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Baby Connectome Project (BCP) is recently started and will acquire and release thousands of infant MRI scans. Due to the fact that infant MRIs are often affected by motion artifacts, low imaging contrast, low SNR, low spatial resolution, and high dropout rate, there are many challenges for the data analysis on this unique dataset:

  • Segmentation of structural MRI
  • Cortical surface reconstruction and mapping
  • Registration and atlas building
  • Structural and functional connectivity analysis
  • Multimodal brain parcellation

Therefore, our keynote speakers will cover these challenging topics in their talks.



Dr. Lin currently serves as the Director of Biomedical Research Imaging Center (BRIC) at the University of North Carolina at Chapel Hill. The BRIC, an institution center, houses a comprehensive collection of human and small animal imaging scanners and 32 faculty members with diverse expertise on imaging related topics. Dr. Lin is the Dixie Lee Boney Soo Distinguished Professor of Neurological Medicine. He is a Professor of Radiology, Neurology, Biomedical Engineering, and Pharmacy and serves as the Vice Chair of Basic Research in the Department of Radiology. Dr. Lin was elected as a Fellow of the American Institute for Medical and Biological Engineering in 2012, and has served on numerous National Institutes of Health (NIH) study sections as a regular or ad hoc member over the past 25 years. He has published more than 250 peer-reviewed articles with research interests focused on early brain functional and structural development, discerning cerebral hemodynamics and oxygen metabolism in patients with neurological diseases, and technical development of hybrid PET/MR imaging approaches. Dr. Lin is the contact principal investigator for the ongoing research on delineating early brain functional and structural development using non-invasive imaging approaches. For this research, recently awarded by the NIH Baby Connectome Project, his team has developed imaging protocols, imaging approaches and novel image analysis tools specifically tailored for analyzing early brain development.


Colin Studholme, Ph.D.

Dr. Studholme’s research is focused on mathematical and computational techniques to study brain anatomy and its change over time, with a particular focus on fetal and premature neonatal brain growth. His research group, the Biomedical Image Computing Group, is working on new techniques to solve the problem of how to produce images of the human fetal head in utero — which is especially challenging because the fetal head is often moving. Dr. Studholme’s group uses magnetic resonance imaging (MRI) to solve this problem and provide the first high–resolution 3D images of early human brain growth. As part of this project, his group has developed a 4–dimensional computational map of tissue volume changes and surface shape changes that occur when the human brain surface begins to fold, in a process that will go on to form the complex anatomy of the adult human brain. In recent papers, Dr. Studholme’s team has mapped the points at which the first differences in the left and right sides of the brain emerge in the developing fetus. The team has developed techniques to accurately compare brain anatomy in premature babies to that of normally developing fetuses. In addition to his ongoing research in this area, he will be a co–investigator on the randomized controlled trial of Epo neuroprotection in extremely preterm infants (PENUT Trial), with the specific aim of evaluating brain growth of Epo treated infants as compared to controls.

Dr. Studholme is also Professor of Pediatrics and Bioengineering, and Adjunct Professor of Radiology. He completed his Ph.D. in medical physics and biophysics from the University of London and a postdoctoral fellowship in diagnostic radiology at Yale University. He has served as faculty at the University of California–San Francisco, visiting faculty member in biomedical engineering at the Mayo Clinic and at the Fields Institute of the University of Toronto, and has recently become an associate editor for the journal IEEE Transactions on Medical Imaging.


Ali Gholipour, Ph.D.

Dr. Gholipour is an Assistant Professor in Radiology at Harvard Medical School, a principal investigator and research faculty in the Computational Radiology Laboratory, and the Director of Translational Research in the Radiology Department at Boston Children’s Hospital. He received all his degrees in Electrical Engineering (PhD’08 at the Univ. of Texas at Dallas, and MS’03, BS’01 at the Univ. of Tehran) and is currently a Senior Member of IEEE. With long-term research interests in signal and image processing and intelligent systems, he has turned his focus to machine learning and medical imaging in the past decade, where he has developed new techniques and tools for brain functional localization, motion and distortion correction in MRI, image registration and segmentation, robust super-resolution volume reconstruction, and motion-robust diffusion-weighted MRI. Dr. Gholipour is the principal investigator of ongoing projects on motion-robust diffusion-weighted MRI of early brain development, and motion-robust imaging technology for quantitative analysis of early brain growth. These technologies along with resources such as the precisely labeled, normative spatiotemporal fetal brain MRI atlas that Dr. Gholipour and his colleagues have developed in the past years have enabled quantitative evaluation of in-utero brain development using MRI. By providing fetal in-vivo neuroimaging at an unprecedented resolution despite intermittent fetal and maternal motion, these image reconstruction, segmentation, and analysis techniques have enabled new investigations into the mechanisms of early brain development and neurodevelopmental disorders in congenital disease, and brain functional and structural connectivity growth in the fetal and neonatal periods.