Low Dimensional Embedding of fMRI datasets

TL;DR

This paper introduces a new low-dimensional embedding technique for fMRI data that preserves local functional relationships, enabling easier detection of activated brain regions during natural stimuli exposure.

Contribution

The novel method uses commute time on a voxel connectivity graph to embed fMRI datasets in low dimensions, improving interpretability over existing techniques.

Findings

Successfully identified visual, auditory, and language areas in fMRI data.

Outperformed linear and nonlinear embedding methods in synthetic and real datasets.

Effective for analyzing fMRI during naturalistic stimuli.

Abstract

We propose a novel method to embed a functional magnetic resonance imaging (fMRI) dataset in a low-dimensional space. The embedding optimally preserves the local functional coupling between fMRI time series and provides a low-dimensional coordinate system for detecting activated voxels. To compute the embedding, we build a graph of functionally connected voxels. We use the commute time, instead of the geodesic distance, to measure functional distances on the graph. Because the commute time can be computed directly from the eigenvectors of (a symmetric version) the graph probability transition matrix, we use these eigenvectors to embed the dataset in low dimensions. After clustering the datasets in low dimensions, coherent structures emerge that can be easily interpreted. We performed an extensive evaluation of our method comparing it to linear and nonlinear techniques using synthetic datasets and in vivo datasets. We analyzed datasets from the EBC competition obtained with subjects interacting in an urban virtual reality environment. Our exploratory approach is able to detect independently visual areas (V1/V2, V5/MT), auditory areas, and language areas. Our method can be used to analyze fMRI collected during ``natural stimuli''.