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Attentional Deficits in Alzheimer’s Disease: Investigating the Role of Acetylcholine with Computational Modelling

  • Eirini MavritsakiEmail author
  • Howard Bowman
  • Li Su
Chapter
Part of the Springer Series in Cognitive and Neural Systems book series (SSCNS, volume 13)

Abstract

Alzheimer’s disease is a neurodegenerative condition that affects the brain’s cognitive processes as well as many other functions for daily life. It is the commonest cause for dementia in older people and can take several years or decades from the time its pathology starts to the time the full clinical symptoms are developed. One of the cognitive processes affected in Alzheimer’s disease is attention. Depletion in attentional processes is linked to acetylcholine function, and attention deficit underlies many cognitive dysfunctions in Alzheimer’s disease. In this work, we are employing computational modelling to provide a neural bio-mechanistic account linking acetylcholine depletion and decreased attentional performance. Although previous research has modelled the decrease of acetylcholine, how neurotransmitter depletion is associated with behavioural impairments in Alzheimer’s disease remains unclear. We employed a spiking Search over Time and Space (sSoTS) model to simulate attentional function and describe the reduction of acetylcholine by changes applied to gNMDA and gAMPA conductance. Our model simulation results showed that changes in acetylcholine function were able to produce a notable reduction in attentional performance similar to what is seen in patients with Alzheimer’s disease. This work provided an architectural and methodological framework under which neurobiological mechanisms and failures of the system can directly explain symptomology, such as attention dysfunctions in Alzheimer’s disease. This framework enables future studies and novel clinical trials targeting acetylcholine pathways in treating Alzheimer’s disease and related conditions.

Keywords

Alzheimer’s Spiking Search over Time and Space model Computational modelling Attention Acetylcholine 

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of PsychologyBirmingham City UniversityBirminghamUK
  2. 2.School of PsychologyUniversity of BirminghamBirminghamUK
  3. 3.School of ComputingUniversity of KentCanterburyUK
  4. 4.Department of PsychiatryUniversity of CambridgeCambridgeUK
  5. 5.Sino-Britain Centre for Cognition and Ageing ResearchSouthwest UniversityChongqingChina

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