This application is in response to the PA-10-067 (Research Project Grant (Parent R01)). Problem statement: Recent neuroimaging studies in the literature, including our own ones, have shown that several brain networks, including arousal regulation, working memory, language, executive function, attention, and vision systems are altered in prenatal cocaine exposure (PCE) affected brains. However, the alterations of structural and functional connectivities in large-scale brain networks and the alterations of structural brain architecture in PCE affected adolescents are largely unknown. The major barrier to the quantitative assessments of large-scale brain connectivities in PCE adolescents and controls is the critical lack of dense brain landmarks that are consistent across different brains and can serve as common network nodes for connectivity mapping. Approaches: Recently, we created and validated a transformative data-driven approach that discovered a dense map of 358 consistent brain landmarks, called Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL), in healthy young adult brains, each of which is defined by consistent white matter fiber connectivity pattern derived from diffusion tensor imaging (DTI) data. Our validation results have shown that these 358 DTI-derived DICCCOL landmarks are strikingly reproducible in separate datasets, have meaningful and accurate functional localizations, and possess automatically-established cross-subjects correspondences. Importantly, these 358 brain landmarks can be accurately and efficiently predicted in a new, single brain with DTI data. Therefore, this set of 358 dense landmarks offers common network nodes for large-scale structural and functional connectivities assessments. In this project, we propose to apply our DICCCOL models and their prediction framework on existing Emory PCE/control datasets and assess large-scale connectivities in PCE affected adolescents. Significance: 1) The discovered common DICCCOL map, together with the consistent structural connection patterns, can be considered and used as the next-generation brain atlas, which will have much finer granularity and better functional homogeneity than the Brodmann brain atlas that has been used for over 100 years. 2) The dissemination of the DICCCOL prediction framework based on the open source platform of Insight Toolkit (ITK) and the DICCCOL map will contribute to numerous applications in brain imaging that rely on accurate localization of brain ROIs. 3) Connectivity alterations in large-scale brain networks of DICCCOL landmarks in PCE are largely unknown. This knowledge gap will be significantly bridged in this project by assessing large-scale networks in multimodal DTI and resting state fMRI datasets of PCE/control brains.

Public Health Relevance

Briefly, this project aims to apply UGA Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOL) models and landmark prediction framework on existing Emory prenatal cocaine exposure (PCE)/control datasets and assess large-scale connectivities in PCE adolescents. This project aims to predict DICCCOL maps in PCE/controls, discover common DICCCOL maps in three populations and assess large-scale structural connectivities in PCE/controls, and assess large-scale functional connectivities in PCE/controls. The outcome will be novel elucidations of PCE's teratogenic effects on widespread connectivity alterations and a collection of connectivity-based markers that are predictive of PCE clinical and behavior measurements.

National Institute of Health (NIH)
National Institute on Drug Abuse (NIDA)
Research Project (R01)
Project #
Application #
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Sirocco, Karen
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Georgia
Biostatistics & Other Math Sci
Schools of Arts and Sciences
United States
Zip Code
Ou, Jinli; Xie, Li; Jin, Changfeng et al. (2015) Characterizing and Differentiating Brain State Dynamics via Hidden Markov Models. Brain Topogr 28:666-79
Zhang, Tuo; Chen, Hanbo; Guo, Lei et al. (2014) Characterization of U-shape streamline fibers: Methods and applications. Med Image Anal 18:795-807
Zhang, Xin; Li, Xiang; Jin, Changfeng et al. (2014) Identifying and characterizing resting state networks in temporally dynamic functional connectomes. Brain Topogr 27:747-65
Deng, Fan; Jiang, Xi; Zhu, Dajiang et al. (2014) A functional model of cortical gyri and sulci. Brain Struct Funct 219:1473-91
Ou, Jinli; Lian, Zhichao; Xie, Li et al. (2014) Atomic dynamic functional interaction patterns for characterization of ADHD. Hum Brain Mapp 35:5262-78
Li, Xiang; Zhu, Dajiang; Jiang, Xi et al. (2014) Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients. Hum Brain Mapp 35:1761-78
Zhu, Dajiang; Zhang, Tuo; Jiang, Xi et al. (2014) Fusing DTI and fMRI data: a survey of methods and applications. Neuroimage 102 Pt 1:184-91
Lv, Peili; Hu, Xintao; Lv, Jinglei et al. (2014) A linear model for characterization of synchronization frequencies of neural networks. Cogn Neurodyn 8:55-69
Chen, Mo; Han, Junwei; Hu, Xintao et al. (2014) Survey of encoding and decoding of visual stimulus via FMRI: an image analysis perspective. Brain Imaging Behav 8:7-23
Lv, Jinglei; Guo, Lei; Zhu, Dajiang et al. (2014) Group-wise FMRI activation detection on DICCCOL landmarks. Neuroinformatics 12:513-34

Showing the most recent 10 out of 21 publications