Predicting longitudinal changes in adaptive function in autism using EEG functional connectivity – A developmental approach: BPNA 2025 Annual Conference, Abstracts of the Annual Meeting, 8-10 January 2025, Oxford, UK

TY - CHAP

T1 - Predicting longitudinal changes in adaptive function in autism using EEG functional connectivity – A developmental approach

T2 - BPNA 2025 Annual Conference, Abstracts of the Annual Meeting, 8-10 January 2025, Oxford, UK

AU - Mehra, Chirag

AU - Laiou, Petroula

AU - Garcés, Pilar

AU - Ewen, Joshua B.

AU - Loth, Eva

AU - Murphy, Declan

AU - Richardson, Mark

AU - O'Muircheartaigh, Jonathan

PY - 2025/1/1

Y1 - 2025/1/1

N2 - Objective: Many autistic people have challenges with adaptive function, impacting education, employment and independent living attainments. Adaptive function outcomes of autistic people vary considerably, but there are few methods to predict them. This makes planning for future independence or support needs challenging. Here, we investigated if quantifying cortical functional connectivity – a neurobiological feature altered in autism – combined with a developmentally sensitive analysis approach, could predict longitudinal changes in adaptive function in autistic people.Methods: We quantified resting state electroencephalography (EEG)-derived functional connectivity in 309 autistic and non-autistic participants, aged 6-31-years. We investigated if the number of functional connections in a brain network (mean degree) and the extent of network small-world organisation (small-world index) could predict longitudinal changes in adaptive function 12-30 months after the EEG was recorded. Predictive performance was quantified using general linear models and cross-validation. We assessed whether predictive performance and functional connectivity differences between autistic and non-autistic people differed by developmental stage. We explored the association between functional connectivity and genetic variation.Results: Small-world organisation of functional connectivity significantly predicted longitudinal changes in adaptive function in autistic people across the entire age-range. Predictive performance was best in 15-21-year-olds, where small-world index and mean degree explained 30% and 33% of the variance in adaptive function outcomes, respectively. In this age-group, functional connectivity measures outperformed measures of intelligence and autistic features in predictive ability. Functional connectivity differences between autistic and non-autistic people also varied by developmental stage. In autistic people, both small-world index and mean degree were associated with polygenic variation in brain volume.Conclusions: EEG-derived mean degree and small-world index may be developed as prognostic biomarkers for adaptive function outcomes in autistic people. Further, considering developmental stage may reconcile the heterogeneity in existing autism connectivity literature.

AB - Objective: Many autistic people have challenges with adaptive function, impacting education, employment and independent living attainments. Adaptive function outcomes of autistic people vary considerably, but there are few methods to predict them. This makes planning for future independence or support needs challenging. Here, we investigated if quantifying cortical functional connectivity – a neurobiological feature altered in autism – combined with a developmentally sensitive analysis approach, could predict longitudinal changes in adaptive function in autistic people.Methods: We quantified resting state electroencephalography (EEG)-derived functional connectivity in 309 autistic and non-autistic participants, aged 6-31-years. We investigated if the number of functional connections in a brain network (mean degree) and the extent of network small-world organisation (small-world index) could predict longitudinal changes in adaptive function 12-30 months after the EEG was recorded. Predictive performance was quantified using general linear models and cross-validation. We assessed whether predictive performance and functional connectivity differences between autistic and non-autistic people differed by developmental stage. We explored the association between functional connectivity and genetic variation.Results: Small-world organisation of functional connectivity significantly predicted longitudinal changes in adaptive function in autistic people across the entire age-range. Predictive performance was best in 15-21-year-olds, where small-world index and mean degree explained 30% and 33% of the variance in adaptive function outcomes, respectively. In this age-group, functional connectivity measures outperformed measures of intelligence and autistic features in predictive ability. Functional connectivity differences between autistic and non-autistic people also varied by developmental stage. In autistic people, both small-world index and mean degree were associated with polygenic variation in brain volume.Conclusions: EEG-derived mean degree and small-world index may be developed as prognostic biomarkers for adaptive function outcomes in autistic people. Further, considering developmental stage may reconcile the heterogeneity in existing autism connectivity literature.

UR - http://www.scopus.com/inward/record.url?scp=85214777344&partnerID=8YFLogxK

U2 - 10.1111/dmcn.16180

DO - 10.1111/dmcn.16180

M3 - Conference paper

VL - 67

T3 - Developmental medicine and child neurology

SP - 6

EP - 22

BT - Developmental Medicine & Child Neurology

ER -