Brain Networks In Schizophrenia Research Articles

On the integrity of functional brain networks in schizophrenia, Parkinson's disease, and advanced age: Evidence from connectivity-based single-subject classification.

Previous whole-brain functional connectivity studies achieved successful classifications of patients and healthy controls but only offered limited specificity as to affected brain systems. Here, we examined whether the connectivity patterns of functional systems affected in schizophrenia (SCZ), Parkinson's disease (PD), or normal aging equally translate into high classification accuracies for these conditions. We compared classification performance between pre-defined networks for each group and, for any given network, between groups. Separate support vector machine classifications of 86 SCZ patients, 80 PD patients, and 95 older adults relative to their matched healthy/young controls, respectively, were performed on functional connectivity in 12 task-based, meta-analytically defined networks using 25 replications of a nested 10-fold cross-validation scheme. Classification performance of the various networks clearly differed between conditions, as those networks that best classified one disease were usually non-informative for the other. For SCZ, but not PD, emotion-processing, empathy, and cognitive action control networks distinguished patients most accurately from controls. For PD, but not SCZ, networks subserving autobiographical or semantic memory, motor execution, and theory-of-mind cognition yielded the best classifications. In contrast, young-old classification was excellent based on all networks and outperformed both clinical classifications. Our pattern-classification approach captured associations between clinical and developmental conditions and functional network integrity with a higher level of specificity than did previous whole-brain analyses. Taken together, our results support resting-state connectivity as a marker of functional dysregulation in specific networks known to be affected by SCZ and PD, while suggesting that aging affects network integrity in a more global way. Hum Brain Mapp 38:5845-5858, 2017. © 2017 Wiley Periodicals, Inc.

Disrupted relationship between “resting state” connectivity and task-evoked activity during social perception in schizophrenia

Schizophrenia has been described as a self-disorder, whereas social deficits are key features of the illness. Changes in “resting state” activity of brain networks involved in self-related processing have been consistently reported in schizophrenia, but their meaning for social perception deficits remains poorly understood. Here, we applied a novel approach investigating the relationship between task-evoked neural activity during social perception and functional organization of self-related brain networks during a “resting state”.“Resting state” functional MRI was combined with task-related functional MRI using a social perception experiment. Twenty-one healthy control participants (HC) and 21 out-patients with a diagnosis of schizophrenia (SCH) were included. There were no significant differences concerning age, IQ, education and gender between the groups.Results showed reduced “resting state” functional connectivity between ventromedial prefrontal cortex and dorsal posterior cingulate cortex in SCH, compared to HC. During social perception, neural activity in dorsal posterior cingulate cortex and behavioral data indicated impaired congruence coding of social stimuli in SCH. Task-evoked activity during social perception in dorsal posterior cingulate cortex co-varied with dorsal posterior cingulate cortex-ventromedial prefrontal cortex functional connectivity during a “resting state” in HC, but not in SCH. Task-evoked activity also correlated with negative symptoms in SCH.These preliminary findings, showing disrupted prediction of social perception measures by “resting state” functioning of self-related brain networks in schizophrenia, provide important insight in the hypothesized link between self and social deficits. They also shed light on the meaning of “resting state” changes for tasks such as social perception.

Measuring alterations in oscillatory brain networks in schizophrenia with resting-state MEG: State-of-the-art and methodological challenges.

Neuroimaging studies provide evidence of disturbed resting-state brain networks in Schizophrenia (SZ). However, untangling the neuronal mechanisms that subserve these baseline alterations requires measurement of their electrophysiological underpinnings. This systematic review specifically investigates the contributions of resting-state Magnetoencephalography (MEG) in elucidating abnormal neural organization in SZ patients. A systematic literature review of resting-state MEG studies in SZ was conducted. This literature is discussed in relation to findings from resting-state fMRI and EEG, as well as to task-based MEG research in SZ population. Importantly, methodological limitations are considered and recommendations to overcome current limitations are proposed. Resting-state MEG literature in SZ points towards altered local and long-range oscillatory network dynamics in various frequency bands. Critical methodological challenges with respect to experiment design, and data collection and analysis need to be taken into consideration. Spontaneous MEG data show that local and global neural organization is altered in SZ patients. MEG is a highly promising tool to fill in knowledge gaps about the neurophysiology of SZ. However, to reach its fullest potential, basic methodological challenges need to be overcome. MEG-based resting-state power and connectivity findings could be great assets to clinical and translational research in psychiatry, and SZ in particular.

