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Li Rui
Associate Professor
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Mailing Address:  Institute of Psychology, Chinese Academy of Sciences, 16 Lincui Road, Chaoyang District, Beijing, China
Email:  lir@psych.ac.cn
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Resume:

2014.3 –         Associate professor, Institute of Psychology, Chinese Academy of Sciences
2011.7 – 2014.2  Assistant professor, Institute of Psychology, Chinese Academy of Sciences
2008 – 2011      PhD,State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
2006 – 2008      MS,  School of Information Science and Technology, Beijing Normal University
2001 – 2005      BE, Liaocheng University, Liaocheng, Shandong, China
Research Interests:

1. Computational methods in neuroimaging analysis
2. Brain network and its association with cognitive aging and disorders
3. The early detection and intervention of mental disorders in older adults
Community service:
Achievements:
Selected Publications:

1.Li R.*, Zhang J., Wu X., Wen X., Han B., 2020. Brain-wide resting-state connectivity regulation by the hippocampus and medial prefrontal cortex is associated with fluid intelligence. Brain Structure and Function 225, 1587-1600

2.Li R., Zhu X., Zheng Z., Wang P., Li J. 2020. Subjective well-being is associated with the functional connectivity network of the dorsal anterior insula. Neuropsychologia 141:107393

3.Li R., Wan W., Li J., 2019. KIBRA polymorphism modulates gray matter volume to influence cognitive ability in the elderly. Brain Imaging and Behavior 14, 1388–1394

4.Li R., Zhang S., Yin S., Ren W., He R., Li J., 2018. The Fronto-Insular Cortex Causally Mediates the Default-Mode and Central-Executive Networks to Contribute to Individual Cognitive Performance in Healthy Elderly. Human Brain Mapping 39:4302–4311

5.Li R., Yin S., Zhu X., Ren W., Yu J., Wang P., Zheng Z., Niu Y.-N., Huang X., Li J., 2017. Linking Inter-Individual Variability in Functional Brain Connectivity to Cognitive Ability in Elderly Individuals. Frontiers in Aging Neuroscience 9:385

6.Wu X., Li Q., Yu X., Chen K., Fleisher A., Guo X., Zhang J., Reiman E., Yao L., Li R.*, 2016. A Triple Network Connectivity Study of Large-Scale Brain Systems in Cognitively Normal APOE4 Carriers. Frontiers in Aging Neuroscience 8, 231

7.Yin S., Zhu X., He R., Li R.*, Li J.*, 2015. Spontaneous activity in the precuneus predicts individual differences in verbal fluency in cognitively normal elderly. Neuropsychology 29, 961-970

8.Ren W. #, Li R. #, Zheng Z., Li J., 2015. Neural correlates of associative memory in the elderly: A resting-state functional MRI study. BioMed Research International, 129180

9.Zheng Z., Zhu X., Yin S., Wang B., Niu Y., Huang X., Li R.*, Li J.*, 2015. Combined cognitive-psychological-physical intervention induces reorganization of intrinsic functional brain architecture in older adults. Neural Plasticity 2015, 713104

10.Wu X., Yu X., Yao L., Li R.*, 2014. Bayesian network analysis revealed the connectivity difference of the default mode network from the resting-state to task-state. Frontiers in Computational Neuroscience 8, 118

11.Zhu X.#, Li R.#, Wang P., Li J., 2014. Aberrant functional connectivity of the hippocampus in older adults with subthreshold depression. Psych Journal 3, 245-253

12.Li R., Zhu X., Yin S., Niu Y., Zheng Z., Huang X., Wang B., Li J., 2014. Multimodal intervention in older adults improves resting-state functional connectivity between the medial prefrontal cortex and medial temporal lobe. Frontiers in Aging Neuroscience 6, 39

13.Li R., Ma Z., Yu J., He Y., Li J., 2014. Altered local activity and functional connectivity of the anterior cingulate cortex in elderly individuals with subthreshold depression. Psychiatry Research: Neuroimaging 222, 29-36

14.Li R., Yu J., Zhang S., Bao F., Wang P., Huang X., Li J., 2013. Bayesian network analysis reveals alterations to default mode network connectivity in individuals at risk for Alzheimer's disease. PLoS One 8, e82104

15.Li R., Wu X., Chen K., Fleisher A.S., Reiman E.M., Yao L., 2013. Alterations of directional connectivity among resting-state networks in Alzheimer's disease. American Journal of Neuroradiology 34, 340-345

16.Li J., Li R., Chen K., Yao L., Wu X., 2012. Temporal and instantaneous connectivity of default mode network estimated using Gaussian Bayesian network frameworks. Neuroscience Letters 513, 62-66

17.Li R., Wu X., Fleisher A.S., Reiman E.M., Chen K., Yao L., 2012. Attention-related networks in Alzheimer's disease: A resting functional MRI study. Human Brain Mapping 33, 1076-1088

18.Li R., Chen K., Fleisher A.S., Reiman E.M., Yao L., Wu X., 2011. Large-scale directional connections among multi resting-state neural networks in human brain: A functional MRI and Bayesian network modeling study. Neuroimage 56, 1035-1042

19.Wu X., Li R., Fleisher A.S., Reiman E.M., Guan X., Zhang Y., Chen K., Yao L., 2011. Altered default mode network connectivity in Alzheimer's disease- A resting functional MRI and Bayesian network study. Human Brain Mapping 32, 1868-1881

20.Li R., Chen K., Zhang N., Fleisher A.S., Yao L., Wu X., 2009. Effective connectivity analysis of default mode network based on the Bayesian network learning approach. Proceedings of SPIE Medical Imaging 7262, 72621W-1-2621W-10
Grants:

1. National Science Foundation of China (31200847): The cognitive neural mechanism of heterogeneity in aging: the studies based on resting-state functional MRI,2013-2015
2. Open-project of National Key Laboratory of Cognitive Neuroscience and Learning (CNLYB1213): The expression of brain network in heterogeneity of cognitive aging,2013-2014
3. Scientific Foundation of Institute of Psychology, Chinese Academy of Sciences (Y1CX251005): Brain network study of explicit and implicit memory mechanisms in Alzheimer disease and its application in the early detection, 2011-2013