Modeling the Sequence of Brain Volumes by Local Mesh Models for Brain Decoding

TL;DR

This paper introduces local mesh models to represent fMRI brain volume sequences, improving brain decoding accuracy by capturing local voxel relationships through linear regression-based edge weights.

Contribution

It proposes a novel graph-based representation of brain activity using spatial and functional local meshes, enhancing decoding performance over existing models.

Findings

Mesh edge weights outperform state-of-the-art models.

Spatial and functional meshes provide complementary information.

Support Vector Machines effectively classify cognitive states using mesh features.

Abstract

We represent the sequence of fMRI (Functional Magnetic Resonance Imaging) brain volumes recorded during a cognitive stimulus by a graph which consists of a set of local meshes. The corresponding cognitive process, encoded in the brain, is then represented by these meshes each of which is estimated assuming a linear relationship among the voxel time series in a predefined locality. First, we define the concept of locality in two neighborhood systems, namely, the spatial and functional neighborhoods. Then, we construct spatially and functionally local meshes around each voxel, called seed voxel, by connecting it either to its spatial or functional p-nearest neighbors. The mesh formed around a voxel is a directed sub-graph with a star topology, where the direction of the edges is taken towards the seed voxel at the center of the mesh. We represent the time series recorded at each seed voxel in terms of linear combination of the time series of its p-nearest neighbors in the mesh. The relationships between a seed voxel and its neighbors are represented by the edge weights of each mesh, and are estimated by solving a linear regression equation. The estimated mesh edge weights lead to a better representation of information in the brain for encoding and decoding of the cognitive tasks. We test our model on a visual object recognition and emotional memory retrieval experiments using Support Vector Machines that are trained using the mesh edge weights as features. In the experimental analysis, we observe that the edge weights of the spatial and functional meshes perform better than the state-of-the-art brain decoding models.