Entropic regression with neurologically motivated applications
Abstract
The ultimate goal of cognitive neuroscience is to understand the mechanistic neural processes underlying the functional organization of the brain. The key to this study is understanding the structure of both the structural and functional connectivity between anatomical regions. In this paper, we use an information theoretic approach, which defines direct information flow in terms of causation entropy, to improve upon the accuracy of the recovery of the true network structure over popularly used methods for this task such as correlation and least absolute shrinkage and selection operator regression. The method outlined above is tested on synthetic data, which is produced by following previous work in which a simple dynamical model of the brain is used, simulated on top of a real network of anatomical brain regions reconstructed from diffusion tensor imaging. We demonstrate the effectiveness of the method of AlMomani et al. [Chaos 30, 013107 (2020)] when applied to data simulated on the realistic diffusion tensor imaging network, as well as on randomly generated small-world and Erdös–Rényi networks.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Nov 01, 2021
- Source ID
- 10.1063/5.0039333
Entities
People
- Abd AlRahman R AlMomani
- Alexander Dewitt
- Erik Bollt
- Jeremie Fish
- Paul J. Laurienti
Organizations
- Clarkson University
- Defense Advanced Research Projects Agency
- Wake Forest School of Medicine