Dynamic Functional Connectivity Using Heat Kernel

Shih Gu Huang, Moo K. Chung, Ian C. Carroll, H. Hill Goldsmith

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations


Sliding and tapered sliding window methods are the most common approaches in computing dynamic correlations between brain regions. However, due to data acquisition and physiological artifacts in resting-state fMRI, the sidelobes of the window functions in spectral domain will cause high-frequency fluctuations in dynamic correlations. To address the problem, we propose to define the heat kernel, a generalization of the Gaussian kernel, on a circle continuously without boundary. The windowless dynamic correlations are then computed by the weighted cosine series expansion, where the weights are related by the heat kernel. The proposed method is applied to the study of dynamic interhemispheric connectivity in the human brain in identifying the state space more accurately than the existing window methods.
Original languageEnglish (US)
Title of host publication2019 IEEE Data Science Workshop (DSW)
Number of pages5
ISBN (Print)9781728107080
StatePublished - Jul 4 2019
Externally publishedYes

Bibliographical note

KAUST Repository Item: Exported on 2022-06-30
Acknowledgements: This study was supported by NIH research grants EB022856, MH101504, P30HD003352, U54HD09025 and UL1TR002373. We would like to thank Siti Balqis Samdin, Chee-Ming Ting and Hernando Ombao of KAUST and Martin Lindquist of JHU for providing valuable discussion and support.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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