Family-based case-control study of homotopic connectivity in first-episode, drug-naive schizophrenia at rest

Homotopic functional connectivity disruptions in schizophrenia and their associated gene expression

It has been revealed that abnormal voxel-mirrored homotopic connectivity (VMHC) is present in patients with schizophrenia, yet there are inconsistencies in the relevant findings. Moreover, little is known about their association with brain gene expression profiles. In this study, transcription-neuroimaging association analyses using gene expression data from Allen Human Brain Atlas and case-control VMHC differences from both the discovery (meta-analysis, including 9 studies with a total of 386 patients and 357 controls) and replication (separate group-level comparisons within two datasets, including a total of 258 patients and 287 controls) phases were performed to identify genes associated with VMHC alterations. Enrichment analyses were conducted to characterize the biological functions and specific expression of identified genes, and Neurosynth decoding analysis was performed to examine the correlation between cognitive-related processes and VMHC alterations in schizophrenia. In the discovery and replication phases, patients with schizophrenia exhibited consistent VMHC changes compared to controls, which were correlated with a series of cognitive-related processes; meta-regression analysis revealed that illness duration was negatively correlated with VMHC abnormalities in the cerebellum and postcentral/precentral gyrus. The abnormal VMHC patterns were stably correlated with 1287 genes enriched for fundamental biological processes like regulation of cell communication, nervous system development, and cell communication. In addition, these genes were overexpressed in astrocytes and immune cells, enriched in extensive cortical regions and wide developmental time windows. The present findings may contribute to a more comprehensive understanding of the molecular mechanisms underlying VMHC alterations in patients with schizophrenia.

Linked patterns of interhemispheric functional connectivity and microstructural characteristics of the corpus callosum in antipsychotic-naive first-episode schizophrenia

OBJECTIVE

Many magnetic resonance imaging (MRI) studies have showed significant structural abnormalities of the corpus callosum (CC) and dysregulated interhemispheric functional connectivity (FC) in schizophrenia. Although the hemispheres are mainly linked through CC, few studies directly examined the relationship between aberrant interhemispheric FC and the white matter deficits of the CC in schizophrenia.

METHODS

One hundred and sixty-nine antipsychotic-naive first-episode schizophrenia patients (AN-FES) and 214 healthy controls (HCs) were recruited. Diffusional and functional MRI data were obtained for each participant, and fractional anisotropy (FA) values of the five CC subregions and interhemispheric FC for each participant were acquired. Between-group differences in these metrics were compared using multivariate analysis of covariance (MANCOVA). Moreover, sparse canonical correlation analysis (sCCA) was conducted to explore correlations of fibers integrity of the CC subregions with dysregulated interhemispheric FC in patients.

RESULTS

Compared with HCs, the patients with schizophrenia showed significantly reduced FA values of the CC subregions and dysregulated connectivity between two cerebral hemispheres. The canonical correlation coefficients identified five significant sCCA modes between FA and FC (r > 0.75, p < 0.001), suggesting strong relationships between FA values of the CC subregions and interhemispheric FC in patients.

CONCLUSION

Our findings support a key role of CC in maintaining ongoing functional communication between two cerebral hemispheres, and suggest that microstructural changes of white matter fibers crossing different CC subregions may affect special interhemispheric FC in schizophrenia.

A method for capturing dynamic spectral coupling in resting fMRI reveals domain-specific patterns in schizophrenia

Introduction

Resting-state functional magnetic resonance imaging (rs-fMRI) is a powerful tool for assessing functional brain connectivity. Recent studies have focused on shorter-term connectivity and dynamics in the resting state. However, most of the prior work evaluates changes in time-series correlations. In this study, we propose a framework that focuses on time-resolved spectral coupling (assessed via the correlation between power spectra of the windowed time courses) among different brain circuits determined via independent component analysis (ICA).

Methods

Motivated by earlier work suggesting significant spectral differences in people with schizophrenia, we developed an approach to evaluate time-resolved spectral coupling (trSC). To do this, we first calculated the correlation between the power spectra of windowed time-courses pairs of brain components. Then, we subgrouped each correlation map into four subgroups based on the connectivity strength utilizing quartiles and clustering techniques. Lastly, we examined clinical group differences by regression analysis for each averaged count and average cluster size matrices in each quartile. We evaluated the method by applying it to resting-state data collected from 151 (114 males, 37 females) people with schizophrenia (SZ) and 163 (117 males, 46 females) healthy controls (HC).

