Neuroimaging   data analysis presents significant challenges due to the high-dimensional,   complex, and spatially structured nature of the data. Effective   representation learning for neuroimaging must not only capture predictive   relationships but also preserve spatial and anatomical context to ensure   interpretability for clinical applications. However, most existing methods   overlook these critical aspects, resulting in representations that fail to   fully utilize structural information and lack clinical relevance. To address   these limitations, we propose a novel approach, Enhanced Fused Sufficient   Representation Learning (EFSRL), which integrates sufficient representation   learning with region detection. Our method's core is HSIC-FUSE, a measure   aggregating normalized Hilbert-Schmidt Independence Criterion (HSIC) values   across multiple kernels to promote sufficient representation without relying   on arbitrary kernel selection. These ensure both robustness and   interpretability, which are essential for clinical tasks. We also introduce a   dual-network architecture that alternates between learning representations and   selecting key regions, facilitating more accurate and meaningful   interpretations. Through extensive experiments on synthetic and real-world   medical imaging data, including the ADNI dataset, we demonstrate that EFSRL   outperforms existing methods. Our approach generates interpretable   representations tailored for various medical imaging tasks, highlighting its   potential for practical applications in healthcare.