ATF3 is a neuron-specific biomarker for spinal cord injury and ischaemic stroke.

BACKGROUND: Although many molecules have been investigated as biomarkers for spinal cord injury (SCI) or ischemic stroke, none of them are specifically induced in central nervous system (CNS) neurons following injuries with low baseline expression. However, neuronal injury constitutes a major pathology associated with SCI or stroke and strongly correlates with neurological outcomes. Biomarkers characterized by low baseline expression and specific induction in neurons post-injury are likely to better correlate with injury severity and recovery, demonstrating higher sensitivity and specificity for CNS injuries compared to non-neuronal markers or pan-neuronal markers with constitutive expressions. METHODS: In animal studies, young adult wildtype and global Atf3 knockout mice underwent unilateral cervical 5 (C5) SCI or permanent distal middle cerebral artery occlusion (pMCAO). Gene expression was assessed using RNA-sequencing and qRT-PCR, while protein expression was detected through immunostaining. Serum ATF3 levels in animal models and clinical human samples were measured using commercially available enzyme-linked immune-sorbent assay (ELISA) kits. RESULTS: Activating transcription factor 3 (ATF3), a molecular marker for injured dorsal root ganglion sensory neurons in the peripheral nervous system, was not expressed in spinal cord or cortex of naïve mice but was induced specifically in neurons of the spinal cord or cortex within 1 day after SCI or ischemic stroke, respectively. Additionally, ATF3 protein levels in mouse blood significantly increased 1 day after SCI or ischemic stroke. Importantly, ATF3 protein levels in human serum were elevated in clinical patients within 24 hours after SCI or ischemic stroke. Moreover, Atf3 knockout mice, compared to the wildtype mice, exhibited worse neurological outcomes and larger damage regions after SCI or ischemic stroke, indicating that ATF3 has a neuroprotective function. CONCLUSIONS: ATF3 is an easily measurable, neuron-specific biomarker for clinical SCI and ischemic stroke, with neuroprotective properties. HIGHLIGHTS: ATF3 was induced specifically in neurons of the spinal cord or cortex within 1 day after SCI or ischemic stroke, respectively. Serum ATF3 protein levels are elevated in clinical patients within 24 hours after SCI or ischemic stroke. ATF3 exhibits neuroprotective properties, as evidenced by the worse neurological outcomes and larger damage regions observed in Atf3 knockout mice compared to wildtype mice following SCI or ischemic stroke.

Correction: Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome.

[This corrects the article DOI: 10.1371/journal.pone.0265254.].

Safety and comparative efficacy of initiating low-molecular-weight heparin within 24 hours of injury or surgery for venous thromboembolism prophylaxis in patients with spinal cord injury: a prospective TRACK-SCI registry study.

OBJECTIVE: Venous thromboembolism (VTE) following traumatic spinal cord injury (SCI) is a significant clinical concern. This study sought to determine the incidence of VTE and hemorrhagic complications among patients with SCI who received low-molecular-weight heparin (LMWH) within 24 hours of injury or surgery and identify variables that predict VTE using the prospective Transforming Research and Clinical Knowledge in SCI (TRACK-SCI) database. METHODS: The TRACK-SCI database was queried for individuals with traumatic SCI from 2015 to 2022. Primary outcomes of interest included rates of VTE (including deep vein thrombosis [DVT] and pulmonary embolism [PE]) and in-hospital hemorrhagic complications that occurred after LWMH administration. Secondary outcomes included intensive care unit and hospital length of stay, discharge location type, and in-hospital mortality. RESULTS: The study cohort consisted of 162 patients with SCI. Fifteen of the 162 patients withdrew from the study, leading to loss of data for certain variables for these patients. One hundred thirty patients (87.8%) underwent decompression and/or fusion surgery for SCI. DVT occurred in 11 (7.4%) of 148 patients, PE in 9 (6.1%) of 148, and any VTE in 18 (12.2%) of 148 patients. The analysis showed that admission lower-extremity motor score (p = 0.0408), injury at the thoracic level (p = 0.0086), admission American Spinal Injury Association grade (p = 0.0070), and younger age (p = 0.0372) were significantly associated with VTE. There were 3 instances of postoperative spine surgery-related bleeding (2.4%) in the 127 patients who had spine surgery with bleeding complication data available, with one requiring return to surgery (0.8%). Thirteen (8.8%) of 147 patients had a bleeding complication not related to spine surgery. There were 2 gastrointestinal bleeds associated with nasogastric tube placement, 3 cases of postoperative non-spine-related surgery bleeding, and 8 cases of other bleeding complications (5.4%) not related to any surgery. CONCLUSIONS: Initiation of LMWH within 24 hours was associated with a low rate of spine surgery-related bleeding. Bleeding complications unrelated to SCI surgery still occur with LMWH administration. Because neurosurgical intervention is typically the limiting factor in initializing chemical DVT prophylaxis, many of these bleeding complications would have likely occurred regardless of the protocol.

