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

Emmanouela Kosteletou; Panagiotis G Simos; Eleftherios Kavroulakis; Despina Antypa; Thomas G Maris; Athanasios P Liavas; Paris A Karakasis; Efrosini Papadaki

doi:10.3389/fnhum.2021.771668

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

Front Hum Neurosci. 2021 Dec 14:15:771668. doi: 10.3389/fnhum.2021.771668. eCollection 2021.

Authors

Emmanouela Kosteletou¹, Panagiotis G Simos^{2

3}, Eleftherios Kavroulakis⁴, Despina Antypa³, Thomas G Maris⁵, Athanasios P Liavas⁶, Paris A Karakasis⁷, Efrosini Papadaki^{2

4}

Affiliations

¹ Institute of Applied and Computational Mathematics, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece.
² Computational Biomedicine Laboratory (CBML), Institute of Computer Science, Foundation for Research and Technology - Hellas (FORTH), Heraklion, Greece.
³ Department of Psychiatry, School of Medicine, University of Crete, Heraklion, Greece.
⁴ Department of Radiology, School of Medicine, University of Crete, Heraklion, Greece.
⁵ Department of Medical Physics, School of Medicine, University of Crete, Heraklion, Greece.
⁶ School of Electrical and Computer Engineering, Technical University of Crete, Chania, Greece.
⁷ Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States.

Abstract

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.

Keywords: action observation; fMRI; gCCA method; signal intensity; signal sensitivity; task-related fMRI.