Joint Quantitative Brownbag


Michael Halquist

Dr. Michael Halquist
Department of Psychology and Neuroscience
University of North Carolina at Chapel Hill

Dr. Michael Hallquist is an Associate Professor in the Clinical Psychology Program and Quantitative Psychology Program in the Department of Psychology and Neuroscience. He is also the Director of the Developmental Personality Neuroscience Laboratory at UNC Chapel Hill. His research seeks to characterize how the disrupted maturation of neurobehavioral systems during adolescence and young adulthood is associated with the emergence of emotional and interpersonal problems. As a clinician, his approach integrates third-wave behavior therapies (especially Acceptance and Commitment Therapy), short-term psychodynamic therapy, and cognitive-behavioral approaches.


Using Multilevel Models to Uncover Spatiotemporal Dynamics of Neural Activity (Or, How I Learned to Stop Worrying and Love the MLM)


Over the past 30 years, functional magnetic resonance imaging (fMRI) has become an extremely popular technology in psychology and neuroscience. Although fMRI has revolutionized human neuroscience, typical data analyses are often mundane and heavy on statistical assumptions. Most analyses build on a version of the general linear model (GLM) that aggregates neural activity across many independent trials in an experiment, thereby eliminating within-person variability while retaining between-person differences. As a result, trial-to-trial variation in neural activity is often ignored, limiting our understanding of brain-behavior relationships that unfold dynamically with learning or repetition.

In this talk, I will describe how the extension of multilevel models across many brain regions and timepoints in an experiment can uncover both between-trial neural variability and within-trial temporal dynamics in task-based fMRI studies. The Multilevel Event-related Deconvolved Signal Analysis (MEDuSA) framework provides a useful tool for interrogating where and when cognitive processes (e.g., as instantiated by computational models) are represented by brain regions. I will show the application of MEDuSA to empirical fMRI data and describe ongoing simulation research on this approach.