用随机最优控制构建脑动力学模型，提升fMRI分析的准确性和泛化能力。
A Foundational Brain Dynamics Model via Stochastic Optimal Control
- 基于随机最优控制与近似推断，建立连续-离散状态空间模型处理噪声复杂的fMRI信号。
- 在UKB、HCP-A等数据集上实现最先进的下游任务表现，涵盖疾病诊断与预后预测。
- 无需仿真即可高效推断，适合神经科学中大规模脑动态建模与迁移学习场景。
我们提出一种基础性脑动力学模型，采用随机最优控制（SOC）与摊销推断。该方法构建了连续-离散状态空间模型（SSM），能稳健应对fMRI信号的复杂性与噪声。为克服计算瓶颈，基于SOC框架设计近似策略；同时提出无需仿真的隐变量动态方法，利用局部线性近似实现高效可扩展推断。为促进表征学习，从SOC形式导出证据下界（ELBO），与自监督学习（SSL）无缝结合，生成鲁棒且可迁移的表示。在大型数据集UKB上预训练后，模型在多项下游任务中达到当前最优性能，包括人口统计预测、特质分析、疾病诊断与预后。在外部数据集HCP-A、ABIDE和ADHD200上的评估进一步验证其跨人群与临床分布的优越性与鲁棒性。本模型为解析脑动力学提供了一种高效可扩展的范式，推动神经科学研究的应用发展。
原文摘要 · Abstract (English)
We introduce a foundational model for brain dynamics that utilizes stochastic optimal control (SOC) and amortized inference. Our method features a continuous-discrete state space model (SSM) that can robustly handle the intricate and noisy nature of fMRI signals. To address computational limitations, we implement an approximation strategy grounded in the SOC framework. Additionally, we present a simulation-free latent dynamics approach that employs locally linear approximations, facilitating efficient and scalable inference. For effective representation learning, we derive an Evidence Lower Bound (ELBO) from the SOC formulation, which integrates smoothly with recent advancements in self-supervised learning (SSL), thereby promoting robust and transferable representations. Pre-trained on extensive datasets such as the UKB, our model attains state-of-the-art results across a variety of downstream tasks, including demographic prediction, trait analysis, disease diagnosis, and prognosis. Moreover, evaluating on external datasets such as HCP-A, ABIDE, and ADHD200 further validates its superior abilities and resilience across different demographic and clinical distributions. Our foundational model provides a scalable and efficient approach for deciphering brain dynamics, opening up numerous applications in neuroscience.
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