Monica D. Rosenberg, Ph.D.

Monica D. Rosenberg's picture
Assistant Professor, University of Chicago
University of Chicago



Assistant Professor, University of Chicago Department of Psychology


Doctor of Philosophy, Psychology, Yale University, 2017

Research Interests:

Monica Rosenberg’s research explores how we pay attention, and how insights from attention research can help improve focus. In particular, her lab builds models that predict individual differences in attention and cognition from functional brain connectivity. This work has revealed, for example, that data collected while a person is simply resting in an MRI scanner can be used predict aspects of their behavior, including how well they pay attention and remember information. Dr. Rosenberg’s work also uses functional MRI, behavioral experiments, and machine learning methods to investigate how attention fluctuates over time, changes across development, and interacts with the rest of the mind.

Selected Publications:

Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T.,
Chun, M. M. (2016). A neuromarker of sustained attention from whole-brain functional
connectivity. Nature Neuroscience, 19(1): 165–171.
Rosenberg, M. D., Scheinost, D., Greene, A. S., Avery, E. W., Kwon, Y. H., Finn, E. S., Ramani,
R., Qiu, M., Constable, R. T., Chun, M. M. (2020). Functional connectivity predicts changes
in attention observed across minutes, days, and months. Proceedings of the National
Academy of Sciences.
Rosenberg, M. D., Finn, E. S., Scheinost, D., Constable, R. T., Chun, M. M. (2017).
Characterizing attention with predictive network models. Trends in Cognitive Sciences,
21(4): 290–302.
Rosenberg, M. D., Zhang, S., Hsu, W.-T., Scheinost, D., Finn, E. S., Shen, X., Constable, R. T.,
Li, C.-S. R., Chun, M. M. (2016). Methylphenidate modulates functional network
connectivity to enhance attention. Journal of Neuroscience, 36(37): 9547–9557.
Rosenberg, M. D., Hsu, W.-T., Scheinost, D., Constable, R. T., Chun, M. M. (2018).
Connectome-based models predict separable components of attention in novel individuals.
Journal of Cognitive Neuroscience, 30(2): 160–173.