Functional MRI Speaker Series

Most years our laboratory provides opportunities for our users and researchers to learn about advances in fields related to Functional MRI.

No RSVP is required. To be added to the Speaker Series email group and receive notifications about the speaker series talks, please contact the Administrator.

If you would like to have these dates on your calendar, here is a link to the fMRI Lab Speaker Series Google Calendar.

Academic Year 2022-2023 Speaker Series

All presentations will take place at 4:00 - 5:30 PM.

October 25, 2022

Dr. Theodore Satterthwaite, University of Pennsylvania Perelman School of Medicine

Topic: Protracted Development of Association Cortex in Youth

During childhood and adolescence, cortical development progresses from lower-order unimodal cortices to higher-order association cortices. Here we will review recent work from large-scale neuroimaging studies that illustrate how this protracted developmental program endows the brain’s association cortices with unique functional properties to support executive function, but also leaves humans at risk for diverse psychopathologies.

November 15, 2022

Dr. Christopher Honey, Johns Hopkins University

Topic: Timescales in Natural and Artificial Intelligence

My lab studies how people integrate information over time, as they seek to understand and learn from their environment. Temporal integration is ubiquitous, because our world unfolds over time: hearing a fragment of sound, we perceive it as part of a mockingbird's melody; reading one word, we understand it as part of a meaningful sentence. The first part of this talk will review empirical findings and computational models describing how regions of the cerebral cortex integrate new input with context from many seconds earlier; we will also examine how and when cortical regions "forget" prior context. The second part of the talk will explore how learning processes are affected by the processing timescales of our brains, and why certain kinds of information tend to linger in our minds.

January 10, 2023

Dr. Bradley MacIntosh, University of Toronto

Topic: Characterizing and Quantifying Neuroimaging Data with Help from Deep Learning

Magnetic resonance imaging (MRI) and Computed Tomography (CT) are diagnostic imaging modalities used heavily to characterize brain disease. Artificial intelligence (AI), specifically deep learning, is a type of machine learning that involves training a computer to recognize patterns in data by processing large amounts of information through multiple layers of neural networks. Deep learning algorithms are well suited to image analysis tasks and have been applied to a wide range of medical imaging applications. To date, the Food and Drug Administration (FDA) has approved over 500 AI solutions as ‘medical devices’ that aid in the clinical decision making.

The presentation will focus on examples where AI deep learning models can be used to synthesize images that provide physiological information, for example cerebral blood flow maps from arterial spin labeling MRI, and cardiac-related brain pulsatility maps from blood oxygenation level dependent (BOLD) contrast images. We will also showcase the academic AI tools developed at the Computational Radiology and AI ( unit at the Oslo University Hospital, Norway. From among these tools, we will describe the development of a CT-based segmentation tool that contours all forms of intracranial hemorrhage from non-contrast CT images.

February 14, 2023

Dr. Molly Bright, Northwestern University

Topic: Adding Vascular Insight to the fMRI Experiment

In fMRI data, numerous physiologic sources contribute to the measured signals. We typically aim to model and remove these effects (e.g., heart rate, breathing changes) during data preprocessing, which is itself an active and evolving area of research. However, we can also intentionally amplify physiological processes and characterize their effects in our data. Our lab capitalizes on the strong relationship between respiration and blood flow, using breathing challenges to modulate blood gases and evoke systemic vasodilation that can be characterized throughout the central nervous system using fMRI. With practical adjustments, typical fMRI experiments can simultaneously generate metrics of neural and vascular function, making fMRI a truly multiparametric imaging modality. Vascular insights complement our assessment of neural activity and connectivity in fMRI data, and allow for new exploration into the coupling between neural and vascular physiology. This is extremely valuable information when applying fMRI in a range of neurological pathologies where the vasculature is often implicated in disease and symptom progression. Furthermore, we reveal long-distance coordination of respiratory-driven vasodilation across the brain, which demonstrates network-like organization that mirrors established functional (neural) networks, offering the intriguing potential for "vascular networks" that directly contribute to or interact with brain network function. Finally, we are beginning to adapt these methods to examine neural and vascular function in the cervical spinal cord, with promising results.

March 14, 2023

Dr. Thomas Sprague, University of California Santa Barbara

Topic: Using Computational Neuroimaging to Characterize Neural Priority Maps Supporting Visual Cognition

Much of the visual system is organized according to visual retinotopic space, and activation patterns within each retinotopically-defined region (e.g., V1) can be considered as neural ‘priority maps’ – maps of the relative importance of different elements in the visual environment. In my lab’s research, we seek to understand how visual regions index aspects of priority based on image-computable stimulus salience and an observer’s behavioral goals. To accomplish this goal, we develop and apply computational neuroimaging methods to reconstruct and quantify population-level neural representations and assay predictive models of neural encoding. In this talk, I will describe the methods we’ve developed, and show results from several key empirical tests of priority map theory establishing how different retinotopic visual regions in human cortex differentially compute priority maps based on stimulus properties (luminance contrast; salience-defining feature) and task demands (behaviorally-relevant location or feature). Additionally, based on data acquired in the absence of visual stimulation, I will show how Bayesian generative models can be used to show how activation patterns in these priority maps support performance on tasks requiring visual working memory. Overall, I hope to convince you that these results support a theoretical framework whereby visual spatial cognition can be understood as operating via multiple interacting neural priority maps, with different regions preferentially indexing stimulus and task-related aspects of priority.

April 11, 2023

Dr. Lirong Yan, Northwestern University

Topic: Arterial Spin Labeling in Neurovascular Imaging: Beyond CBF

Arterial spin labeling (ASL) is a non-invasive MRI technique for measuring cerebral blood flow. Over the past three decades, ASL has undergone rapid technical development, which promotes ASL to become a useful imaging tool for brain perfusion measurement. Except for ASL perfusion imaging, additional physiological information can be derived using ASL during the passage of labeled blood through the cerebral arterial trees into capillaries and tissue, such as dynamic MR angiography, vascular territorial mapping, and other hemodynamic parameter quantification, all of which could also provide useful information in neurovascular applications. In this talk, I will start with a brief overview of ASL technical development, then move on to introduce our recent research in advanced ASL development.