The myelin-weighted connectome in Parkinson’s disease

Background Even though Parkinson's disease (PD) is typically viewed as largely affecting gray matter, there is growing evidence that there are also structural changes in the white matter. Traditional connectomics methods that study PD may not be specific to underlying microstructural changes, such as myelin loss. Objective The primary objective of this study is to investigate the PD-induced changes in myelin content in the connections emerging from the basal ganglia and the brainstem. For the weighting of the connectome, we used the longitudinal relaxation rate as a biologically grounded myelin-sensitive metric. Methods We computed the myelin-weighted connectome in 35 healthy control subjects and 81 patients with PD. We used partial least squares to highlight the differences between patients with PD and healthy control subjects. Then, a ring analysis was performed on selected brainstem and subcortical regions to evaluate each node's potential role as an epicenter for disease propagation. Then, we used behavioral partial least squares to relate the myelin alterations with clinical scores. Results Most connections (~80%) emerging from the basal ganglia showed a reduced myelin content. The connections emerging from potential epicentral nodes (substantia nigra, nucleus basalis of Meynert, amygdala, hippocampus, and midbrain) showed significant decrease in the longitudinal relaxation rate (P < 0.05). This effect was not seen for the medulla and the pons. Conclusions The myelin-weighted connectome was able to identify alteration of the myelin content in PD in basal ganglia connections. This could provide a different view on the importance of myelination in neurodegeneration and disease progression. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Societ

qMRLab: Quantitative MRI analysis, under one umbrella

ABSTRACT: Magnetic resonance imaging (MRI) has revolutionized the way we look at the human body. However, conventional MR scanners are not measurement devices. They produce digital images represented by “shades of grey”, and the intensity of the shades depends on the way the images are acquired. This is why it is difficult to compare images acquired at different clinical sites, limiting the diagnostic, prognostic, and scientific potential of the technology. Quantitative MRI (qMRI) aims to overcome this problem by assigning units to MR images, ensuring that the values represent a measurable quantity that can be reproduced within and across sites. While the vision for quantitative MRI is to overcome site-dependent variations, this is still a challenge due to variability in the hardware and software used by MR vendors to produce quantitative MRI maps. Although qMRI has yet to enter mainstream clinical use, imaging scientists see great promise in the technique’s potential to characterize tissue microstructure. However, most qMRI tools for fundamental research are developed in-house and are difficult to port across sites, which in turn hampers their standardization, reproducibility, and widespread adoption. To tackle this problem, we developed qMRLab, an open-source software package that provides a wide selection of qMRI methods for data fitting, simulation and protocol optimization Figure 1. It not only brings qMRI under one umbrella, but also facilitates its use through documentation that features online executable notebooks, a user friendly graphical user interface (GUI), interactive tutorials and blog posts

Segmentation de la parole et codage a debit variable

SIGLEINIST T 77153 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

The R1-weighted connectome: complementing brain networks with a myelin-sensitive measure

International audienceMyelin plays a crucial role in how well information travels between brain regions. Complementing the structural connectome, obtained with diffusion MRI tractography, with a myelin-sensitive measure could result in a more complete model of structural brain connectivity and give better insight into white-matter myeloarchitecture. In this work we weight the connectome by the longitudinal relaxation rate (R1), a measure sensitive to myelin, and then we assess its added value by comparing it with connectomes weighted by the number of streamlines (NOS). Our analysis reveals differences between the two connectomes both in the distribution of their weights and the modular organization. Additionally, the rank-based analysis shows that R1 can be used to separate transmodal regions (responsible for higher-order functions) from unimodal regions (responsible for low-order functions). Overall, the R1-weighted connectome provides a different perspective on structural connectivity taking into account white matter myeloarchitecture

qMRLab

MRI dataset for qMRLab: http://qmrlab.readthedocs.io/en/master

Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI

Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more timeconsuming, Cartesian-grid scheme. Importantly, we show that simple pre-and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods