Quantifying Correlogram Shape to Analyze Neuronal Firing Dynamics Recorded in TBI-on-a-Chip.

Changes in neuronal network dynamics in response to different treatments or conditions underly brain function and pathology. A multitude of tools, including electroencephalography, voltage or calcium imaging, and microelectrode array (MEA) recordings, exist to record signals from neuronal firing at different length scales. Correlograms are a standard analysis tool to study the relationship between a pair of recorded neuronal signals. Correlogram shape can provide information about signal independence, firing pattern, and firing order. However, such analysis is performed manually and qualitatively, limiting the amount of information gained. To overcome this limitation, a MATLAB algorithm was developed to automate correlogram shape quantification by calculating correlogram uniformity, peak count and location, and area left of zero, which respectively quantify signal independence/dependence, firing pattern, and firing order. Algorithm outputs were validated using three different MEA recordings during which cells were exposed to bicuculline methiodide, pH shock, or impact injury. Algorithm outputs described signaling changes in all three recordings, bridged changes in individual signal pairings to changes in the entire signal population, and agreed with literature studies. Therefore, this algorithm serves as a useful means of automatically quantifying changes in signal dependence, firing pattern, and firing order across time within a single recording, and across different recordings. These features are also common to all recording techniques, and therefore can be compared across different types of recordings. Therefore, the MATLAB algorithm described in this article can help provide insight into how neural network dynamics are altered by different drugs or conditions.