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Home › Research › Brain scans and imaging

Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease

Peter R Millar, Patrick H Luckett, Brian A Gordon, Tammie L S Benzinger, Suzanne E Schindler, Anne M Fagan, Carlos Cruchaga, Randall J Bateman et al.. NeuroImage, 2022-04-20

This page is alzhub.org's plain-English summary of a published study, not the paper itself. It is generated from the record we hold for this study, sourced and linked below — check the original for the full text and methods.

✓ Peer-reviewed alzhub usefulness score 41 out of 100. Brain scans and imaging

Topic: Brain imaging, connectivity and assessment

"Brain-predicted age" quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18-89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker.

Key findings

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Study details

How this study scored 41/100
How this study's score is builtJournal tier 7 of 10; Journal's citation record left out, no data; Study design 0 of 12; Strength of evidence 1 of 5; Peer-review validation 10 of 10; How recent 3 of 5; Sample size 0 of 4; Effect reported 0 of 3; Studied in people 0 of 3; Randomised / blinded 0 of 2; Times cited left out, no data; Citations per year left out, no data; Impact for its field and year left out, no data; Cited by studies here left out, no data; Still being cited left out, no data; Senior author's track record left out, no data; Institution's track record left out, no data; Multi-centre collaboration left out, no data; Indexed in PubMed 2 of 2; Free to read left out, no data. 23 of the 56 points it could be judged on, which is 41 of 100. Total 41 of 100.23 of the 56 points we could judge = 41 of 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. 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, multi-centre collaboration and free to read, so those were left out: it earned 23 of the 56 points we could judge it on, which is 41 out of 100.

ComponentPointsWhy
Journal tier 7 / 10 Tier 3 — peer-reviewed journal (NeuroImage)
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 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 35452806)
Free to read — / 2 Access not recorded — left out of this score
Points 23 / 56 Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 23 of the 56 points we could judge it on is 41 out of 100.
Total 41 / 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

Millar, P. R., Luckett, P. H., Gordon, B. A., Benzinger, T. L. S., Schindler, S. E., Fagan, A. M., et al. (2022). Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease. NeuroImage. https://doi.org/10.1016/j.neuroimage.2022.119228

Vancouver

Millar PR, Luckett PH, Gordon BA, Benzinger TLS, Schindler SE, Fagan AM, et al. Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease. NeuroImage. 2022. doi:10.1016/j.neuroimage.2022.119228. PMID: 35452806.

BibTeX

@article{millar2022-35452806,
  author  = {Millar, Peter R and Luckett, Patrick H and Gordon, Brian A and Benzinger, Tammie L S and Schindler, Suzanne E and Fagan, Anne M and Cruchaga, Carlos and Bateman, Randall J and others},
  title   = {{Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease}},
  journal = {NeuroImage},
  year    = {2022},
  doi     = {10.1016/j.neuroimage.2022.119228},
  pmid    = {35452806},
  url     = {https://doi.org/10.1016/j.neuroimage.2022.119228}
}

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