Understanding recovery from TBI is complex, involving multiple systems and modalities. The current study applied modern data science tools to manage this complexity and harmonize large-scale data to understand relationships between gene expression and behavioral outcomes in a preclinical model of chronic TBI (cTBI). Data collected by the Moody Project for Translational TBI Research included rats with no injury (naïve animals with similar amounts of anesthetic exposure to TBI and sham-injured animals), sham injury, or lateral fluid percussion TBI, followed by recovery periods up to 12 months. Behavioral measures included locomotor coordination (beam balance neuroscore) and memory and cognition assessments (Morris water maze: MWM) at multiple timepoints. Gene arrays were performed using hippocampal and cortical samples to probe 45,610 genes. To reduce the high dimensionality of molecular and behavioral domains and uncover gene–behavior associations, we performed non-linear principal components analyses (NL-PCA), which de-noised the data. Genomic NL-PCA unveiled three interpretable eigengene components (PC2, PC3, and PC4). Ingenuity pathway analysis (IPA) identified the PCs as an integrated stress response (PC2; EIF2-mTOR, corticotropin signaling, etc.), inflammatory factor translation (PC3; PI3K-p70S6K signaling), and neurite growth inhibition (PC4; Rho pathways). Behavioral PCA revealed three principal components reflecting the contribution of MWM overall speed and distance, neuroscore/beam walk, and MWM platform measures. Integrating the genomic and behavioral domains, we then performed a ‘meta-PCA’ on individual PC scores for each rat from genomic and behavioral PCAs. This meta-PCA uncovered three unique multimodal PCs, characterized by robust associations between inflammatory/stress response and neuroscore/beam walk performance (meta-PC1), stress response and MWM performance (meta-PC2), and stress response and neuroscore/beam walk performance (meta-PC3). Multivariate analysis of variance (MANOVA) on genomic–behavioral meta-PC scores tested separately on cortex and hippocampal samples revealed the main effects of TBI and recovery time. These findings are a proof of concept for the integration of disparate data domains for translational knowledge discovery, harnessing the full syndromic space of TBI.

Traumatic brain injury (TBI) is a major public health problem. Despite considerable research deciphering injury pathophysiology, precision therapies remain elusive. Here, we present large-scale data sharing and machine intelligence approaches to leverage TBI complexity. The Open Data Commons for TBI (ODC-TBI) is a community-centered repository emphasizing Findable, Accessible, Interoperable, and Reusable data sharing and publication with persistent identifiers. Importantly, the ODC-TBI implements data sharing of individual subject data, enabling pooling for high-sample-size, feature-rich data sets for machine learning analytics. We demonstrate pooled ODC-TBI data analyses, starting with descriptive analytics of subject-level data from 11 previously published articles (N = 1250 subjects) representing six distinct pre-clinical TBI models. Second, we perform unsupervised machine learning on multi-cohort data to identify persistent inflammatory patterns across different studies, improving experimental sensitivity for pro- versus anti-inflammation effects. As funders and journals increasingly mandate open data practices, ODC-TBI will create new scientific opportunities for researchers and facilitate multi-data-set, multi-dimensional analytics toward effective translation.

Background: Patient-centered outcomes research seeks to answer patient-centered questions. The process includes varied locations and individuals throughout the care continuum to address individual differences and constraints in implementation and dissemination. Problem: This paper intends to answer this question: do academic nurses practice what they preach by assisting patient-centered outcomes research and researchers through their engagement with patients, caregivers, and other community stakeholder partners in nursing research? Approach: This paper provides an overview of how academic nurses in a single institution (the University of Texas Medical Branch at Galveston School of Nursing) began to embrace patient-centered outcomes research. Conclusion: Whether academic nurses are practicing what they preach in terms of patient-centered outcomes research remains uncertain. More examples from academia are required to make that determination. Academic nurses worldwide have embarked on a steep learning curve to embrace patient-centered outcomes research. This journey will require patience and a systematic strategy.

