Deep-learning based tracking of behaviour in preclinical models for mental illness
Human genetic studies to increasingly highlight that disruption of postsynaptic genes is a hub for a range of mental health disorders, namely neurodevelopmental and neuropsychiatric disorders. These include schizophrenia, anxiety and mood disorders (depression, bipolar) and autism spectrum disorders, that share overlapping symptom domains.
While the importance of postsynaptic proteins in synaptic function and plasticity are strongly appreciated, we know much less about the impact of postsynaptic gene mutations in regulating distinct components of cognition and higher order processing.
Identifying the disrupted neural mechanisms that underlie cognitive symptoms in mental illnesses like schizophrenia and mood disorders remains a challenge for the development of novel effective treatments.
Aim
- Investigate disrupted decision-making and learning behaviour in genetic mouse models for complex mental illnesses.
The ability to measure and control behaviour in preclinical models, using automated behavioural systems, while recording real-time neural activity provides advanced experimental approaches to tackle this challenge. Combining these approaches with novel tools for pose estimation with deep learning now allows training of deep neural networks to accurately quantify a range of complex behavioural measures.
This project will use deep learning tools (such as DeepLabCut) to analyse disrupted decision-making and learning behaviour in genetic mouse models for complex mental illnesses.
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