Use of machine learning to re-assess patterns of multivariate functional recovery after fluid percussion injury: Operation brain trauma therapy

Hannah Radabaugh, Jerry Bonnell, Odelia Schwartz, Dilip Sarkar, W. Dalton Dietrich, Helen M. Bramlett

Research output: Contribution to journalArticlepeer-review

Abstract

Traumatic brain injury (TBI) is a leading cause of death and disability. Yet, despite immense research efforts, treatment options remain elusive. Translational failures in TBI are often attributed to the heterogeneity of the TBI population and limited methods to capture these individual variabilities. Advances in machine learning (ML) have the potential to further personalized treatment strategies and better inform translational research. However, the use of ML has yet to be widely assessed in pre-clinical neurotrauma research, where data are strictly limited in subject number. To better establish ML’s feasibility, we utilized the fluid percussion injury (FPI) portion of the rich, rat data set collected by Operation Brain Trauma Therapy (OBTT), which tested multiple pharmacological treatments. Previous work has provided confidence that both unsupervised and supervised ML techniques can uncover useful insights from this OBTT pre-clinical research data set. As a proof-of-concept, we aimed to better evaluate the multi-variate recovery profiles afforded by the administration of nine different experimental therapies. We assessed supervised pairwise classifiers trained on a pre-processed data set that incorporated metrics from four feature groups to determine their ability to correctly identify specific drug treatments. In all but one of the possible pairwise combinations of minocycline, levetiracetam, erythropoietin, nicotinamide, and amantadine, the baseline was outperformed by one or more supervised classifiers, the exception being nicotinamide versus amantadine. Further, when the same methods were employed to assess different doses of the same treatment, the ML classifiers had greater difficulty in understanding which treatment each sample received. Our data serve as a critical first step toward identifying optimal treatments for specific subgroups of samples that are dependent on factors such as types and severity of traumatic injuries, as well as informing the prediction of therapeutic combinations that may lead to greater treatment effects than individual therapies.

Original languageEnglish (US)
Pages (from-to)1662-1669
Number of pages8
JournalJournal of neurotrauma
Volume38
Issue number12
DOIs
StatePublished - Jun 15 2021

Keywords

  • Data analysis
  • Machine learning
  • Pharmacotherapy
  • Traumatic brain injury

ASJC Scopus subject areas

  • Clinical Neurology

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