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Resting‐state functional connectivity (FC) is widely used in multivariate pattern analysis of functional magnetic resonance imaging (fMRI), including identifying the locations of putative brain functional borders, predicting individual phenotypes, and diagnosing clinical mental diseases. However, limited attention has been paid to the analysis of functional interactions from a frequency perspective. In this study, by contrasting coherence‐based and correlation‐based FC with two machine learning tasks, we observed that measuring FC in the frequency domain helped to identify finer functional subregions and achieve better pattern discrimination capability relative to the temporal correlation. This study has proven the feasibility of coherence in the analysis of fMRI, and the results indicate that modeling functional interactions in the frequency domain may provide richer information than that in the time domain, which may provide a new perspective on the analysis of functional neuroimaging. [ABSTRACT FROM AUTHOR]

Featured Application: A general framework for functional integration evaluation of human brain is proposed, which effectively combines whole-brain functional connectivity analysis with effective connectivity analysis based on Dynamic Causal Modeling. Much effort has been devoted towards the identification of brain areas recruited during driving—as one of the most common motor skills of human beings. However, how driving experience impacts on the brain's intrinsic functional architecture has not been fully investigated. Using resting-state fMRI data collected from 20 taxi drivers and 20 nondrivers, this paper asks whether there exists specific brain network integration encoding driving behavior. First, to address this, we proposed a general framework combining whole-brain functional connectivity analysis with effective connectivity analysis based on spectral Dynamic Causal Modeling. The validation results indicated that the application of this framework could effectively discover the brain network that best explained the observed BOLD fluctuations. Second, by segmenting supplementary motor area (SMA) into pre-SMA and SMA proper sub-regions, we used the above framework and discovered a hierarchical architecture with pre-SMA located at the higher level in both driver and control groups. Third, we further evaluated the possibility that driving behavior could be encoded by directed connections among the hierarchy, and found that the effective connectivity from pre-SMA to left superior frontal gyrus could distinguish drivers from nondrivers with a sensitivity of 80%. Our findings provide a new paradigm for analyzing the brain's intrinsic functional integration, and may shed new light on the theory of neuroplasticity that training and experience can remodel the patterns of correlated spontaneous brain activity between specific processing regions. Meanwhile, from a methodological advantage perspective, our proposed framework takes the functional connectivity results as a prior, enabling subsequent spectral DCM to efficiently assess functional integration at a whole-brain scale, which is not available by only using other DCM methods, such as stochastic DCM or the State-of-the-Art multimodal DCM. [ABSTRACT FROM AUTHOR]

Dividing a pre-defined brain region into several heterogenous subregions is crucial for understanding its functional segregation and integration. Due to the high dimensionality of brain functional features, clustering is often postponed until dimensionality reduction in traditional parcellation frameworks occurs. However, under such stepwise parcellation, it is very easy to fall into the dilemma of local optimum since dimensionality reduction could not take into account the requirement of clustering. In this study, we developed a new parcellation framework based on the discriminative embedded clustering (DEC), combining subspace learning and clustering in a common procedure with alternative minimization adopted to approach global optimum. We tested the proposed framework in functional connectivity-based parcellation of the hippocampus. The hippocampus was parcellated into three spatial coherent subregions along the anteroventral–posterodorsal axis; the three subregions exhibited distinct functional connectivity changes in taxi drivers relative to non-driver controls. Moreover, compared with traditional stepwise methods, the proposed DEC-based framework demonstrated higher parcellation consistency across different scans within individuals. The study proposed a new brain parcellation framework with joint dimensionality reduction and clustering; the findings might shed new light on the functional plasticity of hippocampal subregions related to long-term navigation experience. [ABSTRACT FROM AUTHOR]

Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal–spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD. [ABSTRACT FROM AUTHOR]

