analyzing the activity correlations within s1 barrel cortex of neonatal mice after two-photon imaging

Neuronal Activity Mapping in Developing Mouse Sensory Cortex

The cerebral cortex contains several sensory areas with distinct functions. The areas receive inputs originating from their corresponding sensory organs, mostly conveyed through the spinal cord or brainstem and relayed via the thalamus1,2. Notably, neurons in each primary sensory area exhibit uniquely synchronized activity during early developmental stages, which also originate from sensory organs or the lower nervous centers, but essentially differ from the activities observed in the mature cortex3.
In neonatal rodents, for example, the primary visual area (V1) displays wave-like activity, which originates in the retina (retinal wave) and propagates through the entire visual pathway while conserving retinotopy4. The primary auditory area (A1) exhibits synchronous activity organized in band-shaped subregions that correspond to the isofrequency bands in the mature brain. The activity emanates from the cochlea&#39;s inner hair cells5,6. The barrel cortex in the primary somatosensory area (S1) shows a patchwork-like activity pattern in which layer 4 neurons within individual barrels, namely, neurons responsive to individual whiskers, are synchronously activated7. Although proposed to originate from the trigeminal ganglion, the source of the activity remains unknown7. Consequently, neonatal activity patterns are specialized both within each primary sensory area and within intra-areal subfields. The simultaneous visualization of neuronal activity and structure of primary sensory areas may facilitate an inquiry into the contribution of these activity patterns to the development of sensory systems.
In this article, we summarized a series of protocols: (1) to visualize individual neuronal activities using sparse labeling of GCaMP and primary sensory areas using TCA-RFP mice that express red fluorescent protein in thalamocortical axons7, (2) to image single cell-level activity in neonatal mice using two-photon microscopy in vivo, and (3) to analyze the activity correlations within S1 barrel cortex. The representative results show patchwork-like synchronized activity within individual barrels of a postnatal day (P)6 mouse. Despite some limitations, this technique can be used for chronic imaging, wide-field imaging across multiple sensory areas, and various manipulation experiments. The multifaceted analysis of neuronal activity during development will enrich our comprehension of brain circuit formation mechanisms.

Author Spotlight: Deciphering Neural Circuit Formation from Two-Photon Microscopy and Single Neuron Imaging

In Vivo Visualization of Spontaneous Activity in Neonatal Mouse Sensory Cortex at a Single-Neuron Resolution

The purpose of our research is to investigate the formation of neural circuits in the developing cerebral cortex. We aim to understand the role of neuronal activity in this process. In recent years, two-photon microscopy has made it possible to observe the process of neuronal circuit formation in living animals.We have developed a method for imaging single neurons in the neonatal cerebral cortex. By simultaneously observing activity patterns and changes in their morphology, we will be able to clarify which activity patterns are involved in neuronal circuit formation.

Analyzing the Activity Correlations Within S1 Barrel Cortex of Neonatal Mice After Two-Photon Imaging

