Translational and Clinical Science

Exercise and Cognitive Aging

Research Consultant · Excersize and Brain Lab, University of Maryland’s School of Public Health

I collaborate with kinesiology researchers at UMD’s School of Public Health in developing a brain-based prediction model for understanding how fitness relates to cognitive aging.

CPM_Methods

Predictive ModelingCognitive AgingNeuroimagingBrain Networks

  1. Merchant, J. S., Purcell, J. J., Callow, D. D., Won, J., Kommula, Y., Rosenberg, S., … & Smith, J. C. (2025). The Functional Connectome Mediates Associations Between Fitness and Cognition Across the Adult Lifespan. Human Brain Mapping, 46(16), e70404.

Transdiagnostic Executive Function in Pediatric Psychiatry

Research Associate · Georgetown University / Children’s National Hospital

PediatricsMental HealthCognitive NeuroscienceClusteringMachine Learning</span>NeuroimagingPython</p> Psychiatric diagnostic categories are administratively convenient and biologically messy. Children with ADHD, autism, and anxiety diagnoses show overlapping executive function profiles, and the diagnosis often predicts less about a child's actual cognitive difficulties than the profile itself does. This NIH R01 — a collaboration between **Dr. Chandan Vaidya** in Georgetown University's Psychology Department and **Dr. Lauren Kenworthy**, Director of the Center for Autism Studies at Children's National Hospital — took a data-driven approach: cluster children by measured executive function rather than by diagnosis, and see whether the resulting groups map onto brain and behavioral outcomes better than the diagnostic labels do. I built and validated the clustering pipeline, and developed the end-to-end data collection and analysis protocols connecting the university lab to the hospital's clinical population — including web-based assessment tools that let clinical collaborators score and submit data directly. The resulting transdiagnostic dimensions were associated with distinct patterns of cingulate-prefrontal connectivity and with adaptive functioning, supporting a dimensional rather than categorical model of childhood psychopathology. This period is also when I founded **Brainhack Global DC**, a workshop series spanning NIH, Georgetown, and the University of Maryland. 1. Kaminski, A., You, X., Flaharty, K., Jeppsen, C., Li, S., **Merchant, J. S.**, Berl, M. M., Kenworthy, L., & Vaidya, C. J. (2022). Cingulate-Prefrontal Connectivity During Dynamic Cognitive Control Mediates Association Between P-Factor and Adaptive Functioning in a Transdiagnostic Pediatric Sample. *Biological Psychiatry: Cognitive Neuroscience and Neuroimaging*. https://doi.org/10.1016/j.bpsc.2022.07.003 1. Cook, K. M., You, X., Cherry, J. B., **Merchant, J. S.**, Skapek, M., Powers, M. D., ... & Vaidya, C. J. (2021). Neural correlates of schema-dependent episodic memory and association with behavioral flexibility in autism spectrum disorders and typical development. *Journal of neurodevelopmental disorders*, *13*(1), 1-16. 1. Loewenstern, J., You, X., **Merchant, J.**, Gordon, E. M., Stollstorff, M., Devaney, J., & Vaidya, C. J. (2019). Interactive effect of 5-HTTLPR and BDNF polymorphisms on amygdala intrinsic functional connectivity and anxiety. *Psychiatry Research: Neuroimaging*, 285, 1-8. # Neuroscience of Eating Behavior ## Project Coordinator/Lab Manager · Center for Translational Neuroscience, University of Oregon Through collaborations with the Center for Translational Neuroscience at the University of Oregon, I've examined neurocognitive predictors of behavior change. ![EatingReview](/images/EatingReview.png)

Health BehaviorCognitive NeuroscienceNeuroimagingMATLAB

Why do some people successfully change their eating behavior and others do not? The Oregon Eating Study followed higher-BMI adults longitudinally, combining neuroimaging measures of food valuation and craving regulation with real-world dietary outcomes, to ask whether brain measures predict change better than self-report does. I coordinated the study — research design, recruitment, data collection protocols, and analysis — across its lifecycle, and first-authored work showing how neural substrates of food valuation relate to BMI and healthy eating in higher-BMI individuals. Related work from the group demonstrated that brain activity during craving regulation predicted subsequent changes in self-reported craving and consumption. Earlier in the same lab I co-authored work in the *Journal of Neuroscience* showing **training-induced plasticity in inhibitory control networks** — that self-regulation capacity is trainable and the change is visible in the brain. Taken together this is a body of work on habit formation, self-regulation, and behavior change measurement, which is the scientific foundation most digital health and wellness products are built on. 1. **Merchant, J. S.**, Cosme, D., Giuliani, N. R., Dirks, B., & Berkman, E. T. (2020). Neural substrates of food valuation and its relationship with BMI and healthy eating in higher BMI individuals. *Frontiers in Behavioral Neuroscience, 14*, 578676. 1. Berkman, E. T., Kahn, L. E., & **Merchant, J. S.** (2014). Training-induced changes in inhibitory control network activity. *Journal of Neuroscience, 34*(1), 149–157. 1. Giuliani, N. R., **Merchant, J. S.**, Cosme, D., & Berkman, E. T. (2018). Neural predictors of dietary change. *Annals of the New York Academy of Sciences, 1428*(1), 208–220. *(our figure made the cover for the special issue!)*