Dissociation of functional and anatomical brain abnormalities in unaffected siblings of schizophrenia patients

Clinical Neurophysiology 126 (2015) 927–932

Contents lists available at ScienceDirect

Clinical Neurophysiology journal homepage: www.elsevier.com/locate/clinph

Dissociation of functional and anatomical brain abnormalities in unaffected siblings of schizophrenia patients Wenbin Guo a,⇑, Yan Song a, Feng Liu b, Zhikun Zhang a, Jian Zhang a, Miaoyu Yu a, Jianrong Liu a, Changqing Xiao a, Guiying Liu a, Jingping Zhao c a

Mental Health Center, The First Affiliated Hospital, Guangxi Medical University, Nanning, Guangxi 530021, China Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, Sichuan, China c Mental Health Institute of the Second Xiangya Hospital, Key Laboratory of Psychiatry and Mental Health of Hunan Province, Central South University, Changsha, Hunan 410011, China b

a r t i c l e

i n f o

Article history: Accepted 26 August 2014 Available online 6 September 2014 Keywords: Unaffected siblings Schizophrenia Voxel-based morphometry Amplitude of low-frequency fluctuation Fractional amplitude of low-frequency fluctuation

h i g h l i g h t s  A dissociation phenomenon suggested that brain functional and anatomical abnormalities might be

present independently in unaffected siblings of schizophrenia patients.  The recruitment of unaffected siblings offers insight into the pathophysiology of schizophrenia inde-

pendently of the clinical and treatment issues that complicate studies of the patients themselves.  Several possible factors were given to the increase of gray matter volume in the left putamen in the

sibling group.

a b s t r a c t Objective: Schizophrenia patients and their unaffected siblings share similar brain functional and structural abnormalities. However, no study is engaged to investigate whether and how functional abnormalities are related to structural abnormalities in unaffected siblings. This study was undertaken to examine the association between functional and anatomical abnormalities in unaffected siblings. Methods: Forty-six unaffected siblings of schizophrenia patients and 46 age-, sex-, and educationmatched healthy controls underwent structural and resting-state functional magnetic resonance imaging scanning. Voxel-based morphometry (VBM), amplitude of low-frequency fluctuation (ALFF) and fractional ALFF (fALFF) were utilized to analyze imaging data. Results: The VBM analysis showed gray matter volume decreases in the fronto-temporal regions (the left middle temporal gyrus and right inferior frontal gyrus, orbital part) and increases in basal ganglia system (the left putamen). Functional abnormalities measured by ALFF and fALFF mainly involved in the frontolimbic-sensorimotor circuit (decreased ALFF in bilateral middle frontal gyrus and the right middle cingulate gyrus, and decreased fALFF in the right inferior frontal gyrus, orbital part; and increased ALFF in the left fusiform gyrus and left lingual gyrus, and increased fALFF in bilateral calcarine cortex). No significant correlation was found between functional and anatomical abnormalities in the sibling group. Conclusions: A dissociation pattern of brain regions with functional and anatomical abnormalities is observed in unaffected siblings. Significance: Our findings suggest that brain functional and anatomical abnormalities might be present independently in unaffected siblings of schizophrenia patients. Ó 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

1. Introduction ⇑ Corresponding author. Tel.: +86 771 3277200. E-mail address: [email protected] (W. Guo).

Considering schizophrenia is a highly heritable psychiatric disorder, the siblings of schizophrenia patients have an 8–10-fold

http://dx.doi.org/10.1016/j.clinph.2014.08.016 1388-2457/Ó 2014 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