Results

Our proposed approach enables us to observe the change of connectivity strength within each quartile for different subgroups. People with schizophrenia showed highly modularized and significant differences in multiple network domains, whereas males and females showed less modular differences. Both cell count and average cluster size analysis for subgroups indicate a higher connectivity rate in the fourth quartile for the visual network in the control group. This indicates increased trSC in visual networks in the controls. In other words, this shows that the visual networks in people with schizophrenia have less mutually consistent spectra. It is also the case that the visual networks are less spectrally correlated on short timescales with networks of all other functional domains.

Conclusions

The results of this study reveal significant differences in the degree to which spectral power profiles are coupled over time. Importantly, there are significant but distinct differences both between males and females and between people with schizophrenia and controls. We observed a more significant coupling rate in the visual network for the healthy controls and males in the upper quartile. Fluctuations over time are complex, and focusing on only time-resolved coupling among time-courses is likely to miss important information. Also, people with schizophrenia are known to have impairments in visual processing but the underlying reasons for the impairment are still unknown. Therefore, the trSC approach can be a useful tool to explore the reasons for the impairments.

Reduced homotopic interhemispheric connectivity in psychiatric disorders: evidence for both transdiagnostic and disorder specific features

There is considerable interest in the significance of structural and functional connections between the two brain hemispheres in terms of both normal function and in relation to psychiatric disorders. In recent years, many studies have used voxel mirrored homotopic connectivity analysis of resting state data to investigate the importance of connectivity between homotopic regions in the brain hemispheres in a range of neuropsychiatric disorders. The current review summarizes findings from these voxel mirrored homotopic connectivity studies in individuals with autism spectrum disorder, addiction, attention deficit hyperactivity disorder, anxiety and depression disorders, and schizophrenia, as well as disorders such as Alzheimer's disease, mild cognitive impairment, epilepsy, and insomnia. Overall, other than attention deficit hyperactivity disorder, studies across psychiatric disorders report decreased homotopic resting state functional connectivity in the default mode, attention, salience, sensorimotor, social cognition, visual recognition, primary visual processing, and reward networks, which are often associated with symptom severity and/or illness onset/duration. Decreased homotopic resting state functional connectivity may therefore represent a transdiagnostic marker for general psychopathology. In terms of disorder specificity, the extensive decreases in homotopic resting state functional connectivity in autism differ markedly from attention deficit hyperactivity disorder, despite both occurring during early childhood and showing extensive co-morbidity. A pattern of more posterior than anterior regions showing reductions in schizophrenia is also distinctive. Going forward, more studies are needed to elucidate the functions of these homotopic functional connections in both health and disorder and focusing on associations with general psychopathology, and not only on disorder specific symptoms.

Increased Homotopic Connectivity in the Prefrontal Cortex Modulated by Olanzapine Predicts Therapeutic Efficacy in Patients with Schizophrenia

Background

Previous studies have revealed the abnormalities in homotopic connectivity in schizophrenia. However, the relationship of these deficits to antipsychotic treatment in schizophrenia remains unclear. This study explored the effects of antipsychotic therapy on brain homotopic connectivity and whether the homotopic connectivity of these regions might predict individual treatment response in schizophrenic patients.

Methods

A total of 21 schizophrenic patients and 20 healthy controls were scanned by the resting-state functional magnetic resonance imaging. The patients received olanzapine treatment and were scanned at two time points. Voxel-mirrored homotopic connectivity (VMHC) and pattern classification techniques were applied to analyze the imaging data.

Results

Schizophrenic patients presented significantly decreased VMHC in the temporal and inferior frontal gyri, medial prefrontal cortex (MPFC), and motor and low-level sensory processing regions (including the fusiform gyrus and cerebellum lobule VI) relative to healthy controls. The VMHC in the superior/middle MPFC was significantly increased in the patients after eight weeks of treatment. Support vector regression (SVR) analyses revealed that VMHC in the superior/middle MPFC at baseline can predict the symptomatic improvement of the positive and negative syndrome scale after eight weeks of treatment.

Conclusions

This study demonstrated that olanzapine treatment may normalize decreased homotopic connectivity in the superior/middle MPFC in schizophrenic patients. The VMHC in the superior/middle MPFC may predict individual response for antipsychotic therapy. The findings of this study conduce to the comprehension of the therapy effects of antipsychotic medications on homotopic connectivity in schizophrenia.

