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MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression
Lebedeva AK, Westman E, Borza T, Beyer MK, Engedal K, Aarsland D, Selbaek G, Haberg AK.. Frontiers in aging neuroscience, 2017-02-02
Topic: Risk, prevention and care in population studies
Objective: Late-life depression (LLD) is associated with development of different types of dementia. Identification of LLD patients, who will develop cognitive decline, i.e., the early stage of dementia would help to implement interventions earlier. The purpose of this study was to assess whether structural brain magnetic resonance imaging (MRI) in LLD patients can predict mild cognitive impairment (MCI) or dementia 1 year prior to the diagnosis.
Key findings
- Identification of LLD patients, who will develop cognitive decline, i.e., the early stage of dementia would help to implement interventions earlier.
- The purpose of this study was to assess whether structural brain magnetic resonance imaging (MRI) in LLD patients can predict mild cognitive impairment (MCI) or dementia 1 year prior to the diagnosis.
- Methods: LLD patients underwent brain MRI at baseline and repeated clinical assessment after 1-year.
- Structural brain measurements were obtained using Freesurfer software (v. 5.1) from the T1W brain MRI images.
Why it matters
This study assessed whether structural brain MRI in patients with late-life depression can predict mild cognitive impairment or dementia 1 year before diagnosis. It is a small prediction study, so it cannot show that any treatment or intervention would prevent cognitive decline.
Study details
- Participants185
- Evidence strengthLow
How this study scored 49/100
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 | 185 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 83 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 7.5 citations a year over 11 year(s) since 2017 |
| Impact for its field and year | 3 / 6 | Cited 7.0× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 4 other studies in this library |
| Still being cited | 1 / 4 | Cited 14 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Asta Kristine Haberg: h-index 56 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Karolinska Institutet: institution h-index 1,477 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 9 institution(s) in 3 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 28210220) |
| Free to read | 2 / 2 | Free to read |
| Total | 49 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.3389/fnagi.2017.00013
- PubMed: 28210220
- 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. The record names 8 authors; APA and Vancouver show the first six, BibTeX and the downloads carry all of them. Check the citation against the original before you use it.
APA 7th
Lebedeva, A. K., Westman, E., Borza, T., Beyer, M. K., Engedal, K., Aarsland, D., et al. (2017). MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression. Frontiers in aging neuroscience. https://doi.org/10.3389/fnagi.2017.00013
Vancouver
Lebedeva AK, Westman E, Borza T, Beyer MK, Engedal K, Aarsland D, et al. MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression. Frontiers in aging neuroscience. 2017. doi:10.3389/fnagi.2017.00013. PMID: 28210220.
BibTeX
@article{lebedeva2017-28210220,
author = {Lebedeva, A. K. and Westman, E. and Borza, T. and Beyer, M. K. and Engedal, K. and Aarsland, D. and Selbaek, G. and Haberg, A. K.},
title = {{MRI-Based Classification Models in Prediction of Mild Cognitive Impairment and Dementia in Late-Life Depression}},
journal = {Frontiers in aging neuroscience},
year = {2017},
doi = {10.3389/fnagi.2017.00013},
pmid = {28210220},
url = {https://doi.org/10.3389/fnagi.2017.00013}
}
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