# NeuroConn
NeuroConn is a Python package that provides a user-friendly interface for fMRI preprocessing and computing connectivity matrices and gradients. It is designed as a BIDS application, allowing easy integration with BIDS-formatted datasets.
Documentation: https://victoris93.github.io/NeuroConn/
## Features
<font color="red">**NB! If you wish to run `fmriprep` within this package, install Docker Desktop first. Keep it running when you start** `RawDataset.docker_fmriprep()`</font>
- Preprocessing of fMRI data using the fmriprep pipeline
- Computation of connectivity matrices and gradients
- Direct output of gradients or connectivity matrices for any subject without specifying preprocessing parameters
- Handling of BIDS-formatted datasets
## Installation
You can install NeuroConn using pip: `pip install NeuroConn`
## Usage
**1. fMRIPrep**. The class `RawDataset` features a method to run fmriprep within within your Python environment. Before running it:
1. Register with freesurfer and download the license file `freesurfer_license.txt`)
2. Install Docker Desktop.
3. After having activated your environment, run `pip install fmriprep-docker`.
4. Start Docker Desktop.
Then, give this a try:
```
from NeuroConn.preprocessing.preprocessing import RawDataset, FmriPreppedDataSet
from NeuroConn.data.example_datasets import fetch_example_data
ex_data = fetch_example_data() # from https://openneuro.org/datasets/ds002748
data = RawDataset(ex_data)
subject = '52'
data.docker_fmriprep(subject, fs_reconall = False, fs_license = <path_to_freesurfer_license.txt>)
```
**2. Post-fMRIPrep** Here's an example of how to use the `FmriPreppedDataSet` class provided by NeuroConn:
```
from NeuroConn.preprocessing.preprocessing import RawDataset, FmriPreppedDataSet
from NeuroConn.data.example_datasets import fetch_example_data
# Download the dataset preprocessed with fMRIPrep
example_data = fetch_example_data('https://drive.google.com/file/d/1XjF5wDJXHzMyfoAjQE6NW2xcj9PulZzH/view?usp=share_link')
# Initialize the dataset object
dataset = FmriPreppedDataSet(example_data)
# Compute connectivity matrix
conn_matrix = data_prepped.get_conn_matrix(subject, parcellation='schaefer', task='rest', n_parcels=1000, save = True)
# Compute 10 gradients (Margulies et al., 2016)
gradients = get_gradients(data_prepped,subject, task='rest', n_components = 10, approach = "pca")
```
For more detailed information and examples, please refer to the [notebook](https://github.com/victoris93/PyConn/blob/master/NeuroConn.ipynb).
## Contributing
Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on this GitHub repository.
## License
NeuroConn is released under the MIT License. See the LICENSE file for more details.
## Example Data
Bezmaternykh D.D., Melnikov M.Y., Savelov A.A. et al. Brain Networks Connectivity in Mild to Moderate Depression: Resting State fMRI Study with Implications to Nonpharmacological Treatment. Neural Plasticity, 2021. V. 2021. № 8846097. PP. 1-15. DOI: 10.1155/2021/8846097
Raw data
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"description": "# NeuroConn\n\nNeuroConn is a Python package that provides a user-friendly interface for fMRI preprocessing and computing connectivity matrices and gradients. It is designed as a BIDS application, allowing easy integration with BIDS-formatted datasets.\nDocumentation: https://victoris93.github.io/NeuroConn/\n\n## Features\n\n<font color=\"red\">**NB! If you wish to run `fmriprep` within this package, install Docker Desktop first. Keep it running when you start** `RawDataset.docker_fmriprep()`</font>\n- Preprocessing of fMRI data using the fmriprep pipeline\n- Computation of connectivity matrices and gradients\n- Direct output of gradients or connectivity matrices for any subject without specifying preprocessing parameters\n- Handling of BIDS-formatted datasets\n\n## Installation\n\nYou can install NeuroConn using pip: `pip install NeuroConn`\n\n## Usage\n**1. fMRIPrep**. The class `RawDataset` features a method to run fmriprep within within your Python environment. Before running it:\n1. Register with freesurfer and download the license file `freesurfer_license.txt`)\n2. Install Docker Desktop.\n3. After having activated your environment, run `pip install fmriprep-docker`.\n4. Start Docker Desktop.\nThen, give this a try:\n\n```\nfrom NeuroConn.preprocessing.preprocessing import RawDataset, FmriPreppedDataSet\nfrom NeuroConn.data.example_datasets import fetch_example_data\nex_data = fetch_example_data() # from https://openneuro.org/datasets/ds002748\ndata = RawDataset(ex_data)\nsubject = '52'\ndata.docker_fmriprep(subject, fs_reconall = False, fs_license = <path_to_freesurfer_license.txt>)\n```\n\n**2. Post-fMRIPrep** Here's an example of how to use the `FmriPreppedDataSet` class provided by NeuroConn:\n\n```\nfrom NeuroConn.preprocessing.preprocessing import RawDataset, FmriPreppedDataSet\nfrom NeuroConn.data.example_datasets import fetch_example_data\n\n# Download the dataset preprocessed with fMRIPrep\nexample_data = fetch_example_data('https://drive.google.com/file/d/1XjF5wDJXHzMyfoAjQE6NW2xcj9PulZzH/view?usp=share_link') \n# Initialize the dataset object \ndataset = FmriPreppedDataSet(example_data)\n\n# Compute connectivity matrix\nconn_matrix = data_prepped.get_conn_matrix(subject, parcellation='schaefer', task='rest', n_parcels=1000, save = True)\n\n# Compute 10 gradients (Margulies et al., 2016)\ngradients = get_gradients(data_prepped,subject, task='rest', n_components = 10, approach = \"pca\")\n```\n\nFor more detailed information and examples, please refer to the [notebook](https://github.com/victoris93/PyConn/blob/master/NeuroConn.ipynb).\n\n## Contributing\n\nContributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request on this GitHub repository.\n\n## License\n\nNeuroConn is released under the MIT License. See the LICENSE file for more details.\n\n## Example Data\n\nBezmaternykh D.D., Melnikov M.Y., Savelov A.A. et al. Brain Networks Connectivity in Mild to Moderate Depression: Resting State fMRI Study with Implications to Nonpharmacological Treatment. Neural Plasticity, 2021. V. 2021. \u2116 8846097. PP. 1-15. DOI: 10.1155/2021/8846097\n\n\n",
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