Spatiotemporal EEG Dynamics of Prospective Memory in Ageing and Mild Cognitive Impairment

Mark Crook-Rumsey, Christina Howard, Zohreh Doborjeh, Maryam Doborjeh, Josafath Israel Espinosa Ramos, Nikola Kasabov, Alexander Sumich

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
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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.
Original languageEnglish
Pages (from-to)1273-1299
Number of pages27
JournalCognitive Computation
Issue number4
Early online date23 Nov 2022
Publication statusPublished online - 23 Nov 2022

Bibliographical note

Funding Information:
This research is supported in part by the Nottingham Trent University, UK, and in part by the Knowledge Engineering & Discovery institute, AUT, NZ, as part of a jointly funded PhD programme.

Publisher Copyright:
© 2022, The Author(s).


  • prospective memtory
  • MCI
  • spiking neural networks
  • NeuCube
  • EEG
  • Event-related potential
  • Prospective memory
  • Mild cognitive impairment
  • Spiking neural network
  • Ageing
  • Machine learning


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