Code and materials for paper "Contrastive machine learning reveals the structure of neuroanatomical variation within Autism"
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- 01-Train-AutoEncoders.ipynb Trains VAE and CVAE autoencoders
- 02-Extract-Latent-Features.ipynb Extracts latent features (shared, ASD-specific & VAE) using trained autoencoder models
- 03-Analysis-RSA.ipynb RSA analyses (Figure 1B/1C)
- 04-Analysis-Clustering-Results.ipynb Clustering analyses (Figure 1D)
- 05-Jacobian-Make-Jacobians.ipynb Generates the Jacobian Determinant maps using synthethic "TC-Twins"
- 06-Jacobian-Jacobian-Analysis.ipynb Calculates LOSO PCA and plots neuroanatomical associations (Figure 2)
- helper_funcs.py helper functions called by analysis notebooks
- make_models2.py Code defining VAE and CVAE Tensorflow models
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- Generated data and necessary files
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tf_weights/ Trained weights for VAE and CVAE models
- CVAE_weights/
- CVAE_weights.z01
- CVAE_weights.z02
- ...
- VAE_weights/
- VAE_weights.z01
- VAE_weights.z02
- ...
- CVAE_weights/
N.B. The trained weights are zipped into a multi-part zip file (.z01,.z02,.z03 etc.) and need to be unzipped before use.
N.B. SFARI data has been removed to comply with Researcher Distribution Agreement (eRDA). SFARI Award #614379 to Joshua Hartshorne and Stefano Anzellotti. Data can be obtained by authorized researchers from base.sfari.org