About: Brain Decoding Project

Cracking the neural code and neural circuit dynamics face unique sets of problems: 1) The ability to monitor large-scale neural activity patterns during cognitive behaviors; 2) lack of information on the component cell types and their functional contributions to neural coding; 3) require computational tools to investigate complex dynamic patterns of neural activity, thereby understanding real-time neural code; and 4) lack of fundamental knowledge about the organizing principles that govern real-time neural codes across various interacting circuits in the brain as a whole. In the learning and memory field, little is known about: What are real-time memory traces at the neural population level? How do short-term memory traces differ from long-term memory traces? How do distinct memory traces from multiple brain regions get synthesized into a holistic memory engram at the whole brain level?

In this project, we will employ our newly developed large-scale electrodes arrays techniques to record neural activity patterns over a dozen brain sites simultaneously as mice undergo learning, consolidation, and memory recalls of emotionally charge experiences. The large-scale neural recording will be further accompanied with cell type-specific optogenetic manipulations to further reveal the underlying cell assembly architectures and their interaction across the broad memory-processing circuits. We will apply and develop innovative computational and mathematical approaches to uncover real-time memory codes and provide conceptual insights into the network-level organization of fear memory and associative working memory in the mouse brain. Finally, to facilitate the transformative, paradigm-shift in sharing large-scale neurophysiological datasets, we propose to create an open-access Mouse Memory Code Database (MMCD) platform for stable long-term data storage and dissemination to broad communities of researchers.

[link: Decoding the Brain]
[link: History of Brain Decoding Project]

Teams:

The Brain Decoding Project is composed of a team of interdisciplinary investigators of Drs. Joe Tsien, Tianming Liu, Guantao Chen and King-Ip (David) Lin.

Dr. Joe Tsien

Dr. Joe Tsien (http://www.gru.edu/mcg/discovery/bbdi/tsien/) led a team consists of an interdisciplinary team of investigators working collaboratively to address key bottle-necks in the analysis of memory codes. Dr. Tsien has made many fundamental contributions to the neuroscience field, including his pioneer work in developing Cre/loxP-based brain region- and cell type-specific genetic method which now forms a versatile platform for cell type-specific tracing, molecular imaging, optogenetics, voltage imaging, and chemical genetics. He is also known for his discovery of the NR2B subunit for memory enhancement and innovative genetic analysis of temporal and spatial components of memory processes. Since 2005, he has developed 96- and 128-channel recording techniques in mice to study how the hippocampal CA1 encodes episodic experiences. In December 2007, Dr. Tsien launched the Brain Decoding Project, with support from the Georgia Research Alliance, to conduct large-scale recording and decoding of real-time memory traces in mice (http://www.gra.org/page/1040/meaning_of_the_mind.html). This has led to the successful identification of population-level real-time fear memory traces in the CA1 of the mouse hippocampus and how CA1 memory codes are controlled by the NMDA receptors. This forms a strong foundation for extending neural decoding research to other brain regions such as the ventral tegmental area (VTA), anterior cingulate cortex (ACC), and the basolateral amygdala (BLA). These experiences bring a unique perspective to the Brain Activity Mapping efforts.
[link: Profile of Joe Tsien by BioTechniques]

Dr. Tianming Liu

Dr. Tianming Liu is an associate professor in the Computer Science Department at the University of Georgia in Athens (http://cobweb.cs.uga.edu/~tliu/). His innovative work on brain connectivity and interaction mapping has received the prestigious NIH Career Award and the NSF CAREER Award. Drs. Liu and Tsien’s team proposes to develop novel multi-modal, multi-scale structural and functional connectivity maps related to emotional memory circuits in the mouse brain. They will also develop a series of innovative methods to investigate the high-order interaction patterns and dynamics of neural circuits and sparse coding representations.

Dr. Guantao Chen

Dr. Guantao Chen is the Distinguished University Professor and Chair of the Department of Mathematics and Statistics at Georgia State University (http://www.mathstat.gsu.edu/Guantao_Chen.html). He is a leading mathematician in graph theory field, known for his fundamental contributions in solving more than 10 open problems and conjectures in graph theory, including a few well-known long-standing conjectures. His main research interest lies in structural graph theory and its applications in theoretic computer science, bioinformatics, and health information management. In graph theory and has published more than 100 research papers. His team works with Drs. Tsien and Liu to apply graph theories, including the theory of hypergraphs, village graphs, and random graphs, to study complexity of brain-wide cell assembly representations in conjunction with real-time memory formation and retrievals.

Dr. King-Ip (David) Lin

Dr. King-Ip (David) Lin is an associate professor at the Department of Computer Science of the University of Memphis and he is an expert in database research (http://www.msci.memphis.edu/~linki/). He has published in top conferences in databases such as ACM SIGMOD and AAAI Knowledge Discovery and Data mining conferences. He has developed novel index structures and method for indexing and querying big data. His work on indexing was a finalist in the 10-year paper award at the internal Conference on Database Systems for Advanced Applications. Dr. Lin is also an expert in data mining and is a co-PI of an NIH challenge grant in discovery linguistic knowledge from electronic health records. To address the data-sharing plan, Dr. Lin will develop the Mouse Memory Code Database and a Cloud platform which would allow neural data stored in various databases across the Internet can be seamlessly and reliably accessed and managed.

