) proposed that the failure of higher centres (frontal lobes, cingulate gyrus, and supplementary motor area) to inhibit inappropriate motor actions mediated by subcortical regions (e.g. basal ganglia) resulted in dysfunctional supervisory attentional systems. Behrendt (2004) postulated an uncoupling between underconstrained perception and activity pointing towards connectivity perturbations within different circuitries as possible mechanisms to account for the phenomena. Furthermore, neural models of schizophrenia propose that the disorder is associated with deficits in sensory processing and multimodal integration associated with disturbances in thalamo-cortical networks (Sim et al., 2006). The small number of earlier neuroimaging studies on patients with passivity have focussed on understanding the functional and morphometric brain changes in these individuals. Spence et al. (1997) compared the positron emission tomography (PET) results of three groups of subjects (7 patients with schizophrenia and passivity, 6 patients with schizophrenia and no passivity, and 6 healthy controls) and found abnormal activation in the parietal and cingulate regions in the group of subjects with passivity. Blakemore et al. (2003) investigated the neural correlates of active movements correctly attributed to the self and found that identical active movements misattributed to an external source resulted in significantly abnormal activations in the parietal cortex. More recently, Maruff et al. (2005) found reduced cortical volumes in parietal and frontal association cortices in patients with passivity compared with those without passivity. The involvement of the frontal, parietal and cingulate gyrus regions in passivity from neuroimaging studies points towards the possibility of impairments of neurocognitive domains (such as executive function, attention, and spatial perception) associated with these brain regions. However, there is a dearth of data documenting specific correlates of neurocognitive deficits with passivity. Earlier studies had suggested attentional dysfunction, impairment of spatial imagery processes (Danckert et al., 2004), and abnormalities of the perception of self-produced sensory stimuli (Blakemore et al., 2000) underlying passivity. Some of these neurocognitive deficits have been found to be similarly profound in white matter disorders such as multiple sclerosis (Ghaffar and Feinstein, 2007) and metachromatic leukodystrophy (Hyde et al.,1992). The overlap of these domains of cognitive impairment in psychotic disorders with passivity and white matter disorders suggests that white matter abnormalities may underlie symptoms in schizophrenia such as passivity. To the best of our knowledge, there have been no studies to date that examine white matter disruptions and their clinical and neurocognitive correlates in relation to passivity in patients with schizophrenia. In light of the limited data that have directly examined possible disruptions in neural circuits underlying passivity, we sought to study white matter integrity in patients with passivity symptoms. We hypothesized that compared with patients with no passivity: (1) Patients with passivity phenomena would show white matter changes in specific cortical–subcortical regions involving frontal, parietal cortices, cingulate gyrus, thalamus and basal ganglia; and (2) White matter changes within these cortical and subcortical regions would be associated with neuropsychological deficits in executive function, attention, spatial working memory and visuo-spatial tasks.

for DSM-IV disorders-Patient Version (SCID-P) (First et al., 1994). The patients were maintained on a stable dose of antipsychotic medications for at least 2 weeks (18 on second generation antipsychotics, 16 on first generation antipsychotics and 2 were on a combination of first and second generation antipsychotics) and did not have their medication withdrawn for the purpose of the study. There was no history of any significant neurological illness such as seizure disorder, head trauma or cerebrovascular accident, and no subject met DSM-IV criteria for alcohol or other substance abuse within the preceding 3 months. Thirty-two age-, gender- and handedness-matched healthy controls were recruited from the community by advertisements. Control subjects were free of any Axis I psychiatric disorder as determined by the SCID-Patient version (SCID-NP) (First et al., 2002) and had no history of any major neurological, medical illnesses, substance abuse or psychotropic medication use. Psychopathology and symptom severity were assessed using the Positive and Negative Syndrome Scale (PANSS) (Kay et al., 1987). The Global Assessment of Functioning (GAF) Scale was used to assess the level of psychosocial functioning and handedness was evaluated using the Modified Edinburgh Questionnaire (Schachter et al., 1987). Patients were suspected of having passivity phenomena based on clinical interviews, detailed chart reviews as well as retrospective positive rating on the item of ‘delusion of being controlled’ on the SCID-P (First et al., 1994). Cases of patients with passivity phenomena were subsequently identified on the basis of their scoring greater than 4 on four items of the Scale for the Assessment of Passivity Phenomena (SAPP), namely made movements, impulses/decisions to act, made emotions and somatic passivity (Spence et al., 1997; Maruff et al., 2005). Written, informed consent was acquired from all the participants after a detailed explanation of the study procedures. The study protocol was approved by the Institutional Review Boards of both the Institute of Mental Health and the National Neuroscience Institute. 2.2. Neurocognitive assessment Study subjects were administered the following neurocognitive tests by a psychometrist trained in standardised assessment and scoring procedures. Testing generally took 1.5–2 h and, when necessary, occurred over two sessions. The domains assessed included intelligence, attention, executive functioning, working memory, and visuo-spatial skills and the neuropsychological battery comprised the following measures: (1) Raven's Progressive Matrices, which measure abstract reasoning and intelligence (Lezak, 1995); (2) Continuous Performance Test II (CPT II) (Conners, 2000), which measures attention/vigilance; (3) Wisconsin Card Sorting Test (WCST) (Heaton et al., 1993), which assesses the ability to form abstract concepts, shift and maintain set, and utilize feedback; (4) Digit Span of the WAIS-III (Wechsler, 1997), which tests verbal working memory; (5) Spatial Span subtest of the WMS-III (Wechsler, 1997), which examines spatial working memory; and (6) Block Design subtest of the WAIS-III (Wechsler, 1997), which assesses visuo-constructional skills. 2.3. Image acquisition Magnetic resonance images were acquired on a 3 T whole body MRI scanner (Gyroscan Intera, Philips Medical Systems, Eindhoven, The Netherlands). Stability of a high signal to noise ratio was assured through a regular automated quality control

