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.
Highlights
In the last years, one of the major issues of computational neuroscience has been to understand the organization principles that rule brain connectivity datasets
The Active Pixel Sensor (APS) array (Berdondini et al, 2009) and the highdensity CMOS array (Matsuda et al, 2003) make use of 4096 and 11,000 electrodes respectively; using these recording systems means dealing with a huge amount of data
The Graphical User Interface (GUI) offers a drop-down menu that allows the selection and opening of the addressed interfaces designed for the Computational and the Implementation Strategies Section we describe the implementation and computational optimization strategies that we followed to develop the algorithms currently included in TOOLCONNECT: cross-correlation, partial correlation, transfer entropy and joint entropy
Summary
One of the major issues of computational neuroscience has been to understand the organization principles that rule brain connectivity datasets. Thanks to the development of new non-invasive approaches, a large amount of information regarding the structural organization and functional association of different brain areas have been obtained (Bandettini, 2012). Accurate and detailed studies for reconstructing anatomical connectivity have been performed, and a description of the brain’s structural connectivity (i.e., the connectome) is partly available (Sporns et al, 2005). Structural connectivity drives and influences the network dynamics expressed by a neuronal. An important contribution to the characterization of the estimated network maps comes from the BRAIN CONNECTIVITY TOOLBOX devised by the group of Sporns (Rubinov and Sporns, 2010) which embodies a rich collection of metrics developed in different programming languages (Matlab, Python, and C++) used to characterize the graphs describing the connectivity of neuronal networks
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