Functional Connectivity based Machine Learning Approach for Autism Detection in Young Children using MEG Signals

Barik, Kasturi; Watanabe, Katsumi; Bhattacharya, Joydeep and Saha, Goutam. 2023. Functional Connectivity based Machine Learning Approach for Autism Detection in Young Children using MEG Signals. Journal of Neural Engineering, 20(2), 026012. ISSN 1741-2560 [Article]

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Abstract or Description

Objective: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder, and identifying early autism biomarkers plays a vital role in improving detection and subsequent life outcomes. This study aims to reveal hidden biomarkers in the patterns of functional brain connectivity as recorded by the neuro-magnetic brain responses in children with ASD.

Approach: We recorded resting-state MEG signals from thirty children with ASD (4-7 years) and thirty age, gender-matched typically developing (TD) children. We used a complex coherency-based functional connectivity analysis to understand the interactions between different brain regions of the neural system. The work characterizes the large-scale neural activity at different brain oscillations using functional connectivity analysis and assesses the classification performance of coherence-based (COH) measures for autism detection in young children. A comparative study has also been carried out on COH-based connectivity networks both region-wise and sensor-wise to understand frequency-band-specific connectivity patterns and their connections with autism symptomatology. We used Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers in the machine learning framework with a 5-fold cross-validation technique.

Main results: To classify ASD from TD children, the COH connectivity feature yields the highest classification accuracy of 91.66% in the high gamma (50-100 Hz) frequency band. In region-wise connectivity analysis, the second highest performance is in the delta band (1-4 Hz) after the gamma band. Combining the delta and gamma band features, we achieved a classification accuracy of 95.03% and 93.33% in the ANN and SVM classifiers, respectively. Using classification performance metrics and further statistical analysis, we show that ASD children demonstrate significant hyperconnectivity.

Significance: Our findings support the weak central coherency theory in autism detections. Further, despite its lower complexity, we show that region-wise coherence analysis outperforms the sensor-wise connectivity analysis. Altogether, these results demonstrate the functional brain connectivity patterns as an appropriate biomarker of autism in young children.

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Additional Information:

‘This is the Accepted Manuscript version of an article accepted for publication in Journal of Neural Engineering. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at 10.1088/1741-2552/acbe1f.

Funding: This work was partially supported by JSPS KAKENHI 22H00090.

Data Access Statement:

Availability of data and material: The codes would be made available at a reasonable request made to the corresponding author.


Autism Spectrum Disorder; Children; Brain Oscillations; Coherence; MEG; Classification

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21 February 2023Accepted
14 March 2023Published

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Date Deposited:

21 Feb 2023 17:06

Last Modified:

14 Mar 2024 02:26

Peer Reviewed:

Yes, this version has been peer-reviewed.


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