Talk 1: Investigating hemodynamics in human cerebral cortex using biophysical modeling and vessel-specific fMRI; Talk 2: Translating MRI Methods for Perfusion and Anatomy from 3T and 7T into Clinical Applications

Talk 1: Investigating hemodynamics in human cerebral cortex using biophysical modeling and vessel-specific fMRI

Speaker: Jon Polimeni, Ph.D.; Associate Professor, Richard M. Lucas Center for Imaging, Stanford University.
Abstract: All fMRI techniques in use today measure brain function only indirectly, by tracking changes in blood flow, volume, and oxygenation that accompany neuronal activity, and this has often been viewed as a fundamental limitation of the method. Much of what is known about the hemodynamic response to neuronal activity comes from invasive in vivo microscopy in small-animal models, which provides direct observation of dynamics within the microvascular network. Translating these insights to human hemodynamics will require specialized tools that can more directly relate in vivo microscopy measures to human fMRI.

In this seminar I will describe ongoing efforts aimed at deepening our understanding of the hemodynamics underlying human fMRI. I will review our recent work on biophysical modeling of fMRI signals based on realistic microvascular anatomy and dynamics, and its application to BOLD and non-BOLD fMRI contrasts. This framework, known as the Vascular Anatomical Network (VAN) modeling approach, represents all blood vessels within a single voxel, and is inspired by analogous biophysical modeling efforts aimed at extracting microstructural information from diffusion MRI. I will also survey our work on "vessel-specific fMRI," which seeks to provide dynamic measures of physiological quantities (such as blood velocity and vessel diameter) analogous to those obtained with in vivo microscopy. These new physiological measures can then serve as inputs to, and validation of, our biophysical models of human fMRI.

The goal of this work is to connect microvascular dynamics that cannot be observed directly in humans with the fMRI signals we do measure, thereby bridging the gaps across scales, modalities, and species.

Bio: Jonathan R. Polimeni, Ph.D., is an Associate Professor of Radiology at Stanford University School of Medicine and Director of Ultra-High Field MRI Research at the Richard M. Lucas Center for Imaging at Stanford. His training is in electrical engineering, computational neuroscience, and functional MRI physics. He arrived at Stanford in January, 2025; previously he was faculty at Harvard Medical School and at the Massachusetts General Hospital (MGH), within the Athinoula A. Martinos Center for Biomedical Imaging, and affiliated faculty in the Program in Health Sciences and Technology (HST) at MIT. He leads the High-Resolution and Ultra-High-Field Functional Imaging Laboratory, which focuses on developing new technologies for high-resolution functional MRI (mainly at 7 Tesla) and on understanding how the brain’s vascular anatomy and physiology shape the fMRI signals in order to relate these measurements back to the underlying neuronal activity. Several MRI technologies he has developed are now incorporated into the Siemens MRI scanner platform and are used by researchers and clinicians worldwide. He also serves as Handling Editor of the new, open access, non-profit journal Imaging Neuroscience, handling submissions on fMRI physics, fMRI acquisition, and ultra-high field neuroimaging.

Talk 2:  Translating MRI Methods for Perfusion and Anatomy from 3T and 7T into Clinical Applications

Speaker: Kamil Uludag, Ph.D., Professor, Department of Medical Biophysics, University of Toronto

Abstract: This talk presents a translational framework bridging methodological developments in perfusion and structural MRI across 3T and 7T, with applications in brain tumors. At 3T, we have developed and validated advanced perfusion approaches, including multi-echo DSC and hybrid acquisitions, alongside quantitative structural imaging methods sensitive to tissue microstructure. Together, these techniques enable more robust and biologically specific characterization of vascular and anatomical changes, with demonstrated clinical utility - for example, in assessing tumor heterogeneity and treatment response, as well as in evaluating targeted neuromodulation and blood–brain barrier modulation following MR-guided focused ultrasound.

Building on this foundation, we translate these approaches to 7T, leveraging increased signal-to-noise ratio and susceptibility contrast to achieve high-resolution perfusion, structural, and functional imaging. This enables interrogation of microvascular and microstructural alterations even at the level of cortical layers and subcortical circuits, providing new insights into disease mechanisms. Collectively, these developments illustrate how coordinated advances across field strengths can yield precise imaging biomarkers and support emerging therapeutic strategies in neurodegenerative, neuropsychiatric, and neuro-oncological disorders. 

Bio: Dr. Kamil Uludag is a physicist working at the interface of neuroscience and magnetic resonance imaging (MRI). His research focuses on the development of advanced imaging methods and the critical evaluation of existing techniques to enable novel questions about human brain function in health and disease. A central goal of his work is to understand the physiological, anatomical, and physical foundations of imaging signals, integrating principles from both physics and neuroscience. He is the inaugural Scientific Director of Toronto’s human 7T MRI facility at Sunnybrook Research Institute and holds a Tier 1 Canada Research Chair in Ultra-High-Field MRI.

If you are interested in having 1:1 conversation with Jon and Kamil during their visit on 10 Septmber, please contact Chiara (chiara.coletti@ndcn.ox.ac.uk) or Ziyu (ziyu.li@ndcn.ox.ac.uk) so they can arrange it.

Please see the schedule of the talks below: 

• 11:00 - 11:30 Talk 1 

• 11:30 - 11:45 Discussion

• 11:45 - 12:15 Talk 2

• 12:15 - 12:30 Discussion

• 12:30 onwards Lunch provided in the FMRIB kitchen