Course Overview and Introduction
- fMRI data is dirty and needs to be cleaned, aligned, and transformed before being able to use
- There are standards in place which will allow you to effortlessly pull the data that you need for analysis
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
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