Schizophrenia: A Survey of Artificial Intelligence Techniques Applied to Detection and Classification

Artificial Intelligence in healthcare employs machine learning algorithms to emulate human cognition in the analysis of complicated or large sets of data. Specifically, artificial intelligence taps on the ability of computer algorithms and software with allowable thresholds to make deterministic approximate conclusions. In comparison to traditional technologies in healthcare, artificial intelligence enhances the process of data analysis without the need for human input, producing nearly equally reliable, well defined output. Schizophrenia is a chronic mental health condition that affects millions worldwide, with impairment in thinking and behaviour that may be significantly disabling to daily living. Multiple artificial intelligence and machine learning algorithms have been utilized to analyze the different components of schizophrenia, such as in prediction of disease, and assessment of current prevention methods. These are carried out in hope of assisting with diagnosis and provision of viable options for individuals affected. In this paper, we review the progress of the use of artificial intelligence in schizophrenia.

Morphological alterations of the corpus callosum in antipsychotic-naive first-episode schizophrenia before and 1-year after treatment

OBJECTIVE

The corpus callosum (CC) is known to be altered in patients with schizophrenia. However, its morphologic characteristics are less well studied in treatment-naive first-episode schizophrenia patients, as is the effect of antipsychotic treatment on this structure.

METHODS

T-1 weighted MRI scans were obtained from 160 antipsychotic-naïve first-episode schizophrenia patients (AN-FES) and 155 healthy controls (HCs) before treatment initiation. Among the patients, forty-four were available for follow-up studies after one year of antipsychotic treatment, and were divided into good-outcome (n = 31) and poor-outcome subgroups (n = 13) based on whether there was a 50% reduction in Positive and Negative Symptom Scale (PANSS) total scores from baseline. A computer algorithm was applied to automatically identify the mid-sagittal plane (MSP) and obtain morphological measurement parameters of the CC.

RESULTS

Compared with HCs, AN-FES patients showed a significant reduction of thickness in the posterior midbody of the CC. This deficit was correlated with severity of negative symptoms. After one year of antipsychotic treatment, there was no significant change in CC morphological measurements in schizophrenia patients, nor was there a significant difference of CC morphological measurements between good-outcome and poor-outcome subgroups at baseline or at 1-year follow-up.

CONCLUSION

Thickness of the posterior midbody of the CC is reduced in the early course of schizophrenia before treatment. This alteration was not affected by antipsychotic treatment and was unrelated to treatment outcome at 1-year.

EEG Source Network for the Diagnosis of Schizophrenia and the Identification of Subtypes Based on Symptom Severity-A Machine Learning Approach

A precise diagnosis and a comprehensive assessment of symptom severity are important clinical issues in patients with schizophrenia (SZ). We investigated whether electroencephalography (EEG) features obtained from EEG source network analyses could be effectively applied to classify the SZ subtypes based on symptom severity. Sixty-four electrode EEG signals were recorded from 119 patients with SZ (53 males and 66 females) and 119 normal controls (NC, 51 males and 68 females) during resting-state with closed eyes. Brain network features (global and local clustering coefficient and global path length) were calculated from EEG source activities. According to positive, negative, and cognitive/disorganization symptoms, the SZ patients were divided into two groups (high and low) by positive and negative syndrome scale (PANSS). To select features for classification, we used the sequential forward selection (SFS) method. The classification accuracy was evaluated using 10 by 10-fold cross-validation with the linear discriminant analysis (LDA) classifier. The best classification accuracy was 80.66% for estimating SZ patients from the NC group. The best classification accuracy between low and high groups in positive, negative, and cognitive/disorganization symptoms were 88.10%, 75.25%, and 77.78%, respectively. The selected features well-represented the pathological brain regions of SZ. Our study suggested that resting-state EEG network features could successfully classify between SZ patients and the NC, and between low and high SZ groups in positive, negative, and cognitive/disorganization symptoms.

Treatment effects of olanzapine on homotopic connectivity in drug-free schizophrenia at rest

OBJECTIVES

Deficits in homotopic connectivity have been implicated in schizophrenia. However, alterations in homotopic connectivity associated with antipsychotic treatments in schizophrenia remain unclear due to lack of longitudinal studies.

