# Deep-learning based spatial multiple testing for Alzheimer's neuroimaging data

> **NIH NIH R21** · NEW YORK UNIVERSITY · 2020 · $435,875

## Abstract

Project Summary
The broad objective of this research is to develop a powerful deep-learning based multiple testing approach for
high-dimensional spatial data that arise commonly in biomedical imaging studies, in particular, brain imaging
studies. The motivating problem is to detect the cerebral metabolic abnormalities in Alzheimer’s disease (AD)
from Fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET) data. Existing multiple testing
approaches in solving this problem often ignore or inadequately capture the spatial dependence among the
test statistics obtained from brain voxels and thus lose substantial power for the detection. We will develop a
novel spatial multiple testing method that utilizes the deep convolutional neural network (DCNN), a key deep-
learning technique, to well capture the spatial dependence among test statistics and thus to achieve the
optimal power in the sense of minimizing the false nondiscovery rate (FNR) while correctly controlling the false
discovery rate (FDR) at a given level. The proposed DCNN-based FDR controlling method has enhanced
power to discover new AD-related brain regions that are missed by conventional methods, thereby leading to
novel clinical and pathological studies. The specific aims of this proposal include: 1. To develop an optimal
spatial FDR controlling approach by connecting the unsupervised local-significance-index based multiple
testing with the supervised DCNN-based image segmentation; 2. To evaluate the proposed spatial FDR
controlling approach via extensive simulations under various three-dimensional spatial dependence structures,
in comparison with multiple classical and state-of-the-art methods; 3. To apply proposed spatial FDR
controlling approach to detect AD-related brain regions using the FDG-PET datasets from the Alzheimer’s
Disease Neuroimaging Initiative and the Weill Cornell Brain Health Imaging Institute; 4. To develop a user-
friendly and publicly available software package with versions in both Python and R to implement the proposed
spatial FDR controlling approach. The proposed DCNN-based approach will also be widely applicable to large-
scale multiple testing problems in other fields of biomedical research that involve spatial dependence.

## Key facts

- **NIH application ID:** 10107565
- **Project number:** 1R21AG070303-01
- **Recipient organization:** NEW YORK UNIVERSITY
- **Principal Investigator:** Hai Shu
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $435,875
- **Award type:** 1
- **Project period:** 2020-09-15 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10107565

## Citation

> US National Institutes of Health, RePORTER application 10107565, Deep-learning based spatial multiple testing for Alzheimer's neuroimaging data (1R21AG070303-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10107565. Licensed CC0.

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