Citation

Levin H, Li X, McCauley SR, Hanten G, Wilde EA, Swank PR. J. Neurotrauma 2013; 30(8): 625-632.

Affiliation

Baylor College of Medicine, Cognitive Neuroscience Laboratory, 1709 dryden rd, houston, Texas, United States, 77030, 7137987566; harvlev@aol.com.

Copyright

(Copyright © 2013, Mary Ann Liebert Publishers)

DOI

10.1089/neu.2012.2627

PMID

22994927

Abstract

The multitude of variables associated with a battery of outcome measures presents a risk for spurious findings in clinical trials and observational studies of mild traumatic brain injury (mTBI). We have used principal components analysis (PCA) to facilitate data reduction by identifying components which represent subsets of neuropsychological measures that are selectively correlated with each other. By merging data from two concurrent mTBI studies using the same outcome measures, we obtained a cohort of 102 mTBI patients and 85 orthopedic injury (OI) comparison patients whom we recruited from 24 hours to 96 hours post-injury and evaluated at one week, 1 month, and three months post-injury. Cognitive domains included episodic memory, evaluated by both verbal and visual memory tasks, cognitive processing speed tests, and executive function. Post-concussion and stress related symptoms were measured by rating scales. PCA identified four components, including cognitive processing speed, verbal memory, visual memory, and a symptom composite representing post-concussion and stress symptoms. mTBI patients older than the mean age of 18 years had slower cognitive processing than the OI patients, but there was no group difference in cognitive processing speed in younger patients. The symptom component score differed significantly as mTBI patients had more severe symptoms than the OI group at each occasion. Our results encourage replication with other cohorts using either the same outcome measures or at least similar domains. PCA is an approach to data reduction that could mitigate spurious findings and increase efficiency in mTBI research. Key Words: mTBI, data reduction, principal components analysis.

Language: en