Structure-Adaptive E-Value Filter for Detecting Regional Signals in Brain Imaging
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.
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BibTeX
@misc{he2026structureadaptive,
title = {{Structure-Adaptive E-Value Filter for Detecting Regional Signals in Brain Imaging}},
author = {Jingcheng He and Hangjin Jiang and Wenguang Sun and for the Alzheimer's Disease Neuroimaging Initiative},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.07246},
eprint = {2609.07246},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.07246},
}
RIS
TY - UNPB
TI - Structure-Adaptive E-Value Filter for Detecting Regional Signals in Brain Imaging
AU - Jingcheng He
AU - Hangjin Jiang
AU - Wenguang Sun
AU - for the Alzheimer's Disease Neuroimaging Initiative
PY - 2026
DA - 2026/09/07
PB - arXiv
DO - 10.48550/arxiv.2609.07246
UR - https://arxiv.org/abs/2609.07246
AB - 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.
ER -