Spinal cord injury (SCI) produces a multifaceted syndrome characterized by loss of mobility, loss of bladder, bowel and sexual function, pathological pain, and a loss of autonomy. The past 20 years have seen significant progress in our ability to emulate many features of human SCI in animal models, yet few experimental therapies have translated from the laboratory to human patients. One major obstacle to translation is the lack of information about which outcome metrics for SCI are comparable across different laboratories, strains, types of injuries, and species. Identification of these important common outcome metrics is a major goal of the proposed project. We hypothesize that experimental SCI produces a bio-behavioral syndrome that is reflected not by only one outcome, but rather consistent patterns across many different outcomes. This view is implicitly assumed by many experimental SCI researchers when they evaluate experimental therapeutics using several outcomes from the same experimental subjects. However these data have not been analyzed using sophisticated multivariate information processing procedures which are designed to detect, measure, and quantify disease patterns in complex datasets. By pooling data from several laboratories and making cross- species comparisons, we will leverage existing experimental data to identify common metrics of SCI that can be used for evaluating mechanism of SCI that translate across species. We propose the following Aims: 1) Build a pooled database of existing experimental rodent and primate SCI research data to provide a platform for knowledge discovery and multivariate quantification across diverse outcomes and experimental models. We will start with data from 5 major SCI research centers to provide a framework for later contributions from other research groups. 2) Identify syndrome measures in rodent SCI models, using the same multivariate techniques often used by clinical researchers to define and measure complex disease states. 3) Identify which multivariate outcome patterns in rodent models are most sensitive to the effects of graded injury and which are most sensitive to change over time, with the goal of improving sensitivity and streamlining testing of therapeutic interventions. 4) Identify which multivariate outcome patterns in non-human primates are most sensitive to the effects of SCI and recovery over time, providing important information about the most sensitive outcomes for therapeutic testing in this valuable preclinical model. 5) Make translational multivariate comparisons of rodent and primate SCI data to identify which outcome patterns best translate across experimental models and which are species- and model-specific, setting the stage for future multivariate comparisons to human data. This represents a new direction for the field of experimental SCI and we expect this approach to help define outcome metrics that are comparable across species, facilitating translational SCI research.
The proposed Project focuses on spinal cord injury, a devastating syndrome, affecting approximately 250,000 people in the United States, and costing the nation almost 10 billion dollars per year in healthcare and loss-of- productivity. Injured individuals are plagued by loss of mobility;loss of bladder, bowel and sexual function; pathological pain;and a loss of autonomy. These changes profoundly reduce the quality of life for injured individuals and their loved ones.
|Pedoia, Valentina; Haefeli, Jenny; Morioka, Kazuhito et al. (2018) MRI and biomechanics multidimensional data analysis reveals R2 -R1? as an early predictor of cartilage lesion progression in knee osteoarthritis. J Magn Reson Imaging 47:78-90|
|Nielson, Jessica L; Cooper, Shelly R; Yue, John K et al. (2017) Uncovering precision phenotype-biomarker associations in traumatic brain injury using topological data analysis. PLoS One 12:e0169490|
|Haefeli, Jenny; Ferguson, Adam R; Bingham, Deborah et al. (2017) A data-driven approach for evaluating multi-modal therapy in traumatic brain injury. Sci Rep 7:42474|
|Haefeli, J; Mabray, M C; Whetstone, W D et al. (2017) Multivariate Analysis of MRI Biomarkers for Predicting Neurologic Impairment in Cervical Spinal Cord Injury. AJNR Am J Neuroradiol 38:648-655|
|Huie, J Russell; Morioka, Kazuhito; Haefeli, Jenny et al. (2017) What Is Being Trained? How Divergent Forms of Plasticity Compete To Shape Locomotor Recovery after Spinal Cord Injury. J Neurotrauma 34:1831-1840|
|Callahan, Alison; Anderson, Kim D; Beattie, Michael S et al. (2017) Developing a data sharing community for spinal cord injury research. Exp Neurol 295:135-143|
|Zakrasek, E C; Nielson, J L; Kosarchuk, J J et al. (2017) Pulmonary outcomes following specialized respiratory management for acute cervical spinal cord injury: a retrospective analysis. Spinal Cord 55:559-565|
|Haefeli, Jenny; Huie, J Russell; Morioka, Kazuhito et al. (2017) Assessments of sensory plasticity after spinal cord injury across species. Neurosci Lett 652:74-81|
|Lee, Sangmi; Mattingly, Aaron; Lin, Amity et al. (2016) A novel antagonist of p75NTR reduces peripheral expansion and CNS trafficking of pro-inflammatory monocytes and spares function after traumatic brain injury. J Neuroinflammation 13:88|
|Mabray, Marc C; Talbott, Jason F; Whetstone, William D et al. (2016) Multidimensional Analysis of Magnetic Resonance Imaging Predicts Early Impairment in Thoracic and Thoracolumbar Spinal Cord Injury. J Neurotrauma 33:954-62|
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