Psychiatry Research: Neuroimaging 231 (2015) 252–261

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Psychiatry Research: Neuroimaging journal homepage: www.elsevier.com/locate/psychresns

Brain white matter microstructure in deficit and non-deficit subtypes of schizophrenia Gianfranco Spalletta a,n, Pietro De Rossi a,b, Fabrizio Piras a, Mariangela Iorio a, Claudia Dacquino a, Francesca Scanu a,c, Paolo Girardi b, Carlo Caltagirone a,d, Brian Kirkpatrick e, Chiara Chiapponi a a

Laboratory of Clinical and Behavioural Neurology, IRCCS Santa Lucia Foundation, Rome, Italy NESMOS Department, Faculty of Medicine and Psychology, “Sapienza” University of Rome, Rome, Italy c Department of Neurology and Psychiatry, Faculty of Medicine, “Sapienza” University of Rome, Rome, Italy d Department of Neuroscience, “Tor Vergata” University, Rome, Italy e Department of Psychiatry and Behavioral Science, University of Nevada School of Medicine, Reno, NV, USA b

art ic l e i nf o

a b s t r a c t

Article history: Received 29 July 2014 Received in revised form 5 November 2014 Accepted 23 December 2014 Available online 3 January 2015

Dividing schizophrenia into its deficit (SZD) and nondeficit (SZND) subtypes may help to identify specific and more homogeneous pathophysiological characteristics. Our aim was to define a whole brain voxelwise map specifically characterizing white matter tracts of schizophrenia patients with and without the deficit syndrome. We compared microstructural diffusion-related parameters as measured by diffusion tensor imaging in 21 SZD patients, 21 SZND patients, and 21 healthy controls, age- and gender-matched. Results showed that fractional anisotropy was reduced in the right precentral area in SZND patients, and in the left corona radiata of the schizophrenia group as a whole. Axial diffusivity was reduced in the left postcentral area of SZD patients and in the left cerebellum of the whole schizophrenia group. Radial diffusivity was increased in the left forceps minor of SZD patients, in the left internal capsule of SZND patients, and in the right inferior fronto-occipital fasciculus in the whole schizophrenia group. Mean diffusivity was increased from healthy controls to SZD patients to SZND patients in the right occipital lobe. In conclusion, SZD patients are not simply at the extreme end of a severity continuum of white matter disruption. Rather, the SZD and SZND subtypes are associated with distinct and specific brain microstructural anomalies that are consistent with their peculiar psychopathological dimensions. & 2014 Elsevier Ireland Ltd. All rights reserved.

Keywords: Deficit syndrome Neuroimaging DTI TBSS

1. Introduction One approach to the study of schizophrenia is based on the separation of a homogeneous subgroup of patients characterized by negative symptoms that are primary, stable and enduring; the subgroup has been called deficit schizophrenia (SZD) group (Carpenter et al., 1988; Kirkpatrick et al., 2001; Galderisi and Maj, 2009). Subjects with SZD seem to be at the extreme end of a continuum of patients presenting negative symptoms. However, Carpenter and his associates introduced (Carpenter et al., 1988) and then validated (Buchanan et al., 1990; Kirkpatrick et al., 1993, 1994, 2001; Amador et al., 1999) the idea that SZD is a separate disorder and not simply a more severe form of schizophrenia compared with patients characterized as belonging to the non-deficit subgroup (SZND). Several studies have n Correspondence to: Laboratory of Clinical and Behavioural Neurology, Neuropsychiatric Section, IRCCS Santa Lucia Foundation, Via Ardeatina, 306, 00179 Rome, Italy. Tel.: þ 39 651501575l. E-mail address: [email protected] (G. Spalletta).

http://dx.doi.org/10.1016/j.pscychresns.2014.12.006 0925-4927/& 2014 Elsevier Ireland Ltd. All rights reserved.

explored socio-demographic characteristics (Galderisi et al., 2002; Messias et al., 2004), risk factors (Kirkpatrick et al., 2000a), clinical outcome (Carpenter, 1994; Tek et al., 2001), response to treatment (Kirkpatrick et al., 2000b) and neurobiological features (Kirkpatrick and Buchanan, 1990; Ross et al., 1997; Waltrip et al., 1997; Hong et al., 2005) associated with SZD. Some studies have demonstrated double dissociations (SZD patients impaired on measure A but not B; SZND patients impaired on measure B but not A) in metabolic measures (Garcia-Rizo et al., 2012), season of birth (Messias et al., 2004), and electrophysiological variables (Mucci et al., 2007). These studies are consistent with the concept that SDZ is a separate disorder within the syndrome of schizophrenia. Early positron emission tomography investigations described SZD patients as characterized by thalamic, frontal and parietal hypometabolism (Tamminga et al., 1992; Heckers et al., 1999). More recently, single photon emission tomography studies showed reduced cerebral blood flow in SZD with respect to SZND in frontal (Gonul et al., 2003; Kanahara et al., 2013) and bilateral frontodorsolateral (Vaiva et al., 2002) cortices. Moreover, using magnetic

