Psychiatry Research: Neuroimaging 172 (2009) 121–127

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Psychiatry Research: Neuroimaging

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White matter abnormalities and neurocognitive deficits associated with the passivityphenomenon 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 Collinsong, Siow Ann Chongd, Stephan Heckersh, Wieslaw Nowinskib, Christos Pantelisi

aDepartment of General Psychiatry, Woodbridge Hospital, Institute of Mental Health, 10, Buangkok View, 539747 SingaporebBiomedical Imaging Laboratory, Singapore Biomedical Imaging Consortium, Agency for Science, Technology and Research (ASTAR), SingaporecDepartment of Psychology, Woodbridge Hospital, Institute of Mental Health, SingaporedEarly Psychosis Intervention, Institute of Mental Health, SingaporeeDepartment of Neuroradiology, National Neuroscience Institute, SingaporefDepartment of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USAgDepartment of Psychology, National University of Singapore, SingaporehDepartment of Psychiatry, Vanderbilt University, Nashville, TN, USAiMelbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, Victoria, Australia

⁎ Corresponding author. Tel.: +65 63892000; fax: +6E-mail address: kang_sim@imh.com.sg (K. Sim).

0925-4927/$ – see front matter © 2009 Elsevier Irelandoi:10.1016/j.pscychresns.2009.02.003

a b s t r a c t

a r t i c l e i n f o

Article history:

The passivity phenomenon Received 23 July 2008Received in revised form 4 February 2009Accepted 10 February 2009

Keywords:CorticalSubcorticalNeuralFrontalThalamusCingulate

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 examinecerebral white matter integrity in our subjects. We hypothesised that the passivity phenomenon would beassociated 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 withpassivity and 25without passivity)with schizophreniawere comparedwith 32 age-, gender- and handedness-matched healthy controls using diffusion tensor imaging. Neuropsychological testing was administered.Patientswith passivitywere associatedwith increased fractional anisotropywithin the frontal cortex, cingulategyrus, and basal ganglia and decreased fractional anisotropy within the thalamus when compared withpatients without passivity.Within patients with passivity, fractional anisotropy in the frontal cortex correlatedwith the age of onset of illness and neurocognitive deficits related to attention and executive functioning. Thefindings suggest distributed involvement of cortical and subcortical regions underlying passivity and supportthe notion of neural network models underlying specific psychiatric symptoms such as passivity.

© 2009 Elsevier Ireland Ltd. All rights reserved.

1. Introduction

The passivity phenomenon, inwhich a patient feels his experiencesare controlled by an external agency, was considered one of the firstrank symptoms of schizophrenia by Kurt Schneider in the latter half ofthe last century (Schneider, 1959). Passivity or ‘made’ experiencesoccur in about 10–20% of patients with schizophrenia and can presentin various forms, namely ‘made’ emotions, movements, impulses andsomatic passivity (Mellor, 1970). Furthermore, passivity experiencesare often distressing threat/control-override symptoms, which areconsidered 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 theinvestigation of and the quest to understand the underlying neural

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substrates and cognitive mechanisms subserving the basic forms ofpsychopathology found in schizophrenia including the passivityphenomenon (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 phenomenasuch as disordered internal monitoring (Frith and Done, 1989), whichcan be related to and contributed by abnormalities in the awareness ofaction 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 fromthe standpoint of organic brain lesions producing similar experiencessuch as the anarchic limb, somatoparaphrenia and phantom limbphenomena, 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 disruptedconnectivity between higher control centres and lowermotor responses

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

122 K. Sim et al. / Psychiatry Research: Neuroimaging 172 (2009) 121–127

(Shallice and Burgess, 1996). Shallice and Burgess (1996) proposed thatthe failure of higher centres (frontal lobes, cingulate gyrus, and supple-mentarymotor area) to inhibit inappropriatemotor actionsmediatedbysubcortical regions (e.g. basal ganglia) resulted in dysfunctional super-visory attentional systems. Behrendt (2004) postulated an uncouplingbetween underconstrained perception and activity pointing towardsconnectivity perturbations within different circuitries as possiblemechanisms to account for the phenomena. Furthermore, neuralmodels of schizophrenia propose that the disorder is associated withdeficits in sensory processing and multimodal integration associatedwith disturbances in thalamo-cortical networks (Sim et al., 2006).

