BY: MARIO B. PEREZ

Multi-voxel Pattern Analysis (MVPA): Fundamentals & Case of

Study

A bit of context

Traditional fMRI analysis carry out univariate analysis upon individual voxels (mass-univariate)

Conventional analysis try to find voxels that respond significantly to an experimental condition.

To increase sensitivity, this methods spatially average across voxels that respond to that cognition (other steps like spatial smoothing)

The «beauties» of MVPA

MVPA performs multivariate analysis that takes into account the relationships between voxels.

Uses classifier algorithms to learn and then recognise those activity patterns.

It takes into account «fine-grained» activity that is normally discarded in standard fMRI analysis.

Provides an enhanced sensitivity over one-voxel methods: (no spatial smoothing or averaging)

Effects of spatial smoothing: Loss of«fine-grained activity»

Spatial smoothing generally increases the chances of obtaining significant activity, but «blurs» vital information

MVPA basic procedure

Four main steps

Split your data in «training» and «testing» sets.

Feature selection: This first step tries to delimitate a set of voxels that will be used further using training data.

Classifier training: In these stage the training data is used so our classifier learns our activity patterns. Once training has finished a decision plane its generated

Classifier or generalization testing: The classifier is exposed to new data (testing data set) that belongs to the same category. Based on where they “fall” and the identity of the example, the classification was successful or not.

MVPA basic procedure

Classifiers & Decision Plane: How we distinguish between conditions?

Voxels can work as coordinates to define a point in a plane.

We would only need a line to separate our conditions. (linear classifiers)

Chadwick et al., 2010

Goal was to distinguish between memory traces (a.patterns)

Searchlight feature selection (Sphere/radius)Three ROIs were defined (hippocampus, entorhinal

cortex and parahippocampal gyrus)K-fold cross-validation (9 iterations one with each

memory as test)

Chadwick et al., 2010

Results

You have been released!

Overfitting (subtle versions)

When you use all your examples (stimuli) to select your voxels...voilá!

You have included some voxels that are compatible between your testing and your training data.