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.

Academic Year 2017-2018 Speaker Series

November 14, 2017

Time and Location: 4PM at East Hall, room 4464

John Plass, UM Dept. of Psychology

Topic: Diffusion MRI: Introduction and Modern Methods

Diffusion MRI (dMRI) is a non-invasive imaging technique used to probe the microstructure and morphology of white matter structures in the brain. Whereas diffusion-weighted scans have become ubiquitous in recent years, many researchers are often unclear on how to model, analyze, and interpret dMRI data. In this talk, I will use a straightforward pictographic approach to introduce dMRI analysis techniques used to identify and quantify the features of white matter pathways.

After introducing the most commonly used models (diffusion tensor models), I will demonstrate their shortcomings and introduce recently-developed alternatives. These modern alternatives aim to isolate measures of microscopic tissue structure (e.g., fiber density) from potential confounds produced by local fiber geometry (e.g., crossing fibers), allowing for more reliable, biologically meaningful measures of anatomical connectivity. I will demonstrate how these novel approaches can be used to test hypotheses about anatomical connections between regions and their relationships with other variables of interest.

Todd Constable, Yale University Magnetic Resonance Research Center

Topic: Consideration in Relating the Functional Connectome to Behavior: Connectome Based Predictive Modeling

This talk will focus on recent work relating the individual connectome to behavior and/or clinical symptoms. The individual connectome is a connectivity based measure obtained from fMRI data, that reflects the functional organization of an individual's brain. Variations in this functional organization can tell us something about the individual whether this be their capabilities on a behavioral task or some clinical symptom measure. The approach to connectome based predictive modeling will be described and factors that influence model performance discussed. Examples will be shown in which we are able to predict in novel individuals both behavioral and clinical measures obtained outside the scanner. Stated based manipulations will be discussed in the context of revealing trait based features. This work holds tremendous promise for understanding the neurophysiological basis for a range of normal behaviors, developmental trajectories, and neurological diseases and disorders.

March 13, 2018

Alex DaSilva, UM Dept. of Biologic & Material Sciences

Topic: TBD

March 20, 2018

Shella Keilholz, Dept of BME-Georgia Tech/Emory

Topic: TBD