Surmeier Lab to NWB: Zhai 2025 Conversion

NWB conversion scripts for Surmeier lab data from the Zhai et al. 2025 paper on levodopa-induced dyskinesia research. This project converts multimodal neuroscience data to the Neurodata Without Borders format for standardized data sharing and analysis.

Project Overview

This repository contains conversion scripts for data from the Zhai et al. 2025 paper studying levodopa-induced dyskinesia (LID) in a mouse model of Parkinson's disease. The study examines cellular and molecular mechanisms underlying dyskinesia through comprehensive analysis of direct and indirect pathway spiny projection neurons (dSPNs and iSPNs) in the dorsolateral striatum.

Paper Details

• Title: M1 muscarinic receptor-mediated dendritic excitability underlies striatal dysfunction in levodopa-induced dyskinesia
• Preprint: bioRxiv 2025.01.02.631090
• Key findings: M1 muscarinic receptors drive dendritic hyperexcitability in indirect pathway neurons, contributing to dyskinetic behaviors

Data Types Converted

• Electrophysiology: Patch-clamp recordings (MultiClamp 700B)
• Two-photon imaging: Calcium imaging, acetylcholine biosensor (GRABACh3.0), spine density analysis
• Confocal microscopy: High-resolution spine density measurements
• Optogenetics: Light-evoked postsynaptic currents (oEPSCs)
• Behavioral data: AIM scores, contralateral rotations, video recordings
• Pharmacology: Drug treatments and receptor manipulations

Experimental Models

• 6-OHDA lesion model: Parkinson's disease simulation
• Chronic levodopa treatment: Dyskinesia induction
• Cell-type specific analysis: dSPNs vs iSPNs
• Genetic manipulations: CDGI knockout, M1R antagonists

Installation

Installation from GitHub

This package is currently only available from GitHub (not yet released on PyPI). This project requires Python 3.12 or higher.

Option 1: Using uv (Recommended)

The project uses uv for fast and reliable dependency management. Install uv first (installation instructions), then:

bash git clone https://github.com/catalystneuro/surmeier-lab-to-nwb cd surmeier-lab-to-nwb uv sync

This will create a virtual environment and install all dependencies automatically.

Option 2: Using conda

If you prefer conda for environment management (installation instructions):

bash git clone https://github.com/catalystneuro/surmeier-lab-to-nwb cd surmeier-lab-to-nwb conda create -n surmeier-lab-to-nwb python=3.12 conda activate surmeier-lab-to-nwb pip install --editable .

Option 3: Using pip with virtual environment

Alternatively, you can use standard Python tools:

bash git clone https://github.com/catalystneuro/surmeier-lab-to-nwb cd surmeier-lab-to-nwb python -m venv .venv source .venv/bin/activate # On Windows: .venv\Scripts\activate pip install --editable .

Running a specific conversion

Once you have installed the package, you can run any of the conversion scripts in a notebook or a python file:

Figure-specific conversion scripts are located in src/surmeier_lab_to_nwb/zhai2025/conversion_scripts/. Each script handles specific data types and experimental conditions:

```bash

Example: Convert Figure 1 dendritic excitability data

python src/surmeierlabtonwb/zhai2025/conversionscripts/dendriticexcitability/figure1dendriticexcitability.py

Example: Convert Figure 5 acetylcholine biosensor data

python src/surmeierlabtonwb/zhai2025/conversionscripts/acetylcholinebiosensor/figure5acetylcholinebiosensor.py ```

Note: All methods above install the repository in editable mode, allowing you to modify the source code if needed.

Repository structure

Each conversion is organized in a directory of its own in the src directory:

surmeier-lab-to-nwb/
├── LICENSE
├── README.md
├── pyproject.toml
└── src/
    └── surmeier_lab_to_nwb/
        └── zhai2025/
            ├── conversion_notes_folder/  # Detailed conversion documentation
            ├── conversion_scripts/  # Figure-specific conversion scripts
            │   ├── acetylcholine_biosensor/
            │   ├── aim_behavior/
            │   ├── confocal_spine_density/
            │   ├── dendritic_excitability/
            │   ├── optical_stimulation/
            │   ├── somatic_excitability/
            │   ├── spine_density/
            │   └── videos/
            ├── interfaces/         # Custom NWB interfaces
            └── utils/             # Utility functions

Figure-Specific Conversions

The zhai2025 conversion includes specialized scripts for each figure in the paper:

Dendritic Excitability (Figures 1, 3, 6, 7)

• Two-photon calcium imaging with patch-clamp electrophysiology
• Back-propagating action potential measurements
• Cell-type specific analysis (dSPNs vs iSPNs)
• Scripts:
  • Figure 1
  • Figure 3
  • Figure 6
  • Figure 7
  • Figure 7 OxoM

Somatic Excitability (Figures 1, 3, 6, 7, 8)

• Patch-clamp electrophysiology recordings
• Current injection protocols and F-I curves
• Scripts:
  • Figure 1
  • Figure 3
  • Figure 6
  • Figure 7
  • Figure 8

Spine Density Analysis (Figures 2, 4, 6, 7, 8)

• Two-photon and confocal microscopy
• 3D spine reconstruction and counting
• Multiple imaging platforms (Bruker, Nikon, Olympus)
• Scripts:
  • Figure 2
  • Figure 4
  • Figure 4 Confocal (Nikon)
  • Figure 4 Confocal (Olympus)
  • Figure 6
  • Figure 7
  • Figure 8

Acetylcholine Biosensor (Figure 5)

• GRABACh3.0 fluorescent sensor recordings
• Two-photon line scans and full-field imaging
• Real-time acetylcholine dynamics
• Script: Figure 5

Behavioral Analysis (Figures 7, 8)

• AIM (Abnormal Involuntary Movement) scoring
• Contralateral rotation counting
• Video recording integration
• Scripts:
  • Figure 7 AIM
  • Figure 7 Videos
  • Figure 8 AIM
  • Supplementary Figure 3 Videos

Optogenetics (Figures 2, 4)

• Light-evoked postsynaptic currents (oEPSCs)
• Stimulus timing and metadata
• Scripts:
  • Figure 2
  • Figure 4