Multisubject Task-Related fMRI Data Processing via a Two-Stage Generalized Canonical Correlation Analysis

TL;DR

This paper introduces a two-stage generalized canonical correlation analysis method for processing multisubject task-related fMRI data, effectively extracting accurate brain activation maps even at low SNR, outperforming standard GLM approaches.

Contribution

It proposes a novel model and a two-stage analysis technique that jointly estimate task-related and resting-state components in fMRI data, enhancing activation detection accuracy.

Findings

Accurate temporal and spatial estimates at low SNR

Significant improvements over standard GLM methods

Effective in synthetic and real-world data

Abstract

Functional magnetic resonance imaging (fMRI) is one of the most popular methods for studying the human brain. Task-related fMRI data processing aims to determine which brain areas are activated when a specific task is performed and is usually based on the Blood Oxygen Level Dependent (BOLD) signal. The background BOLD signal also reflects systematic fluctuations in regional brain activity which are attributed to the existence of resting-state brain networks. We propose a new fMRI data generating model which takes into consideration the existence of common task-related and resting-state components. We first estimate the common task-related temporal component, via two successive stages of generalized canonical correlation analysis and, then, we estimate the common task-related spatial component, leading to a task-related activation map. The experimental tests of our method with synthetic data reveal that we are able to obtain very accurate temporal and spatial estimates even at very low Signal to Noise Ratio (SNR), which is usually the case in fMRI data processing. The tests with real-world fMRI data show significant advantages over standard procedures based on General Linear Models (GLMs).