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

Shaoqiang Han (), Qian Cui, Ruiping Zheng, Shuying Li, Bingqian Zhou, Keke Fang, Wei Sheng, Baohong Wen, Liang Liu, Yarui Wei, Huafu Chen (), Yuan Chen (), Jingliang Cheng () and Yong Zhang
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Shaoqiang Han: The First Affiliated Hospital of Zhengzhou University
Qian Cui: University of Electronic Science and Technology of China
Ruiping Zheng: The First Affiliated Hospital of Zhengzhou University
Shuying Li: The First Affiliated Hospital of Zhengzhou University
Bingqian Zhou: The First Affiliated Hospital of Zhengzhou University
Keke Fang: Affiliated Cancer Hospital of Zhengzhou University, Henan Cancer Hospital
Wei Sheng: University of Electronic Science and Technology of China
Baohong Wen: The First Affiliated Hospital of Zhengzhou University
Liang Liu: The First Affiliated Hospital of Zhengzhou University
Yarui Wei: The First Affiliated Hospital of Zhengzhou University
Huafu Chen: The First Affiliated Hospital of Zhengzhou University
Yuan Chen: The First Affiliated Hospital of Zhengzhou University
Jingliang Cheng: The First Affiliated Hospital of Zhengzhou University
Yong Zhang: The First Affiliated Hospital of Zhengzhou University

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract The 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.

Date: 2023
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DOI: 10.1038/s41467-023-39861-z

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