Dynamic state with covarying brain activity-connectivity: On the pathophysiology of schizophrenia

• Type of Material: Article
• Authors: Zening Fu, Emory University; Armin Iraji, Emory University; Jessica A Turner, Emory University; Jing Sui, Emory University; Godfrey D Pearlson, Olin Neuropsychiatry Research Center, the Institute of Living; Vince Calhoun, Emory University
• Language: English
• Date: 2021-01-01
• Publisher: ACADEMIC PRESS INC ELSEVIER SCIENCE
• Publication Version: Author Accepted Manuscript (After Peer Review)
• Copyright Statement: © 2020 The Authors. Published by Elsevier Inc.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1016/j.neuroimage.2020.117385
• Title of Journal or Parent Work: NEUROIMAGE
• Volume: 224
• Start Page: 117385
• End Page: 117385
• Keywords: METAANALYSIS; Radiology, Nuclear Medicine & Medical Imaging; Covarying activity-connectivity; Neurosciences; Network efficiency; 1ST-EPISODE SCHIZOPHRENIA; LOW-FREQUENCY FLUCTUATIONS; COMPONENT; Neurosciences & Neurology; Neuroimaging; NETWORKS; AMPLITUDE; Schizophrenia; FUNCTIONAL CONNECTIVITY; Thalamocortical network dynamics; FMRI DATA; Science & Technology; Life Sciences & Biomedicine; OSCILLATIONS; ICA; Reoccurring brain state

Abstract

The human brain is a dynamic system that incorporates the evolution of local activities and the reconfiguration of brain interactions. Reoccurring brain patterns, regarded as “brain states”, have revealed new insights into the pathophysiology of brain disorders, particularly schizophrenia. However, previous studies only focus on the dynamics of either brain activity or connectivity, ignoring the temporal co-evolution between them. In this work, we propose to capture dynamic brain states with covarying activity-connectivity and probe schizophrenia-related brain abnormalities. We find that the state-based activity and connectivity show high correspondence, where strong and antagonistic connectivity is accompanied with strong low-frequency fluctuations across the whole brain while weak and sparse connectivity co-occurs with weak low-frequency fluctuations. In addition, graphical analysis shows that connectivity network efficiency is associated with the fluctuation of brain activities and such associations are different across brain states. Compared with healthy controls, schizophrenia patients spend more time in weakly-connected and -activated brain states but less time in strongly-connected and -activated brain states. schizophrenia patients also show lower efficiency in thalamic regions within the “strong” states. Interestingly, the atypical fractional occupancy of one brain state is correlated with individual attention performance. Our findings are replicated in another independent dataset and validated using different brain parcellation schemes. These converging results suggest that the brain spontaneously reconfigures with covarying activity and connectivity and such co-evolutionary property might provide meaningful information on the mechanism of brain disorders which cannot be observed by investigating either of them alone.