METHODS

Seventeen drug-free patients with recurrent schizophrenia and 24 healthy controls underwent resting-state functional magnetic resonance imaging scans. The patients were scanned at three time points (baseline, at 6 weeks of treatment, and at 6 months of treatment). Voxel-mirrored homotopic connectivity (VMHC) was applied to analyse the imaging data to examine alterations in VMHC associated with antipsychotic treatment.

RESULTS

The results showed that patients with schizophrenia exhibited decreased VMHC in the default-mode network (such as the precuneus and inferior parietal lobule) and the motor and sensory processing regions (such as the lingual gyrus, fusiform gyrus and cerebellum lobule VI), which could be normalised or denormalised by olanzapine treatment. In addition, negative correlations were found between decreased VMHC and symptom severity in the patients at baseline.

CONCLUSIONS

The present study shows that olanzapine treatment can normalise or denormalise decreased homotopic connectivity in schizophrenia. The findings also provide a new perspective to understand treatment effects of antipsychotic drugs on homotopic connectivity in schizophrenia that contribute to the disconnection hypothesis of this disease.

Dysconnectivity of Multiple Brain Networks in Schizophrenia: A Meta-Analysis of Resting-State Functional Connectivity

Background: Seed-based studies on resting-state functional connectivity (rsFC) in schizophrenia have shown disrupted connectivity involving a number of brain networks; however, the results have been controversial. Methods: We conducted a meta-analysis based on independent component analysis (ICA) brain templates to evaluate dysconnectivity within resting-state brain networks in patients with schizophrenia. Seventy-six rsFC studies from 70 publications with 2,588 schizophrenia patients and 2,567 healthy controls (HCs) were included in the present meta-analysis. The locations and activation effects of significant intergroup comparisons were extracted and classified based on the ICA templates. Then, multilevel kernel density analysis was used to integrate the results and control bias. Results: Compared with HCs, significant hypoconnectivities were observed between the seed regions and the areas in the auditory network (left insula), core network (right superior temporal cortex), default mode network (right medial prefrontal cortex, and left precuneus and anterior cingulate cortices), self-referential network (right superior temporal cortex), and somatomotor network (right precentral gyrus) in schizophrenia patients. No hyperconnectivity between the seed regions and any other areas within the networks was detected in patients, compared with the connectivity in HCs. Conclusions: Decreased rsFC within the self-referential network and default mode network might play fundamental roles in the malfunction of information processing, while the core network might act as a dysfunctional hub of regulation. Our meta-analysis is consistent with diffuse hypoconnectivities as a dysregulated brain network model of schizophrenia.

The homotopic connectivity of the functional brain: a meta-analytic approach

Homotopic connectivity (HC) is the connectivity between mirror areas of the brain hemispheres. It can exhibit a marked and functionally relevant spatial variability, and can be perturbed by several pathological conditions. The voxel-mirrored homotopic connectivity (VMHC) is a technique devised to enquire this pattern of brain organization, based on resting state functional connectivity. Since functional connectivity can be revealed also in a meta-analytical fashion using co-activations, here we propose to calculate the meta-analytic homotopic connectivity (MHC) as the meta-analytic counterpart of the VMHC. The comparison between the two techniques reveals their general similarity, but also highlights regional differences associated with how HC varies from task to rest. Two main differences were found from rest to task: (i) regions known to be characterized by global hubness are more similar than regions displaying local hubness; and (ii) medial areas are characterized by a higher degree of homotopic connectivity, while lateral areas appear to decrease their degree of homotopic connectivity during task performance. These findings show that MHC can be an insightful tool to study how the hemispheres functionally interact during task and rest conditions.

Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning

In the literature, there are substantial machine learning attempts to classify schizophrenia based on alterations in resting-state (RS) brain patterns using functional magnetic resonance imaging (fMRI). Most earlier studies modelled patients undergoing treatment, entailing confounding with drug effects on brain activity, and making them less applicable to real-world diagnosis at the point of first medical contact. Further, most studies with classification accuracies >80% are based on small sample datasets, which may be insufficient to capture the heterogeneity of schizophrenia, limiting generalization to unseen cases. In this study, we used RS fMRI data collected from a cohort of antipsychotic drug treatment-naive patients meeting DSM IV criteria for schizophrenia (N = 81) as well as age- and sex-matched healthy controls (N = 93). We present an ensemble model -- EMPaSchiz (read as ‘Emphasis’; standing for ‘Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction’) that stacks predictions from several ‘single-source’ models, each based on features of regional activity and functional connectivity, over a range of different a priori parcellation schemes. EMPaSchiz yielded a classification accuracy of 87% (vs. chance accuracy of 53%), which out-performs earlier machine learning models built for diagnosing schizophrenia using RS fMRI measures modelled on large samples (N > 100). To our knowledge, EMPaSchiz is first to be reported that has been trained and validated exclusively on data from drug-naive patients diagnosed with schizophrenia. The method relies on a single modality of MRI acquisition and can be readily scaled-up without needing to rebuild parcellation maps from incoming training images.