In epidemiological studies, type 2 diabetes mellitus (T2DM) has been associated with cognitive impairment and dementia, but studies about functional network connectivity in T2DM without cognitive impairment are limited. This study aimed to explore network connectivity alterations that may help enhance our understanding of damage-associated processes in T2DM. MRI data were analyzed from 82 patients with T2DM and 66 normal controls. Clinical, biochemical, and neuropsychological assessments were conducted in parallel with resting-state functional magnetic resonance imaging, and the cognitive status of the patients was assessed using the Montreal Cognitive Assessment-B (MoCA-B) score. Independent component analysis revealed a positive correlation between the salience network and the visual network and a negative connection between the left executive control network and the default mode network in patients with T2DM. The differences in dynamic brain network connectivity were observed in the precuneus, visual, and executive control networks. Internal network connectivity was primarily affected in the thalamus, inferior parietal lobe, and left precuneus. Hemoglobin A1c level, body mass index, MoCA-B score, and grooved pegboard (R) assessments indicated significant differences between the two groups (p < 0.05). Our findings show that key changes in functional connectivity in diabetes occur in the precuneus and executive control networks that evolve before patients develop cognitive deficits. In addition, the current study provides useful information about the role of the thalamus, inferior parietal lobe, and precuneus, which might be potential biomarkers for predicting the clinical progression, assessing the cognitive function, and further understanding the neuropathology of T2DM. [ABSTRACT FROM AUTHOR]

Machine learning methods have been frequently applied in the field of cognitive neuroscience in the last decade. A great deal of attention has been attracted to introduce machine learning methods to study the autism spectrum disorder (ASD) in order to find out its neurophysiological underpinnings. In this paper, we presented a comprehensive review about the previous studies since 2011, which applied machine learning methods to analyze the functional magnetic resonance imaging (fMRI) data of autistic individuals and the typical controls (TCs). The all-round process was covered, including feature construction from raw fMRI data, feature selection methods, machine learning methods, factors for high classification accuracy, and critical conclusions. Applying different machine learning methods and fMRI data acquired from different sites, classification accuracies were obtained ranging from 48.3% up to 97%, and informative brain regions and networks were located. Through thorough analysis, high classification accuracies were found to usually occur in the studies which involved task-based fMRI data, single dataset for some selection principle, effective feature selection methods, or advanced machine learning methods. Advanced deep learning together with the multi-site Autism Brain Imaging Data Exchange (ABIDE) dataset became research trends especially in the recent 4 years. In the future, advanced feature selection and machine learning methods combined with multi-site dataset or easily operated task-based fMRI data may appear to have the potentiality to serve as a promising diagnostic tool for ASD. [ABSTRACT FROM AUTHOR]

Abstract Background Recently, a growing number of neuroimaging studies have begun to investigate the brains of schizophrenic patients and their healthy siblings to identify heritable biomarkers of this complex disorder. The objective of this study was to use multiclass pattern analysis to investigate the inheritable characters of schizophrenia at the individual level, by comparing whole-brain resting-state functional connectivity of patients with schizophrenia to their healthy siblings. Methods Twenty-four schizophrenic patients, twenty-five healthy siblings and twenty-two matched healthy controls underwent the resting-state functional Magnetic Resonance Imaging (rs-fMRI) scanning. A linear support vector machine along with principal component analysis was used to solve the multi-classification problem. By reconstructing the functional connectivities with high discriminative power, three types of functional connectivity-based signatures were identified: (i) state connectivity patterns, which characterize the nature of disruption in the brain network of patients with schizophrenia; (ii) trait connectivity patterns, reflecting shared connectivities of dysfunction in patients with schizophrenia and their healthy siblings, thereby providing a possible neuroendophenotype and revealing the genetic vulnerability to develop schizophrenia; and (iii) compensatory connectivity patterns, which underlie special brain connectivities by which healthy siblings might compensate for an increased genetic risk for developing schizophrenia. Results Our multiclass pattern analysis achieved 62.0% accuracy via leave-one-out cross-validation (p Conclusions Based on our experimental results, we saw some indication of differences in functional connectivity patterns in the healthy siblings of schizophrenic patients compared to other healthy individuals who have no relations with the patients. Our preliminary investigation suggested that the use of resting-state functional connectivities as classification features to discriminate among schizophrenic patients, their healthy siblings and healthy controls is meaningful.

