Authors :
Presenting Author: Csaba Kozma, MSc – Newcastle University
Peter Taylor, PhD – Reader in Computational Neurology, School of Computing, Newcastle University; Gabrielle Schroeder, PhD – Post-Doctoral fellow, School of Computing, Newcastle University; Yujiang Wang, PhD – Reader in Computational Neurology, School of Computing, Newcastle University; Tom Owen, MSc – PhD student, School of Computing, Newcastle University
Rationale:
The identification of abnormal electrographic activity is crucial in epilepsy. This identification can be done through spectral band power analyses; however, different factors may drive similarly appearing changes to band power. Recent studies showed that decomposing brain activity into periodic (oscillatory) and aperiodic (trend across all frequencies) activity may identify underlying drivers of spectral change.This study aims to investigate if decomposing the electrographic activity explains epileptogenic abnormalities and patient outcomes.
Methods:
From power spectral density estimations of iEEG recordings we used the specparam toolbox to compute periodic and aperiodic components. Our computations were performed using iEEG from 234 subjects to construct a normative map, and for a separate cohort of 63 patients with epilepsy. The normative map was computed using three approaches: (i) relative band power – as used previously, (ii) relative band power with the aperiodic component removed –
purely periodic activity, (iii) the aperiodic exponent. Corresponding abnormalities were also calculated for each approach in the separate patient cohort. We first investigated the spatial profiles of the three approaches, second assessed their localization ability, and third replicated our findings in a separate modality using MEG.
Results:
The normative maps of relative band power and relative periodic band power had similar spatial profiles. In the aperiodic normative map, exponent values were highest in the temporal lobe. Abnormality estimated through the band power robustly distinguished between ILAE1, 2, and ILAE3+ outcome patients (AUC=0.74, p p< 0.01; MEG AUC=0.69, p=0.025). However, neither periodic band power nor aperiodic exponent abnormalities did the same (P >0.05). Selecting the higher value across periodic and aperiodic abnormalities yielded approximately the same performance as the first approach (iEEG AUC=0.70, p< 0.01; MEG AUC=0.69, p=0.039).