We develop cutting-edge non-invasive and invasive brain imaging and neuromodulation methods.
Magnetic resonance imaging (MRI)
Magnetic resonance imaging (MRI) has millimeter spatial resolution and versatile contrasts. MRI has been extensively used in clinical medicine and neuroscience studies to provide structural, functional, and metabolic information. We devote to advancing the MRI technology in order to improve its spatiotemporal resolution and sensitivity. We aim at developing a tailored combination of receiver coil array, spatial encoding magnetic fields, main magnetic field, pulse sequences, and image reconstruction algorithms in order to optimize MRI in different neuroscience and clinical applications.
Electroencephalography (EEG), magnetoencephalography (MEG), and stereo-EEG (SEEG)
EEG and MEG are methods of studying the neural activities non-invasively using extracranial measurements of magnetic fields and electric potentials, respectively. We continuously develop EEG and MEEG methods in order to better understand how human brain works via studies of MEG/EEG source localization, neuronal oscillations, and multi-modal (EEG/MEG/MRI) integration.
We also develop methods of analyzing SEEG, invasive recording of multple electrodes implanted in medically refractory patients. These data allows for unprecedented spatoitemporal resolution of neuronal activities.
Transcranial magnetic stimulation (TMS)
TMS stimulates neural populations at the cortex via transient and strong magnetic pulses delivied by coils outside the head. We use neuroimaging (MRI/EEG/MEG) and navigation systems to excite and inhibit neural activity in a spatiotemporally accurate way.
Computational neural models
With rich datasets of brain activites characterized by neuronal, hemodynamic, and metabolic measures, we develop computational tools to reveal correlations and causal modulations between brain and behaviors. Specifically, we aim at using advanced machine learning methods to develop models via the combination of large neuroimaging data sets and project-specific data sets with a smaller sample size.