Dysfunction of Large-Scale Brain Networks in Schizophrenia: A Meta-analysis of Resting-State Functional Connectivity.

Schizophrenia is a complex mental disorder with disorganized communication among large-scale brain networks, as demonstrated by impaired resting-state functional connectivity (rsFC). Individual rsFC studies, however, vary greatly in their methods and findings. We searched for consistent patterns of network dysfunction in schizophrenia by using a coordinate-based meta-analysis. Fifty-six seed-based voxel-wise rsFC datasets from 52 publications (2115 patients and 2297 healthy controls) were included in this meta-analysis. Then, coordinates of seed regions of interest (ROI) and between-group effects were extracted and coded. Seed ROIs were categorized into seed networks by their location within an a priori template. Multilevel kernel density analysis was used to identify brain networks in which schizophrenia was linked to hyper-connectivity or hypo-connectivity with each a priori network. Our results showed that schizophrenia was characterized by hypo-connectivity within the default network (DN, self-related thought), affective network (AN, emotion processing), ventral attention network (VAN, processing of salience), thalamus network (TN, gating information) and somatosensory network (SS, involved in sensory and auditory perception). Additionally, hypo-connectivity between the VAN and TN, VAN and DN, VAN and frontoparietal network (FN, external goal-directed regulation), FN and TN, and FN and DN were found in schizophrenia. Finally, the only instance of hyper-connectivity in schizophrenia was observed between the AN and VAN. Our meta-analysis motivates an empirical foundation for a disconnected large-scale brain networks model of schizophrenia in which the salience processing network (VAN) plays the core role, and its imbalanced communication with other functional networks may underlie the core difficulty of patients to differentiate self-representation (inner world) and environmental salience processing (outside world).

Aberrant modulation of brain activation by emotional valence during self-referential processing among patients with delusions of reference

Background and objectivesDelusions of reference are thought to reflect abnormally heightened attributions of salience to mundane events or stimuli that lead to convictions that they are personally significant or directed at the observer. Recent findings highlight abnormal recruitment of brain regions associated with self-referential processes among patients with referential delusions. Given the inherent overlap of emotion, incentive salience, and self-relevance, as well as with aberrant thought processes in psychosis, this study investigated the implicit relations between participants’ perception of the emotional valence of stimuli on neural correlates of self-referent judgments among schizophrenia-spectrum patients with referential delusions. MethodsDuring fMRI scanning, participants indicated whether sentences describing personal characteristics seemed to refer specifically to them. Subsequently, participants rated their perceived emotional valence of each statement. ResultsRegression analyses revealed differential relations between groups across regions associated with self-referential processing, including prefrontal regions, anterior cingulate, insula, precuneus, and dorsal striatum. Within these regions, greater activation related to sentences rated as more positive among healthy comparison participants and more negative among patients. LimitationsThe current results warrant replication and extension with larger and longitudinal samples to assess potential moderating relations of clinical and demographic individual differences. ConclusionsThese findings support aberrant brain activation associated with emotional and salience brain networks in schizophrenia and highlight the importance of considering specific emotional attributes (valence) in discrete domains of delusional thought (self-referential communication).

Disintegration of sensorimotor brain networks in schizophrenia

A large body of literature reported widespread structural and functional abnormalities throughout the brain in schizophrenia spectrum disorders (SZ). Corresponding with the typical symptomatology in SZ where sensory dysfunctions contribute to the core social and cognitive impairment, converging evidence suggests a disturbed interplay between higher-order (cognitive) and lower-order (sensory) regions. This talk will discuss the results of several recent studies, investigating brain connectivity in SZ using functional magnetic resonance imaging data from large samples. Within-network sensorimotor as well as sensorimotor-thalamic aberrations in SZ robustly appear among the core findings across studies, both during performance of cognitive tasks and during rest. We utilized machine learning to distinguish SZ from healthy controls based on connectivity profiles. When classifying on sensorimotor connections alone, not only can we reach accuracies largely above chance but also, these accuracies are as good as when incorporating whole brain connectivity in the classification. Whereas the overall accuracy levels in distinguishing SZ from controls are not useful in a clinical context, these results underline the robustness of the sensorimotor findings on the individual subject level. Targeting the sensory and perceptual domains may thus be key for future research to get a better understanding of the heterogeneity of clinical manifestations in severe mental disorders and to map clinical symptoms to imaging phenotypes.Disclosure of interestThe authors have not supplied their declaration of competing interest.

