Sparse Clustering with Resampling for Subject Classification in PET Amyloid Imaging Studies

Simulations: A dataset of n=60 observations (3 groups of 20) and p=500 variables was generated for each simulation run; only 50 variables were truly different across groups. The dataset was resampled 20 times, Sparse_kM was applied to each sample and average variable weights were calculated. Probabilities of cluster membership, also called confidence levels, were computed (n=60). Simulations were performed 250 times. The 50 truly different variables were identified by variable weights that were 13-32 times greater than those for the 450 uninformative variables.

Human Data: For the PiB PET dataset, images (ECAT HR+, 10-15 mCi, 90 min) were acquired for 64 cognitively normal subjects (74.1±5.4 yrs). Parametric PiB distribution volume ratio images were generated (Logan method, cerebellum reference) and normalized to the MNI template (SPM8) to produce a dataset of n=64 subjects and p=343,099 voxels/image. The dataset was resampled 10 times and Sparse_kM was applied. An average voxel weight image was computed that indicated cortical areas of greatest interest that included precuneus and frontal cortex; these are key areas linked to early amyloid deposition. Seven of 64 subjects were identified as PiB(+) and 47 as PiB(-) with confidence ≥ 90%, where another subject was PiB(+) at lower confidence (80%) and the other 9 subjects were PiB(-) at confidence in the range of 50-70%. In conclusion, Sparse_kM with resampling can help to establish confidence levels for clustering when p≫n and may be a promising method for revealing informative voxels/spatial patterns that distinguish levels of amyloid load, including that at the transitional amyloid +/- boundary.