Longitudinal Recovery Following Repetitive Traumatic Brain Injury.

Importance: One traumatic brain injury (TBI) increases the risk of subsequent TBIs. Research on longitudinal outcomes of civilian repetitive TBIs is limited. Objective: To investigate associations between sustaining 1 or more TBIs (ie, postindex TBIs) after study enrollment (ie, index TBIs) and multidimensional outcomes at 1 year and 3 to 7 years. Design, Setting, and Participants: This cohort study included participants presenting to emergency departments enrolled within 24 hours of TBI in the prospective, 18-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study (enrollment years, February 2014 to July 2020). Participants who completed outcome assessments at 1 year and 3 to 7 years were included. Data were analyzed from September 2022 to August 2023. Exposures: Postindex TBI(s). Main Outcomes and Measures: Demographic and clinical factors, prior TBI (ie, preindex TBI), and functional (Glasgow Outcome Scale-Extended [GOSE]), postconcussive (Rivermead Post-Concussion Symptoms Questionnaire [RPQ]), psychological distress (Brief Symptom Inventory-18 [BSI-18]), depressive (Patient Health Questionnaire-9 [PHQ-9]), posttraumatic stress disorder (PTSD; PTSD Checklist for DSM-5 [PCL-5]), and health-related quality-of-life (Quality of Life After Brain Injury-Overall Scale [QOLIBRI-OS]) outcomes were assessed. Adjusted mean differences (aMDs) and adjusted relative risks are reported with 95% CIs. Results: Of 2417 TRACK-TBI participants, 1572 completed the outcomes assessment at 1 year (1049 [66.7%] male; mean [SD] age, 41.6 [17.5] years) and 1084 completed the outcomes assessment at 3 to 7 years (714 [65.9%] male; mean [SD] age, 40.6 [17.0] years). At 1 year, a total of 60 participants (4%) were Asian, 255 (16%) were Black, 1213 (77%) were White, 39 (2%) were another race, and 5 (0.3%) had unknown race. At 3 to 7 years, 39 (4%) were Asian, 149 (14%) were Black, 868 (80%) were White, 26 (2%) had another race, and 2 (0.2%) had unknown race. A total of 50 (3.2%) and 132 (12.2%) reported 1 or more postindex TBIs at 1 year and 3 to 7 years, respectively. Risk factors for postindex TBI were psychiatric history, preindex TBI, and extracranial injury severity. At 1 year, compared with those without postindex TBI, participants with postindex TBI had worse functional recovery (GOSE score of 8: adjusted relative risk, 0.57; 95% CI, 0.34-0.96) and health-related quality of life (QOLIBRI-OS: aMD, -15.9; 95% CI, -22.6 to -9.1), and greater postconcussive symptoms (RPQ: aMD, 8.1; 95% CI, 4.2-11.9), psychological distress symptoms (BSI-18: aMD, 5.3; 95% CI, 2.1-8.6), depression symptoms (PHQ-9: aMD, 3.0; 95% CI, 1.5-4.4), and PTSD symptoms (PCL-5: aMD, 7.8; 95% CI, 3.2-12.4). At 3 to 7 years, these associations remained statistically significant. Multiple (2 or more) postindex TBIs were associated with poorer outcomes across all domains. Conclusions and Relevance: In this cohort study of patients with acute TBI, postindex TBI was associated with worse symptomatology across outcome domains at 1 year and 3 to 7 years postinjury, and there was a dose-dependent response with multiple postindex TBIs. These results underscore the critical need to provide TBI prevention, education, counseling, and follow-up care to at-risk patients.

