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

Comparison of Distances for Supervised Segmentation of White Matter Tractography

Tractograms are mathematical representations of the main paths of axons within the white matter of the brain, from diffusion MRI data. Such representations are in the form of polylines, called streamlines, and one streamline approximates the common path of tens of thousands of axons. The analysis of tractograms is a task of interest in multiple fields, like neurosurgery and neurology. A basic building block of many pipelines of analysis is the definition of a distance function between streamlines. Multiple distance functions have been proposed in the literature, and different authors use different distances, usually without a specific reason other than invoking the "common practice". To this end, in this work we want to test such common practices, in order to obtain factual reasons for choosing one distance over another. For these reasons, in this work we compare many streamline distance functions available in the literature. We focus on the common task of automatic bundle segmentation and we adopt the recent approach of supervised segmentation from expert-based examples. Using the HCP dataset, we compare several distances obtaining guidelines on the choice of which distance function one should use for supervised bundle segmentation

Improving the reliability of network metrics in structural brain networks by integrating different network weighting strategies into a single graph

Structural brain networks estimated from diffusion MRI (dMRI) via tractography have been widely studied in healthy controls and in patients with neurological and psychiatric diseases. However, few studies have addressed the reliability of derived network metrics both node-specific and network-wide. Different network weighting strategies (NWS) can be adopted to weight the strength of connection between two nodes yielding structural brain networks that are almost full-weighted. Here, we scanned 5 healthy participants 5 times each, using a diffusion-weighted MRI protocol and computed edges between 90 regions of interest (ROIs) from the AAL template. The edges were weighted according to nine different methods.We propose a linear combination of these nine NWS into a single graph using an appropriate diffusion distance metric. We refer to the resulting weighted graph as an integrated weighted structural brain network (ISWBN). Additionally, we consider a topological filtering scheme that maximizes the information flow in the brain network under the constraint of the overall cost of the surviving connections. We compared each of the nine NWS and the ISWBN based on the improvement of : a) intra-class correlation coefficient (ICC) of well-known network metrics, both node-wise and per network level; and b) the recognition accuracy of each subject over the rest of the cohort, as an attempt to access the uniqueness of the structural brain network for each subject; after first applying our proposed topological filtering scheme. Based on a threshold that the network-level ICC should be > 0.90, our findings revealed six out of nine NWS lead to unreliable results at the network-level, while all nine NWS were unreliable at the node-level. In comparison, our proposed ISWBN performed as well as the best-performing individual NWS at the network-level, and the ICC was higher compared to all individual NWS at the node-level. Importantly, both network- and node-wise ICCs of network metrics derived from the topologically filtered ISBWN(ISWBNTF), were further improved compared to non-filtered ISWBN. Finally, in the recognition accuracy tests, we assigned each single ISWBNTF to the correct subject. Overall, these findings suggest that the proposed methodology results in improved characterisation of genuine between-subject differences in connectivit

Mapping hybrid functional-structural connectivity traits in the human connectome

One of the crucial questions in neuroscience is how a rich functional repertoire of brain states relates to its underlying structural organization. How to study the associations between these structural and functional layers is an open problem that involves novel conceptual ways of tackling this question. We here propose an extension of the Connectivity Independent Component Analysis (connICA) framework, to identify joint structural-functional connectivity traits. Here, we extend connICA to integrate structural and functional connectomes by merging them into common hybrid connectivity patterns that represent the connectivity fingerprint of a subject. We test this extended approach on the 100 unrelated subjects from the Human Connectome Project. The method is able to extract main independent structural-functional connectivity patterns from the entire cohort that are sensitive to the realization of different tasks. The hybrid connICA extracted two main task-sensitive hybrid traits. The first, encompassing the within and between connections of dorsal attentional and visual areas, as well as fronto-parietal circuits. The second, mainly encompassing the connectivity between visual, attentional, DMN and subcortical networks. Overall, these findings confirms the potential ofthe hybrid connICA for the compression of structural/functional connectomes into integrated patterns from a set of individual brain networks.Comment: article: 34 pages, 4 figures; supplementary material: 5 pages, 5 figure

Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset?

White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process

Structural and Functional Network-Level Reorganization in the Coding of Auditory Motion Directions and Sound Source Locations in the Absence of Vision

Epub 2022 May 2hMT+/V5 is a region in the middle occipitotemporal cortex that responds preferentially to visual motion in sighted people. In cases of early visual deprivation, hMT+/V5 enhances its response to moving sounds. Whether hMT+/V5 contains information about motion directions and whether the functional enhancement observed in the blind is motion specific, or also involves sound source location, remains unsolved. Moreover, the impact of this cross-modal reorganization of hMT+/V5 on the regions typically supporting auditory motion processing, like the human planum temporale (hPT), remains equivocal. We used a combined functional and diffusion-weighted MRI approach and individual in-ear recordings to study the impact of early blindness on the brain networks supporting spatial hearing in male and female humans. Whole-brain univariate analysis revealed that the anterior portion of hMT+/V5 responded to moving sounds in sighted and blind people, while the posterior portion was selective to moving sounds only in blind participants. Multivariate decoding analysis revealed that the presence of motion direction and sound position information was higher in hMT+/V5 and lower in hPT in the blind group. While both groups showed axis-of-motion organization in hMT+/V5 and hPT, this organization was reduced in the hPT of blind people. Diffusion-weighted MRI revealed that the strength of hMT+/V5-hPT connectivity did not differ between groups, whereas the microstructure of the connections was altered by blindness. Our results suggest that the axis-of-motion organization of hMT+/V5 does not depend on visual experience, but that congenital blindness alters the response properties of occipitotemporal networks supporting spatial hearing in the sighted.SIGNIFICANCE STATEMENT Spatial hearing helps living organisms navigate their environment. This is certainly even more true in people born blind. How does blindness affect the brain network supporting auditory motion and sound source location? Our results show that the presence of motion direction and sound position information was higher in hMT+/V5 and lower in human planum temporale in blind relative to sighted people; and that this functional reorganization is accompanied by microstructural (but not macrostructural) alterations in their connections. These findings suggest that blindness alters cross-modal responses between connected areas that share the same computational goals.The project was funded in part by a European Research Council starting grant MADVIS (Project 337573) awarded to O.C., the Belgian Excellence of Science (EOS) program (Project 30991544) awarded to O.C., a Flagship ERA-NET grant SoundSight (FRS-FNRS PINT-MULTI R.8008.19) awarded to O.C., and by the European Union Horizon 2020 research and innovation program under the Marie Skłodowska-Curie Grant Agreement No. 701250 awarded to V.O. Computational resources have been provided by the supercomputing facilities of the Université catholique de Louvain (CISM/UCL) and the Consortium des Équipements de Calcul Intensif en Fédération Wallonie Bruxelles (CÉCI) funded by the Fond de la Recherche Scientifique de Belgique (F.R.S.-FNRS) under convention 2.5020.11 and by the Walloon Region. A.G.-A. is supported by the Wallonie Bruxelles International Excellence Fellowship and the FSR Incoming PostDoc Fellowship by Université Catholique de Louvain. O.C. is a research associate, C.B. is postdoctoral researcher, and M.R. is a research fellow at the Fond National de la Recherche Scientifique de Belgique (FRS-FNRS)