Abstract
Intensity variations over time in resting BOLD fMRI exhibit spatial correlation patterns consistent with a set of large scale cortical networks. However, visualizations of this data on the brain surface, even after extensive preprocessing, are dominated by local intensity fluctuations that obscure larger scale behavior. Our novel adaptation of non-local means (NLM) filtering, which we refer to as temporal NLM or tNLM, reduces these local fluctuations without the spatial blurring that occurs when using standard linear filtering methods. We show examples of tNLM filtering that allow direct visualization of spatio-temporal behavior on the cortical surface. These results reveal patterns of activity consistent with known networks as well as more complex dynamic changes within and between these networks. This ability to directly visualize brain activity may facilitate new insights into spontaneous brain dynamics. Further, temporal NLM can also be used as a preprocessor for resting fMRI for exploration of dynamic brain networks. We demonstrate its utility through application to graph-based functional cortical parcellation. Simulations with known ground truth functional regions demonstrate that tNLM filtering prior to parcellation avoids the formation of false parcels that can arise when using linear filtering. Application to resting fMRI data from the Human Connectome Project shows significant improvement, in comparison to linear filtering, in quantitative agreement with functional regions identified independently using task-based experiments as well as in test-retest reliability.
Highlights
Low frequency fluctuations in BOLD activity during resting functional MRI exhibit correlations between cortical regions that are known to be physiologically related, as first shown by Biswal et al [1, 2]
LB filtering does not use information about the time course to filter at each point in time and so can mix signals across neighboring vertices with very dissimilar time courses; temporal non-local means (tNLM) filtering on the other hand uses weights based on similarity of the time series and so can avoid mixing of signals from dissimilar vertices
The results shown above support the primary claim of this report: that temporal non-local means filtering is able to denoise resting fMRI data while retaining spatial structure that reflects ongoing dynamic brain activity
Summary
Low frequency fluctuations in BOLD activity during resting functional MRI (rfMRI) exhibit correlations between cortical regions that are known to be physiologically related, as first shown by Biswal et al [1, 2] These correlations are the basis for identification of functional networks from rfMRI in individuals and groups [2,3,4,5]. Temporal Non-Local Means (tNLM) Filtering for Functional MRI preprocessed prior to network analysis with a pipeline that includes compensation for susceptibility-induced distortion, slice timing and subject motion, as well as high-pass filtering of individual time series and removal of ICA-identified temporal noise components [6,7,8] Even after this extensive preprocessing, when visualized as a time series or movie of cortical activity, correlated patterns of BOLD variation reflecting time-varying brain activity are not readily visible in the data. Local variations in network-related BOLD activity and unrelated physiological and other noise in the data mask these underlying patterns
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