State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University

Faculty

Current Faculty

Shuyu Li Ph.D.Neuroimaging computing and applications shuyuli#bnu.edu.cn

Educational Experience

1998-2003: Institute of biophysics, Chinese Academy of Sciences

1993-1998: Department of biomedical engineering, Capital Medical University

Professional Experience

2021- 　: State Key Lab of Cognition Neuroscience & Learning, Beijing Normal University, Professor

2008-2021: School of biological science & medical engineering, Beihang University, Associate professor, Professor

2005-2008: Department of bioengineering, Beihang Univeristy, Assistant professor

2003-2005: National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Postdoc

Research Description

My research focuses on the development of MRI image analysis methods for understanding morphological changes of brain structures under different conditions that can help to recognize the onset of a disease, as well as abnormal developmental and aging processes. My current research addresses the development of cortical and subcortical morphology during childhood and adolescence.

Publications

1. Fu ZR, Zhao MY, He YR, Wang XT, Lu JD, Li SX, Li X, Kang GX, Han Y*, Li SY*. Divergent Connectivity Changes in Grey Matter Structural Covariance Networks in Subjective Cognitive Decline, Amnestic Mild Cognitive Impairment, and Alzheimer’s Disease. Front. Aging Neurosci., 2021.

2. Jin LM, Ma BQ, Zeng DB, Han Y*, Li SY*. Feature Level-Based Group Lasso Method for Amnestic Mild Cognitive Impairment Diagnosis. Computer Methods and Programs in Biomedicine, 2021.

3. Zhao K, Zheng Q, Che TT, Martin D, Li QL, Ding YH, Zheng YJ, Liu Y*, Li SY*. Regional radiomics similarity networks (R2SNs) in the human brain: reproducibility, small-world properties and a biological basis. Network Neuroscience, 2021, https://doi.org/10.1162/netn_a_00200

4. Wang XT, Li QL, Zhao Y, He YR, Ma BQ, Fu ZR, Li SY*. Decomposition of individual-specific and individual-shared components from resting-state functional connectivity using a multi-task machine learning method. NeuroImage, 2021, vol.238, 118252.

5. Zeng DB , Li QL , Ma BQ , Li SY*. Hippocampus Segmentation for Preterm and Aging Brains Using 3D Densely Connected Fully Convolutional Networks. IEEE Access, 2020, PP(99):1-1.

6. Li QL, Wang XT, Wang SY, Xie YQ, Xie YC, Li SY*. More Flexible Integration of Functional Systems After Musical Training in Young Adults. IEEE Transactions on Neural Systems and Rehabilitation Engineering. 2020 Apr;28(4):817-824.

7. Zhao Y, Ma BQ, Jiang PB, Zeng DB, Wang XT and Li SY*, Prediction of Alzheimer's Disease Progression with Multi-Information Generative Adversarial Network. IEEE Journal of Biomedical and Health Informatics. 2020.

8. Ma BQ, Zhao Y., Yang YJ, Zhang XH, Dong XX, Zeng DB, Ma SY, Li SY*. MRI image synthesis with dual discriminator adversarial learning and difficulty-aware attention mechanism for hippocampal subfields segmentation. Computerized Medical Imaging and Graphics. 2020.

9. Guo PF, Li QL, Wang XT, Li XW, Wang SY, Xie YQ, Xie YC, Fu ZR, Z XH, Li SY*. Structural Covariance Changes of Anterior and Posterior Hippocampus During Musical Training in Young Adults. Frontiers in Neuroanatomy, 2020.

10. Fu ZR, Altered topologic of morphological brain networks in individuals with subjective cognitive decline. Journal of Alzheimers Disease, accepted, 2020.

11. Li QL, Wang XT, Wang SY, Xie YQ, Li XW, Xie YC, Li SY*. Dynamic reconfiguration of the functional brain network after musical training in young adults. Brain Struct Funct. 2019, 224(5):1781-1795.

12. Jiang PB, Wang XT, Li QL, Jin LM, Li SY*. Correlation-aware Sparse and Low-rank Constrained Multi-task Learning for Longitudinal Analysis of Alzheimer's Disease. IEEE J Biomed Health Inform. 2019, 23(4): 1450-1456.

