Aberrant resting-state co-activation network dynamics in major depressive disorder

Abstract

Major depressive disorder (MDD) is a globally prevalent and highly disabling disease characterized by dysfunction of large-scale brain networks. Previous studies have found that static functional connectivity is not sufficient to reflect the complicated and time-varying properties of the brain. The underlying dynamic interactions between brain functional networks of MDD remain largely unknown, and it is also unclear whether neuroimaging-based dynamic properties are sufficiently robust to discriminate individuals with MDD from healthy controls since the diagnosis of MDD mainly depends on symptom-based criteria evaluated by clinical observation. Resting-state functional magnetic resonance imaging (fMRI) data of 221 MDD patients and 215 healthy controls were shared by REST-meta-MDD consortium. We investigated the spatial-temporal dynamics of MDD using co-activation pattern analysis and made individual diagnoses using support vector machine (SVM). We found that MDD patients exhibited aberrant dynamic properties (such as dwell time, occurrence rate, transition probability, and entropy of Markov trajectories) in some transient networks including subcortical network (SCN), activated default mode network (DMN), de-activated SCN-cerebellum network, a joint network, activated attention network (ATN), and de-activated DMN-ATN, where some dynamic properties were indicative of depressive symptoms. The trajectories of other networks to deactivated DMN-ATN were more accessible in MDD patients. Subgroup analyses also showed subtle dynamic changes in first-episode drug-naïve (FEDN) MDD patients. Finally, SVM achieved preferable accuracies of 84.69%, 76.77%, and 88.10% in discriminating patients with MDD, FEDN MDD, and recurrent MDD from healthy controls with their dynamic metrics. Our findings reveal that MDD is characterized by aberrant dynamic fluctuations of brain network and the feasibility of discriminating MDD patients using dynamic properties, which provide novel insights into the neural mechanism of MDD.

Early altered directionality of resting brain network state transitions in the TgF344-AD rat model of Alzheimer's disease

“…They offer several advantages over QPPs, including the ability to capture and quantify inter-CAP transitions and higher statistical power due to single-frame resolution. Utilizing CAPs as an analytical tool has shown promise in classifying and differentiating properties associated with various physiological and pathological conditions (Liu et al, 2018;Gutierrez-Barragan et al, 2019Adhikari et al, 2020Adhikari et al, , 2023Cifre et al, 2021;Li et al, 2021;Lee et al, 2023;Vasilkovska et al, 2023;An et al, 2024). Recent studies employing CAP analysis in mouse models of neurodegenerative disorders (Gutierrez-Barragan et al, 2022;Adhikari et al, 2023) have demonstrated their predictive capacity in distinguishing model mice from the wildtype at different stages of the disease.…”