| Autor: |
Sridhar VB; Bioengineering Undergraduate Program, University of California San Diego, La Jolla, CA, USA., Tian P; Department of Neurosciences, University of California San Diego, La Jolla, CA, USA ; Department of Physics, John Carroll University, University Heights OH, USA., Dale AM; Department of Neurosciences, University of California San Diego, La Jolla, CA, USA ; Department of Radiology, University of California San Diego, La Jolla, CA, USA., Devor A; Department of Neurosciences, University of California San Diego, La Jolla, CA, USA ; Department of Radiology, University of California San Diego, La Jolla, CA, USA ; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School Charlestown, MA, USA., Saisan PA; Department of Neurosciences, University of California San Diego, La Jolla, CA, USA. |
| Abstrakt: |
We present a database client software-Neurovascular Network Explorer 1.0 (NNE 1.0)-that uses MATLAB(®) based Graphical User Interface (GUI) for interaction with a database of 2-photon single-vessel diameter measurements from our previous publication (Tian et al., 2010). These data are of particular interest for modeling the hemodynamic response. NNE 1.0 is downloaded by the user and then runs either as a MATLAB script or as a standalone program on a Windows platform. The GUI allows browsing the database according to parameters specified by the user, simple manipulation and visualization of the retrieved records (such as averaging and peak-normalization), and export of the results. Further, we provide NNE 1.0 source code. With this source code, the user can database their own experimental results, given the appropriate data structure and naming conventions, and thus share their data in a user-friendly format with other investigators. NNE 1.0 provides an example of seamless and low-cost solution for sharing of experimental data by a regular size neuroscience laboratory and may serve as a general template, facilitating dissemination of biological results and accelerating data-driven modeling approaches. |