To begin, initiate MATLAB and execute the EZcalcium toolbox to access the initial GUI. Within the initial GUI, select Motion Correction to open the motion correction GUI. Use the Add Files option to upload a TIF file containing the imaging data.Next, set the non-rigid motion correction to blank, upsampling factor to 50, maximum shift to 15, initial batch size and bin width to 200. Click on Run Motion Correction to initiate the correction. Within the initial GUI, activate automated ROI detection to access the ROI detection GUI.Use the Add Files feature to import the motion-corrected imaging data. Set initialization to greedy, search method to ellipse, deconvolution to constrained FOOPSI-SPGL1, and autoregression to decay. Then set the estimated number of ROIs to 60.Assign the estimated ROI width to 17, merge threshold to 0.95, fudge factor to 0.95, spatial and temporal downsampling to one, and temporal iterations to five. Then click Run ROI Detection to initiate the detection process. In the initial GUI, select ROI Refinement to launch the ROI refinement GUI.Use the Low Data button to import ROI data. Select the ROIs with low activity frequency situated beneath the skull or those overlapping with other neurons/neurites. Click Exclude ROI to exclude those ROIs from subsequent analysis.Calculate the delta F by F values using this equation. Choose XLSX as the data export format and execute export data to generate an Excel file populated with the raw delta F by F values. Compute the Pearson's correlation coefficient for the delta F by F values among the ROIs and construct a matrix of the correlation coefficients.Use Fiji software to delineate the barrel boundaries from the TCA-RFP image. Then assign the ROIs to their corresponding barrels or septa. Compare the pairwise correlation coefficients within identical barrels and distinct barrels.Generate 1, 000 to 10, 000 surrogate datasets by randomly permuting the association between ROI positions and calcium ion traces. In each surrogate dataset, compute the mean correlation coefficient within barrels individually and determine the statistical significance of the correlation. Higher pairwise correlation coefficients were observed within the same sensory processing units than across different units.Activities demonstrated stronger synchrony within the same units despite longer distances, surpassing the correlation with physically closer neurons from different units. The average of correlation coefficients within the same barrels was significantly higher than that calculated from 10, 000 sets of surrogate data. The correlation within the same barrels was significantly strong among three different time windows.

analyzing-activity-correlations-within-s1-barrel-cortex-neonatal-mice

Research

JoVE Journal

Neuroscience

The mammalian brain undergoes dynamic developmental changes at both the cellular and circuit levels throughout prenatal and postnatal periods. Following the discovery of numerous genes contributing to these developmental changes, it is now known that neuronal activity also substantially modulates these processes. In the developing cerebral cortex, neurons exhibit synchronized activity patterns that are specialized to each primary sensory area. These patterns markedly differ from those observed in the mature cortex, emphasizing their role in regulating area-specific developmental processes. Deficiencies in neuronal activity during development can lead to various brain diseases. These findings highlight the need to examine the regulatory mechanisms underlying activity patterns in neuronal development. This paper summarizes a series of protocols to visualize primary sensory areas and neuronal activity in neonatal mice, to image the activity of individual neurons within the cortical subfields using two-photon microscopy in vivo, and to analyze subfield-related activity correlations. We show representative results of patchwork-like synchronous activity within individual barrels in the somatosensory cortex. We also discuss various potential applications and some limitations of this protocol.

Primary sensory areas in the neocortex exhibit unique spontaneous activities during development. This article describes how to visualize individual neuron activities and primary sensory areas to analyze area-specific synchronous activities in neonatal mice in vivo.

The mammalian brain undergoes dynamic developmental changes at both the cellular and circuit levels throughout prenatal and postnatal periods. Following ...

Imaging Single Cell-Level Activity in Neonatal Mice Using Two-Photon Microscopy

To begin, affix a two-axis goniometer with a titanium plate to the stage plate for XY positioning under the microscope. Activate the scanning software, set pixels to 512 by 512 bidirectional to on using a 20X objective lens. After performing the cranial window surgery, place the pup expressing GCaMP on one side of the hemisphere on the heating pad.Using screws, secure the head mount titanium bar to the titanium plate. Then adjust the window angle horizontally with the goniometer. Illuminate the brain surface with the backlight and select the imaging area using XY positioning with a 5X objective lens.Under a 20X water immersion lens, administer eyedrops to cranial window. After disabling the backlight, scan the brain surface in one photon mode. Increase the laser intensity to visualize the green autofluorescence from the glass and brain surface.Cover the microscope to prevent external light interference. Start the scanning software to two photon mode for imaging. Then calibrate the laser power and detector gain to optimally visualize GCaMP signals.Locate the depth in an imaging area populated with many GCaMP-expressing neurons and start time-lapse imaging to capture GCaMP signals. Two-photon microscopy revealed layer four neuron activities in the barrel cortex of a postnatal day six pup with green fluorescence. The green GCaMP was more prominent, resulting in signal leakage into the red channel and identification of 14 regions of interest.