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BMC Med. 2012; 10: 64.
Published online Jun 26, 2012. doi:  10.1186/1741-7015-10-64
PMCID: PMC3391175
A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study
Frank H Duffycorresponding author1 and Heidelise Als2
1Department of Neurology, Children's Hospital Boston and Harvard Medical School, 300 Longwood Ave., Boston, MA 02115, USA
2Department of Psychiatry(Psychology), Children's Hospital Boston and Harvard Medical School, 320 Longwood Ave., Boston, MA 02115, USA
corresponding authorCorresponding author.
Frank H Duffy: fhd/at/sover.net; Heidelise Als: heidelise.als/at/childrens.harvard.edu
Received December 1, 2011; Accepted June 26, 2012.
Abstract
Background
The autism rate has recently increased to 1 in 100 children. Genetic studies demonstrate poorly understood complexity. Environmental factors apparently also play a role. Magnetic resonance imaging (MRI) studies demonstrate increased brain sizes and altered connectivity. Electroencephalogram (EEG) coherence studies confirm connectivity changes. However, genetic-, MRI- and/or EEG-based diagnostic tests are not yet available. The varied study results likely reflect methodological and population differences, small samples and, for EEG, lack of attention to group-specific artifact.
Methods
Of the 1,304 subjects who participated in this study, with ages ranging from 1 to 18 years old and assessed with comparable EEG studies, 463 children were diagnosed with autism spectrum disorder (ASD); 571 children were neuro-typical controls (C). After artifact management, principal components analysis (PCA) identified EEG spectral coherence factors with corresponding loading patterns. The 2- to 12-year-old subsample consisted of 430 ASD- and 554 C-group subjects (n = 984). Discriminant function analysis (DFA) determined the spectral coherence factors' discrimination success for the two groups. Loading patterns on the DFA-selected coherence factors described ASD-specific coherence differences when compared to controls.
Results
Total sample PCA of coherence data identified 40 factors which explained 50.8% of the total population variance. For the 2- to 12-year-olds, the 40 factors showed highly significant group differences (P < 0.0001). Ten randomly generated split half replications demonstrated high-average classification success (C, 88.5%; ASD, 86.0%). Still higher success was obtained in the more restricted age sub-samples using the jackknifing technique: 2- to 4-year-olds (C, 90.6%; ASD, 98.1%); 4- to 6-year-olds (C, 90.9%; ASD 99.1%); and 6- to 12-year-olds (C, 98.7%; ASD, 93.9%). Coherence loadings demonstrated reduced short-distance and reduced, as well as increased, long-distance coherences for the ASD-groups, when compared to the controls. Average spectral loading per factor was wide (10.1 Hz).
Conclusions
Classification success suggests a stable coherence loading pattern that differentiates ASD- from C-group subjects. This might constitute an EEG coherence-based phenotype of childhood autism. The predominantly reduced short-distance coherences may indicate poor local network function. The increased long-distance coherences may represent compensatory processes or reduced neural pruning. The wide average spectral range of factor loadings may suggest over-damped neural networks.
Keywords: Autism spectrum disorder, pervasive developmental disorder, PDD, EEG coherence, principal components analysis, PCA, coherence factors, discriminant analysis
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