Post Traumatic Seizure Classification with Missing Data using Multimodal Machine Learning on dMRI, EEG, and fMRI

Late post-traumatic seizure (LPTS) is a complication of traumatic brain injury (TBI), which can lead to a potentially lifelong condition of post-traumatic epilepsy (PTE). Currently, the patho-mechanism that induces epileptogenesis in TBI subjects is unclear. As such, the epilepsy community strives to identify which TBI subjects will develop epilepsy and find potential biomarkers. To that end, this study collects longitudinal multimodal data from TBI subjects at multiple participating institutes. A supervised, binary classification task is formed with data from the LPTS versus no LPTS subjects. Missing modalities in certain subjects is handled in two ways. First, we extend a graphical model based Bayesian estimator to directly classify subjects with missing modality, and second, we investigate standard imputation techniques. The multimodal information is then combined, following several fusion and dimensionality reduction techniques found in literature, and eventually fitted to a kernelor a tree-based classifier. For this fusion, we propose two new algorithms: recursive elimination of correlated components (RECC) which filters information based on correlation, and information decomposition and selective fusion (IDSF) which meaningfully recombines information from decomposed multimodal features. Based on the cross-validated area under the curve (AUC) score, we find the proposed IDSF algorithm provides the best performance. Finally, following statistical analyses of the frequently selected features, we recommend alterations in inferior temporal gyrus as a potential biomarker. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This project was funded by the National Institute of Neurological Disorders and Stroke (NINDS) of the National Institutes of Health (NIH) under award number R01NS111744. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: This work was approved by the University of California, Los Angeles (UCLA) Institutional Review Board (IRB# 16-001 576) and the local review boards at each EpiBioS4Rx Study Group institution. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data analyzed in this study is subject to the following licenses/restrictions: access to data must be requested and approved by the EpiBioS4Rx steering committee. Requests to access these datasets should be directed to epibiossteeringcommittee{at}loni.usc.edu.