We present scilpy, an open-source Python library for diffusion magnetic resonance imaging (dMRI) and tractography. It provides a large collection of well-documented, user-friendly scripts that support nearly every stage of the dMRI processing pipeline, from preprocessing (such as denoising, registration, and local fiber reconstruction) to tractography and post-processing of tractograms, including connectivity and bundle analyses. Scilpy leverages the strength of DIPY, is well-tested through modern continuous integration practices, and is actively maintained.

Tractography is a powerful tool to study brain connectivity in vivo, but it is well known to suffer from an intrinsic trade-off between sensitivity and specificity. A critical - but usually underrated - parameter to choose that can heavily impact the quality of the estimates is the number of streamlines to be reconstructed for a given data set. In fact, sensitivity can be improved by generating more and more streamlines, as all real anatomical connections are likely reconstructed, but lots of false positives are inevitably introduced, too. Consequently, so-called tractography filtering techniques have become increasingly popular to get rid of these false positives and improve specificity. However, increasing number of streamlines introduces redundancy in tractography reconstructions, which may negatively impact the performance of filtering algorithms, especially those based on linear formulations. To address this problem, we introduce a novel streamlines representation, called "blurred streamlines", which drastically reduces the redundancy among streamlines by (i) clustering similar trajectories and (ii) spatially blurring the corresponding signal contributions. We tested the effectiveness of the blurred streamlines both on synthetic and in vivo data. Our results clearly show that this new representation is as accurate as state-of-the-art methods despite using only 5% of the input streamlines, thus significantly decreasing the computational complexity of filtering algorithms as well as storage requirements of the resulting reconstructions.

Tractography is a powerful tool for the investigation of the complex organization of the brain in vivo, as it allows inferring the macroscopic pathways of the major fiber bundles of the white matter based on non-invasive diffusion-weighted magnetic resonance imaging acquisitions. Despite this unique and compelling ability, some studies have exposed the poor anatomical accuracy of the reconstructions obtained with this technique and challenged its effectiveness for studying brain connectivity. In this work, we describe a novel method to readdress tractography reconstruction problem in a global manner by combining the strengths of so-called generative and discriminative strategies. Starting from an input tractogram, we parameterize the connections between brain regions following a bundle-based representation that allows to drastically reducing the number of parameters needed to model groups of fascicles. The parameters space is explored following an MCMC generative approach, while a discrimininative method is exploited to globally evaluate the set of connections which is updated according to Bayes' rule. Our results on both synthetic and real brain data show that the proposed solution, called bundle-o-graphy, allows improving the anatomical accuracy of the reconstructions while keeping the computational complexity similar to other state-of-the-art methods.

Background: Autosomal Recessive Spastic Ataxia of Charlevoix-Saguenay (ARSACS) and Spastic Paraplegia Type 7 (SPG7) are paradigmatic spastic ataxias (SPAX) with suggested white matter (WM) involvement. Aim of this work was to thoroughly disentangle the degree of WM involvement in these conditions, evaluating both macrostructure and microstructure via the analysis of diffusion MRI (dMRI) data. Material and methods: In this multi-center prospective study, ARSACS and SPG7 patients and Healthy Controls (HC) were enrolled, all undergoing a standardized dMRI protocol and a clinimetrics evaluation including the Scale for the Assessment and Rating of Ataxia (SARA). Differences in terms of WM volume or global microstructural WM metrics were probed, as well as the possible occurrence of a spatially defined microstructural WM involvement via voxel-wise analyses, and its correlation with patients’ clinical status. Results: Data of 37 ARSACS (M/F = 21/16; 33.4 ± 12.4 years), 37 SPG7 (M/F = 24/13; 55.7 ± 10.7 years), and 29 HC (M/F = 13/16; 42.1 ± 17.2 years) were analyzed. While in SPG7, only a mild mean microstructural damage was found compared to HC, ARSACS patients present a severe WM involvement, with a reduced global volume (p < 0.001), an alteration of all microstructural metrics (all with p < 0.001), without a spatially defined pattern of damage but with a prominent involvement of commissural fibers. Finally, in ARSACS, a correlation between microstructural damage and SARA scores was found (p = 0.004). Conclusion: In ARSACS, but not SPG7 patients, we observed a complex and multi-faced involvement of brain WM, with a clinically meaningful widespread loss of axonal and dendritic integrity, secondary demyelination and, overall, a reduction in cellularity and volume.

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized by motor and non-motor symptoms and widespread alterations in brain networks. Circulating biomarkers such as neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) reflect neuroaxonal damage and astroglial activation, respectively, but their relationship with large-scale brain connectivity remains poorly understood. Seventy-three PD patients and thirty-four healthy controls underwent diffusion magnetic resonance imaging. Whole-brain tractography was used to reconstruct structural connectivity networks, and graph-theoretical measures were derived. Serum NfL and GFAP levels were quantified, and their associations with network metrics and clinical variables were assessed. PD patients showed significant alterations in global and nodal network organization compared to controls. Higher NfL and GFAP levels were associated with reduced global clustering coefficient and efficiency, as well as increased path length and modularity. At the regional level, higher biomarker levels were associated with reduced network measures in the right thalamus and right cerebellar cortex. No significant associations were observed in healthy controls. These findings demonstrate that circulating biomarkers of neurodegeneration are linked to both global and regional disruptions of structural brain connectivity in PD, supporting the integration of blood-based biomarkers and connectomics to better characterize disease-related network alterations.