928

W. Guo et al. / Clinical Neurophysiology 126 (2015) 927–932

higher risk for developing schizophrenia than general population (Gottesman and Gould, 2003). Moreover, unaffected siblings share similar genetic backgrounds and early-life environments with the patients, and exhibit brain abnormalities that are also observed in the patients (MacDonald et al., 2009; Jang et al., 2011; Pettersson-Yeo et al., 2011; van Buuren et al., 2011, 2012; Guo et al., 2014a,c). Thus, unaffected siblings provide an opportunity for us to examine brain abnormalities related to the pathophysiology of schizophrenia, reducing the progressive effect caused by possible confounders such as medication use and long duration of untreated psychosis. Previous anatomical studies have shown that unaffected siblings may share gray matter volume (GMV) abnormalities with their sick siblings in the amygdala-hippocampal complex, thalamus, and temporal cortices (Seidman et al., 2002; Honea et al., 2008), perhaps most notably in the temporal cortex (Hu et al., 2013). Meanwhile, the major functional abnormalities revealed by functional magnetic resonance imaging (fMRI) in unaffected siblings have been localized in the prefrontal, temporal, and parietal regions (Jang et al., 2011; Guo et al., 2014a), particularly in the default-mode network (DMN) (van Buuren et al., 2012; Guo et al., 2014c). Previously, few studies have examined anatomical and functional abnormalities together in the same sample of schizophrenia patients (Lui et al., 2009; Rubinov and Bassett, 2011; Ren et al., 2013), and the findings exhibited that functional and anatomical abnormalities were in different brain regions, suggesting that functional and anatomical abnormalities are significantly dissociated in schizophrenia. However, no study is devoted to examining anatomical and functional abnormalities together in unaffected siblings of schizophrenia patients, and it remains unclear whether and how the functional abnormalities are related to the anatomical abnormalities in unaffected siblings. Voxel-based morphometry (VBM), a useful automatic technique, is capable of assessing anatomical abnormalities in the whole brain, and avoids operational bias to particular brain regions (Antonova et al., 2005; Honea et al., 2005). Amplitude of lowfrequency fluctuation (ALFF) and fractional ALFF (fALFF) can be applied to identify regional neural activity, which is considered to be physiologically meaningful and associated with regional neural activity (Zang et al., 2007; Zou et al., 2008). Up to now, these methods have been successfully used to examine anatomical and functional abnormalities in clinical studies (Zang et al., 2007; Bora et al., 2011; Liu et al., 2012, 2013; Guo et al., 2013; Yao et al., 2014), including schizophrenia patients and their unaffected siblings (Hoptman et al., 2010; Hu et al., 2013). In the present study, we used VBM, ALFF and fALFF to investigate functional and anatomical abnormalities in unaffected siblings of schizophrenia patients. Our aim was to examine the association between functional and anatomical abnormalities, as well as the relationship of these abnormalities in unaffected siblings. According to the findings from schizophrenia patients (Lui et al., 2009; Rubinov and Bassett, 2011; Ren et al., 2013), we hypothesized that a similar dissociation pattern of brain regions with functional and anatomical abnormalities would be present in unaffected siblings. 2. Materials and methods 2.1. Subjects Forty-six unaffected siblings of schizophrenia patients were recruited from Mental Health Center, the First Affiliated Hospital, Guangxi Medical University, China. The diagnosis of schizophrenia for their sick siblings was confirmed by the Structured Clinical Interview of the DSM-IV (SCID), patient edition (First et al., 1997). To minimize the symptom heterogeneity and potentially

