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White-matter functional topology: a neuromarker for classification and prediction in unmedicated depression.

ABSTRACT: Aberrant topological organization of brain connectomes underlies pathological mechanisms in major depressive disorder (MDD). However, accumulating evidence has only focused on functional organization in brain gray-matter, ignoring functional information in white-matter (WM) that has been confirmed to have reliable and stable topological organizations. The present study aimed to characterize the functional pattern disruptions of MDD from a new perspective-WM functional connectome topological organization. A case-control, cross-sectional resting-state functional magnetic resonance imaging study was conducted on both discovery [91 unmedicated MDD patients, and 225 healthy controls (HCs)], and replication samples (34 unmedicated MDD patients, and 25 HCs). The WM functional networks were constructed in 128 anatomical regions, and their global topological properties (e.g., small-worldness) were analyzed using graph theory-based approaches. At the system-level, ubiquitous small-worldness architecture and local information-processing capacity were detectable in unmedicated MDD patients but were less salient than in HCs, implying a shift toward randomization in MDD WM functional connectomes. Consistent results were replicated in an independent sample. For clinical applications, small-world topology of WM functional connectome showed a predictive effect on disease severity (Hamilton Depression Rating Scale) in discovery sample (r?=?0.34, p?=?0.001). Furthermore, the topologically-based classification model could be generalized to discriminate MDD patients from HCs in replication sample (accuracy, 76%; sensitivity, 74%; specificity, 80%). Our results highlight a reproducible topologically shifted WM functional connectome structure and provide possible clinical applications involving an optimal small-world topology as a potential neuromarker for the classification and prediction of MDD patients.

SUBMITTER: Li J

PROVIDER: S-EPMC7603321 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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White-matter functional topology: a neuromarker for classification and prediction in unmedicated depression.

Li Jiao J Chen Heng H Fan Feiyang F Qiu Jiang J Du Lian L Xiao Jinming J Duan Xujun X Chen Huafu H Liao Wei W

Translational psychiatry 20201030 1

Aberrant topological organization of brain connectomes underlies pathological mechanisms in major depressive disorder (MDD). However, accumulating evidence has only focused on functional organization in brain gray-matter, ignoring functional information in white-matter (WM) that has been confirmed to have reliable and stable topological organizations. The present study aimed to characterize the functional pattern disruptions of MDD from a new perspective-WM functional connectome topological orga ...[more]

PMID: 33127899

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Dataset Information

White-matter functional topology: a neuromarker for classification and prediction in unmedicated depression.

Publications

White-matter functional topology: a neuromarker for classification and prediction in unmedicated depression.

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