G. Spalletta et al. / Psychiatry Research: Neuroimaging 231 (2015) 252–261

resonance spectroscopy, Delamillieure and colleagues interpreted a reduced concentration of N-acetyl aspartate in the medial prefrontal cortex of SZD as an indicator of neuronal impairment in that region (Delamillieure et al., 2000). Structural magnetic resonance imaging (MRI) studies found either structural anatomical anomalies in SZD in comparison with SZND or the contrary. In particular, Buchanan and colleagues (Buchanan et al., 1993) and Galderisi and co-workers (Galderisi et al., 2008), respectively, described increased prefrontal volumes and reduced lateral ventricles in SZD compared with SZND patients. In contrast, the groups of Arango (Arango et al., 2008) and Fischer (Fischer et al., 2012) found SZD patients to be more impaired than SZND patients and healthy controls (HC), observing respectively larger ventricles and smaller temporal gray matter volume in SZD. Damage to white matter (WM) microstructure was also reported to characterize the brains of SZD patients (Rowland et al., 2009; Kitis et al., 2012; Voineskos et al., 2013). The studies examining WM focused on tracts that were a priori selected (Rowland et al., 2009; Voineskos et al., 2013) or on regions of interest (ROIs) (Kitis et al., 2012); they all pinpointed WM disruptions in selected areas as a neurobiological feature of SZD. If on the one hand choosing an a priori region may decrease the possibility of false positive results, on the other hand it may increase the probability of false negative results. Moreover, ROI-based studies in SZD found heterogeneous results and, to date, the picture of neuroanatomical regions that are pivotal in SZD is far from being clear. In this framework, a whole brain voxel-based investigation probing WM changes in SZD and SZND patients with respect to HC subjects is needed. Such an approach is important to detect brain differences in brain WM as a whole, particularly in the attempt to define whether between-group brain alterations are distributed over multiple foci rather than in specific and predetermined brain regions (Voormolen et al., 2010; Perlini et al., 2012). The aim of the present study was to reveal a WM voxel-byvoxel microstructural map defining neuroanatomical areas peculiarly impaired in SZD, SZND or in schizophrenia as a whole. A HC group was selected to test the hypothesis of a severity continuum in the framework of WM microstructure. Given the known WM abnormalities identified in schizophrenia patients in our previous works (Spalletta et al., 2003; Spoletini et al., 2009; Chiapponi et al., 2013), we hypothesized (1) that we would find WM abnormalities in the whole group of patients with respect to HC subjects; (2) that we would find significant differences between SZD and SZND in terms of microstructural diffusion-related indices. However, due to the complexity and variety of results found in previous studies on SZD, we made a general prediction that we would find localized brain regions in which the deficit subgroup would show specific impairment, and other brain regions in which the non-deficit subgroup would be the most impaired.

2. Methods 2.1. Subjects For this study we initially included 42 SZD patients consecutively recruited at the IRCCS Santa Lucia Foundation of Rome. The diagnosis of schizophrenia was made according to the Diagnostic and Statistical Manual of Mental Disorders, 4th edition, text revision (DSM-IV-TR) (American Psychiatric Association, 2000). The clinician treating the patients, who was blind to the aims of the study, made the preliminary diagnosis. Then, a senior research psychiatrist confirmed all preliminary diagnoses using the Structured Clinical Interview for DSM-IV-TR-Patient Edition (SCID-I/P) (First et al., 2002a). A semi-structured interview, the Schedule for the Deficit Syndrome (SDS) (Kirkpatrick et al., 1989), was used to diagnose SZD with standard criteria. The SZD subtype was diagnosed conservatively with the aim of minimizing false positive diagnoses. Using a retrospective method, the same senior psychiatrist who confirmed all the DSM diagnoses reviewed information regarding the patients clinical status during the preceding 12 months. The senior psychiatrist was trained by B.K. at the Maryland Psychiatric Research Center. The information required to compile the SDS was obtained from review of records and interviews

