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.

RehabDS 数据集。
受试对象列表如下：
['GCaMP-ChR2-7', # 对照组（CTRL）
'GCaMP-ChR2-17', # 对照组（CTRL）
"GCaMP-ChR2-23", # 对照组（CTRL）
"GCaMP-ChR2-24", # 对照组（CTRL）

'GCaMP-ChR2-8', # 卒中组（STROKE）
"GCaMP-ChR2-9", # 卒中组（STROKE）
'GCaMP-ChR2-19', # 卒中组（STROKE）
'GCaMP-ChR2-22', # 卒中组（STROKE）
'GCaMP-ChR2-25', # 卒中组（STROKE）
'GCaMP-ChR2-26', # 卒中组（STROKE）

'GCaMP-ChR2-11', # 康复组（REHAB）
'GCaMP-ChR2-12', # 康复组（REHAB）
'GCaMP-ChR2-14', # 康复组（REHAB）
'GCaMP-ChR2-15', # 康复组（REHAB）
'GCaMP16', # 康复组（REHAB）
'GCaMP18']. # 康复组（REHAB）

分组规则详见对应论文。

数据采用日期/受试对象的层级目录结构存储：顶层文件夹代表数据采集日期，子文件夹存储对应受试对象的实验数据。

每个MAT文件（MAT file）均为三维向量，其中第一维度为时间维度，第二、第三维度共同构成200×200尺寸的图像，可通过imshow函数进行可视化。

所有.csv文件均依照艾伦脑图谱（Allan Brain Atlas）进行数据分区，掩码文件存储于masks文件夹中，包含各掩码对应的脑区名称、脑区范围及质心信息。

MAT文件中图像的空间分辨率为60 μm/像素。
前囟点（Bregma）对应的图像坐标为(100, 75)。
卒中造模位点坐标：相对于前囟点，前后轴（AP）偏移0.5 mm、内侧-外侧轴（ML）偏移1.75 mm；对应图像坐标为(129, 67)，计算式为(100+1750/60, 75-500/60)。

掩码文件已更新以匹配MAT文件图像的空间分辨率。MAT文件本质为图像序列堆叠，第一维度为时间维度，其余两个维度对应图像坐标系（可参考https://www.mathworks.com/help/images/image-coordinate-systems.html），具体操作示例如下：

**MATLAB 环境**：
>> imshow(squeeze(data(1,:,:)))

**Python 环境**：
>>> 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])

上述代码用于展示序列中的首张图像。