2. Methods 2.1. Subjects and clinical assessment The patients were consecutively recruited from the Institute of Mental Health, the only state psychiatric hospital in Singapore as well as the main treatment centre for patients with psychotic spectrum disorders such as schizophrenia. Out of 120 potential participants who were identified for the study, 90 (75%) met inclusion and exclusion criteria. Of these, 68 (75.6%) provided written, informed consent for the study. There were no age or gender differences between the participants and nonparticipants. Thus, we studied 36 patients with a DSM-IV diagnosis of schizophrenia and 32 healthy controls. Confirmation of the diagnosis was made for all patients by psychiatrists based on information obtained from the clinical history, existing medical records, interviews with significant others as well as the administration of the Structured Clinical Interview

Fig. 1. Diffusion tensor image showing region of interest in basal ganglia.

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Fig. 2. Diffusion tensor image showing region of interest in the thalamus. Fig. 4. Diffusion tensor image showing region of interest in frontal gyrus. procedure. After automated scout and shimming procedures to optimize field homogeneity, total brain volumetric scans were acquired with a high resolution, 3D magnetisation-prepared rapid acquisition with a gradient echo (MPRAGE) sequence that obtained 180 contiguous axial slices (which were later reformatted to coronal and sagittal slices) of 0.9 mm thickness with no gap, resulting in isovoxels of 0.9 mm3, TR/TE/TI/flip angle 8.4/3.8/3000/8, field of view (FOV) of 240 mm2 and matrix of 256×256. In the same session, diffusion tensor imaging in 15 directions was performed in the same axial plane using single-shot, spin-echo echo planar imaging (EPI) sequence, b value of 0 and 800 s/mm2, TR/TE should be 3700/56, matrix 128 × 128, slice thickness 3 mm with no gap and FOV of 240 mm2. 2.4. Image processing An electronic atlas based region-of-interest approach was utilized during image processing and analyses. The electronic atlas of brain anatomy (Nowinski and Thirunavuukarasuu, 2004) used in neurosurgical procedures was adopted to define the regions of interest where white matter indices were calculated (Figs. 1–5). The atlas was

Fig. 3. Diffusion tensor image showing region of interest in cingulate gyrus.

warped against MPRAGE scans by using a landmark-based and piece-wise linear approach, in that anatomical landmarks identified in the scan were interpolated against corresponding predefined landmarks in the atlas. For this purpose the Fast Talairach Transformation was applied (Nowinski et al., 2006) with dorso-ventral extension (Nowinski and Prakash, 2005) features. The MPRAGE scans were processed to identify the landmarks including the anterior commissure (AC), posterior commissure (PC), brain extents in the left, right, anterior, posterior, dorsal and ventral directions, and the superior midway (SM) and inferior midway (IM) landmarks corresponding to the top of the corpus callosum and the bottom of the orbito-frontal cortex, respectively. These landmarks were set manually for each subject to ensure better accuracy. After these landmarks are identified on the MPRAGE images, the whole brain is divided into 2 by 3

Fig. 5. Diffusion tensor image showing region of interest in parietal lobule.

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Table 1 Demographic and clinical features of the subjects (N = 68). Characteristica

Passivity (N = 11)

No passivity (N = 25)

Age, years 35.45 (8.86) 36.16 (9.16) Gender (M/F) 6/5 21/4 Handedness (L/R) 3/8 4/21 Education level, years 10.64 (2.25) 11.16 (3.16) Education level of mother, 5.02 (2.93) 5.09 (3.77) years Education level of father, years 5.64 (3.20) 6.65 (3.74) Daily CPZ equivalents, mg 327.27 (371.73) 149.20 (93.90) Atypical antipsychotic, 6 (54.54) 13 (52.00) N (%) Duration of illness, years 5.63 (3.47) 9.23 (9.94) Age of onset, years 29.64 (9.04) 26.29 (6.65) GAF total 51.36 (17.28) 46.84 (18.60) PANSS positive 13.00 (5.33) 12.40 (4.84) PANSS negative 13.55 (4.99) 10.24 (3.41) PANSS general 23.55 (7.29) 20.84 (4.39) psychopathology PANSS total 50.09 (13.93) 43.48 (10.44)

