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.
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.
|Wang, Han; Xie, Kun; Lian, Zhichao et al. (2018) Large-Scale Circuitry Interactions Upon Earthquake Experiences Revealed by Recurrent Neural Networks. IEEE Trans Neural Syst Rehabil Eng 26:2115-2125|
|Wang, Han; Xie, Kun; Xie, Li et al. (2018) Functional Brain Connectivity Revealed by Sparse Coding of Large-Scale Local Field Potential Dynamics. Brain Topogr :|
|Yuan, Jing; Li, Xiang; Zhang, Jinhe et al. (2018) Spatio-temporal modeling of connectome-scale brain network interactions via time-evolving graphs. Neuroimage 180:350-369|
|Hu, Xintao; Huang, Heng; Peng, Bo et al. (2018) Latent source mining in FMRI via restricted Boltzmann machine. Hum Brain Mapp 39:2368-2380|
|Ge, Bao; Li, Xiang; Jiang, Xi et al. (2018) A Dictionary Learning Approach for Signal Sampling in Task-Based fMRI for Reduction of Big Data. Front Neuroinform 12:17|
|Zhao, Yu; Dong, Qinglin; Zhang, Shu et al. (2018) Automatic Recognition of fMRI-Derived Functional Networks Using 3-D Convolutional Neural Networks. IEEE Trans Biomed Eng 65:1975-1984|
|Zhang, Tuo; Chen, Hanbo; Razavi, Mir Jalil et al. (2018) Exploring 3-hinge gyral folding patterns among HCP Q3 868 human subjects. Hum Brain Mapp 39:4134-4149|
|Jiang, Xi; Li, Xiang; Lv, Jinglei et al. (2018) Temporal Dynamics Assessment of Spatial Overlap Pattern of Functional Brain Networks Reveals Novel Functional Architecture of Cerebral Cortex. IEEE Trans Biomed Eng 65:1183-1192|
|Zhang, Shu; Zhao, Yu; Jiang, Xi et al. (2018) Joint representation of consistent structural and functional profiles for identification of common cortical landmarks. Brain Imaging Behav 12:728-742|
|Zhao, Yu; Ge, Fangfei; Liu, Tianming (2018) Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization. Med Image Anal 47:111-126|
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