A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify “causal” relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time.

Delivery of pharmacological agents in vitro can often be a difficult, time consuming and costly process. In this paper, we describe an economical method for in vitro delivery using a hydrogel of poly hydroxyethyl methacrylate (PHEMA) that can absorb up to 50% of its weight of any water-solubilized pharmacological agent. This agent will then passively diffuse into surrounding media upon application in vitro. An in vitro test of PHEMA as a drug delivery device was conducted using dissociated rat-cortical neurons cultured on micro-electrode arrays. These micro-electrode arrays permit the real-time measurement of neural activity at 60 different sites across a network of neurons. Neural activity was compared during the application of PHEMA saturated with cell culture media and PHEMA saturated with bicuculline, a widely used pharmacological agent with stereotypical effects on neural activity patterns. Application of PHEMA saturated with bicuculline produced a gradual increase in concentration in vitro. When the minimum effective concentration of bicuculline was reached, which was found to be 0.59 microM using the diffusion properties of PHEMA, it produced the rapid almost periodic synchronized bursting characteristically associated with this agent. In contrast, the application of PHEMA saturated in culture media alone had no effect on neural activity reinforcing its inherent inert properties. Since PHEMA is nontoxic, can be molded into a variety of shapes, quickly manufactured in any laboratory and is inexpensive to produce, the material represents a promising alternative to drug delivery systems on the market today.

An understanding of the in vivo spatial emergence of abnormal brain activity during spontaneous seizure onset is critical to future early seizure detection and closed-loop seizure prevention therapies. In this study, we use Granger causality (GC) to determine the strength and direction of relationships between local field potentials (LFPs) recorded from bilateral microelectrode arrays in an intermittent spontaneous seizure model of chronic temporal lobe epilepsy before, during, and after Racine grade partial onset generalized seizures. Our results indicate distinct patterns of directional GC relationships within the hippocampus, specifically from the CA1 subfield to the dentate gryus, prior to and during seizure onset. Our results suggest sequential and hierarchical temporal relationships between the CA1 and dentate gyrus within and across hippocampal hemispheres during seizure. Additionally, our analysis suggests a reversal in the direction of GC relationships during seizure, from an abnormal pattern to more anatomically expected pattern. This reversal correlates well with the observed behavioral transition from tonic to clonic seizure in time-locked video. These findings highlight the utility of GC to reveal dynamic directional temporal relationships between multichannel LFP recordings from multiple brain regions during unprovoked spontaneous seizures.