Persons With Spinal Cord Injury Report Peripherally Dominant Serotonin-Like Syndrome After Use of Serotonergic Psychedelics.

Psychedelic-assisted therapy (PAT) may treat various mental health conditions. Despite its promising therapeutic signal across mental health outcomes, less attention is paid on its potential to provide therapeutic benefits across complex medical situations within rehabilitation medicine. Persons with spinal cord injury (SCI) have a high prevalence of treatment-resistant mental health comorbidities that compound the extent of their physical disability. Reports from online discussion forums suggest that those living with SCI are using psychedelics, though the motivation for their use is unknown. These anecdotal reports describe a consistent phenomenon of neuromuscular and autonomic hypersensitivity to classical serotonergic psychedelics, such as psilocybin and lysergic acid diethylamide (LSD). Persons describe intense muscle spasms, sweating, and tremors, with an eventual return to baseline and no reports of worsening of their baseline neurological deficits. The discomfort experienced interferes with the subjective beneficial effects self-reported. This phenomenon has not been described previously in the academic literature. We aim to provide a descriptive review and explanatory theoretical framework hypothesizing this phenomenon as a peripherally dominant serotonin syndrome-like clinical picture-that should be considered as such when persons with SCI are exposed to classical psychedelics. Raising awareness of this syndrome may help our mechanistic understanding of serotonergic psychedelics and stimulate development of treatment protocols permitting persons with SCI to safely tolerate their adverse effects. As PAT transitions from research trials into accepted clinical and decriminalized use, efforts must be made from a harm reduction perspective to understand these adverse events, while also serving as an informed consent process aid if such therapeutic approaches are to be considered for use in persons living with SCI.

Emergence of functionally aberrant and subsequent reduction of neuromuscular connectivity and improved motor performance after cervical spinal cord injury in Rhesus.

Introduction: The paralysis that occurs after a spinal cord injury, particularly during the early stages of post-lesion recovery (∼6 weeks), appears to be attributable to the inability to activate motor pools well beyond their motor threshold. In the later stages of recovery, however, the inability to perform a motor task effectively can be attributed to abnormal activation patterns among motor pools, resulting in poor coordination. Method: We have tested this hypothesis on four adult male Rhesus monkeys (Macaca mulatta), ages 6-10 years, by recording the EMG activity levels and patterns of multiple proximal and distal muscles controlling the upper limb of the Rhesus when performing three tasks requiring different levels of skill before and up to 24 weeks after a lateral hemisection at C7. During the recovery period the animals were provided routine daily care, including access to a large exercise cage (5' × 7' × 10') and tested every 3-4 weeks for each of the three motor tasks. Results: At approximately 6-8 weeks the animals were able to begin to step on a treadmill, perform a spring-loaded task with the upper limb, and reaching, grasping, and eating a grape placed on a vertical stick. The predominant changes that occurred, beginning at ∼6-8 weeks of the recovery of these tasks was an elevated level of activation of most motor pools well beyond the pre-lesion level. Discussion: As the chronic phase progressed there was a slight reduction in the EMG burst amplitudes of some muscles and less incidence of co-contraction of agonists and antagonists, probably contributing to an improved ability to selectively activate motor pools in a more effective temporal pattern. Relative to pre-lesion, however, the EMG patterns even at the initial stages of recovery of successfully performing the different motor tasks, the level of activity of most muscle remained higher. Perhaps the most important concept that emerges from these data is the large combinations of adaptive strategies in the relative level of recruitment and the timing of the peak levels of activation of different motor pools can progressively provide different stages to regain a motor skill.

Increased suicidal ideation and suicide attempts in COVID-19 patients in the United States: Statistics from a large national insurance billing database.

Emerging research suggests suicidality may have increased during the COVID-19 pandemic. This cross-sectional study aimed to advance understanding of suicide risk during the pandemic through novel use of a large insurance database. Using logistic regression across time-points, we estimated the effect of exposure to SARS-CoV-2 infection on rates of suicidal ideation and suicide attempts in infected individuals versus uninfected controls during the pandemic (March 2020 - September 2021). In uninfected individuals, we estimated the effect of exposure to the pandemic period versus the pre-pandemic control period (January 2017 to February 2020) on suicidality rates. We also investigated within-pandemic temporal patterns of suicidality. All patients with data in the UnitedHealth Group claims during those intervals were included. ICD-10 codes defined suicidality measures. There were 525,312,717 (62.3% over age 45, 57.7% female) included encounters. From the pandemic subsample (32.8%), 1.7% were COVID+. Adjusted odds ratios showed that COVID+ patients were significantly more likely to have suicidal ideation and suicide attempts than COVID- patients. Among COVID- patients, adjusted odds of suicidality were significantly lower during versus prior to the pandemic. Results were unfortunately limited by the absence of data on deaths by suicide. Further research should examine how SARS-CoV-2 infection may influence suicidality.

