Abstract
We introduce Brain-JEPA, a brain dynamics foundation model with the Joint-Embedding Predictive Architecture (JEPA). This pioneering model achieves state-of-the-art performance in demographic prediction, disease diagnosis/prognosis, and trait prediction through fine-tuning. Furthermore, it excels in off-the-shelf evaluations (e.g., linear probing) and demonstrates superior generalizability across different ethnic groups, surpassing the previous large model for brain activity significantly. Brain-JEPA incorporates two innovative techniques: Brain Gradient Positioning and Spatiotemporal Masking. Brain Gradient Positioning introduces a functional coordinate system for brain functional parcellation, enhancing the positional encoding of different Regions of Interest (ROIs). Spatiotemporal Masking, tailored to the unique characteristics of fMRI data, addresses the challenge of heterogeneous time-series patches. These methodologies enhance model performance and advance our understanding of the neural circuits underlying cognition. Overall, Brain-JEPA is paving the way to address pivotal questions of building brain functional coordinate system and masking brain activity at the AI-neuroscience interface, and setting a potentially new paradigm in brain activity analysis through downstream adaptation.
Abstract (translated)
我们引入了Brain-JEPA,一种基于Joint-Embedding Predictive Architecture(JEPA)的大脑动力学基础模型。这一创新模型通过微调在人口预测、疾病诊断/预后和特征预测方面实现了最先进的性能。此外,它在一系列的预测试评估(例如线性探测)中表现优异,并在不同种族之间表现出卓越的泛化能力,显著超越了前一个大模型。Brain-JEPA采用了两种创新技术:Brain Gradient Positioning和Spatiotemporal Masking。Brain Gradient Positioning引入了一个功能坐标系来对脑功能进行分割编码,从而增强不同关注区域(ROIs)的位置编码。Spatiotemporal Masking专门针对fMRI数据的独特特点,解决了异质时间序列补丁的挑战。这些方法提高了模型性能,并进一步推动了在人工智能与神经科学界面构建脑功能坐标系以及通过下游适应揭示神经活动的全新范式。总的来说,Brain-JEPA为解决在人工智能与神经科学界面构建脑功能坐标系以及揭示神经活动的全新范式铺平了道路。
URL
https://arxiv.org/abs/2409.19407