Electroencephalogram Coherence Patterns in Autism: An Updated Review

Schwartz, Sophie; Kessler, Riley; Gaughan, Thomas; Buckley, Ashura

doi:10.1016/j.pediatrneurol.2016.10.018

Cited by 61 publications

(46 citation statements)

References 87 publications

(233 reference statements)

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“…Given that some studies show that ASD patients and controls have similar FRN amplitudes to reward and punishment feedback (Larson et al, 2011; McPartland et al, 2012), these data underscore the importance of also measuring the consistency of trial-to-trial phase alignment as a measure of neural synchrony. In a reward prediction task we show a unique difference, lower trial-to-trial phase locking in ASD, consistent with several studies highlighting a lack of neural synchrony as an endophenotype in ASD (Catarino et al, 2013; David et al, 2016; Dinstein et al, 2011; Lushchekina et al, 2016; Schwartz et al, 2016). Our findings point to further evidence for reduced ITC in ASD and the benefit of examining more nuanced measures in EEG studies that can differentiate ASD from neurotypical controls.…”

Section: Discussionsupporting

confidence: 90%

“…Some evidence suggest that within-subject variability in the amplitude and timing of early visual P1 ERPs is greater in ASD compared to neuro-typical matched controls (Milne, 2011). Others have examined neural variability in youth and young adults with ASD and found low coherence across multiple frequency bands during resting states (Dinstein et al, 2011; Lushchekina, Khaerdinova, Novototskii-vlasov, & Lushchekin, 2016), stimulus processing (Catarino et al, 2013), and cognitive tasks (Lushchekina et al, 2016), leading some to suggest that a lack of synchrony in neural oscillations reflects an endophenotype of ASD (David et al, 2016; Schwartz, Kessler, Gaughan, & Buckley, 2016). Thus, we consider whether this variability could also be present in the FRN and reflect a lack of synchronization in medial frontal neural systems that mediate feedback processing.…”

Section: Introductionmentioning

confidence: 99%

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Inter-trial coherence of medial frontal theta oscillations linked to differential feedback processing in youth and young adults with autism

Noordt

Venkataraman

et al. 2017

Research in Autism Spectrum Disorders

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Background Impairment in prediction and appreciation for choice outcomes could contribute to several core symptoms of ASD. We examined electroencephalography (EEG) oscillations in 27 youth and young adults diagnosed with autism spectrum disorder (ASD) and 22 IQ-matched neurotypical controls while they performed a chance-based reward prediction task. Method We re-analyzed our previously published ERP data (Larson et al., 2011) and examined theta band oscillations (4–8 Hz) at frontal midline sites, within a timing window that overlaps with the feedback-related negativity (FRN). We focused on event-related changes after presentation of feedback for reward (WIN) and punitive (LOSE) outcomes, both for spectral power and inter-trial phase coherence. Results In our reward prediction task, for both groups, medial frontal theta power and phase coherence were greater following LOSE compared to WIN feedback. However, compared to controls, inter-trial coherence of medial frontal theta was significantly lower overall (across both feedback types) for individuals with ASD. Our results indicate that while individuals with ASD are sensitive to the valence of reward feedback, comparable to their neurotypical peers, they have reduced synchronization of medial frontal theta activity during feedback processing. Conclusions This finding are consistent with previous studies showing neural variability in ASD and suggest that the processes underlying decision-making and reinforcement learning may be atypical and less efficient in ASD.

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Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Inter-trial coherence of medial frontal theta oscillations linked to differential feedback processing in youth and young adults with autism

Noordt

Venkataraman

et al. 2017

Research in Autism Spectrum Disorders

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“…Although the relationships between behavior and oscillatory activity were not tested directly in the present study, some relationships might be hypothesized from prior work, where decreased theta and alpha coherence were shown to lead to impairment in working memory and between-network binding (particularly as related to executive processing, inhibition, and conscious attention), while beta frequency synchrony has been related to successful higher-order cognitive processing (cf. Schwartz et al, 2017). Additionally, atypical pattern of synchronization in the left hemisphere might be related to leftlateralized microstructural abnormalities in ASD (Peterson et al, 2015) Our analysis of fronto-parietal temporal dysregulation suggests a possible underlying mechanism whereby normal functional organization of brain networks in ASD fails to emerge.…”

Section: Discussionmentioning

confidence: 91%

Dysregulation of temporal dynamics of synchronous neural activity in adolescents on autism spectrum

