Autism, spectrum or clusters? An EEG coherence study

Abstract

Background: Autism prevalence continues to grow, yet a universally agreed upon etiology is lacking despite manifold evidence of abnormalities especially in terms of genetics and epigenetics. The authors postulate that the broad definition of an omnibus 'spectrum disorder' may inhibit delineation of meaningful clinical correlations. This paper presents evidence that an objectively defined, EEG based brain measure may be helpful in illuminating the autism spectrum versus subgroups (clusters) question.

Methods: Forty objectively defined EEG coherence factors created in prior studies demonstrated reliable separation of neuro-typical controls from subjects with autism, and reliable separation of subjects with Asperger's syndrome from all other subjects within the autism spectrum and from neurotypical controls. In the current study, these forty previously defined EEG coherence factors were used prospectively within a large (N = 430) population of subjects with autism in order to determine quantitatively the potential existence of separate clusters within this population.

Results: By use of a recently published software package, NbClust, the current investigation determined that the 40 EEG coherence factors reliably identified two distinct clusters within the larger population of subjects with autism. These two clusters demonstrated highly significant differences. Of interest, many more subjects with Asperger's syndrome fell into one rather than the other cluster.

Conclusions: EEG coherence factors provide evidence of two highly significant separate clusters within the subject population with autism. The establishment of a unitary "Autism Spectrum Disorder" does a disservice to patients and clinicians, hinders much needed scientific exploration, and likely leads to less than optimal educational and/or interventional efforts.

Keywords: Asperger’s syndrome (ASP); Autism spectrum disorder (ASD); Cluster analysis; Connectivity; Discriminant analysis; EEG coherence factors; Hierarchical; K-means; NbClust.

Fig. 1

Standard EEG Electrode Names and Positions. Legend: Head in vertex view, nose above, left ear to left. EEG electrodes: Z: Midline: FZ: Midline Frontal; CZ: Midline Central; PZ: Midline Parietal; OZ: Midline Occipital. Even numbers, right hemisphere locations; odd numbers, left hemisphere locations: Fp: Frontopolar; F: Frontal; C: Central; T: Temporal; P: Parietal; O: Occipital. The standard 19, 10–20 electrodes are shown as black circles. An additional subset of five, 10–10 electrodes are shown as open circles. This figure was first published in a 2012 autism manuscript by the current authors [27] and is shown with permission of these authors and publisher, BMC Medicine

Fig. 2

Optimal Cluster Number by Hierarchical Clustering and Program NbClust. Legend: NbClust produced histogram of up to 15 possible cluster groupings formed by Hierarchical clustering. Atop each vertical bar is the total number of the 30 indices used to estimate the optimal cluster grouping. Note that 17 of the 30 indices indicate the two cluster configuration as “optimal”. Cluster configurations never selected are omitted from the X axis as their frequency would be zero

Fig. 3

Optimal Cluster Number by K-means Clustering and Program NbClust. Legend: An NbClust produced histogram of up to 15 possible cluster groupings formed by K-means clustering. Atop each vertical bar is the total number of the 30 indices used to estimate the optimal cluster grouping. Note that 10 of the 30 indices indicate the two cluster configuration as “optimal”. Cluster configurations never selected are omitted from the X axis as their frequency would be zero

Fig. 4

Graphic Representation of 19 Coherence Factor Loadings Used in Separating Clusters 1 and 2. Legend: EEG coherence factor loadings. Heads in top view, scalp left to image left, nose above; Factor number is above heads to left and peak frequency for factor in Hz is above to right. Lines indicate top approximate 15% coherence loadings per factor: Red Lines = increased coherence in Cluster 1; Blue Lines = decreased coherence in Cluster 1. Involved electrodes are shown as white circles. Uninvolved electrodes are not shown; they are blackened-out within the superior scalp area and greened-out for scalp electrodes. Factors are shown in numerical order. See text for factor selection order in discriminant analysis

Fig. 5

C1 and C2 Cluster Groups Along 2-Group DFA by Discriminant ScoreLegend: C1 and C2 histograms (red = C1, blue/green = C2) with X-axis the 2 group discriminant score. Note minimal overlap. Separation by Wilk’s Lambda is significant (p < 0.00001) and overall individual subject classification is approximately 95% correct by jackknifing (see text)

Fig. 6

C1, C2 and CON Groups Along 3-Group DFA by Discriminant Score. Legend: C1, C2, and CON group population distributions (red circle = C1, green triangle = C2, blue + = CON) with X and Y axes the two 3-group discriminant function scores. Note minimal populations overlap. Overall and among group separations are significant. There is very significant three group subject classification (see text). Note, hierarchical clustering results, upon which this figure is based, tend to illustrate linear group boundaries (whereas K-means clustering tend to produce more circular or ovoid boundaries [32])

Fig. 7

C1, C2, CON, and ASP Groups Along 3-Group DFA by Discriminant Score. Legend: C1, C2, and CON group population distributions as for Fig. 6. Now with passive classification of 26 Asperger (ASP) subjects. Note ASP population mostly overlaps with C2 ASD group and nearby regions of C1 group. (red circle = C1, green square = C2, blue x = CON, black square = ASP)