CASIA OpenIR  > 脑图谱与类脑智能实验室  > 脑网络组研究
ICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive Disorder
Li Xiang1,2; Xu Ming1,2; Jiang Rongtao3; Li Xuemei4; Calhoun Vince5; Zhou Xinyu4; Sui Jing6
2023
Conference NameIEEE Engineering in Medicine and Biology
Conference DateJuly 24-27, 2023
Conference PlaceICC Sydney, Australia
Abstract

Major depressive disorder (MDD) is a complex mood disorder characterized by persistent and overwhelming depression. Previous studies have identified large scale structural brain alterations in MDD, yet most are group analyses with atlas-parcellated anatomical regions. Here we proposed a method to measure individual difference by independent component analysis (ICA)-based individual difference structural similarity network (IDSSN). This approach provided a data-adaptive, atlas-free solution that can be applied to new individual subjects. Specifically, we constructed individualized whole-brain structural covariance networks based on network perturbation approach using spatially constrained ICA. First, a set of benchmark independent components (ICs) were generated using gray matter volume (GMV) from all healthy controls. Then individual heterogeneity was obtained by calculating differences and other similarity metrics between ICs derived from “each one patient + all controls” and the benchmark ICs, resulting in 32 imaging features and structural similarity networks for each patient, which can be used for predicting multiple clinical symptoms. We estimated IDSSN for 189 adolescent MDD patients aged 10-20 years and compared them to 112 healthy adolescents. We tested their predictability of the Hamilton Anxiety Scale , the 17-item Hamilton Depression Scale and six clinical syndromes using connectome-based predictive modeling. The prediction results showed that ICA-based IDSSN features reveal more disease-specific information than those using other brain templates. We also found that depression-associated networks mainly involved the default-mode network and visual network. In conclusion, our study proposed an adaptive method that improves the ability to detect GMV deviations and specificity between one individual patient and healthy groups, providing a new perspectives and insights for evaluating individual-level clinical heterogeneity based on brain structures.

Indexed ByEI
Language英语
Sub direction classification脑网络分析
planning direction of the national heavy laboratory认知机理与类脑学习
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/51653
Collection脑图谱与类脑智能实验室_脑网络组研究
Corresponding AuthorSui Jing
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
3.Department of Radiology and Biomedical Imaging, Yale School of Medicine, CT, United States
4.Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
5.Tri-Institutional Centre for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University Georgia Institute of Technology, and Emory University, Atlanta, GA, United States
6.the IDG/McGovern Institute for Brain Research, State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Li Xiang,Xu Ming,Jiang Rongtao,et al. ICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive Disorder[C],2023.
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