Effects of Inducing Gamma Oscillations in Hippocampal Subregions DG, CA3, and CA1 on the Potential Alleviation of Alzheimer’s Disease-Related Pathology: Computer Modeling and Simulations

Abstract

The aim of this study was to evaluate the possibility of the gamma oscillation function (40–130 Hz) to reduce Alzheimer’s disease related pathology in a computer model of the hippocampal network dentate gyrus, CA3, and CA1 (DG-CA3-CA1) regions. Methods: Computer simulations were made for a pathological model in which Alzheimer’s disease was simulated by synaptic degradation in the hippocampus. Pathology modeling was based on sequentially turning off the connections with entorhinal cortex layer 2 (EC2) and the dentate gyrus on CA3 pyramidal neurons. Gamma induction modeling consisted of simulating the oscillation provided by the septo-hippocampal pathway with band frequencies from 40–130 Hz. Pathological models with and without gamma induction were compared with a control. Results: In the hippocampal regions of DG, CA3, and CA1, and jointly DG-CA3-CA1 and CA3-CA1, gamma induction resulted in a statistically significant improvement in terms of increased numbers of spikes, spikes per burst, and burst duration as compared with the model simulating Alzheimer’s disease (AD). The positive maximal Lyapunov exponent was negative in both the control model and the one with gamma induction as opposed to the pathological model where it was positive within the DG-CA3-CA1 region. Gamma induction resulted in decreased transfer entropy in accordance with the information flow in DG → CA3 and CA3 → CA1. Conclusions: The results of simulation studies show that inducing gamma oscillations in the hippocampus may reduce Alzheimer’s disease related pathology. Pathologically higher transfer entropy values after gamma induction returned to values comparable to the control model.

The Computer Simulation of Therapy with the NMDA Antagonist in Excitotoxic Neurodegeneration in an Alzheimer’s Disease-like Pathology

“…Each module is responsible for a different part of the overall synaptic transmission process. The simulation model uses the formalism from previous studies [ 20 , 21 , 22 , 23 , 24 ] and a simplified demo version of the simulation model available on ModelDB-private model ( )—accessed data: 1 June 2019; or ( )—accessed data: 1 September 2017. The diagram of the simulation is shown in Figure 1 .…”

“…Physiological stimulation of the NMDA receptor is essential for neuroplasticity and LTP processes [ 34 ]. The issue of modelling the plasticity of biological synapses is very difficult, but our algorithm mimics quite accurately the biologically most important process of long-term potentiation (LTP), and at the same time takes into account the process of forgetting, i.e., the return of the weight of a given synapse to its initial state in the absence of maintenance mechanisms [ 20 , 21 , 22 , 23 , 24 ]. We used a function that calculates the current synapse weight magnitude and models the biological process of long-term potentiation and synaptic efficacy as an exponent from the LTP function.…”

“…Computer models of synaptic decay in the hippocampus for different phases of synapse loss [ 23 ] are helpful in understanding the process of neurodegeneration in Alzheimer’s disease. In contrast, other simulation studies show that inducing gamma oscillations in the hippocampus can ameliorate the pathology associated with Alzheimer’s disease [ 24 ]. The artificial neural networks have been successfully used in supporting the diagnosis of AD based on cerebral perfusion single-photon emission computed tomography (SPECT) data and in nuclear medicine [ 25 , 26 ].…”

“…In this study, a computer simulation environment of α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic (AMPA) and NMDA receptors was created for virtual excitotoxicity therapy with the NMDA receptor antagonist-memantine using the mathematical formalism from previous simulation studies [ 20 , 21 , 22 , 23 , 24 ]. Memantine inhibits NMDA receptor currents in a concentration-dependent manner.…”

Artificial Intelligence in Dentistry—Narrative Review

“…We can benefit from AI in medicine, mostly in the fields such as radiology, pathomorphology, and oncology (by using “Thermalitics” technique in breast cancer detection), in cardiology (to help in ECG analysis), in psychiatry (to diagnose, prevent and treat mental illnesses), nuclear medicine, and many others [ 6 , 7 , 8 , 9 , 10 ]. The computer models of neural networks are also one of the methods to understand the functioning of the nervous system, which we cannot study in natural conditions due to the limitations of modern research methods [ 11 , 12 , 13 , 14 , 15 ].…”