The small number of earlier neuroimaging studies on patients withpassivity have focussed on understanding the functional and morpho-metric brain changes in these individuals. Spence et al. (1997) comparedthe positron emission tomography (PET) results of three groups ofsubjects (7 patients with schizophrenia and passivity, 6 patients withschizophrenia and no passivity, and 6 healthy controls) and foundabnormal activation in the parietal and cingulate regions in the group ofsubjects with passivity. Blakemore et al. (2003) investigated the neuralcorrelates of activemovements correctly attributed to the self and foundthat identical active movements misattributed to an external sourceresulted in significantly abnormal activations in the parietal cortex.More recently, Maruff et al. (2005) found reduced cortical volumes inparietal and frontal association cortices in patients with passivitycompared with those without passivity.

The involvement of the frontal, parietal and cingulate gyrus regions inpassivity from neuroimaging studies points towards the possibility ofimpairments 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 ofneurocognitive deficits with passivity. Earlier studies had suggestedattentional dysfunction, impairment of spatial imagery processes (Danck-ert et al., 2004), and abnormalities of the perception of self-producedsensory stimuli (Blakemore et al., 2000) underlying passivity. Some ofthese neurocognitive deficits have been found to be similarly profound inwhite matter disorders such as multiple sclerosis (Ghaffar and Feinstein,2007) andmetachromatic leukodystrophy (Hyde et al.,1992). The overlapof these domains of cognitive impairment in psychotic disorders withpassivity and white matter disorders suggests that white matterabnormalities may underlie symptoms in schizophrenia such as passivity.

To the best of our knowledge, there have been no studies to datethat examine white matter disruptions and their clinical and neuro-cognitive correlates in relation to passivity in patients with schizo-phrenia. In light of the limited data that have directly examinedpossible disruptions in neural circuits underlying passivity, we soughtto 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 matterchanges in specific cortical–subcortical regions involving frontal,parietal cortices, cingulate gyrus, thalamus and basal ganglia; and(2)Whitematter changeswithin these cortical and subcortical regionswould be associated with neuropsychological deficits in executivefunction, attention, spatial working memory and visuo-spatial tasks.

2. Methods

2.1. Subjects and clinical assessment

The patients were consecutively recruited from the Institute of Mental Health, theonly state psychiatric hospital in Singapore as well as the main treatment centre forpatients with psychotic spectrum disorders such as schizophrenia. Out of 120 potentialparticipants who were identified for the study, 90 (75%) met inclusion and exclusioncriteria. Of these, 68 (75.6%) provided written, informed consent for the study. Therewere no age or gender differences between the participants and nonparticipants. Thus,we studied 36 patients with a DSM-IV diagnosis of schizophrenia and 32 healthycontrols. Confirmation of the diagnosis was made for all patients by psychiatrists basedon information obtained from the clinical history, existing medical records, interviewswith significant others as well as the administration of the Structured Clinical Interview

for DSM-IV disorders-Patient Version (SCID-P) (First et al., 1994). The patients weremaintained on a stable dose of antipsychotic medications for at least 2 weeks (18 onsecond generation antipsychotics, 16 on first generation antipsychotics and 2 were on acombination of first and second generation antipsychotics) and did not have theirmedication withdrawn for the purpose of the study. There was no history of anysignificant neurological illness such as seizure disorder, head trauma or cerebrovascularaccident, and no subjectmet DSM-IV criteria for alcohol or other substance abusewithinthe preceding 3 months.

Thirty-two age-, gender- and handedness-matched healthy controls were recruitedfrom the community by advertisements. Control subjects were free of any Axis Ipsychiatric 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 abuseor psychotropic medication use.

Psychopathology and symptom severity were assessed using the Positive andNegative Syndrome Scale (PANSS) (Kay et al., 1987). The Global Assessment of Func-tioning (GAF) Scale was used to assess the level of psychosocial functioning andhandedness was evaluated using the Modified Edinburgh Questionnaire (Schachteret al., 1987). Patients were suspected of having passivity phenomena based on clinicalinterviews, 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 withpassivity phenomena were subsequently identified on the basis of their scoring greaterthan 4 on four items of the Scale for the Assessment of Passivity Phenomena (SAPP),namely made movements, impulses/decisions to act, made emotions and somaticpassivity (Spence et al., 1997; Maruff et al., 2005).

Written, informed consent was acquired from all the participants after a detailedexplanation of the study procedures. The study protocol was approved by the Insti-tutional Review Boards of both the Institute of Mental Health and the National Neuro-science Institute.

