Decoding Dynamic Neural Patterns of MDD: A Preliminary Surface-Based MRI Study of Temporal Stability

587 

Abstract Submission 

Xue-Ying Li¹, Chao-Gan Yan²

¹Department of Psychology, University of Chinese Academy of Sciences, Beijing, China, ²Institute of Psychology, Chinese Academy of Sciences, Beijing, China

Xue-Ying Li  
Department of Psychology, University of Chinese Academy of Sciences
Beijing, China

Chao-Gan Yan  
Institute of Psychology, Chinese Academy of Sciences
Beijing, China

Depression poses a common mental health challenge worldwide. While it inflicts substantial burdens and suffering on patients, their families, and society, clinically useful biomarkers for depression diagnosis and treatment response remain elusive. Temporal stability describes the consistency of dynamic functional connectivity in the brain over time and is a key indicator for characterizing the topological properties of brain functional networks. Temporal stability and dynamic functional connectivity, as newly developed approaches, have been increasingly used to measure impact of depressive disorders or interventions on brain functions. Previous studies indicate correlation between these dynamic functional indices and symptomatic improvements in major depressive disorder (MDD) patients. However, clear and powerful conclusions differentiating the dynamic features in brain functional activity between MDD patients and healthy individuals are still lacking, and there is a lack of large-scale surface-based MRI studies to understand the relationship between disease courses and specific symptoms of MDD.
In an ongoing study, we utilized a cross-sectional study design to directly compare the dynamic patterns in brain functional activity between MDD patients and healthy individuals, and further explore their relationship with symptoms and long-term prognosis.

This study utilized a large multi-site MRI database aggregated by the Depression Imaging REsearch ConsorTium (DIRECT), including MDD (n = 1583) and healthy controls (n = 1308). All participants underwent structural and resting-state functional MRI scans, and depression patients were assessed with HAMD by clinicians.
To investigate the temporal stability of the whole-brain functional network, we reconstructed the cortical surface mesh and applied preprocessing based on surface space to resting-state functional MRI data. Vertex-wise dynamic functional connectivity was calculated, establishing the temporal stability of the whole-brain functional network. Stability maps were compared through two-sample t-tests, corrected using permutation tests with Threshold Free Cluster Enhancement (TFCE).
Subsequently, regions with significant stability differences were chosen as seeds to calculate their whole-brain dynamic functional connectivity maps. These maps were statistically compared between MDD patients and healthy controls using the same methods to explore the sources of observed stability differences. Significant results yeild from 3 seeds (showed by arrows and letters in Figure 1). Additionally, correlation analyses were conducted between stability values of significant regions in MDD patients and HAMD-17 item scores, as well as disease duration, unraveling potential associations between stability differences and clinical manifestations.

We found that, compared to healthy subjects, depressed patients exhibited decreased stability in brain areas of visual and somatomotor networks. This reduced stability was primarily contributed by a decrease in dynamic functional connectivity across widespread brain regions of dorsal attention, visual, somatomotor, and frontoparietal networks. Notably, stability in the somatosensory cortex positively correlated with their HAMD scores of insomnia and insight of illness in MDD patients. On the other hand, we observed increased stability in the frontoparietal and limbic networks in MDD patients. This increase could be partially attributed to enhanced dynamic functional connectivity between right insula and left precuneus, and associated with the severity of general somatic symptom in MDD patients.

This study provides preliminary evidence on associations between differentiating dynamic patterns of MDD and specific symptoms. Future examinations of these results in relation to disease courses and recurrence hold promise for identifying MRI-based biomarkers for depression treatment targets.

Psychiatric (eg. Depression, Anxiety, Schizophrenia) ¹

Task-Independent and Resting-State Analysis

BOLD fMRI ²

FUNCTIONAL MRI

Other - Major Depressive Disorder, stability, dynamic functional connectivity, depression symptoms