MRI self-supervised learning (SSL) has largely emphasized segmentation and dense prediction, while controlled studies of how alternative pretraining objectives shape transferable representations for MRI-based disease detection remain limited. We compare reconstruction-based Masked Autoencoders (MAE) and latent-predictive Joint Embedding Predictive Architectures (JEPA) for 3D structural MRI. We further introduce a spectral-domain reconstruction loss for MAE to promote sensitivity to fine-grained anatomical structure and integrate variance-covariance regularization (VCR) into JEPA to encourage broader, less redundant latent representations. Models are pretrained on heterogeneous MRI datasets using individual single-contrast inputs. Across seven dataset-task settings spanning four clinical problem domains, downstream transfer depends on both the pretraining paradigm and how the representational bias induced by a specific objective aligns with the downstream task. Spectral regularization provides its clearest gains for Alzheimer's disease classification and tumor grading, while its benefit is smaller for NC vs. MCI and ASD. VCR yields its strongest improvements when discrimination appears to involve multiple sources of structural variation. Overall, MAE-based pretraining generally outperforms baseline JEPA, with SL-MAE providing the strongest gains in several disease-detection settings. Representation analyses show that fine-tuning reorganizes learned representations in an objective-, depth-, and task-dependent manner. VCR-JEPA maintains broader effective dimensionality across depth and greater cross-task representational alignment than baseline JEPA. We publicly release subject-level pretraining and downstream evaluation splits.

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