DICCCOL: Dense Individualized and Common Connectivity-based Cortical Landmarks (http://dicccol.cs.uga.edu/)
Is there a common structural and functional cortical architecture that can be quantitatively encoded and precisely reproduced across individuals and populations? This question is still largely unanswered due to the vast complexity, variability, and nonlinearity of the cerebral cortex.
In this project, we hypothesize that the common cortical architecture can be effectively represented by group-wise consistent structural fiber connections and take a novel data-driven approach to explore the cortical architecture. We aim to discover a dense and consistent map of cortical landmarks, named Dense Individualized and Common Connectivity-based Cortical Landmarks (DICCCOLs), each of which is defined by group-wise consistent white-matter fiber connection patterns derived from diffusion tensor imaging (DTI) data. The neuroscience basis is that each brain’s cytoarchitectonic area has a unique set of extrinsic inputs and outputs, called as connectional fingerprint, which largely determine the function that each brain area performs. The connectional fingerprint concept is modeled by fiber bundles that are quantitatively described by our trace-map models, such that quantitative description and comparison of fiber patterns within and across individuals can be effectively performed.
This theme of research aims to discover and validate large-scale cortical landmarks with finer granularity, better functional homogeneity, more accurate functional localization, and automatically-established cross-subjects correspondence. Eventually, we hope the DICCCOL system and its prediction framework will facilitate many neuroscience and neuroimaging applications that rely on structural/functional correspondences across individuals, and the DICCCOL map will offer a generic platform to compare and integrate neuroimaging data across individuals and populations.
In addition, based on DICCCOL, we design computational approaches to detect the dynamically changing higher-order functional interactions among structural connectomes, quantitatively characterize these time-dependent functional connectome dynamics and their representative patterns, and elucidate the entire state spaces of the functional connectomes transitions. We are also very interested in the fundamental driving forces and mechanisms of these temporally-changing brain states transitions. We hope this theme of research can contribute to deeper understanding of the fundamental mechanisms of the functioning brain.
Xi Jiang, Tuo Zhang, Dajiang Zhu, Kaiming Li, Hanbo Chen, Jinglei Lv, Xintao Hu, Junwei Han, Dinggang Shen, Lei Guo, Tianming Liu, Anatomy-guided Dense Individualized and Common Connectivity-based Cortical Landmarks (A-DICCCOL), IEEE Transactions on Biomedical Engineering, 2015. vol. 62(4), pp. 1108 - 1119. PDF
Dajiang Zhu, Kaiming Li, Douglas P. Terry, A. Nicholas Puente, Lihong Wang, Dinggang Shen, L. Stephen Miller, Tianming Liu, Connectome-scale Assessments of Structural and Functional Connectivity in MCI, Human Brain Mapping, 2014. vol. 35(7), pp. 2911–2923.PDF
Tuo Zhang, Dajiang Zhu, Xi Jiang, Bao Ge, Xintao Hu, Junwei Han, Lei Guo, Tianming Liu, Predicting Cortical ROIs via Joint Modeling of Anatomical and Connectional Profiles, Medical Image Analysis, 2013. vol. 17(6), pp. 601–615.PDF
