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.

Public Health Relevance

. We are developing new analysis approaches that exploit simultaneous multislice magnetic resonance imaging and the availability of large public datasets, in order to improve the sensitivity and specificity of measures of functional connectivity in clinical populations. These developments are important to our collaborators' search for noninvasive imaging-based biomarkers that can reveal disease mechanisms, improve diagnosis and prognosis, and assess therapeutic interventions, in autism, ADHD, Alzheimer's disease, multiple sclerosis, schizophrenia, primary progressive aphasia, and Huntington's disease

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Biotechnology Resource Grants (P41)
Project #
5P41EB015909-19
Application #
9741485
Study Section
Special Emphasis Panel (ZEB1)
Project Start
Project End
Budget Start
2019-07-01
Budget End
2020-06-30
Support Year
19
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Hugo W. Moser Research Institute Kennedy Krieger
Department
Type
DUNS #
155342439
City
Baltimore
State
MD
Country
United States
Zip Code
21205
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