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

A minimal metadata set (MNMS) to repurpose nonclinical in vivo data for biomedical research

Although biomedical research is experiencing a data explosion, the accumulation of vast quantities of data alone does not guarantee a primary objective for science: building upon existing knowledge. Data collected that lack appropriate metadata cannot be fully interrogated or integrated into new research projects, leading to wasted resources and missed opportunities for data repurposing. This issue is particularly acute for research using animals, where concerns regarding data reproducibility and ensuring animal welfare are paramount. Here, to address this problem, we propose a minimal metadata set (MNMS) designed to enable the repurposing of in vivo data. MNMS aligns with an existing validated guideline for reporting in vivo data (ARRIVE 2.0) and contributes to making in vivo data FAIR-compliant. Scenarios where MNMS should be implemented in diverse research environments are presented, highlighting opportunities and challenges for data repurposing at different scales. We conclude with a 'call for action' to key stakeholders in biomedical research to adopt and apply MNMS to accelerate both the advancement of knowledge and the betterment of animal welfare.

Perspectives on Data Sharing in Persons With Spinal Cord Injury

Open data sharing of clinical research aims to improve transparency and support novel scientific discoveries. There are also risks, including participant identification and the potential for stigmatization. The perspectives of persons participating in research are needed to inform open data-sharing policies. The aim of the current study was to determine perspectives on data sharing in persons with spinal cord injury (SCI), including risks and benefits, and types of data people are most willing to share. A secondary aim was to examine predictors of willingness to share data. Persons with SCIs in the United States and Canada completed a survey developed and disseminated through various channels, including our community partner, the North American Spinal Cord Injury Consortium. The study collected data from 232 participants, with 52.2% from Canada and 42.2% from the United States, and the majority completed the survey in English. Most participants had previously participated in research and had been living with an SCI for ≥5 years. Overall, most participants reported that the potential benefits of data sharing outweighed the negatives, with persons with SCI seen as the most trustworthy partners for data sharing. The highest levels of concern were that information could be stolen and companies might use the information for marketing purposes. Persons with SCI were generally supportive of data sharing for research purposes. Clinical trials should consider including a statement on open data sharing in informed consents to better acknowledge the contribution of research participants in future studies.

Increasing Rigor of Preclinical Research to Maximize Opportunities for Translation

The use of animal models in pre-clinical research has significantly broadened our understanding of the pathologies that underlie traumatic brain injury (TBI)-induced damage and deficits. However, despite numerous pre-clinical studies reporting the identification of promising neurotherapeutics, translation of these therapies to clinical application has so far eluded the TBI research field. A concerted effort to address this lack of translatability is long overdue. Given the inherent heterogeneity of TBI and the replication crisis that continues to plague biomedical research, this is a complex task that will require a multifaceted approach centered around rigor and reproducibility. Here, we discuss the role of three primary focus areas for better aligning pre-clinical research with clinical TBI management. These focus areas are (1) reporting and standardization of protocols, (2) replication of prior knowledge including the confirmation of expected pharmacodynamics, and (3) the broad application of open science through inter-center collaboration and data sharing. We further discuss current efforts that are establishing the core framework needed for successfully addressing the translatability crisis of TBI.

Fighting for recovery on multiple fronts: The past, present, and future of clinical trials for spinal cord injury

Through many decades of preclinical research, great progress has been achieved in understanding the complex nature of spinal cord injury (SCI). Preclinical research efforts have guided and shaped clinical trials, which are growing in number by the year. Currently, 1,149 clinical trials focused on improving outcomes after SCI are registered in the U.S. National Library of Medicine at ClinicalTrials.gov. We conducted a systematic analysis of these SCI clinical trials, using publicly accessible data downloaded from ClinicalTrials.gov. After extracting all available data for these trials, we categorized each trial according to the types of interventions being tested and the types of outcomes assessed. We then evaluated clinical trial characteristics, both globally and by year, in order to understand the areas of growth and change over time. With regard to clinical trial attributes, we found that most trials have low enrollment, only test single interventions, and have limited numbers of primary outcomes. Some gaps in reporting are apparent; for instance, over 75% of clinical trials with "Completed" status do not have results posted, and the Phase of some trials is incorrectly classified as "Not applicable" despite testing a drug or biological compound. When analyzing trials based on types of interventions assessed, we identified the largest representation in trials testing rehab/training/exercise, neuromodulation, and behavioral modifications. Most highly represented primary outcomes include motor function of the upper and lower extremities, safety, and pain. The most highly represented secondary outcomes include quality of life and pain. Over the past 15 years, we identified increased representation of neuromodulation and rehabilitation trials, and decreased representation of drug trials. Overall, the number of new clinical trials initiated each year continues to grow, signifying a hopeful future for the clinical treatment of SCI. Together, our work provides a comprehensive glimpse into the past, present, and future of SCI clinical trials, and suggests areas for improvement in clinical trial reporting.

