EEG-based brain age prediction in infants–toddlers: Implications for early detection of neurodevelopmental disorders
Year: 2024
Type: article
Abstract: of 0.83 and a mean absolute error of 91.7 days. Feature importance analysis that combined hierarchical clustering and Shapley values identified two feature clusters describing periodic alpha and low beta activity as key predictors of age. Application of the model to EEG data from infants later diagnosed with autism or Down syndrome revealed significant underestimations of chronological age, supporting its potential as a clinical tool for early identification of alterations in brain development.
Authors: Winko W. An, Aprotim C. Bhowmik, Charles A. Nelson, Carol L. Wilkinson
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,440
Awards: G1065523186, G1649372110, Neural Markers of Language Development in Fragile X Syndrome and Autism Spectrum Disorder, G8809709093, G9000055236, G9000076522, G9000143013, Boston Children's Hospital/HMS Intellectual and Developmental Disabilities Research Center, Translational Post-doctoral Training in Neurodevelopment, Neural, Physiological, Behavioral, and Environmental Risk Markers of Anxiety from Infancy to Adolescence
OpenAlex ID: W4405531748