Causal discovery replicates symptomatic and functional interrelations of posttraumatic stress across five patient populations.

Introduction: Approximately half of individuals with posttraumatic stress disorder (PTSD) may meet criteria for other psychiatric disorders, and PTSD symptoms are associated with diminished health and psychosocial functioning. However, few studies examine the longitudinal progression of PTSD symptoms concurrent with related symptom domains and functional outcomes, such that may neglect important longitudinal patterns of symptom progression beyond PTSD specifically. Methods: Therefore, we used longitudinal causal discovery analysis to examine the longitudinal interrelations among PTSD symptoms, depressive symptoms, substance abuse, and various other domains of functioning in five longitudinal cohorts representing veterans (n = 241), civilians seeking treatment for anxiety disorders (n = 79), civilian women seeking treatment for post-traumatic stress and substance abuse (n = 116), active duty military members assessed 0-90 days following TBI (n = 243), and civilians with a history of TBI (n = 43). Results: The analyses revealed consistent, directed associations from PTSD symptoms to depressive symptoms, independent longitudinal trajectories of substance use problems, and cascading indirect relations from PTSD symptoms to social functioning through depression as well as direct relations from PTSD symptoms to TBI outcomes. Discussion: Our findings suggest PTSD symptoms primarily drive depressive symptoms over time, tend to show independence from substance use symptoms, and may cascade into impairment in other domains. The results have implications for refining conceptualization of PTSD co-morbidity and can inform prognostic and treatment hypotheses about individuals experiencing PTSD symptoms along with co-occurring distress or impairment.

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.

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.

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.

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.

Statistical Guidelines for Handling Missing Data in Traumatic Brain Injury Clinical Research.

Missing data is a persistent and unavoidable problem in even the most carefully designed traumatic brain injury (TBI) clinical research. Missing data patterns may result from participant dropout, non-compliance, technical issues, or even death. This review describes the types of missing data that are common in TBI research, and assesses the strengths and weaknesses of the statistical approaches used to draw conclusions and make clinical decisions from these data. We review recent innovations in missing values analysis (MVA), a relatively new branch of statistics, as applied to clinical TBI data. Our discussion focuses on studies from the International Traumatic Brain Injury Research (InTBIR) initiative project: Transforming Research and Clinical Knowledge in TBI (TRACK-TBI), Collaborative Research on Acute TBI in Intensive Care Medicine in Europe (CREACTIVE), and Approaches and Decisions in Acute Pediatric TBI Trial (ADAPT). In addition, using data from the TRACK-TBI pilot study (n = 586) and the completed clinical trial assessing valproate (VPA) for the treatment of post-traumatic epilepsy (n = 379) we present real-world examples of typical missing data patterns and the application of statistical techniques to mitigate the impact of missing data in order to draw sound conclusions from ongoing clinical studies.

A Multi-Modal Assessment of Clinical Predictors for Traumatic Brain Injury End-Points.

Traumatic brain injury (TBI) is a complex injury that has a multi-faceted recovery process. The current "gold standard" for classifying severity of TBI symptoms is the Glasgow Outcome Scale (GOSE), a crude measure of overall dysfunction after TBI. Exploratory factor analysis performed on TRACK-TBI Pilot (N = 297) identified candidate multi-variate outcome measures of neuropsychological impairment and cognitive speed and flexibility at 6 months post-TBI that were confirmed in data from the COBRIT study (N = 645) using confirmatory factor analysis. These new outcome measures were used as the dependent variables in an ordinal logistic regression model, using common data elements (CDE) collected in the emergency department as independent variables, including basic demographics, socioeconomic status, medical history, and measures of blood alcohol and blood pressure. We directly compared these prediction models with the GOSE as the 6-month outcome variable and found that in both the TRACK-TBI pilot and COBRIT studies, both neuropsychiatric complications (approx. 36.0% and 22.3% variance explained) and cognitive speed and flexibility (approx. 33.9% and 24.5% variance explained) were better explained by the prediction model, compared with GOSE (approx. 19.9% and 14.4% variance explained), respectively. While differences in overall distributions of impairment between TRACK-TBI pilot and COBRIT exist and should be explored further for applications of these prediction models, we think these multi-variate end-points more accurately characterize patients' functioning at six-months post-TBI. A multi-variate assessment of end-points seems especially important for characterizing TBI outcomes in cases where gross impairment, such as those measured by the GOSE, may be less evident.

