Abstract |
In this study, we aimed to find biomarkers of autism in young children. We recorded magnetoencephalography (MEG) in thirty children (4-7 years) with autism and thirty age, gender-matched controls while they were watching cartoons. We focused on characterizing neural oscillations by amplitude (power spectral density, PSD) and phase (preferred phase angle, PPA). Machine learning based classifier showed a higher classification accuracy (88%) for PPA features than PSD features (82%). Further, by a novel fusion method combining PSD and PPA features, we achieved an average classification accuracy of 94% and 98% for feature-level and score-level fusion, respectively. These findings reveal discriminatory patterns of neural oscillations of autism in young children and provide novel insight into autism pathophysiology.
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Authors | Kasturi Barik, Katsumi Watanabe, Joydeep Bhattacharya, Goutam Saha |
Journal | Journal of autism and developmental disorders
(J Autism Dev Disord)
Vol. 53
Issue 12
Pg. 4830-4848
(Dec 2023)
ISSN: 1573-3432 [Electronic] United States |
PMID | 36192669
(Publication Type: Journal Article)
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Copyright | © 2022. The Author(s). |
Topics |
- Humans
- Child
- Child, Preschool
- Magnetoencephalography
(methods)
- Autistic Disorder
(diagnosis)
- Brain
- Autism Spectrum Disorder
(diagnosis)
- Machine Learning
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