I support open science and doing my best to develop new packages when needed and contribute to existing packages when they already exist. Fork me on github.

You may be interested in the following software I develop:

  • neuropredict :The aim of this python module would be to automatically assess the predictive power of commonly used neuroimaging features (such as resting-state connectivity, fractional anisotropy, subcortical volumes and cortical thickness features) automatically read from the processing of popular tools such as FSL, DTIstudio, AFNI and Freesurfer, and present a comprehensive report on a given dataset. It is mainly aimed (to lower or remove the barriers) at clinical users who would like to understand what features and brain regions are discriminative in their shiny new dataset before diving into the deep grey sea of feature extraction and optimization. This is part of a broader initiative I am pursuing to develop standardized and easy predictive analysis – see here for an overview and the bigger picture idea.
  • graynet : helps extract individualized single-subject networks (pairwise links between ROIs) from T1 mri features such as cortical thickness, gray matter density, subcortical morphometric features, gyrification and curvature.
  • Pyradigm: This is a Python class defining a machine learning dataset to ensure key-based correspondence within samples and maintaining integrity across samples. neuropredict leverages this package to a great effect.
  • hiwenet: Histogram-weighted Networks for Feature Extraction, Connectivity and Advanced Analysis in Neuroscience