Neurophotonics tutorial on making connectivity diagrams from Channelrhodopsin-2 stimulated data
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.245 | 0.062 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".