Autism Detection in Children using Common Spatial Patterns of MEG Signals.

Abstract

Autism exhibits a wide range of developmental disabilities and is associated with aberrant anatomical and functional neural patterns. To detect autism in young children (4-7 years) in an automatic and non-invasive fashion, we have recorded magnetoencephalogram (MEG) signals from 30 autistic and 30 age-matched typically developing (TD) children. We have used a machine learning classification framework with common spatial pattern (CSP)-based logarithmic band power (LBP) features. When comparing the LBP feature to the conventional logarithmic variance (LV) spatial pattern, CSP + LBP (92.77%) has performed better than CSP + LV (90.66%) in the 1-100 Hz frequency range for distinguishing autistic children from TD children. In frequency band-wise analysis using our proposed method, the high gamma frequency band (50-100 Hz) has shown the highest classification accuracy (97.14%). Our findings reveal that the occipital lobe exhibits the most distinct spatial pattern in autistic children over the whole frequency range. This study shows that spatial brain activation patterns can be utilized as potential biomarkers of autism in young children. The improved performance signifies the clinical relevance of the work for autism detection using MEG signals.