1
|
Main KL, Soman S, Pestilli F, Furst A, Noda A, Hernandez B, Kong J, Cheng J, Fairchild JK, Taylor J, Yesavage J, Wesson Ashford J, Kraemer H, Adamson MM. 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. [PMID: 28725550 PMCID: PMC5503837 DOI: 10.1016/j.nicl.2017.06.031] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2016] [Revised: 06/10/2017] [Accepted: 06/23/2017] [Indexed: 01/10/2023]
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.
Collapse
Key Words
- AD, axial diffusivity
- Axon degeneration
- CC, corpus callosum
- Concussion
- DAI, diffuse axonal injury
- DTI, diffusion tensor imaging
- FA, fractional anisotropy
- GN, genu
- Imaging
- LAT, left anterior thalamic tract
- LCG, left cingulum
- LCH, left cingulum – hippocampus
- LCS, left cortico-spinal tract
- LIF, left inferior fronto-occipital fasciculus
- LIL, left inferior longitudinal fasciculus
- LSL, left superior longitudinal fasciculus
- LST, left superior longitudinal fasciculus – temporal
- LUN, left uncinate
- MD, mean diffusivity
- Neurodegeneration
- PTSD, post-traumatic stress disorder
- RAT, right anterior thalamic tract
- RCG, right cingulum
- RCH, right cingulum – Hippocampus
- RCS, right cortico-spinal tract
- RD, radial diffusivity
- RIF, right inferior fronto-occipital fasciculus
- RIL, right inferior longitudinal fasciculus
- ROC, receiver operating characteristic
- RSL, right superior longitudinal fasciculus
- RST, right superior longitudinal fasciculus – temporal
- RUN, right uncinate
- SP, splenium
- TBI, traumatic brain injury
- Traumatic brain injury
Collapse
Affiliation(s)
- Keith L. Main
- 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
| | - Salil Soman
- 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
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
| | - Ansgar Furst
- 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
| | - Art Noda
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Beatriz Hernandez
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Jennifer Kong
- War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States
| | - Jauhtai Cheng
- War Related Illness and Injury Study Center, Veterans Affairs, Palo Alto Health Care System (VAPAHCS), Palo Alto, CA, United States
| | - Jennifer K. Fairchild
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Joy Taylor
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Jerome Yesavage
- 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
| | - J. Wesson Ashford
- 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
| | - Helena Kraemer
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States
| | - Maheen M. Adamson
- 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
| |
Collapse
|