Advanced methodologies resolving dimensionality complications for autism neuroimaging dataset: a comprehensive guide for beginners

Abstract

Autism spectrum disorder (ASD) is gender biased neurodevelopmental condition consisting of a triad of physiological symptoms. Neural images and neurobiology of cognitive disorders are complex but provide significant information and accurate visualization of developmental changes. The diagnosis is time-consuming and necessitates sufficient evidence to distinguish the disorder from other concomitant diseases. The most recent area of interest for cognitive research is neuroimaging, which is used to study the disorder's impact, affected region, and functional connectivity between the regions of interest. The challenges in the domain are the availability of data, the modalities of data, the selection of the correct processing strategies, and the result assessment complications. The study employed machine learning (ML) methods to process the autism data in both structural and functional data formats collected from the autism brain imaging data exchange (ABIDE) consortium. A comparative analysis among image processing methodologies with both data formats was successfully implemented. The variations in the processing pipeline and the outputs strongly suggest an emerging need for 3D/4D images to visualize better, accurate feature extraction and classification. The study aims to support the researchers in identifying the correct image format for specific objectives and the ML techniques, such as Gaussian median filters, segmentation methodologies for 2D data, or a well-defined preprocessing pipeline for 3D data, to achieve reliable and generalized results.