Academic Journal

Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development

التفاصيل البيبلوغرافية
العنوان: Explainable spatio-temporal graph evolution learning with applications to dynamic brain network analysis during development
المؤلفون: Longyun Chen, Chen Qiao, Kai Ren, Gang Qu, Vince D. Calhoun, Julia M. Stephen, Tony W. Wilson, Yu-Ping Wang
المصدر: NeuroImage, Vol 298, Iss , Pp 120771- (2024)
بيانات النشر: Elsevier, 2024.
سنة النشر: 2024
المجموعة: LCC:Neurosciences. Biological psychiatry. Neuropsychiatry
مصطلحات موضوعية: Spatio-temporal dependencies, Explainability, Dynamic functional connectivity, Brain development, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
الوصف: Modeling dynamic interactions among network components is crucial to uncovering the evolution mechanisms of complex networks. Recently, spatio-temporal graph learning methods have achieved noteworthy results in characterizing the dynamic changes of inter-node relations (INRs). However, challenges remain: The spatial neighborhood of an INR is underexploited, and the spatio-temporal dependencies in INRs’ dynamic changes are overlooked, ignoring the influence of historical states and local information. In addition, the model’s explainability has been understudied. To address these issues, we propose an explainable spatio-temporal graph evolution learning (ESTGEL) model to model the dynamic evolution of INRs. Specifically, an edge attention module is proposed to utilize the spatial neighborhood of an INR at multi-level, i.e., a hierarchy of nested subgraphs derived from decomposing the initial node-relation graph. Subsequently, a dynamic relation learning module is proposed to capture the spatio-temporal dependencies of INRs. The INRs are then used as adjacent information to improve the node representation, resulting in comprehensive delineation of dynamic evolution of the network. Finally, the approach is validated with real data on brain development study. Experimental results on dynamic brain networks analysis reveal that brain functional networks transition from dispersed to more convergent and modular structures throughout development. Significant changes are observed in the dynamic functional connectivity (dFC) associated with functions including emotional control, decision-making, and language processing.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 1095-9572
Relation: http://www.sciencedirect.com/science/article/pii/S1053811924002684; https://doaj.org/toc/1095-9572
DOI: 10.1016/j.neuroimage.2024.120771
URL الوصول: https://doaj.org/article/a77a1a3d97a042c496e33666378de184
رقم الانضمام: edsdoj.77a1a3d97a042c496e33666378de184
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:10959572
DOI:10.1016/j.neuroimage.2024.120771