Harmonization-Information Trade-Offs for Sharing Individual Participant Data in Biomedicine

Biomedical practice is evidence-based. Peer-reviewed papers are the primary medium to present evidence and data-supported results to drive clinical practice. However, it could be argued that scientific literature does not contain data, but rather narratives about and summaries of data. Meta-analyses of published literature may produce biased conclusions due to the lack of transparency in data collection, publication bias, and inaccessibility to the data underlying a publication ('dark data'). Co-analysis of pooled data at the level of individual research participants can offer higher levels of evidence, but this requires that researchers share raw individual participant data (IPD). FAIR (findable, accessible, interoperable, and reusable) data governance principles aim to guide data lifecycle management by providing a framework for actionable data sharing. Here we discuss the implications of FAIR for data harmonization, an essential step for pooling data for IPD analysis. We describe the harmonization-information trade-off, which states that the level of granularity in harmonizing data determines the amount of information lost. Finally, we discuss a framework for managing the trade-off and the levels of harmonization. In the coming era of funder mandates for data sharing, research communities that effectively manage data harmonization will be empowered to harness big data and advanced analytics such as machine learning and artificial intelligence tools, leading to stunning new discoveries that augment our understanding of diseases and their treatments. By elevating scientific data to the status of a first-class citizen of the scientific enterprise, there is strong potential for biomedicine to transition from a narrative publication product orientation to a modern data-driven enterprise where data itself is viewed as a primary work product of biomedical research.

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.

Mapping the long rocky road to effective spinal cord injury therapy - A meta-review of pre-clinical and clinical research

Spinal cord injury (SCI) is a rare condition, which even after decades of research, to date still presents an incurable condition with a complex symptomatology. SCI can result in paralysis, pain, loss of sensation, bladder and sexual dysfunction, and muscle degeneration to name but a few. The large number of publications makes it difficult to keep track of current progress in the field and of the many treatment options, which have been suggested and are being proposed with increasing frequency. Scientific databases with user-oriented search options will offer possible solutions, but they are still mostly in the development phase. In this meta-analysis, we summarize and narrow down SCI therapeutic approaches applied in pre-clinical and clinical research. Statistical analyses of treatment clusters - assorted after counting annual publication numbers in PubMed and ClinicalTrials.gov databases - were performed to allow the comparison of research foci and of their translation efficacy into clinical therapy. Using the example of SCI research, our findings demonstrate the challenges that come with the accelerating research progress - an issue, which many research fields are faced with today. The analyses point out similarities and differences in the prioritization of SCI research in pre-clinical versus clinical therapy strategies. Moreover, the results demonstrate the rapidly growing importance of modern (bio-)engineering technologies.

Reflections on Data Sharing Practices in Spinal Cord Injury Research

There are few pharmacological therapeutics available for spinal cord injury despite years of preclinical and clinical research. This brief editorial discusses some of the shortcomings of translational research efforts. In addition, we comment on our previous experiences with data curation and highlight evolving efforts by the spinal cord injury research community to improve prospects for future therapeutic development, especially pertaining to preclinical data sharing through the Open Data Commons for Spinal Cord Injury (ODC-SCI).

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.

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.

Age-Dependent Decline in Neuron Growth Potential and Mitochondria Functions in Cortical Neurons

The age of incidence of spinal cord injury (SCI) and the average age of people living with SCI is continuously increasing. However, SCI is extensively modeled in young adult animals, hampering translation of research to clinical applications. While there has been significant progress in manipulating axon growth after injury, the impact of aging is still unknown. Mitochondria are essential to successful neurite and axon growth, while aging is associated with a decline in mitochondrial functions. Using isolation and culture of adult cortical neurons, we analyzed mitochondrial changes in 2-, 6-, 12- and 18-month-old mice. We observed reduced neurite growth in older neurons. Older neurons also showed dysfunctional respiration, reduced membrane potential, and altered mitochondrial membrane transport proteins; however, mitochondrial DNA (mtDNA) abundance and cellular ATP were increased. Taken together, these data suggest that dysfunctional mitochondria in older neurons may be associated with the age-dependent reduction in neurite growth. Both normal aging and traumatic injury are associated with mitochondrial dysfunction, posing a challenge for an aging SCI population as the two elements can combine to worsen injury outcomes. The results of this study highlight this as an area of great interest in CNS trauma.

