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

by   Itir Onal, et al.

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.


page 4

page 5

page 10

page 11

page 12


Mesh Learning for Classifying Cognitive Processes

A relatively recent advance in cognitive neuroscience has been multi-vox...

Discriminative Functional Connectivity Measures for Brain Decoding

We propose a statistical learning model for classifying cognitive proces...

Hierarchical Multi-resolution Mesh Networks for Brain Decoding

We propose a new framework, called Hierarchical Multi-resolution Mesh Ne...

Self-Replicating Strands that Self-Assemble into User-Specified Meshes

It has been argued that a central objective of nanotechnology is to make...

Locality and low-dimensions in the prediction of natural experience from fMRI

Functional Magnetic Resonance Imaging (fMRI) provides dynamical access i...

Encoding the Local Connectivity Patterns of fMRI for Cognitive State Classification

In this work, we propose a novel framework to encode the local connectiv...

Assessing Dynamic Effects on a Bayesian Matrix-Variate Dynamic Linear Model: an Application to fMRI Data Analysis

In this work, we propose a modeling procedure for fMRI data analysis usi...

Please sign up or login with your details

Forgot password? Click here to reset