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

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).

Improving the Sensitivity of Task-Related Functional Magnetic Resonance Imaging Data Using Generalized Canonical Correlation Analysis.

General Linear Modeling (GLM) is the most commonly used method for signal detection in Functional Magnetic Resonance Imaging (fMRI) experiments, despite its main limitation of not taking into consideration common spatial dependencies between voxels. Multivariate analysis methods, such as Generalized Canonical Correlation Analysis (gCCA), have been increasingly employed in fMRI data analysis, due to their ability to overcome this limitation. This study, evaluates the improvement of sensitivity of the GLM, by applying gCCA to fMRI data after standard preprocessing steps. Data from a block-design fMRI experiment was used, where 25 healthy volunteers completed two action observation tasks at 1.5T. Whole brain analysis results indicated that the application of gCCA resulted in significantly higher intensity of activation in several regions in both tasks and helped reveal activation in the primary somatosensory and ventral premotor area, theoretically known to become engaged during action observation. In subject-level ROI analyses, gCCA improved the signal to noise ratio in the averaged timeseries in each preselected ROI, and resulted in increased extent of activation, although peak intensity was considerably higher in just two of them. In conclusion, gCCA is a promising method for improving the sensitivity of conventional statistical modeling in task related fMRI experiments.

Multi-subject Task-related fMRI Data Analysis via Generalized Canonical Correlation Analysis.

Functional magnetic resonance imaging (fMRI) is one of the most popular methods for studying the human brain. It measures brain activity, by detecting local changes of Blood Oxygen Level Dependent (BOLD) signal in the brain, over time, and can be used in both task-related and resting-state studies. In task-related studies, our aim is to determine which brain areas are activated when a specific task is performed. Various unsupervised multivariate statistical methods are being increasingly employed in fMRI data analysis. Their main goal is to extract information from a dataset, often with no prior knowledge of the experimental conditions. Generalized canonical correlation analysis (gCCA) is a well known statistical method that can be considered as a way to estimate a linear subspace, which is "common" to multiple random linear subspaces. We propose a new fMRI data generating model which takes into consideration the existence of common task-related and resting-state components. We estimate the common spatial task-related component via a two-stage gCCA. We test our theoretical results using real-world fMRI data. Our experimental findings corroborate our theoretical results, rendering our approach a very good candidate for multi-subject task-related fMRI processing.Clinical Relevance-This work provides a set of methods for amplifying and recovering commonalities across subjects that appear in data from multi-subject task-related fMRI experiments.