Description
Dynamic correlation offers far
superior modeling flexibility over static
correlation in functional brain signals (Chung
et al. 2019) [3]. Sliding window (SW) and
tapered sliding window (TSW) 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 method is published in Huang
et al. (2019) [1].The MATLAB code windowless.zip will perform the SW, TSW and windowless heat kernel method in a simulated network data with the ground truth. The state space is estimated using the k-means clustering that was explained in Huang et al., 2018 [2]. If you use any part of code, please reference Huang et al. (2019) [1].
Reference
[1] Huang, S.-G., Chung, M.K., Carroll, I.C., Goldsmith, H.H. 2019 Dynamic functional connectivity using heat kernel. IEEE Data Science Workshop (DSW), in press[2] Huang, S.-G., Samdin, S., Ting, C.M., Ombao, H., Chung, M.K. 2018 Statistical model for dynamically-changing correlation matrices with application to resting-state brain connectivity. arXiv:1812.10050
[3] Chung, M.K, Lee, H., Ombao, H., Solo, V. 2019. Exact topological inference of the resting state brain networks in twins, Network Neuroscience, in press
