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. Quantifying correlogram uniformity (signal independence/dependence), peak count and location (firing pattern diversity), and area left of zero (firing order) provides information on network dynamics. Compared to standard approaches that estimate functional connectivity from correlograms, the above quantification comprises an alternative approach that… Quantifies network dynamics without risk of mistaking correlations as cause Uses metrics easier to compare across different length scales or recording types However, provides less connectivity insight without additional quantification or measurements. A MATLAB algorithm that automates this alternative approach is provided and described. The algorithm is validated with three different microelectrode array recordings during which cells were exposed to bicuculline methiodide, pH shock, or impact injury. Algorithm outputs bridge specific signals to the population of signals and do not lose or conceal information on heterogenous network responses.