Wide-field calcium imaging of cortical activity after combined rehabilitation
RehabDS Dataset. Subjects = ['GCaMP-ChR2-7', # CTRL 'GCaMP-ChR2-17', # CTRL "GCaMP-ChR2-23", # CTRL "GCaMP-ChR2-24", # CTRL 'GCaMP-ChR2-8', # STROKE "GCaMP-ChR2-9", # STROKE 'GCaMP-ChR2-19', # STROKE 'GCaMP-ChR2-22', # STROKE 'GCaMP-ChR2-25', # STROKE 'GCaMP-ChR2-26', # STROKE 'GCaMP-ChR2-11', # REHAB 'GCaMP-ChR2-12', # REHAB 'GCaMP-ChR2-14', # REHAB 'GCaMP-ChR2-15', # REHAB 'GCaMP16', # REHAB 'GCaMP18']. # REHAB Groups are defined accordingly to the paper. Data are arranged in the date/subject format, so that the top folder represents the day when data was acquired and the child folders contain data for the subject Every mat file is 3D vector where first dim = time. second and third dimension makes an image of size 200x200, use imshow to visualize it. Every .csv file has data parceled accordingly to the Allan Brain Atlas, masks files can be found in the masks folders Information about the masks names areas, centroids resolution of images in the mat file is 60 um/pixel Bregma is located at image coordinates 100, 75 Stroke coordinates : 0.5 mm AP 1.75 mm ML from bregma : image coordinates 129 (100+1750/60), 67(75-500/60) The masks have also been updated to match the resolution of the mat file images. The mat files are basically stack of images the first axis is time, the other axes generate the image coordinates (https://www.mathworks.com/help/images/image-coordinate-systems.html), for this you can use: 'MATLAB' >> imshow(squeeze(data(1,:,:))) "PYTHON" >>> import numpy as np >>> %pylab >>> import scipy.io as io >>> data = io.loadmat('171105/GCaMP22/gcamp22_171105_trialx15.aln.mat')['gcamp22_171105_trialx15'] >>> imshow(data[0)) To show the first image of the sequence.