Associations of Microvascular Injury-Related Biomarkers With Traumatic Brain Injury Severity and Outcomes: A Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot Study.

Traumatic brain injury (TBI) is characterized by heterogeneity in terms of injury severity, mechanism, outcome, and pathophysiology. A single biomarker alone is unlikely to capture the heterogeneity of even one injury subtype, necessitating the use of panels of biomarkers. Herein, we focus on traumatic cerebrovascular injury and investigate associations of a panel of 16 vascular injury-related biomarkers with indices of TBI severity and outcomes using data from 159 participants in the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) Pilot Study. Associations of individual biomarkers and clusters of biomarkers identified using non-linear principal components analysis with TBI severity and outcomes were assessed using logistic regression models and Spearman's correlations. As individual biomarkers, higher levels of thrombomodulin, angiopoietin (Ang)-2, von Willebrand factor, and P-selectin were associated with more severe injury; higher levels of Ang-1, Tie2, vascular endothelial growth factor (VEGF)-C, and basic fibroblast growth factor (bFGF) were associated with less severe injury (all p < 0.05 in age-adjusted models). After false discovery rate correction for multiple comparisons, higher levels of Ang-2 remained associated with more severe injury and higher levels of Ang-1, Tie2, and bFGF remained associated with less severe injury at a p < 0.05 level. In principal components analysis, principal component (PC)1, comprised of Ang1, bFGF, P-selectin, VEGF-C, VEGF-A, and Tie2, was associated with less severe injury (age-adjusted odds ratio [OR]: 0.63, 95% confidence interval [CI]: 0.44-0.88 for head computer tomography [CT] positive vs. negative) and PC2 (Ang-2, E-selectin, Flt-1, placental growth factor, thrombomodulin, and vascular cell adhesion protein 1) was associated with greater injury severity (age-adjusted OR: 2.29, 95% CI: 1.49-3.69 for Glasgow Coma Scale [GCS] 3-12 vs. 13-15 and age-adjusted OR 1.59, 95% CI: 1.11-2.32 for head CT positive vs. negative). Neither individual biomarkers nor PCs were associated with outcomes in adjusted models (all p > 0.05). In conclusion, in this trauma-center based population of acute TBI patients, biomarkers of microvascular injury were associated with TBI severity.

Neuroinflammatory Biomarkers for Traumatic Brain Injury Diagnosis and Prognosis: A TRACK-TBI Pilot Study.