Healthy controls (N = 32)

P valueb

34.16 (11.01) 19/13 5/27 14.09 (2.53) 6.72 (4.01)

NS NS⁎ NS⁎ b0.001 NS

8.03 (3.69) – –

NS 0.033 NS

– – – – – –

NS NS NS NS NS NS

–

NS

Abbreviations: GAF, Global Assessment of Functioning scale; PANSS, Positive and Negative Syndrome Scale. a Characteristic described by mean (S.D.) unless otherwise stated. b Kruskal-Wallis test, unless otherwise stated by asterisk (chi-square).

by 4 (totally 24) cuboidal sub-volumes. Within each sub-volume of the brain, the corresponding sub-volume of the Talairach brain atlas is linearly warped to match brain images along all three orthogonal directions, namely anterior to posterior, right to left, and superior to inferior. FA maps were acquired from the diffusion tensor imaging (DTI) images using DTI Studio (Jiang et al., 2006) and were then co-registered automatically to the structural images. Following the registration of the FA maps to the structural scans, the electronic brain atlas was mapped directly to the DTI data. The white matter indices such as FA and mean diffusivity (MD) within delineated structures of interest (frontal, parietal cortices, cingulate gyrus, thalamus and basal ganglia) were compared between patient groups. Figs. 1 and 2 show some of the delineated structures of interest. 2.5. Reliability assessment The test–retest (intra-rater) reliability of the measurement technique of the regional FA/MD was assessed by repeated measurement of eight randomly selected subjects (4 from controls and 4 from patients) over a minimum interval of 2 weeks. On the basis of a two-factor random effects model for intraclass correlation coefficient calculation (Shrout and Fleiss, 1979), alpha values were all greater than 0.90 for the FA/ MD of the five examined cerebral regions. Inter-rater reliability evaluation performed on a separate subset of eight subjects (4 from controls and 4 from patients) revealed alpha values of greater than 0.90 for the white matter indices of all five brain regions. 2.6. Statistical analysis Data were analyzed using the Statistical Package for the Social Sciences-PC version 13.0 (SPSS Inc., Chicago, III). Normality of distributions of continuous measures was checked with the Kolmogorov–Smirnov one-sample test. We tested for group differences with the student t-test/analysis of variance (ANOVA) for normally distributed data, and with the nonparametric Mann–Whitney U test/Kruskal Wallis test for nonnormally distributed continuous data. The white matter indices of the examined regions were subjected to repeated measures ANOVA, using diagnosis (patients with and without passivity and healthy controls) as the between-group factor and both hemisphere and region as within-group factors. Significant main effects and interactions were then explored with post-hoc statistical tests.

We then explored the relationship of white matter indices of affected regions with continuous clinical measures (age at onset, duration of illness, severity of psychopathology, medication dosages, and neurocognitive measures) in patients with passivity. Correlations for normally distributed data were made with linear regression (Pearson's r), and non-normally distributed data were correlated with a rank-method (Spearman's rs). Statistical significance was set at an alpha level of 0.05 (two tailed) for analyses of demographic and clinical data. To take into account the several brain regions (five) included in the other analyses, the corrected statistical significance was set a priori at an alpha level of 0.01 (two tailed).

3. Results Demographic and clinical characteristics of the sample are shown in Table 1. The three groups (patients with and without passivity, and healthy controls) were comparable in age, gender, handedness and parental education. In addition, there was no difference between patients with and without passivity in age of onset, years of education, type of prescribed antipsychotics (typical vs. atypical), duration of illness, GAF and PANSS total and subscale scores and intelligence as measured by Raven's Progressive Matrices. Compared with patients without passivity, patients with passivity had higher daily medication doses in daily chlorpromazine equivalents (327.27 ± 371.73 vs. 149.20 ± 93.90, z = −2.13, P = 0.033). Controls had a significantly higher level of education than the patients with (14.09 ± 2.53 vs. 10.64 ± 2.25, z = − 3.40, P = 0.001) and without (14.09 ± 2.53 vs. 11.16 ± 3.16, z = − 3.54, P = 0.001) passivity. Two main effects explained the differences in white matter FA values: diagnosis (F(1,66) = 44.63, P b 0.001) and region (F(4,63) = 91.73, P b 0.001). There was a significant diagnosis by region interaction (F(4,63) = 3.79, P = 0.008), but the significant FA difference between the groups was not specific for hemisphere (diagnosis by hemisphere interaction: F(1,66) = 2.74, P = 0.103; and diagnosis by region by hemisphere interaction: F(4,63) = 1.29, P = 0.282). Post-hoc analyses revealed that patients with passivity had increased FA in frontal cortex, cingulate gyrus, and basal ganglia but decreased FA within the thalamus compared with patients without passivity (Table 2). There was no significant difference in the MD values in the five brain regions between the two groups of subjects with and without passivity. The main findings did not differ when education or medication dosage was included as a covariate in the repeated measures ANOVA. In terms of neurocognitive assessments, patients with and without passivity both performed worse than controls, with more perseverative responses, and higher perseverative errors on WCST, higher total errors on the CPT, and lower Spatial Span backward scores on the Spatial Span test (Table 3). In patients with passivity, in terms of clinical parameters, correlations of age of onset of illness with FA of the brain regions were as follows: frontal cortex (rs =0.33, P =0.006); cingulate gyrus (rs =0.16, P =0.35); parietal cortex (rs =0.09, P =0.61); thalamus (rs = −0.16, P =0.30); and basal ganglia (rs =0.10, P = 0.55). Correlations of PANSS total score with brain regions were as follows: frontal cortex (rs =−0.31, P = 0.008); cingulate gyrus (rs = −0.23, P = 0.17); parietal cortex (rs = −0.11, P =0.52); thalamus (rs = 0.28, P =0.10); and basal ganglia (rs = −0.10, P =0.55). In addition, executive functioning, as expressed by perseverative errors on the WCST, was correlated with the following brain regions: frontal cortex (rs =−0.36, P =0.008); cingulate cortex (rs = −0.25,