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with psychiatrists and other mental health professionals who treated the patients and had long-standing contact with them. In addition, reports from the patients and first degree relatives were used to integrate the data reported by the clinical staff. According to SDS criteria, the final diagnosis of SZD required some combination of two or more primary negative symptoms always to be present for the 12 months preceding the admission. From the original group of patients confidently diagnosed as SZD, four refused to undergo MRI, 11 were excluded for strong movement artefacts in brain images, and six were excluded for the presence of moderate to severe brain vascular lesions (see exclusion criteria below). The remaining 21 SZD patients were age- and gender-matched with 21 SZND patients. This latter group was selected from an original sample of 96 SZND patients consecutively recruited at the IRCCS Santa Lucia Foundation in Rome. Overall severity of psychiatric symptoms was assessed using the Positive and Negative Syndrome Scale (PANSS) (Kay et al., 1987). Age at onset was defined as age at onset of positive or negative symptoms preceding the first hospitalization, which was investigated in an interview with patients and first degree relatives. All patients were receiving stable oral dosages of one or more atypical antipsychotics such as risperidone, quetiapine, and olanzapine. Antipsychotic dosages were converted to equivalents of olanzapine (Oquendo et al., 2003). Extrapyramidal side effects and abnormal involuntary movements were evaluated using the Simpson-Angus Scale (SAS) (Simpson and Angus, 1970) and the Abnormal Involuntary Movement Scale (AIMS) (Guy, 1976). We also recruited 21 HC subjects carefully matched, one by one, with SZD and SZND patients for age and gender. All HC subjects were screened for a current or lifetime history of DSM-IV-TR Axis I and II disorders using the SCID-I/NP (First et al., 2002b) and SCID-II (First et al., 1997); they were also assessed to confirm that no first degree relative had a history of psychosis. Inclusion criteria for all participants were as follows: (1) age between 18 and 65 years, (2) at least 8 years of education, and (3) suitability for MRI scanning. Exclusion criteria were as follows: (1) history of alcohol or drug abuse in the 2 years before the assessment, (2) lifetime drug dependence, (3) traumatic head injury with loss of consciousness, (4) past or present major medical illness or neurological disorders, (5) any additional psychiatric disorder or mental retardation, (6) dementia or cognitive deterioration according to DSM-IV-TR criteria and Mini-Mental State Examination (MMSE) (Folstein et al., 1975) score lower than 25, consistent with normative data in the Italian population (Measso et al., 1993), and (7) any potential brain abnormality and microvascular lesion as apparent on conventional fluid attenuated inversion recovery (FLAIR) scans; in particular, the presence, severity, and location of vascular lesions were computed according to the semi-automated method recently published by our group (Iorio et al., 2013). Sociodemographic and clinical characteristics of the HC, SZD and SZND samples are shown in Table 1. The study was approved and undertaken in accordance with the guidelines of the Santa Lucia Foundation Ethics Committee. All participants gave their written informed consent to participate after they had received a complete explanation of the study procedures.

2.2. Image acquisition and processing All 63 participants underwent the same imaging protocol, which included 3D T1-weighted, diffusion tensor imaging (DTI), T2-weighted and FLAIR sequences using a 3T Allegra MR imager (Siemens, Erlangen, Germany) with a standard quadrature head coil. Whole-brain T1-weighted images were obtained in the sagittal plane using a modified driven equilibrium Fourier transform sequence (TE/TR ¼2.4/7.92 ms, flip angle 151, voxel size 1  1  1 mm3). Diffusion-weighted volumes were acquired using spin-echo EPI (TE/TR ¼ 89/ 8500 ms, bandwidth ¼2126 Hz/vx; matrix size 128  128; 80 axial slices, voxel size 1.8  1.8  1.8 mm3, scan time 12 min) with 30 isotropically distributed orientations for the diffusion-sensitising gradients at a b-value of 1000 s/mm2 and no diffusion weighted images (b0). Scanning was repeated three times to increase the signal-tonoise ratio (Cherubini et al., 2009). Diffusion-weighted images were processed using FSL 4.1 software (www.fmrib. ox.ac.uk/fsl/). Diffusion-weighted images were corrected for the distortion induced by eddy currents and head motions, by applying a 3D full affine alignment of each image to the mean b0 image. After distortion corrections, DTI data were averaged and concatenated into 31 (1 b0þ 30 b1000) volumes. A diffusion tensor model was fitted at each voxel, generating fractional anisotropy (FA), axial diffusivity (AD) (first eigenvalue of the diffusion tensor), radial diffusivity (RD) (average of the second and third eigenvalues) and mean diffusivity (MD) maps. We used TBSS (Smith et al., 2006) version 1.2, part of FSL for the post-processing and analysis of FA, RD, AD and MD maps in WM. The key features of TBSS overcome the alignment problems (Simon et al., 2005; Vangberg et al., 2006) and smoothing issues (Jones et al., 2005) related to conventional voxel-based morphometry (VBM) whole brain approaches for multi-subject DTI. Briefly, TBSS first projects all subjects’ FA, RD, AD and MD data onto an alignment invariant tract representation, the skeleton, by means of the nonlinear registration tool FNIRT (Andersson et al., 2007a, 2007b), which uses a b-spline representation of the registration warp field (Rueckert et al., 1999). This process of projecting individual maps onto a mean skeleton helps to

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confine the effect of cross-spatial subject variability that remains after classical nonlinear registration. The resulting data are then fed into voxelwise cross-subject statistics (Nichols and Holmes, 2002).

