Data-driven segmentation of cortical calcium dynamics

Demixing signals in transcranial videos of neuronal calcium flux across the cerebral hemispheres is a key step before mapping features of cortical organization. Here we demonstrate that independent component analysis can optimally recover neural signal content in widefield recordings of neuronal cortical calcium dynamics captured at a minimum sampling rate of 1.5×106 pixels per one-hundred millisecond frame for seventeen minutes with a magnification ratio of 1:1. We show that a set of spatial and temporal metrics obtained from the components can be used to build a random forest classifier, which separates neural activity and artifact components automatically at human performance. Using this data, we establish functional segmentation of the mouse cortex to provide a map of ~115 domains per hemisphere, in which extracted time courses maximally represent the underlying signal in each recording. Domain maps revealed substantial regional motifs, with higher order cortical regions presenting large, eccentric domains compared with smaller, more circular ones in primary sensory areas. This workflow of data-driven video decomposition and machine classification of signal sources can greatly enhance high quality mapping of complex cerebral dynamics.Author summary: Researchers have been able to record from a large population of neurons across the cortex using calcium indicators in awake, behaving mice; however, many confounding neuronal signals (ie. neurons from different depths) or tissue dynamics (ie. blood flow) influence these recordings. Our custom pipeline utilizes algorithms to identify distinct signals and spatially segment the signal sources into components that can then be characterized. From these components, we show that neuronal signals are distinct from non-neuronal artifacts; further, we are able to remove the artifacts to clean the neuronal signal. The remaining components are spatial segments of the neuronal signals; we use them to create a data-driven map of functional units, which we call domains. We characterize these domains between subsequent recordings and show that they are highly similar within the same animal, given a long enough recording. This data-driven map can provide information about the limitations one can make from the specific recordings. Furthermore, it will enhance our ability to understand the effects on functional maps from animals that do not have a reference map, including mapping functional changes in development, differences between genetically mutated or varying strains of mice.