Neuroscience
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Neurovascular Network Explorer 2.0: A Simple Tool for Exploring and Sharing a Database of Optogenetically-evoked Vasomotion in Mouse Cortex In Vivo
Chapters
Summary May 4th, 2018
A graphical user interface for exploring and sharing a database of optogenetically-induced vascular responses in mouse somatosensory cortex in vivo measured by 2-photon microscopy is presented. It allows browsing the data, criteria-based selection, averaging, localization of measurements within a 3D volume of vasculature and exporting the data.
Transcript
The presented software called Neurovascular Network Explorer 2.0 or NNE 2.0, is a MATLAB based graphical user interface which allows visualization, inspection, selection, and export of optogenetically-evoked vascular diameter changes measured by two photon microscopy. In any two supports localization of the diameter measurements within a 3D structure of vascular network which can be used in modern studies of brain function. And any two can be used to explore the associated database as well as a template to sharing and exploring users own data structured into database of similar format.
Start the NNE 2.0 program by selecting NNE 2.execute. Images in the left column of the main panel show graphs of time courses and perimeters of all entries in the database vdb. mat Begin by selecting the range for cortical depth in the right column.
A minimum depth of 40 microns and a maximum depth of 560 microns is used here. Then select branching order, left-click on the arrow and choose one of the options from the list shown. Next, select baseline diameter and type in the range.
A minimum diameter of two microns and a maximum diameter of 45 microns is used here. Now, select the subjects to be analyzed in the right column of the panel. Left-click on the arrow and choose from the available options.
Press submit to display and explore the selected data in panel two. Prior to exploring the selected subset of data, group average the data by left-clicking either average by cortical depth or average by branching order. The choice is highlighted in green below.
Then select data based on vessel morphology or subject. Select all data for tree consists of the data for a single diving arterial and its branches. Next, left-click on submit to display selected data in graphs of individual time courses, group average time courses, and scatter plots of onset times, time-to-peak's peak amplitudes, and baseline diameters.
Left-click on a trace in the graph of individual time courses. The selected time courses then highlighted in magenta and its onset time, time-to-peak, peak amplitude, and baseline diameter will be marked by red circles in the graphs below. Then right-click anywhere on panel two with a cross cursor to view and explore all traces for the selected subject ID or tree ID in panel three.
Select a time course in the top graph of the left column by left-clicking on a trace. The selected trace will be highlighted in magenta in the graph and descriptive parameters of the database entry will be displayed on top of the graph. The corresponding onset time, time-to-peak, peak amplitude, and baseline diameter are displayed in the graphs below.
The time course in thick black is the average of all displayed traces. Left-click on the export set button to save traces displayed in the top graph into the NNE 2.0 folder. Please make sure none of the exported files are open during the export action.
If one of the files is open, a warning will appear. In this case, the user should close the exported file and restart NNE 2. To inspect all data for subject instead of tree, close panel three, restart NNE 2.0, then after repeating the selection of categories in panel one as before, select all data for subject in panel two.
Right-click anywhere in panel three with a cross cursor to go to panel four to explore reference images and 3D image stacks for all traces in the top graph of panel three. Please know that panel four will open only if option all data for tree was selected in panel two. If all data for subject were selected instead, the user will be prompted to change the selection and direct it to panel one.
Select a time course by left-clicking on it in the graph on top of the left column. At the bottom right of the left column, explore the corresponding reference image which in this case is loaded automatically from the hana_refs folder. At the bottom left of the left column, explore the corresponding 3D image stack loaded automatically from the hana_stk folder.
Scroll through this stack using the arrows or slider below the figure. When the stack image reaches the level of the reference image, the stack image is highlighted and indicated as frame level. Click export set in the right column to export the highlighted time course into a file ref_stacks_trace.
xls which is saved to the NNE 2 folder. Finally, close panel four to go back to panel one to start exploring a new set of data or close panel one to end the program. To use the functional measurements along with the 3D vascular structure, a corresponding image stack is located in folder hana_stk using its stack reference name displayed in panel four.
To recognize the particular diving arterial, the user can refer to a low magnification map of surface vasculature with diving segments of measured arterial or trees labeled with corresponding numbers. This stack index number at frame level localizes the measurement within appropriate depth. The branching order along with the scan trajectory, localizes the measurement within the imaging plane.
Taken together, the user is provided with 3D topography of vasculature populated by stimulus induced vasomotion at multiple locations within vascular trees. And NNE 2 was written to share the vascular imaging data of a specific study but with the intention of developing a simple tool for sharing and exploring data of similar kind by other users. In the prerequisite for using NNE 2 is a template, is a database in the format of a matrix And this could be achieved either by processing the data directly in MATLAB or using tools to build the matrix from other formats.
In NNE 2 has the potential to help sharing experimental data across research communities. Facilitating further utilization of the data as well as the development for standards for data acquisition and processing.
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