Tractometry#

Tractometry uses diffusion-weighted MRI (dMRI) to extract microstructural tissue properties of major white matter pathways. Here, we maintain a suite of integrated, open-source software that performs all analysis stages:

pyAFQ: Automated Fiber Quantification in Python:
 • post-processing of dMRI data
 • delineation of major white matter pathways
 • modeling of the tissue properties within them
 • expects pre-processed data. Data can be pre-processed with QSIprep

AFQ-Insight: Machine learning and statistics for tractomtery:
 • novel machine learning models such as convolutional neural networks (CNNs) and recurrent neural network (RNNs)
 • more standard approaches, such as ordinary least squares (OLS) and principal component analysis (PCA)
 • integrated with scikit-learn and adapted for tract data, bridging these two worlds

Tractable: R-based statistical analysis of tractometry:
 • focuses on generalized additive models (GAMs)

AFQ-Browser: Interactive exploratory visualization and sharing of tractometry studies:
 • allows researchers to interactively query the data to explore patterns

Tractoscope: Visualization of large openly-available tractometry studies.