A supervised clustering approach for fMRI-based inference of brain states

TL;DR

This paper introduces a supervised clustering method for fMRI data that improves brain state prediction by leveraging hierarchical spatial clustering and feature agglomeration, outperforming traditional voxel-based techniques.

Contribution

The method combines hierarchical clustering with supervised pruning to create a multi-scale, spatially-informed feature set for better brain state inference.

Findings

Higher prediction accuracy than standard voxel-based methods

Explicit regional weighting in the predictive model

Effective dimensionality reduction through supervised clustering

Abstract

We propose a method that combines signals from many brain regions observed in functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior during a scanning session. Such predictions suffer from the huge number of brain regions sampled on the voxel grid of standard fMRI data sets: the curse of dimensionality. Dimensionality reduction is thus needed, but it is often performed using a univariate feature selection procedure, that handles neither the spatial structure of the images, nor the multivariate nature of the signal. By introducing a hierarchical clustering of the brain volume that incorporates connectivity constraints, we reduce the span of the possible spatial configurations to a single tree of nested regions tailored to the signal. We then prune the tree in a supervised setting, hence the name supervised clustering, in order to extract a parcellation (division of the volume) such that parcel-based signal averages best predict the target information. Dimensionality reduction is thus achieved by feature agglomeration, and the constructed features now provide a multi-scale representation of the signal. Comparisons with reference methods on both simulated and real data show that our approach yields higher prediction accuracy than standard voxel-based approaches. Moreover, the method infers an explicit weighting of the regions involved in the regression or classification task.