Hsin-Yi Lai
Ph.D., Principal Investigator
Email: laixinyi@@gdiist.cn
Personal Profile:
Dr. Hsin-Yi Lai is a principal investigator at the Guangdong Institute of Intelligent Science and Technology and the head of the Laboratory of Magnetic Resonance Imaging and Modulation for Cognitive Neural Networks. Her research focuses on: 1) Brain-Machine Fusion Technology, and 2) Advanced Brain Functions in Perception, Cognition, and Metacognition. Dr. Lai earned her Ph.D. in Electrical and Control Engineering from National Chiao Tung University, Taiwan, in 2011. She completed her postdoctoral research at the University of North Carolina at Chapel Hill and Chang Gung University, from 2012 to 2015. From 2015 to 2024, she served as a PI Professor at the I Interdisciplinary Institute of Neuroscience and Technology at Zhejiang University, where she was also responsible for the operation, management, and innovative technology development of the 7T Magnetic Resonance Brain Imaging Center. In November 2024, she joined the Guangdong Institute of Intelligent Science and Technology. Dr. Lai holds several academic positions, including committee memberships in the Chinese Society for Neuroscience (Basic and Clinical Neurology Subcommittee), the Chinese Society for Cognitive Science (Neuroimaging and Psychiatry Subcommittee), and the Zhejiang Neuroscience Society. She is also the Associate Editor for the ACS Chemical Neuroscience and serves as an editorial board member for Psychoradiology, Scientific Reports, and three Frontiers journals. To date, Dr. Lai has led or participated in more than ten national and provincial-level research projects. She has published over 60 papers in SCI journals, holds 10 invention patents (including one in the United States), and has an H-index of 28. Her achievements have been recognized with prestigious awards, including the "Outstanding Young Scholar in Engineering Frontiers" from the Chinese Academy of Engineering and the "Silver Award for Invention and Entrepreneurship" from the China Invention Association.
Laboratory of Magnetic Resonance Imaging and Modulation for Cognitive Neural Networks (MMCNN)
The research group focuses on the innovation and application of brain-machine fusion technology. By integrating and innovating across multiple disciplines—including biology, medicine, engineering, and information sciences—the group is committed to developing multimodal brain-machine fusion technologies that combine optical, acoustic, electrical, and magnetic modalities. Additionally, the group aims to establish multimodal data fusion algorithms to systematically investigate the spatiotemporal dynamic evolution of cognitive neural networks at various levels. Key .
Research directions include:
1. Brain-Machine Fusion Technology
· Development of multimodal-compatible key devices integrating optical, acoustic, electrical, and magnetic modalities.
· Establishment of multimodal data fusion algorithms and achievement of bidirectional integration of brain-machine fusion technology.
· Investigation of brain function mechanisms and the pathological processes of brain diseases.
2. Brain Functions in Perception, Cognition, and Metacognition
· Systematic exploration of the spatiotemporal dynamics of brain functional networks across different stages of perception, cognition, and metacognition using multimodal magnetic resonance imaging techniques.
· Investigation of the mechanisms, long-term effects, and safety of neuromodulation in cognitive enhancement.
Representative publications:
[1] Qu SX, Shi SH, Quan ZY, GaoY, Wang MM, Wang, YM, Pan G, Lai HY*, Roe AW*, Zhang XT* (2023 Aug) Design and Application of a multimodality-compatible 1Tx/6Rx RF coil for monkey brain MRI at 7T, Neuroimage, 276:120185.
[2] Tang WX#, Shen T#, Huang YQ, Zhu WJ, Zhu C, Ma JH, Wang YQ, Zhao JP, Li T*, Lai HY* (2023 Mar) Exploring structural and functional alterations in drug-naïve obsessive-compulsive disorder patients: an ultrahigh field multimodal MRI study, Asian J Psychiatr, 81:103431.
[3] Shen T#, Yue YM#, Ba F, He TT, Tang XC, Yu YX, Lv W*, Zhang BR*, Lai HY* (2022 Dec) Diffusion along perivascular spaces as marker for impairment of glymphatic system in Parkinson’s disease, npj Parkinson Dis, 8(1):174.
[4] Shen T, Yue YM, Zhao S, Xie JJ, Chen YX, Tian J, Lv W, Lo Cy, Hsu YC, Kober T, Zhang BR*, Lai HY* (2021 Feb) The role of brain perivascular space burden in early-stage Parkinson’s disease, npj Parkinson Dis, 7, 12.
[5] Shen T*, Yue YM, He TT, Huang C, Qu BY, Lv W, Lai HY* (2021 Feb) The association between the gut microbiota and Parkinson's disease, a meta-analysis, Front Aging Neurosci, 13:636545. (IF: 5.702, 80/ 272in Neurosiences) High Cited Paper
[6] Chen BW#, Yang SH#, Lo YC, Wang CF, Wang HL, Hsu CY, Kuo YT, Chen JC, Lin SH, Pan HC, Lee SW, Yu X, Qu BY, Kuo CH, Chen YY*, Lai HY* (2020 Sep) Enhancement of hippocampal spatial decoding using a dynamic q-learning method with a relative reward using theta phase precession, Int J Neural Syst, 30(9):2050048.
[7] Lai HY, Younce JR, Albaugh DL, Kao YCJ, Shih YYI* (2014 Jan) Functional MRI reveals frequency-dependent responses during deep brain stimulation at the subthalamic nucleus or internal globus pallidus, NeuroImage, 84:11-18.
[8] Lai HY, Liao LD, Lin CT, Shih YY, Chen YY*, Tsang S, Chang JY (2012 Jun) Design, simulation and experimental validation of a novel flexible neural probe for deep brain stimulation and multichannel recording, J Neural Eng, 9:036001. (IF: 5.379, 42/96 in Engineering, Biomedical category)