Altered balance of functional brain networks in Schizophrenia

Activity in dorsal attention (DAN) and frontoparietal (FPN) functional brain networks is linked to allocation of attention to external stimuli, and activity in the default-mode network (DMN) is linked to allocation of attention to internal representations. Tasks requiring attention to external stimuli shift activity to the DAN/FPN and away from the DMN, and optimal task performance depends on balancing DAN/FPN against DMN activity. The current functional magnetic resonance imaging (fMRI) study assessed the balance of DAN/FPN and DMN activity in 13 schizophrenia patients and 13 healthy controls while they were engaged in a task switching Stroop paradigm which demanded internally directed attention to task instructions. The typical pattern of reciprocity between the DAN/FPN and DMN was observed for healthy controls but not for patients, suggesting a reduction in the internally focussed thought important for maintenance of instructions and strategies in schizophrenia. The observed alteration in the balance between DAN/FPN and DMN in patients may reflect a general mechanism underlying multiple forms of cognitive impairment in schizophrenia, including global processing deficits such as cognitive inefficiency and impaired context processing.

Evidence for Accelerated Decline of Functional Brain Network Efficiency in Schizophrenia.

Previous work suggests that individuals with schizophrenia display accelerated aging of white matter integrity, however, it is still unknown whether functional brain networks also decline at an elevated rate in schizophrenia. Given the known degradation of functional connectivity and the normal decline in cognitive functioning throughout healthy aging, we aimed to test the hypothesis that efficiency of large-scale functional brain networks supporting overall cognition, as well as integrity of hub nodes within those networks, show evidence of accelerated aging in schizophrenia. Using pseudo-resting state data in 54 healthy controls and 46 schizophrenia patients, in which task-dependent signal from 3 tasks was regressed out to approximate resting-state data, we observed a significant diagnosis by age interaction in the prediction of both global and local efficiency of the cingulo-opercular network, and of the local efficiency of the fronto-parietal network, but no interaction when predicting both default mode network and whole brain efficiency. We also observed a significant diagnosis by age interaction for the node degree of the right anterior insula, left dorsolateral prefrontal cortex, and dorsal anterior cingulate cortex. All interactions were driven by stronger negative associations between age and network metrics in the schizophrenia group than the healthy controls. These data provide evidence that is consistent with accelerated aging of large-scale functional brain networks in schizophrenia that support higher-order cognitive ability.

A Review: Detecting Alterations of Brain Connectivity in Schizophrenia based on Structural MRI

Schizophrenia is a common psychiatric disease with brain connectivity changed. Nowadays brain network has become an effective tool to detect the abnormal brain connectivity in patients. Compared with studies focused on functional and white matter connectivity, the research of grey matter connectivity is relatively less and should be paid more attention. The primary approach of grey matter connectivity analysis is based on brain network constructed from structural MRI (sMRI). There are several morphometric features such as cortical thickness, volume, and curvature etc. Which could be used for sMRI brain network construction? This review briefly introduced sMRI brain network construction and analysis, presenting the most frequently used morphometric features with their effectiveness in schizophrenia, highlighting the application of sMRI brain network in schizophrenia, and finally estimated its potential value.

