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Showing 2 results for Autism Spectrum Disorder
Saba Gholami, Sara Motamed, Elham Askari,
Volume 17, Issue 1 (5-2026)
Abstract
Autism spectrum disorder (ASD) is consistently associated with abnormal functional connectivity; resting-state fMRI data were obtained from the ABIDE dataset. Dynamic functional connectivity (DFC) was obtained in an autism-specific subnetwork consisting of 17 regions identified from previous static connectivity analyses. Time-varying connectivity matrices were estimated using a sliding window approach, and recurrent connectivity states were identified using a hidden Markov model. Dynamic measures included state occupancy rate, mean dwell time, and edge-level connectivity variability. Compared with controls, individuals with ASD showed a significant decrease in the occupancy of highly integrated connectivity states (ASD: 28.6 ± 7.4% vs. control: 36.9 ± 8.1%, p < 0.001) and longer dwell times in poorly integrated connectivity states (ASD: 42.3 ± 10.2 vs. control: 31.7 ± 9.5 s, p = 0.002). In contrast, edge-level connectivity variability was significantly increased in ASD, particularly in default mode-limbic connections. Importantly, increased connectivity variability in the default mode network significantly predicted ADOS total scores (β = 0.41), (p = 0.001). These findings suggest a dissociation between reduced network state flexibility and increased moment-to-moment connectivity variability in autism spectrum disorder (ASD).
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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