ICA-based Individualized Differential Structure Similarity Networks for Predicting Symptom Scores in Adolescents with Major Depressive Disorder

作者全名："Li, Xiang; Xu, Ming; Jiang, Rongtao; Li, Xuemei; Calhoun, Vince D.; Zhou, Xinyu; Sui, Jing"

作者地址："[Li, Xiang; Xu, Ming] Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing, Peoples R China; [Li, Xiang; Xu, Ming] Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China; [Jiang, Rongtao] Yale Sch Med, Dept Radiol & Biomed Imaging, New Haven, CT USA; [Li, Xuemei; Zhou, Xinyu] Chongqing Med Univ, Dept Psychiat, Affiliated Hosp 1, Chongqing, Peoples R China; [Calhoun, Vince D.] Georgia State Univ, Georgia Inst Technol, Tri Inst Ctr Translat Res Neuroimaging & Data Sci, Atlanta, GA USA; [Calhoun, Vince D.; Sui, Jing] Emory Univ, Atlanta, GA USA; [Sui, Jing] Beijing Normal Univ, McGovern Inst Brain Res, State Key Lab Cognit Neurosci & Learning, IDG, Beijing, Peoples R China"

通信作者："Sui, J (通讯作者)，Beijing Normal Univ, McGovern Inst Brain Res, State Key Lab Cognit Neurosci & Learning, IDG, Beijing, Peoples R China."

来源："2023 45TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY, EMBC"

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WOS号：WOS:001133788302043

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年份：2023

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文献类型：Proceedings Paper

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摘要："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."

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