scholarly journals Tract-Based Bayesian Multivariate Analysis of Mild Traumatic Brain Injury

2014 ◽  
Vol 2014 ◽  
pp. 1-4 ◽  
Author(s):  
Yongkang Liu ◽  
Tianyao Wang ◽  
Xiao Chen ◽  
Jianhua Zhang ◽  
Guoxing Zhou ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Multivariate Analysis ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Graphical Model ◽  
Diffusion Tensor ◽  
Brain Regions ◽  
Neuroimaging Biomarkers ◽  
Normal Controls ◽  
Local Research

Purpose. Detecting brain regions characterizing mild traumatic brain injury (mTBI) by combining Tract-Based Spatial Statistics (TBSS) and Graphical-model-based Multivariate Analysis (GAMMA).Materials and Methods. This study included 39 mTBI patients and 28 normal controls. Local research ethics committee approved this prospective study. Diffusion-tensor imaging was performed in mTBI patients within 7 days of injury. Skeletonized fractional anisotropy (FA) maps were generated by using TBSS. Brain regions characterizing mTBI were detected by GAMMA.Results. Two clusters of lower frontal white matter FA were present in mTBI patients. We constructed classifiers based on FA values in these two clusters to differentiate mTBI and controls. The mean accuracy, sensitivity, and specificity, across five different classifiers, were 0.80, 0.94, and 0.61, respectively.Conclusions. Combining TBSS and GAMMA can detect neuroimaging biomarkers characterizing mTBI.

scholarly journals Machine Learning Classification of Mild Traumatic Brain Injury Using Whole-Brain Functional Activity: A Radiomics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xiaoping Luo ◽  
Dezhao Lin ◽  
Shengwei Xia ◽  
Dongyu Wang ◽  
Xinmang Weng ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Short Range ◽  
Low Frequency ◽  
Brain Regions ◽  
Classification Performance ◽  
Imaging Parameters ◽  
The Brain ◽  
Normal Controls

Objectives. To investigate the classification performance of support vector machine in mild traumatic brain injury (mTBI) from normal controls. Methods. Twenty-four mTBI patients (15 males and 9 females; mean age, 38.88 ± 13.33 years) and 24 age and sex-matched normal controls (13 males and 11 females; mean age, 40.46 ± 11.4 years) underwent resting-state functional MRI examination. Seven imaging parameters, including amplitude of low-frequency fluctuation (ALFF), fractional amplitude of low-frequency fluctuation (fALFF), regional homogeneity (ReHo), degree centrality (DC), voxel-mirrored homotopic connectivity (VMHC), long-range functional connectivity density (FCD), and short-range FCD, were entered into the classification model to distinguish the mTBI from normal controls. Results. The ability for any single imaging parameters to distinguish the two groups is lower than multiparameter combinations. The combination of ALFF, fALFF, DC, VMHC, and short-range FCD showed the best classification performance for distinguishing the two groups with optimal AUC value of 0.778, accuracy rate of 81.11%, sensitivity of 88%, and specificity of 75%. The brain regions with the highest contributions to this classification mainly include bilateral cerebellum, left orbitofrontal cortex, left cuneus, left temporal pole, right inferior occipital cortex, bilateral parietal lobe, and left supplementary motor area. Conclusions. Multiparameter combinations could improve the classification performance of mTBI from normal controls by using the brain regions associated with emotion and cognition.

170 Myelination and Diffusion Tensor Imaging Findings in Mild Traumatic Brain Injury

Neurosurgery ◽  
2013 ◽  
Vol 60 ◽  
pp. 176-177
Author(s):  
Heather Spader ◽  
Anna Ellermeier ◽  
Lindsay Walker ◽  
Jeffrey Rogg ◽  
Rees Cosgrove ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Diffusion Tensor Imaging ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Diffusion Tensor ◽  
Imaging Findings ◽  
And Diffusion

scholarly journals Acute mild traumatic brain injury is not associated with white matter change on diffusion tensor imaging

Brain ◽  
2014 ◽  
Vol 137 (7) ◽  
pp. 1876-1882 ◽  
Author(s):  
Tero Ilvesmäki ◽  
Teemu M. Luoto ◽  
Ullamari Hakulinen ◽  
Antti Brander ◽  
Pertti Ryymin ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Diffusion Tensor Imaging ◽  
Brain Injury ◽  
White Matter ◽  
Mild Traumatic Brain Injury ◽  
Diffusion Tensor ◽  
White Matter Change

scholarly journals Combat-related Mild Traumatic Brain Injury: Association between Baseline Diffusion-Tensor Imaging Findings and Long-term Outcomes

