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

Abstract

Schizophrenia (SZ) is a severe neuropsychiatric disorder. A leading hypothesis is that SZ is a brain dysconnection syndrome, involving abnormal interactions between wide-spread 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. Wide-spread 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. On the whole, 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.

Neurobiological Correlates of Core Symptoms in Anorexia:Abnormal Spatiotemporal Structure in the Resting-StateActivity and Troubles of the Self

“…[13][14][15]. These findings supporting the alteration of the resting-state's spatial structure in schizophrenia coupled with the evidences of abnormal activity balances between different regions, abnormal functional connectivity within and between networks, and abnormal frequency fluctuations in midline regions lend support to the assumption that schizophrenia is a "dysconnectivity syndrome" that affects the whole brain including its various regions and networks (Stephan, Friston & Frith, 2009) [16].…”

Spatial Variance in Resting fMRI Networks of Schizophrenia Patients: An Independent Vector Analysis

“…32 In this study, we used the IVA-GL as an approach to estimate subject-level functional network spatial maps (similar to the ones from GICA) that enable us to focus on predictions regarding subject-level spatial variability. We were able to examine differences in identified functional networks in SZs compared to HCs and our detection of significant voxelwise differences in weighted amplitude (see figure 2, table 1, and test A) in components representing basal ganglia, bilateral temporal, sensorimotor, and visual networks provides a new perspective on the implication of these regions in schizophrenia from earlier Note: FEW, family wise error; IVA-GL, independent vector analysis Gaussian/Laplace density model.…”

Cerebellar-Thalamic Connectivity in Schizophrenia

“…For example, one such prominent theory, put forth by Andreasen and colleagues, 9 suggested that schizophrenia involves a disruption in the integration of cortical-subcortical-cerebellar circuits, a hypothesis termed "cognitive dysmetria." Although the cognitive dysmetria hypothesis suggested the critical involvement of cerebellar, striatal, and thalamic circuits as well as cortical circuits, the majority of the work on structural and functional connectivity deficits in schizophrenia has focused on cortical and striatal dysfunction and disconnection, [10][11][12][13][14] with the need for additional work on the thalamus. Alterations in the structure [15][16][17] and function of the thalamus are prominent in the schizophrenia literature.…”