Software
I’m an avid supporter of open-source software. These are some of the projects I’ve created or contributed to:
- gallantlab/autoflatten A Python pipeline for automatically creating cortical flatmaps from FreeSurfer surfaces, streamlining the surface flattening process for cortical visualization and analysis.
- gallantlab/pycortex A Python toolkit for surface visualization of fMRI data. Allows interactive exploration of volumetric neuroimaging data on cortical surfaces directly in the browser.
- gallantlab/himalaya Machine learning linear models in Python, focusing on computational efficiency for large numbers of targets. Supports CPU and GPU backends for multiple-target regression problems.
- PyMVPA/PyMVPA Multivariate pattern analysis in Python. A framework for applying machine learning techniques to neuroimaging data, enabling decoding and representational similarity analyses.
- nipy/heudiconv A flexible DICOM converter for organizing brain imaging data into structured directory layouts. Uses customizable heuristics to automatically sort and convert raw scanner data into BIDS-compliant datasets.
- duecredit/duecredit Addresses inadequate citation of scientific software and methods. Automatically collects and reports references for packages, methods, and datasets used in an analysis to ensure proper attribution.
- ReproNim/reproin A turnkey setup for automatic generation of shareable, version-controlled BIDS datasets directly from MR scanners. Part of the ReproNim Center suite of tools for reproducible neuroimaging.
- mvdoc/budapest-fmri-data Quality assurance analyses for an fMRI dataset collected while participants watched "The Grand Budapest Hotel" by Wes Anderson, useful for studying naturalistic stimulus processing.
- nipy/nipype A neuroimaging pipeline framework providing uniform interfaces to tools like FSL, FreeSurfer, AFNI, and SPM. Enables building complex, reproducible processing workflows across heterogeneous software packages.
Additional information can be found on my GitHub profile: mvdoc