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# Data-driven segmentation of cortical calcium dynamics
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# Data-driven cortical domain maps
# Data-driven domain maps of the cortical surface
# Data-driven filtration and segmentation of cortical calcium dynamics
# Data-driven segmentation of cortical calcium dynamics maps a functional domain mosaic
@@ -24,7 +25,7 @@ Sydney C. Weiser¹•, Brian R. Mullen¹•, Desiderio Ascencio², & James B. Ac
## Abstract
Demixing neural signals from artifact signals in videos of neuronal calcium flux across the cerebral hemispheres could help map core functional features of cortical organization. Here we demonstrate that the general solution to multichannel source signal separation, independent component analysis, can optimally recover neural signal content in recordings of neuronal cortical calcium dynamics captured at a rate of 1.5×10⁶ pixels per one-hundred millisecond frame for seventeen minutes. We show that a set of spatial and temporal metrics can be used to build a random forest classifier which separates neural activity and artifact components automatically at human performance. We show how this data produces a functional segmentation of the neocortical sheet, providing a map of 230±14 domains from which extracted time courses maximally represent the underlying signal in each recording. This workflow of data-driven video decomposition and machine classification of signal sources will aid high quality mapping of complex cerebral dynamics.
Demixing neural signals from artifact signals in videos of neuronal calcium flux across the cerebral hemispheres could help map core functional features of cortical organization. Here we demonstrate that the general solution to multichannel source signal separation, independent component analysis, can optimally recover neural signal content in recordings of neuronal cortical calcium dynamics captured at a magnification factor of one and at a sampling rate of 1.5×10⁶ pixels per one-hundred millisecond frame for seventeen minutes. We show that a set of spatial and temporal metrics 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 a functional segmentation of the mouse cerebral sheet, providing a map of 115 domains per {x} cm²-mouse-neocortical-hemisphere from which extracted time courses maximally represent the underlying signal in each recording. This workflow of data-driven video decomposition and machine classification of signal sources will aid high quality mapping of complex cerebral dynamics.