A stable pattern of EEG spectral coherence distinguishes children with autism from neuro-typical controls - a large case control study

BMC Medicine

Heidelise Als²  Frank H Duffy¹ 

[1] Department of Neurology, Children's Hospital Boston and Harvard Medical School, 300 Longwood Ave., Boston, MA 02115, USA;Department of Psychiatry(Psychology), Children's Hospital Boston and Harvard Medical School, 320 Longwood Ave., Boston, MA 02115, USA

关键词: discriminant analysis;    coherence factors;    PCA;    principal components analysis;    EEG coherence;    PDD;    pervasive developmental disorder;    Autism spectrum disorder;   

Others  :  1126069
DOI  :  10.1186/1741-7015-10-64

 received in 2011-12-01, accepted in 2012-06-26,  发布年份 2012

【 摘 要 】

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.

【 授权许可】

   
2012 Duffy and Als; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 2.

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