IC‐03‐06: Six month MRI gray matter declines in Alzheimer's dementia evaluated by voxel‐based morphometry with multivariate network analysis: Preliminary findings from the ADNI study

Alexander, Gene E.; Hanson, Krista D.; Chen, Kewei; Reiman, Eric M.; Bernstein, Matt A.; Kornak, John; Schuff, Norbert; Fox, Nick C.; Thompson, Paul M.; Weiner, Michael W.; Jack, Clifford R.

doi:10.1016/j.jalz.2008.05.011

Cited by 1 publication

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“…As the chosen features are all assessed simultaneously, changes in one region that are not necessarily strong enough on their own to be predictive are assessed in conjunction with all others, and systems of co-occurring changes can be identified (Zhang et al, 2008). This is the premise of several machine learning methods that have been applied to brain mapping, such as multivarate network analysis (Alexander et al, 2008), adaptive boosting, online learning, and their variants (see Morra et al, (2009a; 2009b), for a review).…”

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confidence: 99%

Optimizing power to track brain degeneration in Alzheimer's disease and mild cognitive impairment with tensor-based morphometry: An ADNI study of 515 subjects

Hou¹,

Lee²,

Yanovsky³

et al. 2009

NeuroImage

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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 functions), and different numerical summary measures derived from the maps. Detection power was greatly enhanced by summarizing changes in a statistically- Author contributions were as follows: XH, SL, IY, AL, YC, AH, BG, AT, and PT performed the image analyses; CJ, MB, ER, DH, JK, NS, GA, and MW contributed substantially to the image and data acquisition, study design, quality control, calibration and pre-processing, data-basing and image analysis. We thank Anders Dale for his contributions to the image pre-processing and the ADNI project. NIH Public Access Author ManuscriptNeuroimage. Author manuscript; available in PMC 2010 December 1. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author Manuscript 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. In power analyses, the best method required only 48 AD and 88 MCI subjects to give 80% power to detect a 25% reduction in the mean annual change using a two-sided test (at α = 0.05). This is a drastic sample size reduction relative to using clinical scores as outcome measures (619 AD/6797 MCI for the ADAS-Cog, and 408 AD/796 MCI for the Clinical Dementia Rating sum-ofboxes scores). TBM offers high statistical power to track brain changes in large, multi-site neuroimaging studies and clinical trials of AD.

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