Analysis of Normal High-Frequency Intracranial Pressure Values and Treatment Threshold in Neurocritical Care Patients: Insights into Normal Values and a Potential Treatment Threshold.

Importance: Intracranial pressure (ICP) elevation is a compartment syndrome that impairs blood flow to the brain. Despite the importance of ICP values in neurocritical care, normal ICP values and the precise ICP threshold at which treatment should be initiated remain uncertain. Objective: To refine our understanding of normal ICP values and determine the ICP threshold most strongly associated with outcome. Design, Setting, and Participants: Prospective observational study (2004-2010), with outcomes determined at hospital discharge. The study included neurocritical care patients from a single level I trauma center, San Francisco General Hospital. Three hundred eighty-three patients had a traumatic brain injury with or without craniectomy; 140 patients had another indication for ICP monitoring. Consecutive patients were studied. Data analyses were completed between March 2015 and December 2019. Exposures: Five hundred twenty-three ICP-monitored patients. Main Outcomes and Measures: A computer system prospectively and automatically collected 1-minute physiologic data from patients in the intensive care unit during a 6-year period. Mean ICP was calculated, as was the proportion of ICP values greater than thresholds from 1 to 80 mm Hg in 1-mm Hg increments. The association between these measures and outcome was explored for various epochs up to 30 days from the time of injury. A principal component analysis was used to explore physiologic changes at various ICP thresholds, and elastic net regression was used to identify ICP thresholds most strongly associated with Glasgow Outcome Scale score at discharge. Results: Of the 523 studied patients, 70.7% of studied patients were men (n = 370) and 72.1% had a traumatic brain injury (n = 377). A total of 4 090 964 1-minute ICP measurements were recorded for the included patients (7.78 years of recordings). Intracranial pressure values of 8 to 9 mm Hg were most commonly recorded and could possibly reflect normal values. The principal component analysis suggested state shifts in the physiome occurred at ICPs greater than 19 mm Hg and 24 mm Hg. Elastic net regression identified an ICP threshold of 19 mm Hg as most robustly associated with outcome when considering all neurocritical care patients, patients with TBI, and patients with TBI who underwent craniectomy. Intracranial pressure values greater than 19 mm Hg were associated with mortality, while lower values were associated with outcome in surviving patients. Conclusions and Relevance: This study provides insight into what normal ICP values could be. An ICP threshold of 19 mm Hg was robustly associated with outcome in studied patients, although lower ICP values were associated with outcome in surviving patients.

FAIR SCI Ahead: The Evolution of the Open Data Commons for Pre-Clinical Spinal Cord Injury Research.

Over the last 5 years, multiple stakeholders in the field of spinal cord injury (SCI) research have initiated efforts to promote publications standards and enable sharing of experimental data. In 2016, the National Institutes of Health/National Institute of Neurological Disorders and Stroke hosted representatives from the SCI community to streamline these efforts and discuss the future of data sharing in the field according to the FAIR (Findable, Accessible, Interoperable and Reusable) data stewardship principles. As a next step, a multi-stakeholder group hosted a 2017 symposium in Washington, DC entitled "FAIR SCI Ahead: the Evolution of the Open Data Commons for Spinal Cord Injury research." The goal of this meeting was to receive feedback from the community regarding infrastructure, policies, and organization of a community-governed Open Data Commons (ODC) for pre-clinical SCI research. Here, we summarize the policy outcomes of this meeting and report on progress implementing these policies in the form of a digital ecosystem: the Open Data Commons for Spinal Cord Injury (ODC-SCI.org). ODC-SCI enables data management, harmonization, and controlled sharing of data in a manner consistent with the well-established norms of scholarly publication. Specifically, ODC-SCI is organized around virtual "laboratories" with the ability to share data within each of three distinct data-sharing spaces: within the laboratory, across verified laboratories, or publicly under a creative commons license (CC-BY 4.0) with a digital object identifier that enables data citation. The ODC-SCI implements FAIR data sharing and enables pooled data-driven discovery while crediting the generators of valuable SCI data.

