The human brain demonstrates substantial inter-individual variability in fine-grained functional topography, posing challenges in identifying common neural representations across individuals. Functional alignment has the potential to harmonize these individual differences. However, it typically requires an identical set of stimuli presented to different individuals, which is often unavailable. To address this, we propose a content loss-based neural code converter, designed to convert brain activity from one subject to another representing the same content. The converter is optimized so that the source subject's converted brain activity is decoded into a latent image representation that closely resembles that of the stimulus given to the source subject. We show that converters optimized using hierarchical image representations achieve conversion accuracy comparable to those optimized by paired brain activity as in conventional methods. The brain activity converted from a different individual and even from a different site sharing no stimuli produced reconstructions that approached the quality of within-individual reconstructions. The converted brain activity had a generalizable representation that can be read out by different decoding schemes. The converter required much fewer training samples than that typically required for decoder training to produce recognizable reconstructions. These results demonstrate that our method can effectively combine image representations to convert brain activity across individuals without the need for shared stimuli, providing a promising tool for flexibly aligning data from complex cognitive tasks and a basis for brain-to-brain communication.

翻译：人脑在精细功能拓扑结构上表现出显著的个体间差异，这给识别跨个体的共同神经表征带来了挑战。功能对齐技术有望协调这些个体差异，但通常需要向不同个体呈现相同的刺激集，而这一条件往往难以满足。针对这一问题，我们提出了一种基于内容损失的神经编码转换器，旨在将某个被试的大脑活动转换为另一个被试表征相同内容的大脑活动。该转换器的优化目标为：源被试转换后的大脑活动经解码后生成的潜在图像表征，与源被试所接收刺激的表征高度相似。研究表明，采用分层图像表征优化的转换器，其转换精度可媲美传统方法中利用配对大脑活动优化的转换器。来自不同个体、甚至不同脑区且无共享刺激的大脑活动经转换后，其重建图像质量已接近个体内重建的水平。转换后的大脑活动具有可泛化的表征，能够被不同解码方案读取。与解码器训练所需的典型样本量相比，该转换器仅需更少的训练样本即可生成可识别的重建结果。这些结果表明，我们的方法能够有效结合图像表征实现无需共享刺激的跨个体大脑活动转换，为灵活对齐复杂认知任务数据提供了有前景的工具，并奠定了脑-脑通信的基础。