Voxel-based, brain-wide association study of aberrant functional connectivity in schizophrenia implicates thalamocortical circuitry

Abstract

Background:Wernicke’s concept of ‘sejunction’ or aberrant associations among specialized brain regions is one of the earliest hypotheses attempting to explain the myriad of symptoms in psychotic disorders. Unbiased data mining of all possible brain-wide connections in large data sets is an essential first step in localizing these aberrant circuits.Methods:We analyzed functional connectivity using the largest resting-state neuroimaging data set reported to date in the schizophrenia literature (415 patients vs. 405 controls from UK, USA, Taiwan, and China). An exhaustive brain-wide association study at both regional and voxel-based levels enabled a continuous data-driven discovery of the key aberrant circuits in schizophrenia.Results:Results identify the thalamus as the key hub for altered functional networks in patients. Increased thalamus–primary somatosensory cortex connectivity was the most significant aberration in schizophrenia (P=10−18). Overall, a number of thalamic links with motor and sensory cortical regions showed increased connectivity in schizophrenia, whereas thalamo–frontal connectivity was weakened. Network changes were correlated with symptom severity and illness duration, and support vector machine analysis revealed discrimination accuracies of 73.53–80.92%.Conclusions:Widespread alterations in resting-state thalamocortical functional connectivity is likely to be a core feature of schizophrenia that contributes to the extensive sensory, motor, cognitive, and emotional impairments in this disorder. Changes in this schizophrenia-associated network could be a reliable mechanistic index to discriminate patients from healthy controls.

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

“…The thalamus is connected to all cortical areas and conveys information to the neocortex, including information from the basal ganglia. Though a number of other studies have identified thalamic connectivity changes in the chronic stage of schizophrenia, 17,19,[22][23][24] such studies provide little information about early illness pathophysiology. In contrast, studies of FE schizophrenia have the advantage to specify brain changes at illness onset, thus providing crucial information about the pathogenesis of schizophrenia.…”

“…[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.…”

“…These subjects are from Dataset 1 # (Shanghai Mental Health Centre, prodromal, described in supplementary text S1); Dataset 2 # (Huaxi hospital, 19 38 FE and chronic). Detailed demographics of the patients are provided in table 1.…”

IC‐P‐033: Resting‐State Network Dysfunction in Alzheimer’s Disease: A Systematic Review and Meta‐Analysis

“…However, full-brain studies will be required to get a more comprehensive view on AD-related changes in rsfMRI network connectivity using meta-analyses. The current trend toward large public samples [13,62] is enabling unbiased metaanalyses, pooling neuroimaging data across many studies instead of relying on published coordinates. This will hopefully resolve most of the aforementioned limitations in the future.…”

Fine-scale patterns driving dynamic functional connectivity provide meaningful brain parcellations

“…However, the use of different parcellation schemes could lead to substantially different network structure and statistics [6,9], and there is no consensus on the ideal parcellation resolution, which is a compromise between spatial variability of the functional signals and anatomical interpretability [6,10]. Despite this, due to computational limitations only few studies have explored FC at the voxel resolution with direct assessment of the FC matrix [11][12][13][14][15][16][17]. Others [18,19] circumvented the problem by investigating eigenvector centrality of functional networks, with a fast computation method introduced by [20] that avoids the explicit calculation of the FC matrix.…”