The relationship between systemic inflammation and secondary injury in traumatic brain injury (TBI) is complex. We investigated associations between inflammatory markers and clinical confirmation of TBI diagnosis and prognosis. The prospective TRACK-TBI Pilot (Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot) study enrolled TBI patients triaged to head computed tomography (CT) and received blood draw within 24 h of injury. Healthy controls (HCs) and orthopedic controls (OCs) were included. Thirty-one inflammatory markers were analyzed from plasma. Area under the receiver operating characteristic curve (AUC) was used to evaluate discriminatory ability. AUC >0.7 was considered acceptable. Criteria included: TBI diagnosis (vs. OC/HC); moderate/severe vs. mild TBI (Glasgow Coma Scale; GCS); radiographic TBI (CT positive vs. CT negative); 3- and 6-month Glasgow Outcome Scale-Extended (GOSE) dichotomized to death/greater relative disability versus less relative disability (GOSE 1-4/5-8); and incomplete versus full recovery (GOSE <8/ = 8). One-hundred sixty TBI subjects, 28 OCs, and 18 HCs were included. Markers discriminating TBI/OC: HMGB-1 (AUC = 0.835), IL-1b (0.795), IL-16 (0.784), IL-7 (0.742), and TARC (0.731). Markers discriminating GCS 3-12/13-15: IL-6 (AUC = 0.747), CRP (0.726), IL-15 (0.720), and SAA (0.716). Markers discriminating CT positive/CT negative: SAA (AUC = 0.767), IL-6 (0.757), CRP (0.733), and IL-15 (0.724). At 3 months, IL-15 (AUC = 0.738) and IL-2 (0.705) discriminated GOSE 5-8/1-4. At 6 months, IL-15 discriminated GOSE 1-4/5-8 (AUC = 0.704) and GOSE <8/ = 8 (0.711); SAA discriminated GOSE 1-4/5-8 (0.704). We identified a profile of acute circulating inflammatory proteins with potential relevance for TBI diagnosis, severity differentiation, and prognosis. IL-15 and serum amyloid A are priority markers with acceptable discrimination across multiple diagnostic and outcome categories. Validation in larger prospective cohorts is needed. ClinicalTrials.gov Registration: NCT01565551.

Rehabilitation combined with neural progenitor cell grafts enables functional recovery in chronic spinal cord injury.

We reported previously that neural progenitor cell (NPC) grafts form neural relays across sites of subacute spinal cord injury (SCI) and support functional recovery. Here, we examine whether NPC grafts after chronic delays also support recovery and whether intensive rehabilitation further enhances recovery. One month after severe bilateral cervical contusion, rats received daily intensive rehabilitation, NPC grafts, or both rehabilitation and grafts. Notably, only the combination of rehabilitation and grafting significantly improved functional recovery. Moreover, improved functional outcomes were associated with a rehabilitation-induced increase in host corticospinal axon regeneration into grafts. These findings identify a critical and synergistic role of rehabilitation and neural stem cell therapy in driving neural plasticity to support functional recovery after chronic and severe SCI.

Expert-augmented automated machine learning optimizes hemodynamic predictors of spinal cord injury outcome.

Artificial intelligence and machine learning (AI/ML) is becoming increasingly more accessible to biomedical researchers with significant potential to transform biomedicine through optimization of highly-accurate predictive models and enabling better understanding of disease biology. Automated machine learning (AutoML) in particular is positioned to democratize artificial intelligence (AI) by reducing the amount of human input and ML expertise needed. However, successful translation of AI/ML in biomedicine requires moving beyond optimizing only for prediction accuracy and towards establishing reproducible clinical and biological inferences. This is especially challenging for clinical studies on rare disorders where the smaller patient cohorts and corresponding sample size is an obstacle for reproducible modeling results. Here, we present a model-agnostic framework to reinforce AutoML using strategies and tools of explainable and reproducible AI, including novel metrics to assess model reproducibility. The framework enables clinicians to interpret AutoML-generated models for clinical and biological verifiability and consequently integrate domain expertise during model development. We applied the framework towards spinal cord injury prognostication to optimize the intraoperative hemodynamic range during injury-related surgery and additionally identified a strong detrimental relationship between intraoperative hypertension and patient outcome. Furthermore, our analysis captured how evolving clinical practices such as faster time-to-surgery and blood pressure management affect clinical model development. Altogether, we illustrate how expert-augmented AutoML improves inferential reproducibility for biomedical discovery and can ultimately build trust in AI processes towards effective clinical integration.

Empowering Data Sharing and Analytics through the Open Data Commons for Traumatic Brain Injury Research.

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.

Appendicular Fracture and Polytrauma Correlate with Outcome of Spinal Cord Injury: A Transforming Research and Clinical Knowledge in Spinal Cord Injury Study.

