J. Russell Huie

Chronic pain affects most individuals with spinal cord injury (SCI) and frequently impairs daily function, and nearly half experience neuropathic pain. Neuropathic pain is commonly thought to develop months after injury and to persist once established. These views rest on classifying each patient as having neuropathic pain or not, although patients often report several pains. The same binary label is the usual target of prognostic and biomarker studies that aim to predict life-limiting pain. We tracked up to three of the worst pains per interview from injury to one year in 140 TRACK-SCI participants, using the DN4 interview (DN4i, threshold 3). Within three days of injury, half of all pain areas and three quarters of participants met the threshold, and pain-area prevalence changed little over the year. Individual pains, however, often changed. Only a third remained above the threshold throughout, one third resolved or fluctuated, and pains in the same person often followed different courses. At more than half of interviews with several pains, one pain met the threshold while another did not, so that the patient’s classification depended on the pain considered. We therefore derived a threshold-free Pain Composite Score from the DN4i items and pain intensity. The items formed a single dimension, and the composite was associated with sleep and mood interference whereas the binary classification was not. Neuropathic pain after SCI thus appears early, does not invariably persist, and differs between a person’s pains, and graded pain-level measures may offer a more stable target for predicting life-limiting pain. Summary: Neuropathic pain after spinal cord injury appears early and does not always persist. A threshold-free score, unlike binary classification, tracks how pain limits daily life.

Objective: To test the accuracy of existing traumatic brain injury (TBI) outcome prediction models in older adults with TBI (oaTBI), to determine the independent association of 5 different measures of preinjury health with 6-month outcome, and to investigate whether these preinjury health measures improve outcome prediction. Setting: Two level 1 trauma centers. Participants: A total of 112 adults aged ≥65 years who underwent head computed tomography after acute TBI (95% with Glasgow Coma Scale (GCS) score 13–15, 66% with acute intracranial trauma on head CT). Design: Prospective cohort study. Main Measures: Preinjury health was characterized using activities of daily living (ADL), Functional Activities Questionnaire (FAQ), Clinical Dementia Rating (CDR), Charlson Comorbidity Index (CCI), and Groningen Frailty Index (GFI). The primary outcome was poor recovery (Glasgow Outcome Scale–Extended 0.05). Conclusions: Among older adults with mainly mild TBI, preinjury frailty and cognitive-mediated function were independently associated with poor 6-month outcome, yet these preinjury measures did not significantly improve prediction accuracy. Instruments such as the FAQ and GFI capture clinically meaningful preinjury heterogeneity and may inform the development of more precise oaTBI prognostic models.

Summary: Spinal cord injury (SCI) results in dysfunction of both motor and sensory systems, which can be characterized by neuropathic pain, hypersensitivity, muscular spasticity and rigidity. Most SCIs result from incidents such as vehicle accidents or falls, resulting in polytraumatic SCI that includes peripheral injuries in addition to direct CNS damage. Recent findings suggest that spinal cord synaptic plasticity plays a crucial role in neuropathic pain pathophysiology, specifically in association with spinal sensitization and the consequent onset of AMPA-related maladaptive plasticity. Further findings have demonstrated that nociceptive peripheral stimulation in the acute phase of SCI results in maladaptive spinal synaptic plasticity by overdriving GluA2-lacking calcium-permeable AMPARs (CP-AMPARs). Here, we investigated the effect of a spared nerve injury (SNI) in conjunction with SCI to determine the effect of polytraumatic SCI on maladaptive plasticity in the spinal cord. Near-IR quantitative Western blot analysis demonstrated that SCI+SNI increases spinal GluA1 expression, but not GluA2. Patch-clamp confirmed that AMPAR currents in spinal motorneurons increase after SCI with SNI, and decrease after the administration of NASPM, a CP-AMPAR antagonist. Data-driven analysis using non-linear principal components analysis (NL-PCA) also demonstrated that SCI with SNI produces a multivariate signature of AMPAR plasticity that is observed in other forms of nociceptive peripheral input, indicating a general mechanism for maladaptive plasticity in spinal motor systems in response to polytraumatic SCI.

