Derivation of multivariate syndromic outcome metrics for consistent testing across multiple models of cervical spinal cord injury in rats

• Published in: PloS one Vol. 8; no. 3; p. e59712
• Main Authors: Ferguson, Adam R, Irvine, Karen-Amanda, Gensel, John C, Nielson, Jessica L, Lin, Amity, Ly, Johnathan, Segal, Mark R, Ratan, Rajiv R, Bresnahan, Jacqueline C, Beattie, Michael S
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 27.03.2013; Public Library of Science (PLoS)
• Subjects: Animals; Apoptosis; Behavior, Animal; Biology; Brain research; Cervical Vertebrae - injuries; Cervical Vertebrae - pathology; Contusions; Disease control; Disease Models, Animal; Feature extraction; Female; Gangrene; Information management; Information processing; Injuries; Locomotion; Medicine; Multivariate Analysis; Neurological diseases; Neurological disorders; Principal Component Analysis; Rats; Rats, Long-Evans; Recovery of Function; Reproducibility of Results; Research projects; Severity of Illness Index; Spinal cord injuries; Spinal Cord Injuries - diagnosis; Spinal Cord Injuries - pathology; Spinal Cord Injuries - physiopathology; Surgery; Syndrome; Target recognition; Translation; Trauma; Traumatic brain injury; New York; San Francisco California; California; United States > US
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0059712

Spinal cord injury (SCI) and other neurological disorders involve complex biological and functional changes. Well-characterized preclinical models provide a powerful tool for understanding mechanisms of disease; however managing information produced by experimental models represents a significant challenge for translating findings across research projects and presents a substantial hurdle for translation of novel therapies to humans. In the present work we demonstrate a novel 'syndromic' information-processing approach for capitalizing on heterogeneous data from diverse preclinical models of SCI to discover translational outcomes for therapeutic testing. We first built a large, detailed repository of preclinical outcome data from 10 years of basic research on cervical SCI in rats, and then applied multivariate pattern detection techniques to extract features that are conserved across different injury models. We then applied this translational knowledge to derive a data-driven multivariate metric that provides a common 'ruler' for comparisons of outcomes across different types of injury (NYU/MASCIS weight drop injuries, Infinite Horizons (IH) injuries, and hemisection injuries). The findings revealed that each individual endpoint provides a different view of the SCI syndrome, and that considering any single outcome measure in isolation provides a misleading, incomplete view of the SCI syndrome. This limitation was overcome by taking a novel multivariate integrative approach for leveraging complex data from preclinical models of neurological disease to identify therapies that target multiple outcomes. We suggest that applying this syndromic approach provides a roadmap for translating therapies for SCI and other complex neurological diseases.