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.
Highlights
Functional Magnetic Resonance Imaging is one of the most popular methods for detecting systematic changes in regional brain activity during the performance of cognitive tasks
To characterize the effect of Generalized Canonical Correlation Analysis (gCCA) in more detail, we extracted data on the extent and degree of estimated activation from first-level T-maps focusing on four key regions of the brain network known to be involved in action observation (Simos et al, 2017; Savaki et al, 2021). These regions were selected based on three additional criteria: (1) they cover both posterior and anterior sections of the brain, (2) they include both sensory processing and motor representation cortex areas, and (3) they display a wide range of signal intensities based on our earlier experiment on action observation
Functional Magnetic Resonance Imaging data were obtained from 25 healthy adults without history of neurological or psychiatric disorder, sensory or motor deficit
Summary
Functional Magnetic Resonance Imaging (fMRI) is one of the most popular methods for detecting systematic changes in regional brain activity during the performance of cognitive tasks. In a previous report (Karakasis et al, 2020), a two-stage gCCA method was introduced for single-task multisubject fMRI analysis, under the assumption of a common taskrelated set of spatial and temporal responses The goal of this approach is to capture the basic features of the fMRI signal by taking into consideration both the common task-related spatial component and the common spatial components associated with ongoing, background activity. The goal of the present work was to explore the capacity of an unsupervised method for signal processing in improving the sensitivity of conventional statistical modeling of blockdesign task-related fMRI data This approach relies on gCCA applied to the data after standard preprocessing steps (coregistration to MNI space, anatomic normalization, band-pass filtering/detrending, motion correction, and spatial smoothing). These regions were selected based on three additional criteria: (1) they cover both posterior (occipitotemporal and postcentral gyrus) and anterior sections of the brain (dorsolateral prefrontal), (2) they include both sensory processing (extrastriate visual and primary somatosensory) and motor representation cortex (dorsal and ventral premotor) areas, and (3) they display a wide range of signal intensities based on our earlier experiment on action observation (strong signal in occipitotemporal and dorsal premotor, and weaker, more variable signal across participants in ventral premotor and primary somatosensory cortex)
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