saruther/pcntoolkit-interface repository overview

⁠Repository for PCNtoolkit Normative Models via Docker Container

View on Docker Hub⁠

This is a python-based container that will install the necessary packages to run pre-trained cortical thickness and subcortical volume normative models and transfer these models to an unseen test set. The models are described in Rutherford et al⁠. The transfer test set is currently a multi-site dataset using public data from OpenNeuro⁠. This build currently does not support using your own transfer data set. This feature (uploading your own dataset) is actively being developed and a future release will allow users an option to input their own transfer data set.

⁠Setup Steps:

1. Clone this GitHub repository and cd into the cloned repository.

   git clone [email protected]:saigerutherford/PCNtoolkit-interface.git

   cd PCNtoolkit-interface

2. Pull the Docker image from DockerHub

   docker pull saruther/pcntoolkit-interface

3. Run the container, and mount the GitHub repository using the Volume (-v) flag. Change the path to match the location of the cloned GitHub repository.

   docker run -v /path/to/PCNtoolkit-interface/repo:/home/jovyan/ pcntoolkit-interface python apply_normative_models.py

⁠After the script has run:

There will be a file called Z_predict.txt that is located in each subdirectory in /models/lifespan_57K_82sites/. This file contains the Z-scores (deviation scores) for all subjects in the test set. There is one subdirectory and corresponding Z_predict.txt file for every model that was run.