Neuropsychological Testing and Machine Learning Distinguish Alzheimer’s Disease from Other Causes for Cognitive Impairment
Open accessAbstract
With promising results in recent treatment trials for Alzheimer's disease (AD), it becomes increasingly important to distinguish AD at early stages from other causes for cognitive impairment. However, existing diagnostic methods are either invasive (lumbar punctures, PET) or inaccurate Magnetic Resonance Imaging (MRI). This study investigates the potential of neuropsychological testing (NPT) to specifically identify those patients with possible AD among a sample of 158 patients with Mild Cognitive Impairment (MCI) or dementia for various causes. Patients were divided into an early stage and a late stage group according to their Mini Mental State Examination (MMSE) score and labeled as AD or non-AD patients based on a post-mortem validated threshold of the ratio between total tau and beta amyloid in the cerebrospinal fluid (CSF; Total tau/Aβ(1-42) ratio, TB ratio). All patients completed the established Consortium to Establish a Registry for Alzheimer's Disease-Neuropsychological Assessment Battery (CERAD-NAB) test battery and two additional newly-developed neuropsychological tests (recollection and verbal comprehension) that aimed at carving out specific Alzheimer-typical deficits. Based on these test results, an underlying AD (pathologically increased TB ratio) was predicted with a machine learning algorithm. To this end, the algorithm was trained in each case on all patients except the one to predict (leave-one-out validation). In the total group, 82% of the patients could be correctly identified as AD or non-AD. In the early group with small general cognitive impairment, classification accuracy was increased to 89%. NPT thus seems to be capable of discriminating between AD patients and patients with cognitive impairment due to other neurodegenerative or vascular causes with a high accuracy, and may be used for screening in clinical routine and drug studies, especially in the early course of this disease.
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Machine learning and neuropsychological testing differentiated AD from other causes of cognitive impairment, with around 82% of patients clearly differentiated as AD and non-AD in the total group and accuracy increasing to 89%.
“AD and other causes of cognitive impairment were also differentiated with machine learning and neuropsychological testing. With samples of 158 patients divided on the basis of Mini Mental State Examination (MMSE) into early and a late stage group (labelled as non-AD and AD), around 82% patients were clearly differentiated as AD and non-AD in total group and the accuracy increased to 89% [144].”
From Recent Advancements in Pathogenesis, Diagnostics and Treatment of Alzheimer’s Disease (2020) · cites this paper as [144]
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Recent Advancements in Pathogenesis, Diagnostics and Treatment of Alzheimer’s Disease
Khan, S., Barve, K. H., & Kumar, M. S. (2020) · Current Neuropharmacology · doi:10.2174/1570159x18666200528142429 · Cited by 820 · see citing works
“With samples of 158 patients divided on the basis of Mini Mental State Examination (MMSE) into early and a late stage group (labelled as non-AD and AD), around 82% patients were clearly differentiated as AD and non-AD in total group and the accuracy increased to 89% [144].”
Method
- Authors
- Gurevich, P., Stuke, H., Kastrup, A., Stuke, H., & Hildebrandt, H.
- Affiliations
- Peoples' Friendship University of Russia (RU)
- Freie Universität Berlin (DE)
- Freie Hansestadt Bremen (DE)
- Carl von Ossietzky Universität Oldenburg (DE)
- Corresponding author
- Hildebrandt, Helmut
- Venue
- Frontiers in Aging Neuroscience
- Publisher
- Frontiers Media SA
- Published
- 2017-04-25
- Volume
- 9
- Pages
- 114-114
- ISSN
- 1663-4365 (ISSN-L)
- PMC
- PMC5403832
- OpenAlex
- W2607838029
- Cited by
- 53
- References
- 35
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 8 · 2023: 11 · 2022: 8 · 2021: 4 · 2020: 6 · 2019: 6 · 2018: 1
- FWCI
- 2.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Frontiers in Aging Neuroscience (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Carl von Ossiezky University of Oldenburg (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Refubium (Universitätsbibliothek der Freien Universität Berlin) (submittedVersion, cc-by)
- Universitätsbibliothek der FU Berlin Hochschulschriftenstelle u. Dokumentenserver (cc-by)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
- Social Sciences › Psychology › Applied Psychology › Psychological Testing and Assessment
- Keywords
- Alzheimer's disease, mild cognitive impairment, dementia, neuropsychological assessment, machine learning classification, cerebrospinal fluid biomarkers, early Alzheimer's disease, recollection, verbal comprehension
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