Overview

At our Neuroimaging Lab, we are dedicated to advancing our understanding of the brain through cutting-edge optical neuroimaging techniques. We develop methodologies for a state-of-the-art optical neuroimaging method, functional near-infrared spectroscopy (fNIRS), to enhance its performance. Our second mission is to unravel the complexities of neural processes and contribute to the development of innovative approaches for diagnosing and treating neurological and psychiatric conditions. We aim to bridge the gap between neuroscience and clinical applications, fostering collaborations that enhance both scientific discovery and patient care.

Information

Academic Interests and Expertise

Post-doctoral Fellow Department of Psychiatry and Behavioral Sciences, Stanford University Stanford, 03/2023 – 06/2024

Post-doctoral Associate Department of Biomedical Engineering, Boston University Boston, 11/2020 – 02/2023

Visiting Scholar Department of Civil Engineering, University at Buffalo, 02/2019-10/2019

Researcher Harvard Medical School, 02/2016-08/2016

Ph.D. Department of Mechanical Engineering, Rensselaer Polytechnic Institute, 08/2015-08/2020

MS in Department of Mechanical Engineering, Beihang University Beijing, China, 08/2010–07/2013

BS in Aircraft Environment and Life Security Engineering, Beihang University Beijing, China, 09/2006–07/2010

Areas of Research Interest
  • Neuroimaging;
  • Computational Neuroimaging;
  • Clinical Neuroimaging;
  • Optical Neuroimaging;
  • Functional near-infrared spectroscopy;
  • Optical Simulation;
  • Neuromodulation;
  • Human Motor Learning;
  • Motor Function Recovery;
  • Machine/Deep Learning;
  • Biostatistics
Areas of Teaching Interest
  • BME480 Bioinstrumentation
Publications

9. Yuanyuan Gao, Rihui Li, Qianheng Ma, Kristi L. Bartholomay, Amy A. Lightbody, and Allan L. Reiss. Aberrant neural activation during inhibitory control in girls with fragile x syndrome. Under Preparation

8. Yuanyuan Gao, Rihui Li, Qianheng Ma, Joseph M. Baker, Stephen Rauch, Robert B. Gunier, Ana M. Mora, Katherine Kogut, Asa Bradman, Brenda Eskenazi, Allan L. Reiss, and Sharon K. Sagiv. Childhood exposure to organophosphate pesticides: Functional connectivity and working memory in adolescents. NeuroToxicology, 103:206–214, 2024 2/5

7. Yuanyuan Gao, Rihui Li, Qianheng Ma, Kristi L. Bartholomay, Amy A. Lightbody, and Allan L. Reiss. Longitudinal changes in functional brain activation and habituation during face processing in fragile x syndrome. Biological Psychiatry, In Press.

6. Yuanyuan Gao, De’Ja Rogers, Alexander von Lühmann, Antonio Ortega-Martinez, David A. Boas, and Meryem Yücel. Short-separation regression incorporated diffuse optical tomography image reconstruction modeling for high-density functional near-infrared spectroscopy. Neurophotonics, 10(2):025007, 2023

5. Yuanyuan Gao, Hanqing Chao, Lora Cavuoto, Pingkun Yan, Uwe Kruger, Jack E. Norfleet, Basiel A. Makled, Steven D. Schwaitzberg, Suvranu De, and Xavier R. Intes. Deep learning-based motion artifact removal in functional near-infrared spectroscopy. Neurophotonics, 9(4):041406, 2022

4. Yuanyuan Gao, Lora Cavuoto, Anirban Dutta, Uwe Kruger, Pingkun Yan, Arun Nemani, Jack E. Norfleet, Basiel A. Makled, Jessica Silvestri, Steven Schwaitzberg, Xavier Intes, and Suvranu De. Decreasing the surgical errors by neurostimulation of primary motor cortex and the associated brain activation via neuroimaging. Frontiers in Neuroscience, 15, 2021

3. Yuanyuan Gao, Pingkun Yan, Uwe Kruger, Lora Cavuoto, Steven Schwaitzberg, Suvranu De, and Xavier Intes. Functional brain imaging reliably predicts bimanual motor skill performance in a standardized surgical task. IEEE Transactions on Biomedical Engineering, 68(7):2058–2066, 2021

2. Yuanyuan Gao, Lora Cavuoto, Steven Schwaitzberg, Jack E. Norfleet, Xavier Intes, and Suvranu De. The effects of transcranial electrical stimulation on human motor functions: A comprehensive review of functional neuroimaging studies. Frontiers in Neuroscience, 14, 2020

1. Yuanyuan Gao, Uwe Kruger, Xavier Intes, Steven Schwaitzberg, and Suvranu De. A machine learning approach to predict surgical learning curves. Surgery, 167(2):321–327, 2020