Multiplex Networks to Characterize Seizure Development in Traumatic Brain Injury Patients.

Marianna La Rocca,Evan S Lutkenhoff,Arthur W Toga,Dominique Duncan,Nicola Amoroso,Paul Vespa,Rachael Garner,Martin M Monti

doi:10.3389/fnins.2020.591662

Abstract

Traumatic brain injury (TBI) may cause secondary debilitating problems, such as post-traumatic epilepsy (PTE), which occurs with unprovoked recurrent seizures, months or even years after TBI. Currently, the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) has been enrolling moderate-severe TBI patients with the goal to identify biomarkers of epileptogenesis that may help to prevent seizure occurrence and better understand the mechanism underlying PTE. In this work, we used a novel complex network approach based on segmenting T1-weighted Magnetic Resonance Imaging (MRI) scans in patches of the same dimension (network nodes) and measured pairwise patch similarities using Pearson's correlation (network connections). This network model allowed us to obtain a series of single and multiplex network metrics to comprehensively analyze the different interactions between brain components and capture structural MRI alterations related to seizure development. We used these complex network features to train a Random Forest (RF) classifier and predict, with an accuracy of 70 and a 95% confidence interval of [67, 73%], which subjects from EpiBioS4Rx have had at least one seizure after a TBI. This complex network approach also allowed the identification of the most informative scales and brain areas for the discrimination between the two clinical groups: seizure-free and seizure-affected subjects, demonstrating to be a promising pilot study which, in the future, may serve to identify and validate biomarkers of PTE.

Highlights

Traumatic brain injury (TBI) is the third most common cause of death and debilitating secondary problems in adults and children worldwide
Even though we found the best accuracy for a patch size of 1,000 and 5,000 voxels, and the best Area Under the receiver-operating-characteristic Curve (AUC) for 3,000 voxels, the classification performances at these three scales are statistically comparable
We evaluated the mean percentage of features associated with a certain network metric that are selected as important in a cross validation-round

Summary

Introduction

Traumatic brain injury (TBI) is the third most common cause of death and debilitating secondary problems in adults and children worldwide. Growing attention has been devoted to investigate PTE In this regard, the Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is an international, multi-center project conceived to identify biomarkers of epileptogenesis after a TBI in order to evaluate treatments that could prevent the development of PTE and design clinical trials of antiepileptogenic therapies on an extensive patient population. The Epilepsy Bioinformatics Study for Antiepileptogenic Therapy (EpiBioS4Rx) is an international, multi-center project conceived to identify biomarkers of epileptogenesis after a TBI in order to evaluate treatments that could prevent the development of PTE and design clinical trials of antiepileptogenic therapies on an extensive patient population With this project, the scientific community can be granted access to a large amount of high quality, multi-modal data, including imaging, electrophysiology, and clinical data from both humans and animals

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