Functional connectivity based machine learning approach for autism detection in young children using MEG signals
Kasturi Barik1, Katsumi Watanabe2, Joydeep Bhattacharya3
1Department of Electronics and Electrical Communication Engineering, Indian Institute of Technology Kharagpur, Kharagpur, India.
Insights
This study identifies functional brain connectivity patterns as early autism biomarkers in children. Coherence-based analysis of magnetoencephalogram data achieved high accuracy in detecting autism spectrum disorder (ASD).
Area of Science:
- Neuroscience
- Biomarkers
- Developmental Disorders
Background:
- Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder.
- Early identification of autism biomarkers is crucial for improving outcomes.
- Functional brain connectivity patterns may serve as potential biomarkers.
Purpose of the Study:
- To identify novel biomarkers for autism spectrum disorder (ASD) in children.
- To investigate functional brain connectivity patterns using magnetoencephalogram (MEG) data.
- To assess the efficacy of coherence-based (COH) measures for autism detection.
Main Methods:
- Resting-state MEG signals were recorded from children with ASD and typically developing (TD) controls.
- Complex coherency-based functional connectivity analysis was applied to characterize neural activity.
- Artificial neural network (ANN) and support vector machine (SVM) classifiers were used for autism detection.
Main Results:
- High classification accuracy (91.66%) was achieved using COH features in the high gamma band (50-100 Hz).
- Combining delta (1-4 Hz) and gamma band features yielded accuracies of 95.03% (ANN) and 93.33% (SVM).
- Children with ASD exhibited significant hyperconnectivity, supporting the weak central coherency theory.
Conclusions:
- Functional brain connectivity patterns are effective biomarkers for early autism detection in children.
- Region-wise COH analysis demonstrates superior performance compared to sensor-wise analysis.
- These findings contribute to improved diagnostic approaches for autism spectrum disorder.
Abstract:
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 magnetoencephalogram signals from thirty children with ASD (4-7 years) and thirty age and 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 five-fold CV 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 detection. Further, despite its lower complexity, we show that region-wise COH 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.
