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Surface-based TBM boosts power to detect disease effects on the brain: an N=804 ADNI study
Wang Y, Song Y, Rajagopalan P, An T, Liu K, Chou YY, Gutman B, Toga AW et al.. NeuroImage, 2011-03-23
Topic: Brain proteins, microglia and lab models
Computational anatomy methods are now widely used in clinical neuroimaging to map the profile of disease effects on the brain and its clinical correlates. In Alzheimer's disease (AD), many research groups have modeled localized changes in hippocampal and lateral ventricular surfaces, to provide candidate biomarkers of disease progression for drug trials. We combined the power of parametric surface modeling and tensor-based morphometry to study hippocampal differences associated with AD and mild cognitive impairment (MCI) in 490 subjects (97 AD, 245 MCI, 148 controls) and ventricular differences in 804 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI; 184 AD, 391 MCI, 229 controls). We aimed to show that a new multivariate surface statistic based on multivariate tensor-based morphometry (mTBM) and radial distance provides a more powerful way to detect localized anatomical differences than conventional surface-based analysis. In our experiments, we studied correlations between hippocampal atrophy and ventricular enlargement and clinical measures and cerebrospinal fluid biomarkers. The new multivariate statistics gave better effect sizes for detecting morphometric differences, relative to other statistics including radial distance, analysis of the surface tensor and the Jacobian determinant. In empirical tests using false discovery rate curves, smaller sample sizes were needed to detect associations with diagnosis. The analysis pipeline is generic and automated. It may be applied to analyze other brain subcortical structures including the caudate nucleus and putamen. This publically available software may boost power for morphometric studies of subcortical structures in the brain.
Key findings
- We combined the power of parametric surface modeling and tensor-based morphometry to study hippocampal differences associated with AD and mild cognitive impairment (MCI) in 490 subjects (97 AD, 245 MCI, 148 controls) and ventricular differences in 804 subjects scanned as part of the Alzheimer's Disease Neuroimaging Initiative (ADNI; 184 AD, 391 MCI, 229 controls).
Why it matters
This imaging study of up to 804 participants found that a new multivariate surface statistic detected localized hippocampal and ventricular differences associated with Alzheimer's disease and mild cognitive impairment with better effect sizes than conventional methods. The work is about improving brain imaging measures as candidate biomarkers for drug trials, not about treating, curing or preventing the disease.
Study details
- Participants490
- Evidence strengthLow
How this study scored 51/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported 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 | 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 | 1 / 5 | 2011 — within 20 year(s) of the newest study here |
| Sample size | 2 / 4 | 490 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 5 / 7 | Cited 133 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 7.8 citations a year over 17 year(s) since 2011 |
| 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 | 1 / 6 | Cited by 1 other studies in this library |
| Still being cited | 2 / 4 | Cited 17 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 | 3 / 3 | University of Washington: institution h-index 3,540 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 21440071) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 51 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1016/j.neuroimage.2011.03.040
- PubMed: 21440071
- Original source →
- Free full text: PubMed Central →
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
Wang, Y., Song, Y., Rajagopalan, P., An, T., Liu, K., Chou, Y. Y., et al. (2011). Surface-based TBM boosts power to detect disease effects on the brain: an N=804 ADNI study. NeuroImage. https://doi.org/10.1016/j.neuroimage.2011.03.040
Vancouver
Wang Y, Song Y, Rajagopalan P, An T, Liu K, Chou YY, et al. Surface-based TBM boosts power to detect disease effects on the brain: an N=804 ADNI study. NeuroImage. 2011. doi:10.1016/j.neuroimage.2011.03.040. PMID: 21440071.
BibTeX
@article{wang2011-21440071,
author = {Wang, Y. and Song, Y. and Rajagopalan, P. and An, T. and Liu, K. and Chou, Y. Y. and Gutman, B. and Toga, A. W. and others},
title = {{Surface-based TBM boosts power to detect disease effects on the brain: an N=804 ADNI study}},
journal = {NeuroImage},
year = {2011},
doi = {10.1016/j.neuroimage.2011.03.040},
pmid = {21440071},
url = {https://doi.org/10.1016/j.neuroimage.2011.03.040}
}
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