标题： 赋能功能神经影像：一种用于神经信号统一表示的预训练生成框架 显示英文标题

标题： Empowering Functional Neuroimaging: A Pre-trained Generative Framework for Unified Representation of Neural Signals

摘要： 多模态功能神经影像能够系统地分析大脑机制，并为脑机接口（BCI）解码提供区分性表示。然而，其采集受到高成本和可行性限制的制约。此外，特定群体的代表性不足会削弱BCI解码模型的公平性。为了解决这些挑战，我们通过生成式人工智能（AI）提出了一种统一表示框架来整合多模态功能神经影像。通过将多模态功能神经影像映射到一个统一的表示空间，该框架能够为采集受限的模态和代表性不足的群体生成数据。实验表明，该框架可以生成与真实大脑活动模式一致的数据，为理解大脑机制提供见解，并提高下游任务的性能。更重要的是，它可以通过增强代表性不足群体的数据来提升模型的公平性。总体而言，该框架为降低多模态功能神经影像采集成本以及提升BCI解码模型的公平性提供了新的范式。 显示英文摘要

摘要： Multimodal functional neuroimaging enables systematic analysis of brain mechanisms and provides discriminative representations for brain-computer interface (BCI) decoding. However, its acquisition is constrained by high costs and feasibility limitations. Moreover, underrepresentation of specific groups undermines fairness of BCI decoding model. To address these challenges, we propose a unified representation framework for multimodal functional neuroimaging via generative artificial intelligence (AI). By mapping multimodal functional neuroimaging into a unified representation space, the proposed framework is capable of generating data for acquisition-constrained modalities and underrepresented groups. Experiments show that the framework can generate data consistent with real brain activity patterns, provide insights into brain mechanisms, and improve performance on downstream tasks. More importantly, it can enhance model fairness by augmenting data for underrepresented groups. Overall, the framework offers a new paradigm for decreasing the cost of acquiring multimodal functional neuroimages and enhancing the fairness of BCI decoding models.