%0 Journal Article
%T Operation Brain Trauma Therapy (OBTT): the use of machine learning to re-assess patterns of multivariate functional recovery following fluid percussion injury
%J Journal of neurotrauma
%D 2020
%A Radabaugh, Hannah
%A Bonnell, Jerry
%A Schwartz, Odelia
%A Sarkar, Dilip
%A Dietrich, W. Dalton
%A Bramlett, Helen M.
%V ePub
%N ePub
%P ePub-ePub
%X 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 advance personalized treatment strategies and better inform translational research. However, the use of ML has yet to be widely assessed in preclinical neurotrauma research where data are strictly limited in subject number. To better establish this feasibility, we utilized the fluid percussion injury (FPI) portion of the rich rat dataset collected by Operation Brain Trauma Therapy (OBTT) which tested multiple pharmacological treatments. Previous work 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 multivariate recovery profiles afforded by the administration of nine different experimental therapies. We assessed supervised pairwise classifiers trained on a preprocessed dataset that incorporated metrics from all feature groups to determine their ability to correctly identify specific drug treatments. In all but one possible pairwise combinations of minocycline, levetiracetam, erythropoietin, nicotinamide, and amantadine, the baseline was outperformed by one or more supervised classifiers; the exception being nicotinamide vs. amantadine. Furthermore, 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 towards 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.<p /> <p>Language: en</p>
%G en
%I Mary Ann Liebert Publishers
%@ 0897-7151
%U http://dx.doi.org/10.1089/neu.2020.7357