The purpose of our research is to investigate the formation of neuro 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 animal.
We have developed a method for imaging single neurons in the neonatal cerebral cortex. By simultaneously activity patterns and changes in their morphology, we will be able to clarify which activity patterns are involved in neuronal target formation. 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's 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 the 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, then 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. To begin, initiate MATLAB and execute the easy calcium 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 TIFF 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 Load 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 pair-wise 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 pair-wise 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.
