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Jing Sui, Professor 

Brainnetome Center and National Laboratory of Pattern Recognition  

Institute of Automation, Chinese Academy of Sciences 

Centre for Excellence in Brain Science and Intelligence Technology  

University of Chinese Academy of Sciences, China  

E-mail: jing.sui@nlpr.ia.ac.cn   

Office: 010-82544518 


Brief History 

  • 2013 - present Professor, Brainnetome Center, Institute of Automation 
  • 2012 - 2013 Assistant Professor, The Mind Research Network (MRN), USA 
  • 2010 - 2012 Research Scientist, The Mind Research Network , Albuquerque, NM, USA 
  • 2007 - 2009 Postdoctoral Fellow, The Mind Research Network, Albuquerque, NM, USA 


  • 2007 PhD Optical Engineering (in major of Image/Signal Processing), Beijing Institute of Technology (BIT), Beijing, China 
  • 2002 BS Optical Technology and Photoelectric Instrumental, Beijing Institute of Technology 
  • 2002 Minor Computer Science, Beijing Institute of Technology 

Research Interest

  • Imaging Biomarker /Neuromarker Identification. Individualized Prediction  
  • Multivariate Brain Imaging Data Analysis/ Multimodal Fusion (fMRI,dMRI, sMRI, EEG)  
  • Research on Mental Diseases and Cognitive function, including schizophrenia, depression, bipolar disorder, Autism, ADHD, Alzheimer
  • Machine Learning. Classification. Prediction
  • Big data analysis. Data Mining. Independent Component Analysis

Selected Peer-reviewed Publications 

  • 1. Sui J*, Pearlson GD, Du Y, Yu Q, Thomas JR, Chen J, Jiang T, Bustillo J, & Calhoun VD. 2015. In Search of Multimodal Neuroimaging Biomarkers of Cognitive Deficits in Schizophrenia. Biological Psychiatry. 78 (11) : 794-804. 
  • 2. Qi S, Yang X, Zhao L, Calhoun VD, Perrone-Bizzozero N, Liu S, Jiang R, Jiang T, Sui J*, Ma X*. 2017. MicroRNA132 associated multimodal neuroimaging patterns in unmedicated major depressive disorder. Brain. In press. 
  • 3. Qi S, Calhoun VD, van Erp TGM, Bustillo J, Damaraju E, Turner JA, Du Y, Yang J, Chen J, Yu Q, Mathalon DH, Ford JM, Voyvodic J, Mueller BA, Belger A, McEwen S, Potkin SG, Preda A, Jiang T, Sui J*. 2017. Multimodal Fusion with Reference: Searching for Joint Neuromarkers of Working Memory Deficits in Schizophrenia. IEEE Trans Med Imag. In press. 
  • 4. Jiang R, Abbott CC, Jiang T, Du Y, Espinoza R, Narr KL, Wade B, Yu Q, Song M, Lin D, Chen J, Jones T, Argyelan M, Petrides G, Sui J* , Calhoun VD (2017): SMRI Biomarkers Predict Electroconvulsive Treatment Outcomes: Accuracy with Independent Data Sets. Neuropsychopharmacology. In press. 
  • 5. He H, Sui J*, Du Y, Yu Q, Lin D, Drevets WC, Savitz JB, Yang J, Victor TA, Calhoun VD. 2017. Co-altered functional networks and brain structure in unmedicated patients with bipolar and major depressive disorders. Brain Struct Funct. 222:4051-4064. 
  • 6. Meng X, Jiang R, Lin D, Bustillo J, Jones T, Chen J, Yu Q, Du Y, Zhang Y, Jiang T, Sui J*, Calhoun VD. Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRI data. NeuroImage 2017;145:218-229. 
  • 7. Arbabshirani MR, Plis S, Sui J, Calhoun VD. Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls. NeuroImage 2017;145:137-165. 
  • 8. Abbott CC, Loo D, Sui J. 2016. Determining Electroconvulsive Therapy Response With Machine Learning. JAMA Psychiatry. 73:545-546. 
  • 9. Calhoun VD*, Sui J 2016. Multimodal Fusion of Brain Imaging Data: A Key to Finding the Missing Link(s) in Complex Mental Illness. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. 1:230-244 
  • 10. Sui J, Calhoun VD. 2015. Multivariate approaches for multimodal fusion of structural and functional brain imaging data. Neuromethods Book: fMRI techniques (2nd edition). Chapter 5. Springer . 
  • 11. Sui J*, Huster R, Yu Q, Judith M. Segall, Vince D Calhoun. 2014. Function-Structure Associations of the Brain: Evidence from Multimodal Connectivity and Covariance Studies. Neuroimage. 102:11-23. 
  • 12. Sui J*, He H, Pearlson GD, Adali T, Yu Q, Clark VP, White T, Mueller BA, Ho BC, Andreasen NC, Calhoun VD. 2013. Three-Way (N-way) Fusion of Brain Imaging Data Based on mCCA+jICA and Its Application to Discriminating Schizophrenia. Neuroimage. 2(66):119-132. 
  • 13.Sui J*, Pearlson GD, Adali T, Caprihan A, Liu J, Yamamoto J, Calhoun VD. 2011. Discriminating Schizophrenia and Bipolar Disorder by Fusing FMRI and DTI in a CCA+ICA Based Model. Neuroimage. 57(7):839-855. 
  • 14. Sui J*,, Adali T, Pearlson GD, Yang H, Sponheim SR, White T, Calhoun VD 2010. A CCA+ICA Based Model for Multi-Task Brain Imaging Data Fusion And Its Application to Schizophrenia. Neuroimage. 51(5):123-134. 
  • 15.Sui J*, Adali T, Pearlson GD, Calhoun VD. 2009. An ICA-based method for the identification of optimal FMRI features and components using combined group-discriminative techniques. Neuroimage 46(1):73-86. 
  • 16. Sui J*, Adali T, Pearlson GD, Clark VP, Calhoun VD. 2009. A method for accurate group difference detection by constraining the mixing coefficients in an ICA framework. Hum Brain Mapping 30(9): 2953-2970.