2.3. Statistical analyses Comparisons between the three diagnostic groups on sociodemographic and clinical characteristics were performed using chi-square, t-, and F-tests. We conducted one-way analyses of variance (ANOVAs) to examine potential differences between the three diagnostic groups (independent variable) in brain DTI indices (dependent variables) evaluated voxel-by-voxel in the WM skeleton. Whenever a significant group effect was observed (p o 0.05, cluster threshold 50 voxels), pairwise post-hoc t-tests with family-wise error (FWE) correction were performed (p o 0.05). Voxel-wise comparisons over the WM skeleton were performed using a permutation-based approach, namely the “randomise” command in the FSL package (Nichols and Holmes, 2002). To avoid false positive results (type I errors), the Threshold-Free Cluster Enhancement option (Smith and Nichols, 2009) was used in “randomise” in order to obtain significant differences between groups at p o 0.05, after accounting for multiple comparisons (FWE rate) (Smith et al., 2007). Finally, for each cluster showing a significant difference between diagnostic groups with respect to DTI parameters in the pairwise post-hoc comparisons, we implemented an equations system to verify the relative position of the third diagnostic group not included in the pairwise comparison. In particular, we first binarized the resulting map of each post hoc comparison to have an image in which a voxel was valued 1 whenever the comparison was significant, and 0 otherwise. For each DTI parameter we multiplied the binary post-hoc results as follows: ðHC 4 SZDÞ U ðHC 4SZNDÞU ðSZD 4 SZNDÞ ¼ HC 4 SZD 4SZND ðHC o SZDÞU ðHC o SZNDÞ U ðSZD o SZNDÞ ¼ HC o SZD o SZND ðHC o SZDÞU ðHC o SZNDÞ U ðSZND o SZDÞ ¼ HC o SZND o SZD ðHC o SZDÞU ðHC o SZNDÞ U ðSZD o SZNDÞ ¼ HC o SZD o SZND The other four possible combinations of products gave null results and are therefore not reported. In brain regions where t-tests comparing two groups in the two directions (i.e. group14group2 and group1 o group2) gave no significant results, we assumed that, at the significance level accepted, the two groups did not differ. In the latter case, differences between the three groups were gathered as follows: 8 > < ðHC 4 SZNDÞ  ðSZD 4SZNDÞ ¼ 1 HC 4 SZD ¼ 0 ) ðHC ¼ SZDÞ 4 SZND > : HC o SZD ¼ 0 8 > < ðHC o SZNDÞ  ðSZD o SZNDÞ ¼ 1 HC 4 SZD ¼ 0 ) ðHC ¼ SZDÞ o SZND > : HC o SZD ¼ 0

8 > < ðHC 4 SZDÞ  ðSZND 4 SZDÞ ¼ 1 HC 4 SZND ¼ 0 ) ðHC ¼ SZNDÞ 4 SZD > : HC o SZND ¼ 0 8 > < ðHC o SZDÞ  ðSZND o SZDÞ ¼ 1 HC 4 SZND ¼ 0 ) ðHC ¼ SZNDÞo SZD > : HC o SZND ¼ 0

3. Results 3.1. Clinical characteristics SZD subjects had significantly higher PANSS negative score and significantly lower PANSS positive score than SZND ones. Also, SZD subjects had significantly higher anergy and lower activation and depression scores than SZND patients (see Table 1). 3.2. Brain microstructural characteristics eTable 1 in Appendix A shows results indicating the global effect of diagnosis for each DTI parameter. Table 2 shows results of the post hoc pairwise comparisons indicating the clusters that were significantly different among diagnostic groups. Finally, Table 3 and Figs. 1 and 2 summarize the relative position of the third diagnostic group not included in the post hoc pairwise comparison for each DTI parameter. In particular, results of FA are illustrated in Fig. 1. The SZND group turned out to have reduced FA with respect to SZD patients and HC subjects in clusters distributed over the bilateral cerebellum, the right occipital lobe including the inferior fronto-occipital fasciculus and the forceps major, the right superior corona and the precentral area. In the latter two regions, no differences in FA between HC and SZD groups emerged. Moreover, in the bilateral anterior and left superior and posterior corona radiata, the left forceps minor, the external capsule and cerebral peduncle, and the right body and genu of the corpus callosum, HC subjects had higher FA than both subgroups of patients. In these regions, no differences between SZD and SZND patients emerged, indicating that the schizophrenia group as a whole had decreased FA compared with HC subjects.

Table 1 Sociodemographic and clinical characteristics of 21 SZD, 21 SZND patients and 21 HC subjects. Characteristics

HC (n¼ 21)

SZD (n¼21)

SZND (n ¼21)

t, F, or chi2

df

Age (years 7S.D.) Males n (%) Educational Level (years 7 S.D.) MMSE (mean 7S.D.) MMSE of patients (mean7 S.D.) Age at onset Duration of illness Olanzapine equivalents (mg/day) PANSS positive PANSS negative PANSS general psychopathology PANSS anergya PANSS thought disordera PANSS activationa PANSS paranoid/belligerencea PANSS depressiona SAS AIMS tremors AIMS dyskinesia

33.77 11.5 19 (90.5) 12.17 2.7 29.6 7 0.7 – – – – – – – – – – – – – – –

33.87 12.2 19 (90.5) 11.2 7 2.3 27.0 7 1.7 27.0 7 1.7 22.4 7 7.4 11.4 7 9.6 24.47 23.5 21.6 76.7 27.8 7 8.7 47.4 712.3 14.17 3.9 13.4 7 5.1 7.5 7 3.0 8.2 7 3.4 8.7 7 3.5 6.5 7 5.6 0.2 7 0.6 0.5 7 1.4