Spinal cord injuries (SCIs) frequently occur in combination with other major organ injuries, such as traumatic brain injury (TBI) and injuries to the chest, abdomen, and musculoskeletal system (e.g., extremity, pelvic, and spine fractures). However, the effects of appendicular fractures on SCI recovery are poorly understood. We investigated whether the presence of SCI-concurrent appendicular fractures is predictive of a less robust SCI recovery. Patients enrolled in the Transforming Research and Clinical Knowledge in SCI (TRACK-SCI) prospective cohort study were identified and included in this secondary analysis study. Inclusion criteria resulted in 147 patients, consisting of 120 with isolated SCIs and 27 with concomitant appendicular fracture. The primary outcome was American Spinal Injury Association (ASIA) Impairment Scale (AIS) neurological grades at hospital discharge. Secondary outcomes included hospital length of stay, intensive care unit (ICU) length of stay, and AIS grade improvement during hospitalization. Multivariable binomial logistical regression analyses assessed whether SCI-concomitant appendicular fractures associate with SCI function and secondary outcomes. These analyses were adjusted for age, gender, injury severity, and non-fracture polytrauma. Appendicular fractures were associated with more severe AIS grades at hospital discharge, though covariate adjustments diminished statistical significance of this effect. Notably, non-fracture injuries to the chest and abdomen were influential covariates. Secondary analyses suggested that appendicular fractures also increased hospital length of stay. Our study indicated that SCI-associated polytrauma is important for predicting SCI functional outcomes. Further statistical evaluation is required to disentangle the effects of appendicular fractures, non-fracture solid organ injury, and SCI physiology to improve health outcomes among SCI patients.

Cognitive Outcome 1 Year After Mild Traumatic Brain Injury: Results From the TRACK-TBI Study.

BACKGROUND AND OBJECTIVES: The objectives of this study were to develop and establish concurrent validity of a clinically relevant definition of poor cognitive outcome 1 year after mild traumatic brain injury (mTBI), to compare baseline characteristics across cognitive outcome groups, and to determine whether poor 1-year cognitive outcome can be predicted by routinely available baseline clinical variables. METHODS: Prospective cohort study included 656 participants ≥17 years of age presenting to level 1 trauma centers within 24 hours of mTBI (Glasgow Coma Scale score 13-15) and 156 demographically similar healthy controls enrolled in the Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) study. Poor 1-year cognitive outcome was defined as cognitive impairment (below the ninth percentile of normative data on ≥2 cognitive tests), cognitive decline (change score [1-year score minus best 2-week or 6-month score] exceeding the 90% reliable change index on ≥2 cognitive tests), or both. Associations of poor 1-year cognitive outcome with 1-year neurobehavioral outcomes were performed to establish concurrent validity. Baseline characteristics were compared across cognitive outcome groups, and backward elimination logistic regression was used to build a prediction model. RESULTS: Mean age of participants with mTBI was 40.2 years; 36.6% were female; 76.6% were White. Poor 1-year cognitive outcome was associated with worse 1-year functional outcome, more neurobehavioral symptoms, greater psychological distress, and lower satisfaction with life (all p < 0.05), establishing concurrent validity. At 1 year, 13.5% of participants with mTBI had a poor cognitive outcome vs 4.5% of controls (p = 0.003). In univariable analyses, poor 1-year cognitive outcome was associated with non-White race, lower education, lower income, lack of health insurance, hyperglycemia, preinjury depression, and greater injury severity (all p < 0.05). The final multivariable prediction model included education, health insurance, preinjury depression, hyperglycemia, and Rotterdam CT score ≥3 and achieved an area under the curve of 0.69 (95% CI 0.62-0.75) for the prediction of a poor 1-year cognitive outcome, with each variable associated with >2-fold increased odds of poor 1-year cognitive outcome. DISCUSSION: Poor 1-year cognitive outcome is common, affecting 13.5% of patients with mTBI vs 4.5% of controls. These results highlight the need for better understanding of mechanisms underlying poor cognitive outcome after mTBI to inform interventions to optimize cognitive recovery.

