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.