Deep learning has been extensively applied to the diagnosis of Alzheimer’s disease(AD) based on MRI data. However, these methods often require a substantial amount of labeled images, and the resulting feature representations are hard to interpret. To simultaneously address these two issues, we propose a self-supervised cross-encoder framework, which leverages the temporal information among longitudinal MRI scans as the supervision and yields disentangled representations comprising two components. The first component, obtained by adhering to an additional constraint enforced through contrastive learning, captures static brain information, and the second component, capturing the dynamic information, is a low-dimensional vector representation, which can be readily fine-tuned for downstream AD classification tasks. The proposed method demonstrates superior performance in both classification accuracy and interpretability on the ADNI dataset.