Excavating FAIR Data: the Case of the Multicenter Animal Spinal Cord Injury Study (MASCIS), Blood Pressure, and Neuro-Recovery.

Meta-analyses suggest that the published literature represents only a small minority of the total data collected in biomedical research, with most becoming 'dark data' unreported in the literature. Dark data is due to publication bias toward novel results that confirm investigator hypotheses and omission of data that do not. Publication bias contributes to scientific irreproducibility and failures in bench-to-bedside translation. Sharing dark data by making it Findable, Accessible, Interoperable, and Reusable (FAIR) may reduce the burden of irreproducible science by increasing transparency and support data-driven discoveries beyond the lifecycle of the original study. We illustrate feasibility of dark data sharing by recovering original raw data from the Multicenter Animal Spinal Cord Injury Study (MASCIS), an NIH-funded multi-site preclinical drug trial conducted in the 1990s that tested efficacy of several therapies after a spinal cord injury (SCI). The original drug treatments did not produce clear positive results and MASCIS data were stored in boxes for more than two decades. The goal of the present study was to independently confirm published machine learning findings that perioperative blood pressure is a major predictor of SCI neuromotor outcome (Nielson et al., 2015). We recovered, digitized, and curated the data from 1125 rats from MASCIS. Analyses indicated that high perioperative blood pressure at the time of SCI is associated with poorer health and worse neuromotor outcomes in more severe SCI, whereas low perioperative blood pressure is associated with poorer health and worse neuromotor outcome in moderate SCI. These findings confirm and expand prior results that a narrow window of blood-pressure control optimizes outcome, and demonstrate the value of recovering dark data for assessing reproducibility of findings with implications for precision therapeutic approaches.

Mixture Model Framework for Traumatic Brain Injury Prognosis Using Heterogeneous Clinical and Outcome Data.

Prognoses of Traumatic Brain Injury (TBI) outcomes are neither easily nor accurately determined from clinical indicators. This is due in part to the heterogeneity of damage inflicted to the brain, ultimately resulting in diverse and complex outcomes. Using a data-driven approach on many distinct data elements may be necessary to describe this large set of outcomes and thereby robustly depict the nuanced differences among TBI patients' recovery. In this work, we develop a method for modeling large heterogeneous data types relevant to TBI. Our approach is geared toward the probabilistic representation of mixed continuous and discrete variables with missing values. The model is trained on a dataset encompassing a variety of data types, including demographics, blood-based biomarkers, and imaging findings. In addition, it includes a set of clinical outcome assessments at 3, 6, and 12 months post-injury. The model is used to stratify patients into distinct groups in an unsupervised learning setting. We use the model to infer outcomes using input data, and show that the collection of input data reduces uncertainty of outcomes over a baseline approach. In addition, we quantify the performance of a likelihood scoring technique that can be used to self-evaluate the extrapolation risk of prognosis on unseen patients.

Promoting FAIR Data Through Community-driven Agile Design: the Open Data Commons for Spinal Cord Injury (odc-sci.org).

The past decade has seen accelerating movement from data protectionism in publishing toward open data sharing to improve reproducibility and translation of biomedical research. Developing data sharing infrastructures to meet these new demands remains a challenge. One model for data sharing involves simply attaching data, irrespective of its type, to publisher websites or general use repositories. However, some argue this creates a 'data dump' that does not promote the goals of making data Findable, Accessible, Interoperable and Reusable (FAIR). Specialized data sharing communities offer an alternative model where data are curated by domain experts to make it both open and FAIR. We report on our experiences developing one such data-sharing ecosystem focusing on 'long-tail' preclinical data, the Open Data Commons for Spinal Cord Injury (odc-sci.org). ODC-SCI was developed with community-based agile design requirements directly pulled from a series of workshops with multiple stakeholders (researchers, consumers, non-profit funders, governmental agencies, journals, and industry members). ODC-SCI focuses on heterogeneous tabular data collected by preclinical researchers including bio-behaviour, histopathology findings and molecular endpoints. This has led to an example of a specialized neurocommons that is well-embraced by the community it aims to serve. In the present paper, we provide a review of the community-based design template and describe the adoption by the community including a high-level review of current data assets, publicly released datasets, and web analytics. Although odc-sci.org is in its late beta stage of development, it represents a successful example of a specialized data commons that may serve as a model for other fields.

