Machine learning for NeuroImaging in Python
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Updated
Jun 4, 2024 - Python
Machine learning for NeuroImaging in Python
fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data. The transparent workflow dispenses of manual intervention, thereby ensuring the reproducibility of the results.
Connectome Mapper 3 is a BIDS App that implements full anatomical, diffusion, resting/state functional MRI, and recently EEG processing pipelines, from raw T1 / DWI / BOLD , and preprocessed EEG data to multi-resolution brain parcellation with corresponding connection matrices.
Core tools required for running Canlab Matlab toolboxes. The heart of this toolbox is object-oriented tools that enable interactive analysis of neuroimaging data and simple scripts using high-level commands tailored to neuroimaging analysis.
Forschungszentrum Jülich Neuroimaging Feature Extractor
Working repository to turn the COBIDAS guidelines to report methods and results in neuroimaging into a user friendly checklist
Explain a black-box module in natural language.
Repository for Masharipov, Knyazeva, Korotkov, Cherednichenko & Kireev: "Comparison of whole-brain task-modulated functional connectivity methods for fMRI task connectomics"
python module to compute cerebrovascular reactivity mapping and other related maps
Benchmarks for functional connectivity estimators and FCEst Python package
Neuroimaging analysis in Python
Official AFNI source and documentation
Comparing latent space representations using autoencoders and vision transformers using fMRI data.
Code used to create the Neural Encoding Datasets (NED), and utility functions to generate neural responses to arbitrary images using NED's trained models.
SPM Documentation
Python version of 3dPFM and 3dMEPFM
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