Evaluating Sample-Size Efficiency and Sensitivity of Tractometry in Alzheimer's Disease

Abstract

Tractometry allows quantitative analysis of white matter microstructure along the brains fiber tracts, but the impact of study design parameters--such as sample size and along-tract resolution--on sensitivity and specificity is not well understood. In this study, we conducted tractometry bootstrap analysis using linear-mixed models across four diffusion tensor imaging (DTI) metrics to systematically evaluate how these factors affect the detection of dementia- and amyloidrelated effects. While coarser along-tract segments yield greater sensitivity and higher mean effect sizes, finer segments tend to produce higher peak effect sizes, revealing more spatially localized effects. Dementia-related effects were more widespread and detectable with fewer subjects, whereas amyloid-related effects were more subtle and localized, requiring larger cohorts to detect them. These findings highlight that tractometry offers improved spatial specificity and can reliably detect small, fine-scale effects, but study design should be tailored to specific research questions, considering the expected spatial extent and magnitude of effects, to optimize sample size efficiency and interpretability.