Classification of Autism Spectrum Disorder using Resting State-Functional Magnetic Resonance Imaging and Artificial Neural Network

K. Lakshmipriya; R. Harini; A. Jegadheep Edwin; O. Uma Maheswari

doi:10.36948/ijfmr.2024.v06i02.18204

Classification of Autism Spectrum Disorder using Resting State-Functional Magnetic Resonance Imaging and Artificial Neural Network

Author(s)	K. Lakshmipriya, R. Harini, A. Jegadheep Edwin, O. Uma Maheswari
Country	India
Abstract	Autism Spectrum Disorder (ASD) represents a growing challenge in public health, characterized by irreversible neurodevelopmental abnormalities. Despite a rising prevalence, the underlying aetiology and neural substrates of ASD remain incompletely understood. Resting state-functional Magnetic Resonance Imaging (rs-fMRI) has emerged as a valuable non-invasive tool for probing the organization and cognitive functions of the brain by capturing hemodynamic changes. This study explores the relationship between Functional Connectivity (FC) alterations, measured by rs-fMRI, and the manifestation of ASD-related brain disorders. In this paper, we propose a hybrid approach employing an InfoMax Independent Component Analysis (ICA) algorithm and Artificial Neural Network to distinguish between the ASD subjects and Healthy Controls (HCs). Initially, rs-fMRI datasets are preprocessed using Statistical Parametric Mapping (SPM). Further, these datasets are processed using InfoMax ICA to extract the distinct features such as fractional Amplitude of Low-Frequency Fluctuations (fALFF) and Dynamic Range and visualize the FC alterations by region mapping. In addition to this, the number of activated voxels for each brain regions are estimated to observe the neural abnormalities. Finally, the extracted features are fed as input to train, test and validate the ANN classifier. The efficacy of the proposed network is evaluated using the Autism Brain Imaging Data Exchange (ABIDE) dataset and an accuracy of 75.3 percentage is achieved.
Keywords	Resting state-functional Magnetic Resonance Imaging, Autism Spectrum Disorder, Functional Connectivity, InfoMax Independent Component Analysis, Artificial Neural Network, Fractional Amplitude of Low-Frequency Fluctuations, Dynamic Range
Field	Engineering
Published In	Volume 6, Issue 2, March-April 2024
Published On	2024-04-26
Cite This	Classification of Autism Spectrum Disorder using Resting State-Functional Magnetic Resonance Imaging and Artificial Neural Network - K. Lakshmipriya, R. Harini, A. Jegadheep Edwin, O. Uma Maheswari - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.18204
DOI	https://doi.org/10.36948/ijfmr.2024.v06i02.18204
Short DOI	https://doi.org/gtsg66

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Classification of Autism Spectrum Disorder using Resting State-Functional Magnetic Resonance Imaging and Artificial Neural Network

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