This lesson is in the early stages of development (Alpha version)

Functional Neuroimaging Analysis in Python: Glossary

Key Points

Exploring Preprocessed fMRI Data from fMRIPREP
  • fMRIPrep stores preprocessed data in a ‘BIDS-like’ fashion

  • You can pull files using pyBIDS much like how you can navigate raw BIDS data

Introduction to Image Manipulation using Nilearn
  • MR images are essentially 3D arrays where each voxel is represented by an (i,j,k) index

  • Nilearn is Nibabel under the hood, knowing how Nibabel works is key to understanding Nilearn

Integrating Functional Data
  • fMRI data is represented by spatial (x,y,z) and temporal (t) dimensions, totalling 4 dimensions

  • fMRI data is at a lower resolution than structural data. To be able to combine data requires resampling your data

Preprocessing fMRI Data
  • fmriprep takes care of several of the preprocessing steps

Cleaning Confounders in your Data with Nilearn
  • Nuisance regression is an attempt to make sure your results aren’t driven by non-brain signals

  • With resting state, we don’t actually ever know the true signal - we can only attempt to estimate it

Applying Parcellations to Resting State Data
  • Parcellations group voxels based on criteria such as similarities, orthogonality or some other criteria

  • Nilearn stores several standard parcellations that can be applied to your data

  • Parcellations are defined by assigning each voxel a parcel ‘membership’ value telling you which group the parcel belongs to

  • Parcellations provide an interpretative framework for understanding resting state data. But beware, some of the techniques used to form parcellations may not represent actual brain functional units!

Functional Connectivity Analysis
  • MR images are essentially 3D arrays where each voxel is represented by an (i,j,k) index

  • Nilearn is Nibabel under the hood, knowing how Nibabel works is key to understanding Nilearn

Neuroimaging Fundamentals & Nibabel
  • blah

Introduction to Image Manipulation using Nilearn
  • MR images are essentially 3D arrays where each voxel is represented by an (i,j,k) index

  • Nilearn is Nibabel under the hood, knowing how Nibabel works is key to understanding Nilearn

Exploration of Open Neuroimaging Datasets in BIDS format
  • BIDS is an organizational principle for neuroimaging data for transparent data sharing

  • PyBIDS is a python based tool that allows for easy exploration of BIDS-formatted neuroimaging data

Glossary

FIXME