Temporal Non-Local Means Filtering Reveals Real-Time Whole-Brain Cortical Interactions in Resting fMRI

• Published in: PloS one 2016-07, Vol.11 (7), p.e0158504-e0158504
• Main Authors: Bhushan, Chitresh, Chong, Minqi, Choi, Soyoung, Joshi, Anand A, Haldar, Justin P, Damasio, Hanna, Leahy, Richard M
• Format: Article
• Language: English
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Blurring; Brain; Brain - physiology; Brain Mapping; Computer and Information Sciences; Cortex; Data processing; Engineering and Technology; Evaluation; Filtration; Fluctuations; Functional magnetic resonance imaging; Ground truth; Humans; Magnetic Resonance Imaging; Medicine and Health Sciences; Methods; Models, Theoretical; Natural language processing; Networks; Neurosciences; NMR; Noise; Nuclear magnetic resonance; Parcels; Physiology; Preprocessing; Research and Analysis Methods; Signal processing; Time series; Variations; Visualization (Computer)
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0158504

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.