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.

Public Health Relevance

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.

Agency
National Institute of Health (NIH)
Institute
National Institute of Neurological Disorders and Stroke (NINDS)
Type
Research Project (R01)
Project #
5R01NS067092-04
Application #
8443843
Study Section
Neurotechnology Study Section (NT)
Program Officer
Jakeman, Lyn B
Project Start
2010-05-01
Project End
2015-04-30
Budget Start
2013-05-01
Budget End
2014-04-30
Support Year
4
Fiscal Year
2013
Total Cost
$318,983
Indirect Cost
$112,111
Name
University of California San Francisco
Department
Neurosurgery
Type
Schools of Medicine
DUNS #
094878337
City
San Francisco
State
CA
Country
United States
Zip Code
94143
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
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