Brain Decoding Consortium:

1) Brain and Behavior Discovery Institute, Medical College of Georgia at Georgia Regents University, USA

2) Cortical Architecture Imaging and Discovery (CAID) Lab, Department of Computer Sciences and Bioimaging Research Center (BIRC) at University of Georgia, Athens, USA

3) Department of Mathematics at Georgia State University, USA

4) Department of Computer Sciences at University of Memphis, USA

5) Institute of Brain Functional Genomics at East China Normal University, Shanghai, China

6) Brain Decoding Center at Banna Biomedical Research Institute, Yunnan, China

7) Georgia Research Alliance

8) Northwestern Polytechnical University, Xi'an, China

9) Biomedical Imaging Technology Center, Emory University/Georgia Institute of Technology, Atlanta, USA

Media Resources

Music in the Brain

Our Brain Decoding team has recently made a major breakthrough and provided the very first insight into what real-time memory engrams look like in the hippocampus, a brain region essential for memory formation. Our scientists have translated a portion of memory traces into a music so that you can hear it now! For more information, please see this Memory Decoding paper

"Pavlovian memory symphony" of fear memory traces from the hippocampus of a normal mouse. The audio file reflects the episodic memory patterns generated in the hippocampus of a wild-type mouse when he recalls his previous experiences hearing a beep and then receiving a mild foot-shock in the conditioning chamber. To convert the recall patterns of fear memory traces into "Pavlovian memory symphony", four distinct types of fear memory traces retrieved during the five-minutes contextual recall session are represented by four different frequency tones (notes). Each tenor C, E, G and a soprano C note correspond to a simple CS trace, a simple US trace, a CS-to-US associative trace, and a US-to-CS associative trace, respectively. The audio clip of this 5-minute “Pavlovian memory symphony” is time compressed by a factor of four (into a 1.25-minute clip).

"Pavlovian memory symphony" of fear memory traces from a memory-impaired mouse (missing the key memory switch known as the NMDA receptor in the hippocampus). The audio file reflects the memory patterns generated from the CA1 of the hippocampus of a mutant mouse when he recalls the same kind experiences hearing a beep and then receiving a mild foot-shock in the conditioning chamber.

Structure Connection

Resting Functional Connection

NMDA Receptor Signaling Critical for Habit Formation:

Publications

Joint Publications by the teams:

[1] Li M, Xie K, Kuang H, Liu J, Wang D, Fox GE, Shi Z, Chen L, Zhao F, Mao Y, Tsien JZ. Neural Coding of Cell Assemblies via Spike-Timing Self-Information. Cereb Cortex. 2018 Jul 1;28(7):2563-2576. doi: 10.1093/cercor/bhy081. [link]

[2] Li M, and Tsien JZ. Neural Code-Neural Self-information Theory on How Cell-Assembly Code Rises from Spike Time and Neuronal Variability. Front Cell Neurosci. 2017 Aug 30;11:236. doi: 10.3389/fncel.2017.00236. eCollection 2017. [link]

[3] Kun Xie, Grace E. Fox, Jun Liu, Cheng Lyu, Jason C. Lee, Hui Kuang, Stephanie Jacobs, Meng Li, Tianming Liu, Sen Song and Joe Z. Tsien, Brain Computation Is Organized via Power-of-Two-Based Permutation Logic, Front. Syst. Neurosci, doi:10.3389/fnsys.2016.00095, 2016. [link]

[4] Joe Z. Tsien, A Postulate on the Brain's Basic Wiring Logic, Trend in Neuroscience, doi:10.1016/j.tins.2015.09.002, 2015.[link]

[5] Meng Li, Fang Zhao, Jason Lee, Dong Wang, Hui Kuang, Joe Z. Tsien, Computational Classification Approach to Profile Neuron Subtypes from Brain Activity Mapping Data, Scientific Reports, doi:10.1038/srep12474, 2015.[link]

[6] Hanbo Chen, Tao Liu, Yu Zhao, Tuo Zhang, Yujie Li, Meng Li, Hongmiao Zhang, Hui Kuang, Lei Guo, Joe Tsien, Tianming Liu, Optimization of Large-scale Mouse Brain Connectome via Joint Evaluation of DTI and Neuron Tracing Data, NeuroImage, vol. 115, pp. 202 - 213, 2015.[link]

[7] Joe Z. Tsien, Meng Li, Remus Osan, Guifen Chen, Longian Lin, Phillip Lei Wang, Sabine Frey, Julietta Frey, Dajiang Zhu, Tianming Liu, Fang Zhao, Hui Kuang, On initial Brain Activity Mapping of episodic and semantic memory code in the hippocampus, in press, Neurobiology of Learning and Memory, 2013. [link]

[8] Joe Z. Tsien, Meng Li, Remus Osan, GuiFen Chen, LongNian Lin, Phillip Lei Wang, Sabine Frey, Julietta Frey, DaJiang Zhu, TianMing Liu, Fang Zhao, Hui Kuang, On brain activity mapping: insights and lessons from Brain Decoding Project to map memory patterns in the hippocampus, Vol 56(9), pp. 767-779, Science China Life Sciences, 2013. [link]

[9] Zhichao Lian, Xiang Li, Hongmiao Zhang, Hui Kuang, Kun Xie, Jianchuan Xing, Dajiang Zhu, Joe Z. Tsien, Tianming Liu, Jing Zhang, Detecting cell assembly interaction patterns via Bayesian based change-point detection and graph inference model, ISBI 2014.

