BrainFIBRE：基于信息分解的脑微结构基础模型
BrainFIBRE: A Foundation Model via Information Decomposition for Brain Microstructure
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中文总结 AI 辅助
提出BrainFIBRE，首个脑微结构基础模型，通过自监督部分信息分解（SPID）和反事实候选构建（CCC）从NODDI图谱中解缠独特、协同和冗余信息，在多种预测任务上达到最优性能。
中文摘要 AI 辅助
弥散MRI探测脑微结构，对早期脑血管和神经退行性变化特别敏感。神经突方向分散度和密度成像（NODDI）将弥散信号分解为三个生物物理解释图：神经突密度指数（NDI）、方向分散度指数（ODI）和自由水分数（FWF），分别捕捉神经突堆积、纤维相干性和细胞外液。这些3D图为可迁移的微结构表示提供了丰富基底，但整合它们具有挑战性：标准表示学习难以从共享和协同交互中解缠每个图的独特信息。我们提出BrainFIBRE，首个脑微结构基础模型，在来自55,592名UK Biobank参与者的NODDI衍生图上预训练。我们提出自监督部分信息分解（SPID），首次将PID引导的多模态学习扩展到自监督范式。一种新颖的反事实候选构建（CCC）范式通过模态丢弃和交换扰动模态间对齐，为混合专家架构提供对比信号，以解缠独特、协同和冗余信息，无需任何下游标签。在白种人和亚洲人群队列中，BrainFIBRE在预测年龄、性别、脑血管和神经退行性标志物以及认知的多种任务上达到最先进性能，同时产生神经生物学可解释的表示，揭示任务和队列特定的交互模式。BrainFIBRE为微结构水平的神经影像分析建立了多功能基础。
英文摘要
Diffusion MRI probes 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. These 3D maps offer a rich substrate for transferable microstructural representations, yet integrating them is challenging: standard representation learning struggles to disentangle the unique information in each map from their shared and synergistic interactions. We present BrainFIBRE, the first foundation model for brain microstructure, pretrained on NODDI-derived maps from 55,592 UK Biobank participants. 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 architecture to disentangle unique, synergistic, and redundant information without any downstream label. On both Caucasian and Asian cohorts, BrainFIBRE achieves state-of-the-art performance across diverse tasks predicting age, sex, cerebrovascular and neurodegenerative markers, and cognition, while yielding neurobiologically interpretable representations that reveal task- and cohort-specific interaction patterns. BrainFIBRE establishes a versatile foundation for neuroimaging analysis at the microstructural level.
发表机构
- National University of Singapore（新加坡国立大学）
机构由 AI 辅助整理，请以论文原文为准。
