Cross species neural patterns predict human drug effects in neurological disorders
Cross-species representation learning aligns mouse and human neural dynamics and tracks clinical drug efficacy
Machine Learning
Summary
It is hard to predict if a drug tested in animals will work in humans, especially for brain diseases. The authors created a method that finds similar patterns of brain activity between mice and humans. This helps spot shared disease features and shows when a drug moves animal brain activity closer to a healthy human state. Their method also found common brain activity changes in different genetic conditions affecting the brain. This approach may help test if new treatments will work in humans by comparing brain signals across species.
What this means in practice
- •For pharmaceutical developers: Assess if experimental drugs tested in animal brain models produce neural activity patterns linked to healthy human brain states.
- •For clinical trial designers: Map heterogeneous human patient neural data onto animal models to improve selection of relevant preclinical drug tests.
Authors
Marko Tvrdic, Justin Richmond Domingo, Jae Ann Buenaluz, Jydell Ashley Palomo Penollar, Edmayelle Villavicencio Alforja, Jobi Fallaeria Subosa, Gabriel Ocana-Santero
Abstract
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.