Nan-kuei Chen

Nan-kuei Chen

Associate Professor, BIO5 Institute
Associate Professor, Biomedical Engineering
Primary Department
Department Affiliations
(520) 626-0060

Research Interest

Research Interest
I am an MR physicist with extensive expertise in fast image acquisition methodology, pulse sequence design, and artifact correction for neuro MRI. In the past 18 years, I have developed novel approaches effectively addressing various types of challenging MRI artifacts, ranging from echo-planar imaging (EPI) geometric distortions, to susceptibility effect induced signal loss, to EPI Nyquist artifact, to motion-induced phase errors and aliasing artifacts in interleaved EPI based diffusion-weighted imaging. I am the original developer of multiplexed sensitivity encoded (MUSE) MRI, which can measure human brain connectivity in vivo at high spatial-resolution and accuracy, as shown in the publications listed below. More generally, my research involves the application of MR protocols in translational contexts. I have served as PI on NIH-funded R01, R21 and R03 grants, and have had extensive experience as a co-investigator on NIH-funded projects. The current focus of my research includes: * Development of high-throughput and motion-immune clinical MRI for imaging challenging patient populations * Imaging of neuronal connectivity networks for studies of neurological diseases * High-fidelity and multi-contrast MRI guided intervention * Characterization and correction of MRI artifacts * Signal processing and algorithm development * MRI studies of human development


McClernon, F. J., Conklin, C. A., Kozink, R. V., Adcock, R. A., Sweitzer, M. M., Addicott, M. A., Chou, Y., Chen, N., Hallyburton, M. B., & DeVito, A. M. (2016). Hippocampal and Insular Response to Smoking-Related Environments: Neuroimaging Evidence for Drug-Context Effects in Nicotine Dependence. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology, 41(3), 877-85.

Environments associated with prior drug use provoke craving and drug taking, and set the stage for lapse/relapse. Although the neurobehavioral bases of environment-induced drug taking have been investigated with animal models, the influence of drug-environments on brain function and behavior in clinical populations of substance users is largely unexplored. Adult smokers (n=40) photographed locations personally associated with smoking (personal smoking environments; PSEs) or personal nonsmoking environment (PNEs). Following 24-h abstinence, participants underwent fMRI scanning while viewing PSEs, PNEs, standard smoking and nonsmoking environments, as well as proximal smoking (eg, lit cigarette) and nonsmoking (eg, pencil) cues. Finally, in two separate sessions following 6-h abstinence they viewed either PSEs or PNEs while cue-induced self-reported craving and smoking behavior were assessed. Viewing PSEs increased blood oxygen level-dependent signal in right posterior hippocampus (pHPC; F(2,685)=3.74, p

Song, X., Chen, N., & Gaur, P. (2011). Identification and attenuation of physiological noise in fMRI using kernel techniques. Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2011, 4852-5.

Functional magnetic resonance imaging (fMRI) techniques enable noninvasive studies of brain functional activity under task and resting states. However, the analysis of brain activity could be significantly affected by the cardiac- and respiration-induced physiological noise in fMRI data. In most multi-slice fMRI experiments, the temporal sampling rates are not high enough to critically sample the physiological noise, and the noise is aliased into frequency bands where useful brain functional signal exists, compromising the analysis. Most existing approaches cannot distinguish between the aliased noise and signal if they overlap in the frequency domain. In this work, we further developed a kernel principal component analysis based physiological removal method based on our previous work. Specifically, two kernel functions were evaluated based on a newly proposed criterion that can measure the capability of a kernel to separate the aliased physiological noise from fMRI signal. In addition, a mutual information based criterion was designed to select principal components for noise removal. The method was evaluated by human experimental fMRI studies, and the results demonstrate that the proposed method can effectively identify and attenuate the aliased physiological noise in fMRI data.

Wei, H., Zhang, Y., Gibbs, E., Chen, N., Wang, N., & Liu, C. (2016). Joint 2D and 3D phase processing for quantitative susceptibility mapping: application to 2D echo-planar imaging. NMR in biomedicine.

Quantitative susceptibility mapping (QSM) measures tissue magnetic susceptibility and typically relies on time-consuming three-dimensional (3D) gradient-echo (GRE) MRI. Recent studies have shown that two-dimensional (2D) multi-slice gradient-echo echo-planar imaging (GRE-EPI), which is commonly used in functional MRI (fMRI) and other dynamic imaging techniques, can also be used to produce data suitable for QSM with much shorter scan times. However, the production of high-quality QSM maps is difficult because data obtained by 2D multi-slice scans often have phase inconsistencies across adjacent slices and strong susceptibility field gradients near air-tissue interfaces. To address these challenges in 2D EPI-based QSM studies, we present a new data processing procedure that integrates 2D and 3D phase processing. First, 2D Laplacian-based phase unwrapping and 2D background phase removal are performed to reduce phase inconsistencies between slices and remove in-plane harmonic components of the background phase. This is followed by 3D background phase removal for the through-plane harmonic components. The proposed phase processing was evaluated with 2D EPI data obtained from healthy volunteers, and compared against conventional 3D phase processing using the same 2D EPI datasets. Our QSM results were also compared with QSM values from time-consuming 3D GRE data, which were taken as ground truth. The experimental results show that this new 2D EPI-based QSM technique can produce quantitative susceptibility measures that are comparable with those of 3D GRE-based QSM across different brain regions (e.g. subcortical iron-rich gray matter, cortical gray and white matter). This new 2D EPI QSM reconstruction method is implemented within STI Suite, which is a comprehensive shareware for susceptibility imaging and quantification. Copyright © 2016 John Wiley & Sons, Ltd.

Froeliger, B., Garland, E. L., Kozink, R. V., Modlin, L. A., Chen, N., McClernon, F. J., Greeson, J. M., & Sobin, P. (2012). Meditation-State Functional Connectivity (msFC): Strengthening of the Dorsal Attention Network and Beyond. Evidence-based complementary and alternative medicine : eCAM, 2012, 680407.

Meditation practice alters intrinsic resting-state functional connectivity (rsFC) in the default mode network (DMN). However, little is known regarding the effects of meditation on other resting-state networks. The aim of current study was to investigate the effects of meditation experience and meditation-state functional connectivity (msFC) on multiple resting-state networks (RSNs). Meditation practitioners (MPs) performed two 5-minute scans, one during rest, one while meditating. A meditation naïve control group (CG) underwent one resting-state scan. Exploratory regression analyses of the relations between years of meditation practice and rsFC and msFC were conducted. During resting-state, MP as compared to CG exhibited greater rsFC within the Dorsal Attention Network (DAN). Among MP, meditation, as compared to rest, strengthened FC between the DAN and DMN and Salience network whereas it decreased FC between the DAN, dorsal medial PFC, and insula. Regression analyses revealed positive correlations between the number of years of meditation experience and msFC between DAN, thalamus, and anterior parietal sulcus, whereas negative correlations between DAN, lateral and superior parietal, and insula. These findings suggest that the practice of meditation strengthens FC within the DAN as well as strengthens the coupling between distributed networks that are involved in attention, self-referential processes, and affective response.

Chang, H., Guhaniyogi, S., & Chen, N. (2015). Interleaved diffusion-weighted improved by adaptive partial-Fourier and multiband multiplexed sensitivity-encoding reconstruction. Magnetic resonance in medicine, 73(5), 1872-84.

We report a series of techniques to reliably eliminate artifacts in interleaved echo-planar imaging (EPI) based diffusion-weighted imaging (DWI).