Table 2 White matter indices (FA) of examined cortical and subcortical regions. Regiona

Passivity (N = 11)

No passivity (N = 25)

Healthy Controls (HC) (N=32)

Passivity vs. no passivity (P value)

Passivity vs. HC (P value)

No passivity vs. HC (P value)

Frontal cortex Cingulate gyrus Parietal cortex Thalamus Basal ganglia

0.35 (0.11) 0.38 (0.11) 0.30 (0.08) 0.31 (0.07) 0.43 (0.12)

0.26 (0.03) 0.31 (0.03) 0.31 (0.03) 0.37 (0.04) 0.31 (0.09)

0.31 (0.02) 0.34 (0.03) 0.31 (0.02) 0.33 (0.04) 0.37 (0.09)

PassivityNno PassivityNno NS Passivitybno PassivityNno

NS NS NS NS NS

No passivitybHC (0.001) No passivitybHC (b 0.001) NS No passivityNHC (b 0.001) NS

Abbreviations: FA, fractional anisotropy; HC, healthy controls; NS, no significance. a Regional FA described by mean (S.D.).

passivity (0.005) passivity (0.005) passivity (0.003) passivity (0.002)

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Table 3 Neurocognitive profiles of the subjects (N = 68). Neurocognitive testa

Passivity (N = 11)

No passivity (N = 25)

Healthy Controls (HC) (N = 32)

Passivity vs. HC (P value)

No passivity vs. HC (P value)

Raven's raw score WCST perseverative responses raw score WCST errors raw score CPT trials administered CPT total errors raw score Spatial Span backward raw score Block Design raw score

42.22 (13.44) 26.89 (19.51) 24.00 (16.32) 109.33 (24.77) 42.78 (25.39) 7.00 (2.35) 36.78 (11.60)

44.52 (8.69) 26.05 (24.45) 22.10 (18.58) 105.90 (23.16) 34.71 (22.94) 7.52 (1.99) 39.18 (15.06)

55.80 (3.61) 9.53 (6.55) 8.73 (5.73) 85.20 (15.28) 18.07 (14.87) 9.13 (1.41) 47.73 (10.63)

PassivitybHC PassivityNHC PassivityNHC PassivityNHC PassivityNHC PassivitybHC NS

No passivitybHC No passivityNHC No passivityNHC No passivityNHC No passivityNHC No passivitybHC No passivitybHC

(0.001) (0.041) (0.03) (0.03) (0.012) (0.041)

(0.001) (0.045) (0.013) (0.016) (0.028) (0.016) (0.045)

Abbreviations: CPT, Continuous Performance Test; HC, healthy controls; WCST, Wisconsin Card Sorting Test; NS, no significance. a Neurocognitive domain scores described by mean (S.D.).

