Multivariate Resting-State EEG Organization in Autism: Shared Age-Related Structure and Diagnostic Context Dependent IQ Associations
Abstract
Resting-state electroencephalography (EEG) studies of autism have often sought categorical differentiation between autistic and typical neurodevelopmental groups, with limited stability across features or classifiers. A neurodiversity-informed perspective views autism as part of human neurodevelopmental variation, while recognizing associated disability and support needs. In this view, overlap between autistic and non-autistic EEG distributions is not merely classification noise, but may reflect shared dimensions of neural organization. A complementary question arise is whether position along these dimensions has different functional meaning across diagnostic contexts. We conducted an exploratory secondary analysis of 726 eyes-open resting- state EEG features previously extracted in a published multi-site autism EEG study, with a final analytic sample of 767 children and adolescents: 344 controls (CON), 347 participants classified by the Autism Diagnostic Observation Schedule as Autistic Disorder (ADOS-A), and 76 classified as Autism Spectrum (ADOS-S). Principal component analysis was applied once to the pooled sample without including age, sex, IQ, or diagnostic group. The dominant component (PC1) was interpreted post hoc by examining its EEG feature loading pattern, its association with chronological age, the consistency of this age association across diagnostic groups, and the concentration of age-related variance across components. We then tested whether age-adjusted position on this component was differentially associated with general cognitive performance (i.e., IQ) across groups. PC1 explained 15.94% of feature variance and was characterized by the strongest positive loadings on delta-band power and the strongest negative loadings on sample entropy. PC1 showed a significant negative association with chronological age (𝑟 = −0.846, 𝑝 < .001), indicating that older age was associated with relatively lower delta power and higher sample entropy along this component, consistent with previously reported developmental EEG changes. Age-related EEG variance was concentrated primarily on PC1 rather than on subsequent components, supporting its interpretation as a shared age-related EEG dimension. After removing PC1’s linear association with age, residual PC1 scores (PC1r) were negatively associated with IQ within the ADOS-A group (𝑏 = −0.612, 𝑝 < .001), but not within the control or ADOS-S groups. Pairwise slope contrasts indicated that this association was significantly more negative in ADOS-A than in CON, whereas ADOS-A vs. ADOS-S and ADOS-S vs. CON contrasts were not significant. These findings shift emphasis from categorical EEG abnormality toward individual variation within a broadly shared age-related EEG organization, in which brain–cognition coupling depends on diagnostic context.
Related
- Exploring the role of multivariate associations between brain networks and gut microbiota in (food) value-based decision-making
- SRF3 couples cellular iron homeostasis and defense responses through ubiquitin-dependent nano-organization
- Stage-Specific Associations Between Respiratory Pathogens and Mortality in Sheep: A Multivariate Analysis.
- MMAD-Risk: Multivariate Mixed Survival Analysis for the Prediction of Age-Dependent Disease Risks from Plasma Proteomes
- Eye movements reflect capacity-dependent organization of working memory
BibTeX
@misc{wang2026multivariate,
title = {{Multivariate Resting-State EEG Organization in Autism: Shared Age-Related Structure and Diagnostic Context Dependent IQ Associations}},
author = {Yang WANG and Yingying Wang and Sandra Chan},
howpublished = {PsyArXiv},
year = {2026},
month = sep,
doi = {10.31234/osf.io/vecf8_v1},
url = {https://osf.io/preprints/psyarxiv/vecf8_v1/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Multivariate Resting-State EEG Organization in Autism: Shared Age-Related Structure and Diagnostic Context Dependent IQ Associations
AU - Yang WANG
AU - Yingying Wang
AU - Sandra Chan
PY - 2026
DA - 2026/09/29
PB - PsyArXiv
DO - 10.31234/osf.io/vecf8_v1
UR - https://osf.io/preprints/psyarxiv/vecf8_v1/
AB - Resting-state electroencephalography (EEG) studies of autism have often sought categorical differentiation between autistic and typical neurodevelopmental groups, with limited stability across features or classifiers. A neurodiversity-informed perspective views autism as part of human neurodevelopmental variation, while recognizing associated disability and support needs. In this view, overlap between autistic and non-autistic EEG distributions is not merely classification noise, but may reflect shared dimensions of neural organization. A complementary question arise is whether position along these dimensions has different functional meaning across diagnostic contexts. We conducted an exploratory secondary analysis of 726 eyes-open resting- state EEG features previously extracted in a published multi-site autism EEG study, with a final analytic sample of 767 children and adolescents: 344 controls (CON), 347 participants classified by the Autism Diagnostic Observation Schedule as Autistic Disorder (ADOS-A), and 76 classified as Autism Spectrum (ADOS-S). Principal component analysis was applied once to the pooled sample without including age, sex, IQ, or diagnostic group. The dominant component (PC1) was interpreted post hoc by examining its EEG feature loading pattern, its association with chronological age, the consistency of this age association across diagnostic groups, and the concentration of age-related variance across components. We then tested whether age-adjusted position on this component was differentially associated with general cognitive performance (i.e., IQ) across groups. PC1 explained 15.94% of feature variance and was characterized by the strongest positive loadings on delta-band power and the strongest negative loadings on sample entropy. PC1 showed a significant negative association with chronological age (𝑟 = −0.846, 𝑝 < .001), indicating that older age was associated with relatively lower delta power and higher sample entropy along this component, consistent with previously reported developmental EEG changes. Age-related EEG variance was concentrated primarily on PC1 rather than on subsequent components, supporting its interpretation as a shared age-related EEG dimension. After removing PC1’s linear association with age, residual PC1 scores (PC1r) were negatively associated with IQ within the ADOS-A group (𝑏 = −0.612, 𝑝 < .001), but not within the control or ADOS-S groups. Pairwise slope contrasts indicated that this association was significantly more negative in ADOS-A than in CON, whereas ADOS-A vs. ADOS-S and ADOS-S vs. CON contrasts were not significant. These findings shift emphasis from categorical EEG abnormality toward individual variation within a broadly shared age-related EEG organization, in which brain–cognition coupling depends on diagnostic context.
ER -