34.17 12.1 19 (90.5) 12.9 72.9 27.7 7 2.2 27.7 7 2.2 22.6 76.7 11.5 712.3 17.4 7 15.1 26.3 75.0 18.5 7 6.6 50.7 711.9 9.9 74.5 14.4 7 3.8 9.6 73.8 9.9 72.9 13.5 7 4.0 5.3 75.1 0.4 71.2 0.2 70.9

0.007 0.00 4.02 12.8  1.11  0.09  0.03 1.19  2.62 3.90  0.86 3.27  0.72  2.00  1.68  4.14 0.72  0.48 1.08

2,60 2 2,60 2 40 40 40 40 40 40 40 40 40 40 40 40 40 40 40

p 0.99 4.999 0.02 o0.001 0.27 0.93 0.98 0.24 0.01 o0.001 0.39 0.002 0.47 0.05 0.10 o0.001 0.48 0.63 0.29

SZD ¼deficit schizophrenia patients; SZND ¼ nondeficit schizophrenia patients; HC ¼healthy controls; MMSE ¼Mini-Mental State Examination; PANSS ¼positive and negative syndrome scale; SAS¼Simpson Angus Scale; AIMS¼ Abnormal Involontary Movement Scale; S.D. ¼standard deviation; df ¼degrees of freedom. The significant p-values are highlighted in bold. a

Refers to a factor, not to the single item.

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Table 2 Post-hoc pairwise comparisons between diagnostic groups. Cluster size (number of voxels) MNI coordinates of statistical Location of the statistical peak and discrete anatomical areas within significant clusters Laterality p peak (x, y, z) Fractional anisotropy HC4SZD 7036

671 167 HC4SZND 8078

 14, 47,  12

Forceps minor Anterior, superior and posterior CR External capsule Cerebral peduncle CC Anterior CR

L L L L R R

0.004

Cerebellum Cerebellum Anterior and superior CR External capsule Cerebral peduncle Precentral area Superior CR CC Occipital lobe Forceps major Inferior fronto-occipital fasciculus

R L L L L R R R R R R

0.002

0.002

27, 7, 25

Superior CR

R

0.04

 29,  14, 47  12,  72,  31

Postcentral area Cerebellum

L L

0.02 0.008

 37,  26, 43

Postcentral area

L

0.002

35,  61,  3

Inferior longitudinal fasciculus Inferior fronto-occipital fasciculus Forceps major Occipital lobe Superior longitudinal fasciculus Posterior CR Forceps minor Body of CC Body of CC

R R R L L L L R L

0.02

Frontoparietal lobe Superior CR Posterior CR Internal capsule External capsule Occipital lobe Superior longitudinal fasciculus Body and genu of CC Inferior fronto-occipital fasciculus Forceps major Inferior longitudinal fasciculus Cerebellum Cerebellum

R R L L L L L R R R R R R

 48, 7,  15 25, 5, 20

Internal capsule External capsule Temporal lobe Superior CR

L L L R

0.03 0.05

37,  79,  7

Occipital lobe

R

0.002

24,  85,  9

Occipital lobe

R

0.002

16, 22, 23 19, 42,  3 21,–38,  37

4235

34,  2, 41

1559 112

13, 12, 27 21,  90, 12

SZD4 SZND 99 Axial diffusivity HC4SZD 72 63 SZND 4SZD 80 Radial diffusivity HCoSZD 1444

1211

1026 134 88 HCoSZND 5549

 29,  51, 30

 19, 45, 6 16, 9, 31  15, 11, 30 26, 5, 44

5146

 26,  25, 20

2104 1922

12, 23, 18 18,  80,  4

75 57 SZDo SZND 2062 230 55 Mean diffusivity HCoSZD 1114 HCoSZND 1674

23,  51,  35 18,  38,  32  9, 1,  8

0.01 0.03

0.004 0.04

0.02

0.03 0.05 0.05 0.002 0.002

0.002 0.002

0.03 0.04 0.02

SZD ¼deficit schizophrenia patients; SZND ¼ nondeficit schizophrenia patients; HC ¼ healthy controls; MNI ¼Montreal neurological institute; L ¼ left, R ¼ right. CR¼ corona radiata; CC ¼ corpus callosum. All p values are corrected for multiple comparisons. The lower threshold for cluster size is 50 voxels.

Results of diffusivity parameters are shown in Fig. 2. When diffusivity along WM tracts was examined, SZD patients had decreased AD with respect to both the HC and SZND groups in the left postcentral area, while in the left cerebellum the global schizophrenia population had decreased AD with respect to the HC group.

