Authors :
Presenting Author: vytene Janiukstyte, MSc – Newcastle University
Thomas Owen, MSc – Newcastle University; Umair Chaudhary, MBBS, MSc, PhD, MRCP – National Hospital for Neurology and Neurosurgery; Beate Diehl, Dr – UCL; Louis Lemieux, Prof – UCL Queen Square Institute of Neurology; John Duncan, Prof – National Hospital for Neurology and Neurosurgery; Jane de Tisi, Dr – UCL; Yujiang Wang, Dr – Newcastle University; Peter Taylor, Dr – Newcastle University
Rationale:
A normative electrographic activity map could be a powerful resource to understand normal brain function and identify abnormal activity. Here, we present a normative brain map using scalp EEG in terms of relative band power. In this exploratory study, we investigate its temporal stability, its similarity to other imaging modalities, and explore a potential clinical applicationMethods:
We constructed scalp EEG normative maps of brain dynamics from 17 healthy controls using source-localised resting-state scalp recordings. We then correlated these maps with those acquired from MEG and intracranial EEG to investigate their similarity. Lastly, we use the normative maps to lateralise abnormal regions in epilepsyResults:
Spatial patterns of band powers were broadly consistent with previous literature and stable across recordings (Figure 1). Scalp EEG normative maps were most similar to other modalities in the alpha band, and relatively similar across most bands (Figure 2). Towards a clinical application in epilepsy, we found abnormal temporal regions ipsilateral to the epileptogenic hemisphereConclusions:
Scalp EEG relative band power normative maps are spatially stable across time, in keeping with MEG and intracranial EEG results. Normative mapping is feasible and may be potentially clinically useful in epilepsy. Future studies with larger sample sizes and high-density EEG are now required for validation.
Funding:
B.D. receives support from the NIH National Institute of Neurological Disorders and Stroke U01-NS090407 (Center for SUDEP Research) and Epilepsy Research UK. Y.W. is supported by a UKRI Future Leaders Fellowship (MR/V026569/1). P.N.T. is supported by a UKRI Future Leaders Fellowship (MR/T04294X/1). T.W.O is supported by the Centre for Doctoral Training in Cloud Computing for Big Data (EP/L015358/1). J.S.D. is grateful to Wellcome Trust (WT106882) and Epilepsy Research UK. We are grateful to the Epilepsy Society for supporting the Epilepsy Society MRI scanner. This work was supported by the National Institute for Health Research University College London Hospitals Biomedical Research Centre.