EEG functional connectivity as a prognostic biomarker of adaptive function in autistic people.
17 affiliations
- 1Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK. [email protected].
- 2Wolfson Neurodisability Service, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK. [email protected].
- 3Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- 4Department of Biostatistics & Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- 5Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland.
- 6Developmental & Behavioral Pediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA.
- 7Department of Pediatrics at Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
- 8Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- 9Department of Psychology, University of Cambridge, Cambridge, UK.
- 10Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- 11MRC Centre for Developmental Neurobiology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- 12Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
- 13Human Genetics and Cognitive Functions, Institut Pasteur, UMR3571 CNRS, Université de Paris, 75015, Paris, France.
- 14Department of Medical Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, Netherlands.
- 15Department of Women and Children's Health, Faculty of Life Sciences and Medicine, School of Life Course Sciences, King's College London, London, UK.
- 16Children's Neurosciences, Evelina London Children's Hospital at Guy's and St Thomas' NHS Foundation Trust, London, UK.
- 17Department of Early Life Imaging, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
Abstract
Many autistic people have challenges with adaptive function, impacting education, employment and independent-living goals. Adaptive function outcomes of autistic people vary considerably, which makes planning for future needs challenging. Here, using a developmentally sensitive approach, we investigated if cortico-cortical functional connectivity - a core neurobiological feature that differs in autism - could predict longitudinal changes in adaptive function in autistic people. Using electroencephalography in 150 autistic and 159 non-autistic participants aged 6-31 years, we investigated if mean degree and network organisation (small-world index) predict longitudinal changes in adaptive function over 19-months. We found that small-world index significantly predicted changes in adaptive function in autistic people across the entire age-range. Predictive performance was best for autistic youth (15-24-year-olds), where mean degree and small-world index explained 21 and 30% of additional variance in outcomes, respectively, outperforming measures of intelligence and autistic features. In categorising binary (improved versus not-improved) outcomes, the model containing mean degree had an AUC of 0.84 [95% CI: 0.71-0.97] in 15-24-year-olds, while that containing small-world index had an AUC of 0.76 [95% CI: 0.63-0.89] across the 6-31-year age-range. Both metrics demonstrated properties desired in prognostic biomarkers: high test-retest reliability and convergence with underlying biology (significant associations with genetic variation in brain volume-related genes). Thus, we demonstrate the first evidence that electroencephalography-derived functional connectivity metrics show promise as prognostic biomarkers of adaptive function in autistic people. Potential precision-medicine applications include stratifying participants in clinical trials and identifying those at risk of declining function in clinical settings.
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BibTeX
@article{mehra2026functional,
title = {{EEG functional connectivity as a prognostic biomarker of adaptive function in autistic people.}},
author = {Chirag Mehra and Petroula Laiou and Pilar Garcés and Joshua B Ewen and Eva Loth and Mark H Johnson and Luke Mason and Emily Jh Jones and Tony Charman and Thomas Bourgeron and Jan Buitelaar and Michael Absoud and Mark P Richardson and Declan Murphy and Jonathan O'Muircheartaigh},
journal = {Translational psychiatry},
year = {2026},
month = sep,
doi = {10.1038/s41398-026-04422-7},
url = {https://europepmc.org/article/MED/43009499},
note = {CC BY},
}
RIS
TY - JOUR
TI - EEG functional connectivity as a prognostic biomarker of adaptive function in autistic people.
AU - Chirag Mehra
AU - Petroula Laiou
AU - Pilar Garcés
AU - Joshua B Ewen
AU - Eva Loth
AU - Mark H Johnson
AU - Luke Mason
AU - Emily Jh Jones
AU - Tony Charman
AU - Thomas Bourgeron
AU - Jan Buitelaar
AU - Michael Absoud
AU - Mark P Richardson
AU - Declan Murphy
AU - Jonathan O'Muircheartaigh
PY - 2026
DA - 2026/09/09
JO - Translational psychiatry
DO - 10.1038/s41398-026-04422-7
UR - https://europepmc.org/article/MED/43009499
AB - Many autistic people have challenges with adaptive function, impacting education, employment and independent-living goals. Adaptive function outcomes of autistic people vary considerably, which makes planning for future needs challenging. Here, using a developmentally sensitive approach, we investigated if cortico-cortical functional connectivity - a core neurobiological feature that differs in autism - could predict longitudinal changes in adaptive function in autistic people. Using electroencephalography in 150 autistic and 159 non-autistic participants aged 6-31 years, we investigated if mean degree and network organisation (small-world index) predict longitudinal changes in adaptive function over 19-months. We found that small-world index significantly predicted changes in adaptive function in autistic people across the entire age-range. Predictive performance was best for autistic youth (15-24-year-olds), where mean degree and small-world index explained 21 and 30% of additional variance in outcomes, respectively, outperforming measures of intelligence and autistic features. In categorising binary (improved versus not-improved) outcomes, the model containing mean degree had an AUC of 0.84 [95% CI: 0.71-0.97] in 15-24-year-olds, while that containing small-world index had an AUC of 0.76 [95% CI: 0.63-0.89] across the 6-31-year age-range. Both metrics demonstrated properties desired in prognostic biomarkers: high test-retest reliability and convergence with underlying biology (significant associations with genetic variation in brain volume-related genes). Thus, we demonstrate the first evidence that electroencephalography-derived functional connectivity metrics show promise as prognostic biomarkers of adaptive function in autistic people. Potential precision-medicine applications include stratifying participants in clinical trials and identifying those at risk of declining function in clinical settings.
SN - 2158-3188
ER -