Effects of the Neurogranin Variant rs12807809 on Thalamocortical Morphology in Schizophrenia

Although the genome wide supported psychosis susceptibility neurogranin (NRGN) gene is expressed in human brains, it is unclear how it impacts brain morphology in schizophrenia. We investigated the influence of NRGN rs12807809 on cortical thickness, subcortical volumes and shapes in patients with schizophrenia. One hundred and fifty six subjects (91 patients with schizophrenia and 65 healthy controls) underwent structural MRI scans and their blood samples were genotyped. A brain mapping algorithm, large deformation diffeomorphic metric mapping, was used to perform group analysis of subcortical shapes and cortical thickness. Patients with risk TT genotype were associated with widespread cortical thinning involving frontal, parietal and temporal cortices compared with controls with TT genotype. No volumetric difference in subcortical structures (hippocampus, thalamus, amygdala, basal ganglia) was observed between risk TT genotype in patients and controls. However, patients with risk TT genotype were associated with thalamic shape abnormalities involving regions related to pulvinar and medial dorsal nuclei. Our results revealed the influence of the NRGN gene on thalamocortical morphology in schizophrenia involving widespread cortical thinning and thalamic shape abnormalities. These findings help to clarify underlying NRGN mediated pathophysiological mechanisms involving cortical-subcortical brain networks in schizophrenia.

State-related functional integration and functional segregation brain networks in schizophrenia

Altered topological properties of brain connectivity networks have emerged as important features of schizophrenia. The aim of this study was to investigate how the state-related modulations to graph measures of functional integration and functional segregation brain networks are disrupted in schizophrenia. Firstly, resting state and auditory oddball discrimination (AOD) fMRI data of healthy controls (HCs) and schizophrenia patients (SZs) were decomposed into spatially independent components (ICs) by group independent component analysis (ICA). Then, weighted positive and negative functional integration (inter-component networks) and functional segregation (intra-component networks) brain networks were built in each subject. Subsequently, connectivity strength, clustering coefficient, and global efficiency of all brain networks were statistically compared between groups (HCs and SZs) in each state and between states (rest and AOD) within group. We found that graph measures of negative functional integration brain network and several positive functional segregation brain networks were altered in schizophrenia during AOD task. The metrics of positive functional integration brain network and one positive functional segregation brain network were higher during the resting state than during the AOD task only in HCs. These findings imply that state-related characteristics of both functional integration and functional segregation brain networks are impaired in schizophrenia which provides new insight into the altered brain performance in this brain disorder.

From Lichtheim to Rich Club

As van den Heuvel et al1 point out in their article on rich club organization of brain networks in schizophrenia, the idea of brain network abnormality goes back a long way in psychiatry. Early (mostly German-speaking) pioneers of neuroanatomy and neurology or psychiatry, such as Meynert, Wernicke, Lichtheim, Dejerine, and Freud, appreciated the functional importance of the brain’s central whitematter and its synaptic connectivity. They understood that whitematter was organized as tracts that interconnected regions of cortex andsubcorticalnuclei.Theyreasoned,mainlyby linkingsymptoms to lesions identified by dissection, that disorders could be caused by focal lesions to cortical network nodes or the white matter links between them. Themostexplicitandclinicallysuccessful 19th-centurynetworkmodelwasprobablytheLichtheimlanguagemodel,which locatednodes for sensory,motor, andassociationcomponents of languageinmoreor lessspecificcorticalareas, linkedbywhite matter tracts, eg, the arcuate fasciculus between the Broca areaof left inferior frontal cortex and the Wernicke area of left superior temporal gyrus. This model, although not perfectly complete, has been a useful and enduring road map to guide clinical treatment of aphasic language disorders.2 It certainlyoccurred to theearlynetworkpsychiatrists that psychosis, being also associatedwithvariousdisorders of language,might equallybeexplicable in termsofdisrupted largescale brainnetworkorganization.However, therewasnoclear empiricalverificationof thishypothesis in the19thorearly20th century.Theavailablemethods formedicalbrain researchwere largely limited to clinical examination of patients in life followed by postmortem examination of their brains. This was too blunt an instrument to convincingly map the brain network diagram for psychosis, as Lichtheim had managed for language. However, methods for clinical brain mapping have vastly improved in the last 30 years, as new imaging technologies such as positron emission tomography, magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and magnetoencephalography have been developed and translated to studies of schizophrenia. This has allowed us to assemble a much more convincing evidence base for brain disorder in schizophrenia. It is now beyond doubt that schizophrenia is typically associated with an anatomically distributed set of regional nodes that have locally abnormal structure or function.3 There is also very strong prior evidence for abnormal white matter organization and for structural or functional dysconnectivity between brain regions in schizophrenia.4 Broadly speaking, 20th-century neuroimaging has definitively confirmed the speculative network hypotheses of a few outstanding 19thcentury clinical scientists. In the last decade, there has been a surge of interest and progress in mapping brain network organization at both microscopicandmacroscopic scales.This ispartlya resultof technological developments in brain-measuring instruments, like MRI scanners, but mainly a result of more conceptual developments inhow these brainmeasurements are analyzed.One major recent conceptual trend has been the increasing use of graph theory.5 In this approach, brain networks are representedabstractlyasa setof regionalnodesconnectedbyedges, and their topological properties—or patterns of connectivity— aremeasured. It hasbeen found thatnormalbraingraphshave complex (not random and not regular) topological properties, such as small-worldness, modules, and highly connected hubs. Topological abnormalities of brain graphs have also been demonstrated in case-control neuroimaging studies of patients with schizophrenia.6 In this context, van den Heuvel and colleagues have first focused their attentionon the topologyofbrain structural networks measured using DTI (acquired at 3 T) in a sample of 48 patients with chronic schizophrenia compared with a group of 45 healthy volunteers. In particular, they have asked an innovativequestion: is the rich clubof thehumanbrain connectome abnormal in schizophrenia? Rich club is a technical term innetwork theory that can be written down as a mathematical formula, but its meaning is also quite immediately intuitive. In many networks, not just brain networks, there will be a few nodes that are exceptionally well connected to other nodes. They are the high-degree hubsof thenetwork.The rich clubmetric simplymeasures the tendency of this elite group of hubs to be more densely connected to each other than would be expected by chance. As van den Heuvel and colleagues have previously shown7,8 and have replicated here, humanDTI networks normally have a rich club comprising regions of superior frontal and parietal cortex, precuneus, and thalamus that are efficiently interconnected to each other, often over long distances in anatomical space. To put it another way, the brain’s rich club has a high capacity for integrative information processingbutalsoahighphysical cost in termsof connectiondistance between club hubs. In people with schizophrenia, van den Heuvel and colleagues have clearly demonstrated that rich club organizaRelated article page 783 Opinion

