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) Embodied Perception-Metacognition Interaction. Dr. Lai earned her Ph.D. in Electrical and Control Engineering from National Chiao Tung University, Taiwan, in 2011. From 2012 to 2015, she conducted postdoctoral research at the University of North Carolina at Chapel Hill and Chang Gung University. Between 2015 and 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 managing the 7T Magnetic Resonance Brain Imaging Center and driving its technological innovation. In November 2024, she joined the Guangdong Institute of Intelligent Science and Technology. Dr. Lai is actively involved in academic positions service. She serves as a committee member of 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 on the editorial boards for Psychoradiology, Scientific Reports, and three Frontiers journals. 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 and holds 11 invention patents, including one US patent. She currently holds an H-index of 30. 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)
Our research group focuses on the innovation and application of brain-machine fusion technology. By integrating disciplines across biology, medicine, engineering, and information science, the group is developing a multimodal brain-machine fusion technology that combine optical, acoustic, electrical, and magnetic modalities. The lab also constructs multimodal data integration algorithms to systematically investigate the spatiotemporal dynamics of cognitive neural networks across multiple hierarchical levels. Emphasizing both technological innovation and theoretical advancement, the group is committed to advancing cutting-edge research in brain science and facilitating its clinical translation.
Main Research Direction:
1. Brain-Machine Fusion Technology
· Development of multimodal-compatible devices and builds a brain-machine fusion platform integrating optical, acoustic, electrical, and magnetic modalities.
· Development of multimodal data fusion algorithms and implementation of bidirectional brain-machine fusion technology, enhancing insights into brain mechanisms and disease pathology.
2. Embodied Perception-Metacognition Interaction
· We combine fMRI, electrophysiology, mixed-reality, and neuromodulation to investigate the spatiotemporal dynamics of brain networks across perception, cognition, and metacognition.
· We explore how neuromodulation influences embodied metacognitive processes, providing insights for cognitive enhancement and intervention strategies.
The laboratory is distinguished by its commitment to interdisciplinary, collaborative innovation. It strives to drive breakthroughs in brain-machine fusion technologies and to provide strategic support for theoretical advancement and translational applications in brain science and brain-inspired intelligence.
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)
