Journal
IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING
Volume 10, Issue 7, Pages 1189-1203Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTSP.2016.2600859
Keywords
Functional brain network; fMRI; filtering; graph signal processing; motor learning; network theory
Categories
Funding
- NSF [CCF-01217963, BCS-1441502, BCS-1430087]
- PHS [NS44393]
- ARO ICB [W911NF-09-0001]
- ARL [W911NF-10-2-0022]
- ARO [W911NF-14-1-0679]
- NIH [R01-HD086888]
- John D. and Catherine T. MacArthur foundation
- Alfred P. Sloan foundation
- Direct For Social, Behav & Economic Scie
- Division Of Behavioral and Cognitive Sci [1430087] Funding Source: National Science Foundation
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This paper presents methods to analyze functional brain networks and signals from graph spectral perspectives. The notion of frequency and filters traditionally defined for signals supported on regular domains such as discrete time and image grids has been recently generalized to irregular graph domains and defines brain graph frequencies associated with different levels of spatial smoothness across the brain regions. Brain network frequency also enables the decomposition of brain signals into pieces corresponding to smooth or rapid variations. We relate graph frequency with principal component analysis when the networks of interest denote functional connectivity. The methods are utilized to analyze brain networks and signals as subjects master a simplemotor skill. We observe that brain signals corresponding to different graph frequencies exhibit different levels of adaptability throughout learning. Further, we notice a strong association between graph spectral properties of brain networks and the level of exposure to tasks performed and recognize the most contributing and important frequency signatures at different levels of task familiarity.
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