کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8688158 | 1580951 | 2017 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
DTI measures identify mild and moderate TBI cases among patients with complex health problems: A receiver operating characteristic analysis of U.S. veterans
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کلمات کلیدی
LSTLILRCSLCHLSLDAIRCHCorpus callosumrCGTBIRSTDTIRILLIFROCRSLLCGLCSaxial diffusivity - diffusivity محوریTraumatic brain injury - آسیب تروماتیک مغزDiffuse axonal injury - آسیب پذیری آسیب دیدهRUN - اجرا کنpost-traumatic stress disorder - اختلال استرس پس از ضربهPTSD - اختلال استرسی پس از ضایعه روانیradial diffusivity - انتشار شعاعیImaging - تصویربرداریdiffusion tensor imaging - تصویربرداری تانسور انتشارNeurodegeneration - تولید نوروژنیکConcussion - تکان مغزیAxon degeneration - دژنراسیون آکسونGenu - زانویLAT - سالSplenium - شکم بزرگLun - لونmean diffusivity - متوسط نفوذپذیریRat - موش صحراییfractional anisotropy - ناپیوستگی کسریRif - کد عکسreceiver operating characteristic - گیرنده عامل عامل
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
روانپزشکی بیولوژیکی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage: Clinical - Volume 16, 2017, Pages 1-16
Journal: NeuroImage: Clinical - Volume 16, 2017, Pages 1-16
نویسندگان
Keith L. Main, Salil Soman, Franco Pestilli, Ansgar Furst, Art Noda, Beatriz Hernandez, Jennifer Kong, Jauhtai Cheng, Jennifer K. Fairchild, Joy Taylor, Jerome Yesavage, J. Wesson Ashford, Helena Kraemer, Maheen M. Adamson,