Summer is on it’s way and the Google Summer Of Code is by no means an exception!
The project is titled: mne.set_volumeAverage(True) – preparing Group level analyses for volumetric data
So what’s it all about?
MNE Python is a Open Source software package for Python, providing tools for the analysis of (electrophysiological) brain data. Those tools include functions for neural source reconstructions of MEG/EEG data. In a nutshell, the goal is to infer sources of brain activity given electrophysiological brain data, recorded at multiple locations around the head. One approach to solve this so called inverse problem is the beamforming approach: The anatomical brain is partialized into a volumetric grid and by “scanning” through all the grid points while trying to maximize the explanation of the recorded data, grid points can be identified as relevant generators for a certain set of brain data. Furthermore time resolved (LCMV beamformer) or frequency resolved (DICS beamformer) data for those “virtual channels” can be obtained.
Due to anatomical differences across subjects, grid points might expose different relations. Or in other words: not two brains are alike. Imagine lining up all facial features of two faces. Even though they share almost always the same features it is also almost always impossible to align all features at the same time.
In order to compute group level statistics on volumetric brain data we are facing the same problem. It is hence necessary to transform individual “brain grids” into a common grid space, where functional (and anatomical) similar regions are represented at similar voxel locations in the volumetric grid.
This is were my project becomes relevant:
As a result of the proposed project I would like to see a set of functions, that allow for (non-) linear warping of volumetric grids and creating pseudo-individual MR images, based on subjects head shape. If time allows the respective visualization would be a “nice to have” as well.
Source code produced throughout the project, will be provided via GitHub.
All right! Let’s get started!
Stay tuned for any updates on the grid side of life!