BrainTaskonomy: Learning How to Pretrain and What to Transfer in fMRI Foundation Models

2026-09-09

AI Summary

研究通过Brain-DiT代理和任务拓扑结构优化fMRI基础模型的预训练和适应过程，以解决数据异质性问题，提高下游任务性能。

Abstract

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.

Problem

• fMRI
• pretraining
• adaptation
• learning relations
• foundation models

Innovation

• Brain-DiT
• Priority-guided Curriculum
• Budgeted Integer Programming (BIP)
• Directed Taskonomy
• High-to-Low Noise Scheduling