Bio

Dr. Chuyi Cui is a postdoctoral scholar in the Human Motor Control and Neuromodulation Lab at Stanford University School of Medicine. She earned her Ph.D. in Biomechanics and Motor Control, with a minor in Gerontology, from Purdue University. Her doctoral research focused on understanding gait control and stability in healthy aging, through comprehensive investigations of the kinematics and kinetics of adaptive locomotion in young and older adults. At Stanford, her postdoctoral research expanded to clinical populations, aiming to uncover the mechanisms underlying gait dysfunction in neurodegenerative diseases. Under the guidance of Dr. Helen Bronte-Stewart, she contributes to clinical trials on closed-loop deep brain stimulation (DBS) for Parkinson's Disease, utilizing advanced neuromodulation technology to develop and evaluate DBS therapies that adapt in real time to patients' motor fluctuations and alleviate gait symptoms.

Stanford Advisors

Contact

  • Academic
    chuyicui@stanford.edu
    University - Scholar Department: Adult Neurology Position: Postdoctoral Scholar

Additional Info

All Publications

  • Step by adaptive step: How younger and older adults navigate obstacles. Gait & posture Kulkarni, A., Cui, C., Rietdyk, S., Ambike, S. 2025; 120: 192-198

    Abstract

    Younger adults, while approaching and crossing an obstacle, destabilize step length over several steps to ensure accurate foot placement around the obstacle and thereby avoid a trip. Destabilized step length has two potential effects: it facilitates corrections in foot placements to achieve the required accuracy, but it may also impair balance by perturbing the relation between the base of support and the motion or state of the whole-body center of mass. Therefore, destabilized step length in younger adults reflects a greater concern for tripping versus small variations in step length.Do healthy older adults demonstrate greater step length destabilization than younger adults while approaching and crossing stationary obstacles?Healthy younger and older adults approached and crossed a stationary visible obstacle multiple times. The across-trial foot placement data were analyzed using the uncontrolled manifold method to obtain the inter-step covariance (ISCz) index for several approach steps and the obstacle crossing step. Higher index value indicates higher step length stability and vice-versa.Younger and older adults destabilized step length (ISCz index reduced) while approaching and crossing the obstacle (p < .0001). The ISCz index was 14.5 % lower for older adults indicating that they destabilized step length more than younger adults (p = .02). Given the higher costs of a trip-induced fall, the pattern likely represents a rational adaptation by the older adults to avoid tripping. This pattern in the ISCz index could be used to assess the health of the neuromuscular control system in clinical populations.

    View details for DOI 10.1016/j.gaitpost.2025.04.014

    View details for PubMedID 40262367

  • Closing the loop in DBS: A data-driven approach. Parkinsonism & related disorders Acharyya, P., Daley, K. W., Choi, J. W., Wilkins, K. B., Karjagi, S., Cui, C., Seo, G., Abay, A. K., Bronte-Stewart, H. M. 2025: 107348

    Abstract

    Deep brain stimulation (DBS) has transformed the treatment of movement disorders like Parkinson's Disease (PD). Innovations in DBS technology and experimentation have fostered adaptive DBS (aDBS), which employs a closed-loop system that senses physiological biomarkers to inform precise neuromodulation and personalized therapy. This review analyzes several promising advances in aDBS, including biomarker detection, control policies, mechanisms of efficacy, and a data-driven approach using artificial intelligence to decode motor states from neural signals. Investigations into data-driven approaches have expanded biomarker detection beyond subcortical beta oscillations, leveraging other neural and kinematic signals. Future aDBS systems that accommodate multi-modal inputs have the potential to bolster therapeutic efficacy and address symptoms not addressed by beta-driven aDBS. Continuing investigation is necessary to address existing technical and computational challenges for further clinical translation.

    View details for DOI 10.1016/j.parkreldis.2025.107348

    View details for PubMedID 40037940

  • N2GNet tracks gait performance from subthalamic neural signals in Parkinson's disease. NPJ digital medicine Choi, J. W., Cui, C., Wilkins, K. B., Bronte-Stewart, H. M. 2025; 8 (1): 7

    Abstract

    Adaptive deep brain stimulation (DBS) provides individualized therapy for people with Parkinson's disease (PWP) by adjusting the stimulation in real-time using neural signals that reflect their motor state. Current algorithms, however, utilize condensed and manually selected neural features which may result in a less robust and biased therapy. In this study, we propose Neural-to-Gait Neural network (N2GNet), a novel deep learning-based regression model capable of tracking real-time gait performance from subthalamic nucleus local field potentials (STN LFPs). The LFP data were acquired when eighteen PWP performed stepping in place, and the ground reaction forces were measured to track their weight shifts representing gait performance. By exhibiting a stronger correlation with weight shifts compared to the higher-correlation beta power from the two leads and outperforming other evaluated model designs, N2GNet effectively leverages a comprehensive frequency band, not limited to the beta range, to track gait performance solely from STN LFPs.

    View details for DOI 10.1038/s41746-024-01364-6

    View details for PubMedID 39755754

    View details for PubMedCentralID 1737677

https://orcid.org/0000-0002-0437-323X