Investigating the effect of amyloid-beta on hippocampal dynamics in Alzheimer's disease

Student thesis: Doctoral ThesisDoctor of Philosophy (PhD)

Abstract

Alzheimer’s disease (AD) is a complex and multifactorial, neurodegenerative disease. Accumulation of pathogenic forms of the protein amyloid-beta (Aβ), one of the hallmark features of AD, is thought to have a causal role in this neurodegeneration. Enhanced levels of Aβ are associated with synaptic dysfunction, altered neuronal intrinsic excitability and altered gamma frequency activity within the hippocampus. In this thesis, biophysical models of synapses, neurons and networks are combined
with experimental work to examine how Aβ alters neural activity in the CA1 hippocampal region.

The acute effect of Aβ-infusion on synaptic transmission is investigated by recording
spontaneous miniature excitatory postsynaptic currents (mEPSCs) from CA1 neurons in
cultured hippocampal slices; Aβ is found to cause a rapid increase in mEPSC amplitude. Using a first-order kinetic synapse model parameterised using the mEPSC data it is found that the increase in amplitude can be accounted for by a 50% increase in the synaptic conductance of the model.

Two versions of a single-compartment biophysical model are used to simulate intrinsic excitability measures recorded from CA1 pyramidal neurons in wild type and PDAPP transgenic mice that overexpress Aβ. Both models predict that altered excitability in PDAPP neurons can be accounted for by lowering the transient Na+ and delayed-rectifier K+ (KDR) channel conductances and by slowing the activation rate of the KDR-channel.

The potential impacts of these observations on gamma frequency oscillations are explored using an excitatory-inhibitory network model. The Aβ-mediated increase in synaptic transmission increases the total gamma power of the oscillations, progressively increasing as more synapses are affected. Incorporating the PDAPP neuron model into the network increases the frequency of the gamma oscillations.

These results illustrate how data-informed mathematical models can bring new insights into the underlying mechanisms and implications of Aβ pathology and can contribute to a quantitative and multiscale understanding of AD.
Date of Award24 Jun 2021
Original languageEnglish
Awarding Institution
  • The University of Bristol
SupervisorLucia Marucci (Supervisor) & Krasimira Tsaneva-Atanasova (Supervisor)

Keywords

  • Alzheimer's disease
  • Amyloid-beta
  • Hippocampus
  • Computational
  • CA1
  • Biophysical
  • Gamma oscillations
  • Intrinsic excitability
  • mEPSC

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