Title: Microstructural variations on a theme: a bottom-up approach to diffusion models
Project lead: Matteo Mancini (Twitter: @ingmatman; Mattermost: matman)
Project collaborators: Marco Palombo (who already gave a lot of suggestions!)
Registered Brainhack Global 2020 Event: Brainhack Micro2Macro - https://brainhack-micro2macro.github.io
Project Description: The goal of this project is to quantify in practical terms the differences between different microstructural models focusing on one of the most common targets: the intra-cellular volume fraction. As detailed in Novikov et al. NMR in Biomed 2018, the numerous approaches (with related articles) proposed are “theme variations” on a common model, the sticks model, to the point that someone has started talking about a “standard model” of white matter microstructure. So what happens when we compare these different variations at the ROI or voxel level? How large are the mismatches? And how negligible they become when moving from high-quality data (e.g. acquired with ultra-high-gradients) to clinical-grade data? As a starting point, I proposed to implement using containers several of the common workflows and start looking at simple comparisons (e.g. using scatterplot and correlation). The use of containers will guarantee high reproducibility and will easily allow to process multiple datasets, even locally or on cloud systems. Given the high number of approaches proposed so far, a potential direction could be to open a “call for workflows”, for a larger comparison.
Data to use:
Link to project repository/sources: I will open a repo on my Github account.
Goals for Brainhack Global 2020:
Good first issues:
Tools/Software/Methods to Use: Some methods to start with:
Communication channels: I’ll open the channel #microstructural-variations in Mattermost A Zoom meeting will be set up and the link will be privately shared with interested participants.
Type of project: coding_methods, data_management, documentation, method_development, #pipeline_development, tutorial_recording, #visualization
Project development status: #0_concept_no_content, 1_basic structure, 2_releases_existing
Topic of the projet: Bayesian_approaches, causality, connectome, data_visualisation, deep_learning, #diffusion, diversity_inclusivity_equality, EEG_EventRelatedResponseModelling, EEG_source_modelling, Granger_causality, hypothesis_testing, ICA, information_theory, machine_learning, #MR_methodologies, neural_decoding, neural_encoding, neural_networks, PCA, physiology, reinforcement_learning, #reproducible_scientific_methods, single_neuron_models, statistical_modelling, systems_neuroscience, tractography
Tools used in the project: AFNI, ANTs, BIDS, Brainstorm, CPAC, #Datalad, #DIPY, FieldTrip, fMRIPrep, Freesurfer, #FSL, Jupyter, MNE, MRtrix, Nipype, NWB, SPM
Tools skill level required to enter the project (more than one possible): #comfortable, expert, #familiar, no_skills_required
Programming language used in the project: no_programming_involved, C++, #containerization, documentation, Java, Julia, Matlab, #Python, R, #shell_scripting, #Unix_command_line, Web, workflows
Modalities involved in the project (if any): behavioral, #DWI, ECG, ECOG, EEG, eye_tracking, fMRI, fNIRS, MEG, MRI, PET, TDCS, TMS
Git skills reuired to enter the project (more than one possible): #0_no_git_skills, #1_commit_push, 2_branches_PRs, 3_continuous_integration
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