Altered Amygdala Connectivity Within the Social Brain in Schizophrenia

Impairments in social cognition have been described in schizophrenia and relate to core symptoms of the disorder. Social cognition is subserved by a network of brain regions, many of which have been implicated in schizophrenia. We hypothesized that deficits in connectivity between components of this social brain network may underlie the social cognition impairments seen in the disorder. We investigated brain activation and connectivity in a group of individuals with schizophrenia making social judgments of approachability from faces (n = 20), compared with a group of matched healthy volunteers (n = 24), using functional magnetic resonance imaging. Effective connectivity from the amygdala was estimated using the psychophysiological interaction approach. While making approachability judgments, healthy participants recruited a network of social brain regions including amygdala, fusiform gyrus, cerebellum, and inferior frontal gyrus bilaterally and left medial prefrontal cortex. During the approachability task, healthy participants showed increased connectivity from the amygdala to the fusiform gyri, cerebellum, and left superior frontal cortex. In comparison to controls, individuals with schizophrenia overactivated the right middle frontal gyrus, superior frontal gyrus, and precuneus and had reduced connectivity between the amygdala and the insula cortex. We report increased activation of frontal and medial parietal regions during social judgment in patients with schizophrenia, accompanied by decreased connectivity between the amygdala and insula. We suggest that the increased activation of frontal control systems and association cortex may reflect a compensatory mechanism for impaired connectivity of the amygdala with other parts of the social brain networks in schizophrenia.

Disruption of Anterior Insula Modulation of Large-Scale Brain Networks in Schizophrenia

