SPM Methods, Results and SPM based global indices Kewei Chen, Eric Reiman Banner Alzheimer’s Institute Summary We have been using Statistical Parametric Mapping (SPM) to develop, test, and apply voxelbased algorithms to characterize, in a single index being free of the multiple regional comparisons, the extent to which a) the spatial pattern and magnitude of hypometabolism in a person’s FDG PET image and b) the spatial pattern and magnitude of metabolic decline in sequential PET images corresponds to that in patients with Alzheimer’s disease (AD). We have also been testing voxel-based algorithms to detect and track changes alterations in florbetapir PET measurements of fibrillar amyloid-beta deposition and MRI measurements of whole brain shrinkage.
Method The SPM voxel-based analysis procedure was standard, relatively simple and described in detail in http://adni.loni.ucla.edu/wp-content/uploads/2010/05/BannerPET_Analysis.pdf. We note some updates to that document here: 1) We currently use SPM8 (since 2011); 2) our analysis for ADNI-GO and ADNI-2 also includes patients with eMCI; 3) for the computation of several global indices as described in more detail below we did not smooth the images; 4)we found that global brain count measurement is not always the optimal normalization factor for characterizing the longitudinal change of cerebral metabolic rate of glucose (CMRgl); 5) the SPM procedure is conceptually similar for analyzing amyloid PET data; 6) the bootstrap resampling with replacement was used to assess reliability in the detection of CMRgl decline, as reported in [1]. In addition to the standard SPM analyses, we developed a strategy known as the hypometabolic convergence index (HCI) as detailed in [2]. Briefly, the HCI is intended to reflect in a single measurement the extent to which the pattern and magnitude of cerebral hypometabolism in an individual FDG PET image corresponds to that in probable AD patients, and is generated using a fully automated procedure implemented under the voxel-based image analysis algorithm SPM. It was first coded for SPM99 and we have now made it compatible with SPM8 with some additional setting optimizations and database extension. The HCI was recently extended to amyloid convergence index (ACI) for florbetapir PET and we are in the process of generalizing HCI to measure longitudinal metabolic declines for patients with AD or MCI separately. We had also developed a statistical region of interest (sROI) strategy to help track AD-related metabolic declines and evaluate disease-modifying treatment effects with improved statistical power and freedom from multiple regional comparisons [3]. A training set was used to characterize the set of voxels associated with maximal decline over different intervals and for the patient group (e.g., mild AD dement over 12-month period (sROI), along with the set of voxels that was relative spared (normalization ROI). A test set was then used to independently track Rev Nov 17 2011
declines in the empirically pre-specified sROI in reference to normalization ROI, and estimate the number of patients needed to assess disease-modifying treatment effects. We continue to refine our methods and develop other voxel-based data analysis algorithms to detect and track changes in FDG PET CMRgl measurements, florbetapir PET fibrillar amyloid SUVRs, and brain volume. For our standard SPM analyses, we simply report local maxima (e.g., data corresponding to the voxel associated with the most significant difference or change using the relevant statistical test), extracting data from each subject corresponding to the voxel implicated in the statistical brain map. Since these particular data were not corrected for multiple regional comparisons, we would suggest caution in using these data in the design of clinical trials, and we would instead recommend taking a look at our HCI and sROI data.
References [1] X. Wu, K. Chen, L. Yao, N. Ayutyanont, J. B. Langbaum, A. Fleisher, C. Reschke, W. Lee, X. Liu, G. E. Alexander, D. Bandy, N. L. Foster, P. M. Thompson, D. J. Harvey, M. W. Weiner, R. A. Koeppe, W. J. Jagust, and E. M. Reiman, "Assessing the reliability to detect cerebral hypometabolism in probable Alzheimer's disease and amnestic mild cognitive impairment," J. Neurosci. Methods, vol. 192, no. 2, pp. 277-285, Oct.2010. [2] K. Chen, N. Ayutyanont, J. B. Langbaum, A. S. Fleisher, C. Reschke, W. Lee, X. Liu, D. Bandy, G. E. Alexander, P. M. Thompson, L. Shaw, J. Q. Trojanowski, C. R. Jack, Jr., S. M. Landau, N. L. Foster, D. J. Harvey, M. W. Weiner, R. A. Koeppe, W. J. Jagust, and E. M. Reiman, "Characterizing Alzheimer's disease using a hypometabolic convergence index," Neuroimage, vol. 56, no. 1, pp. 52-60, May2011. [3] K. Chen, J. B. Langbaum, A. S. Fleisher, N. Ayutyanont, C. Reschke, W. Lee, X. Liu, D. Bandy, G. E. Alexander, P. M. Thompson, N. L. Foster, D. J. Harvey, M. J. de Leon, R. A. Koeppe, W. J. Jagust, M. W. Weiner, and E. M. Reiman, "Twelve-month metabolic declines in probable Alzheimer's disease and amnestic mild cognitive impairment assessed using an empirically pre-defined statistical region-of-interest: findings from the Alzheimer's Disease Neuroimaging Initiative," Neuroimage., vol. 51, no. 2, pp. 654-664, June2010.
About the Authors This document was prepared by Kewei Chen, Eric Reiman from Banner Alzheimer’s Institute. For more information please contact Kewei at 602-839-4851 or by email at
[email protected]. Notice: This document is presented by the author(s) as a service to ADNI data users. However, users should be aware that no formal review process has vetted this document and that ADNI cannot guarantee the accuracy or utility of this document.
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