Background: A spinal cord contusion injury is among the most clinically relevant models for studying pathophysiology and for developing potential therapeutic interventions for spinal cord injuries (SCI). Methods: In this study, we implemented an intra-operative ultrasound (IOU) approach to precisely locate and examine the lesion site at 5 and 10 min post-injury after a cervical hemi-contusion injury in a non-human primate (NHP) model. We assessed acute lesion progression from 5 to 10 min and then compared that to the lesion extent as measured by MRI 3 weeks later. Results: We observed a small increase in the rostrocaudal and mediolateral lesion area (mm²) from 5 to 10 min and a further 26% increase in the mediolateral lesion extent when comparing 5 and 10 min to 3 weeks post-injury. Conclusions: By enabling high-resolution ultrasound visualization of the hemicontusion lesion in vivo, this approach can provide critical insights into the early progression of SCI. It can help with further refining this preclinical SCI model and provide significant predictive value for the animals’ recovery post-injury.

Traumatic brain injury (TBI) has long been a leading cause of death and disability, yet research has failed to successfully translate findings from the pre-clinical, animal setting into the clinic. One factor that contributes significantly to this struggle is the heterogeneity observed in the clinical setting where patients present with injuries of varying types, severities, and comorbidities. Modeling this highly varied population in the laboratory remains challenging. Given feasibility constraints, individual laboratories often focus on single injury types and are limited to an abridged set of outcome measures. Furthermore, laboratories tend to use different injury or outcome methodologies from one another, making it difficult to compare studies and identify which pre-clinical findings may be best suited for clinical translation. The NINDS-funded Translational Outcomes Project in Neurotrauma (TOP-NT) is a multi-site consortium designed to address the reproducibility, rigor, and transparency of pre-clinical development and validation of clinically relevant biomarkers for TBI. The current overview article provides a detailed description of the infrastructure and strategic approach undertaken by the consortium. We outline the TOP-NT strategy to address three goals: (1) selection and cross-center validation of biomarker tools, (2) development and population of a data infrastructure to allow for the sharing and reuse of pre-clinical, animal research following findable, accessible, interoperable, and reusable data guidelines, and (3) demonstration of feasibility, reproducibility, and transparency in conducting a multi-center, pre-clinical research trial for TBI biomarker development. The synthesized scientific analysis and results of the TOP-NT efforts will be the topic of future articles.

Effective team science requires procedural harmonization for rigor and reproducibility. Multicenter studies across experimental modalities (domains) can help accelerate translation. The Translational Outcomes Project in NeuroTrauma (TOP-NT) is a pre-clinical traumatic brain injury (TBI) consortium charged with establishing and validating noninvasive TBI assessment tools through team science. Here, we present practical approaches for harmonization of TBI research across five centers providing needed vocabulary and structure to achieve centralized data organization and use. This includes data sharing as an essential step that enables validating data between domains, evaluating reproducibility between sites, and performing multimodal analyses. As part of this process, TOP-NT (1) produced a library of TBI-relevant standard operating procedures to coordinate workflow, (2) aligned 481 pre-clinical and clinical common data elements (CDEs), and (3) generated 272 new pre-clinical TBI CDEs. This consortium then (4) connected diverse data types to validate assessments across domains and to allow multivariable TBI phenotyping. Lastly, TOP-NT (5) specified technical quality controls for pre-clinical studies. These harmonization tools can facilitate reproducibility in team science, help distinguish a wide injury spectrum from technical variability, apply quality-controls, and ease higher level data analyses. TOP-NT uses three rat TBI models across four sites. Each site collects primary outcome measures, including magnetic resonance imaging (MRI) protocols and blood biomarkers of neuronal and glial injury, validated by histopathology and behavioral outcomes. Collected data are organized using the 481 TOP-NT pre-clinical CDEs, covering surgical, behavioral, biomarker, MRI, and quantitative histopathological methods. We report data curation steps suited for data storage using the Open Data Commons for TBI as a centralized data repository, allowing unbiased cross-site analysis. This approach leads to introducing a higher level, syndromic understanding of TBI signatures. TOP-NT authors outline a semantic and structural framework suggesting strategies for robust pre-clinical research in multicenter trials to improve translatability for TBI assessments. [Figure: see text].