Systems level modeling of functional magnetic resonance imaging data has demonstrated dysfunction of several large-scale brain networks in schizophrenia. Anomalies across multiple functional networks associated with schizophrenia could be due to diffuse pathology across multiple networks or, alternatively, dysfunction at converging control(s) common to these networks. The right anterior insula has been shown to modulate activity in the central executive and default mode networks in healthy individuals. We tested the hypothesis that right anterior insula modulation of central executive and default mode networks is disrupted in schizophrenia and associated with cognitive deficits. In 44 patients with schizophrenia and 44 healthy control subjects, we used seed-based resting state functional connectivity functional magnetic resonance imaging analysis to examine connectivity between right insular subregions and central executive/default mode network regions. We also performed two directed connectivity analyses of resting state data: Granger analysis and confirmatory structural equation modeling. Between-group differences in path coefficients were used to evaluate anterior insula modulation of central executive and default mode networks. Cognitive performance was assessed with the rapid visual information processing task, a test of sustained attention. With multiple connectivity techniques, we found compelling, corroborative evidence of disruption of right anterior insula modulation of central executive and default mode networks in patients with schizophrenia. The strength of right anterior insula modulation of these networks predicted cognitive performance. Individuals with schizophrenia have impaired right anterior insula modulation of large-scale brain networks. The right anterior insula might be an emergent pathophysiological gateway in schizophrenia.

Resting-State Networks in Schizophrenia

Schizophrenia has been conceptualized as a disorder of altered brain connectivity (i.e. dysconnectivity). Until relatively recently, it was not feasible to test dysconnectivity hypotheses of schizophrenia in vivo. Resting-state functional magnetic resonance imaging (fMRI) is a powerful tool for mapping functional networks of the brain, such as the default mode network (DMN), and investigating the systems-level pathology of neurological and psychiatric disorders. In this article, we review the latest findings from resting-state fMRI studies on schizophrenia. Despite the wide array of methods used and heterogeneity of patient samples, several tentative conclusions may be drawn from the existing literature. 1) Connectivity of the DMN is altered in schizophrenia. Findings vary across studies; however, a majority of investigations reported hyper-connectivity of the DMN. 2) Resting-state connectivity of the prefrontal cortex (PFC) is reduced in schizophrenia, particularly intra-PFC connectivity. 3) Cortical-subcortical networks, including thalamocortical, frontolimbic, and cortico-cerebellar networks are altered in schizophrenia. 4) Preliminary findings indicate that functional connectivity within auditory/language networks and the basal ganglia is related to specific clinical symptoms, including auditory- verbal hallucinations and delusions. 5) Whole-brain network topology measures based on graph theory indicate that functional brain networks in schizophrenia are characterized by reduced small-worldness, lower degree connectivity of brain hubs, and decreased modularity. 6) Some of the alterations in functional connectivity observed in probands are present in unaffected relatives, raising the possibility that functional dysconnectivity is an endophenotype related to genetic risk for schizophrenia. Combined, these findings provide broad support for dysconnectivity theories of schizophrenia. We conclude our review with a discussion of the limitations of the existing literature and potentially important areas of future research.

Small-world brain networks in schizophrenia.

SummaryOver the last decade the combination of brain neuroimaging techniques and graph theoretical analysis of the complex anatomical and functional networks in the brain have provided an exciting new platform for exploring the etiology of mental disorders such as schizophrenia. This review introduces the current status of this work, focusing on the topological properties of human brain networks – called ‘small-world brain networks’ – and on the disruptions in these networks in schizophrenia. The evidence supporting the findings of reduced efficiency of information exchange in schizophrenia both within local brain regions and globally throughout the brain is reviewed and the potential relationship of these changes to cognitive and clinical symptoms is discussed. Finally we propose some suggestions for future research.

Disrupted correlation between low frequency power and connectivity strength of resting state brain networks in schizophrenia

Altered brain connectivity has emerged as a central feature of schizophrenia. Low frequency oscillations and connectivity strength (CS) of resting state brain networks are altered in patients with schizophrenia (SZs). However, the relationship between these two measures has not yet been studied. Such work may be helpful in understanding the so-called “rich club” organization (i.e. high-CS nodes are more densely connected among themselves than are nodes of a lower CS in the human brain) in healthy controls (HCs) and SZs. Here we present a study of HCs and SZs examining low frequency oscillations and CS by first decomposing resting state fMRI (R-fMRI) data into independent components (ICs) using group independent component analysis (ICA) and computing the low frequency power ratio (LFPR) of each ICA time course. Weighted brain graphs consisting of ICs were built based on correlations between ICA time courses. Positive CS and negative CS of each node in the brain graphs were then examined. The correlations between LFPR and CSs as well as “rich club” coefficients of group mean brain graphs were assessed. Results demonstrate that the LFPR of some ICs were lower in SZs compared to HCs. In addition, LFPR was correlated with positive CS in HCs, but to a lesser extent in SZs. HCs showed higher normalized rich club parameter than SZs. The findings provide new insight into disordered intrinsic brain graphs in schizophrenia.

