Disintegration of sensorimotor brain networks in schizophrenia

Abstract

Background: Schizophrenia is a severe mental disorder associated with derogated function across various domains, including perception, language, motor, emotional, and social behavior. Due to its complex symptomatology, schizophrenia is often regarded a disorder of cognitive processes. Yet due to the frequent involvement of sensory and perceptual symptoms, it has been hypothesized that functional disintegration between sensory and cognitive processes mediates the heterogeneous and comprehensive schizophrenia symptomatology. Methods: Here, using resting-state functional magnetic resonance imaging in 71 patients and 196 healthy controls, we characterized the standard deviation in BOLD (blood-oxygen-level-dependent) signal amplitude and the functional connectivity across a range of functional brain networks. We investigated connectivity on the edge and node level using network modeling based on independent component analysis and utilized the brain network features in cross-validated classification procedures. Results: Both amplitude and connectivity were significantly altered in patients, largely involving sensory networks. Reduced standard deviation in amplitude was observed in a range of visual, sensorimotor, and auditory nodes in patients. The strongest differences in connectivity implicated within-sensorimotor and sensorimotor-thalamic connections. Furthermore, sensory nodes displayed widespread alterations in the connectivity with higher-order nodes. We demonstrated robustness of effects across subjects by significantly classifying diagnostic group on the individual level based on cross-validated multivariate connectivity features. Conclusion: Taken together, the findings support the hypothesis of disintegrated sensory and cognitive processes in schizophrenia, and the foci of effects emphasize that targeting the sensory and perceptual domains may be key to enhance our understanding of schizophrenia pathophysiology.

Global brain connectivity alterations in patients with schizophrenia and bipolar spectrum disorders

“…We also included patients with a bipolar spectrum-disorder (i.e., BDI, BDII, and BD NOS). Both the patient and control samples overlap with that of Kaufmann and colleagues, 22 although the present study includes an additional sample of patients with BD and the methods and objectives do not overlap.…”

“…44,45 Somatosensory and occipital cortices are critical for visual and sensory processing and are known to be affected in patients with schizophrenia. 20,46 In a previous study with an overlapping schizophrenia sample, 22 lower level sensory regions were shown to have reduced signal amplitude and aberrant functional network connectivity, indicating that different methodological approaches point to dysfunction in these regions. Decreased centrality in these regions suggests less coordinated sensory processing, which may be related to the range of cognitive and emotional symptoms through various downstream mechanisms.…”

“…We anticipated centrality reductions in hub regions across patient groups, with stronger reductions associated with more severe symptoms. Further, we hypothesized altered centrality of regions involved in sensorimotor and perceptual processing; [20][21][22] centrality decreases in frontal, anterior cingulate, precuneus and parietal regions in patients with schizophrenia; 18 and aberrant limbic centrality in patients with BD. 15 In order to address questions pertaining to regional specificity, we performed fullbrain analyses with stringent corrections for multiple comparisons using permutation testing.…”

Brain-Wide Analysis of Functional Connectivity in First-Episode and Chronic Stages of Schizophrenia

“…The pathophysiology of schizophrenia involves distributed functional dysconnectivity involving a number of brain regions, 1,2 including the frontal lobe, [3][4][5][6][7][8][9][10] and its language-related areas in the inferior frontal gyrus, 11,12 sensory-motor areas, 13 the temporal lobe, 14 limbic structures, 15,16 and thalamus. [17][18][19][20][21][22][23][24] Despite numerous leads, the reported findings are somewhat inconsistent and the core regions associated with the pathogenesis of schizophrenia still remain controversial.…”

An fMRI Feature Selection Method Based on a Minimum Spanning Tree for Identifying Patients with Autism

“…In recent decades, various noninvasive brain imaging methods have been widely used in ASD studies, such as electroencephalography [11], structural magnetic resonance imaging [12], resting-state magnetic resonance imaging [13,14], and diffusion tensor imaging [15], and these methods have made outstanding contributions to extracting biomarkers that characterize disease characteristics. Recently, machine learning has been used in the field of neuroimaging, because it accurately and automatically distinguishes individuals with schizophrenia from healthy people [16,17]. In light of the ASD auxiliary diagnosis, machine learning is often utilized in three key steps: (1) constructing functional connectivity networks among regions of interest (ROIs) from resting state functional magnetic resonance imaging (rs-fMRI) data, (2) analyzing the connectivity of brain regions and identifying differences in connectivity between patients and normal controls, and (3) building a classification model to discriminate individuals with ASD from non-ASD subjects using filtered functional connectivity features [18].…”