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Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data
Meyer S, Mueller K, Stuke K, Bisenius S, Diehl-Schmid J, Jessen F, Kassubek J, Kornhuber J et al.. NeuroImage. Clinical, 2017-02-06
Topic: Risk, prevention and care in population studies
Purpose Frontotemporal lobar degeneration (FTLD) is a common cause of early onset dementia. Behavioral variant frontotemporal dementia (bvFTD), its most common subtype, is characterized by deep alterations in behavior and personality. In 2011, new diagnostic criteria were suggested that incorporate imaging criteria into diagnostic algorithms. The study aimed at validating the potential of imaging criteria to individually predict diagnosis with machine learning algorithms. Materials & methods Brain atrophy was measured with structural magnetic resonance imaging (MRI) at 3 Tesla in a multi-centric cohort of 52 bvFTD patients and 52 healthy control subjects from the German FTLD Consortium's Study. Beside group comparisons, diagnosis bvFTD vs. controls was individually predicted in each subject with support vector machine classification in MRI data across the whole brain or in frontotemporal, insular regions, and basal ganglia known to be mainly affected based on recent meta-analyses. Multi-center effects were controlled for with a new method, "leave one center out" conjunction analyses, i.e. repeatedly excluding subjects from each center from the analysis. Results Group comparisons revealed atrophy in, most consistently, the frontal lobe in bvFTD beside alterations in the insula, basal ganglia and temporal lobe. Most remarkably, support vector machine classification enabled predicting diagnosis in single patients with a high accuracy of up to 84.6%, where accuracy was highest in a region-of-interest approach focusing on frontotemporal, insular regions, and basal ganglia in comparison with the whole brain approach. Conclusion Our study demonstrates that MRI, a widespread imaging technology, can individually identify bvFTD with high accuracy in multi-center imaging data, paving the road to personalized diagnostic approaches in the future.
Key findings
- In 2011, new diagnostic criteria were suggested that incorporate imaging criteria into diagnostic algorithms.
- Results Group comparisons revealed atrophy in, most consistently, the frontal lobe in bvFTD beside alterations in the insula, basal ganglia and temporal lobe.
Why it matters
This study used structural MRI and machine learning to predict a diagnosis of behavioral variant frontotemporal dementia, not Alzheimer's disease. It concerns diagnosis of a different dementia, so it does not directly inform treating, curing or preventing Alzheimer's.
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Study details
- Study designmeta-analysis
- Effect size84.6%
- Evidence strengthHigh
How this study scored 61/100
Published in a peer-reviewed journal, and it is a meta-analysis pooling many studies. Scored zero for sample size and studied in people because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (NeuroImage. Clinical) |
| Journal's citation record | 1 / 4 | NeuroImage Clinical: journal h-index 133 (OpenAlex, 2026-10-29) |
| Study design | 12 / 12 | A meta-analysis pooling many studies |
| Strength of evidence | 5 / 5 | Rated “high” 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 | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | A review of other studies — randomisation is not credited here |
| Times cited | 4 / 7 | Cited 84 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 7.6 citations a year over 11 year(s) since 2017 |
| Impact for its field and year | 3 / 6 | Cited 7.2× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 2 / 6 | Cited by 7 other studies in this library |
| Still being cited | 1 / 4 | Cited 11 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Matthias Leopold Schroeter: h-index 71 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Technical University of Munich: institution h-index 1,092 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 9 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 28348957) |
| Free to read | 2 / 2 | Free to read |
| Total | 61 / 100 | — |
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Read the original
- DOI: 10.1016/j.nicl.2017.02.001
- PubMed: 28348957
- Original source →
- Free full text: NeuroImage Clinical →
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's author list ends in “et al.”, so not every author is named. Check the citation against the original before you use it.
APA 7th
Meyer, S., Mueller, K., Stuke, K., Bisenius, S., Diehl-Schmid, J., Jessen, F., et al. (2017). Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data. NeuroImage. Clinical. https://doi.org/10.1016/j.nicl.2017.02.001
Vancouver
Meyer S, Mueller K, Stuke K, Bisenius S, Diehl-Schmid J, Jessen F, et al. Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data. NeuroImage. Clinical. 2017. doi:10.1016/j.nicl.2017.02.001. PMID: 28348957.
BibTeX
@article{meyer2017-28348957,
author = {Meyer, S. and Mueller, K. and Stuke, K. and Bisenius, S. and Diehl-Schmid, J. and Jessen, F. and Kassubek, J. and Kornhuber, J. and others},
title = {{Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data}},
journal = {NeuroImage. Clinical},
year = {2017},
doi = {10.1016/j.nicl.2017.02.001},
pmid = {28348957},
url = {https://doi.org/10.1016/j.nicl.2017.02.001}
}
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Referenced by this study and also in the alzhub library, so you can read them here.
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7 studies in the alzhub library list this one in their references.