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To address this problem, this study proposes a novel method, named dynamic functional connections analysis with spectral learning (dCSL), to explore inherently temporal patterns of dFCs and further detect the brain disorders. Concretely, dCSL includes two components, dFCs estimation module and dFCs analysis module.
Sep 17, 2024
Sep 17, 2024 · This study proposes a novel method, named dynamic functional connections analysis with spectral learning (dCSL), to explore inherently temporal patterns of ...
Dynamic functional connections analysis with spectral learning for brain disorder detection. September 2024; Artificial Intelligence in Medicine 157(5):102984.
Dynamic functional connections analysis with spectral learning for brain disorder detection ... Authors: Yanfang Xue; Hui Xue; Pengfei Fang; Shipeng Zhu; Lishan ...
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Spectral analysis based on neural field theory is used to analyze dynamic connectivity via methods based on the physical eigenmodes that are the building blocks ...
Here, we developed a new framework to measure brain connectivity (in static and dynamic formats) and incorporate it to study fMRI data from MS patients and ...
Aug 11, 2024 · The present study aimed to identifying major depressive disorders (MDD) with dynamic functional connectivity (dFC) from resting-state fMRI data.
Numerous studies have applied rs-fMRI to automated detection of brain disorders such as autism spectrum disorders using various machine/deep learning techniques ...
Abstract:Deep learning (DL) methods recently show promise on accurate brain disorder classification using functional connectivity (FC) estimated from ...
Dec 9, 2024 · To this end, in this paper, we propose a multi-scale dynamic graph learning (MDGL) framework to capture multi-scale spatiotemporal dynamic ...