Traumatic brain injury (TBI) induces cognitive deficits clinically and in animal models. Learning and memory testing is critical when evaluating potential therapeutic strategies and treatments to manage the effects of TBI. We evaluated three data analysis methods for the Morris water maze (MWM), a learning and memory assessment widely used in the neurotrauma field, to determine which statistical tool is optimal for MWM data. Hidden platform spatial MWM data aggregated from three separate experiments from the same laboratory were analyzed using 1) a logistic regression model, 2) an analysis of variance (ANOVA) model, and 3) an accelerated failure time (AFT) time-to-event model. The logistic regression model showed no significant evidence of differences between treatments among any swims over all days of the study, p > 0.11. Although the ANOVA model found significant evidence of differences between sham and TBI groups on three out of four swims on the third day, results are potentially biased due to the failure of this model to account for censoring. The time-to-event AFT model showed significant differences between sham and TBI over all swims on the third day, p < 0.045, taking censoring into account. We suggest AFT models should be the preferred analytical methodology for latency to platform associated with MWM studies.

Traumatic brain injury (TBI) is an extremely complex condition due to heterogeneity in injury mechanism, underlying conditions, and secondary injury. Pre-clinical and clinical researchers face challenges with reproducibility that negatively impact translation and therapeutic development for improved TBI patient outcomes. To address this challenge, TBI Pre-clinical Working Groups expanded upon previous efforts and developed common data elements (CDEs) to describe the most frequently used experimental parameters. The working groups created 913 CDEs to describe study metadata, animal characteristics, animal history, injury models, and behavioral tests. Use cases applied a set of commonly used CDEs to address and evaluate the degree of missing data resulting from combining legacy data from different laboratories for two different outcome measures (Morris water maze [MWM]; RotorRod/Rotarod). Data were cleaned and harmonized to Form Structures containing the relevant CDEs and subjected to missing value analysis. For the MWM dataset (358 animals from five studies, 44 CDEs), 50% of the CDEs contained at least one missing value, while for the Rotarod dataset (97 animals from three studies, 48 CDEs), over 60% of CDEs contained at least one missing value. Overall, 35% of values were missing across the MWM dataset, and 33% of values were missing for the Rotarod dataset, demonstrating both the feasibility and the challenge of combining legacy datasets using CDEs. The CDEs and the associated forms created here are available to the broader pre-clinical research community to promote consistent and comprehensive data acquisition, as well as to facilitate data sharing and formation of data repositories. In addition to addressing the challenge of standardization in TBI pre-clinical studies, this effort is intended to bring attention to the discrepancies in assessment and outcome metrics among pre-clinical laboratories and ultimately accelerate translation to clinical research.

The release of excess glutamate following traumatic brain injury (TBI) results in glutamate excitotoxicity and metabolic energy failure. Endogenous mechanisms for reducing glutamate concentration in the brain parenchyma following TBI are poorly understood. Using multiple mass spectrometry approaches, we examined TBI-induced changes to glutamate metabolism. We present evidence that glutamate concentration can be reduced by glutamate oxidation via a "truncated" tricarboxylic acid cycle coupled to the urea cycle. This process reduces glutamate levels, generates carbon for energy metabolism, leads to citrulline accumulation, and produces nitric oxide. Several key metabolites are identified by metabolomics in support of this mechanism and the locations of these metabolites in the injured hemisphere are demonstrated by MALDI-MS imaging. The results of this study establish the advantages of multiple mass spectrometry approaches and provide insights into glutamate metabolism following TBI that could lead to improved treatment approaches.

Translation of traumatic brain injury (TBI) research findings from bench to bedside involves aligning multi-species data across diverse data types including imaging and molecular biomarkers, histopathology, behavior, and functional outcomes. In this review we argue that TBI translation should be acknowledged for what it is: a problem of big data that can be addressed using modern data science approaches. We review the history of the term big data, tracing its origins in Internet technology as data that are "big" according to the "4Vs" of volume, velocity, variety, veracity and discuss how the term has transitioned into the mainstream of biomedical research. We argue that the problem of TBI translation fundamentally centers around data variety and that solutions to this problem can be found in modern machine learning and other cutting-edge analytical approaches. Throughout our discussion we highlight the need to pull data from diverse sources including unpublished data ("dark data") and "long-tail data" (small, specialty TBI datasets undergirding the published literature). We review a few early examples of published articles in both the pre-clinical and clinical TBI research literature to demonstrate how data reuse can drive new discoveries leading into translational therapies. Making TBI data resources more Findable, Accessible, Interoperable, and Reusable (FAIR) through better data stewardship has great potential to accelerate discovery and translation for the silent epidemic of TBI.

