Award and funding


R01-NS102220   Chen (PI)   07/01/2018-03/31/2023


Title: Development of High-Speed and Quantitative Neuro MRI Technologies for Challenging Patient Populations

      In this project we will develop and integrate multidisciplinary approaches to maximize the translatability of advanced MRI technologies to clinical uses for challenging patients. The proposed technologies enables the acquisition of a complete set of high-resolution, artifact-free, multi- contrast and quantitative MR images from challenging patients (e.g., Parkinson’s disease (PD) patients; stroke patients; pediatric populations) within clinically-feasible time.

Role: PI


R56-AG052576   Madden, Chen, Liu (multi-PI)   09/30/2017-08/31/2019


Title: Quantitative susceptibility mapping of iron accumulation in neurocognitive aging

      In this project we test a model of the influence of age-related deep gray matter (DGM) iron accumulation on neurocognitive function, proposing that age-related DGM iron contributes to oxidative stress and consequently to a decline in network connectivity.

Role: multi-PI


R01-NS074045   Chen (PI)   09/30/2011-06/30/2018


Title: Imaging of Intrinsic Connectivity Networks

      We propose high-resolution mapping of phenotype-specific intrinsic connectivity network (ICN) vulnerability, which will make it possible to investigate the mechanistic connection, at the level of neuronal network, among multiple phenotypes of neurological diseases.

Role: PI


R21-EB018419   Chen (PI)   09/25/2014 – 06/30/2018


Title: Motion-immune neuro and body MRI for challenging patient populations

      We propose to develop and integrate novel strategies to effectively eliminate motion-related artifact, which is the major bottleneck to achieving high-quality clinical MRI for challenging patient populations such as children, tremor-dominant Parkinson’s patients and seriously ill patients among others.

Role: PI


XSEDE startup research award ASC160076

Chen (PI)   11/07/2016 – 11/07/2017

Title: Building Virtual Machine for MRI Artifact Correction in Jetstream Cloud

      Many advanced MRI artifact correction procedures cannot be easily shared across the research community due to the difference in the chosen computation environment, raw data format (e.g., with different MRI manufacture provided header information and format for the raw k-space data) and knowledge in MRI physics and data processing. The scientific computation environment and the virtual machine provided by the Jetstream Cloud provide a great opportunity for our group to implement and optimize the MRI artifact correction procedures in a way that all the established procedures can be well integrated and easily shared, in a form as a virtual machine, across the MRI research community. The built virtual machine can also be used as a platform to initiate large-scale processing (e.g., processing of group-level MRI data) in other HPC and HTC environments in Xsede.


R01-MH103790  Belger (PI); Chen (subcontact PI) 07/20/2015-06/30/2020

UNC Chapel Hill

Title: Stress Regulation, Working Memory, and Cognitive Disorganization in Adolescence

      We will examining the neural and physiological systems associated with working memory and stress regulation in adolescence, and their contribution to cognitive disorganization severity.

Role: subcontract PI