发表机构
Exin Therapeutics, Inc.（Exin 治疗有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究开发双规则对比学习框架，通过跨物种神经表征学习对齐小鼠与人类神经动力学，可追踪药物疗效并关联疾病相关神经特征，助力临床前模型转化研究。
AI 中文摘要
临床前模型难以准确预测人类药物疗效，尤其是在神经疾病领域。神经活动是极具价值的转化研究信息源，因为它能捕捉神经系统功能的高维变化，且可在动物与人类中同时测量。然而，其高维特性使得区分保守的疾病相关特征与物种、记录模态及实验背景带来的变异变得困难。本研究测试能否通过学习以生物状态而非物种为组织的表征，直接从电生理数据中识别共享神经动力学。我们开发了双规则对比学习框架，该框架对齐小鼠与人类的对应状态，同时保留不同表型间的区分。此框架恢复了跨物种保守的感觉反应结构，在癫痫研究中，明确了三种小鼠模型与异质性人类患者群体间的不同关系。将处理后的动物投影到冻结的跨物种表征中时，药物诱导其向人类对齐的健康状态移动，可回顾性追踪十种模型-药物组合的已知临床疗效，包括一种疾病特异性有害效应。尽管遗传病因与记录模态存在差异，该框架还识别出Fmr1基因敲除小鼠与人类16p11.2拷贝数变异携带者间共享的疾病相关神经动力学。综上，这些发现表明跨物种神经表征学习具有潜力，可将异质性人类疾病映射到实验易处理的临床前状态，并评估干预措施是否恢复了与人类相关的环路功能。
英文摘要
Preclinical models poorly predict human drug efficacy, particularly in neurological disorders. Neural activity offers a uniquely rich source of translational information because it captures high-dimensional variation in nervous-system function that can be measured in both animals and humans. However, its high dimensionality makes it difficult to distinguish conserved disease-related features from variation arising from species, recording modality and experimental context. Here, we test whether shared neural dynamics can be identified directly from electrophysiology data by learning representations organized by biological state rather than species. We develop a dual-rule contrastive learning framework that aligns corresponding mouse and human states while preserving separation between distinct phenotypes. This framework recovered conserved sensory-response structure across species and, in epilepsy, resolved distinct relationships between three mouse models and heterogeneous human patient populations. When treated animals were projected into a frozen cross-species representation, drug-induced movement towards the human-aligned healthy state retrospectively tracked known clinical efficacy across ten model-drug combinations including a disease-specific detrimental effect. The framework also identified shared disease-associated neural dynamics between Fmr1-knockout mice and human 16p11.2 copy-number variant carriers despite differences in genetic aetiology and recording modality. Together, these findings show the potential of cross-species neural representation learning to map heterogeneous human disease onto experimentally tractable preclinical states and assess whether interventions restore human-relevant circuit function.
Comments33 pages, 5 figures; supplementary material included (2 supplementary figures, 3 supplementary tables)
