Cross-species representation learning aligns mouse and human neural dynamics and tracks clinical drug efficacy

Key points

• We develop a dual-rule contrastive learning framework that aligns corresponding mouse and human states while preserving separation between distinct phenotypes.1
• This framework recovered conserved sensory-response structure across species and, in epilepsy, resolved distinct relationships between three mouse models and heterogeneous human patient populations.1
• 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.1
• 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.1

Sources (1)

• [1]Cross-species representation learning aligns mouse and human neural dynamics and tracks clinical drug efficacy
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 04:19 AM

  Preclinical models poorly predict human drug efficacy, particularly in neurological disorders.

  We develop a dual-rule contrastive learning framework that aligns corresponding mouse and human states while preserving separation between distinct phenotypes.

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