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57/100

✓ Peer-reviewed alzhub usefulness score 57 out of 100. Cognitive tests and assessment

Topic: Amyloid, tau and blood-test diagnosis

Tensor-based morphometry (TBM) is a powerful method to map the 3D profile of brain degeneration in Alzheimer's disease (AD) and mild cognitive impairment (MCI). We optimized a TBM-based image analysis method to determine what methodological factors, and which image-derived measures, maximize statistical power to track brain change. 3D maps, tracking rates of structural atrophy over time, were created from 1030 longitudinal brain MRI scans (1-year follow-up) of 104 AD patients (age: 75.7+/-7.2 years; MMSE: 23.3+/-1.8, at baseline), 254 amnestic MCI subjects (75.0+/-7.2 years; 27.0+/-1.8), and 157 healthy elderly subjects (75.9+/-5.1 years; 29.1+/-1.0), as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI). To determine which TBM designs gave greatest statistical power, we compared different linear and nonlinear registration parameters (including different regularization functi

Key findings

We optimized a TBM-based image analysis method to determine what methodological factors, and which image-derived measures, maximize statistical power to track brain change. 3D maps, tracking rates of structural atrophy over time, were created from 1030 longitudinal brain MRI scans (1-year follow-up) of 104 AD patients (age: 75.7+/-7.2 years; MMSE: 23.3+/-1.8, at baseline), 254 amnestic MCI subjects (75.0+/-7.2 years; 27.0+/-1.8), and 157 healthy elderly subjects (75.9+/-5.1 years; 29.1+/-1.0), as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI).
To determine which TBM designs gave greatest statistical power, we compared different linear and nonlinear registration parameters (including different regularization functions), and different numerical summary measures derived from the maps.
Detection power was greatly enhanced by summarizing changes in a statistically-defined region-of-interest (ROI) derived from an independent training sample of 22 AD patients.
Effect sizes were compared using cumulative distribution function (CDF) plots and false discovery rate methods.

Why it matters

This study compared brain imaging analysis methods to find which best tracks brain shrinkage over time in Alzheimer's disease and mild cognitive impairment. It does not test any treatment, cure, or prevention for Alzheimer's disease.

Study details

Study designlongitudinal study
Evidence strengthModerate

How this study scored 57/100

Published in a peer-reviewed 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.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (NeuroImage)
Journal's citation record	4 / 4	NeuroImage: journal h-index 523 (OpenAlex, 2026-10-29)
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	1 / 5	2009 — within 20 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	5 / 7	Cited 148 times (OpenAlex, 2026-10-10)
Citations per year	2 / 5	7.8 citations a year over 19 year(s) since 2009
Impact for its field and year	4 / 6	Cited 11.8× the average for papers of its field and year (OpenAlex FWCI)
Cited by studies here	3 / 6	Cited by 11 other studies in this library
Still being cited	1 / 4	Cited 14 times in 2024–2026 (2026 still in progress; OpenAlex)
Senior author's track record	4 / 4	Last-listed author Paul M. Thompson: h-index 193 (OpenAlex, whole career)
Institution's track record	2 / 3	University of California, Los Angeles: institution h-index 1,923 (OpenAlex)
Multi-centre collaboration	2 / 3	OpenAlex lists 8 institution(s) in 1 country
Indexed in PubMed	2 / 2	Indexed in PubMed (PMID 19615450)
Free to read	0 / 2	Behind a paywall — scores zero
Total	57 / 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

Hua, X., Lee, S., Yanovsky, I., Leow, A. D., Chou, Y. Y., Ho, A. J., et al. (2009). Optimizing power to track brain degeneration in Alzheimer's disease and mild cognitive impairment with tensor-based morphometry: an ADNI study of 515 subjects. NeuroImage. https://doi.org/10.1016/j.neuroimage.2009.07.011

Vancouver

Hua X, Lee S, Yanovsky I, Leow AD, Chou YY, Ho AJ, et al. Optimizing power to track brain degeneration in Alzheimer's disease and mild cognitive impairment with tensor-based morphometry: an ADNI study of 515 subjects. NeuroImage. 2009. doi:10.1016/j.neuroimage.2009.07.011. PMID: 19615450.

BibTeX

@article{hua2009-19615450,
  author  = {Hua, X. and Lee, S. and Yanovsky, I. and Leow, A. D. and Chou, Y. Y. and Ho, A. J. and Gutman, B. and Toga, A. W. and others},
  title   = {{Optimizing power to track brain degeneration in Alzheimer's disease and mild cognitive impairment with tensor-based morphometry: an ADNI study of 515 subjects}},
  journal = {NeuroImage},
  year    = {2009},
  doi     = {10.1016/j.neuroimage.2009.07.011},
  pmid    = {19615450},
  url     = {https://doi.org/10.1016/j.neuroimage.2009.07.011}
}

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