Computer-aided neurophysiology and imaging with open-source PhysImage

Improved integration between imaging and electrophysiological data has become increasingly critical for rapid interpretation and intervention as approaches have advanced in recent years. Here, we present PhysImage, a fork of the popular public-domain ImageJ that provides a platform for working with these disparate sources of data, and we illustrate its utility using in vitro preparations from murine embryonic and neonatal tissue. PhysImage expands ImageJ's core features beyond an imaging program by facilitating integration, analyses, and display of 2D waveform data, among other new features. Together, with the Micro-Manager plugin for image acquisition, PhysImage substantially improves on closed-source or blended approaches to analyses and interpretation, and it furthermore aids post hoc automated analysis of physiological data when needed as we demonstrate here. Developing a high-throughput approach to neurophysiological analyses has been a major challenge for neurophysiology as a whole despite data analytics methods advancing rapidly in other areas of neuroscience, biology, and especially genomics. NEW & NOTEWORTHY High-throughput analyses of both concurrent electrophysiological and imaging recordings has been a major challenge in neurophysiology. We submit an open-source solution that may be able to alleviate, or at least reduce, many of these concerns by providing an institutionally proven mechanism (i.e., ImageJ) with the added benefits of open-source Python scripting of PhysImage data that eases the workmanship of 2D trace data, which includes electrophysiological data. Together, with the ability to autogenerate prototypical figures shows this technology is a noteworthy advance.