BrainFIBRE: A Foundation Model via Information Decomposition for Brain Microstructure

Diffusion MRI is widely used to probe brain microstructure, with particular sensitivity to early cerebrovascular and neurodegenerative changes. Neurite Orientation Dispersion and Density Imaging (NODDI) decomposes the diffusion signal into three biophysically interpretable maps — neurite density index (NDI), orientation dispersion index (ODI), and free water fraction (FWF) — capturing neurite packing, fiber coherence, and extracellular fluid, respectively. These 3D maps provide a rich substrate for learning transferable microstructural representations for the human brain, yet effectively integrating them remains an open challenge: standard representation learning struggles to disentangle the unique information carried by each of the three brain microstructure maps (i.e., NDI, ODI, and FWF) from their shared and synergistic interactions. Here, we present BrainFIBRE (Brain Foundation Model via Information Decomposition for BRain MicrostructurE), the first foundation model for brain microstructure, pretrained on three NODDI-derived microstructure maps from the UK Biobank dataset (55,592 participants). To achieve this, we propose Self-supervised Partial Information Decomposition (SPID), which extends PID-guided multimodal learning to the self-supervised regime for the first time. A novel Counterfactual Candidate Construction (CCC) paradigm perturbs inter-modality alignment through modality dropping and swapping, providing the contrastive signal for a Mixture-of-Experts (MoE) architecture to disentangle unique, synergistic, and redundant information without any downstream label. Evaluated on both Caucasian and Asian cohorts, our model achieves state-of-the-art performance across diverse downstream tasks predicting age, sex, cerebrovascular disease (CeVD) and neurodegenerative markers, and cognitive performance, while yielding neurobiologically interpretable representations that reveal taskand cohort-specific interaction patterns among microstructural compartments. BrainFIBRE establishes a versatile foundation for neuroimaging analysis at the microstructural level. Code is available at https://github.com/hzlab/BrainFIBRE