Until now, dynamic functional connectivity (dFC) based on functional magnetic resonance imaging is typically estimated on a set of predefined regions of interest (ROIs) derived from an anatomical or static functional atlas which follows an implicit assumption of functional homogeneity within ROIs underlying temporal fluctuation of functional coupling, potentially leading to biases or underestimation of brain network dynamics. Here, we presented a novel computational method based on dynamic functional connectivity degree (dFCD) to derive meaningful brain parcellations that can capture functional homogeneous regions in temporal variance of functional connectivity. Several spatially distributed but functionally meaningful areas that are well consistent with known intrinsic connectivity networks were identified through independent component analysis (ICA) of time‐varying dFCD maps. Furthermore, a systematical comparison with commonly used brain atlases, including the Anatomical Automatic Labeling template, static ICA‐driven parcellation and random parcellation, demonstrated that the ROI‐definition strategy based on the proposed dFC‐driven parcellation could better capture the interindividual variability in dFC and predict observed individual cognitive performance (e.g., fluid intelligence, cognitive flexibility, and sustained attention) based on chronnectome. Together, our findings shed new light on the functional organization of resting brains at the timescale of seconds and emphasized the significance of a dFC‐driven and voxel‐wise functional homogeneous parcellation for network dynamics analyses in neuroscience. [ABSTRACT FROM AUTHOR]

Antisocial behavior (ASB) is believed to have neural substrates; however, the association between ASB and functional brain networks remains unclear. The temporal variability of the functional connectivity (or dynamic FC) derived from resting‐state functional MRI has been suggested as a useful metric for studying abnormal behaviors including ASB. This is the first study using low‐frequency fluctuations of the dynamic FC to unravel potential system‐level neural correlates with ASB. Specifically, we individually associated the dynamic FC patterns with the ASB scores (measured by Antisocial Process Screening Device) of the male offenders (age: 23.29 ± 3.36 years) based on machine learning. Results showed that the dynamic FCs were associated with individual ASB scores. Moreover, we found that it was mainly the inter‐network dynamic FCs that were negatively associated with the ASB severity. Three major high‐order cognitive functional networks and the sensorimotor network were found to be more associated with ASB. We further found that impaired behavior in the ASB subjects was mainly associated with decreased FC dynamics in these networks, which may explain why ASB subjects usually have impaired executive control and emotional processing functions. Our study shows that temporal variation of the FC could be a promising tool for ASB assessment, treatment, and prevention. [ABSTRACT FROM AUTHOR]

Increasing evidence has suggested that the dynamic properties of functional brain networks are related to individual behaviors and cognition traits. However, current fMRI-based approaches mostly focus on statistical characteristics of the windowed correlation time course, potentially overlooking subtle time-varying patterns in dynamic functional connectivity (dFC). Here, we proposed the use of an end-to-end deep learning model that combines the convolutional neural network (CNN) and long short-term memory (LSTM) network to capture temporal and spatial features of functional connectivity sequences simultaneously. The results on a large cohort (Human Connectome Project, n = 1,050) demonstrated that our model could achieve a high classification accuracy of about 93% in a gender classification task and prediction accuracies of 0.31 and 0.49 (Pearson's correlation coefficient) in fluid and crystallized intelligence prediction tasks, significantly outperforming previously reported models. Furthermore, we demonstrated that our model could effectively learn spatiotemporal dynamics underlying dFC with high statistical significance based on the null hypothesis estimated using surrogate data. Overall, this study suggests the advantages of a deep learning model in making full use of dynamic information in resting-state functional connectivity, and highlights the potential of time-varying connectivity patterns in improving the prediction of individualized characterization of demographics and cognition traits. [ABSTRACT FROM AUTHOR]

This project aims to explore if stronger functional connectivity (FC) exists in the maximal BOLD response of EEG/fMRI analysis when it is concordant with seizure-onset-zone (SOZ). Twenty-six patients with drug-resistant focal epilepsy who had an EEG/fMRI and later underwent stereo-EEG implantation were included. Different types of IEDs were labeled in scalp EEG and IED-related maximal BOLD responses were evaluated separately, each constituting one study. After evaluating concordance between maximal BOLD and SOZ, twenty-seven studies were placed in the concordant group and eight in the discordant group. We evaluated the local connectivity and ipsilaterally distant connectivity difference between the maximal BOLD and the contralateral homotopic region. Significantly stronger local FC was found for the maximal BOLD in the concordant group (p < 0.05, Bonferroni corrected). 52% of the studies in the concordant group and 13% in the discordant group had a significant difference compared to healthy subjects (p < 0.05, uncorrected). The finding suggests that, when concordant with the SOZ, the maximal BOLD is more likely to have stronger local FC compared to its contralateral counterpart. This asymmetry in functional connectivity may help to noninvasively improve the specificity of EEG/fMRI analysis. [ABSTRACT FROM AUTHOR]

