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Deep learning-based brain age prediction in normal aging and dementia
Lee J, Burkett BJ, Min HK, Senjem ML, Lundt ES, Botha H, Graff-Radford J, Barnard LR et al.. Nature aging, 2022-05-09
Topic: Brain imaging, connectivity and assessment
Brain aging is accompanied by patterns of functional and structural change. Alzheimer's disease (AD), a representative neurodegenerative disease, has been linked to accelerated brain aging. Here, we developed a deep learning-based brain age prediction model using a large collection of fluorodeoxyglucose positron emission tomography and structural magnetic resonance imaging and tested how the brain age gap relates to degenerative syndromes including mild cognitive impairment, AD, frontotemporal dementia and Lewy body dementia. Occlusion analysis, performed to facilitate the interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging. The elevated brain age gap was highly correlated with cognitive impairment and the AD biomarker. The higher gap also showed a longitudinal predictive nature across clinical categories, including cognitively unimpaired individuals who converted to a clinical stage. However, regions generating brain age gaps were different for each diagnostic group of which the AD continuum showed similar patterns to normal aging.
Key findings
- Occlusion analysis, performed to facilitate the interpretation of the model, revealed that the model learns an age- and modality-specific pattern of brain aging.
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Study details
- Study designlongitudinal study
- Evidence strengthModerate
How this study scored 59/100
Published in a leading specialty journal, and it is a longitudinal study following the same people over time. Scored zero for sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record and multi-centre collaboration, so those were left out: it earned 34 of the 58 points we could judge it on, which is 59 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Nature aging) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| Study design | 7 / 12 | A longitudinal study following the same people over time |
| Strength of evidence | 3 / 5 | Rated “moderate” 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 | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 37118071) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 34 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 34 of the 58 points we could judge it on is 59 out of 100. |
| Total | 59 / 100 | — |
What each of these 20 components measures, and what this score is not →
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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
Lee, J., Burkett, B. J., Min, H. K., Senjem, M. L., Lundt, E. S., Botha, H., et al. (2022). Deep learning-based brain age prediction in normal aging and dementia. Nature aging. https://doi.org/10.1038/s43587-022-00219-7
Vancouver
Lee J, Burkett BJ, Min HK, Senjem ML, Lundt ES, Botha H, et al. Deep learning-based brain age prediction in normal aging and dementia. Nature aging. 2022. doi:10.1038/s43587-022-00219-7. PMID: 37118071.
BibTeX
@article{lee2022-37118071,
author = {Lee, J. and Burkett, B. J. and Min, H. K. and Senjem, M. L. and Lundt, E. S. and Botha, H. and Graff-Radford, J. and Barnard, L. R. and others},
title = {{Deep learning-based brain age prediction in normal aging and dementia}},
journal = {Nature aging},
year = {2022},
doi = {10.1038/s43587-022-00219-7},
pmid = {37118071},
url = {https://doi.org/10.1038/s43587-022-00219-7}
}