Autism, also referred to as autism spectrum disorder (ASD), constitutes a neurodevelopmental disorder characterized by impairment in communication, including language, social skills and comportment often involving rigidity of interests and repetitive, stereotypical behaviors [1
]. Ancillary symptoms may encompass obsessive-compulsive, sleep, hyperactivity, attention, mood, gastrointestinal, self-injurious, ritualistic, and sensory integration disorders. ASD is generally considered a life-long disability of yet undetermined etiology, without an established confirmatory laboratory test, and as yet without universally established, curative pharmacological or behavioral therapy [2
]. The incidence of autism appears to be increasing. In 2011, Manning et al.
] using birth certificate and Early Intervention data reported that in the Commonwealth of Massachusetts between 2001 and 2005 the incidence of ASD diagnosed by 36 months of age increased from 56 to 93 infants per 10,000. Whether this increased incidence reflects better reporting and/or diagnosis or whether other factors are involved remains to be determined. None-the-less, such an increase in incidence is alarming. These data appropriately have spawned much research into the exploration of potential etiologies as well as the development of diagnostic tests, particularly in terms of neuro-imaging and EEG, with the hope of establishing a definitive diagnosis at the earliest possible age, in order to facilitate early intervention, while the immature brain still holds high compensatory promise.
ASD is considered by many to be a genetically determined disorder; three well-known twin studies [6
] estimate heritability at about 90% [9
]. Sibling concordance varies from about 3 to 14%; linkage studies are consistent with a polygenic mode of transmission [10
]. The 2008 finding by the Autism Consortium of a microduplication at 16p11.2 (1% of studied cases) raised hopes that a full ASD genomic pattern might soon be elucidated. However, more recent data suggest the heterogeneity and complexity of genetic abnormalities identified in children with ASD. Sakai et al.
] set out with 26 ASD associated genes and then described an "interactome" of autism-associated proteins that may be necessary to describe common mechanisms underlying ASD. Voineagu et al.
] provided strong evidence to suggest widespread transcriptional and splicing dysregulation as the key mechanism underlying brain dysfunction in ASD. On the basis of a detailed study of twins with autism, Hallmayer et al.
] recently reported, as expected, high twin concordance yet also concluded that ASD has, in addition to moderate heritability, a substantial environmental component. Thus, studies to date suggest a strong genetic component to autism that may, however, be more complex than initially thought, and environmental factors, especially their types and mechanisms of action, also appear to deserve further consideration.
MRI and its derivatives have demonstrated important findings in ASD as has been reviewed extensively [13
]. The earliest anatomical studies involved recognition that young children with ASD have abnormally increased total brain volumes that appear related to both increased grey and white matter volumes, with a differentially higher white matter contribution. Brain size in ASD appears to reach a 10% increase beyond control values by two to four years of age, possibly followed by a plateau. Regional brain growth specificity studies, however, have shown little consistency with the exception of decreased corpus callosum volume in ASD suggesting decreased interhemispheric connectivity. Diffusion magnetic resonance imaging (DMRI) studies in children and adults have demonstrated lower white matter tract fractional anisotropy (FA) in ASD, indicating poorer functional connectivity between brain regions. Supporting this, Just et al.
] published functional MRI (fMRI) studies which demonstrate functional under-connectivity in ASD. However, some studies have provided evidence for several regions with increased FA, that is, likely increased connectivity, in both children and adolescents with ASD [19
As Chen [16
] correctly pointed out, there are "many conflicting... (MRI)... findings in individuals within the ASD...(which result from)...factors such as population age, MRI acquisition parameters, details of the image processing pipeline, feature extraction procedures, analytic methods used to detect group differences and sample sizes...(which have)...contributed to these disparities...". From the entirety of MRI related studies, one may conclude that ASD is typically associated with widely distributed alterations of brain anatomy involving both grey and white matter, and with alterations in functional connectivity, which appear primarily decreased, yet also with some regionally increased connectivity. Despite a number of serious attempts, there are as yet no universally established MRI-based criteria that are usable to diagnose ASD. This no doubt reflects the problematic complexity of factors underlying autism as outlined above.
