Individualized Functional Connectome Enhances Severity Prediction in Major Depressive Disorder
Poster No:
417
Submission Type:
Abstract Submission
Authors:
Liangfang Li1, Jiehui Qian1, Ying Lin1, Zhengjia Dai1
Institutions:
1SUN YAT-SEN UNIVERSITY, Guangzhou, Guangdong
First Author:
Co-Author(s):
Introduction:
Major depressive disorder (MDD) is a debilitating mental health condition characterized by highly heterogeneous clinical symptoms and treatment responses (Sacchet et al., 2024). Aberrant functional connections across brain networks, including the limbic, default, frontoparietal, and salience networks, were found to be related to depressive symptoms (Korgaonkar et al., 2020). However, most previous studies utilized uniform group-level atlases to assess brain functional organization, potentially overlooking person-specific imaging features and critical brain-behavior associations essential for understanding MDD's pathophysiology (Kraus et al., 2023; Michon et al., 2022).
Methods:
We analyzed structural MRI (sMRI), resting-state fMRI (R-fMRI) data, and depression scores from 200 MDD participants and 195 healthy controls from SRPBS Multi-disorder MRI Dataset (Tanaka et al., 2021). Depressive severity was measured using the Beck Depression Inventory-II (BDI-II). R-fMRI data underwent comprehensive preprocessing using the fMRIprep pipeline (Esteban et al., 2019), including tissue segmentation, surface reconstruction, unstable volume removal, slice-timing correction, head motion correction, nuisance variable regression, and filtering. After alignment with structural images, R-fMRI data were smoothed and projected to fsaverage6 surface space. Then, we conducted individualized cortical parcellation for each participant using a multi-session hierarchical Bayesian model (Kong et al., 2021), initialized with a 400-region group-level atlas (Schaefer et al., 2018) and iteratively refining individualized patch boundary at the individual level. After individualized parcellation, we calculated region of interest (ROI) size by counting the number of vertices, and quantified case-control differences in ROI size and size variability. Finally, we trained the relevance vector regression model (10-fold cross-validation and 100 repetitions) to predict individual-level depressive severity based on individualized functional connectivity (FC), with significant correlations to BDI-II scores (p < 0.001). The same analyses directly using a group-level atlas were conducted as a baseline comparison.
Results:
We found that ROI size variability was higher in visual and association cortices compared to the sensorimotor cortex. Compared to healthy controls, MDD participants showed reduced size variability in sensorimotor and limbic networks, and increased size variability in frontoparietal and salience networks. Significant ROI size differences were observed in specific brain regions, including enlarged frontal operculum and inferior temporal gyrus (ps < 0.05, 1000 permutations, FDR-corrected) and reduced posterior cingulate (p = 0.04, 1000 permutations, FDR-corrected). A smaller size of the posterior cingulate was associated with increased depressive severity (r = -0.165, p = 0.026). Prediction analysis revealed that FCs calculated based on individualized ROIs significantly predicted BDI-II scores (r = 0.247, p < 0.001, 1000 permutations), while group-level atlas FC failed to show significant prediction (r = -0.040, p = 0.638, 1000 permutations). Predictive individualized FC distributed among the visual, subcortical, and default networks. Notably, statistical comparisons of correlation values showed that the individualized FC model statistically outperformed the group-atlas FC model (z = 6.553, p < 0.001).
·Fig 1. Individualized parcellation reveals network size variability and ROI size differences between major depressive disorder group and healthy controls.
·Fig 2. Individualized functional connectivity outperforms group-atlas functional connectivity in predicting depressive severity.
Conclusions:
Individualized parcellation facilitates the discovery of neural biomarkers for MDD by revealing unique topographical properties and improving the predictive power of intrinsic FC. These findings underscore the importance of considering individual variability in brain functional organization when exploring neural mechanisms and developing personalized neuromodulation strategies for MDD.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Affective Disorders
FUNCTIONAL MRI
Machine Learning
MRI
Psychiatric
Psychiatric Disorders
1|2Indicates the priority used for review
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