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Multi-feature computational framework for combined signatures of dementia in underrepresented settings
Moguilner S, Birba A, Fittipaldi S, Gonzalez-Campo C, Tagliazucchi E, Reyes P, Matallana D, Parra MA et al.. Journal of neural engineering, 2022-08-25
Topic: Risk, prevention and care in population studies
Objective. The differential diagnosis of behavioral variant frontotemporal dementia (bvFTD) and Alzheimer's disease (AD) remains challenging in underrepresented, underdiagnosed groups, including Latinos, as advanced biomarkers are rarely available. Recent guidelines for the study of dementia highlight the critical role of biomarkers. Thus, novel cost-effective complementary approaches are required in clinical settings. Approach . We developed a novel framework based on a gradient boosting machine learning classifier, tuned by Bayesian optimization, on a multi-feature multimodal approach (combining demographic, neuropsychological, magnetic resonance imaging (MRI), and electroencephalography/functional MRI connectivity data) to characterize neurodegeneration using site harmonization and sequential feature selection. We assessed 54 bvFTD and 76 AD patients and 152 healthy controls (HCs) from a Latin American consortium (ReDLat). Main results . The multimodal model yielded high area under the curve classification values (bvFTD patients vs HCs: 0.93 (±0.01); AD patients vs HCs: 0.95 (±0.01); bvFTD vs AD patients: 0.92 (±0.01)). The feature selection approach successfully filtered non-informative multimodal markers (from thousands to dozens). Results . Proved robust against multimodal heterogeneity, sociodemographic variability, and missing data. Significance . The model accurately identified dementia subtypes using measures readily available in underrepresented settings, with a similar performance than advanced biomarkers. This approach, if confirmed and replicated, may potentially complement clinical assessments in developing countries.
Key findings
- The multimodal model yielded high area under the curve classification values (bvFTD patients vs HCs: 0.93 (±0.01); AD patients vs HCs: 0.95 (±0.01); bvFTD vs AD patients: 0.92 (±0.01)).
- The feature selection approach successfully filtered non-informative multimodal markers (from thousands to dozens).
- Proved robust against multimodal heterogeneity, sociodemographic variability, and missing data.
- The model accurately identified dementia subtypes using measures readily available in underrepresented settings, with a similar performance than advanced biomarkers.
- This approach, if confirmed and replicated, may potentially complement clinical assessments in developing countries.
Why it matters
This study developed a machine learning model combining demographic, neuropsychological, MRI, and connectivity data to distinguish behavioral variant frontotemporal dementia from Alzheimer's disease and healthy controls. It is about diagnosis in underrepresented settings, not about treating or curing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 48/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of neural engineering) |
| Journal's citation record | 2 / 4 | Journal of Neural Engineering: journal h-index 165 (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 | 3 / 5 | 2022 — within 6 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 | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 4 / 7 | Cited 40 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 6.7 citations a year over 6 year(s) since 2022 |
| 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 7 other studies in this library |
| Still being cited | 2 / 4 | Cited 24 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Agustín M. Ibáñez: h-index 81 (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 17 institution(s) in 8 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 35940105) |
| Free to read | 2 / 2 | Free to read |
| Total | 48 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1088/1741-2552/ac87d0
- PubMed: 35940105
- Original source →
- Free full text: Journal of Neural Engineering →
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
Moguilner, S., Birba, A., Fittipaldi, S., Gonzalez-Campo, C., Tagliazucchi, E., Reyes, P., et al. (2022). Multi-feature computational framework for combined signatures of dementia in underrepresented settings. Journal of neural engineering. https://doi.org/10.1088/1741-2552/ac87d0
Vancouver
Moguilner S, Birba A, Fittipaldi S, Gonzalez-Campo C, Tagliazucchi E, Reyes P, et al. Multi-feature computational framework for combined signatures of dementia in underrepresented settings. Journal of neural engineering. 2022. doi:10.1088/1741-2552/ac87d0. PMID: 35940105.
BibTeX
@article{moguilner2022-35940105,
author = {Moguilner, S. and Birba, A. and Fittipaldi, S. and Gonzalez-Campo, C. and Tagliazucchi, E. and Reyes, P. and Matallana, D. and Parra, M. A. and others},
title = {{Multi-feature computational framework for combined signatures of dementia in underrepresented settings}},
journal = {Journal of neural engineering},
year = {2022},
doi = {10.1088/1741-2552/ac87d0},
pmid = {35940105},
url = {https://doi.org/10.1088/1741-2552/ac87d0}
}
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