Effective connectivity: Influence, causality and biophysical modeling

TLDR

The paper concludes a series on identifying interacting brain networks with fMRI, focusing on model selection, causality, and deconvolution. The authors argue that effective connectivity can be discovered only through state‑space models that incorporate biophysically informed observation and state equations. They propose state‑space models that incorporate parameter priors, identifiability checks, and compare Dynamic Causal Modeling, Granger Causal Modeling, and other methods, linking past and present statistical causal modeling through Bayesian dependency graphs and Wiener–Akaike–Granger–Schweder influence measures. The authors demonstrate that several field challenges have promising solutions and outline potential future developments.

Abstract

This is the final paper in a Comments and Controversies series dedicated to "The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution". We argue that discovering effective connectivity depends critically on state-space models with biophysically informed observation and state equations. These models have to be endowed with priors on unknown parameters and afford checks for model Identifiability. We consider the similarities and differences among Dynamic Causal Modeling, Granger Causal Modeling and other approaches. We establish links between past and current statistical causal modeling, in terms of Bayesian dependency graphs and Wiener–Akaike–Granger–Schweder influence measures. We show that some of the challenges faced in this field have promising solutions and speculate on future developments.