Malaia

Ahn

Rubchinsky

2019

Preprint

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Autism spectrum disorder is increasingly understood to be based on atypical signal transfer among multiple interconnected networks in the brain. Relative temporal patterns of neural activity have been shown to underlie both the altered neurophysiology and the altered behaviors in a variety of neurogenic disorders. We assessed brain network dynamics variability in Autism Spectrum Disorders (ASD) using measures of synchronization (phase-locking) strength, and timing of synchronization and desynchronization of neural activity (desynchronization ratio) across frequency bands of resting state EEG. Our analysis indicated that fronto-parietal synchronization is higher in ASD, but with more short periods of desynchronization. It also indicates that the relationship between the properties of neural synchronization and behavior is different in ASD and typically developing populations. Recent theoretical studies suggest that neural networks with high desynchronization ratio have increased sensitivity to inputs. Our results point to the potential significance of this phenomenon to autistic brain. This sensitivity may disrupt production of an appropriate neural and behavioral responses to external stimuli. Cognitive processes dependent on integration of activity from multiple networks may be, as a result, particularly vulnerable to disruption. Lay SummaryParts of the brain can work together by synchronizing activity of the neurons. We recorded electrical activity of the brain in adolescents with autism spectrum disorder, and then compared the recording to that of their peers without the diagnosis. We found that in participants with autism, there were a lot of very short time periods of non-synchronized activity between frontal and parietal parts of the brain. Mathematical models show that the brain system with this kind of activity is very sensitive to external events. IntroductionHuman behavior in health and disease is undergirded by temporal synchronization of distributed networks, which allows for information processing (e.g., Buzsaki, 2006;Harris & Gordon, 2015). The difficulty in understanding of the temporal regulation of brain networks is partially due to the disparity of temporal scales for different types of neural data (e.g. between millisecond scale of brain-activity-driven EEG and second-to-minute scale of blood-flow-based fMRI).Experimental data from different modalities, such as EEG, MEG, and fMRI, indicate that better understanding of temporal dynamics of brain activity may help elucidate mechanisms of brain network organization in ASD population. During rest, ASD participants show increased coherence (or, static overconnectivity) in long-range brain networks (Buckley et al., 2015). However, the increase in coherence coincides with longer dwell times in a globally-disconnected state (Rashid et al., 2018), and increased variability in connectivity over time (Mash et al., 2019). Analyses of EEG and MEG recordings, which provide higher temporal resolution, indicate longrange functional underconnectivity (O'Reilly ...

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“…Coherence provides a measure of the degree of synchronization between two signals, which means that the two signals with the same frequency have the consistent phase relationship over time, and we could also assume there is a high degree of the coordinated brain activity between the underlying brain areas where those two signals come from . EEG coherence is one way to assess the brain functional connectivity, which has proven abnormal in previous studies for ASD children .…”

Section: Methodsmentioning

confidence: 99%

“…Previous studies have shown that abnormalities in brain network connectivity were common in autistic children, which might lead to atypical interactions between brain regions that could lead to the social and cognitive impairment . For ASD, the ongoing changes of the pruning and synaptogenesis disturbed the normal brain development, thus leading to the abnormal neural connectivity . Therefore, differences in EEG signals can be used to compare the children with autism with the typical developmental children .…”

Section: Introductionmentioning

confidence: 99%

Effects of repetitive transcranial magnetic stimulation on children with low‐function autism

et al. 2019

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Background Autism spectrum disorder (ASD) is a very complex neurodevelopmental disorder, characterized by social difficulties and stereotypical or repetitive behavior. Some previous studies using low‐frequency repetitive transcranial magnetic stimulation (rTMS) have proven of benefit in ASD children. Methods In this study, 32 children (26 males and six females) with low‐function autism were enrolled, 16 children (three females and 13 males; mean ± SD age: 7.8 ± 2.1 years) received rTMS treatment twice every week, while the remaining 16 children (three females and 13 males; mean ± SD age: 7.2 ± 1.6 years) served as waitlist group. This study investigated the effects of rTMS on brain activity and behavioral response in the autistic children. Results Peak alpha frequency (PAF) is an electroencephalographic measure of cognitive preparedness and might be a neural marker of cognitive function for the autism. Coherence is one way to assess the brain functional connectivity of ASD children, which has proven abnormal in previous studies. The results showed significant increases in the PAF at the frontal region, the left temporal region, the right temporal region and the occipital region and a significant increase of alpha coherence between the central region and the right temporal region. Autism Behavior Checklist (ABC) scores were also compared before and after receiving rTMS with positive effects shown on behavior. Conclusion These findings supported our hypothesis by demonstration of positive effects of combined rTMS neurotherapy in active treatment group as compared to the waitlist group, as the rTMS group showed significant improvements in behavioral and functional outcomes as compared to the waitlist group.

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Electroencephalogram Coherence Patterns in Autism: An Updated Review

Cited by 61 publications

References 87 publications

Inter-trial coherence of medial frontal theta oscillations linked to differential feedback processing in youth and young adults with autism

Inter-trial coherence of medial frontal theta oscillations linked to differential feedback processing in youth and young adults with autism

Dysregulation of temporal dynamics of synchronous neural activity in adolescents on autism spectrum

Effects of repetitive transcranial magnetic stimulation on children with low‐function autism

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