Resting state EEG classifies developmental status in three-year-old children
Monitoring cognitive development in early childhood enables detection of problems for timely intervention. However, currently recommended methods require lengthy evaluations of task performance, and are resource intense. Here we examined whether 3 minutes of resting-state EEG (rs-EEG) recorded in 70...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-08-01
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| Series: | Developmental Cognitive Neuroscience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1878929325000702 |
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| author | Dhanya Parameshwaran Supriya Bhavnani Debarati Mukherjee Kamal Kant Sharma Jennifer Jane Newson Narayan Puthanmadam Subramaniyam Gauri Divan Vikram Patel Tara C. Thiagarajan |
| author_facet | Dhanya Parameshwaran Supriya Bhavnani Debarati Mukherjee Kamal Kant Sharma Jennifer Jane Newson Narayan Puthanmadam Subramaniyam Gauri Divan Vikram Patel Tara C. Thiagarajan |
| author_sort | Dhanya Parameshwaran |
| collection | DOAJ |
| description | Monitoring cognitive development in early childhood enables detection of problems for timely intervention. However, currently recommended methods require lengthy evaluations of task performance, and are resource intense. Here we examined whether 3 minutes of resting-state EEG (rs-EEG) recorded in 70 33–40-month-old children using a 14-channel portable EEG device in low-resource households could classify performance on five domains of developmental outcomes (cognition, receptive language, expressive language, fine motor and gross motor coordination) as measured by the Bayley’s Scale of Infant and Toddler Development, 3rd Edition (BSID-III). Applying supervised learning models to a combination of spectral features and novel time-domain features derived from EEG data, we predicted BSID-III domain scores with moderate accuracy (AUCs ranging from 0.70 to 0.84 and F1-scores ranging from 0.58 to 0.76). While spectral frequencies significantly correlated with cognitive and language domain scores, time-domain features describing amplitude variability were more significantly correlated and contributed more substantially to model outcomes. Model performance was reliable even with a subset of 4 channels. Overall, this study provides a first demonstration that rs-EEG from low electrode configuration devices can serve as a quick and reliable indicator of cognitive developmental outcomes and aid in identifying those requiring support during early childhood. |
| format | Article |
| id | doaj-art-13afe9ec92bc44d08948c41230d7ae39 |
| institution | OA Journals |
| issn | 1878-9293 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Developmental Cognitive Neuroscience |
| spelling | doaj-art-13afe9ec92bc44d08948c41230d7ae392025-08-20T02:05:12ZengElsevierDevelopmental Cognitive Neuroscience1878-92932025-08-017410157510.1016/j.dcn.2025.101575Resting state EEG classifies developmental status in three-year-old childrenDhanya Parameshwaran0Supriya Bhavnani1Debarati Mukherjee2Kamal Kant Sharma3Jennifer Jane Newson4Narayan Puthanmadam Subramaniyam5Gauri Divan6Vikram Patel7Tara C. Thiagarajan8Sapien Labs Center for Human Brain and Mind at Krea, IFMR, Chennai, India; Correspondence to: Sapien Labs Center for Human Brain and Mind, Krea Admin Office, No: 196, Parthasarathy Garden Street, TT Krishnamachari Rd, Alwarpet, Chennai 600018, India.Child Development Group, Sangath, New Delhi, IndiaIndian Institute of Public Health-Bengaluru, Public Health Foundation of India, Karnataka, IndiaChild Development Group, Sangath, New Delhi, IndiaSapien Labs, Arlington, VA, USASapien Labs, Arlington, VA, USA; Faculty of Medicine and Health Technology, Tampere University, Tampere, FinlandChild Development Group, Sangath, New Delhi, IndiaDepartment of Global Health and Social Medicine, Harvard Medical School, Boston, MA, USA; Harvard Chan School of Public Health, Boston, MA, USASapien Labs Center for Human Brain and Mind at Krea, IFMR, Chennai, India; Sapien Labs, Arlington, VA, USAMonitoring cognitive development in early childhood enables detection of problems for timely intervention. However, currently recommended methods require lengthy evaluations of task performance, and are resource intense. Here we examined whether 3 minutes of resting-state EEG (rs-EEG) recorded in 70 33–40-month-old children using a 14-channel portable EEG device in low-resource households could classify performance on five domains of developmental outcomes (cognition, receptive language, expressive language, fine motor and gross motor coordination) as measured by the Bayley’s Scale of Infant and Toddler Development, 3rd Edition (BSID-III). Applying supervised learning models to a combination of spectral features and novel time-domain features derived from EEG data, we predicted BSID-III domain scores with moderate accuracy (AUCs ranging from 0.70 to 0.84 and F1-scores ranging from 0.58 to 0.76). While spectral frequencies significantly correlated with cognitive and language domain scores, time-domain features describing amplitude variability were more significantly correlated and contributed more substantially to model outcomes. Model performance was reliable even with a subset of 4 channels. Overall, this study provides a first demonstration that rs-EEG from low electrode configuration devices can serve as a quick and reliable indicator of cognitive developmental outcomes and aid in identifying those requiring support during early childhood.http://www.sciencedirect.com/science/article/pii/S1878929325000702Resting-state EEGCognitive healthEarly child developmentBSIDBayley’s Scale of Infant and Toddler DevelopmentMachine learning |
| spellingShingle | Dhanya Parameshwaran Supriya Bhavnani Debarati Mukherjee Kamal Kant Sharma Jennifer Jane Newson Narayan Puthanmadam Subramaniyam Gauri Divan Vikram Patel Tara C. Thiagarajan Resting state EEG classifies developmental status in three-year-old children Developmental Cognitive Neuroscience Resting-state EEG Cognitive health Early child development BSID Bayley’s Scale of Infant and Toddler Development Machine learning |
| title | Resting state EEG classifies developmental status in three-year-old children |
| title_full | Resting state EEG classifies developmental status in three-year-old children |
| title_fullStr | Resting state EEG classifies developmental status in three-year-old children |
| title_full_unstemmed | Resting state EEG classifies developmental status in three-year-old children |
| title_short | Resting state EEG classifies developmental status in three-year-old children |
| title_sort | resting state eeg classifies developmental status in three year old children |
| topic | Resting-state EEG Cognitive health Early child development BSID Bayley’s Scale of Infant and Toddler Development Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S1878929325000702 |
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