underlying pathology, only unaffected siblings with a patient diagnosed as paranoid schizophrenia were enrolled in the study. Patients and unaffected siblings were brothers or sisters who shared an early home environment with an age interval of less than 4 years. Forty-six healthy controls were recruited from the community. All subjects were right-handed and unrelated to each other. All subjects were screened by using SCID, non-patient edition (First et al., 1997), and they endorsed no items of the SCID, non-patient edition. The exclusion criteria for all subjects included neurological or psychiatric disorders, substance-related disorders or mental retardation, or contraindications for MRI scanning. Healthy controls with a first-degree relative suffering from a psychiatric disorder were also excluded. The two subject groups were matched in age, sex and years of education. The study was approved by the ethics committee of the First Affiliated Hospital, Guangxi Medical University. A written informed consent was obtained from each subject. 2.2. Scan acquisition Imaging data were acquired using a Siemens 3T scanner. Subjects were required to lie motionless, keep their eyes closed, and remain awake. Head motion and scanner noise were reduced by using foam padding and earplugs. High-resolution whole brain volumetric T1-weighted images were obtained with a three-dimensional magnetization prepared rapid acquisition gradient echo (3D-MPRAGE) sequence, and the following parameters were used: repetition time = 8.5 ms, echo time = 2.98 ms, inversion time = 900 ms, flip angle = 9°, acquisition matrix = 256  256, field of view = 240 mm  240 mm, slice thickness = 1 mm, no gap, and 176 slices. Resting-state functional images were acquired using a gradient-echo echo-planar imaging (EPI) sequence with the following parameters: repetition time/echo time = 2000/30 ms, 30 slices, 64  64 matrix, 90° flip angle, 24 cm field of view, 4 mm slice thickness, 0.4 mm gap, and 250 volumes (500s). The degree of cooperation for each participant was confirmed by asking some questions after scanning. 2.3. Data processing Each structural image was checked for scan artifacts and gross anatomical alterations. All structural images were processed using the VBM toolbox (VBM8, http://dbm.neuro.uni-jena.de/vbm) with the Statistical Parametric Mapping software package (SPM8, http://www.fil.ion.ucl.ac.uk/spm). The VBM preprocessing is briefly described as follows. First, all T1-weighted anatomical scans were spatially normalized to the customized template (1.5  1.5  1.5 mm3), and then segmented into gray matter, white matter, and cerebrospinal fluid images. After that, an 8 mm fullwidth at half-maximum (FWHM) Gaussian kernel was used to smooth the gray matter images to reduce the individual difference of brain anatomy and to increase the signal to noise ratio. Finally, the resulting images were transformed to z-maps by subtracting the global mean and dividing the global standard deviation. Functional data were preprocessed with Data Processing Assistant for Resting-State fMRI (DPARSF) (Yan and Zang, 2010) in Matlab (Mathworks). After head-motion and slice-timing correction, no participant had more than 2 mm of maximal translation in x, y, or z and 2° of maximal rotation during scanning. Then, images were spatially normalized to the standard Montreal Neurological Institute (MNI) EPI template in SPM8 and resampled to 3  3  3 mm3. The generated images were smoothed with an 8 mm FWHM Gaussian kernel. Finally, the generated images were temporally bandpass filtered (0.01–0.08 Hz) and linearly detrended. ALFF was calculated with the REST software (Song et al., 2011). The time series for each voxel were transformed with a Fast Fourier

W. Guo et al. / Clinical Neurophysiology 126 (2015) 927–932

Transform (FFT) to obtain the power spectrum. As described in a previous study (Zang et al., 2007), the square root was calculated at each frequency of the power spectrum, and the averaged square root was acquired as ALFF across 0.01–0.08 Hz at each voxel. With the REST software, fALFF was calculated according to a previous study (Zou et al., 2008). After ALFF being calculated, the sum of amplitude across 0.01–0.08 Hz was divided by that across the entire frequency range, which was taken as fALFF. For standard purpose, both ALFF and fALFF were z-converted by dividing the global mean ALFF and fALFF values within a brain mask, respectively. 2.4. Statistical analysis Categorical variables were analyzed with Chi-square tests, and continuous variables were analyzed with two-sample t-tests. The anatomical and functional data were analyzed with two-sample t-tests via voxel-wise cross-subject statistics. Age and sex were applied as covariates in the anatomical and functional data comparisons to reduce the potential effect of these variables, although there were no significant group differences in age and sex ratio. The significance level was set at p < 0.005 corrected by multiple comparisons using Gaussian Random Field (GRF) theory (min z > 2.807, cluster significance: p < 0.005). To examine the association between functional and anatomical abnormalities in the siblings, the regions with abnormal GMV, ALFF and fALFF were overlaid on the same template as described in previous studies (Ren et al., 2013; Guo et al., 2014b). Furthermore, we extracted the mean GMV, ALFF and fALFF values from brain regions with abnormal GMV, ALFF and fALFF, respectively. After assessing the normality, Pearson correlation was performed to examine the relationship between the mean GMV values and the mean ALFF values or the mean fALFF values in the sibling group (p < 0.05). 3. Results 3.1. Characteristics of the subjects The unaffected siblings and the controls did not differ significantly in age, sex ratio and education level (Table 1). 3.2. Anatomical differences between groups Compared to the controls, the siblings exhibited significantly decreased GMV in the left middle temporal gyrus and right inferior frontal gyrus (orbital part), and increased GMV in the left putamen (Fig. 1 and Table 2). No other difference of GMV was observed between groups. 3.3. Functional differences between groups The siblings showed decreased ALFF in bilateral middle frontal gyrus and the right middle cingulate gyrus compared to the controls. In contrast, significantly increased ALFF was found in the left

