Publications

Rosenberg, M. D., Casey, B. J., Holmes, A. J. (in press). Prediction complements explanation in understanding the developing brain. Nature Communications.

Beaty, R. E., Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q., Fink, A., Qiu, J., Kwapil, T. R., Kane, M., Silvia, P. J. (2018). Robust prediction of individual creative ability from brain functional connectivity. Proceedings of the National Academy of Sciences. [PDF]

Yoo, K., Rosenberg, M. D., Hsu, W.-T., Zhang, S., Li, C.-S. R., Scheinost, D., Constable, R. T., Chun, M. M. (2018). Connectome-based predictive modeling of attention: Comparing different functional connectivity features and prediction methods across datasets. NeuroImage, 167: 11-22. [PDF]

Jangraw, D. C., Gonzalez-Castillo, J., Handwerker, D. A., Ghane, M., Rosenberg, M. D., Panwar, P., Bandettini, P. A. (2018). A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task. NeuroImage, 166: 99-109. [PDF]

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. [PDF]

List, A., Rosenberg, M. D., Sherman, A., Esterman, M. (2017). Pattern classification of EEG signals reveals perceptual and attentional states. PLOS ONE, 12(4): e0176349. [PDF]

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. [PDF, issue cover]

Shen, X. Finn, E. S., Scheinost, D., Rosenberg, M. D., Chun, M. M., Papademetris, X., Constable, R. T. (2017). Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols, 12(3): 506-518. [PDF]

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. [PDF]

Chekroud, A. M., Ward, E. J., Rosenberg, M. D., Holmes, A. J. (2016). Patterns in the human brain mosaic discriminate males from females. Proceedings of the National Academy of Sciences, pii: 201523888. [PDF]

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. [PDF, News and Views by Smith (2016), The Conversation]

Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., Constable, R. T. (2015). Functional connectome fingerprinting: identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18(11): 1664-1671. [PDF, issue cover]

Rosenberg, M. D., Finn, E. S., Constable, R. T., Chun, M. M. (2015). Predicting moment-to-moment attentional state. NeuroImage, 114: 249-256. [PDF]

Esterman, M., Rosenberg, M. D., Noonan, S. (2014). Intrinsic fluctuations in sustained attention and distractor processing. Journal of Neuroscience, 34(5): 1724-1730. [PDF]

Rosenberg, M., Noonan, S., DeGutis, J., Esterman, M. (2013). Sustaining visual attention in the face of distraction: A novel gradual-onset continuous performance task. Attention, Perception, & Psychophysics, 75(3): 426-439. [PDF]

Esterman, M., Noonan, S., Rosenberg, M., DeGutis, J. (2013). In the zone or zoning out? Tracking neural and behavioral fluctuations in sustained visual attention. Cerebral Cortex, 23(11): 2712-2723. [PDF, gradCPT]

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