Dr. Joe Tsien's Representive Publications:

Research Articles on decoding real-time memory code in the hippocampus:

[1] H. Zhang, G. Chen, H. Kuang, and J. Z. Tsien, "Mapping and deciphering neural codes of NMDA receptor-dependent fear memory engrams in the hippocampus.," PLoS One, vol. 8, no. 11, p. e79454, Jan. 2013.[link]

[2] R. Osan, G. Chen, R. Feng, and J. Z. Tsien, "Differential consolidation and pattern reverberations within episodic cell assemblies in the mouse hippocampus.," PLoS One, vol. 6, no. 2, p. e16507, Jan. 2011.[link]

[3] H. Kuang, L. Lin, and J. Z. Tsien, "Temporal dynamics of distinct CA1 cell populations during unconscious state induced by ketamine.," PLoS One, vol. 5, no. 12, p. e15209, Jan. 2010.[link]

[4] G. Chen, L. P. Wang, and J. Z. Tsien, "Neural population-level memory traces in the mouse hippocampus.," PLoS One, vol. 4, no. 12, p. e8256, Jan. 2009.[link]

[5] J. Z. Tsien, "The Memory Code," Sci. Am., vol. 297, no. 1, pp. 52–59, Jul. 2007.[link]

[6] L. Lin, R. Osan, and J. Z. Tsien, "Organizing principles of real-time memory encoding: neural clique assemblies and universal neural codes.," Trends Neurosci., vol. 29, no. 1, pp. 48–57, Jan. 2006.[link]

[7] L. Lin, R. Osan, S. Shoham, W. Jin, W. Zuo, and J. Z. Tsien, "Identification of network-level coding units for real-time representation of episodic experiences in the hippocampus.," Proc. Natl. Acad. Sci. U. S. A., vol. 102, no. 17, pp. 6125–30, Apr. 2005. [link]

Memory-related Research Articles:

[1] L. P. Wang, F. Li, D. Wang, K. Xie, D. Wang, X. Shen, and J. Z. Tsien, "NMDA receptors in dopaminergic neurons are crucial for habit learning.," Neuron, vol. 72, no. 6, pp. 1055–66, Dec. 2011.[link]

[2] L. Lin, G. Chen, H. Kuang, D. Wang, and J. Z. Tsien, "Neural encoding of the concept of nest in the mouse brain.," Proc. Natl. Acad. Sci. U. S. A., vol. 104, no. 14, pp. 6066–71, Apr. 2007.[link]

[3] D. V Wang and J. Z. Tsien, "Convergent processing of both positive and negative motivational signals by the VTA dopamine neuronal populations.," PLoS One, vol. 6, no. 2, p. e17047, Jan. 2011.[link]

[4] D. V Wang and J. Z. Tsien, "Conjunctive processing of locomotor signals by the ventral tegmental area neuronal population.," PLoS One, vol. 6, no. 1, p. e16528, Jan. 2011. [link]

[5] D. Wang, Z. Cui, Q. Zeng, H. Kuang, L. P. Wang, J. Z. Tsien, and X. Cao, "Genetic enhancement of memory and long-term potentiation but not CA1 long-term depression in NR2B transgenic rats.," PLoS One, vol. 4, no. 10, p. e7486, Jan. 2009.[link]

[6] X. Cao, H. Wang, B. Mei, S. An, L. Yin, L. P. Wang, and J. Z. Tsien, "Inducible and selective erasure of memories in the mouse brain via chemical-genetic manipulation.," Neuron, vol. 60, no. 2, pp. 353–66, Oct. 2008.[link]

[7] H. Wang, R. Feng, L. Phillip Wang, F. Li, X. Cao, and J. Z. Tsien, "CaMKII activation state underlies synaptic labile phase of LTP and short-term memory formation.," Curr. Biol., vol. 18, no. 20, pp. 1546–54, Oct. 2008.[link]

[8] M. H. Cho, X. Cao, D. Wang, and J. Z. Tsien, "Dentate gyrus-specific manipulation of beta-Ca2+/calmodulin-dependent kinase II disrupts memory consolidation.," Proc. Natl. Acad. Sci. U. S. A., vol. 104, no. 41, pp. 16317–22, Oct. 2007.[link]