Analysis of high-frequency PbtO2 measures in traumatic brain injury: insights into the treatment threshold.

OBJECTIVEBrain tissue hypoxia is common after traumatic brain injury (TBI). Technology now exists that can detect brain hypoxia and guide corrective therapy. Current guidelines for the management of severe TBI recommend maintaining partial pressure of brain tissue oxygen (PbtO2) > 15-20 mm Hg; however, uncertainty persists as to the optimal treatment threshold. The object of this study was to better inform the relationship between PbtO2 values and outcome for patients with TBI.METHODSPbtO2 measurements were prospectively and automatically collected every minute from consecutive patients admitted to the San Francisco General Hospital neurological ICU during a 6-year period. Mean PbtO2 values in TBI patients as well as the proportion of PbtO2 values below each of 75 thresholds between 0 mm Hg and 75 mm Hg over various epochs up to 30 days from the time of admission were analyzed. Patient outcomes were determined using the Glasgow Outcome Scale. The authors explored putative treatment thresholds by generating 675 separate receiver operating characteristic curves and 675 generalized linear models to examine each 1-mm Hg threshold for various epochs.RESULTSA total of 1,380,841 PbtO2 values were recorded in 190 TBI patients. A high proportion of PbtO2 measures were below 20 mm Hg irrespective of the examined epoch. Time below treatment thresholds was more strongly associated with outcome than mean PbtO2. A treatment window was suggested: a threshold of 19 mm Hg most robustly distinguished patients by outcome, especially from days 3-5; however, benefit was suggested from maintaining values at least as high as 33 mm Hg.CONCLUSIONSThis analysis of high-frequency physiological data substantially informs the relationship between PbtO2 values and outcome. The results suggest a therapeutic window for PbtO2 in TBI patients along with minimum and preferred PbtO2 treatment thresholds, which may be examined in future studies. Traditional treatment thresholds that have the strongest association with outcome may not be optimal.

Apolipoprotein E epsilon 4 (APOE-ε4) genotype is associated with decreased 6-month verbal memory performance after mild traumatic brain injury.

INTRODUCTION: The apolipoprotein E (APOE) ε4 allele associates with memory impairment in neurodegenerative diseases. Its association with memory after mild traumatic brain injury (mTBI) is unclear. METHODS: mTBI patients (Glasgow Coma Scale score 13-15, no neurosurgical intervention, extracranial Abbreviated Injury Scale score ≤1) aged ≥18 years with APOE genotyping results were extracted from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury Pilot (TRACK-TBI Pilot) study. Cohorts determined by APOE-ε4(+/-) were assessed for associations with 6-month verbal memory, measured by California Verbal Learning Test, Second Edition (CVLT-II) subscales: Immediate Recall Trials 1-5 (IRT), Short-Delay Free Recall (SDFR), Short-Delay Cued Recall (SDCR), Long-Delay Free Recall (LDFR), and Long-Delay Cued Recall (LDCR). Multivariable regression controlled for demographic factors, seizure history, loss of consciousness, posttraumatic amnesia, and acute intracranial pathology on computed tomography (CT). RESULTS: In 114 mTBI patients (APOE-ε4(-)=79; APOE-ε4(+)=35), ApoE-ε4(+) was associated with long-delay verbal memory deficits (LDFR: B = -1.17 points, 95% CI [-2.33, -0.01], p = .049; LDCR: B = -1.58 [-2.63, -0.52], p = .004), and a marginal decrease on SDCR (B = -1.02 [-2.05, 0.00], p = .050). CT pathology was the strongest predictor of decreased verbal memory (IRT: B = -8.49, SDFR: B = -2.50, SDCR: B = -1.85, LDFR: B = -2.61, LDCR: B = -2.60; p < .001). Seizure history was associated with decreased short-term memory (SDFR: B = -1.32, p = .037; SDCR: B = -1.44, p = .038). CONCLUSION: The APOE-ε4 allele may confer an increased risk of impairment of 6-month verbal memory for patients suffering mTBI, with implications for heightened surveillance and targeted therapies. Acute intracranial pathology remains the driver of decreased verbal memory performance at 6 months after mTBI.

Developing a data sharing community for spinal cord injury research.

