Analyzing brain networks with PCA and conditional Granger causality
Southeast University · University of Florida
Abstract
Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA…
Citation impact
- FWCI
- 10.83
- Percentile
- 100%
- References
- 54
Authors
6Topics & keywords
- Granger causality
- Pairwise comparison
- Autoregressive model
- Voxel
- Artificial intelligence
- Pattern recognition (psychology)
- Principal component analysis
- Computer science