Background and Purpose: Previous neuroimaging studies have demonstrated type 2 diabetes (T2D)-related brain structural and functional changes are partly associated with cognitive decline. However, less is known about the underlying mechanisms. Chronic hyperglycemia and microvascular complications are the two of most important risk factors related to cognitive decline in diabetes. Cerebral small vessel diseases (CSVDs), such as those defined by lacunar infarcts, white matter hyperintensities (WMHs) and microhemorrhages, are also associated with an increased risk of cognitive decline and dementia. In this study, we examined brain magnetic resonance imaging (MRI) changes in patients in the early stages of T2D without CSVDs to focus on glucose metabolism factors and to avoid the interference of vascular risk factors on T2D-related brain damage. Methods: T2D patients with disease durations of less than 5 years and without any signs of CSVDs (n = 34) were compared with healthy control subjects (n = 24). Whole-brain region-based functional connectivity was analyzed with network-based statistics (NBS), and brain surface morphology was examined. In addition, the Montreal Cognitive Assessment (MoCA) was conducted for all subjects. Results: At the whole-brain region-based functional connectivity level, thirty-three functional connectivities were changed in T2D patients relative to those in controls, mostly manifested as pathological between-network positive connectivity and located mainly between the sensory-motor network and auditory network. Some of the connectivities were positively correlated with blood glucose level, insulin resistance, and MoCA scores in the T2D group. The network-level analysis showed between-network hyperconnectivity in T2D patients, but no significant changes in within-network connectivity. In addition, there were no significant differences in MoCA scores or brain morphology measures, including cortical thickness, surface area, mean curvature, and gray/white matter volume, between the two groups. Conclusion: The findings indicate that pathological between-network positive connectivity occurs in the early stages of T2D without CSVDs. The abnormal connectivity may indicate that the original balance of mutual antagonistic/cooperative relationships between the networks is broken, which may be a neuroimaging basis for predicting cognitive decline in early T2D patients. [ABSTRACT FROM AUTHOR]

Objective The pathogenesis of benign adult familial myoclonic epilepsy ( BAFME) remains unknown, although cerebellar pathologic changes and brain hyperexcitability have been reported. We used resting-state functional magnetic resonance imaging ( fMRI) to examine the functional connectivity between the cerebellum and cerebrum in a Chinese family with BAFME for the first time. Methods Eleven adults with BAFME and 15 matched healthy controls underwent resting-state blood oxygen level-dependent ( BOLD) fMRI scanning. The cerebellar seeds, including the bilateral crus I, lobule VIII, lobule VIIb, and lobule IV&V, were defined a priori. Next, regional time courses were obtained for each individual by averaging the BOLD time series over all voxels in each seed region. Then, seed-based functional connectivity z-maps were produced by computing Pearson's correlation coefficients (converted to z-scores by Fisher transformation) between each seed signal and the time series from all other voxels within the entire brain. Finally, a second-level random-effect two-sample t-test was performed on the individual z-maps in a voxel-wise manner. Results Reduced functional connectivity of the right cerebellar crus I with the left middle frontal gyrus and right cerebellar lobule IX was observed in the default network of BAFME. Enhanced functional connectivity of the left cerebellar lobule VIII with the bilateral middle temporal gyri, right putamen, and left cerebellar crus I was found in the dorsal attention network of BAFME. Enhanced functional connectivity between the left cerebellar lobule VIIb and right frontal pole was found in the control network of BAFME. Significance Altered cerebellar-cerebral functional connectivity may contribute to the understanding of the nosogenesis of BAFME and explain the cognitive dysfunction in this Chinese family with BAFME. [ABSTRACT FROM AUTHOR]

