Python implementation of Monti Hyvärinen Analysis (MHA)

Jupyter notebook provides details regarding proposed model and provides an example application to resting-state fMRI data

Requirements

• python 3
• nilearn
• numpy

Datasets

• resting state fMRI data from the CamCAN repository (564 subjects)

References

Monti et al., "Interpretable brain age prediction using linear latent variable models of functional connectivity", PLOS One, 2020

Monti & Hyvärinen, "A Unified Probabilistic Model for Learning Latent Factors and Their Connectivities from High-Dimensional Data", UAI 2018