A sex-dependent functional-effective connectivity model for diagnostic classification of Autism Spectrum Disorder using resting-state fMRI

Haghighat, Hossein

doi:10.1016/j.bspc.2023.104837

Cited by 2 publications

(1 citation statement)

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“…Solutions based on machine learning (ML) algorithms can lead to ASD classi cation and increase the reliability and accuracy of ASD diagnosis (Bi, Wang, Shu, Sun, & Xu, 2018; Chaitra, Vijaya, & Deshpande, 2020). Due to the high spatial resolution and no need for subject cooperation in resting-state functional magnetic resonance imaging (rs-fMRI), in recent years, many ML-based models have been proposed using rs-fMRI data to diagnose ASD (Reiter, et al, 2021;Sun, et al, 2021;Haghighat, 2023). Since spontaneous uctuations in brain blood oxygen level-dependent (BOLD) signals can represent the intrinsic function of brain neural networks, rs-fMRI is commonly used to study brain functional connectivity (FC) (Biswal et al, 1995).…”

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confidence: 99%

Diagnostic classification of autism spectrum disorder in the frequency domain using resting-state fMRI

haghighat

2023

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Autism spectrum disorder (ASD) is a neurodevelopmental disorder with problems in social interactions, verbal and non-verbal communication, repetitive behaviors, and limited interests in a person. Considering the problems of diagnosing ASD using behavioral symptoms by experts, it seems necessary to propose computer-aided diagnosis systems (CADS) for ASD. We proposed an age-dependent CADS based on functional connectivity (FC) in the frequency domain for ASD using resting-state functional magnetic resonance imaging (rs-fMRI). Also, the features and classification accuracy obtained in the frequency and time domains were compared. First, preprocessing was performed on the rs-fMRI data. Then, group-independent component analysis (GICA) was used to obtain resting state networks (RSNs). This was followed by obtaining separate components of RSNs for each individual using dual regression. Then, coherence analysis was used to extract the features of FC in the frequency domain between RSNs. To consider the role of age in the classification process, three age groups of children, adolescents, and adults were considered, and feature selection for each age group was applied separately using an embedded approach, in which all WEKA classifiers were used simultaneously. Finally, classification accuracy was obtained for each age group. The proposed CADS was able to classify 95.23% in the children group, 88.1% in the adolescent group, and 92.8% in the adult group. In addition, the frequencies whose features obtained the most distinction in each age group were reported, which may play an effective role in diagnosing and monitoring the rehabilitation process in ASD.

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