The rapid growth in data sharing presents new opportunities across the spectrum of biomedical research. Global efforts are underway to develop practical guidance for implementation of data sharing and open data resources. These include the recent recommendation of 'FAIR Data Principles', which assert that if data is to have broad scientific value, then digital representations of that data should be Findable, Accessible, Interoperable and Reusable (FAIR). The spinal cord injury (SCI) research field has a long history of collaborative initiatives that include sharing of preclinical research models and outcome measures. In addition, new tools and resources are being developed by the SCI research community to enhance opportunities for data sharing and access. With this in mind, the National Institute of Neurological Disorders and Stroke (NINDS) at the National Institutes of Health (NIH) hosted a workshop on October 5-6, 2016 in Bethesda, MD, in collaboration with the Open Data Commons for Spinal Cord Injury (ODC-SCI) titled "Preclinical SCI Data: Creating a FAIR Share Community". Workshop invitees were nominated by the workshop steering committee (co-chairs: ARF and VPL; members: AC, KDA, MSB, KF, LBJ, PGP, JMS), to bring together junior and senior level experts including preclinical and basic SCI researchers from academia and industry, data science and bioinformatics experts, investigators with expertise in other neurological disease fields, clinical researchers, members of the SCI community, and program staff representing federal and private funding agencies. The workshop and ODC-SCI efforts were sponsored by the International Spinal Research Trust (ISRT), the Rick Hansen Institute, Wings for Life, the Craig H. Neilsen Foundation and NINDS. The number of attendees was limited to ensure active participation and feedback in small groups. The goals were to examine the current landscape for data sharing in SCI research and provide a path to its future. Below are highlights from the workshop, including perspectives on the value of data sharing in SCI research, workshop participant perspectives and concerns, descriptions of existing resources and actionable directions for further engaging the SCI research community in a model that may be applicable to many other areas of neuroscience. This manuscript is intended to share these initial findings with the broader research community, and to provide talking points for continued feedback from the SCI field, as it continues to move forward in the age of data sharing.

Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis.

BACKGROUND: Traumatic brain injury (TBI) is a complex disorder that is traditionally stratified based on clinical signs and symptoms. Recent imaging and molecular biomarker innovations provide unprecedented opportunities for improved TBI precision medicine, incorporating patho-anatomical and molecular mechanisms. Complete integration of these diverse data for TBI diagnosis and patient stratification remains an unmet challenge. METHODS AND FINDINGS: The Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot multicenter study enrolled 586 acute TBI patients and collected diverse common data elements (TBI-CDEs) across the study population, including imaging, genetics, and clinical outcomes. We then applied topology-based data-driven discovery to identify natural subgroups of patients, based on the TBI-CDEs collected. Our hypothesis was two-fold: 1) A machine learning tool known as topological data analysis (TDA) would reveal data-driven patterns in patient outcomes to identify candidate biomarkers of recovery, and 2) TDA-identified biomarkers would significantly predict patient outcome recovery after TBI using more traditional methods of univariate statistical tests. TDA algorithms organized and mapped the data of TBI patients in multidimensional space, identifying a subset of mild TBI patients with a specific multivariate phenotype associated with unfavorable outcome at 3 and 6 months after injury. Further analyses revealed that this patient subset had high rates of post-traumatic stress disorder (PTSD), and enrichment in several distinct genetic polymorphisms associated with cellular responses to stress and DNA damage (PARP1), and in striatal dopamine processing (ANKK1, COMT, DRD2). CONCLUSIONS: TDA identified a unique diagnostic subgroup of patients with unfavorable outcome after mild TBI that were significantly predicted by the presence of specific genetic polymorphisms. Machine learning methods such as TDA may provide a robust method for patient stratification and treatment planning targeting identified biomarkers in future clinical trials in TBI patients. TRIAL REGISTRATION: ClinicalTrials.gov Identifier NCT01565551.

A Unilateral Cervical Spinal Cord Contusion Injury Model in Non-Human Primates (Macaca mulatta).

The development of a non-human primate (NHP) model of spinal cord injury (SCI) based on mechanical and computational modeling is described. We scaled up from a rodent model to a larger primate model using a highly controllable, friction-free, electronically-driven actuator to generate unilateral C6-C7 spinal cord injuries. Graded contusion lesions with varying degrees of functional recovery, depending upon pre-set impact parameters, were produced in nine NHPs. Protocols and pre-operative magnetic resonance imaging (MRI) were used to optimize the predictability of outcomes by matching impact protocols to the size of each animal's spinal canal, cord, and cerebrospinal fluid space. Post-operative MRI confirmed lesion placement and provided information on lesion volume and spread for comparison with histological measures. We evaluated the relationships between impact parameters, lesion measures, and behavioral outcomes, and confirmed that these relationships were consistent with our previous studies in the rat. In addition to providing multiple univariate outcome measures, we also developed an integrated outcome metric describing the multivariate cervical SCI syndrome. Impacts at the higher ranges of peak force produced highly lateralized and enduring deficits in multiple measures of forelimb and hand function, while lower energy impacts produced early weakness followed by substantial recovery but enduring deficits in fine digital control (e.g., pincer grasp). This model provides a clinically relevant system in which to evaluate the safety and, potentially, the efficacy of candidate translational therapies.

