Surface-based TBM boosts power to detect disease effects on the brain: An N=804 ADNI study

Yalin Wang, Yang Song, Priya Rajagopalan, Tuo An, Krystal Liu, Yi Yu Chou, Boris Gutman, Arthur W. Toga, Paul M. Thompson

Research output: Contribution to journalArticle

68 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1993-2010
Number of pages18
JournalNeuroImage
Volume56
Issue number4
DOIs
StatePublished - Jun 15 2011

Fingerprint

Alzheimer Disease
Brain
Neuroimaging
Biomarkers
Caudate Nucleus
Putamen
Sample Size
Atrophy
Cerebrospinal Fluid
Disease Progression
Anatomy
Software
Power (Psychology)
Research
Pharmaceutical Preparations
Cognitive Dysfunction

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

Cite this

Wang, Y., Song, Y., Rajagopalan, P., An, T., Liu, K., Chou, Y. Y., ... Thompson, P. M. (2011). Surface-based TBM boosts power to detect disease effects on the brain: An N=804 ADNI study. NeuroImage, 56(4), 1993-2010. https://doi.org/10.1016/j.neuroimage.2011.03.040

Surface-based TBM boosts power to detect disease effects on the brain : An N=804 ADNI study. / Wang, Yalin; Song, Yang; Rajagopalan, Priya; An, Tuo; Liu, Krystal; Chou, Yi Yu; Gutman, Boris; Toga, Arthur W.; Thompson, Paul M.

In: NeuroImage, Vol. 56, No. 4, 15.06.2011, p. 1993-2010.

Research output: Contribution to journalArticle

Wang, Y, Song, Y, Rajagopalan, P, An, T, Liu, K, Chou, YY, Gutman, B, Toga, AW & Thompson, PM 2011, 'Surface-based TBM boosts power to detect disease effects on the brain: An N=804 ADNI study', NeuroImage, vol. 56, no. 4, pp. 1993-2010. https://doi.org/10.1016/j.neuroimage.2011.03.040
Wang, Yalin ; Song, Yang ; Rajagopalan, Priya ; An, Tuo ; Liu, Krystal ; Chou, Yi Yu ; Gutman, Boris ; Toga, Arthur W. ; Thompson, Paul M. / Surface-based TBM boosts power to detect disease effects on the brain : An N=804 ADNI study. In: NeuroImage. 2011 ; Vol. 56, No. 4. pp. 1993-2010.
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