[9] Z. Cui, K. A. Lindl, B. Mei, S. Zhang, and J. Z. Tsien, "Requirement of NMDA receptor reactivation for consolidation and storage of nondeclarative taste memory revealed by inducible NR1 knockout.," Eur. J. Neurosci., vol. 22, no. 3, pp. 755–63, Aug. 2005.[link]

[10] R. Feng, H. Wang, J. Wang, D. Shrom, X. Zeng, and J. Z. Tsien, "Forebrain degeneration and ventricle enlargement caused by double knockout of Alzheimer’s presenilin-1 and presenilin-2.," Proc. Natl. Acad. Sci. U. S. A., vol. 101, no. 21, pp. 8162–7, May 2004.[link]

[11] Z. Cui, H. Wang, Y. Tan, K. A. Zaia, S. Zhang, and J. Z. Tsien, "Inducible and Reversible NR1 Knockout Reveals Crucial Role of the NMDA Receptor in Preserving Remote Memories in the Brain," Neuron, vol. 41, no. 5, pp. 781–793, Mar. 2004.[link]

[12] H. Wang, E. Shimizu, Y.-P. Tang, M. Cho, M. Kyin, W. Zuo, D. A. Robinson, P. J. Alaimo, C. Zhang, H. Morimoto, M. Zhuo, R. Feng, K. M. Shokat, and J. Z. Tsien, "Inducible protein knockout reveals temporal requirement of CaMKII reactivation for memory consolidation in the brain.," Proc. Natl. Acad. Sci. U. S. A., vol. 100, no. 7, pp. 4287–92, Apr. 2003.[link]

[13] G. Wittenberg, "An emerging molecular and cellular framework for memory processing by the hippocampus," Trends Neurosci., vol. 25, no. 10, pp. 501–505, Oct. 2002.[link]

[14] R. Feng, C. Rampon, Y.-P. Tang, D. Shrom, J. Jin, M. Kyin, B. Sopher, G. M. Martin, S.-H. Kim, R. B. Langdon, S. S. Sisodia, and J. Z. Tsien, "Deficient Neurogenesis in Forebrain-Specific Presenilin-1 Knockout Mice Is Associated with Reduced Clearance of Hippocampal Memory Traces," Neuron, vol. 32, no. 5, pp. 911–926, Dec. 2001.[link]

[15] Y.-P. Tang, H. Wang, R. Feng, M. Kyin, and J. . Tsien, "Differential effects of enrichment on learning and memory function in NR2B transgenic mice," Neuropharmacology, vol. 41, no. 6, pp. 779–790, Nov. 2001.[link]

[16] Y. Tang, E. Shimizu, and J. Z. Tsien, "Do ‘smart’ mice feel more pain, or are they just better learners?," Nat. Neurosci., vol. 4, no. 5, pp. 453–4, May 2001.[link]

[17] E. Shimizu, "NMDA Receptor-Dependent Synaptic Reinforcement as a Crucial Process for Memory Consolidation," Science (80-. )., vol. 290, no. 5494, pp. 1170–1174, Nov. 2000.[link]

[18] C. Rampon, Y. P. Tang, J. Goodhouse, E. Shimizu, M. Kyin, and J. Z. Tsien, "Enrichment induces structural changes and recovery from nonspatial memory deficits in CA1 NMDAR1-knockout mice.," Nat. Neurosci., vol. 3, no. 3, pp. 238–44, Mar. 2000.[link]

[19] Y. P. Tang, E. Shimizu, G. R. Dube, C. Rampon, G. A. Kerchner, M. Zhuo, G. Liu, and J. Z. Tsien, "Genetic enhancement of learning and memory in mice.," Nature, vol. 401, no. 6748, pp. 63–9, Sep. 1999.[link]

Decoding techniques and translational application:

[1] R. Osan, L. Zhu, S. Shoham, and J. Z. Tsien, "Subspace projection approaches to classification and visualization of neural network-level encoding patterns.," PLoS One, vol. 2, no. 5, p. e404, Jan. 2007.[link]

[2] J. Liu, W. Wei, H. Kuang, J. Z. Tsien, and F. Zhao, "Heart rate and heart rate variability assessment identifies individual differences in fear response magnitudes to earthquake, free fall, and air puff in mice.," PLoS One, vol. 9, no. 3, p. e93270, Jan. 2014.[link]

[3] F. Zhao, M. Li, Y. Qian, and J. Z. Tsien, "Remote measurements of heart and respiration rates for telemedicine.," PLoS One, vol. 8, no. 10, p. e71384, Jan. 2013.[link]

[4] X. Zhu, M. Li, X. Li, Z. Yang, and J. Z. Tsien, "Robust action recognition using multi-scale spatial-temporal concatenations of local features as natural action structures.," PLoS One, vol. 7, no. 10, p. e46686, Jan. 2012.[link]

[5] X. He, Z. Yang, and J. Z. Tsien, "A hierarchical probabilistic model for rapid object categorization in natural scenes.," PLoS One, vol. 6, no. 5, p. e20002, Jan. 2011.[link]

