Functional connectivity-based classification of autism spectrum disorder using Mtual Connectivity Analysis with Local Models

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 cross-correlation 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.