Bio

Sa Zhou completed her Ph.D. in Biomedical Engineering at The Hong Kong Polytechnic University, under the supervision of Dr. Xiaoling Hu and Prof. Yongping Zheng. Her Ph.D. research focused on quantitative evaluation and targeted therapeutics for sensory-motor rehabilitation in stroke patients. She developed strong interests in developing closed-loop brain-computer interface (BCI)-driven neuromodulation and robotic systems, designing neuro-behavioral measurements, and understanding functional connectivity in brain networks based on multimodal neurophysiological signals. At Stanford, Sa will contribute her interdisciplinary expertise to the field of cognitive enhancement to prevent cognitive decline and brain aging in the elderly at risk for Alzheimer’s disease (AD) or AD related dementia (AD/ADRD). Outside of the lab, you can find Sa engaging in strength training deadlifting/squatting/running in the gym to enhance her own sensory/motor/cognitive functions.

Professional Education

  • Doctor of Philosophy, Hong Kong Polytechnic University (2023)
  • PhD, The Hong Kong Polytechnic University, Biomedical Engineering (2023)

Stanford Advisors

Contact

  • Academic
    sazhou@stanford.edu
    University - Scholar Department: Psychiatry and Behavioral Sciences Position: Postdoctoral Scholar

Additional Info

  • Mail Code: 5717

All Publications

  • Profiles of brain topology for dual-functional stability in old age. GeroScience Zhou, S., Anthony, M., Adeli, E., Lin, F. V. 2024

    Abstract

    Dual-functional stability (DFS) in cognitive and physical abilities is important for successful aging. This study examines the brain topology profiles that underpin high DFS in older adults by testing two hypotheses: (1) older adults with high DFS would exhibit a unique brain organization that preserves their physical and cognitive functions across various tasks, and (2) any individuals with this distinct brain topology would consistently show high DFS. We analyzed two cohorts of cognitively and physically healthy older adults from the UK (Cam-CAN, n = 79) and the US (CF, n = 48) using neuroimaging data and a combination of cognitive and physical tasks. Variability in DFS was characterized using k-mean clustering for intra-individual variability (IIV) in cognitive and physical tasks. Graph theory analyses of diffusion tensor imaging connectomes were used to assess brain network segregation and integration through clustering coefficients (CCs) and shortest path lengths (PLs). Using support vector machine and regression, brain topology features, derived from PLs + CCs, differentiated the high DFS subgroup from low and mix DFS subgroups with accuracies of 65.82% and 84.78% in Cam-CAN and CF samples, respectively, which predicted cross-task DFS score in CF samples at 58.06% and 70.53% for cognitive and physical stability, respectively. Results showed distinctive neural correlates associated with high DFS, notably varying regional brain segregation and integration within critical areas such as the insula, frontal pole, and temporal pole. The identified brain topology profiles suggest a distinctive neural basis for DFS, a trait indicative of successful aging. These insights offer a foundation for future research to explore targeted interventions that could enhance cognitive and physical resilience in older adults, promoting a healthier and more functional lifespan.

    View details for DOI 10.1007/s11357-024-01396-6

    View details for PubMedID 39432149

    View details for PubMedCentralID 7058488