Orthogonal Mixed-Effects Modeling for High-Dimensional Longitudinal Data: An Unsupervised Learning Approach.

Area of Science:

• Biostatistics
• Machine Learning
• Neuroscience

Background:

• Linear mixed-effects models are standard for longitudinal data but struggle with high dimensionality.
• Characterizing global and individual trajectories in complex datasets remains a challenge.

Purpose of the Study:

• To introduce Unsupervised Orthogonal Mixed-Effects Trajectory Modeling (UOMETM) for high-dimensional longitudinal data.
• To separate and represent global and individual trajectories effectively using unsupervised learning.

Main Methods:

• Developed an autoencoder with an orthogonal constraint in the latent space.
• Implemented a cross-reconstruction loss for consistency and enhanced orthogonality.
• Validated using image simulations and longitudinal Alzheimer's disease (AD) datasets.

Main Results:

• UOMETM demonstrated superior performance over state-of-the-art methods in identifying longitudinal patterns.
• Achieved lower reconstruction error, better orthogonality, and higher accuracy in AD classification and conversion forecasting.
• Individual trajectories were more critical for AD classification than global trajectories, indicating successful separation.

Conclusions:

• UOMETM offers a robust and generalizable method for analyzing high-dimensional longitudinal data.
• The model shows significant potential for clinical applications, particularly in Alzheimer's disease research.
• The clear separation of global and individual trajectories enhances interpretability and predictive power.