ToolConnect: A Functional Connectivity Toolbox for In vitro Networks

Abstract

Nowadays, the use of in vitro reduced models of neuronal networks to investigate the interplay between structural-functional connectivity and the emerging collective dynamics is a widely accepted approach. In this respect, a relevant advance for this kind of studies has been given by the recent introduction of high-density large-scale Micro-Electrode Arrays (MEAs) which have favored the mapping of functional connections and the recordings of the neuronal electrical activity. Although, several toolboxes have been implemented to characterize network dynamics and derive functional links, no specifically dedicated software for the management of huge amount of data and direct estimation of functional connectivity maps has been developed. toolconnect offers the implementation of up to date algorithms and a user-friendly Graphical User Interface (GUI) to analyze recorded data from large scale networks. It has been specifically conceived as a computationally efficient open-source software tailored to infer functional connectivity by analyzing the spike trains acquired from in vitro networks coupled to MEAs. In the current version, toolconnect implements correlation- (cross-correlation, partial-correlation) and information theory (joint entropy, transfer entropy) based core algorithms, as well as useful and practical add-ons to visualize functional connectivity graphs and extract some topological features. In this work, we present the software, its main features and capabilities together with some demonstrative applications on hippocampal recordings.

Super-selective reconstruction of causal and direct connectivity with application toin-vitroiPSC neuronal networks

“…It is interesting to observe how the accuracy data for d =1/9, i.e. prior to using the reconstruction algorithm based on the discriminator threshold d ( Figure 4I, d=1/9), showed performances already higher than 75% for a network of 10 neurons, and even higher for larger sizes, thus exceeding the ones obtained before standard thresholding in published connectivity methods [62]. The complete reconstruction algorithm led to a computational accuracy close to 100% for all network sizes due to the further statistical pruning of false positive connections ( Figure 4I, d =1).…”

“…In this work, we demonstrated a new model-free based approach to reconstruct effective and functional connections from in vitro neuronal networks recorded on MEAs. Our algorithm offers several fundamental differences resulting in critical advancements compared to the state-of-theart connectivity techniques, including correlation and transfer entropy variants [25,62]. To the best of our knowledge, model-free connectivity inference techniques are not able to reconstruct the effective -causal and direct -connections of a recorded neuronal network because they are either missing the causality of signaling or include confounding apparent connections (common input or common output) and multi-neuron pathways.…”

A Tutorial for Information Theory in Neuroscience

“…In other words, using three bins instead of four will produce different results, especially in terms of information values (see Daw et al (2003) for a review of discretization methods and their impacts on data analyses from a more general perspective). Several more advanced methods have been developed, some of which are implemented in other software packages, to overcome these problems in certain cases (Nemenman et al, 2002; Goldberg et al, 2009; Ince et al, 2009; Magri et al, 2009; Ito et al, 2011; Lindner et al, 2011; Pastore et al, 2016). Furthermore, even with the basic analysis methods presented herein, statistical testing methods exist to reduce the appearance of false-positive information theory results caused by discretization and bias effects (see Significance Testing ).…”

“…We found that many of these other packages are focused on a narrower type of analysis. Several software packages have been introduced to calculate transfer entropy (Ito et al, 2011; Lindner et al, 2011; Montalto et al, 2014; Pastore et al, 2016), often with the emphasis on estimating neural connectivity. Also, several software packages have focused on estimating entropy and mutual information using more advanced techniques (e.g., binless and kernel estimation techniques, as well as bias correction) than those presented herein to address problems surrounding continuous data and binning (Goldberg et al, 2009; Ince et al, 2009; Magri et al, 2009; Lindner et al, 2011; Lizier, 2014).…”

“…As a result, these packages took significantly longer to perform a given information theory calculation, but this was not a fair comparison due to the additional calculations performed by the other software package. Second, several software packages could not be made to run on our machines or required programming languages with which we are not familiar (Goldberg et al, 2009; Ince et al, 2009; Magri et al, 2009; Pastore et al, 2016; Moore et al, 2017; dit-contributors, 2018). Obviously, our inability to test the software may be a larger reflection on our programming abilities (or our lack of available time to devote to learning new languages) than on the quality of the software itself.…”