Topological data analysis for discovery in preclinical spinal cord injury and traumatic brain injury.

Data-driven discovery in complex neurological disorders has potential to extract meaningful syndromic knowledge from large, heterogeneous data sets to enhance potential for precision medicine. Here we describe the application of topological data analysis (TDA) for data-driven discovery in preclinical traumatic brain injury (TBI) and spinal cord injury (SCI) data sets mined from the Visualized Syndromic Information and Outcomes for Neurotrauma-SCI (VISION-SCI) repository. Through direct visualization of inter-related histopathological, functional and health outcomes, TDA detected novel patterns across the syndromic network, uncovering interactions between SCI and co-occurring TBI, as well as detrimental drug effects in unpublished multicentre preclinical drug trial data in SCI. TDA also revealed that perioperative hypertension predicted long-term recovery better than any tested drug after thoracic SCI in rats. TDA-based data-driven discovery has great potential application for decision-support for basic research and clinical problems such as outcome assessment, neurocritical care, treatment planning and rapid, precision-diagnosis.

Pronounced species divergence in corticospinal tract reorganization and functional recovery after lateralized spinal cord injury favors primates.

Experimental and clinical studies suggest that primate species exhibit greater recovery after lateralized compared to symmetrical spinal cord injuries. Although this observation has major implications for designing clinical trials and translational therapies, advantages in recovery of nonhuman primates over other species have not been shown statistically to date, nor have the associated repair mechanisms been identified. We monitored recovery in more than 400 quadriplegic patients and found that functional gains increased with the laterality of spinal cord damage. Electrophysiological analyses suggested that corticospinal tract reorganization contributes to the greater recovery after lateralized compared with symmetrical injuries. To investigate underlying mechanisms, we modeled lateralized injuries in rats and monkeys using a lateral hemisection, and compared anatomical and functional outcomes with patients who suffered similar lesions. Standardized assessments revealed that monkeys and humans showed greater recovery of locomotion and hand function than did rats. Recovery correlated with the formation of corticospinal detour circuits below the injury, which were extensive in monkeys but nearly absent in rats. Our results uncover pronounced interspecies differences in the nature and extent of spinal cord repair mechanisms, likely resulting from fundamental differences in the anatomical and functional characteristics of the motor systems in primates versus rodents. Although rodents remain essential for advancing regenerative therapies, the unique response of the primate corticospinal tract after injury reemphasizes the importance of primate models for designing clinically relevant treatments.

Leveraging biomedical informatics for assessing plasticity and repair in primate spinal cord injury.

Recent preclinical advances highlight the therapeutic potential of treatments aimed at boosting regeneration and plasticity of spinal circuitry damaged by spinal cord injury (SCI). With several promising candidates being considered for translation into clinical trials, the SCI community has called for a non-human primate model as a crucial validation step to test efficacy and validity of these therapies prior to human testing. The present paper reviews the previous and ongoing efforts of the California Spinal Cord Consortium (CSCC), a multidisciplinary team of experts from 5 University of California medical and research centers, to develop this crucial translational SCI model. We focus on the growing volumes of high resolution data collected by the CSCC, and our efforts to develop a biomedical informatics framework aimed at leveraging multidimensional data to monitor plasticity and repair targeting recovery of hand and arm function. Although the main focus of many researchers is the restoration of voluntary motor control, we also describe our ongoing efforts to add assessments of sensory function, including pain, vital signs during surgery, and recovery of bladder and bowel function. By pooling our multidimensional data resources and building a unified database infrastructure for this clinically relevant translational model of SCI, we are now in a unique position to test promising therapeutic strategies' efficacy on the entire syndrome of SCI. We review analyses highlighting the intersection between motor, sensory, autonomic and pathological contributions to the overall restoration of function. This article is part of a Special Issue entitled SI: Spinal cord injury.