The Role of Structure MRI in Diagnosing Autism

Abstract

This study proposes a Computer-Aided Diagnostic (CAD) system to diagnose subjects with autism spectrum disorder (ASD). The CAD system identifies morphological anomalies within the brain regions of ASD subjects. Cortical features are scored according to their contribution in diagnosing a subject to be ASD or typically developed (TD) based on a trained machine-learning (ML) model. This approach opens the hope for developing a new CAD system for early personalized diagnosis of ASD. We propose a framework to extract the cerebral cortex from structural MRI as well as identifying the altered areas in the cerebral cortex. This framework consists of the following five main steps: (i) extraction of cerebral cortex from structural MRI; (ii) cortical parcellation to a standard atlas; (iii) identifying ASD associated cortical markers; (iv) adjusting feature values according to sex and age; (v) building tailored neuro-atlases to identify ASD; and (vi) artificial neural networks (NN) are trained to classify ASD. The system is tested on the Autism Brain Imaging Data Exchange (ABIDE I) sites achieving an average balanced accuracy score of 97±2%. This paper demonstrates the ability to develop an objective CAD system using structure MRI and tailored neuro-atlases describing specific developmental patterns of the brain in autism.

A face image classification method of autistic children based on the two-phase transfer learning

“…Machine learning algorithms belongs to the second group of methods. ASD can be diagnosed using machine learning by analyzing genes ( Gunning and Pavlidis, 2021 ; Lin et al, 2021 ), brain ( Yahata et al, 2016 ; Eslami et al, 2019 ; Payabvash et al, 2019 ; Conti et al, 2020 ; Jiao et al, 2020 ; Doi et al, 2021 ; ElNakieb et al, 2021 ; Garbulowski et al, 2021 ; Gui et al, 2021 ; Leming et al, 2021 ; Liu et al, 2021 ; Nunes et al, 2021 ; Shi et al, 2021 ; Takahashi et al, 2021 ; Ali et al, 2022 ; Alves et al, 2023 ; ElNakieb et al, 2023 ; Martinez and Chen, 2023 ), retina ( Lai et al, 2020 ), eye activity ( Vabalas et al, 2020 ; Cilia et al, 2021 ; Liu et al, 2021 ; Kanhirakadavath and Chandran, 2022 ), facial activity ( Carpenter et al, 2021 ), human behavior ( Tariq et al, 2018 ; Drimalla et al, 2020 ) or movement ( Alcañiz Raya et al, 2020 ). Quiet a few researchers have reviewed the application of machine learning in autism detection.…”

Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging

“…sMRI is commonly used to examine brain morphology because of its high contrast sensitivity, spatial resolution, and the fact that it does not need exposure to ionizing radiation; this is especially significant for children and adolescents ( Ali et al, 2022 ). sMRI delivers various sequences of brain tissue (e.g., T1, T2, and FLAIR) created by altering excitation and repetition durations to view multiple brain regions ( Eslami et al, 2021 ).…”

“…Morphometric features include two main types, geometric and volumetric features, which can be employed for the MRI-based diagnosis of ASD. Geometric features are two-dimensional surface features associated with the cerebral cortex, such as curvature, surface area, and thickness ( Ali et al, 2022 ). While volumetric features usually refer to the size of the subcortical structures [e.g., white matter (WM) volume] ( Ecker et al, 2010 ).…”

Abnormal gray matter volume and functional connectivity patterns in social cognition‐related brain regions of young children with autism spectrum disorder

“…It is increasingly possible to use MRI to assist in the diagnosis of ASD. One study (Ali et al, 2022) found that ASD could be successfully identified using anatomical features of the brain. Furthermore, another (Dekhil et al, 2020) introduced a computer‐aided diagnosis system (CAD) that enabled the prediction of ASD from TD individuals (accuracy >0.75) using sMRI and rs‐fMRI features of the brain across the ABIDE database.…”