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 2024-2025 Speaker Series
All presentations will take place at 4:00 - 5:30 PM.October 22, 2024
Dr. Nathaniel G. Harnett, Assistant Professor, Department of Psychiatry, Director of the Neurobiology of Affective and Traumautic Experiences Laboratory at McLean Hospital, Harvard University
Topic: Developing generalizable neural signatures of PTSD Posttraumatic stress disorder is a major psychiatric disorder that affects roughly 8% of individuals at any given time. Nearly 90% of individuals will experience a traumatic event, placing them at risk for the disorder however not all trauma survivors go on to develop PTSD. Understanding the mechanisms that underlie individual variability in PTSD susceptibility is key for developing effective and inefficient early prediction and prevention approaches to minimize the emotional, financial, and social burdens of the disorder. The current presentation will review emergent research on the neurobiological mechanisms that are associated with future development of PTSD in trauma survivors. Focusing on magnetic resonance imaging (MRI) research, both functional and structural signatures of PTSD will be discussed. Further, the presentation will discuss current roadblocks to deploying potential signatures for PTSD with specific attention given to the role socioenvironmental disparities can impact generalizability of findings.November 5, 2024
Dr. Bharath Chandrasekaran, Ralph and Jean Sundin Endowed Professor, Department Chair, Communication Sciences and Disorders, School of Communication, Northwestern University
Topic: Neural Systems Underlying Speech Categorization My research program employs a systems neuroscience approach to investigate the computations, maturational constraints, and plasticity underlying auditory and speech categorization. Speech signals are multidimensional, acoustically variable, and temporally ephemeral. A significant computational challenge in speech perception, and more broadly in audition, is categorization—the task of mapping continuous, multidimensional, and variable acoustic signals into discrete behavioral equivalence classes. Despite the complexity of this computational challenge, native speech perception is rapid and automatic. In contrast, learning new non-native speech categories is effortful and is considered a challenging computational task for the mature brain. In this presentation, I will elucidate the mechanisms underlying how novel speech categories are acquired and represented in the mature human brain. I will discuss the neurobiology of two complementary auditory cortico-striatal streams involved in sound-to- rule and sound-to-reward mapping. I will discuss how these systems interact during learning and contribute to individual differences in auditory and speech category learning success. Finally, I will discuss ongoing experiments that leverage the neurobiology of the dual cortico- striatal streams to design optimal behavioral training that reduces inter-individual differences in learning success.December 3, 2024
Dr. Gagan S. Wig, Associate Professor, School of Behavioral and Brain Sciences, The University of Texas at Dallas
Topic: Brain aging across health, exposures, and species: Using network science to chart disparities in brain health and assess Alzheimer’s disease risk & resilience How does brain network failure lead to aging- and disease-related cognitive decline? I will describe efforts from my lab that have helped to develop and incorporate network science to demonstrate how the brain's large-scale functional network organization changes across the lifespan. This work is revealing that functional brain network organization relates to an individual's cognitive ability during healthy adulthood, and that brain network changes are prognostic of cognitive dysfunction in Alzheimer's disease (independent of structural atrophy and neuropathology). Our parallel work has demonstrated that an individual's life course environmental exposures relate to their trajectory of brain network decline, and that these associations are due to the impacts of cumulative stress. Finally, I will touch on our initial steps towards developing non-human animal models of aging-related brain network changes, which we are advancing in order to create translational bridges between brain aging in humans and other species. Collectively, these observations are offering a new approach for deciphering mechanisms of healthy and pathological aging, spotlighting a path for discovering the resilience and vulnerabilities of brain aging that are linked to an individual's past and present exposures, and are catalyzing the development of a novel class of precision health biomarkers that are based on large-scale brain network function.January 21, 2025
Dr. Mbemba Jabbi, Assistant Professor, Department of Psychiatry and Behavorial Sciences, The University of Texas at Austin