P =0.05); parietal cortex (rs =−0.18, P = 0.14); thalamus (rs =0.25, P =0.04); and basal ganglia (rs = −0.20, P =0.08). Correlations of scores of attention (total error raw scores on CPT) with brain regions were as follows: frontal cortex (rs = − 0.30, P = 0.008); cingulate region (rs = −0.24, P = 0.05); parietal cortex (rs = −0.18, P = 0.14); thalamus (rs = 0.35, P = 0.06); and basal ganglia (rs = −0.16, P = 0.30). 4. Discussion There were several main findings from this study of patients with passivity experiences and in comparison with patients without passivity. First, the passivity phenomenon in schizophrenia was associated with varying white matter changes involving the frontal region, cingulate gyrus, thalamus and basal ganglia suggesting involvement of distributed cortical and subcortical regions underlying this psychiatric symptomatology. Second, in patients with passivity, white matter changes in the frontal cortex correlated with neurocognitive deficits in attention and executive functioning. Third, FA of the frontal cortex in passivity was correlated with age at onset of illness and severity of psychopathology. This is, to the best of our knowledge, the first study evaluating white matter changes and clinical and neurocognitive correlates in passivity phenomena. Structural changes in frontal regions and thalamic nuclei have been documented in patients with schizophrenia, compared with healthy controls (Shenton et al., 2001). Previous studies have found reduced volumes in these regions in patients suffering from schizophrenia (Breier et al., 1992; Andreasen et al., 1994; Flaum et al., 1995; Nopoulos et al., 1995). On the other hand, enlargement of the basal ganglia has been noted, with a postulated relationship to treatment with antipsychotics (Chakos et al., 1994). In comparison, structural and functional neuroanatomical changes underlying specific psychiatric symptomatology have been relatively understudied, including the passivity phenomenon. Earlier functional neuroimaging studies on passivity have found increased cerebral blood flow to the cingulate gyrus, right inferior parietal lobe and left premotor cortex on testing with random hand movements (Spence et al., 1997). More recently, Maruff et al. (2005) observed reductions in the gray matter volume of the left prefrontal and right parietal cortices in schizophrenia with passivity. Our findings of underlying white matter abnormalities in frontal cortex and cingulate gyrus further strengthened the neural basis underlying these reported functional and structural brain changes. Furthermore, white matter involvement of subcortical structures such as the thalamus and the basal ganglia in our study argued against the notion of focal white matter aberrations but suggested the involvement of a more distributed and integrated cortical–subcortical circuitry underlying passivity. In terms of neuroanatomy, the lateroposterior thalamic nucleus projects to association cortices which participate in higher order somatosensory and visuo-spatial function (Yeterian and Pandya,1985) and the frontal cortex establishes reciprocal connections with the basal ganglia (Parent and Hazrati,1995; Maurice et al., 1997) as well as the medial group of thalamic nuclei (Barbas and Mesulam, 1985; Goldman-Rakic and Porrino, 1985). Earlier models of passivity have implicated disruptions in attentional, executive and

motor networks (Behrendt, 2004), which could be subserved by these neural networks such as the cortico-thalamo-striato-cortical circuitry. In addition, we found that in patients with passivity, decreased FA in frontal cortex was correlated with poorer performance on tasks related to attention and executive function suggesting that white matter disruptions may underlie these observed neurocognitive deficits. The differences in white matter changes in the involved regions may indicate not only an underlying neural basis for the symptom but also reciprocal interactions between the implicated regions or compensatory white matter changes in patients with passivity. This may be supported by recent studies which reported pliable and developmental changes even within white matter (Madden et al., 2004; Ardekani et al., 2007; Ashtari et al., 2007), suggesting that white matter connections are not dormant and static but may display neuroplasticity over time. Alternatively, these changes could be related to treatment effects. However, there was no difference in the duration of treatment with antipsychotic medication in the patient groups with versus without passivity, and we did not find any change in the main findings after accounting for medication dose as a covariate, thus making this a less likely explanation. Third, the increase in FA within frontal and cingulate regions could represent loss of crossing fibres (Nilsson et al., 2007). A loss of crossing fibres could cause a paradoxical increase in FA within patients with passivity. Increases in FA in different regions have been reported in previous studies of specific psychotic symptomatology. Hubl et al. (2004) found that schizophrenia patients with auditory hallucinations were associated with an increase in FA within the lateral parts of the temporoparietal section of the arcuate fasciculus and in parts of the anterior corpus callosum. Decreased FA of the frontal cortex in our patients was correlated with a younger age of onset of illness which is consistent with previous data suggesting reduced white matter integrity within the frontal lobes and related regions in those patients with an earlier onset of illness (Kumra et al., 2004; Douaud et al., 2007). Decreased anisotropy of the frontal cortex was further correlated with greater PANSS scores. This underscored the importance of clinical evaluation especially in understanding the progress of the psychotic illness from the time of onset as well as a thorough assessment of the severity of symptoms over time, consistent with previous studies which found correlations between decreased FA in frontal white matter with other psychiatric symptoms such as impulsivity, aggression and negative symptoms (Hoptman et al., 2002; Wolkin et al., 2003). Further studies are needed to examine the longitudinal course of these white matter changes and their clinical and functional correlates. The lack of anisotropy change in the parietal lobe in this study does not preclude involvement of the parietal lobe as observed in earlier structural and functional studies (Spence et al., 1997; Maruff et al., 2005). Several explanations could be proffered. First, compensatory white matter changes may have occurred over time and in response to structural changes within parietal regions. Second, any change in white matter architecture within the parietal region may occur independent of underlying structural or functional abnormalities. Third, the degree of white matter change in passivity may not have be detectable with our current techniques. Fourth, it could be that white matter changes in

