Knowledge Commons of Institute of Automation,CAS
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 | |
会议名称 | IEEE Engineering in Medicine and Biology |
会议日期 | July 24-27, 2023 |
会议地点 | ICC Sydney, Australia |
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. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 脑网络分析 |
国重实验室规划方向分类 | 认知机理与类脑学习 |
是否有论文关联数据集需要存交 | 否 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51653 |
专题 | 脑图谱与类脑智能实验室_脑网络组研究 |
通讯作者 | Sui Jing |
作者单位 | 1.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 |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 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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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
2023_lixiang_EMBC_MD(835KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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