Normative Neurodevelopment: Cross-sectional predictive modeling

Environment build

conda create -n neurodev_cs_predictive python=3.7
conda activate neurodev_cs_predictive

# Essentials
pip install jupyterlab ipython pandas numpy seaborn matplotlib nibabel glob3 nilearn ipywidgets tqdm
pip install jupyter_contrib_nbextensions && jupyter contrib nbextension install

# Statistics
pip install scipy statsmodels sklearn pingouin pygam brainspace bctpy shap

# Pysurfer for plotting
pip install vtk==8.1.2
pip install mayavi
pip install PyQt5
jupyter nbextension install --py mayavi --user
jupyter nbextension enable --py mayavi --user
jupyter nbextension enable --py widgetsnbextension
pip install pysurfer

cd /Users/lindenmp/Google-Drive-Penn/work/research_projects/neurodev_cs_predictive
conda env export > environment.yml
pip freeze > requirements.txt

Environment build (cubic, home)

conda create -n neurodev_cs_predictive python=3.7
conda activate neurodev_cs_predictive

# Essentials
pip install ipython pandas numpy glob3

# Statistics
pip install scipy statsmodels sklearn

cd /cbica/home/parkesl/miniconda3/envs/neurodev_cs_predictive/
conda env export > environment.yml
pip freeze > requirements.txt

Code

Processing

• 0_get_sample.ipynb
• 1_compute_node_metrics.ipynb
• 2_compute_gradients.ipynb

Results

• 3_results_demographics_characteristics.ipynb

• 4_results_str_ac_correlations.ipynb

• 5_results_binned_prediction.ipynb

• 6_job_submitter.ipynb

• 7_results_model_performance.ipynb