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Explainable machine learning algorithm for classifying resting-state functional MRI in amyotrophic lateral sclerosis
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AI
DOI：10.1016/j.neunet.2025.108359.png)
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Abstract
En 中文
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease that affects multiple brain systems. Altered brain function can be observed through resting-state functional magnetic resonance imaging (rs-fMRI). While machine learning offers significant advantages in capturing complex signal patterns across numerous voxels, its decision-making process often lacks transparency. This study aimed to develop an explainable machine learning pipeline to classify patients with ALS and healthy control (HC) using rs-fMRI data.