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the parietal lobe may occur but not necessarily occur within a more extensively disrupted and distributed neural network involving several cortical and subcortical regions in patients with passivity. There are a few limitations in this study. First, this is the first study of white matter changes in a relatively small sample of subjects with passivity and these findings await replication in a bigger sample of subjects. Second, noise may artefactually increase FA (Anderson, 2001) and we did not compare the signal to noise ratio between the groups. Third, longitudinal changes of white matter in the various affected regions were not studied. Fourth, we did not specifically study drugnaïve individuals, an approach that may allow a better evaluation of white matter changes which are unaffected by medications or chronicity of illness. Thus, future studies may want to examine white matter changes from the first break of psychotic illness, their patterns of change over time as well as clinical correlates. In sum, passivity was associated with specific cortical and subcortical white matter changes involving fronto-thalamic-basal ganglia circuitry which were correlated with relevant neurocognitive deficits. Such white matter changes may provide evidence of the neural substrate underlying passivity and these findings have added to our understanding of the neural basis of specific symptoms found in schizophrenia. A better understanding of symptom-related white matter and connectivity disturbances and their changes over time may allow for earlier clinical detection, better nosological subtyping, treatment and follow up of our patients with schizophrenia. Acknowledgements The authors thank all the subjects, their families and the staff for their support of this study. This study is also supported by the National Healthcare Group Research Grant (NHG-SIG/05004) and Singapore Bioimaging Consortium Research Grant (SBIC RP C009/2006).

References Anderson, A.W., 2001. Theoretical analysis of the effects of noise on diffusion tensor imaging. Magnetic Resonance in Medicine 46, 1174–1188. Andreasen, N.C., Arndt, S., Swayze Jr., V., Cizadlom, T., Flaum, M., O'Leary, D., Ehrhardt, J.C., Yuh, W.T., 1994. Thalamic abnormalities in schizophrenia visualized through magnetic resonance image averaging. Science 266, 294–298. Appelbaum, P.S., Robbins, P.C., Monahan, J., 2000. Violence and delusions: data from the MacArthur Violence Risk Assessment Study. The American Journal of Psychiatry 157, 566–572. Arboleda-Florez, J., 1998. Mental illness and violence: an epidemiological appraisal of the evidence. Canadian Journal of Psychiatry 43, 989–996. Ardekani, S., Kumar, A., Bartzokis, G., Sinha, U., 2007. Exploratory voxel-based analysis of diffusion indices and hemispheric asymmetry in normal aging. Magnetic Resonance Imaging 25, 154–167. Ashtari, M., Cervellione, K.L., Hasan, K.M., Wu, J., McIlree, C., Kester, H., Ardekani, B.A., Roofeh, D., Szeszko, P.R., Kumra, S., 2007. White matter development during late adolescence in healthy males: a cross-sectional diffusion tensor imaging study. Neuroimage 35, 501–510. Barbas, H., Mesulam, M.M., 1985. Cortical afferent input to the principalis region of the rhesus monkey. Neuroscience 15, 619–637. Behrendt, R.P., 2004. A neuroanatomical model of passivity phenomena. Consciousness and Cognition 13, 579–609. Blakemore, S.J., Oakley, D.A., Frith, C.D., 2003. Delusions of alien control in the normal brain. Neuropsychologia 41, 1058–1067. Blakemore, S.J., Smith, J., Steel, R., Johnstone, C.E., Frith, C.D., 2000. The perception of self-produced sensory stimuli in patients with auditory hallucinations and passivity experiences: evidence for a breakdown in self-monitoring. Psychological Medicine 30, 1131–1139. Breier, A., Buchanan, R.W., Elkashef, A., Munson, R.C., Kirkpatrick, B., Gellad, F., 1992. Brain morphology and schizophrenia. A magnetic resonance imaging study of limbic, prefrontal cortex, and caudate structures. Archives of General Psychiatry 49, 921–926. Chakos, M.H., Lieberman, J.A., Bilder, R.M., Borenstein, M., Lerner, G., Bogerts, B., Wu, H., Kinon, B., Ashtari, M., 1994. Increase in caudate nuclei volumes of first-episode schizophrenic patients taking antipsychotic drugs. The American Journal of Psychiatry 151, 1430–1436. Conners, C.K., 2000. Conner's Continuous Performance Test II for Windows (CPT II). Multi Health Systems, Toronto. Danckert, J., Saoud, M., Maruff, P., 2004. Attention, motor control and motor imagery in schizophrenia: implications for the role of the parietal cortex. Schizophrenia Research 70, 241–261. Douaud, G., Smith, S., Jenkinson, M., Behrens, T., Johansen-Berg, H., Vickers, J., James, S., Voets, N., Watkins, K., Matthews, P.M., James, A., 2007. Anatomically related

