Harnessing Trial-to-Trial Variability of EEG Spectral Characteristics to Understand Autism
By incorporating neural variability into machine learning models, this study introduces a novel framework for improving biomarker-driven assessments. These findings highlight the potential for personalized tools that inform targeted interventions while offering insights into ASD neurophysiology. Future research should integrate longitudinal EEG analyses and multimodal neuroimaging to advance precision diagnostics in autism.
Authors
- Kevin A. Pelphrey (ORCID: https://orcid.org/0000-0001-5407-2577)
- Mirella Dapretto (ORCID: https://orcid.org/0000-0003-2914-6858)
- Susan Y. Bookheimer (ORCID: https://orcid.org/0000-0002-3417-5891)
- James C. McPartland (ORCID: https://orcid.org/0000-0002-2570-0746)
- Raphael Bernier (ORCID: https://orcid.org/0000-0003-1854-2804)
- Deeksha M. Shama (ORCID: https://orcid.org/0009-0008-4154-7113)
- Shafali Jeste (ORCID: https://orcid.org/0000-0003-2955-6902)
- Archana Venkataraman (ORCID: https://orcid.org/0000-0003-2653-5591)
- John D. Van Horn (ORCID: https://orcid.org/0000-0003-1537-0816)
- Stefen Beeler-Duden
- Mei Su
Institutions
- Boston University (US)
- Seattle Pacific University (US)
- Children's Hospital of Los Angeles (US)
- Johns Hopkins University (US)
- University of California, Los Angeles (US)
- Yale University (US)
- Neurobehavioral Systems (US)
- University of Virginia (US)
- University of Baltimore (US)
Publication Details
- Journal
- Journal of Autism and Developmental Disorders
- Published
- 2025-11-22
- DOI
- https://doi.org/10.1007/s10803-025-07125-y
- Primary Topic
- Autism Spectrum Disorder Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- University of Washington
- National Institutes of Health