Synthetic neuronal datasets for benchmarking directed connectivity metrics

Background: Datasets consisting of synthetic neural data generated with quantifiable and controlled parameters are a valuable asset in the process of testing and validating directed connectivity metrics. Considering the recent debate in the neuroimaging community concerning the use of directed functional connectivity metrics for fMRI data, synthetic datasets that emulate BOLD dynamics have played a central role by supporting claims that argue in favor, or against, certain metrics. Generative models often used in studies that simulate neuronal activity, with the aim of gaining insight into specific brain regions and functions, have different requirements from the generative models for benchmarking datasets. Even though the latter must be realistic, there is a tradeoff between realism and computational demand that needs to be contemplated and simulations that efficiently mimic the real behavior of single neurons or neuronal populations are preferred, instead of more cumbersome and marginally precise ones. Methods: this work explores how simple generative models are able to produce neuronal datasets, for benchmarking purposes, that reflect the simulated effective connectivity and, how these can be used to obtain synthetic recordings of EEG and fMRI BOLD. The generative models covered here are AR processes, neural mass models consisting of linear and non-linear stochastic differential equations and populations with thousands of spiking units. Forward models for EEG consist in the simple three-shell head model while fMRI BOLD is modeled with the Balloon-Windkessel model or by convolution with a hemodynamic response function. Results: the simulated datasets are tested for causality with the original spectral formulation for Granger causality. Modeled effective connectivity can be detected in the generated data for varying connection strengths and interaction delays. Discussion: all generative models produce synthetic neuronal data with detectable causal effects although the relation between modeled and detected causality varies and less biophysically realistic models offer more control in causal relations such as modeled strength and frequency location.