With respect to diffusivity perpendicular to WM tracts, SZND patients had increased RD with respect to the SZD and HC groups in the right superior corona radiata, left internal and external capsule and left temporal lobe and in clusters belonging to the right frontoparietal lobe and cerebellum. In the latter two regions, the three diagnostic

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Table 3 Three groups comparison of each DTI parameter in clusters resulting significant from post hoc analyses. Anatomical white matter region Fractional anisotropy HC4SZD 4SZND Cerebellum Occipital lobe Inferior fronto-occipital fasciculus Forceps major (HC ¼SZD) 4SZND Superior CR Precentral area HC4(SZD ¼SZND) Anterior CR Superior and posterior CR Forceps minor External capsule Cerebral peduncle Body and genu of CC Axial diffusivity (HC ¼SZND) 4SZD Postcentral area HC4(SZD ¼SZND) Cerebellum Radial diffusivity (HC ¼SZD) o SZND Superior CR Internal capsule External capsule Temporal lobe HCo SZD o SZND Frontoparietal lobe Cerebellum HCo SZND o SZD Forceps minor HCo (SZD¼ SZND) Superior longitudinal fasciculus Posterior CR Occipital lobe Body of CC Genu of CC Inferior longitudinal fasciculus Inferior fronto-occipital fasciculus Forceps major Mean diffusivity HCo SZD o SZND Occipital lobe

Laterality

L and R R R R R R L and R L L L L R

L L

R L L L R R L L L L L and R R R R R

R

SZD¼ deficit schizophrenia patients; SZND ¼ nondeficit schizophrenia patients; HC ¼ healthy controls; L ¼ left, R¼ right; CR ¼corona radiata; CC ¼ corpus callosum.

groups were differentiated by a continuous RD increase from HC to SZD to SZND. Interestingly, the left forceps minor proved to be the only area in which SZD patients had increased RD with respect to the SZND and HC groups. Furthermore, in clusters belonging to the left superior longitudinal fasciculus, posterior corona radiata, occipital lobe, bilateral corpus callosum, right inferior longitudinal and inferior frontooccipital fasciculi and right forceps major, the whole schizophrenia sample has increased RD relative to the HC group. As for MD, a continuous increase from HC to SZD to SZND groups differentiated the three populations in the right occipital lobe.

4. Discussion In the current study we investigated, for the first time, the differences between SZD, SZND and HC subjects in all four DTIderived parameters. Although the interpretation of the interplaying diffusion indices is not univocal (Zhang et al., 2009) and it is mainly based on animal models (Song et al., 2003), our results could stimulate

the intriguing process of definition of an overarching neurobiological model of the deficit syndrome. Indeed, our results present a map in which neuroanatomical WM areas peculiarly impaired in SZD, SZND or in schizophrenia as a whole can be distinguished. We found WM areas in which AD is reduced (1) specifically in SZD (left postcentral area) and (2) globally in schizophrenia (left cerebellum). Such findings could be due to the degradation of WM microtubules (Meier-Ruge et al., 1992) and/or to a decline in the number and length of myelinated fibres (Marner et al., 2003; Kubicki et al., 2005). Moreover, we found WM areas in which RD is increased (1) specifically in SZND (e.g. left internal capsule), (2) specifically in SZD (left forceps minor) and (3) globally in schizophrenia (e.g., left superior longitudinal fasciculus, right inferior fronto-occipital fasciculus and forceps major, bilateral corpus callosum). Such increased radial (but unchanged axial) diffusivity might be explained in terms of fiber dysmyelination (Song et al., 2002). Also, the MD increase in the right occipital lobe from HC to SZD to SZND may refer to a general loss of WM compactness (Alexander et al., 2007). As for FA, we found WM areas with (1) a selective reduction for SZND group (e.g., right precentral area and occipital lobe) and (2) a general reduction for the whole schizophrenia group (e.g., left corona radiata and right corpus callosum). However, in contrast to the diffusivity indices, FA proved not to be a very specific index, reflecting the tridimensional architecture of WM fibers or their integrity, so the interpretation of its biological correlates is far from being clear and may indicate a composite series of events (Jones et al., 2013). Thus, the picture that emerges is that schizophrenia, when considered as a unitary diagnostic entity, is characterized by a microstructural impairment of important associative tracts such as the superior longitudinal fasciculus (SLF), the inferior longitudinal and fronto-occipital fasciculi, and the main inter-hemispheric connections such as the corpus callosum. These WM tracts, the SLF in particular, represent the main components of a complex cortico-cortical attention network (Mesulam, 1981; Thiebaut de Schotten et al., 2005; Bartolomeo et al., 2007), and they play a central role in supporting attention-control integration across the varying sensory modalities (Chechlacz et al., 2013). Furthermore, patients with schizophrenia generally show reduced FA values with respect to HC subjects across the whole SLF that is apparent from the early phases of illness (Kubicki et al., 2005; Jones et al., 2006), and this finding is significantly correlated with verbal working memory performance (Karlsgodt et al., 2008; Spalletta et al., 2010). This suggests that an attentioncontrol integration deficit across the varying sensory modalities is pivotal in the pathophysiology of schizophrenia, but it is not a specific mechanism responsible for clinical manifestations peculiar to one subtype. However, Gonul et al. (2003) found reduced fronto-parietal regional cerebral blood flow in SZD compared with SZND. This finding is potentially in contrast with our results, although it is not clear how local cerebral perfusion anomalies could be linked to microstructural connectivity parameters (Várkuti et al., 2011). Also, the MD increase in the occipital lobe from HC to SZD first and SZND at the extreme end, could be related to the well-known visual processing abnormalities representing a common pathophysiological feature of the schizophrenia population as a whole (Onitsuka et al., 2007; Coleman et al., 2009; Kantrowitz et al., 2009). Particularly, the anatomical localization of the impairment can be linked to the volumetric decrease in the visual association area (Brodmann area 19) previously described in schizophrenia patients as a neuroanatomical substrate of early visual processing alterations peculiar to the disorder (Onitsuka et al., 2007). Areas of abnormal microstructural parameters unique to SZND (namely, as for RD, the left internal and external capsules and the right superior corona radiata) are generally interpreted as a marker of disconnection of the cortical-strial-pallidal-thalamic-cortical (CSPTC) feedback loops, eventually resulting in higher cognitive function impairment and related to behavioural and intellectual symptoms observed in schizophrenia (Cohen et al., 2007). In fact, a disrupted WM

