We developed an automated analysis pipeline to analyze 3D changes in ventricular morphology; it provides a highly sensitive quantitative marker of Alzheimer's disease (AD) progression for MRI studies. In the ADNI image database, we created expert delineations of the ventricles, as parametric surface meshes, in 6 brain MRI scans. These 6 images and their embedded surfaces were fluidly registered to MRI scans of 80 AD patients, 80 individuals with mild cognitive impairment (MCI), and 80 healthy controls. Surface averaging within subjects greatly reduced segmentation error. Surface-based statistical maps revealed powerful correlations between surface morphology at baseline and (1) diagnosis, (2) cognitive performance (MMSE scores), (3) depression, and (4) predicted future decline, over a 1 year interval, in 3 standard clinical scores (MMSE, global and sum-of-boxes CDR). We used a false discovery rate method (FDR) method based on cumulative probability plots to find that 40 subjects were sufficient to discriminate AD from normal groups. 60 and 119 subjects, respectively, were required to correlate ventricular enlargement with MMSE and clinical depression. Surface-based FDR, along with multi-atlas fluid registration to reduce segmentation error, will allow researchers to (1) estimate sample sizes with adequate power to detect groups differences, and (2) compare the power of mapping methods head-to-head, optimizing cost-effectiveness for future clinical trials.