# Semiparametric Inference for Psychiatric Neuroimaging

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2022 · $409,823

## Abstract

PROJECT SUMMARY
Identifying brain-behavior associations for the purpose of informing individual differences, illness trajectories,
and neural mechanisms is one of the primary goals of psychiatric neuroimaging. The massively multivariate
nature of neuroimaging data, which consists of spatially detailed images of brain structure and function,
combined with high-dimensional behavioral data pose significant challenges to meeting this goal. The
emerging replication crisis in neuroimaging research has exposed limitations of commonly used spatial extent
inference (SEI) methods for analyzing imaging data. These include unrealistic assumptions about the spatial
covariance function of the imaging data that lead to highly inflated error rates. This project will develop a new
robust semiparametric inference framework for neuroimages to address the need for methods that are robust
in real-world data, integrate these methods into the pbj R package, and develop a graphical user interface
(GUI) to make the methods accessible to neuroimaging scientists. We will use the methods to study how
multidimensional symptoms of psychosis are related to brain function and structure in the Psychiatric
Genotype-Phenotype Project (PGPP) collected and Vanderbilt University Psychiatric Hospital (VUPH) and to
study cross-sectional and longitudinal changes in functional connectivity in the public-access Nathan Kline
Institute Rockland Sample (NKI-RS). We will evaluate the methods using realistic bootstrap-based
neuroimaging simulations. In Aim 1 we will develop a multidimensional semiparametric procedure for SEI that
will leverage computationally efficient parametric and nonparametric bootstraps for inference. In Aim 2 we will
expand the framework to repeated measurement models (including longitudinal data), that will allow scientists
to robustly model associations of subject-level covariate measurements and brain structure or function. In Aim
3, to address the need for alternatives to hypothesis testing in psychiatric neuroimaging, we will develop
semiparametric Coverage Probability Excursion (CoPE) sets that can be used to construct spatial confidence
intervals for semiparametric effect sizes. These methods will be made available to the neuroimaging
community through the pbj R package and GUI, and disseminated at neuroimaging conferences.

## Key facts

- **NIH application ID:** 10434748
- **Project number:** 5R01MH123563-03
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Simon Neil Vandekar
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $409,823
- **Award type:** 5
- **Project period:** 2020-08-01 → 2025-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10434748

## Citation

> US National Institutes of Health, RePORTER application 10434748, Semiparametric Inference for Psychiatric Neuroimaging (5R01MH123563-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10434748. Licensed CC0.

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