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Fig. 1. Voxel-wise FA comparison between HC, deficit schizophrenia (SZD) and nondeficit schizophrenia (SZND) groups. Panel (a): clusters where a continuous decrease of FA characterized the progression from HC to SZD to SZND (bilateral cerebellum Z¼  32 and Z¼  22; right occipital lobe Z¼  22 and Z¼ 15). Panel (b): clusters where FA is selectively decreased in SZND (right corona radiata Z¼ 26; right precentral area Z¼ 34). Panel (c): clusters where FA decreased in whole schizophrenia group (left cerebral peduncle, corona radiata and forceps minor Z¼  10; left superior corona radiata and external capsule Z¼ 10; left posterior corona radiata, right corpus callosum and anterior corona radiata Z¼ 30). Background brain is the MNI template. WM skeleton is represented in green. Results are superimposed in red and are graphically thickened to improve visualization. R ¼right, L ¼ left. MNI coordinates are marked.

microstructure of the internal capsule has been reported in schizophrenia (Park et al., 2004; Kubicki et al., 2005; Szeszko et al., 2005; Buchsbaum et al., 2006; Federspiel et al., 2006; Mitelman et al., 2007; Levitt et al., 2012) even in medication-naïve subjects (Zou et al., 2008). The neurobiological rationale for this interpretation is that the thalamo-cortical projection through the anterior limb of the internal capsule is the common final pathway for all the CSPTC feedback loops (Nolte, 1999). These loops are involved in the fine regulation of functions such as motor and impulse control, planning of goal-directed behaviour, and learning of problem-solving strategies (Kopell and Greenberg, 2008). In this background, results might reflect a greater importance of disruption of higher cognitive and behavioural integration within the SZND subtype. The anomalies found within the corona radiata are in line with this scenario representing a neurobiological substrate of impaired cortico-cortical and corticospinal/corticopontine communication, a kind of damage which is highly specific and present even in the early-onset forms of schizophrenia (Davenport et al., 2010). On the other hand, WM areas such as the left forceps minor, as evident from RD findings, and others within the primary somatosensory cortex, as emerged from AD results, proved to be unique to SZD. These areas are part of neural networks subserving informationprocessing speed (Duering et al., 2012), somatosensory perception and somatosensory temporal discrimination (Conte et al., 2012), and they have been previously reported to be disrupted in schizophrenia

(Skelly et al., 2008). Particularly, the forceps minor fibres are functionally interconnected with regions such as the thalamus, the cingulum and the parahippocampal gyrus (Duering et al., 2012). Therefore, especially with respect to their connections to the cingulate cortex, a microstructural disruption of these fibres may also be related to the particular impairment in motivational and reward-related psychopathological dimensions clinically observed in SZD. Moreover, WM disruption within the primary somatosensory cortex in SZD could be linked to the prominent autistic features peculiar to this subtype of schizophrenia, as primary somatosensory cortices show significant activations in association with “affective” words and during verbal descriptions of feelings (Saxbe et al., 2013). Some results of previous comparison studies on WM microstructure in SZD and SZND patients are in contrast with ours, describing SZD subjects as more impaired than both SZND and HC groups in several “a priori” selected neuroanatomical regions. In particular, Rowland and colleagues (Rowland et al., 2009) described reduced FA in the right SLF in SZD patients compared with HC subjects. However, as the authors acknowledged, their results must be viewed as preliminary due to the small sample size (9 SZD, 10 SZND and 11 HC). Other studies comparing SZND and SZD patients found reduced FA in the uncinate fasciculus (Kitis et al., 2012) and increased MD in the right inferior longitudinal fasciculus, the left uncinate fasciculus, and the right arcuate fasciculus (Voineskos et al., 2013), indicating

