A Deep Learning Approach to Predict Autism Spectrum Disorder Using Multisite Resting-State fMRI

Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this lim...

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Bibliographic Details
Published inApplied sciences Vol. 11; no. 8; p. 3636
Main Authors Subah, Faria Zarin, Deb, Kaushik, Dhar, Pranab Kumar, Koshiba, Takeshi
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.04.2021
Subjects
ABIDE
Accuracy
Autism
autism spectrum disorder
Automation
Brain
Brain mapping
Classification
Comparative analysis
connectivity matrix
Datasets
Deep learning
functional connectivity
Functional magnetic resonance imaging
Magnetic resonance imaging
Model accuracy
Neural networks
Neuroimaging
predefined brain atlas
resting-state fMRI
Software
Time series
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Summary:Autism spectrum disorder (ASD) is a complex and degenerative neuro-developmental disorder. Most of the existing methods utilize functional magnetic resonance imaging (fMRI) to detect ASD with a very limited dataset which provides high accuracy but results in poor generalization. To overcome this limitation and to enhance the performance of the automated autism diagnosis model, in this paper, we propose an ASD detection model using functional connectivity features of resting-state fMRI data. Our proposed model utilizes two commonly used brain atlases, Craddock 200 (CC200) and Automated Anatomical Labelling (AAL), and two rarely used atlases Bootstrap Analysis of Stable Clusters (BASC) and Power. A deep neural network (DNN) classifier is used to perform the classification task. Simulation results indicate that the proposed model outperforms state-of-the-art methods in terms of accuracy. The mean accuracy of the proposed model was 88%, whereas the mean accuracy of the state-of-the-art methods ranged from 67% to 85%. The sensitivity, F1-score, and area under receiver operating characteristic curve (AUC) score of the proposed model were 90%, 87%, and 96%, respectively. Comparative analysis on various scoring strategies show the superiority of BASC atlas over other aforementioned atlases in classifying ASD and control.
ISSN:2076-3417
2076-3417
DOI:10.3390/app11083636