[6] J. Xu, Z. Yang, and J. Z. Tsien, "Emergence of visual saliency from natural scenes via context-mediated probability distributions coding.," PLoS One, vol. 5, no. 12, p. e15796, Jan. 2010. [link]

[7] L. Lin, G. Chen, K. Xie, K. A. Zaia, S. Zhang, and J. Z. Tsien, "Large-scale neural ensemble recording in the brains of freely behaving mice.," J. Neurosci. Methods, vol. 155, no. 1, pp. 28–38, Jul. 2006. [link]

[8] J. Z. Tsien, D. F. Chen, D. Gerber, C. Tom, E. H. Mercer, D. J. Anderson, M. Mayford, E. R. Kandel, and S. Tonegawa, "Subregion- and Cell Type–Restricted Gene Knockout in Mouse Brain," Cell, vol. 87, no. 7, pp. 1317–1326, Dec. 1996.[link]

Dr. Tianming Liu's Representive Publications:

[1] J. Ou, Z. Lian, L. Xie, X. Li, P. Wang, Y. Hao, D. Zhu, R. Jiang, Y. Wang, Y. Chen, J. Zhang, and T. Liu, "Atomic Dynamic Functional Interaction Patterns for Characterization of ADHD," Hum. Brain Mapp., 2014.

[2] J. Lv, L. Guo, D. Zhu, T. Zhang, X. Hu, J. Han, and T. Liu, "Group-Wise FMRI Activation Detection on DICCCOL Landmarks.," Neuroinformatics, Apr. 2014.

[3] T. Zhang, H. Chen, L. Guo, K. Li, L. Li, S. Zhang, D. Shen, X. Hu, and T. Liu, "Characterization of U-shape Streamline Fibers: Methods and Applications," Med. Image Anal., Apr. 2014.

[4] X. Zhang, X. Li, C. Jin, H. Chen, K. Li, D. Zhu, X. Jiang, T. Zhang, J. Lv, X. Hu, J. Han, Q. Zhao, L. Guo, L. Li, and T. Liu, "Identifying and Characterizing Resting State Networks in Temporally Dynamic Functional Connectomes," Brain Topogr., 2014.

[5] B. He, T. Coleman, G. M. Genin, G. Glover, X. Hu, N. Johnson, T. Liu, S. Makeig, P. Sajda, and K. Ye, "Grand challenges in mapping the human brain: NSF workshop report.," IEEE Trans. Biomed. Eng., vol. 60, no. 11, pp. 2983–92, Nov. 2013.

[6] D. Zhu, T. Zhang, X. Jiang, X. Hu, H. Chen, N. Yang, J. Lv, J. Han, L. Guo, and T. Liu, "Fusing DTI and fMRI data: A survey of methods and applications.," Neuroimage, Oct. 2013.

[7] J. Zhang, X. Li, C. Li, Z. Lian, X. Huang, G. Zhong, D. Zhu, K. Li, C. Jin, X. Hu, J. Han, L. Guo, X. Hu, L. Li, and T. Liu, "Inferring functional interaction and transition patterns via dynamic bayesian variable partition models.," Hum. Brain Mapp., Nov. 2013.

[8] D. Zhu, K. Li, D. P. Terry, A. N. Puente, L. Wang, D. Shen, L. S. Miller, and T. Liu, "Connectome-scale assessments of structural and functional connectivity in MCI.," Hum. Brain Mapp., Sep. 2013.

[9] X. Zhang, L. Guo, X. Li, T. Zhang, D. Zhu, K. Li, H. Chen, J. Lv, C. Jin, Q. Zhao, L. Li, and T. Liu, "Characterization of task-free and task-performance brain states via functional connectome patterns.," Med. Image Anal., vol. 17, no. 8, pp. 1106–22, Dec. 2013.

[10] F. Deng, X. Jiang, D. Zhu, T. Zhang, K. Li, L. Guo, and T. Liu, "A functional model of cortical gyri and sulci.," Brain Struct. Funct., May 2013.

[11] H. Chen, K. Li, D. Zhu, X. Jiang, Y. Yuan, P. Lv, T. Zhang, L. Guo, D. Shen, and T. Liu, "Inferring group-wise consistent multimodal brain networks via multi-view spectral clustering.," IEEE Trans. Med. Imaging, vol. 32, no. 9, pp. 1576–86, Sep. 2013.

[12] J. Han, X. Ji, X. Hu, D. Zhu, K. Li, X. Jiang, G. Cui, L. Guo, and T. Liu, "Representing and retrieving video shots in human-centric brain imaging space.," IEEE Trans. Image Process., vol. 22, no. 7, pp. 2723–36, Jul. 2013.

[13] T. Zhang, D. Zhu, X. Jiang, B. Ge, X. Hu, J. Han, L. Guo, and T. Liu, "Predicting cortical ROIs via joint modeling of anatomical and connectional profiles.," Med. Image Anal., vol. 17, no. 6, pp. 601–15, Aug. 2013.

[14] X. Li, D. Zhu, X. Jiang, C. Jin, X. Zhang, L. Guo, J. Zhang, X. Hu, L. Li, and T. Liu, "Dynamic functional connectomics signatures for characterization and differentiation of PTSD patients.," Hum. Brain Mapp., vol. 35, no. 4, pp. 1761–78, Apr. 2014.

