A concerted effort to tackle the global health problem posed by traumatic brain injury (TBI) is long overdue. TBI is a public health challenge of vast, but insufficiently recognised, proportions. Worldwide, more than 50 million people have a TBI each year, and it is estimated that about half the world's population will have one or more TBIs over their lifetime. TBI is the leading cause of mortality in young adults and a major cause of death and disability across all ages in all countries, with a disproportionate burden of disability and death occurring in low-income and middle-income countries (LMICs). It has been estimated that TBI costs the global economy approximately $US400 billion annually. Deficiencies in prevention, care, and research urgently need to be addressed to reduce the huge burden and societal costs of TBI. This Commission highlights priorities and provides expert recommendations for all stakeholders—policy makers, funders, health-care professionals, researchers, and patient representatives—on clinical and research strategies to reduce this growing public health problem and improve the lives of people with TBI.Additional co-authors: Endre Czeiter, Marek Czosnyka, Ramon Diaz-Arrastia, Jens P Dreier, Ann-Christine Duhaime, Ari Ercole, Thomas A van Essen, Valery L Feigin, Guoyi Gao, Joseph Giacino, Laura E Gonzalez-Lara, Russell L Gruen, Deepak Gupta, Jed A Hartings, Sean Hill, Ji-yao Jiang, Naomi Ketharanathan, Erwin J O Kompanje, Linda Lanyon, Steven Laureys, Fiona Lecky, Harvey Levin, Hester F Lingsma, Marc Maegele, Marek Majdan, Geoffrey Manley, Jill Marsteller, Luciana Mascia, Charles McFadyen, Stefania Mondello, Virginia Newcombe, Aarno Palotie, Paul M Parizel, Wilco Peul, James Piercy, Suzanne Polinder, Louis Puybasset, Todd E Rasmussen, Rolf Rossaint, Peter Smielewski, Jeannette Söderberg, Simon J Stanworth, Murray B Stein, Nicole von Steinbüchel, William Stewart, Ewout W Steyerberg, Nino Stocchetti, Anneliese Synnot, Braden Te Ao, Olli Tenovuo, Alice Theadom, Dick Tibboel, Walter Videtta, Kevin K W Wang, W Huw Williams, Kristine Yaffe for the InTBIR Participants and Investigator

We report evidence of longitudinal changes in cognitive functioning and cerebral WM integrity after chemotherapy as well as an association between both.

Purpose:To assess the diagnostic accuracy of diffusion kurtosis magnetic resonance imaging parameters in grading gliomas. Materials and Methods:The institutional review board approved this prospective study, and informed consent was obtained from all patients. Diffusion parameters-mean diffusivity (MD), fractional anisotropy (FA), mean kurtosis, and radial and axial kurtosis-were compared in the solid parts of 17 high-grade gliomas and 11 low-grade gliomas (P , .05 significance level, Mann-Whitney-Wilcoxon test, Bonferroni correction). MD, FA, mean kurtosis, radial kurtosis, and axial kurtosis in solid tumors were also normalized to the corresponding values in contralateral normal-appearing white matter (NAWM) and the contralateral posterior limb of the internal capsule (PLIC) after age correction and were compared among tumor grades. Results:Mean, radial, and axial kurtosis were significantly higher in high-grade gliomas than in low-grade gliomas (P = .02, P = .015, and P = .01, respectively). FA and MD did not significantly differ between glioma grades. All values, except for axial kurtosis, that were normalized to the values in the contralateral NAWM were significantly different between high-grade and low-grade gliomas (mean kurtosis, P = .02; radial kurtosis, P = .03; FA, P = .025; and MD, P = .03). When values were normalized to those in the contralateral PLIC, none of the considered parameters showed significant differences between high-grade and low-grade gliomas. The highest sensitivity and specificity for discriminating between high-grade and low-grade gliomas were found for mean kurtosis (71% and 82%, respectively) and mean kurtosis normalized to the value in the contralateral NAWM (100% and 73%, respectively). Optimal thresholds for mean kurtosis and mean kurtosis normalized to the value in the contralateral NAWM for differentiating high-grade from low-grade gliomas were 0.52 and 0.51, respectively. Conclusion:There were significant differences in kurtosis parameters between high-grade and low-grade gliomas; hence, better separation was achieved with these parameters than with conventional diffusion imaging parameters.q RSNA, 2012 1

In November 2017, the Lancet Neurology Commission on Traumatic Brain Injury (TBI) highlighted existing deficiencies in epidemiology, patient characterization, identifying best practice, outcome assessment, and evidence generation. The Commission concluded that C needed to address deficiencies in prevention , and made a recommendation for large collaborative studies which could provide the framework for precision medicine and comparative effectiveness research (CER).

