HAFNI: Holistic Atlases of Functional Networks and Interactions (http://hafni.cs.uga.edu/)
After decades of active research using in-vivo functional neuroimaging techniques such as fMRI, there has been mounting evidence that the total human brain function emerges from and is realized by the interaction of multiple concurrent neural networks. However, due to the lack of effective computational brain mapping approaches and the limitation of functional neuroimaging data quality/quantity, it is still challenging to robustly and faithfully reconstruct concurrent functional networks from fMRI (either task fMRI (tfMRI) or resting state fMRI (rsfMRI)) data and quantitatively measure their network-level interactions. Thus, it is largely unknown to what extent those multiple interacting functional networks spatially overlap with each other and jointly realize the total brain function. In response, recently, by developing innovative sparse representation of whole-brain fMRI signals and by using the publicly released large-scale Human Connectome Project (HCP) high-quality fMRI data, our pilot studies have shown that a large number of reproducible and robust functional networks, including both task-evoked and resting state networks, are simultaneously distributed in distant neuroanatomic areas while substantially spatially overlapping with each other, thus forming an initial collection of holistic atlases of functional networks and interactions (HAFNI). Based on these promising preliminary results, the objective of this research theme is to create, evaluate and establish a novel theory of reciprocal organizational architecture of human brain function based on the HAFNIs.
Applying HAFNI methods on massive fMRI big-data
Please visit our HELPNI site: http://bd.hafni.cs.uga.edu/HELPNI/.
Bao Ge, Milad Makkie, Jin Wang, Shijie Zhao, Xi Jiang, Xiang Li, Jinglei Lv, Shu Zhang, Wei Zhang, Junwei Han, Lei Guo, Tianming Liu, Signal sampling for efficient sparse representation of resting state FMRI data, in press, Brain Imaging and Behavior, 2015.PDF
Milad Makkie, Shijie Zhao, Xi Jiang, Jinglei Lv, Yu Zhao, Bao Ge, Xiang Li, Junwei Han, Tianming Liu, HAFNI-Enabled Largescale Platform for Neuroimaging Informatics (HELPNI), Brain Informatics, 2015. vol. 2(4), pp. 1-17.PDF
Xi Jiang, Xiang Li, Jinglei Lv, Tuo Zhang, Shu Zhang, Lei Guo, Tianming Liu, Sparse Representation of HCP Grayordinate Data Reveals Novel Functional Architecture of Cerebral Cortex, Human Brain Mapping, 2015. vol. 36(12), pp. 5301-5319.PDF
Jinglei Lv*, Xi Jiang*, Xiang Li*, Dajiang Zhu*, Shu Zhang, Shijie Zhao, Hanbo Chen, Tuo Zhang, Xintao Hu, Junwei Han, Jieping Ye, Lei Guo, Tianming Liu, Holistic Atlases of Functional Networks and Interactions Reveal Reciprocal Organizational Architecture of Cortical Function, IEEE Transactions on Biomedical Engineering, 2015. vol. 62(4), pp. 1120-1131. *These authors contributed equally to this work.PDF
Jinglei Lv, Xi Jiang, Xiang Li, Dajiang Zhu, Shijie Zhao, Tuo Zhang, Xintao Hu, Junwei Han, Lei Guo, Zhihao Li, Claire Coles, Xiaoping Hu*, Tianming Liu*, Assessing Effects of Prenatal Alcohol Exposure Using Group-wise Sparse Representation of FMRI Data, Psychiatry Research: Neuroimaging, 2015. vol. 233(2), pp. 254-268. *Joint correspondence authors. PDF
Jinglei Lv*, Xi Jiang*, Xiang Li*, Dajiang Zhu, Hanbo Chen, Tuo Zhang, Shu Zhang, Xintao Hu, Junwei Han, Heng Huang, Jing Zhang, Lei Guo, Tianming Liu, Sparse Representation of Whole-brain FMRI Signals for Identification of Functional Networks, Medical Image Analysis, 2015. vol. 20(1), pp. 112–134. *These authors contributed equally to this work. PDF
Shu Zhang, Xiang Li, Jinglei Lv, Xi Jiang, Lei Guo, Tianming Liu, Characterizing and Differentiating Task-based and Resting State FMRI Signals via Two-stage Sparse Representations, Brain Imaging and Behavior, 2014. in press.PDF
Xintao Hu, Cheng Lv, Gong Cheng, Jinglei Lv, Lei Guo, Junwei Han, Tianming Liu, Sparsity Constrained fMRI Decoding of Visual Saliency in Naturalistic Video Streams, IEEE Transactions on Autonomous Mental Development, 2015. vol. 7(2), pp. 65-75.PDF
Shijie Zhao, Junwei Han, Jinglei Lv, Xi Jiang, Xintao Hu, Yu Zhao, Bao Ge, Lei Guo, Tianming Liu, Supervised Dictionary Learning for Inferring Concurrent Brain Networks, in press, IEEE Transactions on Medical Imaging, 2015. vol. 34(10), pp. 2036-45.PDF