[15] X. Hu, D. Zhu, P. Lv, K. Li, J. Han, L. Wang, D. Shen, L. Guo, and T. Liu, "Fine-granularity functional interaction signatures for characterization of brain conditions.," Neuroinformatics, vol. 11, no. 3, pp. 301–17, Jul. 2013.

[16] D. Zhang, L. Guo, D. Zhu, K. Li, L. Li, H. Chen, Q. Zhao, X. Hu, and T. Liu, "Diffusion tensor imaging reveals evolution of primate brain architectures," Brain Struct. Funct., vol. 218, no. 6, pp. 1429–1450, Nov. 2013.

[17] Y. Yixuan, J. Xi, Z. Dajiang, C. Hanbo, L. Kaiming, L. Peili, Y. Xiang, L. Xiaojin, Z. Shu, Z. Tuo, H. Xintao, H. Junwei, G. Lei, and L. Tianming, "Meta-analysis of Functional Roles of DICCCOLs," Neuroinformatics, 2012.

[18] B. Ge, L. Guo, T. Zhang, X. Hu, J. Han, and T. Liu, "Resting state fMRI-guided fiber clustering: methods and applications.," Neuroinformatics, vol. 11, no. 1, pp. 119–33, Jan. 2013.

[19] X. Li, C. Lim, K. Li, L. Guo, and T. Liu, "Detecting brain state changes via fiber-centered functional connectivity analysis.," Neuroinformatics, vol. 11, no. 2, pp. 193–210, Apr. 2013.

[20] F. Deng, D. Zhu, J. Lv, L. Guo, and T. Liu, "FMRI signal analysis using empirical mean curve decomposition.," IEEE Trans. Biomed. Eng., vol. 60, no. 1, pp. 42–54, Jan. 2013.

[21] H. Chen, T. Zhang, L. Guo, K. Li, X. Yu, L. Li, X. X. Hu, J. Han, and T. Liu, "Coevolution of gyral folding and structural connection patterns in primate brains.," Cereb. Cortex, vol. 23, no. 5, pp. 1208–17, May 2013.

[22] J. Sun, X. Hu, X. Huang, Y. Liu, K. Li, X. Li, J. Han, L. Guo, T. Liu, and J. Zhang, "Inferring consistent functional interaction patterns from natural stimulus FMRI data.," Neuroimage, vol. 61, no. 4, pp. 987–99, Jul. 2012.

[23] K. Li, L. Guo, C. Faraco, D. Zhu, H. Chen, Y. Yuan, J. Lv, F. Deng, X. Jiang, T. Zhang, X. Hu, D. Zhang, L. S. Miller, and T. Liu, "Visual analytics of brain networks.," Neuroimage, vol. 61, no. 1, pp. 82–97, May 2012.

[24] D. Zhu, K. Li, L. Guo, X. Jiang, T. Zhang, D. Zhang, H. Chen, F. Deng, C. Faraco, C. Jin, C.-Y. Wee, Y. Yuan, P. Lv, Y. Yin, X. Hu, L. Duan, X. Hu, J. Han, L. Wang, D. Shen, L. S. Miller, L. Li, and T. Liu, "DICCCOL: Dense Individualized and Common Connectivity-Based Cortical Landmarks.," Cereb. Cortex, Apr. 2012.

[25] K. Li, D. Zhu, L. Guo, Z. Li, M. E. Lynch, C. Coles, X. Hu, and T. Liu, "Connectomics signatures of prenatal cocaine exposure affected adolescent brains.," Hum. Brain Mapp., vol. 34, no. 10, pp. 2494–510, Oct. 2013.

[26] K. Li, L. Guo, D. Zhu, X. Hu, J. Han, and T. Liu, "Individual functional ROI optimization via maximization of group-wise consistency of structural and functional profiles.," Neuroinformatics, vol. 10, no. 3, pp. 225–42, Jul. 2012.

[27] J. Nie, L. Guo, K. Li, Y. Wang, G. Chen, L. Li, H. Chen, F. Deng, X. Jiang, T. Zhang, L. Huang, C. Faraco, D. Zhang, C. Guo, P.-T. Yap, X. X. Hu, G. Li, J. Lv, Y. Yuan, D. Zhu, J. Han, D. Sabatinelli, Q. Zhao, L. S. Miller, B. Xu, P. Shen, S. Platt, D. Shen, and T. Liu, "Axonal Fiber Terminations Concentrate on Gyri.," Cereb. Cortex, vol. 22, no. 12, pp. 2831–9, Dec. 2012.

[28] D. Zhu, K. Li, C. C. Faraco, F. Deng, D. Zhang, L. Guo, L. S. Miller, and T. Liu, "Optimization of functional brain ROIs via maximization of consistency of structural connectivity profiles.," Neuroimage, vol. 59, no. 2, pp. 1382–93, Jan. 2012.

