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A Functional Connectivity Analysis Toolbox for Multiple Spike Trains Data: “ToolConnect”

Vito P. Pastore1*, Aleksandar Godjoski1, Sergio Martinoia1 and Paolo Massobrio1
  • 1 University of Genova, DIBRIS, Italy

INTRODUCTION In contemporary neuroscience understanding how network dynamics arises from the properties of groups of neurons (at different levels of organization) and especially from their connectivity is an extremely challenging job. The structural connections shape large-scale neuronal dynamics which can be captured as patterns of functional and effective connectivity. Functional connectivity describes statistical patterns of dynamic interactions among regions, also called “functional networks,” while effective connectivity attempts to discern networks of causal influences [1]. In most experimental models the anatomical connectivity is only partially known and/or can be partially estimated, therefore, functional connectivity techniques are often used to assess network interaction [2]. The analysis of multiple neural spike trains recorded from experimental models has gained tremendous relevance with the widespread application of Micro-Electrode Arrays (MEAs). The computational challenge is to come out with a host of data processing and analysis techniques that would enable reliable inference of the underlying functional connectivity patterns [3]. To the best of our knowledge, there is no available dedicated software that implements a collection of different methods for functional connectivity analysis of multiple neural spike trains. Therefore, the purpose of this work was to develop a user-friendly toolbox [4] to provide the researchers community with a powerful tool to perform functional connectivity analysis on in vitro neuronal networks coupled to standard and high-density MEAs. The main objective was to guarantee computational efficiency and accuracy. MATERIALS AND METHODS We used Microsoft Visual Studio with .NET 4.5 framework environment to develop TOOLCONNECT as a standalone windows Graphical User Interface (GUI) application written in C#, with a modular windows forms based implementation. Some dedicated interfaces of the GUI allow the user to: plot the correlograms between each couple of electrodes (for cross- and partial- correlation); thresh and plot the Connectivity Matrix (CM) and the connectivity graph; extract the main topological features (degree, cluster coefficient, path length). One major feature is the toolbox's independence from the acquisition system (e.g., Multi Channel Systems – Reutlingen, Germany, Qwane Biosciences - Lausanne, Switzerland, 3Brain – Wädenswil, Switzerland) and from the MEA layout (number of electrodes and spatial organization). The toolbox offers powerful tools to manipulate data and to perform functional connectivity analysis based on two correlation methods (cross-correlation and partial correlation) and two information theory based methods (transfer entropy and joint entropy). I) Cross Correlation (CC) measures the frequency at which one particular neuron or electrode fires (“target”) as a function of time, relative to the firing of an event in another network (“reference”). Mathematically, CC reduces to a probability Cxy(τ) of observing an event in a train Y at time (t+τ), because of an event in another train X at time t (τ is the time shift or time lag) [5]. II) Partial Correlation analysis allows to distinguish between direct and indirect connections and common input by removing the portion of the relationship between two neural spike trains that can be attributed to linear relationships with recorded spike trains from other neurons [6], [7]. III) Transfer Entropy (TE) is an information theoretic measure able to estimate causal relationships from time series taking into account their past activity [8]. TE is not symmetric with respect to the exchange of the variables (spike trains) X and Y. IV) Joint Entropy is based on the analysis of the cross Inter-Spike-Intervals (cISI): if two neurons are strongly connected the cISI histogram will show a peak and Joint Entropy will be close to zero, otherwise the cISI histogram will be almost flat, and the Joint Entropy will be high [9]. RESULTS We designed TOOLCONNECT taking care to satisfy the user-friendliness requirement and developed it hiding the algorithm‘s implementation specifics and code’s details. The toolbox’s GUI is intuitive and straightforward to use and permits also to inexperienced users to specify all the input parameters, to perform functional connectivity analysis and to obtain a graphical representation of the results. Figure 1 shows a screenshot of the last release of TOOLCONNECT’s GUI. To assess the performances of TOOLCONNECT in terms of reliability, accuracy and computational efficiency, we performed functional-connectivity analysis based on the cross-correlation method on cortical neuronal networks coupled to the MEA60 and the MEA2100 acquisition systems of Multi Channel Systems (MCS) [10] and the BioCam acquisition system of 3Brain Systems [12]. Figure 2 shows the connectivity graphs obtained from the analysis (for further details see [4]). CONCLUSIONS To the best of our knowledge, TOOLCONNECT is the first functional connectivity toolbox dedicated to the analysis of multiple spike trains recorded from in-vitro neural networks coupled to MEAs, that offers a collection of different connectivity methods and powerful tools to represent all the results in a graphical form through a user-friendly GUI. We developed TOOLCONNECT and implemented all the methods taking care to obtain an optimal resource usage (RAM) and to reduce the computational time. We obtained acceptable performances with computational time lower than 2 minutes (for 10 minutes of recording sampled at 10 kHz), that make TOOLCONNECT suitable for analysis of data from high-density recording systems (e.g., the 4096 electrodes of the 3brain system). Therefore, it will be possible to perform functional connectivity analysis on neural networks with dimensions of thousands of neurons preserving an acceptable spatial and temporal resolution, allowing to obtain realistic and complete information on the dynamics and the topology of such systems. TOOLCONNECT is available on INCF-Software center at the following web address: http://software.incf.org/software/toolconnect.

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Available: http://www.halcyon.com/pub/journals/ 21ps03-vidmar
[10] Multi Channel Systems – Reutlingen, Germany [Website] : http://www.multichannelsystems.com
[11] Qwane Biosciences - Lausanne, Switzerland [Website]: http://www.qwane.com
[12] 3Brain – Wädenswil, Switzerland [Website]: http://www.3brain.com

Keywords: functional connectivity, spike train analysis, correlational methods, Information Theory, MEA - Multi-electrodes arrays

Conference: Neuroinformatics 2016, Reading, United Kingdom, 3 Sep - 4 Sep, 2016.

Presentation Type: Poster

Topic: Computational neuroscience

Citation: Pastore VP, Godjoski A, Martinoia S and Massobrio P (2016). A Functional Connectivity Analysis Toolbox for Multiple Spike Trains Data: “ToolConnect”. Front. Neuroinform. Conference Abstract: Neuroinformatics 2016. doi: 10.3389/conf.fninf.2016.20.00035

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Received: 29 Apr 2016; Published Online: 18 Jul 2016.

* Correspondence: MD. Vito P Pastore, University of Genova, DIBRIS, GENOVA, Italy, vitopaolopastore@gmail.com