Teaching

How clean fMRI data with FIX-ICA

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.

How to classify ICA components

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.

Using independent component analysis and machine learning classification to analyse fMRI data

Functional data analysis

Using independent component analysis and machine learning classification to analyse fMRI data

Functional data analysis

How to inspect surface data

Guide to inspecting different types of imaging data. Particular focus on surface visualisation. Nomalization and registration checks coming soon.

Graph Theory and Network Neuroscience

Introductory lecture on Graph Theory and Network Neuroscience. Part of the Cognitive Neuroscience Skills Training (COGNESTIC) at the University of Cambridge.

How to fieldmap correct

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.

Graph Theory

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.

Cerebellum

Cerebellar Structure and Function

Cerebellum

Lecture introducing cerebellar structure and function. Part of the Motor Systems Module of the MSc Neuroscience in Oxford.