Table 1 Characteristics of the subjects.

a b

Demographic data

Siblings (n = 46)

Controls (n = 46)

p value

Sex (male/female) Age (years) Years of education (years)

29/17 22.96 ± 4.01 11.50 ± 2.21

23/23 23.30 ± 2.30 11.34 ± 1.78

0.21a 0.62b 0.85b

The p value for gender distribution was obtained by chi-square test. The p values were obtained by two samples t-tests.

929

fusiform gyrus and left lingual gyrus in the sibling group (Fig. 2 and Table 2). As shown in Fig. 3 and Table 2, the siblings exhibited decreased fALFF in the right inferior frontal gyrus (orbital part) and increased fALFF in bilateral calcarine cortex. 3.4. Association between anatomical and functional findings in the siblings No overlap of brain regions with functional and anatomical abnormalities was found in the sibling group. Furthermore, the mean GMV values in the brain regions with structural abnormalities were not significantly correlated with the mean ALFF and fALFF values in the brain regions with functional abnormalities in the sibling group. 4. Discussion Using the VBM, ALFF and fALFF methods, the present study revealed both anatomical and functional abnormalities in unaffected siblings of schizophrenia patients. GMV abnormalities were observed in the fronto-temporal regions (the left middle temporal gyrus and right inferior frontal gyrus, orbital part) and basal ganglia system (the left putamen), while functional abnormalities mainly involved in the fronto-limbic-sensorimotor circuit (decreased ALFF in bilateral middle frontal gyrus and the right middle cingulate gyrus, and decreased fALFF in the right inferior frontal gyrus, orbital part; and increased ALFF in the left fusiform gyrus and left lingual gyrus, and increased fALFF in bilateral calcarine cortex). There was no significant correlation between brain regions with functional and anatomical abnormalities in the sibling group. There are several interesting aspects in the present study. First, we observed a dissociation phenomenon of brain regions with anatomical and functional abnormalities, like GMV abnormalities in the fronto-temporal regions and basal ganglia system, and ALFF and fALFF abnormalities in the fronto-limbic-sensorimotor circuit in the present study. The dissociation phenomenon may be due to the different analysis methods, which evaluate different aspects of gray matter in the sibling group. Anatomical abnormalities revealed by VBM may reflect more stable and long-standing abnormalities, while functional abnormalities revealed by ALFF and fALFF may represent physiological abnormalities related to early stage of disease (Ren et al., 2013; Guo et al., 2014b). This possible explanation is supported by evidence that functional abnormalities are normalized after clinical remission (Lui et al., 2010), while anatomical abnormalities remain stable and possibly decrease progressively over illness duration in schizophrenia patients (Chan et al., 2011; Asami et al., 2012). Moreover, similar dissociation of anatomical and functional abnormalities has been reported in schizophrenia (Lui et al., 2009; Rubinov and Bassett, 2011; Ren et al., 2013). Consistent with these studies, the present study revealed that functional and anatomical abnormalities are located in different brain regions, indicating that functional and anatomical abnormalities might alter independently in unaffected siblings. In the absence of phenotypic information, we are not sure that these abnormalities represent deficits. They may represent compensatory activity or simply be differences with no functional significance. Also, it has not been established that these differences are, in fact, related to schizophrenia. Future study with a group of schizophrenia patients would warrant or refute the present findings. The second interesting aspect of the present study is that GMV increase in the left putamen is found in the sibling group. Several factors merit consideration in interpreting this increase. First, we

930

W. Guo et al. / Clinical Neurophysiology 126 (2015) 927–932

Fig. 1. Statistical maps showing gray matter volume differences between groups. Blue and red denote decreased and increased gray matter volume.