For nearly 350 years, the process of disseminating scientific knowledge has remained largely unchanged. Scientists conduct experiments, analyze the data, and publish their findings in the form of scientific articles. Since the turn of the century, this process has been challenged by numerous open science and data sharing efforts to enhance transparency, reproducibility, and replicability of scientific research. Big data approaches, together with machine learning and artificial intelligence, are frequently used to gain insight into the ever-growing complexity of biological systems and biomedical research. To utilize these approaches and harness the continuously increasing computational power requires data to be both machine readable and, ideally, harmonized across studies. Therein lies the challenge: understanding how to organize and describe data is a critical skill for scientists, yet one that is rarely explicitly taught. Common data elements (CDEs), standardized definitions, and reporting structures for data represent a practical solution to this challenge. With the goal of creating a common language to describe and share pre-clinical spinal cord injury (SCI) research data, the open data commons for SCI, in collaboration with the National Institute of Neurological Disorders and Stroke, kicked off this process with the "Preclinical SCI Common Data Elements (CDE) Workshop," held in conjunction with the National Neurotrauma Symposium in San Francisco, California in June 2024. In this report, we discuss the workshop proceedings, summarize the input provided by the SCI research community, share insights from related CDE efforts, and provide a pragmatic approach to creating CDEs for pre-clinical SCI research.

Western blot is a popular biomolecular analysis method for measuring the relative quantities of independent proteins in complex biological samples. However, variability in quantitative western blot data analysis poses a challenge in designing reproducible experiments. The lack of rigorous quantitative approaches in current western blot statistical methodology may result in irreproducible inferences. Here we describe best practices for the design and analysis of western blot experiments, with examples and demonstrations of how different analytical approaches can lead to widely varying outcomes. To facilitate best practices, we have developed the blotRig tool for designing and analyzing western blot experiments to improve their rigor and reproducibility. The blotRig application includes functions for counterbalancing experimental design by lane position, batch management across gels, and analytics with covariates and random effects.

BACKGROUND AND PURPOSE: MR imaging of the brain provides unbiased neuroanatomic evaluation of brain injury and is useful for neurologic prognostication following cardiac arrest. Regional analysis of diffusion imaging may provide additional prognostic value and help reveal the neuroanatomic underpinnings of coma recovery. The purpose of this study was to evaluate global, regional, and voxelwise differences in diffusion-weighted MR imaging signal in patients in a coma after cardiac arrest. MATERIALS AND METHODS: We retrospectively analyzed diffusion MR imaging data from 81 subjects who were comatose for >48 hours following cardiac arrest. Poor outcome was defined as the inability to follow simple commands at any point during hospitalization. ADC differences between groups were evaluated across the whole brain, locally by using voxelwise analysis and regionally by using ROI-based principal component analysis. RESULTS: Subjects with poor outcome had more severe brain injury as measured by lower average whole-brain ADC (740 [SD, 102] × 10⁻⁶ mm²/s versus 833 [SD, 23] × 10⁻⁶ mm²/s, P < .001) and larger average volumes of tissue with ADC below 650 × 10⁻⁶ mm^s/s (464 [SD, 469] mL versus 62 [SD, 51] mL, P < .001). Voxelwise analysis showed lower ADC in the bilateral parieto-occipital areas and perirolandic cortices for the poor outcome group. ROI-based principal component analysis showed an association between lower ADC in parieto-occipital regions and poor outcome. CONCLUSIONS: Brain injury affecting the parieto-occipital region measured with quantitative ADC analysis was associated with poor outcomes after cardiac arrest. These results suggest that injury to specific brain regions may influence coma recovery.

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.