Independent Component Analysis (ICA) can identify patterns in fMRI data. Some of the components reflect BOLD signal and others are driven by noise. This post explains how to identify signal components and noise components in your data.
In resting-state fMRI processing we often apply Independent Component Analysis to clean the data from noise. Automated approaches for ICA-based cleaning can automatically label components as noise or signal, but often need to be trained on data-specific labels. This post explains how to train an automated ICA component classifier and use it to denoise fMRI data.
Using FIX on a cluster where it hasn't been set up can be tricky. Here are a few troubleshooting tips.
Guide to inspecting different types of imaging data. Particular focus on surface visualisation. Nomalization and registration checks coming soon.
Guide to creating and using fieldmaps to correct MRI data for B0 field inhomogeneities. Particularly focussed on visual inspection and troubleshooting, as there are a few pitfalls when doing fieldmap correction.
Tutor for MRI Analysis, WIN Graduate Programme. Teaching principles of neuroimaging data analysis and practical application of the FSL toolbox.