Analysis of voxel-mirrored homotopic connectivity in medication-free, current major depressive disorder

BACKGROUND

Recent neuroimaging studies suggest that abnormal function connectivity exists in patients with major depressive disorder (MDD). The aim of this study was to further analyze the underlying neural mechanism of MDD and explore whether clinical characteristics are correlated with the alerted homotopic connectivity in patients with MDD.

METHODS

Using voxel-mirrored homotopic connectivity (VMHC) during resting state, we compared 80 medication-free patients having current episodes of MDD and 124 never-depressed healthy controls (HCs) matched for age and gender.

RESULTS

We found decreased VMHC in patients with MDD in bilateral posterior cingulate cortex (PCC) extending to precuneus (Pre) compared with the HCs, which provided strong support for the potential role of PCC/Pre in recognizing interhemispheric connectivity deficits of MDD. Negative correlation between illness course and VMHC in PCC was observed as well.

LIMITATIONS

First, we just compared the functional connectivity at a rest state but not under a specific task. Second, we did not mitigate the delayed effect on the measurable alterations in homotopic brain activity. Third, we did not make a longitudinal comparison after patients receiving therapeutic drugs.

CONCLUSIONS

These findings that linking illness course with functional brain changes in depression help us understand the neural architecture of MDD.

Voxel-Mirrored Homotopic Connectivity of Resting-State Functional Magnetic Resonance Imaging in Blepharospasm

Objective: Several networks in human brain are involved in the development of blepharospasm. However, the underlying mechanisms for this disease are poorly understood. A voxel-mirrored homotopic connectivity (VMHC) method was used to quantify the changes in functional connectivity between two hemispheres of the brain in patients with blepharospasm.

Methods: Twenty-four patients with blepharospasm and 24 healthy controls matched by age, sex, and education were recruited. The VMHC method was employed to analyze the fMRI data. The support vector machine (SVM) method was utilized to examine whether these abnormalities could be applied to distinguish the patients from the controls.

Results: Compared with healthy controls, patients with blepharospasm showed significantly high VMHC in the inferior temporal gyrus, interior frontal gyrus, posterior cingulate cortex, and postcentral gyrus. No significant correlation was found between abnormal VMHC values and clinical variables. SVM analysis showed a combination of increased VMHC values in two brain areas with high sensitivities and specificities (83.33 and 91.67% in the combined inferior frontal gyrus and posterior cingulate cortex; and 83.33 and 87.50% in the combined inferior temporal gyrus and postcentral gyrus).

Conclusion: Enhanced homotopic coordination in the brain regions associated with sensory integration networks and default-mode network may be underlying the pathophysiology of blepharospasm. This phenomenon may serve as potential image markers to distinguish patients with blepharospasm from healthy controls.

Resting-state functional brain networks in first-episode psychosis: A 12-month follow-up study

INTRODUCTION

Schizophrenia is increasingly conceived as a disorder of brain network connectivity and organization. However, reports of network abnormalities during the early illness stage of psychosis are mixed. This study adopted a data-driven whole-brain approach to investigate functional connectivity and network architecture in a first-episode psychosis cohort relative to healthy controls and whether functional network properties changed abnormally over a 12-month period in first-episode psychosis.

METHODS

Resting-state functional connectivity was performed at two time points. At baseline, 29 first-episode psychosis individuals and 30 healthy controls were assessed, and at 12 months, 14 first-episode psychosis individuals and 20 healthy controls completed follow-up. Whole-brain resting-state functional connectivity networks were mapped for each individual and analyzed using graph theory to investigate whether network abnormalities associated with first-episode psychosis were evident and whether functional network properties changed abnormally over 12 months relative to controls.

