YSPH Biostatistics Seminar: “Using Dynamic Networks to Study the Brain: New Methods and an Application to a Weight-loss Study"

NOTE: BIS 525 students are required to attend in person. Others are invited to attend in person, but may also attend via Zoom.

SPEAKER: Heather Marie Shappell, PhD, Assistant Professor, Department of Biostatistics and Data Science, Wake Forest University School of Medicine

TITLE: “Using Dynamic Networks to Study the Brain: New Methods and an Application to a Weight-loss Study"

ABSTRACT: Network neuroscience has transformed human neuroscience. Networks estimated from functional magnetic resonance imaging (fMRI) data contain information about regional brain interactions that support cognition and behavior. Over 1 million articles have been published that use functional brain network analyses. Previous studies were largely based on one average network constructed across the entire scan lasting many minutes (i.e., static functional connectivity), but emerging evidence indicates that network topology exhibits meaningful variations on the order of seconds (i.e., dynamic functional connectivity- dFC). We are now seeing a major shift, with over 50% of the functional brain network papers last year being based on dFC. Network dynamics are becoming vital for understanding neuropsychiatric disorders and may be effective biomarkers for early prevention, diagnosis, and treatment, but the statistical methods necessary for extracting meaningful dynamic network data and identifying network state changes are in their infancy. We propose a novel hidden semi-Markov model (HSMM) approach for inferring functional brain networks from fMRI data. Specifically, we propose using HSMMs to find each subject's most probable series of network states, the cumulative time in each state, and the networks associated with each state. This modeling framework is then demonstrated on a weight-loss study in older adults with obesity (i.e., the EMPOWER study). Overall, we found significant differences in participant occupancy time, dwell time, and transition probabilities of brain network states between successful and unsuccessful weight-loss groups in older adults with obesity during a food-cue task. We feel that identifying these dynamic connectivity pattern differences is an important next step for guiding clinical decision-making in weight management.

YSPH values inclusion and access for all participants. If you have questions about accessibility or would like to request an accommodation, please contact Charmila Fernandes at Charmila.fernandes@yale.edu. We will try to provide accommodations requested by November 30, 2023.