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concatminiscope

MATLAB algorithm for the concatenation of miniscope recorded sessions.

https://github.com/almeida-filhodg/concatminiscope

Science Score: 57.0%

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Repository

MATLAB algorithm for the concatenation of miniscope recorded sessions.

Basic Info
  • Host: GitHub
  • Owner: Almeida-FilhoDG
  • License: gpl-3.0
  • Language: MATLAB
  • Default Branch: master
  • Size: 1.63 MB
Statistics
  • Stars: 4
  • Watchers: 2
  • Forks: 2
  • Open Issues: 0
  • Releases: 1
Created almost 6 years ago · Last pushed almost 4 years ago
Metadata Files
Readme License Citation

README.md

ConcatMiniscope

DOI: 10.5281/zenodo.5676164

MATLAB algorithm for the concatenation of miniscope recorded sessions. This pipeline is based on the Miniscope Analysis Package developped by Guillaume Etter - McGill University.

Requirements: 1. Matlab 2016 or later 2. NormCorre algorithm for motion correction 3. CNMF-e algorithm for cell detection 4. msDeleteROI algorithm: Neuron Deletion GUI used for deleting ROIs that were mistakenly detected as neurons (optional)

Obs.: The current repository has a version of CNMFe and NoRMCorre (MatlabPath_CNMFe_and_NoRMCorre folder) which shows minor changes from the original repositories for updating or speed optimization.

Getting Started

  1. Install the checkNoisyCells.mlappinstall Matlab app through the Install App button in the APPS tab on Matlab.
  2. Set Matlab path to the MatlabPath_CNMFe_and_NoRMCorre folder:
    1. Click on the HOME tab in Matlab, then Set Path;
    2. Click Add with Subfolders..., browse to the MatlabPath_CNMFe_and_NoRMCorre folder and save.
  3. Organize your data with a parent folder for each dataset (e.g., animal):
    1. Within each parent folder, place all the information from each session to be concatenated within a child folder.
  4. Follow the steps in the concatSessionsPipeline.m file.
    1. In the Parameters section, choose the parameters marked with %%%****************%%%.
    2. Pay attention to the concatInfo.order parameter in which you need to inform the order sessions should be concatenated based on their order in the concatInfo.Sessions variable.
    3. After running CNMF-e on the concatenated video (Step 4), you may delete ROIs that do not correspond to real neurons using the msDeleteROI (optional) based on ROIs' spatial and temporal shapes.
    4. On Step 6 select the downsampling factor (dSFactor) you want to use on the calcium traces for spike inference.
    5. Step 7 is a Matlab app (checkNoisyCells) used for deleting neurons that are too noisy and show poor spike inference from calcium traces.
    6. The last step (Step 8) joins the raw calcium traces and the putative related firing rate of the cells into a single Matlab variable (concatResults.mat).

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Almeida-Filho"
  given-names: "Daniel"
  orcid: "https://orcid.org/0000-0002-3583-3796"
title: "ConcatMiniscope Pipeline: From minsicope recordings to tracking inidividual cells across multiple sessions"
version: 1.0.0
doi: 10.5281/zenodo.5676164
date-released: 2021-11-11
url: "https://github.com/Almeida-FilhoDG/ConcatMiniscope"

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