RESULTS

This study found no evidence of abnormal resting-state functional connectivity or topology in first-episode psychosis individuals relative to healthy controls at baseline or at 12-months follow-up. Furthermore, longitudinal changes in network properties over a 12-month period did not significantly differ between first-episode psychosis individuals and healthy control. Network measures did not significantly correlate with symptomatology, duration of illness or antipsychotic medication.

CONCLUSIONS

This is the first study to show unaffected resting-state functional connectivity and topology in the early psychosis stage of illness. In light of previous literature, this suggests that a subgroup of first-episode psychosis individuals who have a neurotypical resting-state functional connectivity and topology may exist. Our preliminary longitudinal analyses indicate that there also does not appear to be deterioration in these network properties over a 12-month period. Future research in a larger sample is necessary to confirm our longitudinal findings.

Research Progress in Biological Studies of Schizophrenia in China in 2017

Schizophrenia is a severe mental disorder and its etiology and pathological mechanism are unknown. This article mainly introduces the progress of biological studies of schizophrenia in China in 2017, including neuroimaging, genetics, and immunology studies. It also introduces the research progress of high-risk psychotic syndrome and physiotherapy.

Decreased Resting-State Interhemispheric Functional Connectivity Correlated with Neurocognitive Deficits in Drug-Naive First-Episode Adolescent-Onset Schizophrenia

BACKGROUND

Given that adolescence is a critical epoch in the onset of schizophrenia, studying aberrant brain changes in adolescent-onset schizophrenia, particularly in patients with drug-naive first-episode schizophrenia, is important to understand the biological mechanism of this disorder. Previous resting-state functional magnetic resonance imaging studies have shown abnormal functional connectivity in separate hemispheres in patients with adult-onset schizophrenia. Our aim to study adolescent-onset schizophrenia can provide clues for the early aetiology of schizophrenia.

METHOD

A total of 48 drug-naïve, first-episode, adolescent-onset schizophrenia outpatients and 31 healthy controls underwent resting-state functional magnetic resonance imaging scans. Data were subjected to voxel-mirrored homotopic connectivity and support vector machine analyses.

RESULTS

Compared with the healthy controls, the adolescent-onset schizophrenia group showed significantly lower voxel-mirrored homotopic connectivity values in different brain regions, including the fusiform gyrus, superior temporal gyrus/insula, precentral gyrus, and precuneus. Decreased voxel-mirrored homotopic connectivity values in the superior temporal gyrus/insula were significantly correlated with Trail-Making Test: Part A performance (r = -0.437, P = .002). A combination of the voxel-mirrored homotopic connectivity values in the precentral gyrus and precuneus may be used to discriminate patients with adolescent-onset schizophrenia from controls with satisfactory classification results, which showed sensitivity of 100%, specificity of 87.09%, and accuracy of 94.93%.

CONCLUSION

Our findings highlight resting-state interhemispheric FC abnormalities within the sensorimotor network of patients with adolescent-onset schizophrenia and confirm the relationship between adolescent-onset schizophrenia and adult-onset schizophrenia. These findings suggest that reduced interhemispheric connectivity within the sensorimotor network has a pivotal role in the pathogenesis of schizophrenia.

Using short-range and long-range functional connectivity to identify schizophrenia with a family-based case-control design

Abnormal short-range and long-range functional connectivities (FCs) have been implicated in the neurophysiology of schizophrenia. This study was conducted to examine the potential of short-range and long-range FCs for differentiating the patients from the controls with a family-based case-control design. Twenty-eight first-episode, drug-naive patients with schizophrenia, 28 unaffected siblings of the patients (family-based controls, FBCs), and 40 healthy controls (HCs) underwent resting-state functional magnetic resonance imaging (fMRI) scans. The data were analyzed by short-range and long-range FC analyses, receiver operating characteristic curve (ROC) and support vector machine (SVM). Compared with the FBCs/HCs, the patients exhibit increased short-range positive FC strength (spFCS) and/or long-range positive FC strength (lpFCS) in the default-mode network (DMN) and decreased spFCS and lpFCS in the sensorimotor circuits. Furthermore, a combination of the spFCS values in the right superior parietal lobule and the lpFCS values in the left fusiform gyrus/cerebellum VI can differentiate the patients from the FBCs with high sensitivity and specificity. The findings highlight the importance of the DMN and sensorimotor circuits in the pathogenesis of schizophrenia. Combining with family-based case-control design may be a viable option to limit the confounding effects of environmental risk factors in neuroimaging studies of schizophrenia.