Functional imaging of neural cell populations is critical for mapping intra- and inter-regional network dynamics across the neocortex. Recently we showed that an unsupervised machine learning decomposition of densely sampled recordings of cortical calcium dynamics results in a collection of components comprised of neuronal signal sources distinct from optical, movement, and vascular artifacts. Here we build a supervised learning classifier that automatically separates neural activity and artifact components, using a set of extracted spatial and temporal metrics that characterize the respective components. We demonstrate that the performance of the machine classifier matches human identification of signal components in novel data sets. Further, we analyze control data recorded in glial cell reporter and non-fluorescent mouse lines that validates human and machine identification of functional component class. This combined workflow of data-driven video decomposition and machine classification of signal sources will aid robust and scalable mapping of complex cerebral 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.
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