Machine Learning in the AOMIC dataset - predicting audio features with functional connectivity

We use the open Amsterdam Open MRI Collection (AOMIC) to explore Machine Learning algorithms with different predictors and outcome variables.

Name		Name	Last commit message	Last commit date
Latest commit History 59 Commits
audio_features		audio_features
ml_models		ml_models
.gitignore		.gitignore
MLPtryout.ipynb		MLPtryout.ipynb
MLPtryout_2.ipynb		MLPtryout_2.ipynb
MLregression_test.ipynb		MLregression_test.ipynb
Pliers.ipynb		Pliers.ipynb
README.md		README.md
allSubStr.csv		allSubStr.csv
anatomical_features.ipynb		anatomical_features.ipynb
another_ML_test.ipynb		another_ML_test.ipynb
audio_feature_slices.ipynb		audio_feature_slices.ipynb
audio_features.py		audio_features.py
audio_ml.ipynb		audio_ml.ipynb
basic_ml_model.ipynb		basic_ml_model.ipynb
data_peek.ipynb		data_peek.ipynb
difumo_script.ipynb		difumo_script.ipynb
fetcher.py		fetcher.py
final_mask.nii.gz		final_mask.nii.gz
final_voxels_mask.ipynb		final_voxels_mask.ipynb
fs_feat.ipynb		fs_feat.ipynb
harvard_oxford_parcellation.ipynb		harvard_oxford_parcellation.ipynb
mean_slices_audio_features.npy		mean_slices_audio_features.npy
music_functional_connectivity.nii.gz		music_functional_connectivity.nii.gz
neuroquery_rois.ipynb		neuroquery_rois.ipynb
parcellation_test.ipynb		parcellation_test.ipynb
upperTriCorr.csv		upperTriCorr.csv
windowed_cms.npy		windowed_cms.npy

jainraj/NHA2022_AOMIC_ID1000