发表机构
Southern University of Science and Technology; University of Warwick; Shenzhen Loop Area Institute; Omni-Intelligence（南方科技大学; 华威大学; 深圳河套学院; 全智能公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出BrainTaskonomy，利用学习关系组织fMRI基础模型的预训练和迁移，通过优先级课程和任务图谱显著提升性能。
AI 中文摘要
fMRI基础模型日益聚合跨脑状态、队列和采集设置的异质数据，但预训练域通常被视为平坦混合，下游任务则独立适应。我们研究能否在不修改骨干网络的情况下，用测量的学习关系来组织这两个阶段。在预训练阶段，一个轻量级Brain-DiT代理估计十个fMRI域的难度和有向促进，产生优先级引导的累积域课程，结合从高到低噪声的时间步调度和联合巩固。在适应阶段，跨十五个任务的受控一阶和高阶迁移构建有向任务图谱，从中预算整数规划（BIP）选择直接监督的源任务和目标特定路径。联合优先级域和高到低时间步课程相对于在两个维度上的均匀采样，分别将v-NMSE、PSD-NMSE和FC-MSE降低了6.5%、16.3%和10.5%，并在六个域内和域外任务中显示出强大的下游性能。任务图谱揭示了不对称的、依赖目标的迁移，而探索性密封测试评估显示，当高阶路径空间可用时，BIP策略比匹配的随机对照获得更大的描述性收益。总之，这些发现支持通过测量的学习关系来组织fMRI预训练和适应，而不是将域和任务视为独立的平坦集合。
英文摘要
fMRI foundation models increasingly aggregate heterogeneous data across brain states, cohorts, and acquisition settings, yet pretraining domains are commonly treated as a flat mixture and downstream tasks are adapted independently. We study whether measured learning relations can organize both stages without modifying the backbone. During pretraining, a lightweight Brain-DiT proxy estimates difficulty and directed facilitation across ten fMRI domains, yielding a priority-guided cumulative domain curriculum combined with high-to-low-noise timestep scheduling and joint consolidation. During adaptation, controlled first- and higher-order transfer across fifteen tasks constructs a directed taskonomy, from which budgeted integer programming (BIP) selects directly supervised source tasks and target-specific routes. The joint priority-domain and high-to-low-timestep curriculum reduces v-NMSE, PSD-NMSE, and FC-MSE by 6.5%, 16.3%, and 10.5%, respectively, relative to uniform sampling over both dimensions, and shows strong downstream performance across six in- and out-of-domain tasks. The taskonomy reveals asymmetric, target-dependent transfer, while exploratory sealed-test evaluation shows larger descriptive gains for BIP policies when higher-order route spaces are available than for matched random controls. Together, these findings support organizing fMRI pretraining and adaptation by measured learning relations rather than treating domains and tasks as independent flat sets.