Table 2 Differences in GMV, ALFF and fALFF between groups. Measure, and cluster location

GMV Siblings < Controls Left middle temporal gyrus Right inferior frontal gyrus (orbital part) Siblings > Controls Left putamen ALFF Siblings < Controls Right middle frontal gyrus Right middle frontal gyrus Left middle frontal gyrus Right middle cingulate gyrus Siblings > Controls Left fusiform gyrus Left lingual gyrus fALFF Siblings < Controls Right inferior frontal gyrus (orbital part) Siblings > Controls Bilateral calcarine cortex

Number of voxels (mm3)

Peak (MNI) x

y

57 39

6 21

30

12

42 33 39 6

33 21 33 24

33 12

51 96

15 0

t value

z

24 19.5 3

178.88

27 51 21 33

2808.00 1323.00 1566.00 1296.00

39 78

165.38 141.75

9

15 12

1296.00 2322.00

21

1134.00 1080.00

3.9192 4.4794 3.6271

4.0497 3.7978 3.9277 3.7966 4.0045 3.7363

3.9328 3.1831

MNI = Montreal Neurological Institute; GMV = gray matter volume; ALFF = amplitude of low-frequency fluctuation; fALFF = fractional ALFF.

Fig. 2. Statistical maps showing ALFF differences between groups. Blue and red denote decreased and increased ALFF. ALFF = amplitude of low-frequency fluctuation.

used VBM8, a method providing an optimized method of segmentation and normalization to detect tiny GMV abnormalities (Lui et al., 2009). Second, a relatively large sample size was used in the present study with a statistical power to identify GMV increases not reported frequently in previous studies (Ren et al.,

2013). Finally, some possible neuronal pathology, such as preapoptotic osmotic abnormalities or hypertrophy, could introduce increases of regional GMV like increases of GMV in the left putamen in the present study. However, one review indicates small volume increases in the left putamen in patients with chronic

W. Guo et al. / Clinical Neurophysiology 126 (2015) 927–932

931

Fig. 3. Statistical maps showing fALFF differences between groups. Blue and red denote decreased and increased fALFF. fALFF = fractional amplitude of low-frequency fluctuation.

schizophrenia (Wright et al., 2000). Therefore, it remains unclear of the underlying mechanism of volume increases in the left putamen in unaffected siblings. Future large sample size studies including schizophrenia patients, unaffected siblings, and healthy controls are needed to support our results. The third interesting aspect of the present study is the recruitment of unaffected siblings of schizophrenia patients. Previous studies have shown that schizophrenia patients and their unaffected siblings share similar brain functional and anatomical abnormalities (MacDonald et al., 2009; Jang et al., 2011; Pettersson-Yeo et al., 2011; van Buuren et al., 2011, 2012). This inclusion criterion offers insight into the pathophysiology of schizophrenia independently of the clinical and treatment issues that complicate studies of the patients themselves (Jang et al., 2011; Guo et al., 2014a,c). In addition, we found GMV reductions in the fronto-temporal regions (left middle temporal gyrus and right inferior frontal gyrus, orbital part) in the sibling group. There is evidence for gray matter reductions of the frontal (particularly medial and inferior) and temporal gyri in schizophrenia (Shepherd et al., 2012). The temporal gyrus has proven a common target for volumetric study in both schizophrenia patients and their siblings (Hu et al., 2013). In line with these studies, the present study provided a clue for GMV reduction in the fronto-temporal regions in unaffected siblings of schizophrenia patients. We also found significantly decreased neural activity in the fronto-limbic regions of the fronto-limbic-sensorimotor circuit in the siblings. Several studies suggested that the fronto-limbic regions are functionally hypo-activated in schizophrenia in both task-related and resting state (Adams and David, 2007; Loberg et al., 2012). Therefore, the present findings added an additional literature of decreased activity in the fronto-limbic regions of the fronto-limbic-sensorimotor circuit and extended the findings from schizophrenia patients to their unaffected siblings. Additionally, increased neural activity in the sensorimotor regions of the fronto-limbic-sensorimotor circuit was observed in the siblings. Increased neural activity of the sensorimotor regions has been reported in schizophrenia patients, which may introduce the occurrence of hallucination in patients (Hoptman et al., 2010). Although the ALFF and fALFF results are inconsistent in details due to different analysis methods, both results exhibited a pattern

