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pjtoussaint's Projects

appian icon appian

APPIAN is an open-source automated software pipeline for analyzing PET images in conjunction with MRI. The goal of APPIAN is to facilitate reproducible research and to make PET tracer kinetic data analysis easier for users with moderate computing skills.

brainswag icon brainswag

BrainSpell solely houses neuroimaging stereotaxic coordinates. Expanding the BrainSpell database to include ‘omics’ data from neuronal, neurovascular and cerebrospinal fluid (CSF) studies would better facilitate multimodal analysis. In concert with the neuronal component, a healthy cerebral circulation is essential for maintaining brain perfusion and function. The CSF supports/cushions the brain, circulates nutrients obtained from the blood, and removes waste products. Observed changes in brain function in health or disease could be due to underlying alterations in any or all of the above components. The proposed changes to BrainSpell will enable users to integrate multiple streams of information and have a better view of the big picture.

fmriprep icon fmriprep

fMRIprep is a functional magnetic resonance image pre-processing pipeline that is designed to provide an easily accessible, state-of-the-art interface that is robust to differences in scan acquisition protocols and that requires minimal user input, while providing easily interpretable and comprehensive error and output reporting.

microdraw icon microdraw

Collaborative vectorial annotation tool for ultra high resolution data

pna-notebooks icon pna-notebooks

Notebooks for the Berkeley course on practical neuroimaging

pyminc icon pyminc

A python interface to the MINC 2 library, allowing use of numpy arrays to access MINC data, and other such similar goodies, developed by Jason Lerch

rminc icon rminc

Statistics for MINC volumes: A library to integrate voxel-based statistics for MINC volumes into the R environment. Supports getting and writing of MINC volumes, running voxel-wise linear models, correlations, etc.; correcting for multiple comparisons using the False Discovery Rate, and more. With contributions from Jason Lerch, Jim Nikelski and Matthijs van Eede. Some additional information can be found here:

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