Neurophotonics tutorial on making connectivity diagrams from Channelrhodopsin-2 stimulated data

<p>This package of sample data and matlab codes will process, filter, and average raw voltage sensitive dye images, generate a comprehensive connectivity matrix and a network diagram (as seen in Lim et al., 2012, 2014).</p> <p>Requirements: Matlab (with Bioinformatics Toolbox and Brain Connectivity Toolbox)</p> <p>Inputs: sample data; Matlab functions; Mask.tif (as included in .zip file)</p> <p>Outputs: dF/F0(%) VSD connectivity matrix; Figure of network properties as a function of threshold levels; Network diagram</p> <p> To use this package: <ol> <li>Unzip data files and ensure all Matlab functions are in the same directory as the NetworkAnalysis_VSD.m</li> <li>Open NetworkAnalysis_VSD.m with Matlab</li> <li>In NetworkAnalysis_VSD.m, enter the folder name where the data is stored (line 5)</li> <li>Execute code.</li> </ol> </p> <p> See corresponding articles: Lim et al.(2012). In vivo Large-Scale Cortical Mapping Using Channelrhodopsin-2 Stimulation in Transgenic Mice Reveals Asymmetric and Reciprocal Relationships between Cortical Areas. Front Neural Circuits 6:11. Lim et al.(2014). Optogenetic mapping after stroke reveals network-wide scaling of functional connections and heterogeneous recovery of the peri-infarct. J Neurosci 34:16455-16466. </p> <p> See also: Brain connectivity toolbox: https://sites.google.com/site/bctnet/ Rubinov and Sporns (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059-1069. </p> <p>Diana Lim and Jeffrey LeDue, University of British Columbia, 2015.</p>