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Alpha oscillations are dysrhythmic in Fragile X syndrome
Peyton Siekierski1,2, Yanchen Liu1, Grace Westerkamp1
1Division of Child and Adolescent Psychiatry, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
Alpha oscillations in Fragile X syndrome (FXS) show altered burst dynamics, with prolonged durations and elevated amplitudes, particularly in males. This reveals underlying temporal features contributing to neurodevelopmental disorders.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Genetics
Background:
- Alpha oscillations are key brain rhythms for neural coordination and inhibitory control.
- Dysfunctional alpha dynamics are linked to neuropsychiatric and neurodevelopmental disorders.
- Fragile X syndrome (FXS) presents paradoxical alpha power (elevated absolute, reduced relative), requiring deeper analysis beyond simple power metrics.
Purpose of the Study:
- To investigate alpha oscillation temporal dynamics in FXS using cycle-by-cycle burst analysis.
- To decompose nonspecific alpha abnormalities in FXS into specific temporal features.
- To model alpha burst features and identify group, sex, and regional differences.
Main Methods:
- Utilized cycle-by-cycle (bycycle) alpha burst analysis on resting-state EEG data.
- Analyzed data from 70 individuals with FXS and 71 typically developing controls.
- Employed statistical modeling (generalized linear mixed-effects models) to examine burst features.
Main Results:
- FXS males showed reduced alpha burst count; both sexes exhibited prolonged burst durations and elevated amplitudes (especially males).
- Timing dysregulation was found in cognitive-control regions, while amplitude elevations occurred in sensory cortices.
- Alpha burst amplitude correlated with hyperactivity and inversely with intelligence in FXS; burst count correlated with age.
Conclusions:
- Resolved paradoxical alpha power in FXS by identifying specific temporal burst features.
- Findings suggest interneuron dysfunction as a potential mechanism in FXS.
- Demonstrated the utility of burst-level analysis for hypothesis generation and biomarker development in neurodevelopmental disorders.
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