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Example code for the multivariate template creation process used for Myelin imaging in the central nervous system: Comparison of multi-echo T2 relaxation and steady-state approaches

LORIS is an open-source data management platform for neuroscience research and data sharing, built by the McGill Centre for Integrative Neuroscience (MCIN.ca) at the Montreal Neurological Institute-Hospital and led by Dr. Alan Evans and Samir Das. For more information on LORIS -- * Visit LORIS.ca * Fork github.com/aces/loris * Try Demo.loris.ca LORIS (Longitudinal Online Research and Imaging System) supports many Open Science projects, storing and processing and sharing behavioural, clinical, neuroimaging, electrophysiology and genetic data. LORIS makes it easy to manage large datasets acquired over time in a longitudinal study, or at different locations in a large multi-site study.

{"references": ["Bautista, T. et al. (2021) 'Removal of Gibbs ringing artefacts for 3D acquisitions using subvoxel shifts', in Proc. Intl. Soc. Mag. Reson. Med., p. 3535.", "Chakravarty, M. M. et al. (2013) 'Performing label-fusion-based segmentation using multiple automatically generated templates', Human Brain Mapping. doi: 10.1002/hbm.22092.", "Eckstein, K. et al. (2018) 'Computationally Efficient Combination of Multi-channel Phase Data From Multi-echo Acquisitions (ASPIRE)', Magnetic Resonance in Medicine, 79(6), pp. 2996\u20133006. doi: 10.1002/mrm.26963.", "Friedel, M. et al. (2014) 'Pydpiper: A flexible toolkit for constructing novel registration pipelines', Frontiers in Neuroinformatics. doi: 10.3389/fninf.2014.00067.", "Gudbjartsson, H. and Patz, S. (1995) 'The rician distribution of noisy MRI data', Magnetic Resonance in Medicine, 34(6), pp. 910\u2013914. doi: 10.1002/mrm.1910340618.", "Jenkinson, M. (2003) 'Fast, automated, N-dimensional phase-unwrapping algorithm', Magnetic Resonance in Medicine, 49(1), pp. 193\u2013197. doi: 10.1002/mrm.10354.", "Kellner, E. et al. (2016) 'Gibbs-ringing artifact removal based on local subvoxel-shifts', Magnetic Resonance in Medicine. doi: 10.1002/mrm.26054.", "Li, W. et al. (2015) 'A method for estimating and removing streaking artifacts in quantitative susceptibility mapping', NeuroImage. doi: 10.1016/j.neuroimage.2014.12.043.", "Li, W., Wu, B. and Liu, C. (2011) 'Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition', NeuroImage. doi: 10.1016/j.neuroimage.2010.11.088.", "Robinson, S. D. et al. (2017) 'Combining phase images from array coils using a short echo time reference scan (COMPOSER)', Magnetic Resonance in Medicine. doi: 10.1002/mrm.26093.", "Schweser, F. et al. (2011) 'Quantitative imaging of intrinsic magnetic tissue properties using MRI signal phase: An approach to in vivo brain iron metabolism?', NeuroImage. doi: 10.1016/j.neuroimage.2010.10.070.", "Tisca, C. et al. (2021) 'Vcan mutation induces sex-specific changes in white matter microstructure in mice', in Proc. Intl. Soc. Mag. Reson. Med. 29, p. 1226. Available at: https://index.mirasmart.com/ISMRM2021/PDFfiles/1226.html.", "Tisca, C. et al. (2022) 'White matter microstructure changes in a Bcan knockout mouse model', in Proc. Intl. Soc. Mag. Reson. Med. 31.", "Wang, C. et al. (2020) 'Methods for quantitative susceptibility and R2* mapping in whole post-mortem brains at 7T applied to amyotrophic lateral sclerosis', NeuroImage. Elsevier Inc., 222(May), p. 117216. doi: 10.1016/j.neuroimage.2020.117216.", "Wang, C. et al. (2022) 'Phenotypic and genetic associations of quantitative magnetic susceptibility in UK Biobank brain imaging', Nature Neuroscience, 25(6), pp. 818\u2013831. doi: 10.1038/s41593-022-01074-w."]} This repository contains all scripts to run the ex vivo R2*- and QSM post-processing pipelines on data acquired at WIN's 7T Bruker facility. It should be compatible with any other data acquired on a similar Bruker scanner using an equivalent protocol. This resource contains anonymised file-paths which will need to be edited to enable running on a cluster facility. The commands for submitting jobs to the cluster also need to be edited. This pipeline is based on the code developed by Chaoyue Wang and Benjamin Tendler and published here: https://doi.org/10.1016/j.neuroimage.2020.117216. The scripts were either written or adapted by Cristiana Tisca. The outputs of the pipeline include QSM and R2* maps. Additional funding sources: Wellcome Trust Senior Research Fellowship (Renewal), Prof Karla Miller, 224573/Z/21/Z

PyPrep is a Python library to facilitate preprocessing of EEG data. More information can be found at pyprep.readthedocs.io.

