| Paper: | PS-2B.29 |
| Session: | Poster Session 2B |
| Location: | Symphony/Overture |
| Session Time: | Friday, September 7, 19:30 - 21:30 |
| Presentation Time: | Friday, September 7, 19:30 - 21:30 |
| Presentation: |
Poster
|
| Publication: |
2018 Conference on Cognitive Computational Neuroscience, 5-8 September 2018, Philadelphia, Pennsylvania |
| Paper Title: |
Controllability Analysis on Functional Brain Networks |
| Manuscript: |
Click here to view manuscript |
| DOI: |
https://doi.org/10.32470/CCN.2018.1168-0 |
| Authors: |
Shi Gu, Shikuang Deng, University of Electronic Science and Technology of China, China |
| Abstract: |
Controllability analysis on brain networks is an emerging subfield of network neuroscience. It utilizes both the classical and modern control theories to understand the roles of control regions and their energetic properties in certain neural circuits. The previous framework is based on the structural networks built from diffusion imaging thus lacks the adaptability to the functional networks. Here, we apply the system identification algorithm to the BOLD time series to infer the effective connectivity matrix and noise structure, followed by transferring the recognized stochastic dynamics into the linear system and quantifying the controllability via the minimal control sets, the global controllability, the average controllability and the synchronizability. This work provides a complementary part of the structural controllability analysis and enables the investigation of controllability on functional brain networks. |
