Automated phenotyping of behavior (autotyping)

We report an open source toolbox for automating the scoring of several common behavior tasks used by the neuroscience community. Software package (MATLAB source code files and standalone executables) can be downloaded using the links below. Latest version is 15.03 (released in March 2015). Please view the accompanying manuscript in Frontiers in Behavioral Neuroscience and the Getting Started user guide below for instructions on use and installation.

Please visit the links below for details on how to run the software to analyze specific behaviors. For each behavior, we include detailed step-by-step instructions, video demonstration of use of the software, and sample experiment video for you to try running the analysis on your computer.

Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP)

We have developed a set of MATLAB routines and a graphical user interface to facilitate the analysis of fluorescent calcium imaging of neuronal networks. The Fluorescence Single Neuron and Network Analysis Package (FluoroSNNAP) allows users to:

  • • Interactively view an image stack
  • • Perform image registration and remove motion artifact
  • • Perform both automated (independent component analysis and active contour evolution) and manual image segmentation to identify neuronal cell bodies
  • • Identify ROI-based changes in fluorescence
  • • Automatically determine the onset of calcium transients and compute the kinetics of each transient
  • • Perform synchronization cluster analysis to reveal groups of neurons that are phase-locked
  • • Infer functional connectivity using cross-correlation, partial correlation, instantaneous phase, Granger causality or transfer entropy methods
  • • Perform network controllability analysis to identify driver nodes
  • • Identify network ensembles

The MATLAB source code can be downloaded below. Please read the User Guide and our accompanying publication in the Journal of Neuroscience Methods for more details. To get started, simply download the source code, extract the zip file, run MATLAB and change directory to FluoroSNNAP. Enter FluoroSNNAP at the command window to start the software.
A sample experiment of the spontaneous activity of cultured primary rat cortical neurons is also made available to test the software. For this experiment, neurons were transduced with AAV2-GCaMP6 and the activity was measured at 10Hz for 2 minutes. Note: the sample experiment, titled "baseline.tif", is a large file (800MB) so be patient when downloading. Alternatively, use the baseline.csv file which contains fluorescence vs. frame data for each neuron in the field view as a comma-separated format (columns = neurons, rows = frames).

Screencast showing the use of FluoroSNNAP on a Macbook Pro with MacOSX10.9, 2.5GHz core i7 processor and 16 GB RAM, running MATLAB 2014a:

If your computer does not have sufficient memory, the ICA based batch segmentation feature may fail and MATLAB may freeze. In this case, you may still continue to use the software by choosing semi-automated segmentation methods - thresholding, and active coutour evolution. The screencast below demonstrates these features. It was recorded on a Windows 7, i7 930 computer with 12GB RAM, running MATLAB 2013b.

Cytosolic PSD-95 Interactor Regulates AMPA Receptor Signaling Independent of PSD-95 Binding
(Cypin Overexpression Simulation and Analysis)

We developed a model to simulate cypin overexpression by modifying AMPAR conductance, connection density, and presynaptic current in Python using Brian2. To analyze the spiking data from this model we calculated spike rate, inter-spike interval, Fano factor, coefficient of variation, and burst rate. We also calculated the graph theoretical measures global efficiency, number of communities, and the community statistic Q (modularity). We further modeled how the network responded to current input, measuring spike rate in the stimulated (input) versus unstimulated (output) neurons, and the network changes pre- and post-stimulation by measuring the change in global efficiency, number of communities and the community statistic Q (modularity).

The Python code (Python3.7) used to stimulate the neuronal networks and the MATLAB code (MATLAB2018a or later) to analyze the simulation data can be downloaded below. Please read the Read Me file and our accompanying publication in Network Neuroscience for more details.