peak alpha frequency
Research Papers
Peak alpha frequency is a neural marker of cognitive function across the autism spectrum
Cognitive function varies substantially and serves as a key predictor of outcome and response to intervention in autism spectrum disorder (ASD), yet we know little about the neurobiological mechanisms that underlie cognitive function in children with ASD. The dynamics of neuronal oscillations in the alpha range (6-12 Hz) are associated with cognition in typical development. Peak alpha frequency is also highly sensitive to developmental changes in neural networks, which underlie cognitive function, and therefore, it holds promise as a developmentally sensitive neural marker of cognitive function in ASD. Here, we measured peak alpha band frequency under a task-free condition in a heterogeneous sample of children with ASD (N = 59) and age-matched typically developing (TD) children (N = 38). At a group level, peak alpha frequency was decreased in ASD compared to TD children. Moreover, within the ASD group, peak alpha frequency correlated strongly with non-verbal cognition. As peak alpha frequency reflects the integrity of neural networks, our results suggest that deviations in network development may underlie cognitive function in individuals with ASD. By shedding light on the neurobiological correlates of cognitive function in ASD, our findings lay the groundwork for considering peak alpha frequency as a useful biomarker of cognitive function within this population which, in turn, will facilitate investigations of early markers of cognitive impairment and predictors of outcome in high risk infants.
View Full Paper →EEG PHENOTYPES PREDICT TREATMENT OUTCOME TO STIMULANTS IN CHILDREN WITH ADHD
This study demonstrates that the EEG Phenotypes as described by Johnstone, Gunkelman & Lunt [19] are clearly identifiable EEG patterns with good inter-rater reliability. Furthermore, it was also demonstrated that these EEG phenotypes occurred in both ADHD subjects as well as healthy control subjects. The Frontal Slow, the Slow Alpha Peak Frequency and the Low Voltage EEG Phenotype seemed to discriminate ADHD subjects best from the control group, however not significantly. The Frontal Slow group responded to a stimulant with a clinically relevant decreased number of false negative errors on the CPT. The Frontal Slow and Slowed Alpha Peak Frequency phenotypes, have very different etiologies as evidenced by the treatment response to stimulants. In previous research the slowed alpha peak frequency has most likely erroneously shown up as a Frontal Theta sub-group. This implies that future research employing EEG measures in ADHD should avoid using traditional frequency bands, but clearly dissociate slowed alpha peak frequency from frontal theta by taking the individual alpha peak frequency into account. Furthermore, the divergence from normal of the frequency bands pertaining to the various phenotypes is greater in the clinical group than in the controls. Investigating EEG Phenotypes seems to be a promising new way to approach EEG data, explaining much of the variance in EEG’s, and thereby potentially leading to more specific prospective treatment outcomes.
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