Identifying epileptogenic abnormality by decomposing intracranial EEG and MEG power spectra
Poster No:
1623
Submission Type:
Abstract Submission
Authors:
Csaba Kozma1, Gabrielle Schroeder1, Thomas Owen1, John Duncan2, Yujiang Wang1, Peter Taylor1
Institutions:
1Newcastle University, Newcastle, United Kingdom, 2UCL Queen Square Institute of Neurology, London, United Kingdom
First Author:
Co-Author(s):
Introduction:
Improved EEG biomarkers of the epileptogenic zone are important, as around half of individuals have recurrent seizures after surgical treatment . Substantial research has focused on interictal EEG biomarkers using normative maps of power in different frequencies . This approach involves outlining the spatial characteristics and ranges of the feature of interest in a healthy context and comparing patient data to identify abnormalities. Recent studies proposed using patient iEEG data from regions outside the epileptogenic zone to create normative maps . The band power of different frequency bands can be used to infer expected healthy spatial profiles of EEG activity. This approach showed promising results to identify abnormalities and classify patient outcomes across different modalities. It is unclear what spectral features drive the observed band power abnormalities. One way to explore these features is to decompose the power spectra into rhythmic (periodic) and non-rhythmic (aperiodic) components. The aperiodic component can be described by its offset and exponent, while periodic components form peaks in the power spectra. Both components should be considered, as power changes observed in specific frequency bands can be attributed to either changes in the peak or the offset. Despite the growing interest in decomposing power spectra, previous studies have not investigated which power spectrum components contribute to abnormal interictal brain activity. Here we investigate if interictal abnormality is driven by periodic components, aperiodic components, or a combination of both components.
Methods:
Using iEEG data from 234 subjects, we constructed a normative map and compared this with a separate cohort of 63 patients with refractory focal epilepsy being considered for neurosurgery. The normative map was computed using three approaches: (i) relative complete band power, (ii) relative band power with the aperiodic component removed (iii) the aperiodic exponent. Corresponding abnormalities were also calculated for each approach in the separate patient cohort. We investigated the spatial profiles of the three approaches, assessed their localising ability, and replicated our findings in a separate modality using MEG (HC=70, patients=33).
Results:
The normative maps of relative complete 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 complete band power robustly distinguished between good and bad outcome patients (AUC=0.71, p$<$0.01; MEG AUC=0.69, p=0.03). Neither periodic band power nor aperiodic exponent abnormalities distinguished seizure outcome groups. Combining periodic and aperiodic abnormalities improved performance, similar to the complete band power approach (iEEG AUC=0.64, p=0.05; MEG AUC=0.69, p=0.039).
Conclusions:
Our findings suggest that sparing cerebral tissue that generates abnormalities in either periodic or aperiodic activity may lead to a poor surgical outcome. Both periodic and aperiodic abnormalities are necessary to distinguish patient outcomes, with neither sufficient in isolation. Future studies could investigate whether periodic or aperiodic abnormalities are affected by the cerebral location or pathology.
Modeling and Analysis Methods:
EEG/MEG Modeling and Analysis 1
Methods Development 2
Neuroinformatics and Data Sharing:
Brain Atlases
Novel Imaging Acquisition Methods:
EEG
MEG
Keywords:
Computing
Epilepsy
MEG
Statistical Methods
1|2Indicates the priority used for review
Provide references using author date format
2. Taylor et al. (2022).: Normative brain mapping of interictal intracranial eeg to localize epileptogenic tissue. Brain. 145:939–949. doi: 10.1093/brain/awab380.501.
3. Owen et al. (2023).: Meg abnromalities and mechanisms of surgical failure in neocortical epilepsy. Epilepsia. 64:692–704. doi: 10.1111/epi.17503.
4. Donoghue T. (2020). Parameterizing neural power spectra into periodic and aperiodic components. Nature Neuroscience, 23:1655–1665. doi: 10.1038/s41593-020-00744-x.