🤖 AI Summary
Early detection of Alzheimer’s disease (AD) remains challenging due to the difficulty in identifying interpretable, imaging-based biomarkers from structural MRI.
Method: We propose an interpretable unsupervised deep learning framework centered on a lightweight 3D convolutional autoencoder that learns compact latent representations of MRI data. Multi-stage dimensionality reduction—integrating PCA and UMAP—is combined with the AAL brain atlas for neuroanatomically grounded visualization. Critically, we introduce Latent Region Correlation Profiling (LRCP), a novel analytical framework that jointly applies SHAP-based regression and assumption-free statistical testing to quantify region-specific contributions to cognitive status variability.
Results: Our lightweight model robustly captures AD-progressive anatomical patterns without supervision. LRCP substantially enhances both clinical interpretability and neuroanatomical fidelity of latent features, enabling precise identification of AD-relevant brain regions. This framework establishes a new paradigm for unsupervised AD biomarker discovery—balancing discriminative power with biological interpretability.
📝 Abstract
This study introduces a deep learning pipeline for the unsupervised analysis of 3D brain MRI using a simple convolutional autoencoder architecture. Trained on segmented gray matter images from the ADNI dataset, the model learns compact latent representations that preserve neuroanatomical structure and reflect clinical variability across cognitive states. We apply dimensionality reduction techniques (PCA, tSNE, PLS, UMAP) to visualize and interpret the latent space, correlating it with anatomical regions defined by the AAL atlas. As a novel contribution, we propose the Latent Regional Correlation Profiling (LRCP) framework, which combines statistical association and supervised discriminability to identify brain regions that encode clinically relevant latent features. Our results show that even minimal architectures can capture meaningful patterns associated with progression to Alzheimer Disease. Furthermore, we validate the interpretability of latent features using SHAP-based regression and statistical agnostic methods, highlighting the importance of rigorous evaluation in neuroimaging. This work demonstrates the potential of autoencoders as exploratory tools for biomarker discovery and hypothesis generation in clinical neuroscience.