This study introduces a deep learning framework for the inferential exploration of latent representations in 3D brain MRI, leveraging a simple convolutional autoencoder with a hierarchical encoder and a compact latent space. Trained on segmented gray matter images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, the model learns latent representations that preserve neuroanatomical structure and reflect clinical variability across cognitive status. Dimensionality reduction techniques (PCA, t-SNE, PLS, UMAP) were applied to visualize and interpret the latent space, correlating it with anatomical regions defined by the AAL atlas. As a novel contribution, the Latent-Regional Correlation Profiling (LRCP) framework, which combines statistical association and supervised discriminability to identify brain regions that encode clinically relevant latent information is proposed. Our results show that even minimal architectures capture meaningful patterns associated with progression to Alzheimer's disease. Interpretability is assessed by applying SHAP-based regression to a post-hoc model that predicts reconstruction error from atlas-based regional gray matter intensities, thereby identifying anatomically meaningful regions involved in class-specific reconstruction strategies. These findings are further validated using 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.

翻译：本研究提出了一种深度学习框架，用于对三维脑部MRI的潜在表征进行推理性探索。该框架采用具有层级编码器与紧凑潜空间的简单卷积自编码器，在阿尔茨海默病神经影像学倡议（ADNI）数据集的灰质分割图像上训练后，模型习得既保留神经解剖结构特征又反映临床认知状态变异性的潜在表征。通过应用PCA、t-SNE、PLS、UMAP等降维技术对潜空间进行可视化与解读，并将其与AAL图谱定义的解剖区域建立关联。作为创新贡献，本文提出了潜-区域相关性剖图（LRCP）框架——该框架结合统计关联性与监督判别性，用于识别编码临床相关潜在信息的脑区。结果表明，即使是最简化的网络架构也能捕获与阿尔茨海默病进展相关的有意义模式。通过将基于SHAP的回归方法应用于后验模型（该模型根据图谱定义的区域灰质强度预测重建误差），可识别参与类别特异性重建策略的解剖学关键区域，从而评估模型可解释性。这些发现通过不可知统计方法得到进一步验证，凸显了神经影像学中严谨评估的重要性。本研究证明了自编码器作为临床神经科学中生物标志物发现与假说生成探索性工具的潜力。