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Neuropsychological Testing and Machine Learning Distinguish Alzheimer's Disease from Other Causes for Cognitive Impairment
Gurevich P, Stuke H, Kastrup A, Stuke H, Hildebrandt H.. Frontiers in aging neuroscience, 2017-04-25
Topic: Amyloid, tau and blood-test diagnosis
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.
Key findings
- 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.
Why it matters
In 158 patients with mild cognitive impairment or dementia from various causes, this study tested whether neuropsychological testing combined with machine learning could identify those with possible Alzheimer disease. It is a diagnostic study and does not test any treatment or prevention.
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Study details
- Participants158
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Frontiers in aging neuroscience) |
| Journal's citation record | 2 / 4 | Frontiers in Aging Neuroscience: journal h-index 174 (OpenAlex, 2026-10-29) |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 2 / 5 | 2017 — within 10 year(s) of the newest study here |
| Sample size | 2 / 4 | 158 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 53 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 4.8 citations a year over 11 year(s) since 2017 |
| Impact for its field and year | 2 / 6 | Cited 2.6× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 6 other studies in this library |
| Still being cited | 2 / 4 | Cited 17 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Helmut Hildebrandt: h-index 35 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Charité - Universitätsmedizin Berlin: institution h-index 1,116 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 28487650) |
| Free to read | 2 / 2 | Free to read |
| Total | 48 / 100 | — |
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Read the original
- DOI: 10.3389/fnagi.2017.00114
- PubMed: 28487650
- Original source →
- Free full text: Frontiers in Aging Neuroscience →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Gurevich, P., Stuke, H., Kastrup, A., & Hildebrandt, H. (2017). Neuropsychological Testing and Machine Learning Distinguish Alzheimer's Disease from Other Causes for Cognitive Impairment. Frontiers in aging neuroscience. https://doi.org/10.3389/fnagi.2017.00114
Vancouver
Gurevich P, Stuke H, Kastrup A, Hildebrandt H. Neuropsychological Testing and Machine Learning Distinguish Alzheimer's Disease from Other Causes for Cognitive Impairment. Frontiers in aging neuroscience. 2017. doi:10.3389/fnagi.2017.00114. PMID: 28487650.
BibTeX
@article{gurevich2017-28487650,
author = {Gurevich, P. and Stuke, H. and Kastrup, A. and Hildebrandt, H.},
title = {{Neuropsychological Testing and Machine Learning Distinguish Alzheimer's Disease from Other Causes for Cognitive Impairment}},
journal = {Frontiers in aging neuroscience},
year = {2017},
doi = {10.3389/fnagi.2017.00114},
pmid = {28487650},
url = {https://doi.org/10.3389/fnagi.2017.00114}
}
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