Fiber-Flux Diffusion Density for White Matter Tracts Analysis: Application to Mild Anomalies Localization in Contact Sports Players

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 along-tract 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. Along-tract 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

Evaluating 35 Methods to Generate Structural Connectomes Using Pairwise Classification

There is no consensus on how to construct structural brain networks from diffusion MRI. How variations in pre-processing steps affect network reliability and its ability to distinguish subjects remains opaque. In this work, we address this issue by comparing 35 structural connectome-building pipelines. We vary diffusion reconstruction models, tractography algorithms and parcellations. Next, we classify structural connectome pairs as either belonging to the same individual or not. Connectome weights and eight topological derivative measures form our feature set. For experiments, we use three test-retest datasets from the Consortium for Reliability and Reproducibility (CoRR) comprised of a total of 105 individuals. We also compare pairwise classification results to a commonly used parametric test-retest measure, Intraclass Correlation Coefficient (ICC).Comment: Accepted for MICCAI 2017, 8 pages, 3 figure

Dynamic Changes in White Matter Abnormalities Correlate With Late Improvement and Deterioration Following TBI: A Diffusion Tensor Imaging Study.

OBJECTIVE: Traumatic brain injury (TBI) is not a single insult with monophasic resolution, but a chronic disease, with dynamic processes that remain active for years. We aimed to assess patient trajectories over the entire disease narrative, from ictus to late outcome. METHODS: Twelve patients with moderate-to-severe TBI underwent magnetic resonance imaging in the acute phase (within 1 week of injury) and twice in the chronic phase of injury (median 7 and 21 months), with some undergoing imaging at up to 2 additional time points. Longitudinal imaging changes were assessed using structural volumetry, deterministic tractography, voxel-based diffusion tensor analysis, and region of interest analyses (including corpus callosum, parasagittal white matter, and thalamus). Imaging changes were related to behavior. RESULTS: Changes in structural volumes, fractional anisotropy, and mean diffusivity continued for months to years postictus. Changes in diffusion tensor imaging were driven by increases in both axial and radial diffusivity except for the earliest time point, and were associated with changes in reaction time and performance in a visual memory and learning task (paired associates learning). Dynamic structural changes after TBI can be detected using diffusion tensor imaging and could explain changes in behavior. CONCLUSIONS: These data can provide further insight into early and late pathophysiology, and begin to provide a framework that allows magnetic resonance imaging to be used as an imaging biomarker of therapy response. Knowledge of the temporal pattern of changes in TBI patient populations also provides a contextual framework for assessing imaging changes in individuals at any given time point

Groupwise Structural Parcellation of the Cortex: A Sound Approach Based on Logistic Models

International audienceCurrent theories hold that brain function is highly related with long-range physical connections through axonal bundles, namely extrinsic connectivity. However, obtaining a groupwise cortical parcella-tion based on extrinsic connectivity remains challenging. Current par-cellation methods are computationally expensive; need tuning of several parameters or rely on ad-hoc constraints. Furthermore, none of these methods present a model for the cortical extrinsic connectivity. To tackle these problems, we propose a parsimonious model for the extrinsic con-nectivity and an efficient parcellation technique based on clustering of tractograms. Our technique allows the creation of single subject and groupwise parcellations of the whole cortex. The parcellations obtained with our technique are in agreement with anatomical and functional par-cellations in the literature. In particular, the motor and sensory cortex are subdivided in agreement with the human homunculus of Penfield. We illustrate this by comparing our resulting parcels with an anatomical atlas and the motor strip mapping included in the Human Connectome Project data

brainlife.io: A decentralized and open source cloud platform to support neuroscience research

Neuroscience research has expanded dramatically over the past 30 years by advancing standardization and tool development to support rigor and transparency. Consequently, the complexity of the data pipeline has also increased, hindering access to FAIR data analysis to portions of the worldwide research community. brainlife.io was developed to reduce these burdens and democratize modern neuroscience research across institutions and career levels. Using community software and hardware infrastructure, the platform provides open-source data standardization, management, visualization, and processing and simplifies the data pipeline. brainlife.io automatically tracks the provenance history of thousands of data objects, supporting simplicity, efficiency, and transparency in neuroscience research. Here brainlife.io's technology and data services are described and evaluated for validity, reliability, reproducibility, replicability, and scientific utility. Using data from 4 modalities and 3,200 participants, we demonstrate that brainlife.io's services produce outputs that adhere to best practices in modern neuroscience research

Supervised classification of white matter fibers based on neighborhood fiber orientation distributions using an ensemble of neural networks

White matter fibers constitute the main information transfer network of the brain and their accurate digital representation and classification is an important goal of neuroscience image computing. In current clinical practice, the reconstruction of desired fibers generally involves manual selection of regions of interest by an expert, which is time-consuming and subject to user bias, expertise and fatigue. Hence, automation of the process is desired. To that end, we propose a supervised classification approach that utilizes an ensemble of neural networks. Each streamline is represented by the fiber orientation distributions in its neighborhood, while the resolved fiber orientations are obtained by generalized q-sampling imaging (GQI) and a subsequent diffusion decomposition method. In order to make the supervised fiber classification succeed in a real scenario where a substantial portion of reconstructed fiber tracts contain spurious fibers, we present a way to create an “invalid” class label through a dedicated training set creation scheme with an ensemble of networks. The performance of the proposed classification method is demonstrated on major fiber pathways in the brainstem. 30 subjects from Human Connectome Project (HCP)’s publicly available “WU-Minn 500 Subjects + MEG2 dataset” are used as the dataset

VEGF isoforms and receptors expression throughout acute acetaminophen-induced liver injury and regeneration

Acetaminophen (APAP) is a widely-used analgesic and a known hepatotoxic agent. Vascular endothelial growth factor (VEGF) is a growth factor with multiple functional roles. VEGF plays an important role in angiogenesis and hepatic regeneration. The aim of this study was to determine the expression of VEGF isoforms and its receptors throughout liver regeneration after the administration of a toxic dose of APAP in rats. Ten groups of adult male rats received a dose of 3.5 g/kg b.w. of APAP per os. The rats were killed post administration at 0-288 h. Blood and liver tissue were extracted. Determination of serum transaminases and alkaline phophatase activities was performed. Liver injury and regeneration were assessed with hematoxylin-eosin specimens, morphometric analysis, hepatic thymidine kinase assay and Ki-67 expression. Reverse transcription-polymerase chain reaction and immunohistochemical methods were used for assessment of VEGF isoforms and receptors differential expression. High activities of aspartate aminotransferase were observed at 24 and 36 h with another peak of activity at 192 h post administration. Alanine aminotransferase was highest at 36 h. Alkaline phophatase was increased post 24 h being higher at 72,192 and 240 h. Centrilobular necrosis was observed at 48-72 h and thorough restoration of the liver microarchitecture was observed at 288 h. Liver regeneration lasted from 24-192 h according to the results from thymidine kinase activity and Ki-67 expression. VEGF and VEGF receptor-2 m-RNA levels presented with a three-peak pattern of expression at 12-24, 72-96 and 192-240 h post administration. Significant difference was noted between periportal and centrilobular immunohistochemical expression. VEGF proves to play a critical role during APAP-induced liver regeneration as it presents with three points of higher expression. The first two time points are associated with the initial inflammatory reaction to the noxious stimulus and the hepatocyte regenerative process where as the third one is indicative of the potential involvement of VEGF in processes of remodeling © 2007 Springer-Verlag