[29] T. Zhang, L. Guo, K. Li, C. Jing, Y. Yin, D. Zhu, G. Cui, L. Li, and T. Liu, "Predicting functional cortical ROIs via DTI-derived fiber shape models.," Cereb. Cortex, vol. 22, no. 4, pp. 854–64, Apr. 2012.

[30] J. Nie, L. Guo, G. Li, C. Faraco, L. Stephen Miller, and T. Liu, "A computational model of cerebral cortex folding.," J. Theor. Biol., vol. 264, no. 2, pp. 467–78, May 2010.

[31] G. Li, L. Guo, J. Nie, and T. Liu, "An automated pipeline for cortical sulcal fundi extraction.," Med. Image Anal., vol. 14, no. 3, pp. 343–59, Jun. 2010.

[32] G. Li, L. Guo, J. Nie, and T. Liu, "Automatic cortical sulcal parcellation based on surface principal direction flow field tracking.," Neuroimage, vol. 46, no. 4, pp. 923–37, Jul. 2009.

[33] T. Liu, J. Nie, A. Tarokh, L. Guo, and S. T. C. Wong, "Reconstruction of central cortical surface from brain MRI images: method and application.," Neuroimage, vol. 40, no. 3, pp. 991–1002, Apr. 2008.

[34] T. Liu, H. Li, K. Wong, A. Tarokh, L. Guo, and S. T. C. Wong, "Brain tissue segmentation based on DTI data.," Neuroimage, vol. 38, no. 1, pp. 114–23, Oct. 2007.

[35] T. Liu, G. Young, L. Huang, N.-K. Chen, and S. T. C. Wong, "76-space analysis of grey matter diffusivity: methods and applications.," Neuroimage, vol. 31, no. 1, pp. 51–65, May 2006.

[36] T. Liu, D. Shen, and C. Davatzikos, "Deformable registration of cortical structures via hybrid volumetric and surface warping," Neuroimage, vol. 22, no. 4, pp. 1790–1801, 2004.

[37] B. S. Chang, T. Katzir, T. Liu, K. Corriveau, M. Barzillai, K. A. Apse, A. Bodell, D. Hackney, D. Alsop, S. T. Wong, S. Wong, and C. A. Walsh, "A structural basis for reading fluency: white matter defects in a genetic brain malformation.," Neurology, vol. 69, no. 23, pp. 2146–54, Dec. 2007.

[38] V. C. T. Mok, T. Liu, W. W. M. Lam, A. Wong, X. Hu, L. Guo, X. Y. Chen, W. K. Tang, K. S. Wong, and S. Wong, "Neuroimaging predictors of cognitive impairment in confluent white matter lesion: volumetric analyses of 99 brain regions.," Dement. Geriatr. Cogn. Disord., vol. 25, no. 1, pp. 67–73, Jan. 2008.

[39] S. Kantarci, L. Al-Gazali, R. S. Hill, D. Donnai, G. C. M. Black, E. Bieth, N. Chassaing, D. Lacombe, K. Devriendt, A. Teebi, M. Loscertales, C. Robson, T. Liu, D. T. MacLaughlin, K. M. Noonan, M. K. Russell, C. A. Walsh, P. K. Donahoe, and B. R. Pober, "Mutations in LRP2, which encodes the multiligand receptor megalin, cause Donnai-Barrow and facio-oculo-acoustico-renal syndromes.," Nat. Genet., vol. 39, no. 8, pp. 957–9, Aug. 2007.

[40] T. Liu, G. Li, J. Nie, A. Tarokh, X. Zhou, L. Guo, J. Malicki, W. Xia, and S. T. C. Wong, "An automated method for cell detection in zebrafish.," Neuroinformatics, vol. 6, no. 1, pp. 5–21, Jan. 2008.

[41] G. Li, T. Liu, J. Nie, L. Guo, J. Malicki, A. Mara, S. A. Holley, W. Xia, and S. T. C. Wong, "Detection of blob objects in microscopic zebrafish images based on gradient vector diffusion.," Cytometry. A, vol. 71, no. 10, pp. 835–45, Oct. 2007.

[42] T. Liu, J. Nie, G. Li, L. Guo, and S. T. C. Wong, "ZFIQ: a software package for zebrafish biology.," Bioinformatics, vol. 24, no. 3, pp. 438–9, Feb. 2008.

[43] W. A. Campbell, H. Yang, H. Zetterberg, S. Baulac, J. A. Sears, T. Liu, S. T. C. Wong, T. P. Zhong, and W. Xia, "Zebrafish lacking Alzheimer presenilin enhancer 2 (Pen-2) demonstrate excessive p53-dependent apoptosis and neuronal loss.," J. Neurochem., vol. 96, no. 5, pp. 1423–40, Mar. 2006.

[44] T. Liu, J. Lu, Y. Wang, W. A. Campbell, L. Huang, J. Zhu, W. Xia, and S. T. C. Wong, "Computerized image analysis for quantitative neuronal phenotyping in zebrafish.," J. Neurosci. Methods, vol. 153, no. 2, pp. 190–202, Jun. 2006.

