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: How Neurotechnologies are Providing New Insights In Vivo Into the Treatment of Migraine and other Chronic Pain Disorders

While understanding brain mechanisms in chronic pain is important, equally important is applying these concepts in the clinical environment. For example, recent in vivo molecular imaging studies have demonstrated that there is a dysfunctional μ-opioid and dopamine neurotransmission in certain brain regions of migraineurs during spontaneous headache attacks and allodynia. In parallel, other studies have shown scientific evidence that novel non-invasive neuromodulation tools can change endogenous neurotransmission and also provide relatively lasting pain relief in some pain disorders, including chronic migraine, TMD and fibromyalgia. Our overall goal is to discuss novel advances in pain neuroimaging (e.g., PET, fNIRS), with a focus on clinical applications, even with their combination of augmented reality. We will also discuss an emerging neuroimaging technology, functional near-infrared spectroscopy (fNIRS), that can now provide better understating of the ongoing impact of affective and sensory experience in the brain before, during, and after clinical pain.

March 20, 2018

Shella Keilholz, Dept of BME-Georgia Tech/Emory

Topic: Functional MRI of the Dynamic Brain, Quasiperiodic Pattern, Brain States, and Trajectiories

Resting state functional magnetic resonance imaging (rs-fMRI) can capture activity patterns throughout the whole brain as a function of time. The whole brain patterns can be characterized into functional networks of brain areas whose activity maintains a statistical dependence over the course of the scan, a feature described as functional connectivity. More recently, researchers have moved beyond measures of functional connectivity that average across an entire scan (typically 5-10 minutes) to methods that can describe the dynamic features of the brain over the course of the scan. These include point process analysis, windowed functional connectivity techniques, hidden Markov models, and more. This talk will focus on a prominent dynamic feature of rs-fMRI data, quasiperiodic spatiotemporal patterns of activity (QPPs). The QPPs contribute substantially to average measures of functional connectivity, are tied to infraslow activity and behavioral performance, and are altered in patient groups. Preliminary data suggests that they reflect neuromodulatory input from deep brain nuclei and may allow this neurophysiological signal to be isolated from standard rs-fMRI exams.

April 17, 2018

Dustin Scheinost, Assistant Professor of Radiology & Biomedical Imaging, Yale School of Medicine

Topic: Ways to Improve Behavioral Predictions from Functional Connectivity Data

Assessment of neural networks at the level of large-scale systems has the potential to provide novel insight into brain-behavior relationships. However, how best to probe these brain-behavior relationships remains unclear. This talk will describe how factors such as data reliability, brain state, and individualized functional parcellations moderate behavioral prediction performance of a recently developed functional connectivity analysis approach, connectome-based predictive modeling (CPM). Similarly, I will present an extension to CPM, labeled multidimensional CPM, that incorporates information from multiple tasks/modalities into a single model to improve behavioral predictions.