Brain Connectivity Networks in Schizophrenia Underlying Resting State Functional Magnetic Resonance Imaging

Schizophrenia (SZ) is a severe neuropsychiatric disorder. A leading hypothesis is that SZ is a brain dysconnection syndrome, involving abnormal interactions between widespread brain networks. Resting state functional magnetic resonance imaging (R-fMRI) is a powerful tool to explore the dysconnectivity of brain networks in SZ and other disorders. Seed-based functional connectivity analysis, spatial independent component analysis (ICA), and graph theory-based analysis are popular methods to quantify brain network connectivity in R-fMRI data. Widespread network dysconnectivity in SZ has been observed using both seed-based analysis and ICA, although most seed-based studies report decreased connectivity while ICA studies report both increases and decreases. Importantly, most of the findings from both techniques are also associated with typical symptoms of the illness. Disrupted topological properties and altered modular community structure of brain system in SZ have been shown using graph theory-based analysis. Overall, the resting-state findings regarding brain networks deficits have advanced our understanding of the underlying pathology of SZ. In this article, we review aberrant brain connectivity networks in SZ measured in R-fMRI by the above approaches, and discuss future challenges.

Resting-State Networks in Schizophrenia

Schizophrenia has been conceptualized as a disorder of altered brain connectivity (i.e. dysconnectivity). Until relatively recently, it was not feasible to test dysconnectivity hypotheses of schizophrenia in vivo. Resting-state functional magnetic resonance imaging (fMRI) is a powerful tool for mapping functional networks of the brain, such as the default mode network (DMN), and investigating the systems-level pathology of neurological and psychiatric disorders. In this article, we review the latest findings from resting-state fMRI studies on schizophrenia. Despite the wide array of methods used and heterogeneity of patient samples, several tentative conclusions may be drawn from the existing literature. 1) Connectivity of the DMN is altered in schizophrenia. Findings vary across studies; however, a majority of investigations reported hyper-connectivity of the DMN. 2) Resting-state connectivity of the prefrontal cortex (PFC) is reduced in schizophrenia, particularly intra-PFC connectivity. 3) Cortical-subcortical networks, including thalamocortical, frontolimbic, and cortico-cerebellar networks are altered in schizophrenia. 4) Preliminary findings indicate that functional connectivity within auditory/language networks and the basal ganglia is related to specific clinical symptoms, including auditory- verbal hallucinations and delusions. 5) Whole-brain network topology measures based on graph theory indicate that functional brain networks in schizophrenia are characterized by reduced small-worldness, lower degree connectivity of brain hubs, and decreased modularity. 6) Some of the alterations in functional connectivity observed in probands are present in unaffected relatives, raising the possibility that functional dysconnectivity is an endophenotype related to genetic risk for schizophrenia. Combined, these findings provide broad support for dysconnectivity theories of schizophrenia. We conclude our review with a discussion of the limitations of the existing literature and potentially important areas of future research.

Abnormal topological organization of structural brain networks in schizophrenia

Schizophrenia is a debilitating mental disorder characterized by disturbances of thought and emotion as well as neurocognitive deficits. It is hypothesized that the core symptoms of schizophrenia arise from the inability to integrate neural processes segregated across distributed brain regions. Graph theory allows us to verify this hypothesis at large-scale structural network level. In this study, a sample of 101 schizophrenic patients and 101 healthy controls was included. We sought to investigate the abnormality of network topological organization in patients with schizophrenia by using the cortical thickness measurement from magnetic resonance imaging. Brain networks were constructed by thresholding cortical thickness correlation matrices of 78 regions and analyzed using graph theoretical approaches. Compared to healthy controls, patients showed increased characteristic path length and clustering coefficient in the structural cortical networks. Moreover, schizophrenia patients were associated with reduced nodal centrality in several regions of the default network and increased nodal centrality mainly in primary cortex and paralimbic cortex regions. These findings suggest that the structural networks of schizophrenic patients have a less optimal topological organization, resulting in reduced capacity to integrate information across brain regions.