Recent resting-state functional connectivity magnetic resonance imaging studies have shown significant group differences in several regions and networks between patients with major depressive disorder and healthy controls. The objective of the present study was to investigate the whole-brain resting-state functional connectivity patterns of depressed patients, which can be used to test the feasibility of identifying major depressive individuals from healthy controls. Multivariate pattern analysis was employed to classify 24 depressed patients from 29 demographically matched healthy volunteers. Permutation tests were used to assess classifier performance. The experimental results demonstrate that 94.3% (P < 0.0001) of subjects were correctly classified by leave-one-out cross-validation, including 100% identification of all patients. The majority of the most discriminating functional connections were located within or across the default mode network, affective network, visual cortical areas and cerebellum, thereby indicating that the disease-related resting-state network alterations may give rise to a portion of the complex of emotional and cognitive disturbances in major depression. Moreover, the amygdala, anterior cingulate cortex, parahippocampal gyrus and hippocampus, which exhibit high discriminative power in classification, may play important roles in the pathophysiology of this disorder. The current study may shed new light on the pathological mechanism of major depression and suggests that whole-brain resting-state functional connectivity magnetic resonance imaging may provide potential effective biomarkers for its clinical diagnosis. [ABSTRACT FROM PUBLISHER]

Functional connectivity, representing a statistical coupling relationship between different brain regions or electrodes, is an influential concept in clinical medicine and cognitive neuroscience. Electroencephalography-derived functional connectivity (EEG-FC) provides relevant characteristic information about individual differences in cognitive tasks and personality traits. However, it remains unclear whether these individual-dependent EEG-FCs remain relatively permanent across long-term sessions. This manuscript utilizes machine learning algorithms to explore the individual specificity and permanence of resting-state EEG connectivity patterns. We performed six recordings at different intervals during a six-month period to examine the variation and permanence of resting-state EEG-FC over a long period. The results indicated that the EEG-FC networks are quite subject-specific with a high-precision identification accuracy of greater than 90%. Meanwhile, the individual specificity remained stable and only varied slightly after six months. Furthermore, the specificity is mainly derived from the internal connectivity of the frontal lobe. Our work demonstrates the existence of specific and permanent EEG-FC patterns in the brain, providing potential information for biometric applications. [ABSTRACT FROM AUTHOR]

Background: Persistent neurocognitive deficits are often associated with poor outcomes of major depressive disorder (MDD). Executive dysfunction is the most common cognitive deficit in MDD. However, it remains unclear which subcomponent of executive dysfunction is state-independent with distinct neural substrates.Methods: A comprehensive neurocognitive test battery was used to assess four subcomponents of executive function (working memory, inhibition, shifting, and verbal fluency) in 95 MDD patients and 111 matched healthy controls (HCs). After 6 months of paroxetine treatment, 56 patients achieved clinical remission (rMDD) and completed the second-time neurocognitive test. Network-based statistics analysis was utilized to explore the changes in functional connectivity (FC).Results: Compared with the HCs, all the four subcomponents of MDD patients were significantly impaired. After treatment, there was a significant improvement in working memory, inhibition, and verbal fluency in the rMDD group. And shifting and verbal fluency of the rMDD group remained impaired compared with the HCs. Fifteen functional connections were interrupted in the MDD group, and 11 connections remained in a disrupted state after treatment. Importantly, verbal fluency was negatively correlated with the disrupted FC between the right dorsal prefrontal cortex and the left inferior parietal lobule in patients with MDD and remitted MDD.Limitations: The correlation analysis of the association between cognitive impairment and connectivity alterations precluded us from making causal inferences.Conclusions: Verbal fluency is the potential state-independent cognitive deficit with distinct neural basis in patients with MDD. [ABSTRACT FROM AUTHOR]

Abstract: Introduction: As a major interface between the hippocampus and the neocortex, the entorhinal cortex (EC) is widely known to play a pivotal role in spatial memory and navigation. Previous studies have suggested that the EC can be divided into the anterior‐lateral (alEC) and the posterior‐medial subregions (pmEC), with the former receiving object‐related information from the perirhinal cortex and the latter receiving scene‐related information from the parahippocampal cortex. However, the functional connectivity maps of the EC subregions in the context of extensive navigation experience remain elusive. In this study, we analyzed the functional connectivity of the EC in subjects with long‐term navigation experience and aimed to find the navigation‐related change in the functional properties of the human EC. Methods: We investigated the resting‐state functional connectivity changes in the EC subregions by comparing the EC functional connectivity maps of 20 taxi drivers with those of 20 nondriver controls. Furthermore, we examined whether the functional connectivity changes of the EC were related to the number of taxi driving years. Results: Significantly reduced functional connectivity was found in the taxi drivers between the left pmEC and the right anterior cingulate cortex (ACC), right angular gyrus, and bilateral precuneus as well as some temporal regions, and between the right pmEC and the left inferior temporal gyrus. Notably, the strength of the functional connectivity between the left pmEC and the left precuneus, as well as the right ACC, was negatively correlated with the years of taxi driving. Conclusion: This is the first study to explore the impact of long‐term navigation experience on the connectivity patterns of the EC, the results of which may shed new light on the potential influence of extensive navigational training on the functional organization of the EC in healthy human brains. [ABSTRACT FROM AUTHOR]

