The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4R) Scientific Premise is: epileptogenesis after traumatic brain injury (TBI) can be prevented with specific treatments; the identification of relevant biomarkers and performance of rigorous preclinical trials will permit the future design and performance of economically feasible full-scale clinical trials of antiepileptogenic therapies.
The Duncan Lab is specifically contributing to the EpiBioS4Rx Consortium by identifying multimodal biomarkers of epileptogenesis. We apply data science, mathematics, and machine learning to glean insight into what factors may induce the development of seizures after brain injury so we can ultimately predict which patients will develop epilepsy. Our work centers around biomarker identification in scalp and depth electrophysiology and multimodal imaging data.
Automating Lesion Segmentation
TBI patients often have significant brain deformations due to hemorrhagic and contusional injuries. These deformations impact downstream imaging analysis by disrupting visual landmarks and voxel properties used to determine tissue class and perform registration. Therefore, a clear map of affected brain tissue is essential for accurate analysis. However, there is no toolbox for lesion segmentation of TBI patients, so we are performing manual segmentation for hundreds of patients to establish ground truths to train machine learning classifers to perform automated lesion segmentation. Individual lesion masks can be stacked to evaluate the degree of overlap across the EpiBioS4Rx population.
Exploring How Lesion Characteristics Influence Seizure Development
Once lesion masks are generated, we can use them to evaluate how specific characteristics of the injury (e.g. location and volume) relate to developing post-traumatic epilepsy.
Improving High Frequency Oscillation Detection
High frequency oscillations are one potentially pathogenic pattern found in electroencephalography that may distinguish TBI patients who develop subsequent epilepsy. Although insightful, HFOs are difficult to detect because they are brief and transient. Manual detection is tedious and time-intensive, and existing detection algorithms yield high false positive rates. Therefore, we are working towards developing a machine-learning based classifier that can improve the sensitivity of HFO detection.
Once HFOs are identified, we are using them to build predictive models of HFO progression in rodents after induced injury.
This work is supported by the National Institute of Neurological Disorders and Stroke (NINDS) at the National Institutes of Health (NIH), award numbers U54NS100064 and R01NS111744.
For more information, please visit https://epibios.loni.usc.edu
Project Specialist: Alexis B. Bennett
Related Publications:
Manual lesion segmentations for traumatic brain injury characterization. Bennett, A., Garner, R., Morris, M. D., La Rocca, M., Barisano, G., Cua, R., … & Duncan, D. (2023). Frontiers in Neuroimaging, 2, 14.
Functional connectivity alterations in traumatic brain injury patients with late seizures. La Rocca, M., Barisano, G., Garner, R., Ruf, S. F., Amoroso, N., Monti, M., … & EpiBioS4Rx Study Group. (2023). Neurobiology of Disease, 179, 106053.
Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEG. Yıldız Potter, İ., Zerveas, G., Eickhoff, C., & Duncan, D. (2023). arXiv e-prints, arXiv-2301.
Unsupervised seizure identification on EEG. Yıldız İ, Garner R, Lai M, Duncan D. Comput Methods Programs Biomed. 2022;215:106604. doi:https://doi.org/10.1016/j.cmpb.2021.106604
Multiplex Networks to Characterize Seizure Development in Traumatic Brain Injury Patients. La Rocca M, Garner R, Amoroso N, Lutkenhoff ES, Monti MM, Vespa P, Toga AW, Duncan D.Front Neurosci. 2020 Nov 30;14:591662. doi: 10.3389/fnins.2020.591662. eCollection 2020.PMID: 33328863
Early brain biomarkers of post-traumatic seizures: initial report of the multicentre epilepsy bioinformatics study for antiepileptogenic therapy (EpiBioS4Rx) prospective study. Lutkenhoff ES, Shrestha V, Ruiz Tejeda J, Real C, McArthur DL, Duncan D, La Rocca M, Garner R, Toga AW, Vespa PM, Monti MM; EpiBioS4Rx Study Group.J Neurol Neurosurg Psychiatry. 2020 Nov;91(11):1154-1157. doi: 10.1136/jnnp-2020-322780. Epub 2020 Aug 26.PMID: 32848013
Imaging biomarkers of posttraumatic epileptogenesis.Garner R, La Rocca M, Vespa P, Jones N, Monti MM, Toga AW, Duncan D.Epilepsia. 2019 Nov;60(11):2151-2162. doi: 10.1111/epi.16357. Epub 2019 Oct 8.PMID: 31595501
Harmonization of pipeline for preclinical multicenter MRI biomarker discovery in a rat model of post-traumatic epileptogenesis.Immonen R, Smith G, Brady RD, Wright D, Johnston L, Harris NG, Manninen E, Salo R, Branch C, Duncan D, Cabeen R, Ndode-Ekane XE, Gomez CS, Casillas-Espinosa PM, Ali I, Shultz SR, Andrade P, Puhakka N, Staba RJ, O’Brien TJ, Toga AW, Pitkänen A, Gröhn O.Epilepsy Res. 2019 Feb;150:46-57. doi: 10.1016/j.eplepsyres.2019.01.001. Epub 2019 Jan 7.PMID: 30641351
The epilepsy bioinformatics study for anti-epileptogenic therapy (EpiBioS4Rx) clinical biomarker: Study design and protocol.Vespa PM, Shrestha V, Abend N, Agoston D, Au A, Bell MJ, Bleck TP, Blanco MB, Claassen J, Diaz-Arrastia R, Duncan D, Ellingson B, Foreman B, Gilmore EJ, Hirsch L, Hunn M, Kamnaksh A, McArthur D, Morokoff A, O’Brien T, O’Phelan K, Robertson CL, Rosenthal E, Staba R, Toga A, Willyerd FA, Zimmermann L, Yam E, Martinez S, Real C, Engel J Jr.Neurobiol Dis. 2019 Mar;123:110-114. doi: 10.1016/j.nbd.2018.07.025. Epub 2018 Jul 23.PMID: 30048805
Big data sharing and analysis to advance research in post-traumatic epilepsy.Duncan D, Vespa P, Pitkänen A, Braimah A, Lapinlampi N, Toga AW.Neurobiol Dis. 2019 Mar;123:127-136. doi: 10.1016/j.nbd.2018.05.026. Epub 2018 Jun 1.PMID: 29864492
Analytic Tools for Post-traumatic Epileptogenesis Biomarker Search in Multimodal Dataset of an Animal Model and Human Patients.Duncan D, Barisano G, Cabeen R, Sepehrband F, Garner R, Braimah A, Vespa P, Pitkänen A, Law M, Toga AW.Front Neuroinform. 2018 Dec 20;12:86. doi: 10.3389/fninf.2018.00086. eCollection 2018.PMID: 30618695
DETECTING FEATURES OF EPILEPTOGENESIS IN EEG AFTER TBI USING UNSUPERVISED DIFFUSION COMPONENT ANALYSIS.Duncan D, Vespa P, Toga AW.Discrete Continuous Dyn Syst Ser B. 2018 Jan;23(1):161-172. doi: 10.3934/dcdsb.2018010.PMID: 30369835