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Neurophotonics tutorial on making connectivity diagrams from Channelrhodopsin-2 stimulated data Lim, Diana; LeDue, Jeffrey; Murphy, Timothy H

Description

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).

Requirements: Matlab (with Bioinformatics Toolbox and Brain Connectivity Toolbox)

Inputs: sample data; Matlab functions; Mask.tif (as included in .zip file)

Outputs: dF/F0(%) VSD connectivity matrix; Figure of network properties as a function of threshold levels; Network diagram

To use this package: Unzip data files and ensure all Matlab functions are in the same directory as the NetworkAnalysis_VSD.m Open NetworkAnalysis_VSD.m with Matlab In NetworkAnalysis_VSD.m, enter the folder name where the data is stored (line 5) Execute code.

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.

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.

Diana Lim and Jeffrey LeDue, University of British Columbia, 2015.



Item Metadata

Title
Neurophotonics tutorial on making connectivity diagrams from Channelrhodopsin-2 stimulated data
Creator
Lim, Diana; LeDue, Jeffrey; Murphy, Timothy H
Contributor
Bhudia, Ashutosh Mukund
Date Issued
2019-07-08
Description

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).

Requirements: Matlab (with Bioinformatics Toolbox and Brain Connectivity Toolbox)

Inputs: sample data; Matlab functions; Mask.tif (as included in .zip file)

Outputs: dF/F0(%) VSD connectivity matrix; Figure of network properties as a function of threshold levels; Network diagram

To use this package: Unzip data files and ensure all Matlab functions are in the same directory as the NetworkAnalysis_VSD.m Open NetworkAnalysis_VSD.m with Matlab In NetworkAnalysis_VSD.m, enter the folder name where the data is stored (line 5) Execute code.

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.

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.

Diana Lim and Jeffrey LeDue, University of British Columbia, 2015.
Subject
Medicine, Health and Life Sciences; Neurophotonics; Mesoscale
Type
Dataset
Date Available
2019-07-08
Provider
University of British Columbia Library
License
CC-BY 4.0
DOI
10.14288/1.0379775
URI
https://doi.org/10.5683/SP2/CCHOVV
Publisher DOI
https://doi.org/10.5683/SP2/CCHOVV
Rights URI
http://creativecommons.org/licenses/by/4.0
Aggregated Source Repository
Dataverse

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License

CC-BY 4.0