Given that altered brain connectivity is considered a typical characteristic of ASD, a number of studies have compared EEG coherence findings between ASD and neuro-typical control populations [21
]. On a frequency by frequency basis, EEG spectral coherence represents the consistency of the phase difference between two EEG signals when compared over time. According to Srinvasan et al.
"...coherence is a measure of synchronization between two... (EEG)...signals based mainly on phase consistency; that is, two signals may have different phases... but high coherence occurs when this phase difference tends to remain constant. In each frequency band, coherence measures whether two signals can be related by a linear time invariant transformation, in other words a constant amplitude ratio and phase shift (delay). In practice, EEG coherence depends mostly on the consistency of phase differences between channels" [29
]. High coherence values are taken as a measure of strong connectivity between the brain regions that produce the compared EEG signals [30
There is general agreement among coherence study results that ASD patients and neuro-typical subjects differ markedly in terms of coherence findings; however, as for MRI, study details also differ markedly. Cantor et al.
], who studied a small group of 4- to -12-year-old children with ASD, reported greater between-hemisphere coherence in the children with autism than in comparable age children with mental handicaps other than autism. Murias et al.
] evaluated 18 adults with ASD and found locally elevated theta coherence, especially in the left hemisphere. Alpha coherence was reduced within the frontal and between the frontal and other regions. Coben et al.
] studied 20 6- to 11-year-old children with ASD and reported decreased overall coherence compared to neuro-typical control group children. The children with ASD demonstrated decreased intrahemispheric delta and theta for both short and long inter-electrode distances as well as similarly decreased interhemispheric coherence. Lazarev et al.
] evaluated, with EEG during photic stimulation at different frequencies, 14 6- to 14-year-old children with ASD in comparison to a neuro-typical control group. The authors reported an ASD-specific coherence increase at the frequencies of stimulation in the left but not the right hemisphere, as compared to the neuro-typical subjects. Resting, that is, not specifically stimulated, coherence did not differ between the two hemispheres for either group. Isler et al.
] evaluated coherence between two homologous regions of visual cortex during visual stimulation (long latency evoked potentials) in nine children with ASD as compared to neuro-typical controls. The children with ASD demonstrated significantly reduced coherence in the delta and theta spectral bands and essentially no interhemispheric synchronization above the theta band, whereas the neuro-typical children sustained interhemispheric synchrony to higher frequencies. This suggested diminished functional connectivity between the bihemispheric visual regions during visual stimulation in ASD. Leveille et al.
] assessed resting EEG coherence during REM sleep in nine subjects with ASD compared to neuro-typical controls and reported greater coherence between the left occipital area and both local and distant regions for the children with ASD. They also reported lower coherence over right frontal regions for the children with ASD as compared to the control group. Sheikhani et al.
] reported bilaterally increased coherence in the gamma band, especially involving the temporal lobes, in 17 subjects with ASD, ranging in age from 6 to 11 years, when compared to a healthy control group. Barttfeld et al.
] evaluated 10 adults with ASD and noted that the subjects demonstrated reduced long-distance and also increased short- distance coherence when compared to an adult control group.
Study differences in experimental design, including choice of spectral bands, brain regions, brain states (activated or resting) and type of analysis, as well as small sample sizes, differences in sample age ranges, diversity of severity of impairment, lack of replication tests and disparity of results make difficult a meaningful summary of spectral coherence findings in ASD. Furthermore, few studies considered the reality of ASD group-specific EEG artifacts, including eye blink and muscle movement, and their potential spurious effects upon coherence. Also, few studies addressed the confounding effect of differing EEG recording reference techniques upon coherence [31
]. This leaves wide open the question of whether the reported diverse study findings reflect marked variability of brain function within the ASD population as suggested by Happé [32
] and recently demonstrated by Milne [33
], or whether they primarily reflect methodological variability.
The current study attempts to answer the as yet open question of coherence differences between children with ASD and neuro-typical healthy controls. To this end, EEG coherence data were evaluated in a large sample of children with ASD and compared to a large neuro-typical, medically healthy, normal, age-comparable control group. Care was taken to minimize the effects of EEG artifact upon coherence data and to avoid a priori selection of coherences from among the very large number of created coherence variables.