EPHierStats: a statistical tool to model the hierarchical relationships in electrophysiological data

Electrophysiological datasets are typically analyzed under the assumption that repeated measurements of the same unit of analysis (i.e. neuron or animal) can be treated as statistically independent. Recently, this assumption has been questioned and our data confirms and quantifies this skepticism using ex vivo slice recordings of synaptic currents in D1R+ medium spiny neurons in the nucleus accumbens. We therefore present EPHierStats as a statistical framework to analyze electrophysiological datasets with large numbers of measurements (>100) per unit of analysis. This novel analysis framework enables encoding of the full hierarchical relationships between measurements in a mixed-effects general linear model while also analyzing the distribution of values in assessed variables. Our method can easily be adapted to analyze a wide range of repeated-measures electrophysiological experiments. Implementation of the EPHierStats tool will aid the adaption of modern statistical approaches that prevent pseudoreplication and its associated false discovery rate while enabling statistical assessments of the complex relationships inherent to the field of neuroscience.