Spatial Dynamic Functional Connectivity Analysis Identifies Distinctive Biomarkers in Schizophrenia

Abstract

Dynamic functional network connectivity (dFNC) analysis is a widely-used to study associations between dynamic functional correlations and cognitive abilities. Traditional methods analyze time-varying association of different spatial networks while assuming that the spatial network itself is stationary. However, there has been very little work focused on voxelwise spatial variability. Exploiting the variability across both the temporal and spatial domains provide a more promising direction to obtain reliable dynamic functional patterns. However, methods for extracting time-varying spatio-temporal patterns from large-scale functional magnetic resonance imaging (fMRI) data present some challenges, such as degradation in performance with respect to increase in size of the data, estimation of the number of dynamic components, and the potential sensitivity of the resulting dFNCs to selection of the networks. In this work, we implement subsequent extraction of exemplars and dynamics using a constrained independent vector analysis, a data-driven method that efficiently estimates spatial and temporal dynamics from large-scale resting-state fMRI data. We explore the benefits of analyzing spatial dFNC (sdFNC) patterns over temporal dFNC (tdFNC) patterns in the context of differentiating healthy controls and patients with schizophrenia. Our results indicate that for resting-state fMRI data, sdFNC patterns were able to better classify patients and controls, and yield more distinguishing features compared with tdFNC patterns. We also estimate structured patterns of connectivity/states using sdFNC patterns, an area that has not been studied so far, and observe that sdFNC was able to successfully capture distinct information from healthy controls and patients with schizophrenia. In addition, sdFNC patterns were also able to identify functional patterns that associate with signs of paranoia and abnormalities in the patients group. We also observe that patients with schizophrenia tend to switch to or stay in a state corresponding to a hyperconnected brain network.

Altered Dynamic Functional Connectivity of Cuneus in Schizophrenia Patients: A Resting-State fMRI Study

“…In our study, SZ patients exhibited decreased cuneus mean strength connectivity (dFC-Str) in several brain areas, such as the left cuneus and right calcarine, the right cuneus and right calcarine, the right cuneus and right lingual gyrus, and between the right cuneus and right-middle temporal gyrus. These changes were all observed in dFC and not in sFC, this may be because some brain disorders need short-time-scale analyses in order to capture subtle deficits., Previous and current rsfMRI researches on SZ have increasingly focused on dFC [1,22,23], especially in specific time intervals (windows) [15,23,[103][104][105][106][107]. Our results are in accordance with Damaraju et al in SZ and Nguyen et al in Bipolar Disorder studies, who demonstrated how dynamic FC has more predictive accuracy than static FC [15,108].…”

Aberrant Dynamic Functional Connectivity of Default Mode Network in Schizophrenia and Links to Symptom Severity

“…Unlike conventional sFC, which is obtained from the correlation within an entire time series, dynamic functional connectivity (dFC) or its network analog, dynamic functional network connectivity (dFNC) refers to the connectivity between pairs of brain regions (or networks) within sub-intervals of time series (Calhoun et al, 2014 ). In fact, dFC research suggests that cognitive deficits and clinical symptoms associated with many psychiatric disorders not only depend on the strength of the connectivity between any brain regions but also on the variation of connectivity strength between those regions over time (Calhoun et al, 2014 ; Damaraju et al, 2014 ; Du et al, 2015 ; Engels et al, 2018 ; Vergara et al, 2018 ; Bhinge et al, 2019 ; Sanfratello et al, 2019 ; Schumacher et al, 2019 ).…”

Abnormal Brain Functional Network Dynamics in Acute CO Poisoning

“…As dynamical methods advance, a growing interest in the temporal properties of the brain has increased, of which dynamic functional network connectivity (dFNC) is a promising research direction. As a new approach, dFNC has demonstrated its effectiveness in various diseases and conditions ( Bhinge et al, 2019 ; Salman et al, 2019 ; Zhu et al, 2019 ). However, no systematic studies have been conducted to explore the dynamic FC characteristics of CO intoxication as a disorder with severe neurobehavioral or cognitive sequelae.…”