Inferring parameters of pyramidal neuron excitability in mouse models of Alzheimer’s disease using biophysical modeling and deep learning

Saghafi, S., Rumbell, T., Gurev, V., Kozloski, J., Tamagnini, F. ORCID: https://orcid.org/0000-0002-8741-5094, Wedgwood, K. C. A. and Diekman, C. O. (2024) Inferring parameters of pyramidal neuron excitability in mouse models of Alzheimer’s disease using biophysical modeling and deep learning. Bulletin of Mathematical Biology, 86 (5). 46. ISSN 1522-9602 doi: 10.1007/s11538-024-01273-5

Abstract/Summary

Alzheimer’s disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We used deep hybrid modeling (DeepHM), a recently developed parameter inference technique that combines deep learning with biophysical modeling, to map experimental data recorded from hippocampal CA1 neurons in transgenic AD mice and age-matched wildtype littermate controls to the parameter space of a conductance-based CA1 model. Although mechanistic modeling and machine learning methods are by themselves powerful tools for approximating biological systems and making accurate predictions from data, when used in isolation these approaches suffer from distinct shortcomings: model and parameter uncertainty limit mechanistic modeling, whereas machine learning methods disregard the underlying biophysical mechanisms. DeepHM addresses these shortcomings by using conditional generative adversarial networks to provide an inverse mapping of data to mechanistic models that identifies the distributions of mechanistic modeling parameters coherent to the data. Here, we demonstrated that DeepHM accurately infers parameter distributions of the conductance-based model on several test cases using synthetic data generated with complex underlying parameter structures.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/119119
Identification Number/DOI	10.1007/s11538-024-01273-5
Refereed	Yes
Divisions	Interdisciplinary Research Centres (IDRCs) > Centre for Integrative Neuroscience and Neurodynamics (CINN) Life Sciences > School of Chemistry, Food and Pharmacy > School of Pharmacy > Division of Pharmacology
Publisher	Springer
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