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Compensating for synaptic loss in Alzheimer’s disease

journal contribution
posted on 2023-07-26, 13:56 authored by Kamal J. Abu-Hassan, Damien Coyle, Ammar Belatreche, Liam Maguire
Confirming that synaptic loss is directly related to cognitive deficit in Alzheimer’s disease (AD) has been the focus of many studies. Compensation mechanisms counteract synaptic loss and prevent the catastrophic amnesia induced by synaptic loss via maintaining the activity levels of neural circuits. Here we investigate the interplay between various synaptic degeneration and compensation mechanisms, and abnormal cortical oscillations based on a large-scale network model consisting of 100,000 neurons exhibiting several cortical firing patterns, 8.5 million synapses, short-term plasticity, axonal delays and receptor kinetics. The structure of the model is inspired by the anatomy of the cerebral cortex. The results of the modelling study suggest that cortical oscillations respond differently to compensation mechanisms. Local compensation preserves the baseline activity of theta (5-7 Hz) and alpha (8-12 Hz) oscillations whereas delta (1-4 Hz) and beta (13-30 Hz) oscillations are maintained via global compensation. Applying compensation mechanisms independently shows greater effects than combining both compensation mechanisms in one model and applying them in parallel. Consequently, it can be speculated that enhancing local compensation might recover the neural processes and cognitive functions that are associated with theta and alpha oscillations whereas inducing global compensation might contribute to the repair of neural (cognitive) processes which are associated with delta and beta band activity. Compensation mechanisms may vary across cortical regions and the activation of inappropriate compensation mechanism in a particular region may fail to recover network dynamics and/or induce secondary pathological changes in the network.

History

Refereed

  • Yes

Volume

36

Issue number

1

Page range

19-37

Publication title

Journal of Computational Neuroscience

ISSN

1573-6873

Publisher

Springer

Language

  • other

Legacy posted date

2016-10-10

Legacy creation date

2016-09-28

Legacy Faculty/School/Department

ARCHIVED Faculty of Science & Technology (until September 2018)

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