A MULTIVARIATE EXTENSION OF MUTUAL INFORMATION IN DEVELOPING NEURAL NETWORKS IS A DISCRIMINATIVE MEASURE OF COMPOUND EFFECTS ON NETWORK ACTIVITY
Citation:
Ball, K. AND C. Grant. A MULTIVARIATE EXTENSION OF MUTUAL INFORMATION IN DEVELOPING NEURAL NETWORKS IS A DISCRIMINATIVE MEASURE OF COMPOUND EFFECTS ON NETWORK ACTIVITY. Society of Toxicology Annual meeting, Baltimore, MD, March 12 - 16, 2017.
Impact/Purpose:
This abstract describes a new metric by which to characterize chemical effects on neural network connectivity that is a more robust and sensitive metric than other metrics of connectivity.
Description:
Recordings of neural network activity in vitro are increasingly being used to assess the development of neural network activity and the effects of drugs, chemicals and disease states on neural network function. The high-content nature of the data derived from such recordings can be used to infer effects of compounds or disease states on a variety of important neural functions, including network synchrony. Historically, synchrony of networks in vitro has been assessed either by determination of correlation coefficients (e.g. Pearsons correlation), by determination of cross-correlation histograms between pairs of active electrodes, and/or by pairwise mutual information and related measures. The present study examines the application of Normalized Multiinformation (NMI) as a scalar measure of shared information content in a multivariate network that is robust with respect to changes in network size. Theoretical simulations are implemented to investigate NMI as a measure of complexity and synchrony in a developing network relative to several alternative approaches. Finally, the NMI approach is applied to data collected during exposure of in vitro neural networks to neuroactive compounds during the first 12 days in vitro, and compared to other common measures, including correlation coefficients and mean firing rates of neurons. NMI is shown to be more sensitive to developmental effects than first order synchronous and nonsynchronous measures of network complexity. Further, as a scalar measure of global mutual information in a multivariate network, NMI is framework that is well-suited for making population comparisons between recordings from different networks to test for developmental neurotoxicity effects.