2.2. Neurocognitive assessment

Study subjects were administered the following neurocognitive tests by a psy-chometrist trained in standardised assessment and scoring procedures. Testing gene-rally took 1.5–2 h and, when necessary, occurred over two sessions. The domainsassessed included intelligence, attention, executive functioning, working memory, andvisuo-spatial skills and the neuropsychological battery comprised the followingmeasures: (1) Raven's Progressive Matrices, which measure abstract reasoning andintelligence (Lezak, 1995); (2) Continuous Performance Test II (CPT II) (Conners, 2000),which measures attention/vigilance; (3) Wisconsin Card Sorting Test (WCST) (Heatonet al., 1993), which assesses the ability to form abstract concepts, shift and maintain set,and utilize feedback; (4) Digit Span of theWAIS-III (Wechsler, 1997), which tests verbalworking memory; (5) Spatial Span subtest of the WMS-III (Wechsler, 1997), whichexamines 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 ahigh signal to noise ratio was assured through a regular automated quality control

Fig. 2. Diffusion tensor image showing region of interest in the thalamus.

Fig. 4. Diffusion tensor image showing region of interest in frontal gyrus.

123K. Sim et al. / Psychiatry Research: Neuroimaging 172 (2009) 121–127

procedure. After automated scout and shimming procedures to optimize field homo-geneity, total brain volumetric scans were acquired with a high resolution, 3D mag-netisation-prepared rapid acquisition with a gradient echo (MPRAGE) sequence thatobtained 180 contiguous axial slices (which were later reformatted to coronaland 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 240mm2andmatrixof 256×256.

In the same session, diffusion tensor imaging in 15 directions was performed in thesame 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 thickness3 mm with no gap and FOV of 240 mm2.

2.4. Image processing

An electronic atlas based region-of-interest approach was utilized duringimage processing and analyses. The electronic atlas of brain anatomy (Nowinski andThirunavuukarasuu, 2004) used in neurosurgical procedures was adopted to define theregions of interest wherewhitematter 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 linearapproach, in that anatomical landmarks identified in the scanwere interpolated againstcorresponding predefined landmarks in the atlas. For this purpose the Fast TalairachTransformation was applied (Nowinski et al., 2006) with dorso-ventral extension(Nowinski and Prakash, 2005) features. The MPRAGE scans were processed to identifythe landmarks including the anterior commissure (AC), posterior commissure (PC),brain extents in the left, right, anterior, posterior, dorsal and ventral directions, and thesuperior midway (SM) and inferior midway (IM) landmarks corresponding to the top ofthe corpus callosum and the bottom of the orbito-frontal cortex, respectively. Theselandmarks were set manually for each subject to ensure better accuracy. After theselandmarks 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.

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

Characteristica Passivity(N=11)

No passivity(N=25)

Healthycontrols(N=32)

P valueb

Age, years 35.45 (8.86) 36.16 (9.16) 34.16 (11.01) NSGender (M/F) 6/5 21/4 19/13 NS⁎Handedness (L/R) 3/8 4/21 5/27 NS⁎Education level, years 10.64 (2.25) 11.16 (3.16) 14.09 (2.53) b0.001Education level of mother,

years5.02 (2.93) 5.09 (3.77) 6.72 (4.01) NS

Education level of father, years 5.64 (3.20) 6.65 (3.74) 8.03 (3.69) NSDaily CPZ equivalents, mg 327.27 (371.73) 149.20 (93.90) – 0.033Atypical antipsychotic,

N (%)6 (54.54) 13 (52.00) – NS

Duration of illness, years 5.63 (3.47) 9.23 (9.94) – NSAge of onset, years 29.64 (9.04) 26.29 (6.65) – NSGAF total 51.36 (17.28) 46.84 (18.60) – NSPANSS positive 13.00 (5.33) 12.40 (4.84) – NSPANSS negative 13.55 (4.99) 10.24 (3.41) – NSPANSS general

psychopathology23.55 (7.29) 20.84 (4.39) – NS

PANSS total 50.09 (13.93) 43.48 (10.44) – NS

Abbreviations: GAF, Global Assessment of Functioning scale; PANSS, Positive andNegative Syndrome Scale.

a Characteristic described by mean (S.D.) unless otherwise stated.b Kruskal-Wallis test, unless otherwise stated by asterisk (chi-square).

