# TR&D 3: Advanced Statistical Methods for Functional MRI

> **NIH NIH P41** · HUGO W. MOSER RES INST KENNEDY KRIEGER · 2020 · $163,627

## Abstract

TR&D 3.
Advanced Statistical Methods for Functional MRI.
 Principle Investigators:
James J. Pekar, PhD., Associate Professor of Radiology
 Brian S. Caffo, Ph.D., Professor of Biostatistics
SUMMARY
The biological description of the brain as an evolved ensemble of distributed neural networks underlies the
significance of applying imaging measures of functional connectivity to clinical research. Our collaborative
projects use blood oxygenation level dependent functional MRI (BOLD fMRI) to assess changes in brain
networks in autism, ADHD, Alzheimer's disease, multiple sclerosis, schizophrenia, primary progressive
aphasia, and Huntington's disease, seeking to develop noninvasive imaging-based biomarkers in order to
reveal disease mechanisms, improve diagnosis and prognosis, and assess therapeutic interventions. Their
studies are limited by the sensitivity and specificity of BOLD fMRI acquisitions. The overarching goal of this
TR&D is to work with our collaborators to enhance the sensitivity and specificity of their functional connectivity
measures by developing novel empirical Bayesian analysis approaches that exploit two ongoing
transformations that are dramatically improving the acquisition and availability of fMRI data, namely
simultaneous multi-slice (SMS) MRI, and the availability of large public datasets. Accordingly, we have
developed three specific aims: 1. To develop time-invariant approaches to autoregressive modeling, and
optimize them for SMS fMRI data. 2. To develop time-invariant approaches to nuisance regression, and
optimize them for SMS fMRI data. 3. To design, implement, and assess empirical Bayesian methods for
combining information from large public databases with data obtained from single subject/small sample
studies.

## Key facts

- **NIH application ID:** 9997695
- **Project number:** 5P41EB015909-20
- **Recipient organization:** HUGO W. MOSER RES INST KENNEDY KRIEGER
- **Principal Investigator:** JAMES J. PEKAR
- **Activity code:** P41 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $163,627
- **Award type:** 5
- **Project period:** 2000-07-01 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9997695

## Citation

> US National Institutes of Health, RePORTER application 9997695, TR&D 3: Advanced Statistical Methods for Functional MRI (5P41EB015909-20). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/9997695. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*