Quantitative EEG in autistic children

Elhabashy, Hala; Raafat, Omnia; Afifi, Lamia; Raafat, Hebatallah; Abdullah, Khaled

doi:10.4103/1110-1083.162031

Cited by 29 publications

(13 citation statements)

References 3 publications

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“…While Daoust et al, Tani et al, Elhabashy et al, and Yang et al show increased power in the theta band in two studies performed during sleep, one during a relaxed eyes open and one involving a cognitive task (52, 60, 61, 65); three studies show a reduction in theta power in relaxed eyes open conditions and during a cognitive task (55, 56, 64). Variations in the participants’ age, as well as small sample sizes might lead to the lack of consistency in these results.…”

Section: Resultsmentioning

confidence: 95%

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

2017

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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.

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Section: Resultsmentioning

confidence: 95%

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

2017

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“…In addition, Clarke et al and Coben et al found similar results of reduced interhemispheric coherence but in the beta frequency band [33,38]. Elhabashy also found reductions in alpha coherence among short- and medium- connections spanning between the frontal, temporal, and posterior regions as well, but intrahemispherically within the left and right hemispheres, in children with ASD during eyes-open resting state [36]. In contrast, Sheikhani and colleagues found only a few statistically significant reductions in the beta frequency over the left temporal-posterior region in the ASD group [40].…”

Section: School-aged Children: Default Network Coherencementioning

confidence: 86%

“…However, these contradictory findings are less compelling because the total sample size was less than two-thirds the size of that in the Coben et al study [33] and did not uniformly correct for multiple comparisons [35]. Elhabashy and colleagues also found evidence for increased interhemispheric coherence over the temporal region in the delta frequency, despite general reductions between the frontal and central regions [36]. Along with these significant, but apparently contradictory, findings of reduced and increased low-frequency coherence, there were two moderately sized studies that reported no intra- and interhemispheric differences between ASD and TD children during resting-state [37,38].…”

Section: School-aged Children: Default Network Coherencementioning

confidence: 99%

“…In contrast, Sheikhani and colleagues found only a few statistically significant reductions in the beta frequency over the left temporal-posterior region in the ASD group [40]. Elhabashy et al and Carson et al did not focus on the analysis of beta or gamma band frequency, while Clarke et al, and Coben et al did not analyze gamma, so no information about those bands can be inferred [33,36,38,39]. Findings on long-range connections in these higher frequency bands are more mixed, with a few studies showing an ASD-specific increase in long-range coherence but most finding no significant differences between groups.…”

Section: School-aged Children: Default Network Coherencementioning

confidence: 99%

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Electroencephalogram Coherence Patterns in Autism: An Updated Review

Schwartz

Kessler

Gaughan

et al. 2017

Pediatric Neurology

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Recent electrophysiological studies suggest that autism spectrum disorder is characterized by aberrant anatomical and functional neural circuitry. During normal brain development, pruning and synaptogenesis facilitate ongoing changes in both short- and long-range neural wiring. In developmental disorders such as autism, this process may be perturbed leading to abnormal neural connectivity. Careful analysis of electrophysiological connectivity patterns using EEG coherence may provide a way to probe the resulting differences in neurological function between people with and without autism. There is general consensus that EEG coherence patterns differ between individuals with and without autism spectrum disorders, however the exact nature of the differences and their clinical significance remain unclear. Here we review recent literature comparing EEG coherence patterns between patients with autism spectrum disorders or at high risk for autism and their non-autistic or low risk for autism peers.

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“…NASESE has been validated and verified according to a published ASD clinical dataset, concerning cerebral functioning evaluation according to EEG signals [40]. The quantitative EEG analysis described in the study of reference, was performed between 2 groups of children, 21 with ASD and 21 without ASD and differences in the cerebral functioning were reported in the intrahemispheric and interhemispheric coherences [40]. Simulation data [41] was generated and processed according to the specific study in order to ensure the efficacy of tests and results between the non-ASD and ASD groups.…”

Section: Software Validationmentioning

confidence: 99%