The field of neuroimaging is moving towards developing more robust and generalizable solutions for large-scale multicenter studies. Researchers are focusing on creating innovative frameworks for MRI harmonization, which can reduce inter-site variability and improve downstream model performance. Another area of interest is the development of foundation models for 3D brain MRI, which can enable general-purpose feature learning from large-scale, unlabeled datasets. These models have shown promise in improving diagnostic accuracy and reducing dependency on extensive expert annotations. Noteworthy papers in this area include: Scanner-Agnostic MRI Harmonization via SSIM-Guided Disentanglement, which presents a novel image-based harmonization framework for 3D T1-weighted brain MRI. Towards Generalisable Foundation Models for 3D Brain MRI, which introduces BrainFound, a self-supervised foundation model for brain MRI that consistently outperforms existing self-supervised pretraining strategies and supervised baselines.