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Updated: Jun 29, 2025

Using Enzyme-based Biosensors to Measure Tonic and Phasic Glutamate in Alzheimer's Mouse Models
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Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer's Disease Using Biophysical

Soheil Saghafi1,2, Timothy Rumbell3, Viatcheslav Gurev3

  • 1Department of Mathematical Sciences, New Jersey Institute of Technology, University Heights, Newark, NJ, 07102, USA.

Bulletin of Mathematical Biology
|March 26, 2024
PubMed
Summary

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Deep hybrid modeling (DeepHM) precisely maps Alzheimer's disease (AD) mouse brain data to biophysical models. This reveals key ion channel alterations in AD, including potassium and sodium channels, offering new insights into disease mechanisms.

Area of Science:

  • Neuroscience
  • Computational Biology
  • Biophysics

Background:

  • Alzheimer's disease (AD) involves amyloid beta and tau protein aggregation, leading to neuronal dysfunction.
  • Hippocampal neurons in AD mouse models show altered excitability.
  • Existing modeling approaches have limitations: mechanistic models face uncertainty, while machine learning ignores biophysical mechanisms.

Purpose of the Study:

  • To apply deep hybrid modeling (DeepHM) to infer ion channel parameter distributions in AD mouse models.
  • To identify specific ion channels affected by amyloidopathy, tauopathy, and aging in the hippocampus.
  • To overcome limitations of isolated mechanistic and machine learning methods.

Main Methods:

  • Utilized deep hybrid modeling (DeepHM), a technique combining deep learning and biophysical modeling.
Keywords:
Generative adversarial networkParameter inferencePopulation of modelsPyramidal neuron excitability

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  • Applied DeepHM to experimental data from hippocampal CA1 neurons in transgenic AD mice and wildtype controls.
  • Employed conditional generative adversarial networks for inverse mapping of data to mechanistic models.

    • Main Results:

    • DeepHM accurately inferred parameter distributions using synthetic data.
    • Identified disrupted ion channel conductances in AD mouse models at 12 and 24 months.
    • Found delayed rectifier potassium, transient sodium, and hyperpolarization-activated potassium channels are significantly altered by tauopathy, amyloidopathy, and aging, respectively.

    Conclusions:

    • DeepHM is a powerful tool for integrating biophysical modeling with machine learning to analyze complex biological data.
    • Specific ion channels, including potassium and sodium subtypes, are critically affected in Alzheimer's disease progression.
    • This study provides a mechanistic understanding of neuronal dysfunction in AD mouse models.