Data-driven approach to integrating genomic and behavioral preclinical traumatic brain injury research.

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.

Toward a causal model of chronic back pain: Challenges and opportunities.

Chronic low back pain (cLBP) afflicts 8. 2% of adults in the United States, and is the leading global cause of disability. Neuropsychiatric co-morbidities including anxiety, depression, and substance abuse- are common in cLBP patients. In particular, cLBP is a risk factor for opioid addiction, as more than 50% of opioid prescriptions in the United States are for cLBP. Misuse of these prescriptions is a common precursor to addiction. While associations between cLBP and neuropsychiatric disorders are well established, causal relationships for the most part are unknown. Developing effective treatments for cLBP, and associated co-morbidities, requires identifying and understanding causal relationships. Rigorous methods for causal inference, a process for quantifying causal effects from observational data, have been developed over the past 30 years. In this review we first discuss the conceptual model of cLBP that current treatments are based on, and how gaps in causal knowledge contribute to poor clinical outcomes. We then present cLBP as a "Big Data" problem and identify how advanced analytic techniques may close knowledge gaps and improve clinical outcomes. We will focus on causal discovery, which is a data-driven method that uses artificial intelligence (AI) and high dimensional datasets to identify causal structures, discussing both constraint-based (PC and Fast Causal Inference) and score-based (Fast Greedy Equivalent Search) algorithms.

FAIR Data Reuse in Traumatic Brain Injury: Exploring Inflammation and Age as Moderators of Recovery in the TRACK-TBI Pilot.

The guiding principle for data stewardship dictates that data be FAIR: findable, accessible, interoperable, and reusable. Data reuse allows researchers to probe data that may have been originally collected for other scientific purposes in order to gain novel insights. The current study reuses the Transforming Research and Clinical Knowledge for Traumatic Brain Injury (TRACK-TBI) Pilot dataset to build upon prior findings and ask new scientific questions. Specifically, we have previously used a multivariate analytics approach to multianalyte serum protein data from the TRACK-TBI Pilot dataset to show that an inflammatory ensemble of biomarkers can predict functional outcome at 3 and 6 months post-TBI. We and others have shown that there are quantitative and qualitative changes in inflammation that come with age, but little is known about how this interaction affects recovery from TBI. Here we replicate the prior proteomics findings with improved missing value analyses and non-linear principal component analysis and then expand upon this work to determine whether age moderates the effect of inflammation on recovery. We show that increased age correlates with worse functional recovery on the Glasgow Outcome Scale-Extended (GOS-E) as well as increased inflammatory signature. We then explore the interaction between age and inflammation on recovery, which suggests that inflammation has a more detrimental effect on recovery for older TBI patients.

Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury.

Background: Predicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients. Methods: Intra-operative monitoring records and neurological outcome data were extracted (n = 118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods. Results: Network analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76-[104-117] mmHg associated with neurological recovery. Conclusions: We show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention. Funding: NIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB).