Internet addiction (IA) is a condition characterized by loss of control over Internet use, leading to a variety of negative psychosocial consequences. Recent neuroimaging studies have begun to identify IA-related changes in specific brain regions and connections. However, whether and how the interactions within and between the large-scale brain networks are disrupted in individuals with IA remain largely unexplored. Using group independent component analysis, we extracted five intrinsic connectivity networks (ICNs) from the resting-state fMRI data of 26 adolescents with IA and 43 controls, including the anterior and posterior default mode network (DMN), left and right fronto-parietal network (FPN), and salience network (SN). We then examined the possible group differences in the functional connectivity within each ICN and between the ICNs. We found that, compared with controls, IA subjects showed: (1) reduced inter-hemispheric functional connectivity of the right FPN, whereas increased intra-hemispheric functional connectivity of the left FPN; (2) reduced functional connectivity in the dorsal medial prefrontal cortex (mPFC) of the anterior DMN; (3) reduced functional connectivity between the SN and anterior DMN. Our findings suggest that IA is associated with imbalanced interactions among the DMN, FPN and SN, which may serve as system-level neural underpinnings for the uncontrollable Internet-using behaviors. [ABSTRACT FROM AUTHOR]

The default mode network (DMN) plays an important role in the physiopathology of schizophrenia. Previous studies have suggested that the cerebellum participates in higher-order cognitive networks such as the DMN. However, the specific contribution of the cerebellum to the DMN abnormalities in schizophrenia has yet to be established. In this study, we investigated cerebellar functional connectivity differences between 60 patients with schizophrenia and 60 healthy controls from a public resting-state fMRI database. Seed-based correlation analysis was performed by using seeds from the left Crus I, right Crus I and Lobule IX, which have previously been identified as being involved in the DMN. Our results revealed that, compared with the healthy controls, the patients showed significantly reduced cerebellar functional connectivity with the thalamus and several frontal regions including the middle frontal gyrus, anterior cingulate cortex, and supplementary motor area. Moreover, the positive correlations between the strength of frontocerebellar and thalamocerebellar functional connectivity observed in the healthy subjects were diminished in the patients. Our findings implicate disruptive changes of the fronto-thalamo-cerebellar circuit in schizophrenia, which may provide further evidence for the “cognitive dysmetria” concept of schizophrenia. [ABSTRACT FROM AUTHOR]

Abstract: Brain functional asymmetry abnormalities have previously been reported in schizophrenia. In the present study, we hypothesized that the pattern of functional asymmetry in schizophrenia may differ between patients suffering from positive and negative symptoms. We examined the relationship between altered asymmetry of functional connectivity and symptom type (positive/negative) using resting-state functional magnetic resonance imaging. We selected the dorsolateral prefrontal cortex, superior temporal gyrus, middle temporal gyrus and hippocampus as regions of interest, and analyzed functional connectivity patterns between these and other brain regions. Furthermore, a voxel-based two-level asymmetry analysis was conducted to investigate differences in the asymmetry of functional connectivity patterns within and between groups. Our results showed that patients exhibiting positive symptoms had significantly increased leftward asymmetry of functional connectivity. The negative symptom group, in contrast, exhibited increased rightward asymmetry of functional connectivity. The strength of the asymmetry in these regions was found to be significantly correlated with symptom ratings obtained using the Positive and Negative Syndrome Scale. These results suggest that predominantly positive and predominantly negative schizophrenia may have different neural underpinnings, and that certain regions in the frontal and temporal lobes, as well as the cingulate gyrus and precuneus, play important roles in mediating the symptoms of this complex disease. Our study also provided further evidence for the hypothesis that schizophrenia is related to abnormalities in functional brain networks. [Copyright &y& Elsevier]