124 K. Sim et al. / Psychiatry Research: Neuroimaging 172 (2009) 121–127

by 4 (totally 24) cuboidal sub-volumes. Within each sub-volume of the brain, thecorresponding sub-volume of the Talairach brain atlas is linearly warped to match brainimages 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 DTIStudio (Jiang et al., 2006) and were then co-registered automatically to the structuralimages. Following the registration of the FA maps to the structural scans, the electronicbrain atlas wasmapped directly to the DTI data. Thewhitematter indices such as FA andmean diffusivity (MD) within delineated structures of interest (frontal, parietalcortices, cingulate gyrus, thalamus and basal ganglia) were compared between patientgroups. 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 theregional FA/MD was assessed by repeated measurement of eight randomly selectedsubjects (4 from controls and 4 from patients) over a minimum interval of 2 weeks. Onthe basis of a two-factor random effects model for intraclass correlation coefficientcalculation (Shrout and Fleiss, 1979), alpha values were all greater than 0.90 for the FA/MDof the five examined cerebral regions. Inter-rater reliability evaluation performed ona separate subset of eight subjects (4 from controls and 4 from patients) revealed alphavalues 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 version13.0 (SPSS Inc., Chicago, III). Normality of distributions of continuous measures waschecked with the Kolmogorov–Smirnov one-sample test. We tested for group dif-ferences with the student t-test/analysis of variance (ANOVA) for normally distributeddata, and with the nonparametric Mann–Whitney U test/Kruskal Wallis test fornonnormally distributed continuous data. The white matter indices of the examinedregions were subjected to repeated measures ANOVA, using diagnosis (patients withand without passivity and healthy controls) as the between-group factor and bothhemisphere and region aswithin-group factors. Significantmain effects and interactionswere then explored with post-hoc statistical tests.

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

Regiona Passivity(N=11)

No passivity(N=25)

Healthy Controls (HC)(N=32)

Frontal cortex 0.35 (0.11) 0.26 (0.03) 0.31 (0.02)Cingulate gyrus 0.38 (0.11) 0.31 (0.03) 0.34 (0.03)Parietal cortex 0.30 (0.08) 0.31 (0.03) 0.31 (0.02)Thalamus 0.31 (0.07) 0.37 (0.04) 0.33 (0.04)Basal ganglia 0.43 (0.12) 0.31 (0.09) 0.37 (0.09)

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

We then explored the relationship of white matter indices of affected regions withcontinuous clinical measures (age at onset, duration of illness, severity of psychopathology,medicationdosages, andneurocognitivemeasures) inpatientswithpassivity. Correlations fornormally distributed dataweremadewith linear regression (Pearson's r), and non-normallydistributed datawere correlated with a rank-method (Spearman's rs). Statistical significancewas 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, thecorrected 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 inTable 1. The three groups (patients with and without passivity, andhealthy controls) were comparable in age, gender, handedness andparental education. In addition, there was no difference betweenpatients with and without passivity in age of onset, years of education,type of prescribed antipsychotics (typical vs. atypical), duration ofillness, GAF and PANSS total and subscale scores and intelligence asmeasured by Raven's Progressive Matrices. Compared with patientswithout passivity, patients with passivity had higher daily medica-tion doses in daily chlorpromazine equivalents (327.27±371.73 vs.149.20±93.90, z=−2.13, P=0.033). Controls had a significantlyhigher 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.

Twomain effects explained the differences inwhitematter FAvalues:diagnosis (F(1,66)=44.63, Pb0.001) and region (F(4,63)=91.73,Pb0.001). There was a significant diagnosis by region interaction(F(4,63)=3.79, P=0.008), but the significant FA difference betweenthe groups was not specific for hemisphere (diagnosis by hemisphereinteraction: F(1,66)=2.74, P=0.103; and diagnosis by region byhemisphere interaction: F(4,63)=1.29, P=0.282). Post-hoc analysesrevealed that patients with passivity had increased FA in frontal cortex,cingulate gyrus, and basal ganglia but decreased FAwithin the thalamuscompared with patients without passivity (Table 2). There was no sig-nificant difference in the MD values in the five brain regions betweenthe two groups of subjects with and without passivity. The mainfindings did not differ when education or medication dosage wasincluded as a covariate in the repeated measures ANOVA.

In terms of neurocognitive assessments, patients with and withoutpassivity both performed worse than controls, with more persevera-tive responses, and higher perseverative errors on WCST, higher totalerrors on the CPT, and lower Spatial Span backward scores on theSpatial Span test (Table 3).

