Parsing altered gray matter morphology of depression using a framework integrating the normative model and non-negative matrix factorization

Author:

Han ShaoqiangORCID,Cui Qian,Zheng Ruiping,Li Shuying,Zhou Bingqian,Fang Keke,Sheng Wei,Wen Baohong,Liu Liang,Wei Yarui,Chen HuafuORCID,Chen YuanORCID,Cheng JingliangORCID,Zhang Yong

Abstract

AbstractThe high inter-individual heterogeneity in individuals with depression limits neuroimaging studies with case-control approaches to identify promising biomarkers for individualized clinical decision-making. We put forward a framework integrating the normative model and non-negative matrix factorization (NMF) to quantitatively assess altered gray matter morphology in depression from a dimensional perspective. The proposed framework parses altered gray matter morphology into overlapping latent disease factors, and assigns patients distinct factor compositions, thus preserving inter-individual variability. We identified four robust disease factors with distinct clinical symptoms and cognitive processes in depression. In addition, we showed the quantitative relationship between the group-level gray matter morphological differences and disease factors. Furthermore, this framework significantly predicted factor compositions of patients in an independent dataset. The framework provides an approach to resolve neuroanatomical heterogeneity in depression.

Publisher

Springer Science and Business Media LLC

Subject

General Physics and Astronomy,General Biochemistry, Genetics and Molecular Biology,General Chemistry,Multidisciplinary

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