Effective Connectivity from Single Trial fMRI Data by Sampling Biologically Plausible Models

TL;DR

This paper introduces a novel method combining an adaptive importance sampler with EM to estimate brain causal connectivity from single-trial fMRI data, revealing the influence of neuronal timescales on estimation accuracy.

Contribution

It presents an innovative approach for estimating effective brain connectivity from fMRI data using biologically plausible models and examines the impact of neuronal timescales on estimation reliability.

Findings

The method accurately recovers network structure and connection directions from synthetic data.

Slow neuronal dynamics can bias connectivity estimates if data is generated by fast processes.

Faster neuronal timescales reduce BOLD signal sensitivity to connectivity changes.

Abstract

The estimation of causal network architectures in the brain is fundamental for understanding cognitive information processes. However, access to the dynamic processes underlying cognition is limited to indirect measurements of the hidden neuronal activity, for instance through fMRI data. Thus, estimating the network structure of the underlying process is challenging. In this article, we embed an adaptive importance sampler called Adaptive Path Integral Smoother (APIS) into the Expectation-Maximization algorithm to obtain point estimates of causal connectivity. We demonstrate on synthetic data that this procedure finds not only the correct network structure but also the direction of effective connections from random initializations of the connectivity matrix. In addition--motivated by contradictory claims in the literature--we examine the effect of the neuronal timescale on the sensitivity of the BOLD signal to changes in the connectivity and on the maximum likelihood solutions of the connectivity. We conclude with two warnings: First, the connectivity estimates under the assumption of slow dynamics can be extremely biased if the data was generated by fast neuronal processes. Second, the faster the time scale, the less sensitive the BOLD signal is to changes in the incoming connections to a node. Hence, connectivity estimation using realistic neural dynamics timescale requires extremely high-quality data and seems infeasible in many practical data sets.