Pre-Clinical Common Data Elements for Traumatic Brain Injury Research: Progress and Use Cases

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

The Life of a Trailing Spouse

In 1981, I published a paper in the first issue of the Journal of Neuroscience with my postdoctoral mentor, Alan Pearlman. It reported a quantitative analysis of the receptive field properties of neurons in reeler mouse visual cortex and the surprising conclusion that although the neuronal somas were strikingly malpositioned, their receptive fields were unchanged. This suggested that in mouse cortex at least, neuronal circuits have very robust systems in place to ensure the proper formation of connections. This had the unintended consequence of transforming me from an electrophysiologist into a cellular and molecular neuroscientist who studied cell adhesion molecules and the molecular mechanisms they use to regulate axon growth. It took me a surprisingly long time to appreciate that your science is driven by the people around you and by the technologies that are locally available. As a professional puzzler, I like all different kinds of puzzles, but the most fun puzzles involve playing with other puzzlers. This is my story of learning how to find like-minded puzzlers to solve riddles about axon growth and regeneration.

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.

The neuroanatomical-functional paradox in spinal cord injury

Although lesion size is widely considered to be the most reliable predictor of outcome after CNS injury, lesions of comparable size can produce vastly different magnitudes of functional impairment and subsequent recovery. This neuroanatomical-functional paradox is likely to contribute to the many failed attempts to independently replicate findings from animal models of neurotrauma. In humans, the analogous clinical-radiological paradox could explain why individuals with similar injuries can respond differently to rehabilitation. We describe the neuroanatomical-functional paradox in the context of traumatic spinal cord injury (SCI) and discuss the underlying mechanisms of the paradox, including the concepts of lesion-affected and recovery-related networks. We also consider the various secondary complications that further limit the accuracy of outcome prediction in SCI and provide suggestions for how to increase the predictive, translational value of preclinical SCI models.

Behavioral testing in animal models of spinal cord injury

This review is based on a lecture presented at the Craig H. Neilsen Foundation sponsored Spinal Cord Injury Training Program at Ohio State University. We discuss the advantages and challenges of injury models in rodents and theory relation to various behavioral outcome measures. We offer strategies and advice on experimental design, behavioral testing, and on the challenges, one will encounter with animal testing. This review is designed to guide those entering the field of spinal cord injury and/or involved with in vivo animal testing.

Considerations for Studying Sex as a Biological Variable in Spinal Cord Injury

In response to NIH initiatives to investigate sex as a biological variable in preclinical animal studies, researchers have increased their focus on male and female differences in neurotrauma. Inclusion of both sexes when modeling neurotrauma is leading to the identification of novel areas for therapeutic and scientific exploitation. Here, we review the organizational and activational effects of sex hormones on recovery from injury and how these changes impact the long-term health of spinal cord injury (SCI) patients. When determining how sex affects SCI it remains imperative to expand outcomes beyond locomotor recovery and consider other complications plaguing the quality of life of patients with SCI. Interestingly, the SCI field predominately utilizes female rodents for basic science research which contrasts most other male-biased research fields. We discuss the unique caveats this creates to the translatability of preclinical research in the SCI field. We also review current clinical and preclinical data examining sex as biological variable in SCI. Further, we report how technical considerations such as housing, size, care management, and age, confound the interpretation of sex-specific effects in animal studies of SCI. We have uncovered novel findings regarding how age differentially affects mortality and injury-induced anemia in males and females after SCI, and further identified estrus cycle dysfunction in mice after injury. Emerging concepts underlying sexually dimorphic responses to therapy are also discussed. Through a combination of literature review and primary research observations we present a practical guide for considering and incorporating sex as biological variable in preclinical neurotrauma studies.

The Influence of Neuron-Extrinsic Factors and Aging on Injury Progression and Axonal Repair in the Central Nervous System

In the aging western population, the average age of incidence for spinal cord injury (SCI) has increased, as has the length of survival of SCI patients. This places great importance on understanding SCI in middle-aged and aging patients. Axon regeneration after injury is an area of study that has received substantial attention and made important experimental progress, however, our understanding of how aging affects this process, and any therapeutic effort to modulate repair, is incomplete. The growth and regeneration of axons is mediated by both neuron intrinsic and extrinsic factors. In this review we explore some of the key extrinsic influences on axon regeneration in the literature, focusing on inflammation and astrogliosis, other cellular responses, components of the extracellular matrix, and myelin proteins. We will describe how each element supports the contention that axonal growth after injury in the central nervous system shows an age-dependent decline, and how this may affect outcomes after a SCI.