This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.The overarching goal of the White Matter Architecture core is to pursue technologicaldevelopments that improve the understanding of white matter anatomy. Suchunderstanding, particularly owing to the small dimension of the neural pathways relative to current imaging resolution, is vital to developing novel methods and techniques for the analysis of anatomical structures, and for the application of those analyses to understanding neural diseases. This information naturally complements atlas information obtained from standard structural analysis.The clinical objective is to detect and localize white matter brain abnormalities inschizophrenia using techniques in diffusion MRI and post-process imaging, although weexpect that the methods developed will be applicable to a broad range of white matterdisease. To address the issue of white matter structure and abnormalities, we havedeveloped a new technology for analyzing white matter along with tools for visualization and interactive exploration of 3D diffusion MRI data. In addition to supporting the specific needs of this core, these tools have been applied directly to the early detection of white matter damage in the developing infant brain; exploration and characterization of white matter disruptions caused by MS lesions; and visualization of important white matter fiber tracts during neurosurgical procedures (e.g., the cortical spinal tract connecting the motor cortex with the lower motor centers of the spinal cord.)
| Saito, Yukiko; Kubicki, Marek; Koerte, Inga et al. (2018) Impaired white matter connectivity between regions containing mirror neurons, and relationship to negative symptoms and social cognition, in patients with first-episode schizophrenia. Brain Imaging Behav 12:229-237 |
| Gallardo, Guillermo; Wells 3rd, William; Deriche, Rachid et al. (2018) Groupwise structural parcellation of the whole cortex: A logistic random effects model based approach. Neuroimage 170:307-320 |
| Ratner, Vadim; Gao, Yi; Lee, Hedok et al. (2017) Cerebrospinal and interstitial fluid transport via the glymphatic pathway modeled by optimal mass transport. Neuroimage 152:530-537 |
| Sastry, Rahul; Bi, Wenya Linda; Pieper, Steve et al. (2017) Applications of Ultrasound in the Resection of Brain Tumors. J Neuroimaging 27:5-15 |
| Chen, Yongxin; Georgiou, Tryphon T; Ning, Lipeng et al. (2017) Matricial Wasserstein-1 Distance. IEEE Control Syst Lett 1:14-19 |
| Niethammer, Marc; Pohl, Kilian M; Janoos, Firdaus et al. (2017) ACTIVE MEAN FIELDS FOR PROBABILISTIC IMAGE SEGMENTATION: CONNECTIONS WITH CHAN-VESE AND RUDIN-OSHER-FATEMI MODELS. SIAM J Imaging Sci 10:1069-1103 |
| Chen, Yongxin; Cruz, Filemon Dela; Sandhu, Romeil et al. (2017) Pediatric Sarcoma Data Forms a Unique Cluster Measured via the Earth Mover's Distance. Sci Rep 7:7035 |
| Schabdach, Jenna; Wells 3rd, William M; Cho, Michael et al. (2017) A Likelihood-Free Approach for Characterizing Heterogeneous Diseases in Large-Scale Studies. Inf Process Med Imaging 10265:170-183 |
| Wachinger, Christian; Brennan, Matthew; Sharp, Greg C et al. (2017) Efficient Descriptor-Based Segmentation of Parotid Glands With Nonlocal Means. IEEE Trans Biomed Eng 64:1492-1502 |
| Chen, Yongxin; Georgiou, Tryphon; Pavon, Michele et al. (2017) Robust transport over networks. IEEE Trans Automat Contr 62:4675-4682 |
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