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Showing 1 results for Brain Modeling
Dr Elham Askari,
Volume 17, Issue 1 (5-2026)
Abstract
Autism Spectrum Disorder is associated with atypical brain function and altered neural patterns. In this study, an unsupervised and interpretable model based on Growing Self-Organizing Maps is proposed for modeling brain patterns in individuals with ASD. Neuroimaging data from the ABIDE I T1-weighted structural MRI dataset are utilized, and discriminative features, including wavelet coefficients, entropy measures, intensity histograms, and edge-based descriptors, are extracted from brain images. The GSOM model dynamically adapts its topology to the underlying data distribution, enabling effective structural representation of brain patterns. While the learning process is fully unsupervised, class labels are introduced only in a post-hoc evaluation step to assess model performance. Experimental results demonstrate that the proposed framework achieves a classification accuracy of 94.2%, while providing clearer cluster separation and improved structural modeling compared to fixed-topology self-organizing networks. These findings indicate that GSOM is a promising tool for adaptive brain modeling and structural analysis of ASD beyond conventional classification-oriented approaches.
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