Granger causality based approaches are popular in unveiling directed interactions among brain regions. The present work advocates a multi-kernel based nonlinear model for obtaining the effective connectivity between brain regions, by wedding the merits of partial correlation in undirected topology identification with the ability of partial Granger causality (PGC) to estimate edge directionality. The premise is that existing linear PGC approaches may be inadequate for capturing certain dependencies, whereas available nonlinear connectivity models lack data adaptability that multi-kernel learning methods can offer. The proposed approach is tested on both synthetic and real resting-state fMRI data, with the former illustrating the gains in directed edge presence detection performance, as compared to existing PGC methods, and with the latter highlighting differences in the estimated test statistics.
|Original language||English (US)|
|Title of host publication||2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||5|
|State||Published - Jun 16 2017|
|Event||2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017 - New Orleans, United States|
Duration: Mar 5 2017 → Mar 9 2017
|Name||ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings|
|Other||2017 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2017|
|Period||3/5/17 → 3/9/17|
Bibliographical noteFunding Information:
This work was supported by NSF 1500713, 1514056, and NIH 1R01GM104975-01.
© 2017 IEEE.
- kernel-based regression
- multiple kernel learning
- partial Granger causality
- partial correlation