Radiology ◽  
2016 ◽  
Vol 280 (1) ◽  
pp. 212-219 ◽  
Author(s):  
Jeffrey B. Ware ◽  
Rosette C. Biester ◽  
Elizabeth Whipple ◽  
Keith M. Robinson ◽  
Richard J. Ross ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Diffusion Tensor Imaging ◽  
Brain Injury ◽  
Mild Traumatic Brain Injury ◽  
Diffusion Tensor ◽  
Imaging Findings ◽  
Long Term Outcomes

scholarly journals Diffusion Tensor Imaging of the Evolving Response to Mild Traumatic Brain Injury in Rats

2019 ◽  
Vol 13 ◽  
pp. 117906951985862 ◽  
Author(s):  
Wouter S Hoogenboom ◽  
Todd G Rubin ◽  
Kenny Ye ◽  
Min-Hui Cui ◽  
Kelsey C Branch ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Diffusion Tensor Imaging ◽  
Brain Injury ◽  
White Matter ◽  
Mild Traumatic Brain Injury ◽  
Diffusion Tensor ◽  
Axonal Injury ◽  
Mean Diffusivity ◽  
Multiple Time ◽  
Post Injury

Mild traumatic brain injury (mTBI), also known as concussion, is a serious public health challenge. Although most patients recover, a substantial minority suffers chronic disability. The mechanisms underlying mTBI-related detrimental effects remain poorly understood. Although animal models contribute valuable preclinical information and improve our understanding of the underlying mechanisms following mTBI, only few studies have used diffusion tensor imaging (DTI) to study the evolution of axonal injury following mTBI in rodents. It is known that DTI shows changes after human concussion and the role of delineating imaging findings in animals is therefore to facilitate understanding of related mechanisms. In this work, we used a rodent model of mTBI to investigate longitudinal indices of axonal injury. We present the results of 45 animals that received magnetic resonance imaging (MRI) at multiple time points over a 2-week period following concussive or sham injury yielding 109 serial observations. Overall, the evolution of DTI metrics following concussive or sham injury differed by group. Diffusion tensor imaging changes within the white matter were most noticeable 1 week following injury and returned to baseline values after 2 weeks. More specifically, we observed increased fractional anisotropy in combination with decreased radial diffusivity and mean diffusivity, in the absence of changes in axial diffusivity, within the white matter of the genu corpus callosum at 1 week post-injury. Our study shows that DTI can detect microstructural white matter changes in the absence of gross abnormalities as indicated by visual screening of anatomical MRI and hematoxylin and eosin (H&E)-stained sections in a clinically relevant animal model of mTBI. Whereas additional histopathologic characterization is required to better understand the neurobiological correlates of DTI measures, our findings highlight the evolving nature of the brain’s response to injury following concussion.

scholarly journals Elevated Intraindividual Variability in Executive Functions and Associations with White Matter Microstructure in Veterans with Mild Traumatic Brain Injury

2020 ◽  
pp. 1-10
Author(s):  
Scott F. Sorg ◽  
Victoria C. Merritt ◽  
Alexandra L. Clark ◽  
Madeleine L. Werhane ◽  
Kelsey A. Holiday ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
White Matter ◽  
Executive Functions ◽  
Mild Traumatic Brain Injury ◽  
Diffusion Tensor ◽  
Intraindividual Variability ◽  
Ptsd Symptoms ◽  
White Matter Microstructure ◽  
History Of

Abstract Objective: We examined whether intraindividual variability (IIV) across tests of executive functions (EF-IIV) is elevated in Veterans with a history of mild traumatic brain injury (mTBI) relative to military controls (MCs) without a history of mTBI. We also explored relationships among EF-IIV, white matter microstructure, and posttraumatic stress disorder (PTSD) symptoms. Method: A total of 77 Veterans (mTBI = 43, MCs = 34) completed neuropsychological testing, diffusion tensor imaging (DTI), and PTSD symptom ratings. EF-IIV was calculated as the standard deviation across six tests of EF, along with an EF-Mean composite. DSI Studio connectometry analysis identified white matter tracts significantly associated with EF-IIV according to generalized fractional anisotropy (GFA). Results: After adjusting for EF-Mean and PTSD symptoms, the mTBI group showed significantly higher EF-IIV than MCs. Groups did not differ on EF-Mean after adjusting for PTSD symptoms. Across groups, PTSD symptoms significantly negatively correlated with EF-Mean, but not with EF-IIV. EF-IIV significantly negatively correlated with GFA in multiple white matter pathways connecting frontal and more posterior regions. Conclusions: Veterans with mTBI demonstrated significantly greater IIV across EF tests compared to MCs, even after adjusting for mean group differences on those measures as well as PTSD severity. Findings suggest that, in contrast to analyses that explore effects of mean performance across tests, discrepancy analyses may capture unique variance in neuropsychological performance and more sensitively capture cognitive disruption in Veterans with mTBI histories. Importantly, findings show that EF-IIV is negatively associated with the microstructure of white matter pathways interconnecting cortical regions that mediate executive function and attentional processes.

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