Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods
| Autor: | Clark Glymour, Ruben Sanchez-Romero, Joseph D. Ramsey, Biwei Huang, Madelyn R. K. Glymour, Kun Zhang |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
lcsh:RC321-571 Directed networks 03 medical and health sciences 0302 clinical medicine Artificial Intelligence Resampling Effective connectivity lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Research Articles 030304 developmental biology 0303 health sciences Feedback networks Series (mathematics) Orientation (computer vision) Applied Mathematics General Neuroscience fMRI Linear system Feed forward Regression Graphical causal models Computer Science Applications Skewness Adjacency list Algorithm 030217 neurology & neurosurgery |
| Zdroj: | Network Neuroscience, Vol 3, Iss 2, Pp 274-306 (2019) Network Neuroscience |
| ISSN: | 2472-1751 |
| DOI: | 10.1162/netn_a_00061 |
| Popis: | We test the adequacies of several proposed and two new statistical methods for recovering the causal structure of systems with feedback from synthetic BOLD time series. We compare an adaptation of the first correct method for recovering cyclic linear systems; Granger causal regression; a multivariate autoregressive model with a permutation test; the Group Iterative Multiple Model Estimation (GIMME) algorithm; the Ramsey et al. non-Gaussian methods; two non-Gaussian methods by Hyvärinen and Smith; a method due to Patel et al.; and the GlobalMIT algorithm. We introduce and also compare two new methods, Fast Adjacency Skewness (FASK) and Two-Step, both of which exploit non-Gaussian features of the BOLD signal. We give theoretical justifications for the latter two algorithms. Our test models include feedback structures with and without direct feedback (2-cycles), excitatory and inhibitory feedback, models using experimentally determined structural connectivities of macaques, and empirical human resting-state and task data. We find that averaged over all of our simulations, including those with 2-cycles, several of these methods have a better than 80% orientation precision (i.e., the probability of a directed edge is in the true structure given that a procedure estimates it to be so) and the two new methods also have better than 80% recall (probability of recovering an orientation in the true structure). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|