Performance and validation of a digital memory test across the Alzheimer’s disease continuum

Digital cognitive testing using online platforms has emerged as a potentially transformative tool in clinical neuroscience. In theory, it could provide a powerful means of screening for and tracking cognitive performance in people at risk of developing conditions such as Alzheimer's disease. Here we investigate whether digital metrics derived from an in-person administered, tablet-based short-term memory task-the 'What was where?' Oxford Memory Task-were able to clinically stratify patients at different points within the Alzheimer's disease continuum and to track disease progression over time. Performance of these metrics compared to traditional neuropsychological pen-and-paper screening tests of cognition was also analysed. A total of 325 people participated in this study: 49 patients with subjective cognitive decline, 57 with mild cognitive impairment, 63 with Alzheimer's disease dementia and 156 elderly healthy controls. Most digital metrics were able to discriminate between healthy controls and patients with mild cognitive impairment and between mild cognitive impairment and Alzheimer's disease patients. Some, including Absolute Localization Error, also differed significantly between patients with subjective cognitive decline and mild cognitive impairment. Identification accuracy was the best predictor of hippocampal atrophy, performing as well as standard screening neuropsychological tests. A linear support vector model combining digital metrics achieved high accuracy and performed at par with standard testing in discriminating between elderly healthy controls and subjective cognitive decline (area under the curve 0.82) and between subjective cognitive decline and mild cognitive impairment (area under the curve 0.92), while performing worse in classifying between mild cognitive impairment and Alzheimer's disease patients (area under the curve 0.75). Memory imprecision was able to predict cognitive decline on standard cognitive tests over one year. Overall, these findings show how it might be possible to use a digital memory test in clinics and clinical trial contexts to stratify and track performance across the Alzheimer's disease continuum.

Authors

Institutions

Publication Details

Journal
Brain Communications
Published
2024-12-24
DOI
https://doi.org/10.1093/braincomms/fcaf024
Citations
7
Primary Topic
Dementia and Cognitive Impairment Research
Type
article
Field-Weighted Citation Impact
2.70

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Performance and validation of a digital memory test across the Alzheimer’s disease continuum

Bahaaeddin Attaallah, Sanjay Manohar, Mohamad Imran Idris, Vicky Turner et al.
7 citations
Brain Communications
Dementia and Cognitive Impairment Research
2.70
article

Performance and validation of a digital memory test across the Alzheimer’s disease continuum

Bahaaeddin Attaallah, Sanjay Manohar, Mohamad Imran Idris, Vicky Turner, Christopher Robert Butler, Masud Husain, Sian Thompson, Sofia Toniolo, Annie Srowig, Youssuf Saleh, Verena Klar, Kathrin Finke, Maria Raquel Maio, Younes Adam Tabi, Elitsa Slavkova, Christoph Preul
article en
7 citations

Abstract

Digital cognitive testing using online platforms has emerged as a potentially transformative tool in clinical neuroscience. In theory, it could provide a powerful means of screening for and tracking cognitive performance in people at risk of developing conditions such as Alzheimer's disease. Here we investigate whether digital metrics derived from an in-person administered, tablet-based short-term memory task-the 'What was where?' Oxford Memory Task-were able to clinically stratify patients at different points within the Alzheimer's disease continuum and to track disease progression over time. Performance of these metrics compared to traditional neuropsychological pen-and-paper screening tests of cognition was also analysed. A total of 325 people participated in this study: 49 patients with subjective cognitive decline, 57 with mild cognitive impairment, 63 with Alzheimer's disease dementia and 156 elderly healthy controls. Most digital metrics were able to discriminate between healthy controls and patients with mild cognitive impairment and between mild cognitive impairment and Alzheimer's disease patients. Some, including Absolute Localization Error, also differed significantly between patients with subjective cognitive decline and mild cognitive impairment. Identification accuracy was the best predictor of hippocampal atrophy, performing as well as standard screening neuropsychological tests. A linear support vector model combining digital metrics achieved high accuracy and performed at par with standard testing in discriminating between elderly healthy controls and subjective cognitive decline (area under the curve 0.82) and between subjective cognitive decline and mild cognitive impairment (area under the curve 0.92), while performing worse in classifying between mild cognitive impairment and Alzheimer's disease patients (area under the curve 0.75). Memory imprecision was able to predict cognitive decline on standard cognitive tests over one year. Overall, these findings show how it might be possible to use a digital memory test in clinics and clinical trial contexts to stratify and track performance across the Alzheimer's disease continuum.

Brain CommunicationsVol. 7(1)
Queen Mary University of London (GB), University of Oxford (GB), Jena University Hospital (DE), Imperial College London (GB), Ludwig-Maximilians-Universität München (DE)
Wellcome Trust, National Institute for Health and Care Research, Friedrich-Ebert-Stiftung, Centro Singular de Investigación de Galicia
Openalex Percentile: Top 8%
Dementia and Cognitive Impairment Research
2.70
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.