Welcome to MR NeuroImaging Lab

Our research is focused on developing magnetic resonance imaging system technology, neuroimaging methods and computational analysis approaches. The research target is to explore complex brain connectomics and connective network. We have applied the developed neuroimaging methods on a variety of applications, including pre-clinical animal models (e.g., stroke and traumatic brain injury), neurological diseases and psychiatric disorders (e.g., Alzheimer’s disease and heroin addiction), and cognitive neuroscience (e.g., vocal emotion processing and social adaptation).

MRI System Development

The first aim of our research is to develop a multi-scale and high-performance MRI system for improving the angular and spatiotemporal resolutions to reveal finer details of the brain connectomics. This advanced MRI system incorporates two high-strength gradient coils with two different inner diameters, i.e. 48 and 12 cm, for imaging human and small animal, corresponding to gradient strengths of 150 and 675 mT/m, respectively. During the past few years, we have successfully established the multi-scale high-performance 3 T MRI system at NHRI.

Selected publications:

- Kuan-Hung Cho, Sheng-Min Huang, Chang-Hoon Choi, Ming-Jye Chen, Hsuan-Han Chiang, Richard P. Buschbeck, Ezequiel Farrher, N. Jon Shah, Ruslan Garipov, Ching-Ping Chang, Hsu Chang and Li-Wei Kuo. “Development, integration and use of an ultra-high-strength gradient system on a human-size 3 T magnet for small animal MRI,” PLoS One, 14(6):e0217916, 3 Jun 2019, doi: 10.1371/journal.pone.0217916.

MRI Neuroimaging Methods

The second aim of our research is to focus on developing MR neuroimaging methods and computational analysis. Specifically, we have focused on developing the novel high-angular resolution diffusion MRI methods and performed the optimization to improve the capability of resolving the complex tissue microstructures. We have optimized the diffusion kurtosis imaging, a novel technique to resolve tissue complex microstructures. Essentially, all these works would facilitate the applications on pre-clinical animal models and brain diseases in clinical settings.

Selected publications:

- Chia-Wen Chiang, Shih-Yen Lin, Kuan-Hung Cho, Kou-Jen Wu, Yun Wang and Li-Wei Kuo. “Effects of signal averaging, gradient encoding scheme, and spatial resolution on diffusion kurtosis imaging: An empirical study using 7T MRI,” Journal of Magnetic Resonance Imaging, 16 Apr 2019, doi: 10.1002/jmri.26755.

- G. Russell Glenn, Li-Wei Kuo, Yi-Ping Chao, Chu-Yu Lee, Joseph A. Helpern and Jens H. Jensen. “Mapping the Orientation of White Matter Fiber Bundles: A Comparative Study between Diffusion Tensor Imaging (DTI), Diffusional Kurtosis Imaging (DKI), and Diffusion Spectrum Imaging (DSI),” American Journal of Neuroradiology, March 3 2016, doi: 10.3174/ajnr.A4714.

- Li-Wei Kuo, Wen-Yang Chiang, Fang-Cheng Yeh, Van J. Wedeen, Wen-Yih I. Tseng. “Diffusion Spectrum MRI Using Body-centered-cubic and Half-sphere Sampling Schemes,” Journal of Neuroscience Methods, 212(1) p143-155, 2013, doi: 10.1016/j.jneumeth.2012.09.028.

Computational Analysis Approaches

Graph-theoretical analysis has been developed as an important tool for investigating brain connective networks. Using graph-theoretical analysis to analyze the topological organization of brain structures and functions has been recently developed and considered as a new approach to explore the brain neuroscience. During the past few years, we have focused on optimizing the workflow of graph-theoretical analysis and further facilitating the use of graph-theoretical analysis on clinical applications. In our recent work, we investigated the alterations in brain network topology in the patients with mild cognitive impairment and Alzheimer’s disease. We also investigated the alteration of functional connectivity of default mode network in heroin users with different treatment modalities. Other than clinical applications, we also investigated the vocal emotion processing using the graph theoretical analysis.

Selected publications:

- Li-Wei Kuo, Pei-Sheng Lin, Shih-Yen Lin, Ming-Fang Liu, Hengtai Jan, Hsin-Chien Lee and Sheng-Chang Wang. “Functional Correlates of Resting-state Connectivity in the Default Mode Network of Heroin Users on Methadone Treatment and Medication-free Therapeutic Community Program,” Frontiers in Psychiatry, 10:381, 6 June 2019, doi: 10.3389/fpsyt.2019.00381.

- Shih-Yen Lin, Chi-Chun Lee, Yong-Sheng Chen, Li-Wei Kuo. “Investigation of functional brain network reconfiguration during vocal emotional processing using graph-theoretical analysis,” Social Cognitive and Affective Neuroscience, 14(5):529-538, 31 May 2019, doi: 10.1093/scan/nsz025.

- Shih-Yen Lin, Chen-Pei Lin, Tsung-Jen Hsieh, Chung-Fen Lin, Sih-Huei Chen, Yi-Ping Chao, Yong-Sheng Chen, Chih-Cheng Hsu and Li-Wei Kuo. “Multiparametric Graph Theoretical Analysis Reveals Altered Structural and Functional Network Topology in Alzheimer’s Disease,” Neuroimage: Clinical, 22:101680, 2019, doi: 10.1016/j.nicl.2019.101680.