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 2023-2024 Speaker Series

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

October 24, 2023

Dr. Tommy Sprague, Ph.D., UC 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.

January 16,2024

Dr. Caterina Gratton, Ph.D., Florida State University

Topic: Gaining Precision: Studying Individuals to Provide New Insights into Human Brain Networks and their Role in Control

Different regions of the brain interact with one another through large-scale networks. These network interactions are important to many complex processes, including goal-directed control. In my lab, we study large-scale network organization in humans and the principles by which it can vary -- and how these variations contribute to control functions and their breakdown. In the presentation, I will review a sampling of recent studies from our lab investigating forms of variation in large-scale networks within and across people. I will then discuss how these variations influence our studies of control.

February 13, 2024

Dr. Catie Chang, Ph.D., Vanderbilt University

Topic: Investigating Time-Resolved fMRI Patterns in the Individual Brain

Functional magnetic resonance imaging (fMRI) provides a valuable window into the large-scale network organization of the human brain. Recent work has also demonstrated the potential for fMRI to track dynamic internal states on time- scales of seconds to minutes. In this talk, we will discuss our studies on investigating the dynamics of fMRI signals and linking whole-brain fMRI patterns with physiological states, such as levels of wakefulness. We will also discuss the potential for dynamic fMRI patterns to provide clinical biomarkers.

March 12, 2024

Dr. Scott Langenecker, Ph.D., Ohio State University

Topic: Neural, Performance and Trait Mechanisms of and Predictors in Mood Disorders

This talk will provide an overview of our work in rumination, trauma, and executive functioning in mood disorders. There will be a specific focus on putative mechanisms leading to risk for mood disorders, exacerbation of or poor treatment response in mood disorders, and predictors of treatment response and course in mood disorders. The work includes samples from early adulthood, adolescence, and late childhood.