Repetitive mild traumatic brain injury in American football players has garnered increasing public attention following reports of chronic traumatic encephalopathy, a progressive tauopathy. While the mechanisms underlying repetitive mild traumatic brain injury-induced neurodegeneration are unknown and antemortem diagnostic tests are not available, neuropathology studies suggest a pathogenic role for microvascular injury, specifically blood–brain barrier dysfunction. Thus, our main objective was to demonstrate the effectiveness of a modified dynamic contrast-enhanced MRI approach we have developed to detect impairments in brain microvascular function. To this end, we scanned 42 adult male amateur American football players and a control group comprising 27 athletes practicing a non-contact sport and 26 non-athletes. MRI scans were also performed in 51 patients with brain pathologies involving the blood–brain barrier, namely malignant brain tumours, ischaemic stroke and haemorrhagic traumatic contusion. Based on data from prolonged scans, we generated maps that visualized the permeability value for each brain voxel. Our permeability maps revealed an increase in slow blood-to-brain transport in a subset of amateur American football players, but not in sex- and age-matched controls. The increase in permeability was region specific (white matter, midbrain peduncles, red nucleus, temporal cortex) and correlated with changes in white matter, which were confirmed by diffusion tensor imaging. Additionally, increased permeability persisted for months, as seen in players who were scanned both on- and off-season. Examination of patients with brain pathologies revealed that slow tracer accumulation characterizes areas surrounding the core of injury, which frequently shows fast blood-to-brain transport. Next, we verified our method in two rodent models: rats and mice subjected to repeated mild closed-head impact injury, and rats with vascular injury inflicted by photothrombosis. In both models, slow blood-to-brain transport was observed, which correlated with neuropathological changes. Lastly, computational simulations and direct imaging of the transport of Evans blue-albumin complex in brains of rats subjected to recurrent seizures or focal cerebrovascular injury suggest that increased cellular transport underlies the observed slow blood-to-brain transport. Taken together, our findings suggest dynamic contrast-enhanced-MRI can be used to diagnose specific microvascular pathology after traumatic brain injury and other brain pathologies.

We present DeepTract, a deep-learning framework for estimating white matter fibers orientation and streamline tractography. We adopt a data-driven approach for fiber reconstruction from diffusionweighted images (DWI), which does not assume a specific diffusion model. We use a recurrent neural network for mapping sequences of DWI values into probabilistic fiber orientation distributions. Based on these estimations, our model facilitates both deterministic and probabilistic streamline tractography. We quantitatively evaluate our method using the Tractometer tool, demonstrating competitive performance with state-of-theart classical and machine learning based tractography algorithms. We further present qualitative results of bundle-specific probabilistic tractography obtained using our method. The code is publicly available at: https

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We present the concept of fiber-flux density for locally quantifying white matter (WM) fiber bundles. By combining scalar diffusivity measures (e.g., fractional anisotropy) with fiber-flux measurements, we define new local descriptors called Fiber-Flux Diffusion Density (FFDD) vectors. Applying each descriptor throughout fiber bundles allows alongtract coupling of a specific diffusion measure with geometrical properties, such as fiber orientation and coherence. A key step in the proposed framework is the construction of an FFDD dissimilarity measure for sub-voxel alignment of fiber bundles, based on the fast marching method (FMM). The obtained aligned WM tract-profiles enable meaningful inter-subject comparisons and group-wise statistical analysis. We demonstrate our method using two different datasets of contact sports players. Alongtract pairwise comparison as well as group-wise analysis, with respect to non-player healthy controls, reveal significant and spatially-consistent FFDD anomalies. Comparing our method with along-tract FA analysis shows improved sensitivity to subtle structural anomalies in football players over standard FA measurements.

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