About Dr Jinglei Lv

Dr. Jinglei Lv’s research interests include building new theories about fundamental neuroscience, developing novel methodology for neuroimage analysis and discovering imaging biomarker for the diagnosis and treatment of mental disorders, such as schizophrenia, dementia and depression.

Dr. Jinglei Lv is an expert not only on structural and functional brain MRI imaging, but also on developing advanced computational and machine learning methods for multi-scale, multi-model image processing and data analysis

Dr. Jinglei Lv has developed a series of machine learning based methodology to map the human brain into a multi-demand and multi-purpose hierarchical system. He investigated the relationship between the brain structure and function based on diffusion and functional MRI, and translated the methods and important findings into clinical research which aims to define biomarkers of neurological/psychiatric disorders, such as dementia, ADHD, schizophrenia and autism.

Students from engineering, computer science, mathematics, physics or neuroscience background are all welcome. An open mind for interdisciplinary research is desired.

Selected publications

Lv, J., Jiang, X., Li, X., Zhu, D., Chen, H., Zhang, T., ... & Zhang, J. (2015). Sparse representation of whole-brain fMRI signals for identification of functional networks. Medical image analysis, 20(1), 112-134.

• _____________________________________________________________________________ Lv, J., Jiang, X., Li, X., Zhu, D., Zhang, S., Zhao, S., ... & Ye, J. (2014). Holistic atlases of functional networks and interactions reveal reciprocal organizational architecture of cortical function. IEEE Transactions on Biomedical Engineering, 62(4), 1120-1131.

• _____________________________________________________________________________ Lv, J., Jiang, X., Li, X., Zhu, D., Zhao, S., Zhang, T., ... & Coles, C. (2015). Assessing effects of prenatal alcohol exposure using group-wise sparse representation of fMRI data. Psychiatry Research: Neuroimaging, 233(2), 254-268.

• _____________________________________________________________________________ Lv, J., Lin, B., Li, Q., Zhang, W., Zhao, Y., Jiang, X., ... & Ye, J. (2017). Task fMRI data analysis based on supervised stochastic coordinate coding. Medical image analysis, 38, 1-16.

• _____________________________________________________________________________ Ren, Y., Nguyen, V. T., Sonkusare, S., Lv, J., Pang, T., Guo, L., ... & Guo, C. C. (2018). Effective connectivity of the anterior hippocampus predicts recollection confidence during natural memory retrieval. Nature communications, 9(1), 1-10.

• _____________________________________________________________________________ Lv, J., Nguyen, V. T., van der Meer, J., Breakspear, M., & Guo, C. C. (2017, September). N-way Decomposition: Towards Linking Concurrent EEG and fMRI Analysis During Natural Stimulus. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 382-389). Springer, Cham.

• _____________________________________________________________________________ Lv, J., Iraji, A., Ge, F., Zhao, S., Hu, X., Zhang, T., ... & Liu, T. (2016, October). Temporal concatenated sparse coding of resting state fMRI data reveal network interaction changes in mTBI. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 46-54). Springer, Cham.

• _____________________________________________________________________________ Zhu, D., Zhang, T., Jiang, X., Hu, X., Chen, H., Yang, N., ... & Liu, T. (2014). Fusing DTI and fMRI data: a survey of methods and applications. NeuroImage, 102, 184-191.

• _____________________________________________________________________________ Makkie, M., Zhao, S., Jiang, X., Lv, J., Zhao, Y., Ge, B., ... & Liu, T. (2015). HAFNI-enabled largescale platform for neuroimaging informatics (HELPNI). Brain informatics, 2(4), 225-238.

• _____________________________________________________________________________ Lv, J., Guo, L., Li, K., Hu, X., Zhu, D., Han, J., & Liu, T. (2011, July). Activated fibers: fiber-centered activation detection in task-based fMRI. In Biennial International Conference on Information Processing in Medical Imaging (pp. 574-587). Springer, Berlin, Heidelberg.