of decreased activity in the fronto-limbic regions and increased activity in the sensorimotor regions within the fronto-limbic-sensorimotor circuit in unaffected siblings. Together with the abovementioned studies, our results suggested that schizophrenia patients and their unaffected siblings seem to share a dissociation pattern of brain activity in the fronto-limbic-sensorimotor circuit despite of the lack of supporting information from a patient group in the present study. The cerebellum is viewed as a heterogeneous region with a fundamental role in the more extensive deficits in schizophrenia (Schmahmann, 2010; Liu et al., 2011). Previous anatomical studies have observed volumetric reductions in the cerebellum in schizophrenia patients (Rusch et al., 2007). Decreased blood flow in the fronto-thalamic-cerebellar circuit was reported in schizophrenia (Andreasen et al., 1998). Hence, no cerebellar findings of anatomical and functional deficits in the present sibling group is somewhat surprising. Several possibilities might account for this phenomenon. First, the siblings are individuals with no (sub)clinical symptoms, and previous studies have indicated that unaffected siblings share a similar but milder pattern of brain functional and anatomical abnormalities with the patients (MacDonald et al., 2009; Jang et al., 2011; Pettersson-Yeo et al., 2011; van Buuren et al., 2011, 2012; Guo et al., 2014a,c). Therefore, it is possible that cerebellar deficits in the siblings are too tiny to be detected by the present analysis methods. Second, the cerebellar deficits might not be an endophenotype for schizophrenia, which could be present in the patients but might not be necessarily present in the siblings. Finally, the cerebellum has reciprocal links with the cerebrum (Chen et al., 2013). It is possible the cerebellar contributions to the pathophysiology of the schizophrenia siblings might be reflected by the abnormalities of other nodes of a certain circuit (for example, the fronto-thalamic-cerebellar circuit). Some limitations should be addressed. First, psychological assessments were not performed in the present study, and we were not able to know the association between anatomical and functional abnormalities and psychological parameters. Second, a longitudinal study is needed to clarify the differences of anatomical and functional abnormalities between the siblings who will later develop schizophrenia and those who will never. Third, the use of MNI template generated from a Caucasian population may affect the normalization accuracy for the recruitment Chinese