Inpatientswith passivity, in termsof clinical parameters, correlationsof 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); andbasal ganglia (rs=0.10, P=0.55). Correlations of PANSS total score withbrain 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 persevera-tive errors on theWCST, was correlatedwith the following brain regions:frontal cortex (rs=−0.36, P=0.008); cingulate cortex (rs=−0.25,

Passivity vs. no passivity(P value)

Passivity vs. HC(P value)

No passivity vs. HC(P value)

PassivityNno passivity (0.005) NS No passivitybHC (0.001)PassivityNno passivity (0.005) NS No passivitybHC (b0.001)NS NS NSPassivitybno passivity (0.003) NS No passivityNHC (b0.001)PassivityNno passivity (0.002) NS NS

Table 3Neurocognitive 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 42.22 (13.44) 44.52 (8.69) 55.80 (3.61) PassivitybHC (0.001) No passivitybHC (0.001)WCST perseverative responses raw score 26.89 (19.51) 26.05 (24.45) 9.53 (6.55) PassivityNHC (0.041) No passivityNHC (0.045)WCST errors raw score 24.00 (16.32) 22.10 (18.58) 8.73 (5.73) PassivityNHC (0.03) No passivityNHC (0.013)CPT trials administered 109.33 (24.77) 105.90 (23.16) 85.20 (15.28) PassivityNHC (0.03) No passivityNHC (0.016)CPT total errors raw score 42.78 (25.39) 34.71 (22.94) 18.07 (14.87) PassivityNHC (0.012) No passivityNHC (0.028)Spatial Span backward raw score 7.00 (2.35) 7.52 (1.99) 9.13 (1.41) PassivitybHC (0.041) No passivitybHC (0.016)Block Design raw score 36.78 (11.60) 39.18 (15.06) 47.73 (10.63) NS No passivitybHC (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.).

125K. Sim et al. / Psychiatry Research: Neuroimaging 172 (2009) 121–127

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 scoresof attention (total error raw scores on CPT) with brain regions wereas 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 withpassivity experiences and in comparison with patients withoutpassivity. First, the passivity phenomenon in schizophrenia wasassociated with varying white matter changes involving the frontalregion, cingulate gyrus, thalamus and basal ganglia suggestinginvolvement of distributed cortical and subcortical regions underlyingthis psychiatric symptomatology. Second, in patients with passivity,white matter changes in the frontal cortex correlated with neurocog-nitive deficits in attention and executive functioning. Third, FA of thefrontal cortex in passivity was correlated with age at onset of illnessand severity of psychopathology. This is, to the best of our knowledge,the first study evaluating white matter changes and clinical andneurocognitive correlates in passivity phenomena.

Structural changes in frontal regions and thalamic nuclei have beendocumented in patients with schizophrenia, compared with healthycontrols (Shenton et al., 2001). Previous studies have found reducedvolumes in these regions in patients suffering from schizophrenia(Breier et al., 1992; Andreasen et al., 1994; Flaum et al., 1995; Nopouloset al., 1995). On the other hand, enlargement of the basal ganglia hasbeen noted, with a postulated relationship to treatment withantipsychotics (Chakos et al., 1994). In comparison, structural andfunctional neuroanatomical changes underlying specific psychiatricsymptomatology have been relatively understudied, including thepassivity phenomenon. Earlier functional neuroimaging studies onpassivity have found increased cerebral blood flow to the cingulategyrus, right inferior parietal lobe and left premotor cortex on testingwith random hand movements (Spence et al., 1997). More recently,Maruff et al. (2005) observed reductions in the gray matter volume ofthe left prefrontal and right parietal cortices in schizophrenia withpassivity. Our findings of underlying white matter abnormalities infrontal cortex and cingulate gyrus further strengthened the neural basisunderlying these reported functional and structural brain changes.

Furthermore, white matter involvement of subcortical structuressuch as the thalamus and the basal ganglia in our study argued againstthe notion of focal white matter aberrations but suggested theinvolvement of a more distributed and integrated cortical–subcorticalcircuitry underlying passivity. In terms of neuroanatomy, the latero-posterior thalamic nucleus projects to association cortices whichparticipate in higher order somatosensory and visuo-spatial function(Yeterian and Pandya,1985) and the frontal cortex establishes reciprocalconnectionswith thebasal ganglia (Parent andHazrati,1995;Maurice etal., 1997) as well as the medial group of thalamic nuclei (Barbas andMesulam, 1985; Goldman-Rakic and Porrino, 1985). Earlier models ofpassivity have implicated disruptions in attentional, executive and

motor networks (Behrendt, 2004), which could be subserved by theseneural networks suchas the cortico-thalamo-striato-cortical circuitry. Inaddition,we found that inpatientswithpassivity, decreasedFA in frontalcortex was correlated with poorer performance on tasks related toattention and executive function suggesting that white matter disrup-tions may underlie these observed neurocognitive deficits.

