Autistic individuals often have difficulties expressing or controlling emotions and have poor eye contact, among other symptoms. The prevalence of autism is increasing globally, posing a need to address this concern. Current diagnostic systems have particular limitations; hence, some individuals go undiagnosed or the diagnosis is delayed. In this study, an effective autism diagnostic system using electroencephalogram (EEG) signals, which are generated from electrical activity in the brain, was developed and characterized. The pre-processed signals were converted to two-dimensional images using the higher-order spectra (HOS) bispectrum. Nonlinear features were extracted thereafter, and then reduced using locality sensitivity discriminant analysis (LSDA). Significant features were selected from the condensed feature set using Student’s t-test, and were then input to different classifiers. The probabilistic neural network (PNN) classifier achieved the highest accuracy of 98.70% with just five features. Ten-fold cross-validation was employed to evaluate the performance of the classifier. It was shown that the developed system can be useful as a decision support tool to assist healthcare professionals in diagnosing autism.

/pdf/autism-spectrum-disorder-diagnostic-system-using-hos-4z5baa972k.pdf

Autism Spectrum Disorder Diagnostic System Using HOS Bispectrum with EEG Signals

Electroencephalogram Coherence Patterns in Autism: An Updated Review.

Autism spectrum disorders (ASD) are thought to be associated with abnormal neural connectivity. Presently, neural connectivity is a theoretical construct that cannot be easily measured. Research in network science and time series analysis suggests that neural network structure, a marker of neural activity, can be measured with electroencephalography (EEG). EEG can be quantified by different methods of analysis to potentially detect brain abnormalities. The aim of this review is to examine evidence for the utility of three methods of EEG signal analysis in the ASD diagnosis and subtype delineation. We conducted a review of literature in which 40 studies were identified and classified according to the principal method of EEG analysis in three categories: functional connectivity analysis, spectral power analysis, and information dynamics. All studies identified significant differences between ASD patients and non-ASD subjects. However, due to high heterogeneity in the results, generalizations could not be inferred and none of the methods alone are currently useful as a new diagnostic tool. The lack of studies prevented the analysis of these methods as tools for ASD subtypes delineation. These results confirm EEG abnormalities in ASD, but as yet not sufficient to help in the diagnosis. Future research with larger samples and more robust study designs could allow for higher sensitivity and consistency in characterizing ASD, paving the way for developing new means of diagnosis.

/pdf/how-useful-is-electroencephalography-in-the-diagnosis-of-3b74shhclt.pdf

How Useful Is Electroencephalography in the Diagnosis of Autism Spectrum Disorders and the Delineation of Subtypes: A Systematic Review.

https://escholarship.org/content/qt3gv354c3/qt3gv354c3.pdf?t=pc083l

Interhemispheric alpha-band hypoconnectivity in children with autism spectrum disorder.

Understanding the development of the neuronal circuitry underlying autism spectrum disorder (ASD) is critical to shed light into its etiology and for the development of treatment options. Resting state EEG provides a window into spontaneous local and long-range neuronal synchronization and has been investigated in many ASD studies, but results are inconsistent. Unbiased investigation in large and comprehensive samples focusing on replicability is needed.We quantified resting state EEG alpha peak metrics, power spectrum (PS, 2-32 Hz) and functional connectivity (FC) in 411 children, adolescents and adults (n = 212 ASD, n = 199 neurotypicals [NT], all with IQ > 75). We performed analyses in source-space using individual head models derived from the participants' MRIs. We tested for differences in mean and variance between the ASD and NT groups for both PS and FC using linear mixed effects models accounting for age, sex, IQ and site effects. Then, we used machine learning to assess whether a multivariate combination of EEG features could better separate ASD and NT participants. All analyses were embedded within a train-validation approach (70%-30% split).In the training dataset, we found an interaction between age and group for the reactivity to eye opening (p = .042 uncorrected), and a significant but weak multivariate ASD vs. NT classification performance for PS and FC (sensitivity 0.52-0.62, specificity 0.59-0.73). None of these findings replicated significantly in the validation dataset, although the effect size in the validation dataset overlapped with the prediction interval from the training dataset.The statistical power to detect weak effects-of the magnitude of those found in the training dataset-in the validation dataset is small, and we cannot fully conclude on the reproducibility of the training dataset's effects.This suggests that PS and FC values in ASD and NT have a strong overlap, and that differences between both groups (in both mean and variance) have, at best, a small effect size. Larger studies would be needed to investigate and replicate such potential effects. 

/pdf/resting-state-eeg-power-spectrum-and-functional-connectivity-14zh97al.pdf

Resting state EEG power spectrum and functional connectivity in autism: a cross-sectional analysis

Background Autism spectrum disorder is a neurodevelopmental disorder that is characterized mainly by difficulties in social interaction and communication. Studies have suggested abnormal neural connectivity patterns in the brains of patients with autism. Objective The current work aimed to study the quantitative electroencephalography (EEG) findings in autistic children and compare it with those of normal controls. Methods The EEG recordings of 21 autistic children between 4 and 12 years of age were compared with those of 21 age-matched and sex-matched controls under an eyes-opened condition. Differences in cerebral functioning were examined using measurements of absolute and relative power and intrahemispheric and interhemispheric coherence. Results There were statistically significant differences in EEG power between the autistic and control groups, with greater absolute of delta and theta power especially at the frontal region in autistic children. There was also global reduction in relative alpha and beta power especially in the frontal, central, and posterior regions in autistic children. In addition, there was a pattern of underconnectivity and overconnectivity when measuring the intrahemispheric and interhemispheric coherence in the autistic compared with the control group. Conclusion These results suggested regional dysfunction of the brain in autistic children, along with a pattern of abnormal neural connectivity, which could explain the autistic symptomatology.

Khaled Abdullah

Papers

Quantitative EEG in autistic children