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Improving brain age prediction models: incorporation of amyloid status in Alzheimer's disease
Maria Ly, Gary Z Yu, Helmet T Karim, Nishita R Muppidi, Akiko Mizuno, William E Klunk, Howard J Aizenstein, Alzheimer's Disease Neuroimaging Initiative. Neurobiology of aging, 2019-11-14
Topic: Brain imaging, connectivity and assessment
Brain age prediction is a machine learning method that estimates an individual's chronological age from their neuroimaging scans. Brain age indicates whether an individual's brain appears "older" than age-matched healthy peers, suggesting that they may have experienced a higher cumulative exposure to brain insults or were more impacted by those pathological insults. However, contemporary brain age models include older participants with amyloid pathology in their training sets and thus may be confounded when studying Alzheimer's disease (AD). We showed that amyloid status is a critical feature for brain age prediction models. We trained a model on T1-weighted MRI images participants without amyloid pathology. MRI data were processed to estimate gray matter density voxel-wise, which were then used to predict chronological age. Our model performed accurately comparable to previous models. Notably, we demonstrated more significant differences between AD diagnostic groups than other models. In addition, our model was able to delineate significant differences in brain age relative to chronological age between cognitively normal individuals with and without amyloid. Incorporation of amyloid status in brain age prediction models ultimately improves the utility of brain age as a biomarker for AD.
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APA 7th
Ly, M., Yu, G. Z., Karim, H. T., Muppidi, N. R., Mizuno, A., Klunk, W. E., et al. (2020). Improving brain age prediction models: incorporation of amyloid status in Alzheimer's disease. Neurobiology of aging. https://doi.org/10.1016/j.neurobiolaging.2019.11.005
Vancouver
Ly M, Yu GZ, Karim HT, Muppidi NR, Mizuno A, Klunk WE, et al. Improving brain age prediction models: incorporation of amyloid status in Alzheimer's disease. Neurobiology of aging. 2020. doi:10.1016/j.neurobiolaging.2019.11.005. PMID: 31843257.
BibTeX
@article{ly2020-31843257,
author = {Ly, Maria and Yu, Gary Z and Karim, Helmet T and Muppidi, Nishita R and Mizuno, Akiko and Klunk, William E and Aizenstein, Howard J and {Alzheimer's Disease Neuroimaging Initiative}},
title = {{Improving brain age prediction models: incorporation of amyloid status in Alzheimer's disease}},
journal = {Neurobiology of aging},
year = {2020},
doi = {10.1016/j.neurobiolaging.2019.11.005},
pmid = {31843257},
url = {https://doi.org/10.1016/j.neurobiolaging.2019.11.005}
}
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.
Key findings
- Brain age indicates whether an individual's brain appears "older" than age-matched healthy peers, suggesting that they may have experienced a higher cumulative exposure to brain insults or were more impacted by those pathological insults.
- We showed that amyloid status is a critical feature for brain age prediction models.
- Notably, we demonstrated more significant differences between AD diagnostic groups than other models.
- In addition, our model was able to delineate significant differences in brain age relative to chronological age between cognitively normal individuals with and without amyloid.
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Study details
- Evidence strengthLow
How this study scored 39/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomized / 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, multicenter collaboration and free to read, so those were left out: it earned 22 of the 56 points we could judge it on, which is 39 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Neurobiology of aging) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 2020 — 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 | 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) |
| Randomized / blinded | 0 / 2 | No randomization 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 |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 31843257) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 22 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 22 of the 56 points we could judge it on is 39 out of 100. |
| Total | 39 / 100 | — |
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