Structure-Adaptive E-Value Filter for Detecting Regional Signals in Brain Imaging

2026-09-07

AI Summary

研究使用SEFT方法处理结构MRI数据，检测阿尔茨海默病不同临床阶段的脑区灰质差异，控制多重比较错误发现率。

Abstract

Structural MRI provides a noninvasive view of the neuroanatomical differences associated with cognitive impairment and dementia. Using data from the Alzheimer's Disease Neuroimaging Initiative, we investigate which anatomical regions exhibit widespread gray-matter differences and how these regional patterns vary across the clinical spectrum. Addressing this goal requires translating spatially dependent voxel-level evidence into regional conclusions while controlling multiplicity across anatomical regions. We propose the Structure-adaptive E-value FilTer (SEFT), which uses flexible working models to construct spatially adaptive voxel-level scores and aggregates them into regional partial-conjunction e-values. When combined with the e-value Benjamini--Hochberg (e-BH) procedure, these e-values provide finite-sample control of the set-wise false discovery rate under arbitrary interregional dependence. The ADNI analysis reveals a coherent neuroanatomical pattern: the exploratory analysis shows that differences between normal cognition and mild cognitive impairment are concentrated in medial-temporal regions, whereas the differences between mild cognitive impairment and dementia extend more broadly into temporal--limbic and posterior association regions. Both patterns largely overlap the normal-cognition--dementia benchmark, identifying a shared anatomical core across the clinical comparisons.

Problem

• cognitive impairment
• dementia
• neuroanatomical differences
• multiplicity control
• brain imaging

Innovation

• Structure-adaptive E-value Filter
• spatially adaptive voxel-level scores
• regional partial-conjunction e-values
• e-BH procedure
• false discovery rate