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Crook-Rumsey, M. 
ORCID: 0000-0003-3031-3502, Howard, C.J. ORCID: 0000-0002-8755-1109, Doborjeh, Z., Doborjeh, M., Ramos, J.I.E., Kasabov, N. and Sumich, A. ORCID: 0000-0003-4333-8442,
2022.
Spatiotemporal EEG dynamics of prospective memory in ageing and mild cognitive impairment.
Cognitive Computation.
ISSN 1866-9956
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Abstract
Prospective memory (PM, the memory of future intentions) is one of the first complaints of those that develop dementia-related disease. Little is known about the neurophysiology of PM in ageing and those with mild cognitive impairment (MCI). By using a novel artificial neural network to investigate the spatial and temporal features of PM related brain activity, new insights can be uncovered. Young adults (n = 30), healthy older adults (n = 39) and older adults with MCI (n = 27) completed a working memory and two PM (perceptual, conceptual) tasks. Time-locked electroencephalographic potentials (ERPs) from 128-electrodes were analysed using a brain-inspired spiking neural network (SNN) architecture. Local and global connectivity from the SNNs was then evaluated. SNNs outperformed other machine learning methods in classification of brain activity between younger, older and older adults with MCI. SNNs trained using PM related brain activity had better classification accuracy than working memory related brain activity. In general, younger adults exhibited greater local cluster connectivity compared to both older adult groups. Older adults with MCI demonstrated decreased global connectivity in response to working memory and perceptual PM tasks but increased connectivity in the conceptual PM models relative to younger and healthy older adults. SNNs can provide a useful method for differentiating between those with and without MCI. Using brain activity related to PM in combination with SNNs may provide a sensitive biomarker for detecting cognitive decline. Cognitively demanding tasks may increase the amount connectivity in older adults with MCI as a means of compensation.
| Item Type: | Journal article | ||||||
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| Publication Title: | Cognitive Computation | ||||||
| Creators: | Crook-Rumsey, M., Howard, C.J., Doborjeh, Z., Doborjeh, M., Ramos, J.I.E., Kasabov, N. and Sumich, A. | ||||||
| Publisher: | Springer Science and Business Media LLC | ||||||
| Date: | 23 November 2022 | ||||||
| ISSN: | 1866-9956 | ||||||
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| Rights: | © The Author(s) 2022. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | ||||||
| Divisions: | Schools > School of Social Sciences | ||||||
| Record created by: | Laura Ward | ||||||
| Date Added: | 24 Apr 2023 08:52 | ||||||
| Last Modified: | 24 Apr 2023 08:52 | ||||||
| URI: | https://irep.ntu.ac.uk/id/eprint/48802 |
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