Using quantitative and analytic EEG methods in the understanding of connectivity in autism spectrum disorders: a theory of mixed over- and under-connectivity

Abstract

Neuroimaging technologies and research has shown that autism is largely a disorder of neuronal connectivity. While advanced work is being done with fMRI, MRI-DTI, SPECT and other forms of structural and functional connectivity analyses, the use of EEG for these purposes is of additional great utility. Cantor et al. (1986) were the first to examine the utility of pairwise coherence measures for depicting connectivity impairments in autism. Since that time research has shown a combination of mixed over and under-connectivity that is at the heart of the primary symptoms of this multifaceted disorder. Nevertheless, there is reason to believe that these simplistic pairwise measurements under represent the true and quite complicated picture of connectivity anomalies in these persons. We have presented three different forms of multivariate connectivity analysis with increasing levels of sophistication (including one based on principle components analysis, sLORETA source coherence, and Granger causality) to present a hypothesis that more advanced statistical approaches to EEG coherence analysis may provide more detailed and accurate information than pairwise measurements. A single case study is examined with findings from MR-DTI, pairwise and coherence and these three forms of multivariate coherence analysis. In this case pairwise coherences did not resemble structural connectivity, whereas multivariate measures did. The possible advantages and disadvantages of different techniques are discussed. Future work in this area will be important to determine the validity and utility of these techniques.

Dysfunction of sensory oscillations in Autism Spectrum Disorder

“…Postmortem anatomical inquiries have likewise indicated that the microstructure of cortical circuitry is fundamentally altered in ASD (Casanova et al, 2006; McKavanagh et al, 2015). Investigations examining connectivity on more rapid time scales utilizing electroencephalography (EEG) (Coben et al, 2014) and magnetoencephalography (MEG) (Ye et al, 2014) have similarly indicated that connectivity alterations are a characteristic feature of ASD. These connectivity alterations have been proposed as both a leading biomarker and the origin of the behavioral dysfunction characteristic of the disorder (Geschwind and Levitt, 2007).…”

Electrophysiological signatures of atypical intrinsic brain connectivity networks in autism

“…Inherent heterogeneity of ASD (Betancur, 2011; Di Martino et al, 2014), maturational processes (Uddin et al, 2013b), task demands (Muller et al, 2011), and data analysis methods (Nair et al, 2014), are all believed to impact indices of functional connectivity and their measurement in ASD. While no consensus has been reached, a mixed profile of long-range hypo-connectivity and local hyper-connectivity has been frequently reported in the literature (Coben et al, 2014; Courchesne and Pierce, 2005; Kitzbichler et al, 2015; Murias et al, 2007; Vissers et al, 2012; Wass, 2011), indicating impaired functional integration and segregation of distributed neural networks in ASD (Rudie et al, 2012). …”

Neuroimages in Autism

“…However, the relation between abnormal circuitry and the behavioral patterns observed in ASD is not straightforward. A recent study showed that standard EEG coherence analysis did not resemble structural connectivity in autism (Coben et al 2014). More advanced approaches to functional and anatomical analysis may provide more detailed and accurate information about the complicated picture of connectivity anomalies in ASD.…”