[45] G. Li, T. Liu, J. Nie, L. Guo, J. Chen, J. Zhu, W. Xia, A. Mara, S. Holley, and S. T. C. Wong, "Segmentation of touching cell nuclei using gradient flow tracking.," J. Microsc., vol. 231, no. Pt 1, pp. 47–58, Jul. 2008.

[46] G. Li, T. Liu, A. Tarokh, J. Nie, L. Guo, A. Mara, S. Holley, and S. T. C. Wong, "3D cell nuclei segmentation based on gradient flow tracking.," BMC Cell Biol., vol. 8, no. 1, p. 40, Jan. 2007.

[47] T. Liu, X. Hu, X. Li, M. Chen, J. Han, and L. Guo, "Merging Neuroimaging and Multimedia: Methods, Opportunities, and Challenges," IEEE Trans. Human-Machine Syst., vol. 44, no. 2, pp. 270–280, Apr. 2014.

[48] J. Han, K. Li, L. Shao, X. Hu, S. He, L. Guo, J. Han, and T. Liu, "Video abstraction based on fMRI-driven visual attention model," Inf. Sci. (Ny)., Jan. 2014.

[49] J. Han, S. He, X. Qian, D. Wang, L. Guo, and T. Liu, "An Object-Oriented Visual Saliency Detection Framework Based on Sparse Coding Representations," IEEE Trans. Circuits Syst. Video Technol., vol. 23, no. 12, pp. 2009–2021, Dec. 2013.

[50] X. Hu, K. Li, J. Han, X. Hua, L. Guo, and T. Liu, "Bridging the Semantic Gap via Functional Brain Imaging," IEEE Trans. Multimed., vol. 14, no. 2, pp. 314–325, Apr. 2012.

[51] X. Hu, D. Zhang, A. Mesbah, J. Han, X. Hua, L. Xie, S. Miller, L. Guo, T. Liu, F. Deng, K. Li, T. Zhang, H. Chen, X. Jiang, J. Lv, D. Zhu, and C. Faraco, "Bridging low-level features and high-level semantics via fMRI brain imaging for video classification," in Proceedings of the international conference on Multimedia - MM ’10, 2010, p. 451.

[52] T. Liu, H.-J. Zhang, W. Qi, and F. Qi, "A systematic rate controller for MPEG-4 FGS video streaming," Multimed. Syst., vol. 8, no. 5, pp. 369–379, Dec. 2002.

[53] T. Liu, H.-J. Zhang, and F. Qi, "A novel video key-frame-extraction algorithm based on perceived motion energy model," IEEE Trans. Circuits Syst. Video Technol., vol. 13, no. 10, pp. 1006–1013, Oct. 2003.

Dr. Guantao Chen's Representive Publications:

[1] G. CHEN, H. REN, and S. SHAN, "Homeomorphically Irreducible Spanning Trees in Locally Connected Graphs," Comb. Probab. Comput., vol. 21, no. 1–2, pp. 107–111, Feb. 2012.

[2] G. Chen, X. Yu, and W. Zang, "The circumference of a graph with no -minor, II," J. Comb. Theory, Ser. B, vol. 102, no. 6, pp. 1211–1240, Nov. 2012.

[3] G. Chen, Y. Chen, S. Gao, and Z. Hu, "Linked graphs with restricted lengths," J. Comb. Theory, Ser. B, vol. 98, no. 4, pp. 735–751, Jul. 2008.

[4] G. Chen, R. J. Faudree, R. J. Gould, and M. S. Jacobson, "Cycle Extendability of Hamiltonian Interval Graphs," SIAM J. Discret. Math., vol. 20, no. 3, pp. 682–689, Jan. 2006.

[5] G. Chen and S. Shan, "Homeomorphically irreducible spanning trees," J. Comb. Theory, Ser. B, vol. 103, no. 4, pp. 409–414, Jul. 2013.

Dr. King-Ip (David) Lin's Representive Publications:

[1] E. Lo, K. Y. Yip, K.-I. Lin, and D. W. Cheung, "Progressive skylining over Web-accessible databases," Data Knowl. Eng., vol. 57, no. 2, pp. 122–147, May 2006.

[2] E. G. M. Petrakis, C. Faloutsos, and K.-I. Lin, "ImageMap: an image indexing method based on spatial similarity," IEEE Trans. Knowl. Data Eng., vol. 14, no. 5, pp. 979–987, Sep. 2002.

[3] X. Wang, J. T. L. Wang, K.-I. Lin, D. Shasha, B. A. Shapiro, and K. Zhang, "An Index Structure for Data Mining and Clustering," Knowl. Inf. Syst., vol. 2, no. 2, pp. 161–184, Jun. 2000.

[4] K.-I. Lin, H. V. Jagadish, and C. Faloutsos, "The TV-tree: An index structure for high-dimensional data," VLDB J., vol. 3, no. 4, pp. 517–542, Oct. 1994.

[Link: download the full list of representive publications.]

Brain Decoding Project

---A BRAIN Project: Brain Activity Mapping of Neural Codes for memory