Summary
BrainTaskonomy (arXiv 2609.10518) proposes organizing both pretraining and adaptation of fMRI foundation models using measured learning relations, without modifying the backbone. During pretraining, a lightweight Brain-DiT proxy estimates difficulty and directed facilitation across ten fMRI domains, producing 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 experiments across fifteen tasks build a directed taskonomy, from which budgeted integer programming (BIP) selects directly supervised source tasks and target-specific transfer 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 versus uniform sampling, with strong downstream performance on six in- and out-of-domain tasks. The taskonomy reveals asymmetric, target-dependent transfer, and exploratory sealed-test evaluation shows larger gains for BIP policies when higher-order route spaces are available.
Paper Overview
- Field: Computer Vision (CV)
- Authors: Junfeng Xia, Wenhao Ye, Junxiang Zhang, Jiayu Zuo, Mo Wang, Quanying Liu
- Published: 2026-09-09
- arXiv: 2609.10518
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. This work studies whether measured learning relations can organize both stages without modifying the backbone.Pretraining stage: 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.
Adaptation stage: 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.
Key Results
- 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.
- Strong downstream performance across six in- and out-of-domain tasks.
- The taskonomy reveals asymmetric, target-dependent transfer.
- Exploratory sealed-test evaluation shows larger descriptive gains for BIP policies when higher-order route spaces are available than for matched random controls.
Conclusion
The findings support organizing fMRI pretraining and adaptation by measured learning relations rather than treating domains and tasks as independent flat sets.
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