Effective connectivity: Influence, causality and biophysical modeling

By Pedro A. Valdes-Sosa, Alard Roebroeck, Jean Daunizeau, Karl J. Friston. NeuroImage, 2011

400 citations recorded by the source corpus.

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.

Subfields: Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fdb44830dca75db75c0edf9a62e5c9ff92901832

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Molecular Biology

Citations in the corpus

Structure and function of complex brain networks (2013, background): “Effective connectivity represents a third and increasingly important mode of representing and analyzing brain networks.(11,15) Effective connectivity attempts to capture a network of directed causal effects between neural elements.”
Contributions and challenges for network models in cognitive neuroscience (2014, methodology): “Detecting patterns of effective connectivity involves the formulation of appropriate neural models that combine biophysically based nodal dynamics and an anatomically based coupling structure, followed by model inversion and inference 85 .”
A critical assessment of connectivity measures for EEG data: A simulation study (2013, methodology): “Algorithms implementing this definition are often subsumed under the term Granger-causal modeling (Valdes-Sosa et al., 2011), although the technique that is known as Granger causality is only one way to measure time-lagged influence.”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, neutral): “Regional differences in vasculature, neurovascular coupling and other hemodynamic parameters can alter the temporal relations between regional BOLD signal changes relative to those observed at the neuronal level and thus distort measures of directed connectivity based on BOLD time series alone (Davi”
The future of FMRI connectivity (2012, background): “One class is lag-based (more generally, multivariate autoregressive modelling (Valdes-Sosa et al., 2011)), the most common example being Granger causality (Granger, 1969).”
Advancing functional connectivity research from association to causation (2019, methodology): “Specifically, assumptions should be made explicit if they are judged to: (1) be essential for interpreting a methodological result or inference; and/or (2) be uncertain or have the potential to be contentious.”
Analysing connectivity with Granger causality and dynamic causal modelling (2013, background): “variations explicitly and tries to explain the data at the level of hidden neuronal states — in other words, it tries to get beneath the surface structure of the data to explain how they were generated: see [45,46 ] for further discussion.”
Functional and Effective Connectivity: A Review (2011, neutral): “There are many other interesting debates about the use of Granger causality in fMRI time series analysis [see Valde´s-Sosa et al. (2011) for a full discussion of these issues].”