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Updated: Jul 31, 2025

Functional Calcium Imaging in Developing Cortical Networks

Functional Calcium Imaging in Developing Cortical Networks

Published on: October 22, 2011

Data-driven segmentation of cortical calcium dynamics.

Sydney C Weiser¹, Brian R Mullen¹, Desiderio Ascencio²

¹Department of Molecular, Cell, and Developmental Biology, University of California Santa Cruz, Santa Cruz, California, United States of America.

Plos Computational Biology

|May 1, 2023

Summary

This summary is machine-generated.

Independent component analysis (ICA) effectively demixes neuronal calcium signals in transcranial videos. This method enables automated classification of neural activity and artifacts, facilitating precise mapping of cortical organization.

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Area of Science:

Neuroscience
Computational Neuroscience
Biophysics

Background:

Mapping cortical organization requires accurate demixing of neuronal calcium signals from transcranial videos.
Current methods face challenges in separating neural activity from artifacts in complex cerebral dynamics.

Purpose of the Study:

To develop and validate a method for optimal recovery of neural signal content from widefield calcium imaging data.
To establish a data-driven approach for functional segmentation of the mouse cortex.

Main Methods:

Utilized independent component analysis (ICA) for signal demixing in widefield neuronal calcium recordings.
Developed a random forest classifier using spatial and temporal metrics to distinguish neural activity from artifacts.
Applied the workflow to generate functional segmentation maps of the mouse cerebral cortex.

Main Results:

ICA optimally recovered neural signal content at a minimum sampling rate of 1.5×10^6 pixels/frame.
The random forest classifier achieved human-level performance in separating neural and artifact components.
Generated functional segmentation maps identifying approximately 115 domains per hemisphere.
Revealed distinct regional motifs in domain morphology, with higher-order regions showing larger, eccentric domains.

Conclusions:

The demonstrated workflow of data-driven video decomposition and machine classification enhances high-quality mapping of cerebral dynamics.
This approach provides a robust method for functional segmentation of the mouse cortex.
The findings contribute to a deeper understanding of cortical organization and neural signal processing.