Quantitative electroencephalographic profiles for children with autistic spectrum disorder.

Chan, Agnes S.; Sze, Sophia L.; Cheung, Mei-chun

doi:10.1037/0894-4105.21.1.74

Cited by 86 publications

(69 citation statements)

References 45 publications

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“…First, significant differences in the alpha band were shown by five studies with relaxed eyes open condition (41, 42, 50, 54, 55). While four studies show a decrease in absolute spectral power in ASD in children of similar ages (41, 42, 50, 55), another showed elevated absolute alpha power in adults (54). The inconsistencies might be attributed to the age differences between participants and differing developmental trajectories in children with ASD.…”

Section: Resultsmentioning

confidence: 99%

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: 99%

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

2017

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“…They found that children with ASD showed significantly less relative alpha power. This was not specific to restricted location(s) but was a widespread pattern (Chan et al 2007). Less alpha may indicate a brain that does not rest appropriately, which is perhaps the flip side of the picture of spindling beta and low SMR frequently found in clients with AS.…”

Section: Nfb Trainingmentioning

confidence: 95%

Functional Neuroanatomy and the Rationale for Using EEG Biofeedback for Clients with Asperger’s Syndrome

Thompson

Reid

2009

Appl Psychophysiol Biofeedback

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This paper reviews the symptoms of Asperger's Syndrome (AS), a disorder along the autism continuum, and highlights research findings with an emphasis on brain differences. Existing theories concerning AS are described, including theory of mind (Hill and Frith in Phil Trans Royal Soc Lond, Bull 358:281-289, 2003), mirror neuron system (Ramachandran and Oberman in Sci Am 295(5):62-69, 2006), and Porges' (Ann N Y Acad Sci 1008:31-47, 2003, The neurobiology of autism, Johns Hopkins University Press, Baltimore, 2004) polyvagal theory. (A second paper, Outcomes using EEG Biofeedback Training in Clients with Asperger's Syndrome, summarizes clinical outcomes obtained with more than 150 clients.) Patterns seen with QEEG assessment are then presented. Single channel assessment at the vertex (CZ) reveals patterns similar to those found in Attention-Deficit/Hyperactivity Disorder. Using 19-channel data, significant differences (z-scores > 2) were found in the amplitude of both slow waves (excess theta and/or alpha) and fast waves (beta) at various locations. Differences from the norm were most often found in mirror neuron areas (frontal, temporal and temporal-parietal). There were also differences in coherence patterns, as compared to a normative database (Neuroguide). Low Resolution Electromagnetic Tomography Analysis (Pascual-Marqui et al. in Methods Find Exp Clin Pharmacol 24C:91-95, 2002) suggested the source of the abnormal activity was most often the anterior cingulate. Other areas involved included the amygdala, uncus, insula, hippocampal gyrus, parahippocampal gyrus, fusiform gyrus, and the orbito-frontal and/or ventromedial areas of the prefrontal cortex. Correspondence between symptoms and the functions of the areas found to have abnormalities is evident and those observations are used to develop a rationale for using EEG biofeedback, called neurofeedback (NFB), intervention. NFB training is targeted to improve symptoms that include difficulty reading and mirroring emotions, poor attention to the outside world, poor self-regulation skills, and anxiety. Porges' polyvagal theory is used to emphasize the need to integrate NFB with biofeedback (BFB), particularly heart rate variability training. We term this emerging understanding the Systems Theory of Neural Synergy. The name underscores the fact that NFB and BFB influence dynamic circuits and emphasizes that, no matter where we enter the nervous system with an intervention, it will seek its own new balance and equilibrium.

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“…Relative power was used for analyses on the theta band based on previous findings linking relative power measures to better estimates for the theta band because (1) relative power measurements tend to give larger estimates for the dominant frequency range (Klimesch 1999), and (2) individual variations are eliminated by computing the proportion of an individual frequency band relative to the others (Chan et al 2007). To examine the changes in relative theta power in response to the two treatment conditions, a 2 9 29 4 (Condition9 Time9 Area) threeway analyses of variance (ANOVA) with repeated measures was performed, with Condition (two levels: music, TBRT), Time (two levels: baseline, during treatment condition), and Area (four levels: anterior, posterior, left, right) as within-subject factors.…”

Section: Relative Thetamentioning

confidence: 99%

Electroencephalographic (EEG) Measurements of Mindfulness-based Triarchic Body-pathway Relaxation Technique: A Pilot Study

Chan

Han

Cheung

2008

Appl Psychophysiol Biofeedback

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Quantitative electroencephalographic profiles for children with autistic spectrum disorder.

Cited by 86 publications

References 45 publications

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

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

Functional Neuroanatomy and the Rationale for Using EEG Biofeedback for Clients with Asperger’s Syndrome

Electroencephalographic (EEG) Measurements of Mindfulness-based Triarchic Body-pathway Relaxation Technique: A Pilot Study

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