Team – Childhood Epilepsy https://childhoodepilepsy.org Chiildhood epilepsy research and fundraising Tue, 02 Aug 2022 11:15:46 +0000 en-US hourly 1 https://wordpress.org/?v=5.8.3 New Publication: ‘Heterogeneity of Resting-State EEG Features in Juvenile Myoclonic Epilepsy and Controls’ https://childhoodepilepsy.org/research-news/new-publication-heterogeneity-of-resting-state-eeg-features-in-juvenile-myoclonic-epilepsy-and-controls/ Tue, 02 Aug 2022 11:15:45 +0000 https://childhoodepilepsy.org/?p=2509 A new paper has been published in Brain Communications. In this work, we identified differences in a variety of resting-state EEG features between patients with juvenile myoclonic epilepsy and healthy...

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]]> A new paper has been published in Brain Communications. In this work, we identified differences in a variety of resting-state EEG features between patients with juvenile myoclonic epilepsy and healthy controls, such as alpha frequency and network ictogenicity.
We also identified factors influencing these measures within and between cohorts, such as age, time of day and specific seizure characteristics.
Lead author Dr Amy Shakeshaft. Thanks to all collaborators, including those involved in the BIOJUME study, and funders who contributed to this work.

Brain Communications, Volume 4, Issue 4, 2022, fcac180, https://doi.org/10.1093/braincomms/fcac180

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]]> RADAR-EEG Poster Presented at ICTALS Conference 2022 https://childhoodepilepsy.org/conferences/radar-eeg-poster-presented-at-ictal-conference-2022/ Thu, 28 Jul 2022 15:53:27 +0000 https://childhoodepilepsy.org/?p=2488 Andrea Biondi (Pal Lab, Richardson Lab) presented RADAR-EEG, a collaboration with Dr Petroula Laiou (Department of Biostatistics and Health Informatics) using EEG data collected during the RADAR Study 1, at...

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]]> Andrea Biondi (Pal Lab, Richardson Lab) presented RADAR-EEG, a collaboration with Dr Petroula Laiou (Department of Biostatistics and Health Informatics) using EEG data collected during the RADAR Study 1, at the ICTALS Conference 2022 in Bern, Switzerland.

Several studies have shown that in people with epilepsy, brain network features computed from interictal scalp EEG recordings could serve as potential biomarkers for epilepsy diagnosis. In addition, evolving brain networks derived from intracranial EEG recordings have shown topological changes prior to seizure occurrence.


In this small project we investigate the daily evolution of brain network features derived from multi-day scalp EEG and examine whether they change prior to seizure occurrence.

We observed a statistically significant difference (p-value <0.05 corrected for multiple comparisons) of the brain network metrics between the day before the seizure occurrence when compared to previous days. Brain network distributions were studied in multiple frequency bands and alpha and theta band were found to be the more informative.

Interictal scalp EEG data may be used for the characterization of topological changes that occur prior to seizures. In addition, they show promise as potential biomarkers for the estimation of seizure risk and their incorporation in the study pipelines for a long-term wearable EEG mobile monitoring for epilepsy forecasting and management.

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