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Fig. 2. Voxel-wise comparison of diffusivity parameters between HC, deficit schizophrenia (SZD) and nondeficit schizophrenia (SZND) groups. RD: Panel (a): clusters where RD is selectively increased in SZND (left temporal lobe Z¼  10; left internal and external capsule Z¼ 0 and Z¼ 10; left superior corona radiata Z¼10). Panel (b): clusters where a continuous increase of RD characterized the progression from HC to SZD to SZND (right cerebellum Z¼  32; right frontoparietal lobe Z ¼43). Panel (c): cluster where a continuous increase of RD characterized the progression from HC to SZND to SZD (left forceps minor Z¼ 2). Panel (d): clusters where RD increased in whole schizophrenia group (corpus callosum, left superior longitudinal fasciculus, posterior corona radiata and occipital lobe Z¼ 18; right longitudinal and fronto-occipital fasciculi and forceps major Z¼25 and Z¼ 35; bilateral corpus callosum Z¼ 35). AD: Panel (a): clusters where AD is selectively decreased in SZD (left postcentral area Z ¼51). Panel (b): clusters where AD decreased in whole schizophrenia group (right cerebellum Z¼  30). MD: Cluster with an MD increase from HC to SZD to SZND (right occipital lobe Z ¼3). R¼ right, L ¼ left. MNI coordinates are marked. Background brain is the MNI template. WM skeleton is represented in green. Results are superimposed in red and they are graphically thickened to improve visualization.

increased WM microstructural damage. In our study the SLF and other WM skeleton structures within the temporal lobe, including the inferior longitudinal fasciculus, were equally impaired in both SZD and SZND subgroups compared with HC subjects. Generally, the different techniques used for data processing (i.e., tractography vs. voxel-wise approaches) may account for these discrepancies in results. In fact, if tractography has the advantage of overcoming alignment issues by operating in the individual space of subjects and may be more sensitive to detect subtle WM microstructural changes becoming evident when averaging diffusion properties over the whole tract, TBSS has been designed to bring together the strengths of both tractography and classic VBM-style approaches being fully automated,

solving the alignment and smoothing problems and investigating the whole WM tissue without the need to pre-specify the tracts of interest (Smith et al., 2006). There were some limitations of our study. First, the SZND sample suffered from more severe positive psychotic symptoms than the SZD. In fact, the dichotomization of schizophrenia patients into deficit and nondeficit subtypes does not usually result in groups that display significantly different positive symptom severity scores according to a meta-analytic study on the psychiatric symptomatology of deficit schizophrenia (Cohen et al., 2010). In this regard, it should be underlined that, by definition, the greater negative symptoms characterizing SZD patients cannot be attributed to positive symptoms or depression

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in order to make a reliable diagnosis of this subtype. This could have contributed to a slightly biased trend towards the selection of a deficit sample with less severe positive symptoms to achieve as much specificity as possible in defining a SZD diagnosis. Such an imbalance in positive symptom severity between the two schizophrenia groups could have played a role in our results as, in previous reports, FA values in the main associative tracts showed an inverse relationship with positive symptom scores (Skelly et al., 2008; Szeszko et al., 2008; Rotarska-Jagiela et al., 2009). However, in our sample there was no significant correlation between the severity of positive symptoms (PANSS positive score) and DTI parameters in either group of schizophrenia patients (e.g. for FA: Deficit: R2 ¼0.02, R¼0.14, p¼0.5; Nondeficit: R2 ¼0.11, R¼0.34, p¼0.12; additional data available on request). Second, the more heterogeneous group of SZND subjects had sufficient size for the deficit/non-deficit distinction, but it may be underpowered to assess whether this subtype is valid or further subgroups within the SZND category exist. Finally, there is little evidence of antipsychotic medication or duration of illness effects on diffusivity parameters in WM (Voineskos et al., 2010). These two parameters did not differ between SZD and SZND patients, so any effect on our main results seems unlikely. Despite these limitations, our data suggest that SZD patients suffering from primary, stable and enduring negative symptoms are not invariably at the extreme end of a severity continuum of WM disruption, with SZND at an intermediate level. The two subtypes of schizophrenia are rather associated with distinct and specific WM microstructural anomalies that are consistent with the main psychopathological dimensions peculiar to each subtype. Further studies are needed to clarify the eventual role of associative WM tracts in schizophrenia and its subtypes. In particular, other potential neuroanatomical hallmarks of SZD and SZND have to be investigated through different neuroimaging methods, including the analysis of volume and shape of subcortical structures and more sophisticated cortical gray matter parameters (namely cortical thickness, surface area and local gyrification). In the case of SZD, for example, since the clinical core of this subtype is apathetic, anhedonic and avolitional in nature, particular attention should be directed to brain structures and circuitries that are critical for motivational processes and reward-related mechanisms. In conclusion, this is the first study to provide a global, voxel-based microstructural neuroanatomical map of schizophrenia and its deficit and nondeficit subtypes. Our results support the hypothesis that the SZD subtype is not at the extreme end of a severity continuum (Quarantelli et al., 2002; Galderisi et al., 2008; Galderisi and Maj, 2009), and they provide evidence that SZD should be considered as a specific type of mental disorder with brain structural characteristics which are merely different from, and not more severe than, those that pertain to the SZND subtype.

Acknowledgments This work was supported by RC08-09-10-11-12-13/A grants from the Italian Ministry of Health.

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