932

W. Guo et al. / Clinical Neurophysiology 126 (2015) 927–932

subjects. Finally, there is possible that the regions showing structural differences and the regions showing functional differences are component parts of a distributed structural or functional network and that differences in ALFF and fALFF in one node are related to GMV differences in another node. However, the present findings of no correlation between functional and anatomical abnormalities in the sibling group seem to refute this possibility. To our knowledge, this is the first to examine the association between functional and anatomical abnormalities in unaffected siblings of schizophrenia patients. A dissociation pattern of brain regions with anatomical and functional abnormalities is observed in unaffected siblings. The present findings suggest that brain functional and anatomical abnormalities might be present independently in unaffected siblings of schizophrenia patients. Acknowledgments This study was supported by grants from the Natural Science Foundation of China (Grant No. 81260210), the Natural Science Foundation of Guangxi Province for Distinguished Young Scientists (Grant No. 2014GXNSFGA118010), and the Natural Science Foundation of Guangxi Province (Grant No. 2013GXNSFAA019107). Conflict of interest: None of the authors have potential conflicts of interest to be disclosed. References Adams R, David AS. Patterns of anterior cingulate activation in schizophrenia: a selective review. Neuropsychiatr Dis Treat 2007;3:87–101. Andreasen NC, Paradiso S, O’Leary DS. ‘‘Cognitive dysmetria’’ as an integrative theory of schizophrenia: a dysfunction in cortical–subcortical–cerebellar circuitry? Schizophr Bull 1998;24:203–18. Antonova E, Kumari V, Morris R, Halari R, Anilkumar A, Mehrotra R, et al. The relationship of structural alterations to cognitive deficits in schizophrenia: a voxel-based morphometry study. Biol Psychiatry 2005;58:457–67. Asami T, Bouix S, Whitford TJ, Shenton ME, Salisbury DF, McCarley RW. Longitudinal loss of gray matter volume in patients with first-episode schizophrenia: DARTEL automated analysis and ROI validation. Neuroimage 2012;59:986–96. Bora E, Fornito A, Radua J, Walterfang M, Seal M, Wood SJ, et al. Neuroanatomical abnormalities in schizophrenia: a multimodal voxelwise meta-analysis and meta-regression analysis. Schizophr Res 2011;127:46–57. Chan RC, Di X, McAlonan GM, Gong QY. Brain anatomical abnormalities in high-risk individuals, first-episode, and chronic schizophrenia: an activation likelihood estimation meta-analysis of illness progression. Schizophr Bull 2011;37:177–88. Chen YL, Tu PC, Lee YC, Chen YS, Li CT, Su TP. Resting-state fMRI mapping of cerebellar functional dysconnections involving multiple large-scale networks in patients with schizophrenia. Schizophr Res 2013;149:26–34. First MB, Spitzer RL, Gibbon M, Williams JBW. Structured Clinical Interview for DSMIV axis I Disorders (SCID). Washington, DC: American Psychiatric Press; 1997. Gottesman II, Gould TD. The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry 2003;160:636–45. Guo W, Jiang J, Xiao C, Zhang Z, Zhang J, Yu L, et al. Decreased resting-state interhemispheric functional connectivity in unaffected siblings of schizophrenia patients. Schizophr Res 2014a;152:170–5. Guo W, Liu F, Yu M, Zhang J, Zhang Z, Liu J, et al. Functional and anatomical brain deficits in drug-naive major depressive disorder. Prog Neuropsychopharmacol Biol Psychiatry 2014b;54:1–6. Guo W, Su Q, Yao D, Jiang J, Zhang J, Zhang Z, et al. Decreased regional activity of default-mode network in unaffected siblings of schizophrenia patients at rest. Eur Neuropsychopharmacol 2014c;24:545–52. Guo WB, Liu F, Xun GL, Hu MR, Guo XF, Xiao CQ, et al. Reversal alterations of amplitude of low-frequency fluctuations in early and late onset, first-episode, drug-naive depression. Prog Neuropsychopharmacol Biol Psychiatry 2013;40:153–9. Honea R, Crow TJ, Passingham D, Mackay CE. Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. Am J Psychiatry 2005;162:2233–45. Honea RA, Meyer-Lindenberg A, Hobbs KB, Pezawas L, Mattay VS, Egan MF, et al. Is gray matter volume an intermediate phenotype for schizophrenia? A voxelbased morphometry study of patients with schizophrenia and their healthy siblings. Biol Psychiatry 2008;63:465–74.

