Wu Research

Chengyuan Wu, MD, MSBmE


Name: Chengyuan Wu, MD, MSBmE
Position: Associate Professor, Neurological Surgery & Radiology
Organization: Jefferson Integrated Magnetic Resonance Imaging Center

909 Walnut Street
1st Floor
Philadelphia, PA 19107

Telephone: 215-503-5560
optimization of diffusion

Dr. Chengyuan Wu is a board-certified fellowship-trained Stereotactic and Functional Neurosurgeon with a background in the fields of Computer and Biomedical engineering.  For this reason, he is uniquely positioned in the field of neuroscience research.  Specifically, he is able to blend his knowledge of signal and image processing techniques with his clinical expertise in Neurological Surgery.  His work began during his graduate education at Tufts University School of Engineering, where he focused his thesis on the subject of signal processing for automated analysis of electroencephalograms (EEGs).  For more than a decade, he has continued to dedicate his research efforts to the fields of epilepsy.  As part of the Comprehensive Epilepsy Center at Thomas Jefferson University, he has had an opportunity to be part of a clinically busy practice, which performs approximately 100 surgeries for epilepsy each year covering the spectrum from traditional resective surgeries and invasive implantations to minimally invasive interventions and neurostimulation.  He routinely integrates advanced neuroimaging and image post-processing to not only deliver the highest level of care to his patients. In addition to patients with epilepsy, Dr. Wu also applies these advanced imaging techniques to patients undergoing deep brain stimulation (DBS) for movement disorders.  He continues to work on advancing the field of neuroimaging in order to not only improve the future of epilepsy and DBS surgery, but also to further our understanding of the brain and the networks embedded within. 

Research Projects

Prediction of Patient Response to Dopamine and DBS using Advanced Imaging Methods

prediction of patient response to dopamine

Neuronal loss and dopamine depletion alters motor signal processing and propagation between the cortical motor areas, the basal ganglia and the thalamus resulting in the motor manifestation of Parkinson’s disease (PD). Abnormal neural connections and activity within these circuits have been demonstrated in animal models of disease as well as in humans suffering from movement disorders.  With the aim of better understanding changes in functional connectivity (FC) and anatomical connectivity (AC) that occur with disease progression, we are exploring how both FC and AC of cortico-basal-ganglia-thalamic circuits change in patients with advanced PD. Our findings to date have helped to translate our understanding of these networks from animal studies to human patients.  We believe that advanced MR imaging may reveal a useful imaging biomarker that can help to predict response to treatment in patients suffering from PD.

Understanding the Effects of Anesthesia on Resting State Functional MRI

Computer assisted diagnostic

Blood Oxygen Level Dependent (BOLD) imaging forms the basis of functional magnetic resonance imaging (fMRI), which has great potential to identify distributed associative neural networks and reveal features of brain organization. While capable of representing brain regions synchronized by monosynaptic or polysynaptic connections, fMRI suffers from significant variability and artifacts induced by motion, patient physiology, and differences in the task being performed by the subject. Resting state fMRI (rs-fMRI) has grown in popularity over recent years because it obviates the need for specific task paradigms and has been shown to be comparable to task-based fMRI. Some may argue, however, that rs-fMRI still represents a task state – as patients are asked to passively rest or stare at a fixation point. As such, rs-fMRI is likely influenced not only by anatomical connections, but also by thought processes during this “resting task”. We ultimately do not have a clear understanding of the true resting state of the human brain. As a result, the test-retest reliability of rs-fMRI remains poor to moderate and the between-subject variation remains high. Both of these factors limit the utility of rs-fMRI for longitudinal research and in clinical applications. 

Despite its potential, rs-fMRI of the brain has limited clinical utility because of its poor test-retest reliability and high between-subject variation. For these reasons, rs-fMRI must be interpreted carefully and rarely can be used decisively as a clinical tool. Its research applications are also impaired as comparisons across patients to evaluate neurological diseases as well as its ability to longitudinally follow disease progression in a single patient has been limited. We believe that this is largely attributable to the way in which rs-fMRI is currently acquired. Patients are often asked to passively rest or stare at a fixation point – but thought processes during this “resting task” cannot be controlled. Since induction and maintenance of general anesthesia generates a consistent mental state, virtually eliminates head motion, and increases our ability to control certain aspects of patient physiology, BOLD imaging and resultant rs-fMRI under these conditions is expected to be more consistent between subjects and within subjects with repeat testing. If we are able to understand the effects of volatile gaseous anesthetics (vGA) on rs-fMRI and demonstrate improved reproducibility in this setting, we will be able to significantly expand its indications for clinical neuroscience research. We expect that the pervasive use of vGA and rs-fMRI will make our findings readily generalizable, as long as protocols developed in our work are adhered to by other researchers. With a truly reliable means of measuring functional brain states, neuroscientists will be able to better understand pathophysiology, improve diagnosis, and to quantify the effect of treatment in neurological diseases such as epilepsy, Alzheimer’s disease, Parkinson’s Disease, or even with normal aging.