The differences in white matter changes in the involved regionsmay indicate not only an underlying neural basis for the symptom butalso reciprocal interactions between the implicated regions or com-pensatorywhitematter changes in patientswith passivity. Thismay besupported by recent studies which reported pliable and develop-mental changes even within white matter (Madden et al., 2004;Ardekani et al., 2007; Ashtari et al., 2007), suggesting that whitematter connections are not dormant and static but may displayneuroplasticity over time. Alternatively, these changes could be relatedto treatment effects. However, there was no difference in the durationof treatment with antipsychotic medication in the patient groups withversus without passivity, and we did not find any change in the mainfindings after accounting for medication dose as a covariate, thusmaking this a less likely explanation. Third, the increase in FA withinfrontal and cingulate regions could represent loss of crossing fibres(Nilsson et al., 2007). A loss of crossing fibres could cause a paradoxicalincrease in FA within patients with passivity. Increases in FA indifferent regions have been reported in previous studies of specificpsychotic symptomatology. Hubl et al. (2004) found that schizophre-nia patients with auditory hallucinations were associated with anincrease in FAwithin the lateral parts of the temporoparietal section ofthe arcuate fasciculus and in parts of the anterior corpus callosum.

Decreased FA of the frontal cortex in our patients was correlatedwith a younger age of onset of illnesswhich is consistentwith previousdata suggesting reduced white matter integrity within the frontallobes and related regions in those patients with an earlier onset ofillness (Kumra et al., 2004; Douaud et al., 2007). Decreased anisotropyof the frontal cortex was further correlated with greater PANSS scores.This underscored the importance of clinical evaluation especially inunderstanding the progress of the psychotic illness from the time ofonset as well as a thorough assessment of the severity of symptomsover time, consistent with previous studies which found correlationsbetween decreased FA in frontal white matter with other psychiatricsymptoms such as impulsivity, aggression and negative symptoms(Hoptman et al., 2002;Wolkin et al., 2003). Further studies are neededto examine the longitudinal course of these white matter changes andtheir clinical and functional correlates.

The lack of anisotropy change in the parietal lobe in this study doesnot preclude involvement of the parietal lobe as observed in earlierstructural and functional studies (Spence et al., 1997; Maruff et al.,2005). Several explanations could be proffered. First, compensatorywhite matter changes may have occurred over time and in response tostructural changeswithinparietal regions. Second, any change inwhitematter architecture within the parietal region may occur independentof underlying structural or functional abnormalities. Third, the degreeof white matter change in passivity may not have be detectable withour current techniques. Fourth, it could be thatwhitematter changes in

126 K. Sim et al. / Psychiatry Research: Neuroimaging 172 (2009) 121–127

the parietal lobe may occur but not necessarily occur within a moreextensively disrupted and distributed neural network involvingseveral cortical and subcortical regions in patients with passivity.

There are a few limitations in this study. First, this is the first studyof white matter changes in a relatively small sample of subjects withpassivity and these findings await replication in a bigger sample ofsubjects. Second, noisemayartefactually 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 affectedregions were not studied. Fourth, we did not specifically study drug-naïve individuals, an approach that may allow a better evaluation ofwhite matter changes which are unaffected by medications orchronicity of illness. Thus, future studies may want to examine whitematter changes from the first break of psychotic illness, their patternsof change over time as well as clinical correlates.

In sum, passivity was associated with specific cortical and sub-cortical white matter changes involving fronto-thalamic-basal gangliacircuitry which were correlated with relevant neurocognitive deficits.Such white matter changes may provide evidence of the neural sub-strate underlying passivity and these findings have added to ourunderstanding of the neural basis of specific symptoms found inschizophrenia. A better understanding of symptom-related whitematter and connectivity disturbances and their changes over timemayallow 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 thisstudy. This study is also supported by the National Healthcare Group Research Grant(NHG-SIG/05004) and Singapore Bioimaging Consortium Research Grant (SBIC RP C-009/2006).

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