Spinal cord injury is a devastating condition that involves damage to the nerve fibers connecting the brain with the spinal cord, often leading to permanent changes in strength, sensation and body functions, and in severe cases paralysis. Scientists around the world work hard to find ways to treat or even repair spinal cord injuries but few patients with complete immediate paralysis recover fully. Immediate paralysis is caused by direct damage to neurons and their extension in the spinal cord. Previous research has shown that blood pressure regulation may be key in saving these damaged neurons, as spinal cord injuries can break the communication between nerves that is involved in controlling blood pressure. This can lead to a vicious cycle of dysregulation of blood pressure and limit the supply of blood and oxygen to the damaged spinal cord tissue, exacerbating the death of spinal neurons. Management of blood pressure is therefore a key target for spinal cord injury care, but so far, the precise thresholds to enable neurons to recover are poorly understood. To find out more, Torres-Espin, Haefeli et al. used machine learning software to analyze previously recorded blood pressure and heart rate data obtained from 118 patients that underwent spinal cord surgery after acute spinal cord injury. The analyses revealed that patients who suffered from either low or high blood pressure during surgery had poorer prospects of recovery. Statistical models confirming these findings showed that the optimal blood pressure range to ensure recovery lies between 76 to 104-117 mmHg. Any deviation from this narrow window would dramatically worsen the ability to recover. These findings suggests that dysregulated blood pressure during surgery affects to odds of recovery in patients with a spinal cord injury. Torres-Espin, Haefeli et al. provide specific information that could improve current clinical practice in trauma centers. In the future, such machine learning tools and models could help develop real-time models that could predict the likelihood of a patient's recovery following spinal cord injury and related neurological conditions.

Modeling and Bioinformatics Identify Responders to G-CSF in Patients With Amyotrophic Lateral Sclerosis.

Objective: Developing an integrative approach to early treatment response classification using survival modeling and bioinformatics with various biomarkers for early assessment of filgrastim (granulocyte colony stimulating factor) treatment effects in amyotrophic lateral sclerosis (ALS) patients. Filgrastim, a hematopoietic growth factor with excellent safety, routinely applied in oncology and stem cell mobilization, had shown preliminary efficacy in ALS. Methods: We conducted individualized long-term filgrastim treatment in 36 ALS patients. The PRO-ACT database, with outcome data from 23 international clinical ALS trials, served as historical control and mathematical reference for survival modeling. Imaging data as well as cytokine and cellular data from stem cell analysis were processed as biomarkers in a non-linear principal component analysis (NLPCA) to identify individual response. Results: Cox proportional hazard and matched-pair analyses revealed a significant survival benefit for filgrastim-treated patients over PRO-ACT comparators. We generated a model for survival estimation based on patients in the PRO-ACT database and then applied the model to filgrastim-treated patients. Model-identified filgrastim responders displayed less functional decline and impressively longer survival than non-responders. Multimodal biomarkers were then analyzed by PCA in the context of model-defined treatment response, allowing identification of subsequent treatment response as early as within 3 months of therapy. Strong treatment response with a median survival of 3.8 years after start of therapy was associated with younger age, increased hematopoietic stem cell mobilization, less aggressive inflammatory cytokine plasma profiles, and preserved pattern of fractional anisotropy as determined by magnetic resonance diffusion tensor imaging (DTI-MRI). Conclusion: Long-term filgrastim is safe, is well-tolerated, and has significant positive effects on disease progression and survival in a small cohort of ALS patients. Developing and applying a model-based biomarker response classification allows use of multimodal biomarker patterns in full potential. This can identify strong individual treatment responders (here: filgrastim) at a very early stage of therapy and may pave the way to an effective individualized treatment option.

Reproducible analysis of disease space via principal components using the novel R package syndRomics.

Biomedical data are usually analyzed at the univariate level, focused on a single primary outcome measure to provide insight into systems biology, complex disease states, and precision medicine opportunities. More broadly, these complex biological and disease states can be detected as common factors emerging from the relationships among measured variables using multivariate approaches. 'Syndromics' refers to an analytical framework for measuring disease states using principal component analysis and related multivariate statistics as primary tools for extracting underlying disease patterns. A key part of the syndromic workflow is the interpretation, the visualization, and the study of robustness of the main components that characterize the disease space. We present a new software package, syndRomics, an open-source R package with utility for component visualization, interpretation, and stability for syndromic analysis. We document the implementation of syndRomics and illustrate the use of the package in case studies of neurological trauma data.