Zhongming Liu

Principal Investigator, Associate Professor

2119 Gerstacker

zmliu at umich dot edu

Associate Professor, Department of Biomedical Engineering
Associate Professor, Department of Electrical Engineering and Computer Science
Principal Investigator, Laboratory of Integrated Brain Imaging
Director, Engineering Preclinical Imaging Center

Zhongming Liu received B.S. ('00) and M.S. ('03) in Electrical Engineering from Zhejiang University, and Ph.D. ('08) in Biomedical Engineering from the University of Minnesota advised by Bin He. Then he was a research fellow in Advanced MRI Section at the National Institutes of Health advised by Jeff Duyn. From 2013 to 2019, he was an Assistant/Associate Professor in both Biomedical Engineering and Electrical and Computer Engineering at Purdue University. In 2020, he joined the University of Michigan as a tenured Associate Professor in the Department of Biomedical Engineering, the Electrical and Computer Engineering Division of the Department of Electrical Engineering and Computer Science. At U-M, he is also the Director of Engineering Preclinical Imaging Center and a faculty member affiliated with Michigan Institute of Data Science, Neuroscience Graduate Program, and Precision Health. He is a Senior Member of IEEE, Associate Editor for IEEE Transactions on Biomedical Engineering, and Editorial Board Member for NeuroImage. He is a member of ISMRM, OHBM, IEEE EMBS, and SfN.

His lab develops and uses advanced techniques for imaging, recording, stimulating and modeling the brain to accelerate progress in neurosciences, neural engineering, and artificial intelligence. His research has been continuously funded by NIH, NSF, DARPA etc., and has been recognized with a number of awards, including the Innovative New Scientist in Biobehavioral Research from National Institute of Mental Health, Faculty Award of Excellence from Purdue University, Best Dissertation Award from University of Minnesota, and 19 paper or abstract awards from international conferences. 

Selected Publications (see the full list in google scholar)

Computational neuroscience 

  • Kim J-H, Zhang Y, Han K, Wen Z, Choi M, Liu Z., “Representation learning of resting state fMRI with variational auto-encoder,” NeuroImage, 241: 118423, 2021.  
  • Zhang Y, Han K, Worth RM, Liu Z, “Connecting concepts in the brain by mapping cortical representations of semantic relations,” Nature Communications, 11:1877, 2020.
  • Wen H, Shi J., Zhang Y, Lu K-H, Cao J, Liu Z, “Neural encoding and decoding with deep learning for dynamic natural vision,” Cerebral Cortex, 28(2): 4136-4160, 2018.
  • Han K, Wen H, Shi J, Lu K-H, Zhang Y, Di Fu, Liu Z, "Variational auto-encoder: an unsupervised model for encoding and decoding fMRI activity in visual cortex," NeuroImage, 198: 125-136, 2019.
  • Shi J, Wen H, Zhang Y, Han K, Liu Z, "Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision," Human Brain Mapping, 39(5): 2269-2282, 2018. 
  • Wen H, Shi J, Chen W, Liu Z, “Transferring and generalizing deep-learning-based neural encoding models across subjects,” NeuroImage, 176: 152-163, 2018.
  • Wen H, Shi J, Chen W, Liu Z. “Deep residual network predicts cortical representation and organization of visual features for rapid categorization,” Scientific Reports, 8(1): 3752, 2018.

Artificial Intelligence

  • Zhang Y, Choi M, Han K, Liu Z, "Explainable semantic space by grounding language to vision with cross-modal contrastive learning," Advances in Neural Information Processing Systems (NeurIPS), 2021.
  • Han K, Wen H, Zhang Y, Fu D, Culurciello E, Liu Z, "Deep predictive coding network with local recurrent processing for object recognition," Advances in Neural Information Processing Systems (NeurIPS), 2018.
  • Wen H, Han K, Shi J, Zhang Y, Culurciello E, Liu Z, “Deep predictive coding network for object recognition,” International Conference on Machine Learning (ICML), 2018.

Functional MRI

  • Zhang Y, Kim JH, Brang D, Liu Z, "Naturalistic stimuli: a paradigm for multi-scale functional characterization of the human brain," Current Opinion in Biomedical Engineering, 19:100298, 2021.
  • Marussich L, Lu K-H, Wen H, Liu Z, "Mapping white-matter functional organization at rest and during naturalistic visual perception," NeuroImage, 146: 1128-1141, 2017.
  • Liu Z., de Zwart J.A., van Gelderen P., Duan Q., Chang C. Duyn J.H., “Neuroelectrical decomposition of spontaneous brain activity patterns measured with functional magnetic resonance imaging,” Cerebral Cortex, 24(11): 3080-3089, 2014.
  • Chang C., Liu Z., Chen M.C., Liu X., Duyn J.H., “EEG correlates of time-varying BOLD functional connectivity,” NeuroImage, 15(72): 227-236, 2013.
  • Liu Z., de Zwart J.A., Yao B., van Gelderen P., Kuo L., Duyn J.H., “Finding thalamic BOLD correlates to posterior alpha EEG,” NeuroImage, 63(3): 1060-1069, 2012.
  • Liu Z., Zhang N., Rios C., Yang L., Chen W., He B., “Linear and nonlinear relationships between visual stimuli, EEG and BOLD fMRI signals,” NeuroImage, 50(3): 1054-1066, 2010. 

Neuromodulation

  • Cao J, Wang X, Powley TL, Liu Z, "Gastric neurons in the nucleus tractus solitarius are selective to the orientation of gastric electrical stimulation," Journal of Neural Engineering, 2021.
  • Cao J, Lu K-H, Oleson ST, Phillips RJ, Jaffey DM, Hendren CL, Powley TL, Liu Z, "Gastric stimulation drives fast BOLD responses of neural origin," NeuroImage, 197: 200-211, 2019.
  • Lu K-H, Cao J, Oleson ST, Ward MP, Phillips RL, Powley TL, Liu Z, “Vagus nerve stimulation promotes gastric emptying by increasing pyloric opening measured with magnetic resonance imaging,” Neurogastroenterology and Motility, 30(10): e13380, 2018.
  • Cao J, Lu K-H, Powley TL, Liu Z, “Vagal nerve stimulation triggers widespread responses and alters large-scale functional connectivity in the rat brain,” PLoS ONE, 12(2): e0189518, 2017.

Neurophysiology

  • He B., Sobrabpour A., Brown E., Liu Z., “Electrophysiological source imaging: a non-invasive window to brain dynamics,” Annual Review of Biomedical Engineering, 20: 171-196, 2018.
  • Wen H, Liu Z, “Broadband electrophysiological dynamics contribute to global resting-state fMRI signal,” Journal of Neuroscience, 36(22): 6030-6040, 2016.
  • Wen H, Liu Z, "Separating fractal and oscillatory components in the power spectrum of neurophysiological signal," Brain Topography, 29(1): 13-26, 2016.
  • Liu Z., Fukunaga M, de Zwart J.A., Duyn J.H., “Large-scale spontaneous fluctuations and correlations in brain electrical activity observed with magnetoencephalography,” NeuroImage, 51(1): 102-111, 2010.
  • Liu Z., He B., “FMRI-EEG integrated cortical source imaging by use of time-variant spatial constraints,” NeuroImage, 39(3): 1198-1214, 2008.