Altered spatio-temporal state patterns for functional dynamics estimation in first-episode drug-naive major depression

Abstract

Patients with major depressive disorder (MDD) display affective and cognitive impairments. Although MDD-associated abnormalities of brain function and structure have been explored in depth, the relationships between MDD and spatio-temporal large-scale functional networks have not been evaluated in large-sample datasets. We employed data from International Big-Data Center for Depression Research (IBCDR), and comparable 543 healthy controls (HC) and 314 first-episode drug-naive (FEDN) MDD patients were included. We used a multivariate pattern classification method to learn informative spatio-temporal functional states. Brain states of each participant were extracted for functional dynamic estimation using an independent component analysis. Then, a multi-kernel pattern classification method was developed to identify discriminative spatio-temporal states associated with FEDN MDD. Finally, statistical analysis was applied to intrinsic and clinical brain characteristics. Compared with HC, FEDN MDD patients exhibited altered spatio-temporal functional states of the default mode network (DMN), the salience network, a hub network (centered on the dorsolateral prefrontal cortex), and a relatively complex coupling network (visual, DMN, motor-somatosensory and subcortical networks). Multi-kernel classification models to distinguish patients from HC obtained areas under the receiver operating characteristic curves up to 0.80. Classification scores correlated with Hamilton Depression Rating Scale scores and age at MDD onset. FEDN MDD patients had multiple abnormal spatio-temporal functional states. Classification scores derived from these states were related to symptom severity. The assessment of spatio-temporal states may represent a powerful clinical and research tool to distinguish between neuropsychiatric patients and controls. Supplementary Information The online version contains supplementary material available at 10.1007/s11682-022-00739-1.

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

“…In this study, we found dynamic fMRI metrics achieved accuracies of 84.69%, 76.77%, and 88.10% in distinguishing MDD patients, FEDN MDD patients, and recurrent MDD patients from healthy controls, indicating that dynamic functional properties may serve as potential biomarkers for stratifying MDD patients from healthy controls. Our model constructed with SVM and transient networks is comparable to those studies using static FC [ 12 – 14 , 61 ], dynamic FC [ 62 – 64 ], the joint of two FCs [ 62 ] as input features and is superior to models constructed with structural features [ 65 ] in MDD.…”