Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes in Schizophrenia

TL;DR

This study combines fMRI and EEG data using a tensor-based fusion approach to identify distinct brain activity patterns in schizophrenia patients versus healthy controls, enhancing understanding of neurological differences.

Contribution

It introduces a coupled matrix and tensor factorization model that preserves EEG data structure for improved multimodal brain activity analysis in schizophrenia.

Findings

Joint EEG-fMRI analysis captures meaningful brain signatures.

Including multiple electrodes enhances interpretability.

The method differentiates patients from controls effectively.

Abstract

Neuroimaging modalities such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG) provide information about neurological functions in complementary spatiotemporal resolutions; therefore, fusion of these modalities is expected to provide better understanding of brain activity. In this paper, we jointly analyze fMRI and multi-channel EEG signals collected during an auditory oddball task with the goal of capturing brain activity patterns that differ between patients with schizophrenia and healthy controls. Rather than selecting a single electrode or matricizing the third-order tensor that can be naturally used to represent multi-channel EEG signals, we preserve the multi-way structure of EEG data and use a coupled matrix and tensor factorization (CMTF) model to jointly analyze fMRI and EEG signals. Our analysis reveals that (i) joint analysis of EEG and fMRI using a CMTF model can capture meaningful temporal and spatial signatures of patterns that behave differently in patients and controls, and (ii) these differences and the interpretability of the associated components increase by including multiple electrodes from frontal, motor and parietal areas, but not necessarily by including all electrodes in the analysis.