13. Zhao WN, Wang XT, Yin CH, He MF, Li SY*, Han Y*. Trajectories of the Hippocampal Subfields Atrophy in the Alzheimer's Disease: A Structural Imaging Study. Frontiers in neuroinformatics. 2019,13

14. Yu KX, Wang XT, Li QL, Zhang XH, Li XW, Li SY*. Individual Morphological Brain Network Construction Based on Multivariate Euclidean Distances Between Brain Regions. Front. Hum. Neurosci., 2018, 12:204. doi: 10.3389/fnhum.2018.00204.

15. Wang XT, Yu Y, Zhao WN, Li QL, Li XW, Li SY*, Yin C*, Han Y*. Altered Whole-Brain Structural Covariance of the Hippocampal Subfields in Subcortical Vascular Mild Cognitive Impairment and Amnestic Mild Cognitive Impairment Patients. Front Neurol. 2018, 9:342. doi: 10.3389/fneur.2018.00342.

16. Li XW, Li QL, Wang XT, Li DY, Li SY*. Differential Age-Related Changes in Structural Covariance Networks of Human Anterior and Posterior Hippocampus. Front. Physiol. 2018, 9:518. doi: 10.3389/fphys.2018.00518.

17. Li QL, Wang XT, Wang SY, Xie YQ, Li XW, Xie YC, Li SY*. Musical training induces functional and structural auditory-motor network plasticity in young adults. Hum Brain Mapp. 2018, 39(5): 2098-2110.

18. Li XW, Li DY, Li QL, Li YX, Li KC, Li SY*, Han Y. Hippocampal subfield volumetry in patients with subcortical vascular mild cognitive impairment. Scientific reports 2016, 6:20873.

19. Li QL, Li XW, Wang XT, Li YX, Li KC, Yu Y, Yin CH, Li SY*, Han Y. Topological properties of large-scale cortical networks based on multiple morphological features in amnestic mild cognitive impairment. Neural Plasticity 2016, http://dx.doi.org/10.1155/2016/3462309.

20. Li SY, Xie YQ, Li H, Li XW, Zhang ZX, Zhao QL, Xie S, Gong GL*. Morphological changes of amygdala in Turner Syndrome patients. CNS Neurosci Ther 2016, 22:194-199.

21. Li XW, Li QL, Li SY*, Li DY*. Local manifold learning for multiatlas segmentation: application to hippocampal segmentation in healthy population and Alzheimer's disease. CNS Neurosci Ther 2015, 21(10): 826-836.

22. Li SY*, Wang SY, Li XW, Li QL, Li XB. Abnormal surface morphology of the central sulcus in children with attention-deficit/hyperactivity disorder. Front Neuroanat 2015. V9, article 14.

23. Li SY*, Yuan XK, Pu F, Li DY, Fan YB, Wu LY, Chao W, Chen N, He Y, Han Y*. Abnormal Changes of Multidimensional Surface Features Using Multivariate Pattern Classification in Amnestic Mild Cognitive Impairment Patients. J Neurosci 2014: 34(32):10541-10553.

24. Li XW, Cao QJ, Pu F, Li DY, Fan YB, An L, Wang P, Wu ZM, Sun L, Li SY*, Wang YF*. Abnormalities of structural covariance networks in drug-naïve boys with attention deficit hyperactivity disorder. Psychiatry Research: Neuroimaging 2015, 231(3): 273-278.

25. Pu F, Sun SC, Wang L, Li Y, Yu HM, Yang Y, Zhao YY, Li SY*. Investigation of key factors affecting the balance function of older adults. Aging Clin Exp Res 2015, 27(2): 139-147.

26. Li XW, Pu F, Fan YB, Niu HJ, Li SY*, Li DY*. Age-related changes in brain structural covariance networks. Front Hum Neurosci 2013, 7(98). doi:10.3389/fnhum.2013.00098.

27. Pu F, Xu LQ, Li DY, Fan YB, Niu HJ, Li SY*. Comparison of two nonlinear registration techniques to investigate brain atrophy patterns in normal aging. Journal of neuroradiology 2013, 40(5):326-34.

28. Li SY, Wang YN, Xu PF, Pu F, Li DY, Fan YB, Gong GL, Luo YJ. Surface morphology of amygdale is associated with trait anxiety. PLos One 2012, 7(10):e47817.

29. Li SY, Xia MR, Pu F, Li DY, Fan YB, Niu HJ, Pei BQ, He Y. Age-related changes in the surface morphology of the central sulcus. NeuroImage, 2011, 58(2):381-390.