Chronic systemic inflammatory conditions, such as atherosclerosis, diabetes and obesity are associated with increased risk of stroke, which suggests that systemic inflammation may contribute to the development of stroke in humans. The hypothesis that systemic inflammation may induce brain pathology can be tested in animals, and this was the key objective of the present study. First, we assessed inflammatory changes in the brain in rodent models of chronic, systemic inflammation. PET imaging revealed increased microglia activation in the brain of JCR-LA (corpulent) rats, which develop atherosclerosis and obesity, compared to the control lean strain. Immunostaining against Iba1 confirmed reactive microgliosis in these animals. An atherogenic diet in apolipoprotein E knock-out (ApoE−/−) mice induced microglial activation in the brain parenchyma within 8 weeks and increased expression of vascular adhesion molecules. Focal lipid deposition and neuroinflammation in periventricular and cortical areas and profound recruitment of activated myeloid phagocytes, T cells and granulocytes into the choroid plexus were also observed. In a small, preliminary study, patients at risk of stroke (multiple risk factors for stroke, with chronically elevated C-reactive protein, but negative MRI for brain pathology) exhibited increased inflammation in the brain, as indicated by PET imaging. These findings show that brain inflammation occurs in animals, and tentatively in humans, harbouring risk factors for stroke associated with elevated systemic inflammation. Thus a “primed” inflammatory environment in the brain may exist in individuals at risk of stroke and this can be adequately recapitulated in appropriate co-morbid animal models.

This Neuroimaging data management plan (DMP) template is designed to be completed in two phases: Phase 1 questions probe at a high-level, seeking information about the general direction of the study. Normally, researchers will be able to respond to phase 1 questions at the outset of a project. Phase 2 questions seek greater detail. It is understood that these answers will often depend on the outcome of several steps in the research project, such as: a literature review, imaging protocol design and experimental design, or running multiple pilot subjects and interpreting the outcome. As these details become known, the DMP can and should be revisited. This approach underscores that DMPs are living documents that evolve throughout a research project.

We introduce Sleep, a new Python open-source graphical user interface (GUI) dedicated to visualization, scoring and analyses of sleep data. Among its most prominent features are: (1) Dynamic display of polysomnographic data, spectrogram, hypnogram and topographic maps with several customizable parameters, (2) Implementation of several automatic detection of sleep features such as spindles, K-complexes, slow waves, and rapid eye movements (REM), (3) Implementation of practical signal processing tools such as re-referencing or filtering, and (4) Display of main descriptive statistics including publication-ready tables and figures. The software package supports loading and reading raw EEG data from standard file formats such as European Data Format, in addition to a range of commercial data formats. Most importantly, Sleep is built on top of the VisPy library, which provides GPU-based fast and high-level visualization. As a result, it is capable of efficiently handling and displaying large sleep datasets. Sleep is freely available (http://visbrain.org/sleep) and comes with sample datasets and an extensive documentation. Novel functionalities will continue to be added and open-science community efforts are expected to enhance the capacities of this module.

This project publishes the code used in the following publication: Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer’s disease, NeuroImage: Clinical, 2021 Link: https://doi.org/10.1016/j.nicl.2021.102712, arxiv.org/2012.08769 Starting point: Overview.ipynb {"references": ["Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer's disease, NeuroImage: Clinical, 2021"]} https://gitlab.com/radiology/neuro/bron-cross-cohort/-/tree/v1.0

{"references": ["Andersson, J. L. R., Skare, S. and Ashburner, J. (2003) 'How to correct susceptibility distortions in spin-echo echo-planar images: Application to diffusion tensor imaging', NeuroImage. doi: 10.1016/S1053-8119(03)00336-7.", "Andersson, J. L. R. and Sotiropoulos, S. N. (2016) 'An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging', NeuroImage. doi: 10.1016/j.neuroimage.2015.10.019.", "Bautista, T. et al. (2021) 'Removal of Gibbs ringing artefacts for 3D acquisitions using subvoxel shifts', in Proc. Intl. Soc. Mag. Reson. Med., p. 3535.", "Behrens, T. E. J. et al. (2007) 'Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?', NeuroImage, 34(1), pp. 144\u2013155. doi: 10.1016/j.neuroimage.2006.09.018.", "Hernandez-Fernandez, M. et al. (2019) 'Using GPUs to accelerate computational diffusion MRI: From microstructure estimation to tractography and connectomes', NeuroImage. doi: 10.1016/j.neuroimage.2018.12.015.", "Kaden, E. et al. (2016) 'Multi-compartment microscopic diffusion imaging', NeuroImage. doi: 10.1016/j.neuroimage.2016.06.002.", "Kellner, E. et al. (2016) 'Gibbs-ringing artifact removal based on local subvoxel-shifts', Magnetic Resonance in Medicine. doi: 10.1002/mrm.26054.", "Smith, S. M. et al. (2004) 'Advances in functional and structural MR image analysis and implementation as FSL', in NeuroImage. doi: 10.1016/j.neuroimage.2004.07.051.", "Tisca, C. et al. (2021) 'Vcan mutation induces sex-specific changes in white matter microstructure in mice', in Proc. Intl. Soc. Mag. Reson. Med. 29, p. 1226. Available at: https://index.mirasmart.com/ISMRM2021/PDFfiles/1226.html.", "Tisca, C. et al. (2022) 'White matter microstructure changes in a Bcan knockout mouse model', in Proc. Intl. Soc. Mag. Reson. Med. 31.", "Zhang, H. et al. (2012) 'NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain', NeuroImage. doi: 10.1016/j.neuroimage.2012.03.072."]} This repository contains all scripts to run the ex vivo diffusion-weighted MRI (dMRI) post-processing pipeline on data acquired at WIN's 7T Bruker facility. It should be compatible with any other data acquired on a similar Bruker scanner and using an equivalent protocol. This resource contains anonymised file-paths which will need to be edited to enable running on a cluster facility. The commands for submitting jobs to the cluster also need to be edited. The outputs of the pipeline include the standard DTI (FA, MD, V1) and DKI (FA, AK, RK, K1) outputs from FSL's dtifit and the standard NODDI outputs (OD, ICVF, ISOVF) from cuDIMOT. Additional funding sources: Wellcome Trust Senior Research Fellowship (Renewal), Prof Karla Miller, 224573/Z/21/Z