INTRODUCTION:United States (US) and European Union (EU) funders mandate data sharing, joining publishers with deposition requirements. Although funders require data sharing and curation plans be included in grant budgets, costs are difficult to forecast and there is pressing need for guidance. METHODS:We estimated costs of data maintenance, ongoing curation, and consultation for shared data from several major US and EU initiatives, providing benchmarks to help researchers, clinicians, administrators, and governmental agencies better forecast hidden costs of biomedical data sharing. RESULTS:We observe that costs to prepare, deposit, and share data in dynamic, federated environments, may subsume approximately 20% of a traditional operational research budget. The value of sharing these data for secondary analyses and validation/replication is substantial, but difficult to precisely estimate. One estimate suggests that the return on investment of specialized repositories ranges from 2700% to 6500%, measured by the initial cost to acquire their data holdings. CONCLUSIONS:Academic cultivation and advancement across the range of professionals and their diverse data acquisition, curation, and maintenance skillsets is essential. Close partnership between research community stakeholders paired with government mandates and funding to support data management and sharing standards can facilitate accurate and efficient translation of knowledge into clinical practice.

In visual perception, an effect known as surround suppression occurs wherein the apparent contrast of a center stimulus is reduced when it is presented within a higher-contrast surrounding stimulus. Many key aspects of visual perception involve surround suppression, yet the neuromodulatory processes involved remain unclear. Psilocybin is a serotonergic psychedelic compound known for its robust effects on visual perception, particularly texture, color, object, and motion perception. We asked whether surround suppression is altered under peak effects of psilocybin. Using a contrast-matching task with different center-surround stimulus configurations, we measured surround suppression after 25 mg of psilocybin compared with placebo (100 mg niacin). Data on harms were collected, and no serious adverse events were reported. After taking psilocybin, participants (n = 6) reported stronger surround suppression of perceived contrast compared to placebo. Furthermore, we found that the intensity of subjective psychedelic visuals induced by psilocybin correlated positively with the magnitude of surround suppression. We note the potential relevance of our findings for the field of psychiatry, given that studies have demonstrated weakened visual surround suppression in both major depressive disorder and schizophrenia. Our findings are thus relevant to understanding the visual effects of psilocybin, and the potential mechanisms of visual disruption in mental health disorders.

The complex and heterogeneous nature of spinal cord injury has limited translational bench-to-bedside results. The wide variety of data, including injury parameters, biochemical, histological, and behavioral outcome measures represent a ‘big data’ problem, calling for modern data science solutions. There are some instances in which SCI researchers collect sensitive data that needs to remain private, such as datasets designed to meet regulatory approval, sensitive intellectual property, and non-human primate studies. For these types of data, we have developed a Private Data Commons for SCI (PDC-SCI). Our objective is to give an overview of this novel data commons, describing how this type of commons works, how it can benefit the research community, and the cases in which it would be most useful. This private infrastructure is ideal for multi-lab transdisciplinary studies that require a well-organized, scalable data commons for rapid data sharing within a closed, distributed team. As a use-case for the PDC-SCI, we demonstrate the VA Gordon Mansfield SCI Consortium, in which multimodal data from behavior, biomechanics of injury, hospital records, imaging, and histology are integrated, shared, and analyzed to facilitate insights and knowledge discovery.

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.

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.

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 in-fluence suicidality.

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.

IntroductionApproximately 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.MethodsTherefore, 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).ResultsThe 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.DiscussionOur 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.

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); DOE: DE-AC02-05CH11231 (DM).

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.

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.

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.

Traumatic spinal cord injury (SCI) produces a complex syndrome that is expressed across multiple endpoints ranging from molecular and cellular changes to functional behavioral deficits. Effective therapeutic strategies for CNS injury are therefore likely to manifest multi-factorial effects across a broad range of biological and functional outcome measures. Thus, multivariate analytic approaches are needed to capture the linkage between biological and neurobehavioral outcomes. Injury-induced neuroinflammation (NI) presents a particularly challenging therapeutic target, since NI is involved in both degeneration and repair. Here, we used big-data integration and large-scale analytics to examine a large dataset of preclinical efficacy tests combining five different blinded, fully counter-balanced treatment trials for different acute anti-inflammatory treatments for cervical spinal cord injury in rats. Multi-dimensional discovery, using topological data analysis (TDA) and principal components analysis (PCA) revealed that only one showed consistent multidimensional syndromic benefit: intrathecal application of recombinant soluble TNFα receptor 1 (sTNFR1), which showed an inverse-U dose response efficacy. Using the optimal acute dose, we showed that clinically-relevant 90 min delayed treatment profoundly affected multiple biological indices of NI in the first 48 h after injury, including reduction in pro-inflammatory cytokines and gene expression of a coherent complex of acute inflammatory mediators and receptors. Further, a 90 min delayed bolus dose of sTNFR1 reduced the expression of NI markers in the chronic perilesional spinal cord, and consistently improved neurological function over 6 weeks post SCI. These results provide validation of a novel strategy for precision preclinical drug discovery that is likely to improve translation in the difficult landscape of CNS trauma, and confirm the importance of TNFα signaling as a therapeutic target.

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.

ABSTRACT Background Approximately half of patients with posttraumatic stress disorder (PTSD) also meet criteria for internalizing disorders, yet few studies assess reciprocal longitudinal relations among these symptoms. Methods We used longitudinal causal discovery in a veteran cohort for hypothesis-generation about PTSD and internalizing symptom drivers (n=240), followed by hypothesis-testing in two independent civilian cohorts with similar symptom assessments over time (n=79 and n=116). Results In the veteran cohort, causal discovery revealed PTSD symptoms drove internalizing symptoms, which subsequently impacted social functioning; all independent of problematic alcohol use. This replicated in treatment-seeking anxiety disorders (AD, n = 79) and substance abuse (SA, n = 116) samples with significantly better model fit for PTSD symptoms driving internalizing symptoms, versus internalizing symptoms driving PTSD symptoms (BIC change for AD sample = 175.1, p<.001; BIC change for SA sample = 571.6, p<.001). We also found better model fit with PTSD symptoms driving anxiety symptoms, versus anxiety symptoms driving PTSD symptoms (BIC change for AD sample = 71.8, p < .001; BIC change for SA sample = 568.9, p < .001). Posthoc analysis in the veteran sample revealed that hyperarousal and cognitive and affective disturbance bridged between other PTSD symptoms and internalizing symptoms. Conclusions Our findings suggest that internalizing symptoms that emerge in the context of PTSD are more likely to be driven by PTSD symptoms. These results highlight the need for a PTSD- and trauma-informed approach to treating internalizing symptoms, and provide preliminary evidence for cognition and mood disruption as a factor driving comorbidity.

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.

In the original version of this book, the following correction has been incorporated: In Chapter 3, the name citation of “John Pasquina” has been removed. The book and the chapter have been updated with the change.