EEG-based brain age prediction in infants–toddlers: Implications for early detection of neurodevelopmental disorders
The infant brain undergoes rapid developmental changes in the first three years of life. Understanding these changes through the prediction of chronological age using neuroimaging can provide insights into typical and atypical brain development. We utilized 938 resting-state EEG recordings from 457...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Developmental Cognitive Neuroscience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1878929324001543 |
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| author | Winko W. An Aprotim C. Bhowmik Charles A. Nelson Carol L. Wilkinson |
| author_facet | Winko W. An Aprotim C. Bhowmik Charles A. Nelson Carol L. Wilkinson |
| author_sort | Winko W. An |
| collection | DOAJ |
| description | The infant brain undergoes rapid developmental changes in the first three years of life. Understanding these changes through the prediction of chronological age using neuroimaging can provide insights into typical and atypical brain development. We utilized 938 resting-state EEG recordings from 457 typically developing infants, 2 to 38 months old, to develop age prediction models. The multilayer perceptron model demonstrated the highest accuracy with an R2 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. |
| format | Article |
| id | doaj-art-854829e2f1b24f6683ff84385809c21f |
| institution | Kabale University |
| issn | 1878-9293 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Developmental Cognitive Neuroscience |
| spelling | doaj-art-854829e2f1b24f6683ff84385809c21f2025-01-22T05:41:18ZengElsevierDevelopmental Cognitive Neuroscience1878-92932025-01-0171101493EEG-based brain age prediction in infants–toddlers: Implications for early detection of neurodevelopmental disordersWinko W. An0Aprotim C. Bhowmik1Charles A. Nelson2Carol L. Wilkinson3Developmental Medicine, Boston Children’s Hospital, 300 Longwood Avenue, Boston, 02115, MA, USA; Harvard Medical School, 25 Shattuck St, Boston, 02115, MA, USADonald and Barbara Zucker School of Medicine at Hofstra/Northwell, 500 Hofstra Blvd, Hempstead, 11549, NY, USA; Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, 21205, MD, USADevelopmental Medicine, Boston Children’s Hospital, 300 Longwood Avenue, Boston, 02115, MA, USA; Harvard Medical School, 25 Shattuck St, Boston, 02115, MA, USA; Harvard Graduate School of Education, 13 Appian Way, Cambridge, 02138, MA, USADevelopmental Medicine, Boston Children’s Hospital, 300 Longwood Avenue, Boston, 02115, MA, USA; Harvard Medical School, 25 Shattuck St, Boston, 02115, MA, USA; Corresponding author.The infant brain undergoes rapid developmental changes in the first three years of life. Understanding these changes through the prediction of chronological age using neuroimaging can provide insights into typical and atypical brain development. We utilized 938 resting-state EEG recordings from 457 typically developing infants, 2 to 38 months old, to develop age prediction models. The multilayer perceptron model demonstrated the highest accuracy with an R2 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.http://www.sciencedirect.com/science/article/pii/S1878929324001543Brain ageEEGNeurodevelopmentAutismDown syndrome |
| spellingShingle | Winko W. An Aprotim C. Bhowmik Charles A. Nelson Carol L. Wilkinson EEG-based brain age prediction in infants–toddlers: Implications for early detection of neurodevelopmental disorders Developmental Cognitive Neuroscience Brain age EEG Neurodevelopment Autism Down syndrome |
| title | EEG-based brain age prediction in infants–toddlers: Implications for early detection of neurodevelopmental disorders |
| title_full | EEG-based brain age prediction in infants–toddlers: Implications for early detection of neurodevelopmental disorders |
| title_fullStr | EEG-based brain age prediction in infants–toddlers: Implications for early detection of neurodevelopmental disorders |
| title_full_unstemmed | EEG-based brain age prediction in infants–toddlers: Implications for early detection of neurodevelopmental disorders |
| title_short | EEG-based brain age prediction in infants–toddlers: Implications for early detection of neurodevelopmental disorders |
| title_sort | eeg based brain age prediction in infants toddlers implications for early detection of neurodevelopmental disorders |
| topic | Brain age EEG Neurodevelopment Autism Down syndrome |
| url | http://www.sciencedirect.com/science/article/pii/S1878929324001543 |
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