Abstract
Objective: Assessing brain connectivity from electrophysiological signals is of great relevance in neuroscience, but results are still debated and depend crucially on how connectivity is defined and on mathematical instruments utilized. Aim of this work is to assess the capacity of bivariate Transfer Entropy (TE) to evaluate connectivity, using data generated from simple neural mass models of connected Regions of Interest (ROIs).Approach: Signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. We investigated whether the presence of a statistically significant connection can be detected and if connection strength can be quantified.Main Results: Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions, and if the epoch lengths are longer than 10 s. In case of multivariate networks, some spurious connections can emerge (i.e., a statistically significant TE even in the absence of a true connection); however, quite a good correlation between TE and synaptic strength is still preserved. Moreover, TE appears more robust for distal regions (longer delays) compared with proximal regions (smaller delays): an approximate a priori knowledge on this delay can improve the procedure. Finally, non-linear phenomena affect the assessment of connectivity, since they may significantly reduce TE estimation: information transmission between two ROIs may be weak, due to non-linear phenomena, even if a strong causal connection is present.Significance: Changes in functional connectivity during different tasks or brain conditions, might not always reflect a true change in the connecting network, but rather a change in information transmission. A limitation of the work is the use of bivariate TE. In perspective, the use of multivariate TE can improve estimation and reduce some of the problems encountered in the present study.
Highlights
Cognitive phenomena originate from the interaction among several mutually interconnected, specialized brain regions, which exchange information via long range synapses
Taking in mind the previous limitations of former works, the present study was conceived with the following major aims: (i) to analyse the relationship between the TE metrics and the strength of the connectivity parameters using neural mass models (NMMs), in order to assess whether changes in TE from one trial to another can be used to infer an underlying change in connectivity between region of interest (ROI); (ii) to study the role of synapses targeting to excitatory vs. inhibitory populations in affecting functional connectivity (FC); (iii) to reveal how non-linearities can dramatically affect the inference of connection strength, leading to different conclusions on connectivity among regions depending on the particular working condition
Using the open source toolbox Trentool, and neural mass models to generate biologically realistic signals, the present study provides indications on whether brain connectivity can be assessed from bivariate TE
Summary
Cognitive phenomena originate from the interaction among several mutually interconnected, specialized brain regions, which exchange information via long range synapses. Connectivity is often estimated from fMRI neuroimaging techniques (Horwitz, 2003; Friston, 2009; van den Heuvel and Hulshoff Pol, 2010) Thanks to their higher temporal dynamics, electrophysiological data, obtained from electro- or magneto-encephalography, joined with methods for cortical source localization (Koenig et al, 2005; Astolfi et al, 2007; Sakkalis, 2011; Rossini et al, 2019) are receiving increasing attention. DCM assumes that the signals are produced by a state space model (see Table 1 in Valdes-Sosa et al, 2011, for a list of possible equations used in recent papers). This framework requires strong a priori knowledge about the input to the system and the connectivity network. The more suitable network is often chosen among various possible alternatives using Bayesian selection methods (Penny et al, 2004)
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