Publications Holger

Mohr, H., & Ruge, H. (2021). Fast Estimation of L1-Regularized Linear Models in the Mass-Univariate Setting. Neuroinformatics, 19(3), 385–392. https://doi.org/10.1007/s12021-020-09489-1

Muller, E. J., Munn, B., Mohr, H., Ruge, H., & Shine, J. M. (2021). Brain state kinematics and the trajectory of task performance improvement. Neuroimage, 243, 118510. https://doi.org/10.1016/j.neuroimage.2021.118510

Sheffield, J. M., Mohr, H., Ruge, H., & Barch, D. M. (2021). Disrupted Salience and Cingulo-Opercular Network Connectivity During Impaired Rapid Instructed Task Learning in Schizophrenia. Clinical Psychological Science, 9(2), 210–221. https://doi.org/Artn%202167702620959341%2010.1177/2167702620959341

Fechtelpeter, J., Ruge, H., & Mohr, H. (2019). The cingulo-opercular network controls stimulus- response transformations with increasing efficiency over the course of learning. 2019 Conference on Cognitive Computational Neuroscience. https://doi.org/10.32470/CCN.2019.1060-0

Mohr, H., Cichy, R. M., & Ruge, H. (2019). Deep neural networks can predict human behavior in arcade games. 2019 Conference on Cognitive Computational Neuroscience. https://doi.org/10.32470/CCN.2019.1043-0

Ruge, H., Schafer, T. A., Zwosta, K., Mohr, H., & Wolfensteller, U. (2019). Neural representation of newly instructed rule identities during early implementation trials. Elife, 8, e48293. https://doi.org/10.7554/eLife.48293

Sheffield, J., Mohr, H., Ruge, H., & Barch, D. (2019). Disrupted Salience and Cingulo-Opercular Network Connectivity Underlies Impaired Rapid Task-Learning in Schizophrenia. Biological Psychiatry, 85(10), S359–S359. https://doi.org/DOI%2010.1016/j.biopsych.2019.03.911

Mohr, H., Wolfensteller, U., & Ruge, H. (2018). Large-scale coupling dynamics of instructed reversal learning. Neuroimage, 167, 237–246. https://doi.org/10.1016/j.neuroimage.2017.11.049

Mohr, H., Zwosta, K., Markovic, D., Bitzer, S., Wolfensteller, U., & Ruge, H. (2018). Deterministic response strategies in a trial-and-error learning task. PLoS Computational Biology, 14(11), e1006621. https://doi.org/10.1371/journal.pcbi.1006621

Ruge, H., Legler, E., Schäfer, T. A. J., Zwosta, K., Wolfensteller, U., & Mohr, H. (2018). Unbiased Analysis of Item-Specific Multi-Voxel Activation Patterns Across Learning. Frontiers in Neuroscience, 12, 723. https://doi.org/10.3389/fnins.2018.00723

Frimmel, S., Wolfensteller, U., Mohr, H., & Ruge, H. (2016). The neural basis of integrating pre- and post-response information for goal-directed actions. Neuropsychologia, 80, 56–70. https://doi.org/10.1016/j.neuropsychologia.2015.10.035

Mohr, H., Wolfensteller, U., Betzel, R. F., Misic, B., Sporns, O., Richiardi, J., & Ruge, H. (2016). Integration and segregation of large-scale brain networks during short-term task automatization. Nature Communications, 7, 13217. https://doi.org/10.1038/ncomms13217

Mohr, H., Wolfensteller, U., Frimmel, S., & Ruge, H. (2015). Sparse regularization techniques provide novel insights into outcome integration processes. Neuroimage, 104(0), 163–176. https://doi.org/10.1016/j.neuroimage.2014.10.025