Estimating feedforward and feedback effective connections from fMRI time series: Assessments of statistical methods

Network Neuroscience
Ruben Sanchez-RomeroClark Glymour

Abstract

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 str...Continue Reading

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Citations

Oct 16, 2019·Nature Neuroscience·Andrew T ReidMichael W Cole
May 20, 2020·Journal of Cognitive Neuroscience·Ruben Sanchez-Romero, Michael W Cole
Aug 20, 2020·Journal of Cognitive Neuroscience·Sebastian Weichwald, Jonas Peters
Oct 30, 2020·NeuroImage·Stefan FrässleKlaas E Stephan

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Software Mentioned

GlobalMIT
CCD
ALasso
LiNGAM
MVGC
FASK
ICA
MyConnectome
FAS
Java

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