This repository is a template for the NeuroHackademy 2026 week-1
projects. The goal of the project is to examine data from the
Reproducible Brain Charts (RBC) database
and to use any of its data to predict the p_factor of a set of participants.
Project details can be found below.
This repository is a template for your own analysis of the RBC dataset. Please fork this repository then clone your forked copy locally into your JupyterHub environment:
git clone git@github.com:USERNAME/nh2026-week1-projectWe encourage all group members to fork the repository (but only one group member should upload the predicted data and make a pull request at the end; these steps are documented in the analysis notebook).
Once you have cloned your fork of the repository locally on the JupyterHub you
can open the analysis.ipynb notebook in the repository root. This file
contains an example workflow that loads in the surface area of Brodmann Area 1
for each subject, trains a linear regression model to predict the p_factor
based on that surface area in the set of training participants, then uses the
trained model to predict the p_factor in for the test participants based on
their BA1 surface areas. Feel free to make your own notebook or to modify this
analysis notebook for your project.
The RBC database contains many independent datasets; in this project, we will focus on just one of these datasets, the Philadelphia Neurodevelopmental Cohort (PNC). The data from this dataset have been collected into three GitHub repositories:
Although you are welcome to use any data from the dataset to model and predict
the p_factor, we encourage you to focus on the data available in the
PNC_FreeSurfer repository, as it does not require sophisticated preprocessing
and is fairly easy to model in a short amount of time.
The p_factor that you are tasked with modeling and predicting is a general
psychopathology factor that has been calculated for each subject. The details
of this calculation are not critical for this project, but more information can
be found in this RBC publication.
We have provided two TSV files on the NeuroHackademy JupyterHub:
/home/jovyan/shared/data/RBC/train_participants.tsv/home/jovyan/shared/data/RBC/test_participants.tsv
The first of these files, train_participants.tsv contains a subset of the PNC
participants and various meta-data (including the p_factor) for each. The
test_participants.tsv file contains the left-out test subjects, and all of
the p_factor cells in this data table have been set to NaN. Although it is
possible to find and extract the true p_factor values for these test
subjects, we encourage you not to do so, or at least not to do so until after
you have generated predictions. (We will provide the p_factor data to you
when the projects are completed).