Fang-Cheng (Frank) Yeh, MD, PhD, joined the Department of Neurological Surgery in 2016 as a tenure-track assistant professor.
Prior to joining the faculty at the University of Pittsburgh, Dr. Yeh received his MD degree from National Taiwan University and completed his PhD study in biomedical engineering at Carnegie Mellon University in 2014.
Dr. Yeh is currently working on diffusion MRI and its role as image biomarkers for neurological and psychiatric disorders. His research focuses on novel applications of computational methods to brain connectome research, a challenging field with a lot of known unknowns and unsolved questions that require extensive technological development. He has developed several diffusion MRI methods and applied them to both clinical and translational studies.
Dr. Yeh is known for his development of DSI Studio, an integrated platform for diffusion MRI analysis, fiber tracking, and 3D tractography visualization. In 2020 alone, DSI Studio facilitated more than 100 peer-reviewed publications. DSI Studio provides the core technique for “high accuracy fiber tracking,” which has been widely used by many research groups to investigate how major fiber pathways are affected by neurological and psychiatric diseases. In an open competition sponsored by the International Society for Magnetic Resonance in Medicine (ISMRM) in 2015, Dr. Yeh’s method achieved the highest valid connection score (92.49%, ID:03) among 96 different approaches submitted by a total of 20 groups from around the world
Specialized Areas of Interest
Professional Organization Membership
Education & Training
- MD, National Taiwan University, 2006
- PhD, Biomedical Engineering, Carnegie Mellon University, 2014
Honors & Awards
- Chancellor’s Commercialization Fund Award, Pitt Ventures First Gear Program, University of Pittsburgh, 2019
Yeh FC, Vettel JM, Singh A, Poczos B, Grafton ST, Erickson KI, Tseng WI, Verstynen TD. Quantifying Differences and Similarities in Whole-Brain White Matter Architecture Using Local Connectome Fingerprints. PLoS Comput Biol 12(11):e1005203, 2016.
Yeh FC, Badre D, Verstynen T. Connectometry: A statistical approach harnessing the analytical potential of the local connectome. Neuroimage 125:162-71, 2016.
Fernández-Miranda JC, Wang Y, Pathak S, Stefaneau L, Verstynen T, Yeh FC. Asymmetry, connectivity, and segmentation of the arcuate fascicle in the human brain. Brain Struct Funct 220(3):1665-80, 2014.
Wang Y, Fernández-Miranda JC, Verstynen T, Pathak S, Schneider W, Yeh FC. Rethinking the role of the middle longitudinal fascicle in language and auditory pathways. Cereb Cortex 23(10):2347-56, 2013.
Yeh FC, Verstynen TD, Wang Y, Fernández-Miranda JC, Tseng WY. Deterministic diffusion fiber tracking improved by quantitative anisotropy. PLoS One 8(11):e80713, 2013.
Yeh FC, Tseng WY. NTU-90: a high angular resolution brain atlas constructed by q-space diffeomorphic reconstruction. Neuroimage 58(1):91-9, 2011.
Yeh FC, Wedeen VJ, Tseng WY. (2010). Generalized q-sampling imaging. IEEE Trans Med Imaging 29(9):1626-35, 2010.
Yeh FC, Parwani AV, Pantanowitz L, Ho C. Automated grading of renal cell carcinoma using whole slide imaging. Journal of Pathology Informatics 5:23, 2014.
Yeh FC, Ye Q, Hitchens TK, Wu YL, Parwani AV, Ho C. Mapping stain distribution in pathology slides using whole slide imaging. Journal of Pathology Informatics 5:1, 2014.
A complete list of Dr. Yeh's publications can be reviewed through the National Library of Medicine's publication database.
Population-Based Tractography Connectome of Human Brain and Its Hierarchical Topology
A conventional connectome records region-to-region connectivity but does not inform how regions are connected through brain pathways. Here Dr. Yeh leveraged high-throughput automated tractography to map 52 pathways in 1065 subjects and explored the population probability of a pathway connecting to each cortical region. The results can be accumulated into a novel tractography connectome that records the tract-to-region connection probability of the young adult population. Using the connective pattern as the feature vector, Dr. Yeh further applied unsupervised hierarchical clustering to reveal the hierarchical relation of cortical regions. The clustering results showed that cortical regions are grouped into three distinctly separated structural networks, including the dorsal, ventral, and limbic network, matching the dual stream models in the language or visual processing, and each of the networks further has its downward hierarchical structures. The tractography connectome and its hierarchical topology provides a data-driven perspective toward cortical segmentation to elucidates the structure-function relation.
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