Analysis of visual cortical neurons of mice

Authors	email
Cristian Bargiacchi	cristian.bargiacchi@edu.unifi.it
Christian Mancini	christian.mancini1@edu.unifi.it

Project description

This is the final project of Statistical Analysis of Network Data at University of Florence.

The aim of the project is to analyse some network data with the techniques seen during the course.

We choose da dataset including the network of visual cortex of mice [2].

Data represents cell-to-cell mapping of axonal tracts between neurons, created from cellular data like electron microscopy.

Graphs are provided by default in graphML format.

The result of scope of the study and the results can be found in the paper Network anatomy and in vivo physiology of visual cortical neurons.

Scope of the paper

The study shows how the neurons of the brain of a mouse interacts between them in the primary visual cortex.

Data of the study are collected thanks to the advance of two techniques:

1. Two-photon calcium imaging, to "create" specific visual stimulus,
2. Large-scale electron microscopy (EM), to trace a portion of these neurons’ local network.

Result of the paper

The are neurons that stops and regulate neural activity and some that promote it.

Researchers found that the neurons that promote neural activity have preference of specific visual stimulus type (i.e. horizontal, vertical oblique).

This opens the doors of further research for understanding the brain.

What we have

A graph of the synaps of a specific stimulus was created to understand better the structure and the connections of the network. We do not have all the data and imaging of the study since wold be to big but just the network of a synaps.

We will use statistical techniques seen during the course to analyse this kind of data.

Caution

It's important to have a good understanding of the domain we are working in when analyzing data. Without this understanding, the analysis can be misleading and influenced by random factors. For more detailed information about the data components, you can refer to the Notebook provided.

Run the Notebook

Tip

An IDE like Pycharm will detect the requirements and install a virtual environment for you in the project folder, we encourage you to use this mechanism to run the Notebook. Otherwise you can follow these steps to manually install the requirements.

Manually install the requirement

In the project folder run the following commands:

python3 -m venv .venv

Note

The name of the virtual environment will be the same as the name of hidden folder, in this case .venv.

The virtual environment can be activated with:

source .venv/bin/activate

The requirements can be installed with:

pip install --upgrade pip & pip install -r requirements.txt

We just now need to make the virtual environment a Jupyter kernel.

python -Xfrozen_modules=off -m ipykernel install --user --name=sand-2024

Now you can choose sand-2024 as a Kernel.

We can see the installed kernels with:

jupyter kernelspec list

The output should be something like this:

Available kernels:
  python3      /home/mancio/PycharmProjects/sand-2024/.venv/share/jupyter/kernels/python3
  sand-2024    /home/mancio/.local/share/jupyter/kernels/sand-2024

Note

You can remove a kernel with the following command:

jupyter kernelspec uninstall sand-2024 -y

Converting the Notebook to a pdf

The notebook can be converted with the following command

jupyter nbconvert --to pdf notebooks/Cortical.ipynb --LatexPreprocessor.title "Analysis of visual cortical neurons of mice" --LatexPreprocessor.date "May, 2024" --LatexPreprocessor.author_names "Cristian Bargiacchi,Christian Mancini"

References

[1] Neurodata repository.

[2] Mouse_visual.cortex_2

[3] Network anatomy and in vivo physiology of visual cortical neurons.