Preserving What Matters: Efficient-Coding-Inspired Information Bottleneck for Whole-Brain Decoding and Voxel Attribution

Vision-language pretrained models have greatly advanced brain decoding, but most decoders remain opaque, offering limited evidence about which patterns in fMRI activity influence the decoding decision. We propose an interpretable whole-brain decoding framework inspired by the efficient coding principle of preserving what matters. Concretely, we encourage brain-VLPM alignment through contrastive learning with variational Information Bottleneck regularization to compress redundant ROI-free whole-brain fMRI activity. Built on the whole-brain decoder, our method further explains individual decoding decisions through bottleneck-based attribution, producing voxel-level saliency maps for brain-image/text alignment. Experiments on the Natural Scenes Dataset show strong retrieval performance and quantitatively evaluated interpretability. Through ROI localization and voxel ablation analyses, the highly ranked voxels are shown to strongly influence the trained decoder's alignment scores: removing them rapidly degrades the alignment score, whereas inserting them rapidly restores and even can exceed the full-input alignment score. These results establish our framework as a practical approach to identifying informative voxels supporting individual decoding decisions.