Association between superior longitudinal fasciculus, motor recovery, and motor outcome after stroke: a cohort study.

Introduction: Parieto-frontal interactions are mediated by the superior longitudinal fasciculus (SLF) and are crucial to integrate visuomotor information and mediate fine motor control. In this study, we aimed to characterize the relation of white matter integrity of both parts of the SLF (SLF I and SLF II) to both motor outcome and recovery and its evolution over time in stroke patients with upper limb motor deficits. Materials and methods: Fractional anisotropy (FA) values over the SLF I, SLF II, and corticospinal tract (CST) and upper limb motor performance evaluated by both the upper limb Fugl-Meyer Assessment score and maximum grip strength were measured for 16 patients at 3 weeks, 6 weeks, and 12 weeks poststroke. FA changes were assessed over time using repeated-measures Friedman ANOVA, and correlations between motor recovery, motor outcome at 12 weeks, and FA values in the CST, SLF I, and SLF II at 3 weeks were performed using Spearman's rank-order correlation. Results: FA values in the affected hemisphere's SLF I and SLF II at 3 weeks correlated with motor recovery at 12 weeks when assessed by the Fugl-Meyer Assessment for upper limb extremity (rho: 0.502, p: 0.04 and rho: 0.510, p: 0.04, respectively) but not when assessed by grip strength. FA values in the SLF I and SLF II were not correlated with motor outcomes. FA values in the SLF II in the affected hemisphere changed significantly over time (p: 0.016). Conclusion: Both SLF I and SLF II appeared to participate in poststroke motor recovery of complex movements but not in the motor outcome. These results argue that visually/spatially oriented motor tasks as well as more complex motor tasks using parietal associative areas should be used for poststroke rehabilitation strategies.

Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies.

We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

Reproducible Evaluation of Diffusion MRI Features for Automatic Classification of Patients with Alzheimer's Disease.

Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of Alzheimer's disease. However, classification performance obtained with different approaches is difficult to compare because of variations in components such as input data, participant selection, image preprocessing, feature extraction, feature rescaling (FR), feature selection (FS) and cross-validation (CV) procedures. Moreover, these studies are also difficult to reproduce because these different components are not readily available. In a previous work (Samper-González et al. 2018), we propose an open-source framework for the reproducible evaluation of AD classification from T1-weighted (T1w) MRI and PET data. In the present paper, we first extend this framework to diffusion MRI data. Specifically, we add: conversion of diffusion MRI ADNI data into the BIDS standard and pipelines for diffusion MRI preprocessing and feature extraction. We then apply the framework to compare different components. First, FS has a positive impact on classification results: highest balanced accuracy (BA) improved from 0.76 to 0.82 for task CN vs AD. Secondly, voxel-wise features generally gives better performance than regional features. Fractional anisotropy (FA) and mean diffusivity (MD) provided comparable results for voxel-wise features. Moreover, we observe that the poor performance obtained in tasks involving MCI were potentially caused by the small data samples, rather than by the data imbalance. Furthermore, no extensive classification difference exists for different degree of smoothing and registration methods. Besides, we demonstrate that using non-nested validation of FS leads to unreliable and over-optimistic results: 5% up to 40% relative increase in BA. Lastly, with proper FR and FS, the performance of diffusion MRI features is comparable to that of T1w MRI. All the code of the framework and the experiments are publicly available: general-purpose tools have been integrated into the Clinica software package ( www.clinica.run ) and the paper-specific code is available at: https://github.com/aramis-lab/AD-ML .

Novel charged sodium and calcium channel inhibitor active against neurogenic inflammation.

Voltage-dependent sodium and calcium channels in pain-initiating nociceptor neurons are attractive targets for new analgesics. We made a permanently charged cationic derivative of an N-type calcium channel-inhibitor. Unlike cationic derivatives of local anesthetic sodium channel blockers like QX-314, this cationic compound inhibited N-type calcium channels more effectively with extracellular than intracellular application. Surprisingly, the compound is also a highly effective sodium channel inhibitor when applied extracellularly, producing more potent inhibition than lidocaine or bupivacaine. The charged inhibitor produced potent and long-lasting analgesia in mouse models of incisional wound and inflammatory pain, inhibited release of the neuropeptide calcitonin gene-related peptide (CGRP) from dorsal root ganglion neurons, and reduced inflammation in a mouse model of allergic asthma, which has a strong neurogenic component. The results show that some cationic molecules applied extracellularly can powerfully inhibit both sodium channels and calcium channels, thereby blocking both nociceptor excitability and pro-inflammatory peptide release.

Amyloidosis and neurodegeneration result in distinct structural connectivity patterns in mild cognitive impairment.

Alzheimer's disease (AD) is increasingly considered as a disconnection syndrome. Previous studies of the structural connectome in early AD stages have focused on mild cognitive impaired subjects (MCI), considering them as a homogeneous group. We studied 168 subjects from the Alzheimer's Disease Neuroimaging Initiative database (116 MCI and 52 cognitively normal subjects). Biomarker-based stratification using amyloid biomarkers (AV45 PET) and neurodegeneration biomarkers (MRI and FDG PET) led to 4 subgroups based on amyloid positivity (A+/-) and neurodegeneration positivity (N+/-): A-N-, A+N-, A-N+, and A+N+. Using diffusion MRI, we showed that both MCI A-N+ and MCI A+N+ subjects displayed an alteration of the white matter in the fornix and a significant bihemispheric network of decreased connections. These network alterations in MCI A+N+ are stronger and more focal than those of MCI A-N+. Only MCI A+N+ subjects exhibited specific changes in hippocampal connectivity and an AD-like alteration pattern. Our results indicate that the connectome disintegration pattern of MCI subgroups differ with respect to brain amyloid and neurodegeneration. Each of these 2 AD biomarkers induces a connectome alteration that is maximal when they coexist.