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DTI measures identify mild and moderate TBI cases among patients with complex health problems: A receiver operating characteristic analysis of U.S. veterans.
Neuroimage Clin. 2017; 16:1-16.NC

Abstract

Standard MRI methods are often inadequate for identifying mild traumatic brain injury (TBI). Advances in diffusion tensor imaging now provide potential biomarkers of TBI among white matter fascicles (tracts). However, it is still unclear which tracts are most pertinent to TBI diagnosis. This study ranked fiber tracts on their ability to discriminate patients with and without TBI. We acquired diffusion tensor imaging data from military veterans admitted to a polytrauma clinic (Overall n = 109; Age: M = 47.2, SD = 11.3; Male: 88%; TBI: 67%). TBI diagnosis was based on self-report and neurological examination. Fiber tractography analysis produced 20 fiber tracts per patient. Each tract yielded four clinically relevant measures (fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity). We applied receiver operating characteristic (ROC) analyses to identify the most diagnostic tract for each measure. The analyses produced an optimal cutpoint for each tract. We then used kappa coefficients to rate the agreement of each cutpoint with the neurologist's diagnosis. The tract with the highest kappa was most diagnostic. As a check on the ROC results, we performed a stepwise logistic regression on each measure using all 20 tracts as predictors. We also bootstrapped the ROC analyses to compute the 95% confidence intervals for sensitivity, specificity, and the highest kappa coefficients. The ROC analyses identified two fiber tracts as most diagnostic of TBI: the left cingulum (LCG) and the left inferior fronto-occipital fasciculus (LIF). Like ROC, logistic regression identified LCG as most predictive for the FA measure but identified the right anterior thalamic tract (RAT) for the MD, RD, and AD measures. These findings are potentially relevant to the development of TBI biomarkers. Our methods also demonstrate how ROC analysis may be used to identify clinically relevant variables in the TBI population.

Authors+Show Affiliations

War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States. Defense and Veterans Brain Injury Center (DVBIC), Silver Spring, MD, United States. General Dynamics Health Solutions (GDHS), Fairfax, VA, United States.War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States. Department of Radiology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States.War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, United States.Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States.Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States.War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States.War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States.Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States.Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States.War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States.War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States. Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States.Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States.Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States. Department of Neurosurgery, Stanford School of Medicine, Stanford, CA, United States. Defense and Veterans Brain Injury Center (DVBIC), Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

28725550

Citation

Main, Keith L., et al. "DTI Measures Identify Mild and Moderate TBI Cases Among Patients With Complex Health Problems: a Receiver Operating Characteristic Analysis of U.S. Veterans." NeuroImage. Clinical, vol. 16, 2017, pp. 1-16.
Main KL, Soman S, Pestilli F, et al. DTI measures identify mild and moderate TBI cases among patients with complex health problems: A receiver operating characteristic analysis of U.S. veterans. Neuroimage Clin. 2017;16:1-16.
Main, K. L., Soman, S., Pestilli, F., Furst, A., Noda, A., Hernandez, B., Kong, J., Cheng, J., Fairchild, J. K., Taylor, J., Yesavage, J., Wesson Ashford, J., Kraemer, H., & Adamson, M. M. (2017). DTI measures identify mild and moderate TBI cases among patients with complex health problems: A receiver operating characteristic analysis of U.S. veterans. NeuroImage. Clinical, 16, 1-16. https://doi.org/10.1016/j.nicl.2017.06.031
Main KL, et al. DTI Measures Identify Mild and Moderate TBI Cases Among Patients With Complex Health Problems: a Receiver Operating Characteristic Analysis of U.S. Veterans. Neuroimage Clin. 2017;16:1-16. PubMed PMID: 28725550.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - DTI measures identify mild and moderate TBI cases among patients with complex health problems: A receiver operating characteristic analysis of U.S. veterans. AU - Main,Keith L, AU - Soman,Salil, AU - Pestilli,Franco, AU - Furst,Ansgar, AU - Noda,Art, AU - Hernandez,Beatriz, AU - Kong,Jennifer, AU - Cheng,Jauhtai, AU - Fairchild,Jennifer K, AU - Taylor,Joy, AU - Yesavage,Jerome, AU - Wesson Ashford,J, AU - Kraemer,Helena, AU - Adamson,Maheen M, Y1 - 2017/06/24/ PY - 2016/08/15/received PY - 2017/06/10/revised PY - 2017/06/23/accepted PY - 2017/7/21/entrez PY - 2017/7/21/pubmed PY - 2018/4/24/medline KW - AD, axial diffusivity KW - Axon degeneration KW - CC, corpus callosum KW - Concussion KW - DAI, diffuse axonal injury KW - DTI, diffusion tensor imaging KW - FA, fractional anisotropy KW - GN, genu KW - Imaging KW - LAT, left anterior thalamic tract KW - LCG, left cingulum KW - LCH, left cingulum – hippocampus KW - LCS, left cortico-spinal tract KW - LIF, left inferior fronto-occipital fasciculus KW - LIL, left inferior longitudinal fasciculus KW - LSL, left superior longitudinal fasciculus KW - LST, left superior longitudinal fasciculus – temporal KW - LUN, left uncinate KW - MD, mean diffusivity KW - Neurodegeneration KW - PTSD, post-traumatic stress disorder KW - RAT, right anterior thalamic tract KW - RCG, right cingulum KW - RCH, right cingulum – Hippocampus KW - RCS, right cortico-spinal tract KW - RD, radial diffusivity KW - RIF, right inferior fronto-occipital fasciculus KW - RIL, right inferior longitudinal fasciculus KW - ROC, receiver operating characteristic KW - RSL, right superior longitudinal fasciculus KW - RST, right superior longitudinal fasciculus – temporal KW - RUN, right uncinate KW - SP, splenium KW - TBI, traumatic brain injury KW - Traumatic brain injury SP - 1 EP - 16 JF - NeuroImage. Clinical JO - Neuroimage Clin VL - 16 N2 - Standard MRI methods are often inadequate for identifying mild traumatic brain injury (TBI). Advances in diffusion tensor imaging now provide potential biomarkers of TBI among white matter fascicles (tracts). However, it is still unclear which tracts are most pertinent to TBI diagnosis. This study ranked fiber tracts on their ability to discriminate patients with and without TBI. We acquired diffusion tensor imaging data from military veterans admitted to a polytrauma clinic (Overall n = 109; Age: M = 47.2, SD = 11.3; Male: 88%; TBI: 67%). TBI diagnosis was based on self-report and neurological examination. Fiber tractography analysis produced 20 fiber tracts per patient. Each tract yielded four clinically relevant measures (fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity). We applied receiver operating characteristic (ROC) analyses to identify the most diagnostic tract for each measure. The analyses produced an optimal cutpoint for each tract. We then used kappa coefficients to rate the agreement of each cutpoint with the neurologist's diagnosis. The tract with the highest kappa was most diagnostic. As a check on the ROC results, we performed a stepwise logistic regression on each measure using all 20 tracts as predictors. We also bootstrapped the ROC analyses to compute the 95% confidence intervals for sensitivity, specificity, and the highest kappa coefficients. The ROC analyses identified two fiber tracts as most diagnostic of TBI: the left cingulum (LCG) and the left inferior fronto-occipital fasciculus (LIF). Like ROC, logistic regression identified LCG as most predictive for the FA measure but identified the right anterior thalamic tract (RAT) for the MD, RD, and AD measures. These findings are potentially relevant to the development of TBI biomarkers. Our methods also demonstrate how ROC analysis may be used to identify clinically relevant variables in the TBI population. SN - 2213-1582 UR - https://www.unboundmedicine.com/medline/citation/28725550/DTI_measures_identify_mild_and_moderate_TBI_cases_among_patients_with_complex_health_problems:_A_receiver_operating_characteristic_analysis_of_U_S__veterans_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S2213-1582(17)30162-6 DB - PRIME DP - Unbound Medicine ER -