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.
Introductory lecture on Graph Theory and Network Neuroscience. Part of the Cognitive Neuroscience Skills Training (COGNESTIC) at the University of Cambridge.
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.
Lecture introducing concepts from Graph Theory and Network Neuroscience, as part of the Introduction to Neuroimaging Methods lecture series at the University of Cambridge MRC CBU. The seminar includes practical exercises. For exercise material and solutions see links below.