This proposal for an NIH Mentored Quantitative Research Career Award requests support for Dr. Yongmei Michelle Wang as she embarks on a faculty career focused on imaging studies which examine the influence of longitudinal interventions on brain structure and function, and its relationship to cognition and performance, in older adults. The application proposes a research career development plan in the field of neuroimaging, bridging engineering, statistics, and neuroscience. The plan includes two overlapping phases: 1) a didactic phase that emphasizes training, including coursework and laboratory work in the area of cognitive neuroscience, imaging, aging, and interventions to complement Dr. Wang's doctoral training in Electrical Engineering and existing experience in Statistics; and 2) a development phase that focuses on intense development of the proposed research. These two phases will be closely supervised by the mentor and advisor in the area of cognitive neuroscience, brain plasticity, biomedical imaging, aging and interventions. Neuroimaging techniques, such as magnetic resonance imaging (MRI) and functional MRI (fMRI), have been shown to be powerful for characterizing and understanding the structure and function of the human brain. There remains a need, however, for robust and efficient statistical image analysis methods due to the limitations of existing approaches. It is crucial that these analysis techniques be developed with a full understanding of the neuroimaging methods used and the relevant cognitive neuroscience. We propose to develop, implement, and validate integrated computational algorithms for reliable and sensitive analysis of brain MRI and fMRI images, with the following specific aims: 1) Develop, validate and combine novel and efficient univariate and multivariate morphometry analysis methods. 2) Develop and evaluate integrated functional hemodynamic response and connectivity study approaches. 3) Apply these methods to the MRI and fMRI data being collected from separately funded NIA project of the mentor, to examine the effects of aerobic fitness training on brain structure and function of older adults; the neuroscience hypothesis to be tested are: improvements in aerobic fitness, over the course of a 1 year intervention, will result in i) increases in gray and white matter volume and shape changes of subcortical structures of the human brain; and ii) changes in the underlying neural circuits. 4) Develop a brain image analysis toolbox implementing the above methods.

Public Health Relevance

The development of quantitative image analysis methodology for the characterization of brain structural and functional changes caused by interventions (such as fitness) on the aging brain is of tremendous importance to understanding the effects of interventions on brain, and to the design of interventions that optimize the effects on cognition and brain health. PROJECT NARRATIVE The development of quantitative image analysis methodology for the characterization of brain structural and functional changes caused by interventions (such as fitness) on the aging brain is of tremendous importance to understanding the effects of interventions on brain, and to the design of interventions that optimize the effects on cognition and brain health.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Mentored Quantitative Research Career Development Award (K25)
Project #
5K25AG033725-05
Application #
8723715
Study Section
Neuroscience of Aging Review Committee (NIA)
Program Officer
Wagster, Molly V
Project Start
2010-09-15
Project End
2017-05-31
Budget Start
2015-06-01
Budget End
2017-05-31
Support Year
5
Fiscal Year
2015
Total Cost
Indirect Cost
Name
University of Illinois Urbana-Champaign
Department
Psychology
Type
Schools of Arts and Sciences
DUNS #
041544081
City
Champaign
State
IL
Country
United States
Zip Code
61820
Zwilling, Chris E; Wang, Michelle Y (2016) Covariance based outlier detection with feature selection. Conf Proc IEEE Eng Med Biol Soc 2016:2606-2609
Zwilling, Chris E; Wang, Michelle Yongmei (2014) Multivariate Voronoi Outlier Detection for Time Series. Health Innov Point Care Conf 2014:300-303
Xia, Jing; Wang, Michelle Yongmei (2014) PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS. Adv Appl Stat 40:61-74
Zhou, Chunxiao; Zwilling, Chris E; Calhoun, Vince D et al. (2014) Efficient Blockwise Permutation Tests Preserving Exchangeability. Int J Stat Med Res 3:145-152
Voss, Michelle W; Erickson, Kirk I; Prakash, Ruchika Shaurya et al. (2013) Neurobiological markers of exercise-related brain plasticity in older adults. Brain Behav Immun 28:90-9
Vo, Loan T K; Walther, Dirk B; Kramer, Arthur F et al. (2011) Predicting individuals' learning success from patterns of pre-learning MRI activity. PLoS One 6:e16093
Voss, Michelle W; Prakash, Ruchika S; Erickson, Kirk I et al. (2010) Plasticity of brain networks in a randomized intervention trial of exercise training in older adults. Front Aging Neurosci 2:
Sako?lu, Unal; Pearlson, Godfrey D; Kiehl, Kent A et al. (2010) A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia. MAGMA 23:351-66
Wang, Yongmei Michelle; Xia, Jing (2009) Unified framework for robust estimation of brain networks from FMRI using temporal and spatial correlation analyses. IEEE Trans Med Imaging 28:1296-307
Zhou, Chunxiao; Wang, Huixia Judy; Wang, Yongmei Michelle (2009) Efficient Moments-based Permutation Tests. Adv Neural Inf Process Syst 22:2277-2285