Mesh Learning for Classifying Cognitive Processes

TL;DR

This paper introduces a Mesh Learning Model for analyzing neural data from fMRI, using mesh structures around each voxel to improve pattern classification of cognitive states.

Contribution

The paper proposes a novel Mesh Learning approach that constructs star meshes around voxels and uses arc weights as features for classifying brain activity.

Findings

Mesh Learning improves classification accuracy of cognitive states.

Mesh Arc Descriptors effectively capture neural activity patterns.

The method outperforms traditional MVPA techniques.

Abstract

A relatively recent advance in cognitive neuroscience has been multi-voxel pattern analysis (MVPA), which enables researchers to decode brain states and/or the type of information represented in the brain during a cognitive operation. MVPA methods utilize machine learning algorithms to distinguish among types of information or cognitive states represented in the brain, based on distributed patterns of neural activity. In the current investigation, we propose a new approach for representation of neural data for pattern analysis, namely a Mesh Learning Model. In this approach, at each time instant, a star mesh is formed around each voxel, such that the voxel corresponding to the center node is surrounded by its p-nearest neighbors. The arc weights of each mesh are estimated from the voxel intensity values by least squares method. The estimated arc weights of all the meshes, called Mesh Arc Descriptors (MADs), are then used to train a classifier, such as Neural Networks, k-Nearest Neighbor, Na\"ive Bayes and Support Vector Machines. The proposed Mesh Model was tested on neuroimaging data acquired via functional magnetic resonance imaging (fMRI) during a recognition memory experiment using categorized word lists, employing a previously established experimental paradigm (\"Oztekin & Badre, 2011). Results suggest that the proposed Mesh Learning approach can provide an effective algorithm for pattern analysis of brain activity during cognitive processing.