Topic: Translating neuroimaging markers of affect neurobiology into postmortem molecular markers for affective disorder morbidity and mortality phenotypes. Over 12% of the world’s population will, at some point during their lifetime, struggle with maintaining emotional health or suffer from mood disorders. Furthermore, close to a million people who are often living with mood disorders die by suicide annually. Neuroimaging studies have contributed relevant knowledge on how specific brain changes, including reductions in the anatomical and physiological integrity of particular brain regions, are present in individuals with mood disorders. However, the molecular substrates underlying these well-documented brain changes are poorly understood. We apply imaging-guided transcriptomics in human postmortem brain RNA-sequencing to translate neuroimaging findings into measures of gene expression changes (GECs) in the anterior insula (Ant-Ins) and subgenual anterior cingulate (sACC) cortical regions that are known to play mediate the sensing and regulation of emotions and bodily feeling states and known to harbor reduced anatomical and physiological integrity in individuals living with mood disorders. By integrating data-driven factor analysis with gene co-expression, differential expression, and genetic pathway-enrichment analyses, we identified innate immune and inflammatory GECs in the Ant-Ins related to mood disorders and related psychiatric morbidity. In contrast, longevity, despite living with a mood disorder, was associated with GECs associated with metabolic and biosynthesis processes in the sACC. In comparison, suicide-associated GECs were linked to inflammatory, metabolic, and cellular developmental molecular processes in the sACC. Our results reveal brain region-specific and regionally overlapping gene expression (molecular repertoires) and provide a framework for defining molecular biological mechanisms underlying emotion and mood regulatory pathologies and mortality risks.March 18, 2025
Dr. Monica Rosenberg, Assistant Professor, Department of Psychology, The University of Chicago
Topic: Characterizing cognitive and attentional dynamics with large-scale brain dynamics Our everyday experiences are characterized by ever-changing cognitive and attentional states. At some moments we may be engaged in what we are doing whereas at others we may be distracted and mind wandering. How does the brain give rise to these richly varying experiences? In this talk, I will present work demonstrating how we can use fMRI data to decode individuals' cognitive and attentional states as they perform tasks, watch movies, and rest quietly. I will first show that dynamic functional connectivity tracks moment-to-moment fluctuations in attention across datasets and contexts. I will next present evidence that these same dynamics predict the content of ongoing thought in the absence of any explicit task. Taken together, these results suggest that brain dynamics robustly encode everyday mental state dynamics.April 15, 2025
Dr. Joaquín Goñi Cortes, Associate Professor, Edwardson School of Industrial Engineering and Weldon School of Biomedical Engineering, Purdue University
Topic: Human Brain Functional Connectivity: Fingerprinting and Reconfiguration Functional connectomes (FCs), typically represented as correlation matrices, can be transformed using tangent space projections based on Riemannian geometry, resulting in tangent-FCs. These tangent-FCs have shown improved predictive power for brain phenotypes and were recently found to have a stronger individual fingerprint compared to standard FCs across various fMRI conditions, scanning length, parcellation granularities, and when assessing test-retest as well as MZ and DZ twins. Building on this foundation, we applied this framework to study brain functional reconfiguration—the dynamic change in connectivity when transitioning between cognitive states—and its relationship to alcohol use disorder (AUD) risk factors. In a study involving a stop-signal task, we quantified functional reconfiguration using correlation distances between tangent-FCs derived from rest-to-task and task-to-rest conditions. We found that engaging functional reconfiguration was diminished in individuals with a family history of AUD, while disengaging functional reconfiguration was reduced in those with greater recent alcohol consumption. These findings were consistent across analyses using multi-linear regression models, indicating that heritable and behavioral AUD risk factors are associated with functional reconfiguration when engaging into (heritable), and disengaging from (behavioral) a task. Our results suggest that functional reconfiguration, as assessed via tangent-FCs, may serve as a novel biomarker for identifying and understanding vulnerability to AUD. Overall, this line of work highlights the value of Riemannian geometry-based methods in capturing fine-grained individual differences in brain function.