Andersen, Clark R. MS; Hawkins, Bridget E. PhD, MBA; Prough, Donald S. MD Author Information

Though there have been studies on the histopathological and behavioral effects of blast exposure, fewer have been dedicated to blast's cerebral vascular effects. Impact (i.e., non-blast) traumatic brain injury (TBI) is known to decrease pressure autoregulation in the cerebral vasculature in both humans and experimental animals. The hypothesis that blast-induced traumatic brain injury (bTBI), like impact TBI, results in impaired cerebral vascular reactivity was tested by measuring myogenic dilatory responses to reduced intravascular pressure in rodent middle cerebral arterial (MCA) segments from rats subjected to mild bTBI using an Advanced Blast Simulator (ABS) shock tube. Adult, male Sprague-Dawley rats were anesthetized, intubated, ventilated and prepared for Sham bTBI (identical manipulation and anesthesia except for blast injury) or mild bTBI. Rats were randomly assigned to receive Sham bTBI or mild bTBI followed by sacrifice 30 or 60 min post-injury. Immediately after bTBI, righting reflex (RR) suppression times were assessed, euthanasia at the time points post-injury was completed, the brain was harvested and the individual MCA segments were collected, mounted and pressurized. As the intraluminal pressure perfused through the arterial segments was reduced in 20 mmHg increments from 100 to 20 mmHg, MCA diameters were measured and recorded. With decreasing intraluminal pressure, MCA diameters steadily increased significantly above baseline in the Sham bTBI groups while MCA dilator responses were significantly reduced (p < 0.05) in both bTBI groups as evidenced by the impaired, smaller MCA diameters recorded for the bTBI groups. In addition, RR suppression in the bTBI groups was significantly (p < 0.05) higher than in the Sham bTBI groups. MCA's collected from the Sham bTBI groups exhibited typical vasodilatory properties to decreases in intraluminal pressure while MCA's collected following bTBI exhibited significantly impaired myogenic vasodilatory responses to reduced pressure that persisted for at least 60 min after bTBI.

Despite the large number of promising neuroprotective agents identified in experimental traumatic brain injury (TBI) studies, none has yet shown meaningful improvements in long-term outcome in clinical trials. To develop recommendations and guidelines for pre-clinical testing of pharmacological or biological therapies for TBI, the Moody Project for Translational Traumatic Brain Injury Research hosted a symposium attended by investigators with extensive experience in pre-clinical TBI testing. The symposium participants discussed issues related to pre-clinical TBI testing including experimental models, therapy and outcome selection, study design, data analysis, and dissemination. Consensus recommendations included the creation of a manual of standard operating procedures with sufficiently detailed descriptions of modeling and outcome measurement procedures to permit replication. The importance of the selection of clinically relevant outcome variables, especially related to behavior testing, was noted. Considering the heterogeneous nature of human TBI, evidence of therapeutic efficacy in multiple, diverse (e.g., diffuse vs. focused) rodent models and a species with a gyrencephalic brain prior to clinical testing was encouraged. Basing drug doses, times, and routes of administration on pharmacokinetic and pharmacodynamic data in the test species was recommended. Symposium participants agreed that the publication of negative results would reduce costly and unnecessary duplication of unsuccessful experiments. Although some of the recommendations are more relevant to multi-center, multi-investigator collaborations, most are applicable to pre-clinical therapy testing in general. The goal of these consensus guidelines is to increase the likelihood that therapies that improve outcomes in pre-clinical studies will also improve outcomes in TBI patients.