Harnessing forgetfulness: can episodic-memory tests predict early Alzheimer’s disease?

for the Alzheimer’s Disease Neuroimaging Initiative

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

A rapid increase in the number of patients with Alzheimer’s disease (AD) is expected over the next decades. Accordingly, there is a critical need for early-stage AD detection methods that can enable effective treatment strategies. In this study, we consider the ability of episodic-memory measures to predict mild cognitive impairment (MCI) to AD conversion and thus, detect early-stage AD. For our analysis, we studied 307 participants with MCI across four years using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Using a binary logistic regression, we compared episodic-memory tests to each other and to prominent neuroimaging methods in MCI converter (MCI participants who developed AD) and MCI non-converter groups (MCI participants who did not develop AD). We also combined variables to test the accuracy of mixed-predictor models. Our results indicated that the best predictors of MCI to AD conversion were the following: a combined episodic-memory and neuroimaging model in year one (59.8%), the Rey Auditory Verbal Learning Test in year two (71.7%), a mixed episodic-memory predictor model in year three (77.7%) and the Logical Memory Test in year four (77.2%) of ADNI. Overall, we found that individual episodic-memory measure and mixed models performed similarly when predicting MCI to AD conversion. Comparatively, individual neuroimaging measures predicted MCI conversion worse than chance. Accordingly, our results indicate that episodic-memory tests could be instrumental in detecting early-stage AD and enabling effective treatment.

Original languageEnglish
Pages (from-to)2925-2937
Number of pages13
JournalExperimental Brain Research
Volume239
Issue number9
DOIs
Publication statusPublished - Sep 2021

Keywords

  • Alzheimer’s disease (AD)
  • Alzheimer’s disease neuroimaging initiative (ADNI)
  • Big data
  • Disease prediction
  • Episodic memory
  • Mild cognitive impairment (MCI)

ASJC Scopus subject areas

  • Neuroscience(all)

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