The USC Andrew and Erna Viterbi School of Engineering USC Signal and Image Processing Institute USC Ming Hsieh Department of Electrical Engineering University of Southern California

Technical Report USC-SIPI-423

“Non-Local Means Filtering Reveals Real-Time Whole-Brain Cortical Interactions in Resting fRI”

by Chitresh Bhushan, Minqi Chong, Soyoung Choi, Anand A. Joshi, Justin P. Haldar, Hanna Damasio, and Richard M. Leahy

August 2015

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 the development of new insights into spontaneous brain dynamics. 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 parcellation, showing improved performance relative to un filltered and Laplace-Beltrami smoothed data.


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