Abstract
We propose non-local analysis of functional magnetic resonance imaging (fMRI) data in order to detect more brain activity. Our non-local approach combines the ideas of regular fMRI analysis with those of functional connectivity analysis, and was inspired by the non-local means algorithm that commonly is used for image denoising. We extend canonical correlation analysis (CCA) based fMRI analysis to handle more than one activity area, such that information from different parts of the brain can be combined. Our non-local approach is compared to fMRI analysis by the general linear model (GLM) and local CCA, by using simulated as well as real data.
Highlights
It is a well known fact that many parts of the brain work together to solve a given task
Our non-local approach combines the ideas of regular functional magnetic resonance imaging (fMRI) analysis with those of functional connectivity analysis, and was inspired by the non-local means algorithm that commonly is used for image denoising
We extend canonical correlation analysis (CCA) based fMRI analysis to handle more than one activity area, such that information from different parts of the brain can be combined
Summary
It is a well known fact that many parts of the brain work together to solve a given task. A reading task normally results in activation of the visual cortex, Wernicke’s area and Broca’s area Despite this fact, present methods for analysis of functional magnetic resonance imaging (fMRI) data are normally local. We propose to combine information from different parts of the brain, in contrast to only using local averaging, to detect more brain activity. It has been mentioned [1] that linear multi-voxel pattern analysis (MVPA) approaches to fMRI analysis [2, 3, 4] only use information from neighbouring voxels, and are thereby blind to non-local connections. If we know the size of an activity area we know the optimal filter, according to the matched filter theorem, to use a different filter would give worse results
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