Research Summary

Our research aims to enhance our knowledge of the neural processes behind typical and atypical human behaviors through a combination of multimodal brain imaging techniques and computational methods. Specifically, we are interested in:

• Development of integrated multimodal brain imaging algorithms (fNIRS, DOT, EEG, fMRI) for advanced brain imaging with high spatiotemporal resolution
• Establish computational models to elucidate the brain-physiology-behavior association of human brain disorders (neuropsychiatric, neurodegenerative, and neurodevelopmental disorders)
• Static and dynamic human brain network analyses to decode the process of human behaviors (social interaction, team collaboration)

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG suffers from poor spatial resolution while praesenting high temporal resolution. In contrast, fNIRS offers better spatial resolution tough it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that, both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal EEG-fNIRS integration analysis approach. The main goal of this research is to develop and implement multimodal EEG/fNIRS integration methods for brain research.

Leveraging the high spatial resolution of fNIRS and high temporal resolution of EEG, we have explored feasible approaches to study the human brain dynamics related to various clinically-seen brain disorders. We developed an fNIRS-informed EEG source localization approach to investigate the brain network alterations induced by Alzheimer’s disease (Li, R., et al. 2019). We then demonstrated how the proposed fNIRS-informed EEG source localization approach can be used to characterize brain plasticity during longitudinal post-stroke rehabilitation (Li, R., et al. 2020). On the other hand, we also proposed a framework showing how EEG signals may help to enhance the fNIRS-based general linear model analysis (Li, R., et al. 2020).

• Li, Rihui, et al. “Dynamic cortical connectivity alterations associated with Alzheimer’s disease: An EEG and fNIRS integration study.” NeuroImage: Clinical 21 (2019): 101622.
• Li, Rihui, et al. “Multimodal neuroimaging using concurrent EEG/fNIRS for poststroke recovery assessment: an exploratory study.” Neurorehabilitation and Neural Repair 34.12 (2020): 1099-1110.
• Li, Rihui, et al. “Enhancing fNIRS analysis using EEG rhythmic signatures: an EEG-informed fNIRS analysis study.” IEEE Transactions on Biomedical Engineering 67.10 (2020): 2789-2797.