The most common method of analysis of fMRI data consists of comparing the BOLD signal in the brain with time series representing the various conditions of the experiment (e.g. task vs rest).

Similarly, resting-state data is often analyzed by selecting a region of interest, computing the average BOLD signal in that region, and comparing it with signal elsewhere in the brain.

Both of these methods produce statistical maps (for activation or functional connectivity) which then give insight into the underlying brain processes. Attractive by their simplicity, these types of analyses have the drawback of being heavily assumption-driven and usually do not extract all the interesting information contained in the data. To complement these, so-called data-driven methods such as Independent Component Analysis (ICA) have been developed that give a much richer view of the information content of the data. However, these methods can be a little intimidating as they rely on complex numerical analysis algorithms, and require special knowledge from the user to be used correctly.

The goal of this workshop is to allow participants to confidently use ICA (as implemented in MELODIC in the FSL package) to extract and interpret results from their fMRI and resting-state data. We will first introduce the basic concepts behind the ICA method and how they are implemented in MELODIC to produce statistical maps. We will then apply ICA to test fMRI and resting-state datasets and go over many important steps encountered in using ICA in real life situation: classification of components into signal of interest and artifacts, results interpretation, data cleanup, network reconstruction and evaluation of functional connectivity changes. We will also cover group ICA analysis using temporal concatenation ICA as well as tensor ICA and dual regression.

This will be a theoretical workshop (no practical sessions are organized) and it requires a minimal background in data analysis to benefit from attending. Participants with nonmathematical background are encouraged to read through the text (PDF) to introduce themselves to the necessary notions of matrix algebra.

If you are interested in participating in this workshop, please register at: Introductory Workshop on MRI data analysis using Independent Component Analysis at BRAMS 

Thomas Gisiger, Research Associate at the CRBLM