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Calc Cobra (TODO) is a data pipeline for processing and analysing task-specific (widefield) calcium imaging data through neural decoding. Here, calcium activity is a proxy for neuronal activations. It provides stand-alone functionalities to visualize the data as well as enabling the export of processed data for other visualization purposes.

Feature Overview

• Brain Alignment (between different sessions and subjects)
  • Automatic registration with novel MesoNet (under development)
• Different data-driven & anatomical parcellations
  • Including novel locaNMF to obtain interpretable, data-driven brain sub-regions
• Different brain connectivity measurements
  • Functional connectivity (statistical relationship)
  • Effective connectivity (est. causal influence)
    • Novel MOU-EC fits multivariate ornstein uhlenbeck process as generative network model
    • Can be constrained by structural connectivity (under development)

Installation

• Required Software
  • Python >= 3.8
  • Anaconda
  • Snakemake
• Required Files
  • Put the experimental data into resources/experiment/"mouse-id"/"experiment-date"/ with "mouse-id" and "experiment-date" being provided by you
    • e.g. ../repository/resources/experiment/GN06/2021-01-20_10-15-16
• Have a look at the Trouble Shooting if you encounter problems during the setup

Usage | Test installation

All commands assume you followed the default install procedure for Snakemake within a conda virtual environment

1. Activate conda virtual environment
   • conda activate base
2. Customize config file (or use default config to test pipeline installation)
   • Detailed description can be found here
3. Run pipeline
   • snakemake -j4 "rule" with "rule" being replace by:
     • test_installation covers all processing steps
     • decoding_performance performs neural decoding with full feature space and plots results across all features and parcellations
     • reduce_biomarkers performs recursive feature elimination to select most discriminative features and visualizes them in an interactive glassbrain plot

Results

• Visualizations (for direct interpretation)
  • Decoding performance plotted...
    • ...for each feature and decoder individually
    • ...across all features and decoders for each parcellation
    • ...across all parcellations and decoders for feature
  • Interactive Glassbrain Plot of Biomarkers (Example)
• Processed Data (for further processing/visualizing outside of pipeline)
  • Aligned calcium activity
  • Parcellated calcium activity
  • Calculated features (including brain connectivity measurements)
  • Trained decoders
  • Decoders accuracy on test sets
  • Selected biomarkers

Development

• File Structure

• Pipeline Steps

  • Loading & Preprocessing
  • Parcellation
  • Filtering
  • Condition Extraction
  • Feature Calculation
  • Recursive Feature Elimination
  • Deconding
  • Plotting