Understanding the underlying mechanisms of the brain and behavior is an essential requirement for improving the diagnosis of and treatments for mental health diseases. There is an intense interest in generating different modulates of neuroimaging and behavioral data to gain new insights into brain functionality and its connections with behavior outcomes. Challenges in studying the relationships, particularly the alignment, among brain- behavior outcomes from different sources include: (1) the outcomes measured from different modalities are diverse and complex, often in different scales (continuous, ordinal) and of different data representations(scalar, vector, matrix);(2) the neuroimaging data is high dimensional and reflects not only the desired signal but also the background noise;(3) there are obstacles to compare neuroimaging data from multi-center studies due to considerable between-center variability in brain images obtained from different scanners and processing protocols. Currently, there are very limited statistical methods available to address these issues. The overall objective of this proposal is to develop a unified statistical framework that fills in the aforementioned gaps. Our proposal of adopting agreement-based methodology provides a novel perspective for investigating the alignment between behavior outcomes and the biology of the brain (neuroimaging). While standard agreement methodology has been limited to the evaluation of outcomes that are made on the same scale, our seminal work on "broad sense agreement (BSA)" (Peng et al. 2011, a featured JASA article) that characterizes the agreement/alignment between a continuous and an ordinal variable lays the foundation for a promising framework proposed in this application. Specifically, we plan to fulfill our research goals by characterizing the alignment among outcomes with different scales and data-representations;incorporating covariates;assessing the strength of alignment between neuroimaging biomarkers and symptom domains;identifying relevant features in high-dimensional neuroimaging data that align with specific symptom clusters;and assessing agreement and calibrating images from multi-center studies. The proposed statistical methods will be applied to an ongoing PTSD study and a national multi-center imaging study, and user-friendly software will be developed and made available to general research communities. Our proposed method developments will directly benefit mental health research, and they are ubiquitous enough to be generally useful contributions to statistical practice.

Public Health Relevance

The proposed research project will develop new analytical methods for assessing alignment between brain images and psychological instruments. In addition, we plan to provide methods that can be used to compare combine the data from different institutions in multi-center studies. These methods will help to increase the understanding of the connections between psychological instruments and brain images, thereby increasing the accurate diagnosis of psychiatric diseases and the treatment strategies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
2R01MH079448-04A1
Application #
8639058
Study Section
Special Emphasis Panel (ZRG1-HDM-T (02))
Program Officer
Borja, Susan
Project Start
2006-12-01
Project End
2018-08-31
Budget Start
2013-09-26
Budget End
2014-08-31
Support Year
4
Fiscal Year
2013
Total Cost
$387,900
Indirect Cost
$137,900
Name
Emory University
Department
Biostatistics & Other Math Sci
Type
Schools of Public Health
DUNS #
066469933
City
Atlanta
State
GA
Country
United States
Zip Code
30322
Yang, Jing; Peng, Limin (2016) A new flexible dependence measure for semi-competing risks. Biometrics 72:770-9
Cordova, J Scott; Gurbani, Saumya S; Holder, Chad A et al. (2016) Semi-Automated Volumetric and Morphological Assessment of Glioblastoma Resection with Fluorescence-Guided Surgery. Mol Imaging Biol 18:454-62
Wang, Yikai; Kang, Jian; Kemmer, Phebe B et al. (2016) An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation. Front Neurosci 10:123
Peng, Limin; Manatunga, Amita; Wang, Ming et al. (2016) A general approach to categorizing a continuous scale according to an ordinal outcome. J Stat Plan Inference 172:23-25
Kemmer, Phebe B; Guo, Ying; Wang, Yikai et al. (2015) Network-based characterization of brain functional connectivity in Zen practitioners. Front Psychol 6:603
Zheng, Qi; Peng, Limin; He, Xuming (2015) GLOBALLY ADAPTIVE QUANTILE REGRESSION WITH ULTRA-HIGH DIMENSIONAL DATA. Ann Stat 43:2225-2258
Cordova, James S; Schreibmann, Eduard; Hadjipanayis, Costas G et al. (2014) Quantitative tumor segmentation for evaluation of extent of glioblastoma resection to facilitate multisite clinical trials. Transl Oncol 7:40-7
Shim, Hyunsuk; Wei, Li; Holder, Chad A et al. (2014) Use of high-resolution volumetric MR spectroscopic imaging in assessing treatment response of glioblastoma to an HDAC inhibitor. AJR Am J Roentgenol 203:W158-65
Guo, Ying; Li, Ruosha; Peng, Limin et al. (2013) New agreement measures based on survival processes. Biometrics 69:874-82
Guo, Ying; Tang, Li (2013) A hierarchical model for probabilistic independent component analysis of multi-subject fMRI studies. Biometrics 69:970-81

Showing the most recent 10 out of 15 publications