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Increased Intra-Participant Variability in Children with Autistic Spectrum Disorders: Evidence from Single-Trial Analysis of Evoked EEG
Open accessAbstract
Intra-participant variability in clinical conditions such as autistic spectrum disorder (ASD) is an important indicator of pathophysiological processing. The data reported here illustrate that trial-by-trial variability can be reliably measured from EEG, and that intra-participant EEG variability is significantly greater in those with ASD than in neuro-typical matched controls. EEG recorded at the scalp is a linear mixture of activity arising from muscle artifacts and numerous concurrent brain processes. To minimize these additional sources of variability, EEG data were subjected to two different methods of spatial filtering. (i) The data were decomposed using infomax independent component analysis, a method of blind source separation which un-mixes the EEG signal into components with maximally independent time-courses, and (ii) a surface Laplacian transform was performed (current source density interpolation) in order to reduce the effects of volume conduction. Data are presented from 13 high functioning adolescents with ASD without co-morbid ADHD, and 12 neuro-typical age-, IQ-, and gender-matched controls. Comparison of variability between the ASD and neuro-typical groups indicated that intra-participant variability of P1 latency and P1 amplitude was greater in the participants with ASD, and inter-trial α-band phase coherence was lower in the participants with ASD. These data support the suggestion that individuals with ASD are less able to synchronize the activity of stimulus-related cell assemblies than neuro-typical individuals, and provide empirical evidence in support of theories of increased neural noise in ASD.
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“decreased inter-trial alpha-band phase coherence during a visual spatial discrimination task (Milne, 2011)”
From Behavioral, Perceptual, and Neural Alterations in Sensory and Multisensory Function in Autism Spectrum Disorder (2015) · cites this paper as Milne, 2011
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Fadeev, K. A., Reyes, I. V. R., Goiaeva, D. E., Obukhova Tatiana, S., Ovsiannikova, T. M., Prokofyev, A. O., Rytikova, A. M., Novikov, A. Y., Stroganova, T. A., & Orekhova, E. V. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.10.648101
Metadata
- Authors
- Milne, E.
- Affiliations
- University of Sheffield (GB)
- Corresponding author
- Milne, Elizabeth
- ORCID
- Milne, Elizabeth
- Venue
- Frontiers in Psychology
- Publisher
- Frontiers Media SA
- Published
- 2011
- Volume
- 2
- Pages
- 51-51
- ISSN
- 1664-1078 (ISSN-L)
- PMC
- PMC3110871
- OpenAlex
- W2031410002
- Cited by
- 217
- References
- 68
- Citations by year
- 2026: 11 · 2025: 11 · 2024: 19 · 2023: 18 · 2022: 17 · 2021: 17 · 2020: 13 · 2019: 19 · 2018: 16 · 2017: 16 · 2016: 16 · 2015: 14 · 2014: 14 · 2013: 7 · 2012: 7
- FWCI
- 4.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Psychology (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- autism spectrum disorder, trial-to-trial variability, single-trial EEG, P1 component, neuronal synchronization, neural noise, volume conduction
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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