Megan Finnegan is named a 2022 Beckman Institute Graduate Fellow


Congratulations to Megan Finnegan on being named to the 2022 class of Backman Institute Graduate Fellows!

Megan is currently pursuing two Ph.D.s in psychology and neuroscience. 

Finnegan is examining the relationship between female teenagers’ well-being and a type of cyberbullying called online exclusion. She is interested in how the biological processes that occur in the brain during exclusion can help predict future mental health outcomes. Using the neuroimaging facilities in Beckman’s Biomedical Imaging Center, she will monitor participants’ brain activity during a computer-simulated online exclusion scenario.

The project will involve creating new software to analyze dynamic (i.e., time-varying) brain connectivity with well-established metrics and a more recent approach called probabilistic graphical models. 

Graphical models assume that a lower-dimensional latent construct links the changes in brain activity we observe even though we can't directly observe the latent construct. This type of model intuitively maps on to how we think about the relationship between brain and cognition. The actions the brain is physically implementing in neural firing patterns represent the information processing constructs we often refer to as cognition, which we can't directly observe but infer exist. Prior research shows that the well-established ways of analyzing dynamic brain connectivity seem to align well with changes in human behavior, for example, switching between a math task and a visual search task, but it's unknown if the graphical model approach will provide a more accurate mapping between changes in the brain and behavior or potentially allow us to track changes in the subprocesses the brain engages to accomplish a task. 

The online exclusion experiments is called a naturalistic experiment and is designed to better capture the type of processes that people engage in everyday life. Naturalistic experiments appear to show much better test-retest reliability and they may be better for understanding how individuals differ in their brain response patterns. Still, just like everyday life, there are not often any clear boundaries when one task ends, and the other begins. Without clear boundaries to determine what part of the data to look at, the decisions can be somewhat arbitrary and be muddied by cognitive processes that aren't relevant to the research question. So, by developing this software and establishing the most accurate method, Megan hopes to expand the repertoire of tools cognitive neuroscientists have at their disposal to query the changing patterns of human cognition.

Once the best method is established, then she will see what features extracted from that approach are predictive of future depression and suicidal ideation in contrast to in-the-moment low mood and affect and how this relates to the levels of peer victimization girls experience.

The work is mentored by an interdisciplinary team spanning psychology, neuroscience, electrical and computer engineering, and statistics including Psychology faculty members Wendy Heller, Karen Rudolph, and Sepideh Sadaghiani.