Assessment of White Matter Injury and Outcome in Severe Brain Trauma. A Prospective Multicenter Cohort

Abstract

Background: Existing methods to predict recovery after severe traumatic brain injury lack accuracy. The aim of this study is to determine the prognostic value of quantitative diffusion tensor imaging (DTI).

Long-Term White Matter Changes after Severe Traumatic Brain Injury: A 5-Year Prospective Cohort

“…However, a recent study has indicated variations in apparent diffusion coefficients and FA values when images are acquired on different scanners or when different acquisition protocols are used. 19 Therefore, we implemented a normalization step, which makes our estimates of DTI parameters comparable with results found in other groups despite slightly different MR imaging parameters. We believe that this approach helps to overcome data heterogeneity in quantitative DTI studies and can be an important step in clinical application of quantitative DTI measures.…”

“…23 This atlas consists of 48 white matter tracts. Because some of these ROIs are very small and are subject to partial volume effects, we merged the initial 48 white matter tracts into 20 larger regions as published previously 19,24 (in posterior fossa: 1-middle cerebellar peduncle; 2-anterior brain stem; 3-posterior brain stem; 4-right cerebral peduncle; 5-left cerebral peduncle; in deep brain: 6-genu of the corpus callosum; 7-body of the corpus callosum; 8-splenium of the corpus callosum; 9-right side anterior arm of the internal capsule; 10-left side anterior arm of the internal capsule; 11-right side posterior arm of the internal cap-sule; 12-left side posterior arm of the internal capsule; in superficial brain regions: 13-right stratum sagittale; 14-left stratum sagittale; 15-right superior longitudinal fasciculus; 16-left superior longitudinal fasciculus; 17-right external capsule; 18-left external capsule; 19-right corona radiata; 20-left corona radiata). The regional DTI parameter extraction consisted of 3 steps: a nonlinear registration of the FA map to a template (provided by FSL), a projection of FA onto the FA template skeleton representing the centers of all tracts (also provided by FSL), and averaging of FA measures within the 20 ROIs restricted to the skeleton.…”

“…We have constituted a very large dataset of reference values for our scanner that we use routinely for normalization in our clinical workflow and in previous studies. 19,24 For this study, the raw FA, MD, L1, and Lt values in each of the 20 preselected ROIs of both patients and control participants were divided by the mean values of the corresponding reference group. Further details of MR image processing and normalization are provided in a previous publication.…”

Disorders of consciousness after acquired brain injury: the state of the science

“…Older studies have shown the possible prognostic value of 'classic' structural MRI sequences to predict DOC outcome; for example, the presence of corpus callosum and dorsolateral brainstem lesions correlates with lack of recovery at the group level. 63 However, recently developed quantitative diffusion tensor imaging (DTI) techniques, which permit assessment of structural white matter damage, have been shown to outperform clinical markers in predicting 1-year functional outcome at the individual-patient level in patients with traumatic 64 or anoxic 65 brain injury. In our view, DTI-MRI techniques offer a unique opportunity to quantify the structural integrity of the white matter, and can also quantify the primary and secondary axonal damage encountered in DOC.…”

A Pilot Study Evaluating Presurgery Neuroanatomical Biomarkers for Postoperative Cognitive Decline After Total Knee Arthroplasty in Older Adults

“…Diffusion-weighted sequences allow quantification of tissue integrity before changes are seen on traditional clinical sequences (89). Researchers have shown that tensor-based algorithms have relevancy for understanding one-year survival after major cardiac and brain trauma (90,91), as well as delirium (92). Resting and functional task-based sequences can be incorporated for understanding neuronal network risk profiles.…”