Evaluating Sample-Size Efficiency and Sensitivity of Tractometry in Alzheimer's Disease
Feng, Y.; Villalon-Reina, J. E.; Gari, I. B.; Alibrando, J. D.; Nir, T. M.; Jahanshad, N.; Chandio, B. Q.; Thompson, P. M.
Show 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.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- White matter microstructure links with brain, bodily and genetic attributes in adolescence, mid- and late life 94%
- Divergent functional connectivity changes associated with white matter hyperintensities 94%
- Reliability and sensitivity of two whole-brain segmentation approaches included in FreeSurfer - ASEG and SAMSEG 94%
Similar papers in this journal
- The impact of multiband and in-plane acceleration on white matter microstructure analysis 95%
- Beware of White Matter Hyperintensities Causing Systematic Errors in Grey Matter Segmentations! 95%
- WMH-DualTasker: A weakly-supervised deep learning model for automated white matter hyperintensities segmentation and visual rating prediction 95%
Similar papers in this journal
Similar papers in this journal
- Cortical thickness and grey-matter volume anomaly detection in individual MRI scans: Comparison of two methods 94%
- Distinct and joint effects of low and high levels of Aβ and tau deposition on cortical thickness 94%
- Medial temporal atrophy in preclinical dementia: visual and automated assessment during six year follow-up 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.