Home › Research › Digital health tools
Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging
Donnelly-Kehoe PA, Pascariello GO, García AM, Hodges JR, Miller B, Rosen H, Manes F, Landin-Romero R et al.. Alzheimer's & dementia (Amsterdam, Netherlands), 2019-08-28
Topic: Brain proteins, microglia and lab models
Introduction Timely diagnosis of behavioral variant frontotemporal dementia (bvFTD) remains challenging because it depends on clinical expertise and potentially ambiguous diagnostic guidelines. Recent recommendations highlight the role of multimodal neuroimaging and machine learning methods as complementary tools to address this problem. Methods We developed an automatic, cross-center, multimodal computational approach for robust classification of patients with bvFTD and healthy controls. We analyzed structural magnetic resonance imaging and resting-state functional connectivity from 44 patients with bvFTD and 60 healthy controls (across three imaging centers with different acquisition protocols) using a fully automated processing pipeline, including site normalization, native space feature extraction, and a random forest classifier. Results Our method successfully combined multimodal imaging information with high accuracy (91%), sensitivity (83.7%), and specificity (96.6%). Discussion This multimodal approach enhanced the system's performance and provided a clinically informative method for neuroimaging analysis. This underscores the relevance of combining multimodal imaging and machine learning as a gold standard for dementia diagnosis.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Donnelly-Kehoe, P. A., Pascariello, G. O., García, A. M., Hodges, JR., Miller, B., Rosen, H., et al. (2019). Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging. Alzheimer's & dementia (Amsterdam, Netherlands). https://doi.org/10.1016/j.dadm.2019.06.002
Vancouver
Donnelly-Kehoe PA, Pascariello GO, García AM, Hodges JR, Miller B, Rosen H, et al. Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging. Alzheimer's & dementia (Amsterdam, Netherlands). 2019. doi:10.1016/j.dadm.2019.06.002. PMID: 31497638.
BibTeX
@article{donnellykehoe2019-31497638,
author = {Donnelly-Kehoe, P. A. and Pascariello, G. O. and García, A. M. and Hodges, JR. and Miller, B. and Rosen, H. and Manes, F. and Landin-Romero, R. and others},
title = {{Robust automated computational approach for classifying frontotemporal neurodegeneration: Multimodal/multicenter neuroimaging}},
journal = {Alzheimer's \& dementia (Amsterdam, Netherlands)},
year = {2019},
doi = {10.1016/j.dadm.2019.06.002},
pmid = {31497638},
url = {https://doi.org/10.1016/j.dadm.2019.06.002}
}
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.
Key findings
- Results Our method successfully combined multimodal imaging information with high accuracy (91%), sensitivity (83.7%), and specificity (96.6%).
Why it matters
In 44 people with behavioural-variant frontotemporal dementia and 60 healthy people scanned at three centres with different MRI setups, a fully automated computer pipeline combining brain-structure and resting-brain-activity scans sorted people into the right group 91% of the time (it caught 83.7% of patients and correctly cleared 96.6% of healthy people). The study compared patients only with healthy people, not with Alzheimer's or other conditions that look similar, so it shows promise for a research tool rather than a ready clinical test. It does not test any treatment, cure or prevention.
New to this subject? Start with our plain-English guide: Digital health →
Study details
- Participants44
- Effect size83.7%; 96.6%
- Evidence strengthLow
How this study scored 52/100
Published in a leading specialty journal. Scored zero for study design and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Alzheimer's & dementia (Amsterdam, Netherlands)) |
| Journal's citation record | 1 / 4 | Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring: journal h-index 86 (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 | 2019 — within 10 year(s) of the newest study here |
| Sample size | 1 / 4 | 44 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| 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 44 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 4.9 citations a year over 9 year(s) since 2019 |
| Impact for its field and year | 3 / 6 | Cited 5.1× 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 | 1 / 4 | Cited 7 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Lucas Sedeño: h-index 44 (OpenAlex, whole career) |
| Institution's track record | 2 / 3 | University of California, San Francisco: institution h-index 1,915 (OpenAlex) |
| Multi-centre collaboration | 3 / 3 | OpenAlex lists 14 institution(s) in 4 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31497638) |
| Free to read | 2 / 2 | Free to read |
| Total | 52 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.dadm.2019.06.002
- PubMed: 31497638
- Original source →
- Free full text: Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.
Later studies that cite this one
6 studies in the alzhub library list this one in their references.
Related studies in Digital health tools
Also tagged “bodies”
1,017 studies in the library carry the topic tag “bodies”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 1,017 tagged studies.