Hoptman MJ, Zuo XN, Butler PD, Javitt DC, D’Angelo D, Mauro CJ, et al. Amplitude of low-frequency oscillations in schizophrenia: a resting state fMRI study. Schizophr Res 2010;117:13–20. Hu M, Li J, Eyler L, Guo X, Wei Q, Tang J, et al. Decreased left middle temporal gyrus volume in antipsychotic drug-naive, first-episode schizophrenia patients and their healthy unaffected siblings. Schizophr Res 2013;144:37–42. Jang JH, Jung WH, Choi JS, Choi CH, Kang DH, Shin NY, et al. Reduced prefrontal functional connectivity in the default mode network is related to greater psychopathology in subjects with high genetic loading for schizophrenia. Schizophr Res 2011;127:58–65. Liu F, Guo W, Liu L, Long Z, Ma C, Xue Z, et al. Abnormal amplitude low-frequency oscillations in medication-naive, first-episode patients with major depressive disorder: a resting-state fMRI study. J Affect Disord 2013;146:401–6. Liu F, Guo W, Yu D, Gao Q, Gao K, Xue Z, et al. Classification of different therapeutic responses of major depressive disorder with multivariate pattern analysis method based on structural MR scans. PLoS One 2012;7:e40968. Liu H, Fan G, Xu K, Wang F. Changes in cerebellar functional connectivity and anatomical connectivity in schizophrenia: a combined resting-state functional MRI and diffusion tensor imaging study. J Magn Reson Imaging 2011;34:1430–8. Loberg EM, Nygard M, Berle JO, Johnsen E, Kroken RA, Jorgensen HA, et al. An fMRI study of neuronal activation in schizophrenia patients with and without previous cannabis use. Front Psychiatry 2012;3:94. Lui S, Deng W, Huang X, Jiang L, Ma X, Chen H, et al. Association of cerebral deficits with clinical symptoms in antipsychotic-naive first-episode schizophrenia: an optimized voxel-based morphometry and resting state functional connectivity study. Am J Psychiatry 2009;166:196–205. Lui S, Li T, Deng W, Jiang L, Wu Q, Tang H, et al. Short-term effects of antipsychotic treatment on cerebral function in drug-naive first-episode schizophrenia revealed by ‘‘resting state’’ functional magnetic resonance imaging. Arch Gen Psychiatry 2010;67:783–92. MacDonald 3rd AW, Thermenos HW, Barch DM, Seidman LJ. Imaging genetic liability to schizophrenia: systematic review of FMRI studies of patients’ nonpsychotic relatives. Schizophr Bull 2009;35:1142–62. Pettersson-Yeo W, Allen P, Benetti S, McGuire P, Mechelli A. Disconnectivity in schizophrenia: where are we now? Neurosci Biobehav Rev 2011;35:1110–24. Ren W, Lui S, Deng W, Li F, Li M, Huang X, et al. Anatomical and functional brain abnormalities in drug-naive first-episode schizophrenia. Am J Psychiatry 2013;170:1308–16. Rubinov M, Bassett DS. Emerging evidence of connectomic abnormalities in schizophrenia. J Neurosci 2011;31:6263–5. Rusch N, Spoletini I, Wilke M, Bria P, Di Paola M, Di Iulio F, et al. Prefrontalthalamic-cerebellar gray matter networks and executive functioning in schizophrenia. Schizophr Res 2007;93:79–89. Schmahmann JD. The role of the cerebellum in cognition and emotion: personal reflections since 1982 on the dysmetria of thought hypothesis, and its historical evolution from theory to therapy. Neuropsychol Rev 2010;20:236–60. Seidman LJ, Faraone SV, Goldstein JM, Kremen WS, Horton NJ, Makris N, et al. Left hippocampal volume as a vulnerability indicator for schizophrenia: a magnetic resonance imaging morphometric study of nonpsychotic first-degree relatives. Arch Gen Psychiatry 2002;59:839–49. Shepherd AM, Laurens KR, Matheson SL, Carr VJ, Green MJ. Systematic meta-review and quality assessment of the structural brain alterations in schizophrenia. Neurosci Biobehav Rev 2012;36:1342–56. Song XW, Dong ZY, Long XY, Li SF, Zuo XN, Zhu CZ, et al. REST: a toolkit for restingstate functional magnetic resonance imaging data processing. PLoS One 2011;6:e25031. van Buuren M, Vink M, Kahn RS. Default-mode network dysfunction and selfreferential processing in healthy siblings of schizophrenia patients. Schizophr Res 2012;142:237–43. van Buuren M, Vink M, Rapcencu AE, Kahn RS. Exaggerated brain activation during emotion processing in unaffected siblings of patients with schizophrenia. Biol Psychiatry 2011;70:81–7. Wright IC, Rabe-Hesketh S, Woodruff PW, David AS, Murray RM, Bullmore ET. Metaanalysis of regional brain volumes in schizophrenia. Am J Psychiatry 2000;157:16–25. Yan C, Zang Y. DPARSF: a MATLAB toolbox for ‘‘pipeline’’ data analysis of restingstate fMRI. Front Syst Neurosci 2010;4:13. Yao Z, Yan R, Wei M, Tang H, Qin J, Lu Q. Gender differences in brain activity and the relationship between brain activity and differences in prevalence rates between male and female major depressive disorder patients: a resting-state fMRI study. Clin Neurophysiol 2014;125:2232–9. Zang YF, He Y, Zhu CZ, Cao QJ, Sui MQ, Liang M, et al. Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev 2007;29:83–91. Zou QH, Zhu CZ, Yang Y, Zuo XN, Long XY, Cao QJ, et al. An improved approach to detection of amplitude of low-frequency fluctuation (ALFF) for resting-state fMRI: fractional ALFF. J Neurosci Methods 2008;172:137–41.