grey and white matter abnormalities in adolescent-onset schizophrenia. Brain 130, 2375–2386. Feinberg, T.E., Schindler, R.J., Flanagan, N.G., Haber, L.D., 1992. Two alien hand syndromes. Neurology 42, 19–24. First, M.B., Spitzer, R.L., Gibbon, M., Williams, J.B.W., 1994. Structured Clinical Interview for DSM-IV Axis I Disorders-Patient Version (SCID-P). American Psychiatric Press, Washington, DC. First, M.B., Spitzer, R.L., Gibbon, M., Williams, J.B.W., 2002. Structured Clinical Interview for DSM-IV Axis I Disorders-Non-patient version (SCID-NP). American Psychiatric Press, Washington, DC. Flashman, L.A., McAllister, T.W., Andreasen, N.C., Saykin, A.J., 2000. Smaller brain size associated with unawareness of illness in patients with schizophrenia. The American Journal of Psychiatry 157, 1167–1169. Flaum, M., Swayze Jr., V.W., O'Leary, D.S., Yuh, W.T., Ehrhardt, J.C., Arndt, S.V., Andreasen, N.C., 1995. Effects of diagnosis, laterality, and gender on brain morphology in schizophrenia. The American Journal of Psychiatry 152, 704–714. Frith, C.D., Done, D.J., 1989. Experiences of alien control in schizophrenia reflect a disorder in the central monitoring of action. Psychological Medicine 19, 359–363. Ghaffar, O., Feinstein, A., 2007. The neuropsychiatry of multiple sclerosis: a review of recent developments. Current Opinion in Psychiatry 20, 278–285. Goldman-Rakic, P.S., Porrino, L.J., 1985. The primate mediodorsal (MD) nucleus and its projection to the frontal lobe. The Journal of Comparative Neurology 242, 535–560. Heaton, R.K., Chelune, G.J., Talley, J.L., Kay, G., Curtiss, G., 1993. Wisconsin Card Sorting Test, Manual. Psychological Assessment Resources, Odessa, Florida. Hoffman, R.E., Hawkins, K.A., Gueorguieva, R., Boutros, N.N., Rachid, F., Carroll, K., Krystal, J.H., 2003. Transcranial magnetic stimulation of left temporoparietal cortex and medication-resistant auditory hallucinations. Archives of General Psychiatry 60, 49–56. Hoptman, M.J., Volavka, J., Johnson, G., Weiss, E., Bilder, R.M., Lim, K.O., 2002. Frontal white matter microstructure, aggression, and impulsivity in men with schizophrenia: a preliminary study. Biological Psychiatry 52, 9–14. Hubl, D., Koenig, T., Strik, W., Federspiel, A., Kreis, R., Boesch, C., Maier, S.E., Schroth, G., Lovblad, K., Dierks, T., 2004. Pathways that make voices: white matter changes in auditory hallucinations. Archives of General Psychiatry 61, 658–668. Hyde, T.M., Ziegler, J.C., Weinberger, D.R., 1992. Psychiatric disturbances in metachromatic leukodystrophy. Insights into the neurobiology of psychosis. Archives of Neurology 49, 401–406. Jiang, H., van Zijl, P.C., Kim, J., Pearlson, G.D., Mori, S., 2006. DtiStudio: resource program for diffusion tensor computation and fiber bundle tracking. Computer Methods and Programs in Biomedicine 81, 106–116. Kay, S.R., Fiszbein, A., Opler, L.A., 1987. The positive and negative syndrome scale (PANSS) for schizophrenia. Schizophrenia Bulletin 13, 261–276. Keefe, R.S., Arnold, M.C., Bayen, U.J., Harvey, P.D.,1999. Source monitoring deficits in patients with schizophrenia: a multinomial modelling analysis. Psychological Medicine 29, 903–914. Kumra, S., Ashtari, M., McMeniman, M., Vogel, J., Augustin, R., Becker, D.E., Nakayama, E., Gyato, K., Kane, J.M., Lim, K., Szeszko, P., 2004. Reduced frontal white matter integrity in early-onset schizophrenia: a preliminary study. Biological Psychiatry 55, 1138–1145. Lahti, A.C., Holcomb, H.H., Medoff, D.R., Weiler, M.A., Tamminga, C.A., Carpenter Jr, W.T., 2001. Abnormal patterns of regional cerebral blood flow in schizophrenia with primary negative symptoms during an effortful auditory recognition task. The American Journal of Psychiatry 158, 1797–1808. Leiguarda, R., Starkstein, S., Nogues, M., Berthier, M., Arbelaiz, R., 1993. Paroxysmal alien hand syndrome. Journal of Neurology, Neurosurgery and Psychiatry 56, 788–792. Lezak, M.D., 1995. Neuropsychological Assessment, 3rd edition. Oxford University Press, New York. MacDonald, P.A., Paus, T., 2003. The role of parietal cortex in awareness of selfgenerated movements: a transcranial magnetic stimulation study. Cerebral Cortex 13, 962–967. Madden, D.J., Whiting, W.L., Huettel, S.A., White, L.E., MacFall, J.R., Provenzale, J.M., 2004. Diffusion tensor imaging of adult age differences in cerebral white matter: relation to response time. Neuroimage 21, 1174–1181. Maruff, P., Wilson, P., Currie, J., 2003. Abnormalities of motor imagery associated with somatic passivity phenomena in schizophrenia. Schizophrenia Research 60, 229–238. Maruff, P., Wood, S.J., Velakoulis, D., Smith, D.J., Soulsby, B., Suckling, J., Bullmore, E.T., Pantelis, C., 2005. Reduced volume of parietal and frontal association areas in patients with schizophrenia characterized by passivity delusions. Psychological Medicine 35, 783–789. Maurice, N., Deniau, J.M., Menetrey, A., Glowinski, J., Thierry, A.M.,1997. Position of the ventral pallidum in the rat prefrontal cortex-basal ganglia circuit. Neuroscience 80, 523–534. Mellor, C.S., 1970. First rank symptoms of schizophrenia. I. The frequency in schizophrenics on admission to hospital. II. Differences between individual first rank symptoms. The British Journal of Psychiatry 117, 15–23. Mesulam, M.M., 1981. A cortical network for directed attention and unilateral neglect. Annals of Neurology 10, 309–325. Ngan, E.T., Vouloumanos, A., Cairo, T.A., Laurens, K.R., Bates, A.T., Anderson, C.M., Werker, J.F., Liddle, P.F., 2003. Abnormal processing of speech during oddball target detection in schizophrenia. Neuroimage 20, 889–897. Nilsson, C., Markenroth Bloch, K., Brockstedt, S., Lätt, J., Widner, H., Larsson, E.M., 2007. Tracking the neurodegeneration of parkinsonian disorders—a pilot study. Neuroradiology 49, 111–119. Nopoulos, P., Torres, I., Flaum, M., Andreasen, N.C., Ehrhardt, J.C., Yuh, W.T., 1995. Brain morphology in first-episode schizophrenia. The American Journal of Psychiatry 152, 1721–1723.

