The Importance of Electroencephalogram Assessment for Autistic Disorders
Autistic disorders are a set of complex syndromes that lead to challenges impacting communication, behavior repertoire, and social skills. The etiology of autism is unknown but is likely epigenetic in nature. It is likely associated with an inflammatory process leading to neuroinflammation in early childhood. Autistic disorders include seizures in approximately one-third of the cases and there are often regions of brain dysfunction associated with neural connectivity anomalies. The electroencephalogram (EEG) is presented as a premiere tool to assess these difficulties due to its' non-invasive nature, availability and utility in detailing these difficulties. Techniques for seizure detection, monitoring, and tracing their propagation are shown. Similar approaches can then be utilized for assessing EEG oscillations, which are at the heart of these neuronal regulation dysfunctions. Autistic disorders are clearly associated with regions of dysfunction and quantitative electroencephalogram strategies for assessing these impairments are shown. These include techniques for increasing the specificity and spatial resolution of the EEG such as source localization and independent components analysis. Lastly, advanced methods for assessing the neural connectivity problems that underlie the difficulties of these children are presented. EEG assessment, when processed and analyzed with the most advanced techniques, can be invaluable in the evaluation of autistic disorders.Abstract

Spike and wave pattern detected by the Persyst/reveal–spike and seizure detection system (longitudinal montage) with voltage mapping.

Summary of spike reveal/reveal with component mapping, time, and perception.

Spike review propogation mapping.

Mu oscillation with EEG, spectral display, and topographical mapping (laplacian montage).

Right posterior slow activity with EEG, spectral display, and topographical mapping (longitudinal montage).

Topographical mapping of EEG magnitude data in single Hz bins (weighted average montage). Mu seen maximally at 9–11 Hz.

Source localization of mu pattern performed and displayed with sLoreta slice viewer. Maximal localization shown in white, red, yellow, and green. A color version of this figure will be posted, along with this article, at http://www.aapb.org/magazine.html.

Independent component analysis (ICA) (runica function of EEGLAB) of 10 Hz mu activity showing ICA. Maximal activity is shown in darker colors.

Topographical mapping of EEG magnitude data in single Hz bins (weighted average montage). Maximal right posterior slow activity shown in white–gray from 1–4 Hz.

Source localization of right posterior slow activity pattern performed and displayed with sLoreta slice viewer. Maximal localization shown in white, purple, red, yellow, and green. A color version of this figure will be posted, along with this article, at http://www.aapb.org/magazine.html.

ICA (runica function of EEGLAB) of 2 Hz activity showing independent components of activity. Maximal activity is shown in darker colors with special emphasis on components 1, 6, 8, and 9.

Multivariate connectivity analysis (Hudspeth, 2008) in the horizontal view showing findings in the alpha band for mu activity.

Multivariate connectivity analysis (Hudspeth, 2008) in the horizontal view showing findings in the delta band for slow activity over the right posterior regions.

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