Evaluation of the Performance of Information Theory-Based Methods and Cross-Correlation to Estimate the Functional Connectivity in Cortical Networks

Abstract

Functional connectivity of in vitro neuronal networks was estimated by applying different statistical algorithms on data collected by Micro-Electrode Arrays (MEAs). First we tested these “connectivity methods” on neuronal network models at an increasing level of complexity and evaluated the performance in terms of ROC (Receiver Operating Characteristic) and PPC (Positive Precision Curve), a new defined complementary method specifically developed for functional links identification. Then, the algorithms better estimated the actual connectivity of the network models, were used to extract functional connectivity from cultured cortical networks coupled to MEAs. Among the proposed approaches, Transfer Entropy and Joint-Entropy showed the best results suggesting those methods as good candidates to extract functional links in actual neuronal networks from multi-site recordings.

Functional network inference of the suprachiasmatic nucleus

“…Although fast-scale and electrical connections between individual neurons have been identified by methods such as mutual information, transfer entropy, directed transfer functions, Granger causality, or betweensample analysis of connectivity (BSAC) (27,(38)(39)(40)(41), these methods are not suitable here due to nonstationary gene expression and the slow-scale nature of VIP and GABA feedback to the core oscillator, resulting in the damping of high-frequency signals (16,41,42). High-frequency GABA signals affect the firing of SCN neurons and have been mapped previously (27); however, fast scale GABA is not thought to affect the core oscillator (16).…”

Connection‐Strength Estimation of Neuronal Networks by Fitting for Izhikevich Model

“…Finally, a convergence criterion was applied and the final maximum likelihood estimate was obtained by repeating Step 1 and Step 2 until convergence of the fluctuations. In the previous methods [1][2][3], the connection strength is found only between two neurons; in contrast, the proposed method deals with overall relations. In addition the repetitive procedure involving Step 1 and Step 2 is similar to the EM algorithm [7].…”

Kernel Analysis for Estimating the Connectivity of a Network with Event Sequences

“…Estimating connectivity of a network has been an active research area [13,11]. Analyzing cortical neural networks has been one of the prominent examples of this task [10].…”

“…Okatan et al defined a likelihood function for connectivity and proposed a method of inference [24]. Garofalo et al compared various methods of connectivity estimation and reported that transfer entropy and joint entropy give the best results [11].…”

“…There are already various proposals for estimating the connections of a network, including ones that use Granger causality [13], transfer entropy [35,43], a directed transfer function [17,18], directed information [30], dynamic causal modeling [10], a likelihood function [24], and joint entropy [11]. This paper proposes a new approach that uses a positive definite kernel on event sequences.…”