Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.784224
Title: Computational methods toward early detection of neuronal deterioration
Author: Sadegh-Zadeh, Seyed-Ali
ISNI:       0000 0004 7969 7858
Awarding Body: University of Hull
Current Institution: University of Hull
Date of Award: 2019
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Abstract:
In today's world, because of developments in medical sciences, people are living longer, particularly in the advanced countries. This increasing of the lifespan has caused the prevalence of age-related diseases like Alzheimer's and dementia. Researches show that ion channel disruptions, especially the formation of permeable pores to cations by Aβ plaques, play an important role in the occurrence of these types of diseases. Therefore, early detection of such diseases, particularly using non-invasive tools can aid both patients and those scientists searching for a cure. To achieve the goal toward early detection, the computational analysis of ion channels, ion imbalances in the presence of Aβ pores in neurons and fault detection is done. Any disruption in the membrane of the neuron, like the formation of permeable pores to cations by Aβ plaques, causes ionic imbalance and, as a result, faults occur in the signalling of the neuron. The first part of this research concentrates on ion channels, ion imbalances and their impacts on the signalling behaviour of the neuron. This includes investigating the role of Aβ channels in the development of neurodegenerative diseases. Results revealed that these types of diseases can lead to ionic imbalances in the neuron. Ion imbalances can change the behaviour of neuronal signalling. Therefore, by identifying the pattern of these changes, the disease can be detected in the very early stages. Then the role of coupling and synchronisation effects in such diseases were studied. After that, a novel method to define minimum requirements for synchronicity between two coupled neurons is proposed. Further, a new computational model of Aβ channels is proposed and developed which mimics the behaviour of a neuron in the course of Alzheimer's disease. Finally, both fault computation and disease detection are carried out using a residual generation method, where the residuals from two observers are compared to assess their performance.
Supervisor: Kambhampati, Chandra Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.784224  DOI: Not available
Keywords: Computer science
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