Yixuan Yuan; Xi Jiang; Dajiang Zhu; Hanbo Chen; Kaiming Li; Peili Lv; Xiang Yu; Xiaojin Li; Shu Zhang; Tuo Zhang; Xintao Hu; Junwei Han; Lei Guo, Tianming Liu, Meta-analysis of Functional Roles of DICCCOLs, Neuroinformatics, 2013. vol. 11(1), pp. 47-63.PDF
Dajiang Zhu*, Kaiming Li*, Lei Guo, Xi Jiang, Tuo Zhang, Degang Zhang, Hanbo Chen, Fan Deng, Carlos Faraco, Changfeng Jin, Chong-Yaw Wee, Yixuan Yuan, Peili Lv, Yan Yin, Xiaolei Hu, Lian Duan, Xintao Hu, Junwei Han, Lihong Wang, Dinggang Shen, L Stephen Miller, Lingjiang Li, Tianming Liu, DICCCOL: Dense Individualized and Common Connectivity-based Cortical Landmarks, *Joint first authors, Cerebral Cortex, 2013. vol. 23(4), pp. 786-800.PDF
Kaiming Li*, Dajiang Zhu*, Lei Guo, Zhihao Li, Mary Ellen Lynch, Claire Coles, Xiaoping Hu**, Tianming Liu**, Connectomics Signatures of Prenatal Cocaine Exposure Affected Adolescent Brains, *Joint first authors, **Joint corresponding authors, Human Brain Mapping, 2013. vol. 34(10), pp. 2494–2510.PDF
Kaiming Li; Lei Guo; Carlos Faraco; Dajiang Zhu; Hanbo Chen; Yixuan Yuan; Jinglei Lv; Fan Deng; Xi Jiang; Tuo Zhang; Xintao Hu; Degang Zhang; Lloyd Miller, Tianming Liu, Visual Analytics of Brain Networks, NeuroImage, 2012. vol. 61(1), pp. 82–97.PDF
Dajiang Zhu, Kaiming Li, Carlos Faraco, Fan Deng, Degang Zhang, Xi Jiang, Hanbo Chen, Lei Guo, Stephen Miller, Tianming Liu, Optimization of Functional Brain ROIs via Maximization of Consistency of Structural Connectivity Profiles, NeuroImage, 2012. vol. 59(2), pp. 1382–1393.PDF
Tuo Zhang, Lei Guo, Kaiming Li, Changfeng Jing, Yan Yin, Dajing Zhu, Guangbin Cui, Lingjiang Li, Tianming Liu, Predicting Functional Cortical ROIs via DTI-derived Fiber Shape Models, Cerebral Cortex, 2012. vol. 22(4), pp. 854-864.PDF
Jinli Ou*, Zhichao Lian*, Li Xie, Xiang Li, Peng Wang, Yun Hao, Dajiang Zhu, Rongxin Jiang, Yufeng Wang, Yaowu Chen, Jing Zhang**, Tianming Liu**, Atomic Dynamic Functional Interaction Patterns for Characterization of ADHD, Human Brain Mapping, 2014. vol. 35(10), pp. 5262-78. *Joint first authors, **Joint corresponding authors.PDF
Xiang Li, Dajiang Zhu, Xi Jiang, Changfeng Jin, Xin Zhang, Lei Guo, Jing Zhang, Xiaoping Hu, Jingjiang Li, Tianming Liu. Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients, Human Brain Mapping, 2013. vol. 35(4), pp. 1761 - 78.PDF
Hanbo Chen, Kaiming Li, Dajiang Zhu, Xi Jiang, Yixuan Yuan, Peili Lv, Tuo Zhang, Lei Guo, Dinggang Shen*, Tianming Liu*. Inferring Group-wise Consistent Multimodal Brain Networks via Multi-view Spectral Clustering, IEEE Transactions on Medical Imaging, 2013. vol. 32(9), pp. 1576 - 1586. *Joint corresponding authors.PDF
Xin Zhang, Lei Guo, Xiang Li, Tuo Zhang, Dajiang Zhu, Kaiming Li, Hanbo Chen, Jinglei Lv, Changfeng Jin, Qun Zhao, Lingjiang Li, Tianming Liu. Characterization of Task-free and Task-performance Brain States via Functional Connectome Patterns, Medical Image Analysis, 2013. vol. 17(8), pp. 1106 - 22.PDF
Jing Zhang*, Xiang Li, Cong Li, Zhichao Lian, Xiu Huang, Guocheng Zhong, Dajiang Zhu, Kaiming Li, Changfeng Jin, Xintao Hu, Junwei Han, Lei Guo, Xiaoping Hu, Lingjiang Li, Tianming Liu*. Inferring Functional Interaction and Transition Patterns via Dynamic Bayesian Variable Partition Models, Human Brain Mapping, 2014. vol. 35(7), pp. 3314–3331. *Joint corresponding authors.PDF