Functional connectivity-based classification of autism spectrum disorder using mutual connectivity analysis with local models

Akhil Kasturi; Ali Vosoughi; Nathan Hadjiyski; Larry Stockmaster; Axel Wismüller

doi:10.1117/12.3027895

4 October 2024 Functional connectivity-based classification of autism spectrum disorder using mutual connectivity analysis with local models

Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, Axel Wismüller

Author Affiliations +

Proceedings Volume 13118, Emerging Topics in Artificial Intelligence (ETAI) 2024; 1311805 (2024) https://doi.org/10.1117/12.3027895
Event: Nanoscience + Engineering, 2024, San Diego, California, United States

Abstract

In this research we explored the use of Mutual Connectivity Analysis with local models for classifying Autism Spectrum Disorder (ASD) within the ABIDE II dataset. The focus was on understanding brain region differences between individuals with ASD and healthy controls. We conducted a Multi-Voxel Pattern Analysis (MVPA), using a data-driven method to model non-linear dependencies between pairs of time series. This resulted in high-dimensional feature vectors representing the connectivity measures of the subjects, used for ASD classification. To reduce the dimensionality of the features, we used Kendall’s coefficient method, preparing the vectors for classification using a kernel-based SVM classifier. We compared our approach with methods based on crosscorrelation and Pearson correlation. The results are consistent with current literature, suggesting our method could be a useful tool in ASD research. Further studies are required to refine our method.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Akhil Kasturi, Ali Vosoughi, Nathan Hadjiyski, Larry Stockmaster, and Axel Wismüller "Functional connectivity-based classification of autism spectrum disorder using mutual connectivity analysis with local models", Proc. SPIE 13118, Emerging Topics in Artificial Intelligence (ETAI) 2024, 1311805 (4 October 2024); https://doi.org/10.1117/12.3027895

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