Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/DSW.2019.8755550 | 2019 IEEE Data Science Workshop (DSW) |
Keywords | Field | DocType |
Dynamic functional brain connectivity,windowless dynamic correlation,heat kernel,sliding window method,resting state fMRI | Sliding window protocol,Computer science,Resting state fMRI,Algorithm,Heat kernel,Series expansion,Dynamic functional connectivity,Gaussian function,State space,Window function | Conference |
ISBN | Citations | PageRank |
978-1-7281-0709-7 | 0 | 0.34 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shih-Gu Huang | 1 | 43 | 6.44 |
| Moo K. Chung | 2 | 707 | 60.36 |
| Ian C. Carroll | 3 | 0 | 0.34 |
| H. Hill Goldsmith | 4 | 0 | 1.01 |