Transdiagnostic time‐varying dysconnectivity across major psychiatric disorders

Abstract

Dynamic functional connectivity (DFC) analysis can capture time‐varying properties of connectivity. However, studies on large samples using DFC to investigate transdiagnostic dysconnectivity across schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD) are rare. In this study, we used resting‐state functional magnetic resonance imaging and a sliding‐window method to study DFC in a total of 610 individuals (150 with SZ, 100 with BD, 150 with MDD, and 210 healthy controls [HC]) at a single site. Using k‐means clustering, DFCs were clustered into three functional connectivity states: one was a more frequent state with moderate positive and negative connectivity (State 1), and the other two were less frequent states with stronger positive and negative connectivity (State 2 and State 3). Significant 4‐group differences (SZ, BD, MDD, and HC groups; q < .05, false‐discovery rate [FDR]‐corrected) in DFC were nearly only in State 1. Post hoc analyses (q < .05, FDR‐corrected) in State 1 showed that transdiagnostic dysconnectivity patterns among SZ, BD and MDD featured consistently decreased connectivity within most networks (the visual, somatomotor, salience and frontoparietal networks), which was most obvious in both range and extent for SZ. Our findings suggest that there is more common dysconnectivity across SZ, BD and MDD than we previously expected and that such dysconnectivity is state‐dependent, which provides new insights into the pathophysiological mechanism of major psychiatric disorders.

Brain network construction and analysis for patients with mild cognitive impairment and Alzheimer's disease based on a highly‐available nodes approach

“…The first 10 volumes of the functional images’ session were discarded to allow for equilibrations of the magnetic field and slice‐timing correction to the last slice. All the remaining volumes were realigned for head movement compensation correct using the least‐squares minimization (Li et al., 2020). Without subjects had head rotations greater than 1° or head movements exceeding 2 mm on any axis.…”

Clustering of Multiple Psychiatric Disorders Using Functional Connectivity in the Data-Driven Brain Subnetwork

“…This demonstrated the extent to which the clustering results were valid and the level of statistical consistency of the distribution pattern of cluster labels at the individual subject level. One possible interpretation of this result is dynamic FC, a phenomenon in which FC presumably changes dynamically (45,46). The dynamic nature of FC may contribute to the variation in classification results for a single subject, possibly because of the insufficient number of fMRI volumes.…”

Frequency-specific coactivation patterns in resting-state and their alterations in schizophrenia: an fMRI study

“…Moreover, recurring connectivity configurations across windows could be grouped as FC-states (Allen et al 2014). These FC-states were found to be related to cognitive and physiological states such as vigilance (Wang, Ong, et al 2016), self‐generated thought (Marusak et al 2017), eyes open and closed (Weng et al 2020), and also disease alterations (Guo et al 2019; Li, Dong, et al 2020; Damaraju et al 2014). However, the choice of window length and window shape remained to be optimized (Zalesky and Breakspear 2015; Shakil, Lee, and Keilholz 2016), and the temporal resolution is also relatively low as the recommended window length is about 30-50 seconds (Hutchison et al 2013).…”

“…Moreover, recurring connectivity configurations across windows could be grouped as FC-states (Allen et al 2014). These FC-states were found to be related to cognitive and physiological states such as vigilance (Wang, Ong, et al 2016), self-generated thought (Marusak et al 2017), eyes open and closed (Weng et al 2020), and also disease alterations (Guo et al 2019;Li, Dong, et al 2020;Damaraju et al 2014).…”