With diffusion tensor imaging, the diffusion of water molecules through brain structures is quantified by parameters, which are estimated assuming monoexponential diffusion-weighted signal attenuation. The estimated diffusion parameters, however, depend on the diffusion weighting strength, the b-value, which hampers the interpretation and comparison of various diffusion tensor imaging studies. In this study, a likelihood ratio test is used to show that the diffusion kurtosis imaging model provides a more accurate parameterization of both the Gaussian and non-Gaussian diffusion component compared with diffusion tensor imaging. As a result, the diffusion kurtosis imaging model provides a b-value-independent estimation of the widely used diffusion tensor parameters as demonstrated with diffusionweighted rat data, which was acquired with eight different b-values, uniformly distributed in a range of [0,2800 sec/mm 2 ]. In addition, the diffusion parameter values are significantly increased in comparison to the values estimated with the diffusion tensor imaging model in all major rat brain structures. As incorrectly assuming additive Gaussian noise on the diffusionweighted data will result in an overestimated degree of nonGaussian diffusion and a b-value-dependent underestimation of diffusivity measures, a Rician noise model was used in this study. Diffusion tensor magnetic resonance imaging (DTI) is an important medical imaging modality in neuroscience research, because it allows the study of the complex network of myelinated axons, in vivo and noninvasively (1,2). In DTI, the diffusion of water molecules through brain structures is mathematically described by a second order 3D diffusion tensor (DT). It is generally accepted that the first eigenvector of the tensor, corresponding to the direction of maximal diffusion, is aligned with the underlying fiber structures. Furthermore, the diffusion is often quantified with diffusion parameters (i.e., fractional anisotropy (FA) and mean (MD), radial (D ⊥ ) and axial (D ) diffusivity), which provide insight in the organization, structural integrity, and development of white matter (WM) structures of the normal and pathological brain (3-9).In DTI, the diffusion of water molecules along a certain gradient direction is assumed to occur in an unrestricted environment. Consequently, the molecules' probability of diffusing from one location to another in a given time is described by a Gaussian distribution of which the standard deviation relates to the apparent diffusion coefficient (ADC). As a result, the normalized diffusion-weighted signal that is measured along a certain axis can be described by a monoexponential function; the exponent equals the ADC, weighted by the diffusion weighting strength that is given by the b-value. Several DTI studies, however, reported that the estimation of diffusion parameters depends on the b-value that is used during data acquisition. Therefore, the comparison and interpretation of various DTI studies are hampered. Jones and Basser (10) and Ande...

A subgroup of patients with breast cancer suffers from mild cognitive impairment after chemotherapy. To uncover the neural substrate of these mental complaints, we examined cerebral white matter (WM) integrity after chemotherapy using magnetic resonance diffusion tensor imaging (DTI) in combination with detailed cognitive assessment. Postchemotherapy breast cancer patients (n = 17) and matched healthy controls (n = 18) were recruited for DTI and neuropsychological testing, including the self-report cognitive failure questionnaire (CFQ). Differences in DTI WM integrity parameters [fractional anisotropy (FA) and mean diffusivity (MD)] between patients and healthy controls were assessed using a voxel-based two-sample-t-test. In comparison with healthy controls, the patient group demonstrated decreased FA in frontal and temporal WM tracts and increased MD in frontal WM. These differences were also confirmed when comparing this patient group with an additional control group of nonchemotherapy-treated breast cancer patients (n = 10). To address the heterogeneity observed in cognitive function after chemotherapy, we performed a voxel-based correlation analysis between FA values and individual neuropsychological test scores. Significant correlations of FA with neuropsychological tests covering the domain of attention and processing/psychomotor speed were found in temporal and parietal WM tracts. Furthermore, CFQ scores correlated negatively in frontal and parietal WM. These studies show that chemotherapy seems to affect WM integrity and that parameters derived from DTI have the required sensitivity to quantify neural changes related to chemotherapy-induced mild cognitive impairment.

The location and extent of white matter lesions on magnetic resonance imaging (MRI) are important criteria for diagnosis, follow-up and prognosis of multiple sclerosis (MS). Clinical trials have shown that quantitative values, such as lesion volumes, are meaningful in MS prognosis. Manual lesion delineation for the segmentation of lesions is, however, time-consuming and suffers from observer variability. In this paper, we propose MSmetrix, an accurate and reliable automatic method for lesion segmentation based on MRI, independent of scanner or acquisition protocol and without requiring any training data. In MSmetrix, 3D T1-weighted and FLAIR MR images are used in a probabilistic model to detect white matter (WM) lesions as an outlier to normal brain while segmenting the brain tissue into grey matter, WM and cerebrospinal fluid. The actual lesion segmentation is performed based on prior knowledge about the location (within WM) and the appearance (hyperintense on FLAIR) of lesions. The accuracy of MSmetrix is evaluated by comparing its output with expert reference segmentations of 20 MRI datasets of MS patients. Spatial overlap (Dice) between the MSmetrix and the expert lesion segmentation is 0.67 ± 0.11. The intraclass correlation coefficient (ICC) equals 0.8 indicating a good volumetric agreement between the MSmetrix and expert labelling. The reproducibility of MSmetrix' lesion volumes is evaluated based on 10 MS patients, scanned twice with a short interval on three different scanners. The agreement between the first and the second scan on each scanner is evaluated through the spatial overlap and absolute lesion volume difference between them. The spatial overlap was 0.69 ± 0.14 and absolute total lesion volume difference between the two scans was 0.54 ± 0.58 ml. Finally, the accuracy and reproducibility of MSmetrix compare favourably with other publicly available MS lesion segmentation algorithms, applied on the same data using default parameter settings.

A computational framework to obtain an accurate quantification of the Gaussian and non-Gaussian component of water molecules' diffusion through brain tissues with diffusion kurtosis imaging, is presented. The diffusion kurtosis imaging model quantifies the kurtosis, the degree of non-Gaussianity, on a direction dependent basis, constituting a higher order diffusion kurtosis tensor, which is estimated in addition to the well-known diffusion tensor. To reconcile with the physical phenomenon of molecular diffusion, both tensor estimates should lie within a physically acceptable range. Otherwise, clinically and artificially significant changes in diffusion (kurtosis) parameters might be confounded. To guarantee physical relevance, we here suggest to estimate both diffusional tensors by maximizing the joint likelihood function of all Rician distributed diffusion weighted images given the diffusion kurtosis imaging model while imposing a set of nonlinear constraints. As shown in this study, correctly accounting for the Rician noise structure is necessary to avoid significant overestimation of the kurtosis values. The performance of the constrained estimator was evaluated and compared to more commonly used strategies during simulations. Human brain data were used to emphasize the need for constrained estimators as not imposing the constraints give rise to constraint violations in about 70% of the brain voxels. Magn Reson Med 66:678-686, 2011.