1p_BLA_sync_permutation_social_valence

DOI: https://doi.org/10.1101/2023.10.17.562740

Genetically- and spatially-defined basolateral amygdala neurons control food consumption and social interaction

Highlights:

1. Classification of molecularly-defined glutamatergic neuron types in mouse BLA with distinct spatial expression patterns.
2. BLALypd1 neurons are positive-valence neurons innately responding to food and promoting normal feeding.
3. BLAEtv1 neurons innately respond to aversive and social stimuli.
4. BLAEtv1 neurons promote fear learning and social interactions.

"Step by step" for codes for BLA neuron calcium imaging analysis

1. Either using our demo files or your calcium imaging and behavior CSV files need to be used: Demo lists: 1. Figure4_food, 2. Figure5_social, 3. Figure5_fear_conditioning, 4. suppl.fig10_longitudinal_footshock_social.

Note

Demo files are already preprocessed H5 files, so you can skip 2-3steps.

2. you need to download "core_codes" (logger.py, preprocessing.py, util.py) to synchronize the extracted behavior statistics with calcium traces (The TTL emission-reception delay is negligible (less than 30ms), therefore the behavioral statistics time series can be synchronized with calcium traces by the emission/receival time on both devices) and it would generate combined one H5 file (behavior+calcium data)
3. Run Synchrnoize_h5generation.py code to apply core-codes (preprocessing) to your data:

import core.preprocessing as prep

4. You can run each code (5 codes) described in the paper to generate the results: 1. Fear Conditioning, 2.social, 3. food, 4. permutation, 5, suppl.10 social_footshock.py code number 4 is the percentage comparison with shuffling of data in Figure4-5 as bar graph you can reproduce using the code: Figure4_5_permutation_bargraph.

required

Note

-System requirements Python (3.10.8): we used a Python IDE for professional developers by JetBrains, Pycharm. Packages: suite2p : https://github.com/MouseLand/suite2p

pip install git+https://github.com/MouseLand/suite2p.git

matplotlib

pip install matplotlib
pip install PyQt5

numpy

Warning

our code included : matplotlib Qt5Agg An Error is happening because Google Colab and Jupyter run on virtual environments which do not support GUI outputs as you cannot open new windows through a browser. Running it locally on a code editor(Spyder, or even IDLE) ensures that it can open a new window for the GUI to initialize.

Tip

• "our analysis pipeline is based on basic python packages:"

>  import numpy as np
>import h5py as h5
>import pandas as pd

Installation guide above

• Demo -Demo_data.zip

Acknowledgement

Yue Zhang