K. Sim et al. / Psychiatry Research: Neuroimaging 172 (2009) 121–127 Nowinski, W.L., Prakash, K.N., 2005. Dorsoventral extension of the talairach transformation and its automatic calculation for magnetic resonance neuroimages. Journal of Computer Assisted Tomography 29, 863–879. Nowinski, W.L., Qian, G., Bhanu Prakash, K.N., Hu, Q., Aziz, A., 2006. Fast Talairach Transformation for magnetic resonance neuroimages. Journal of Computer Assisted Tomography 30, 629–641. Nowinski, W.L., Thirunavuukarasuu, A., 2004. The Cerefy Clinical Atlas on CD-ROM. Thieme, New York. Parent, A., Hazrati, L.N., 1995. Functional anatomy of the basal ganglia. I. The corticobasal ganglia-thalamo-cortical loop. Brain Research Reviews 20, 91–127. Schachter, S.C., Ransil, B.J., Geschwind, N., 1987. Associations of handedness with hair color and learning disabilities. Neuropsychologia 25, 269–276. Schneider, K., 1959. Clinical Psychopathology. Grune and Stratton, New York. Shallice, T., Burgess, P., 1996. The domain of supervisory processes and temporal organization of behaviour. Philosophical Transactions of the Royal Society of London B: Biological Sciences 351, 1405–1412. Shenton, M.E., Dickey, C.C., Frumin, M., McCarley, R.W., 2001. A review of MRI findings in schizophrenia. Schizophrenia Research 49, 1–52.

127

Shrout, P.E., Fleiss, J.L., 1979. Intraclass correlations: uses in assessing rater reliability. Psychological Bulletin 86, 420–428. Sim, K., Cullen, T., Ongur, D., Heckers, S., 2006. Testing models of thalamic dysfunction in schizophrenia using neuroimaging. Journal of Neural Transmission 113, 907–928. Spence, S.A., Brooks, D.J., Hirsch, S.R., Liddle, P.F., Meehan, J., Grasby, P.M., 1997. A PET study of voluntary movement in schizophrenic patients experiencing passivity phenomena delusions of alien control. Brain 120, 1997–2011. Wechsler, D., 1997. Wechsler Adult Intelligence Scale-III. The Psychological Corporation, San Antonio, Texas. Wolkin, A., Choi, S.J., Szilagyi, S., Sanfilipo, M., Rotrosen, J.P., Lim, K.O., 2003. Inferior frontal white matter anisotropy and negative symptoms of schizophrenia: a diffusion tensor imaging study. American Journal of Psychiatry 160, 572–574. Woodward, T.S., Menon, M., Hu, X., Keefe, R.S., 2006. Optimization of a multinomial model for investigating hallucinations and delusions with source monitoring. Schizophrenia Research 85, 106–112. Yeterian, E.H., Pandya, D.N., 1985. Corticothalamic connections of the posterior parietal cortex in the rhesus monkey. The Journal of Comparative Neurology 237, 408–426.