Dr. Holger Mohr
Postdoc
Contact Information
TU Dresden
Department of Psychology
Chair of General Psychology
Zellescher Weg 17
01069 Dresden
Phone: +49 (0)351 463-36495
E-Mail:
Research Statement
Analysis methods for neuroimaging data: Machine learning, deep learning, connectivity methods, control theory, dynamic causal modeling
Scientific Education
| 2012-2016 | TU Dresden, Dr. rer. nat. on Methods for Neuroimaging Data |
| 2004-2009 | TU Berlin, Major Mathematics, Minor Psychology, Diplom |
| 2002-2004 | University Freiburg, Major Mathematics, Minor Psychology, Vordiplom |
Professional Experience
| Since 7/2020 | Postdoc at the Chair of General Psychology and Neuroimaging Center, Department of Psychology, TU Dresden |
| 2012-2020 |
Core Imaging Facility of the SFB 940, Department of Psychology, TU Dresden |
| 2010-2012 | Systems Neuroscience Lab at the Department of Psychiatry, University Medical Center Göttingen |
| 2009-2010 | Junior Consultant at Mercer Deutschland |
Publications
Boehm, I., Mohr, H., King, J.A., Steding, J., Geisler, D., Wronski, M.-L., Weigel, K., Roessner, V., Ruge, H., and Ehrlich, S. (2021). Aberrant neural representation of food stimuli in women with acute anorexia nervosa predicts treatment outcome and is improved in weight restored individuals. Transl. Psychiatry 11, 532.
Müller, E.J., Munn, B., Mohr, H., Ruge, H., and Shine, J.M. (2021). Brain state kinematics and the trajectory of task performance improvement. NeuroImage 243, 118510.
Krönke, K.-M., Mohr, H., Wolff, M., Kräplin, A., Smolka, M.N., Bühringer, G., Ruge, H., and Goschke, T. (2021). Real-Life Self-Control is Predicted by Parietal Activity During Preference Decision Making: A Brain Decoding Analysis. Cogn. Affect. Behav. Neurosci.
Sheffield, J.M., Mohr, H., Ruge, H., and Barch, D.M. (2021). Disrupted salience and cingulo-opercular network connectivity during impaired rapid instructed task learning in schizophrenia. Clin. Psychol. Sci. 9(2), 210-221
Mohr, H., and Ruge, H. (2020). Fast Estimation of L1-Regularized Linear Models in the Mass-Univariate Setting. Neuroinformatics
Knorr, F., Neukam, P., Fröhner, J., Mohr, H., Smolka, M., and Marxen, M. (2020). A Comparison of fMRI and Behavioral Models for Predicting Inter-Temporal Choices. NeuroImage 211, 116634.
Krönke, K.-M., Wolff, M., Mohr, H., Kräplin, A., Smolka, M.N., Bühringer, G., and Goschke, T. (2020). Predicting Real-Life Self-Control From Brain Activity Encoding the Value of Anticipated Future Outcomes. Psychol. Sci. 31, 268-279.
Ruge, H., Schäfer, T.A., Zwosta, K., Mohr, H., and Wolfensteller, U. (2019). Neural representation of newly instructed rule identities during early implementation trials. ELife 8, e48293.
Mohr, H., Cichy, R.M., and Ruge, H. Deep neural networks can predict human behavior in arcade games. Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany. DOI: 10.32470/CCN.2019.1043-0
Fechtelpeter, J., Ruge, H., and Mohr, H. The cingulo-opercular network controls stimulus-response transformations with increasing efficiency over the course of learning. Proceedings of the 2019 Conference on Cognitive Computational Neuroscience, Berlin, Germany. DOI: 10.32470/CCN.2019.1060-0
Mohr, H., Zwosta, K., Markovic, D., Bitzer, S., Wolfensteller, U., and Ruge, H. (2018). Deterministic response strategies in a trial-and-error learning task. PLoS Comput. Biol. 14, 11.
Ruge, H., Legler, E., Schäfer, T.A.J., Zwosta, K., Wolfensteller, U., and Mohr, H. (2018). Unbiased Analysis of Item-Specific Multi-Voxel Activation Patterns Across Learning. Front. Neurosci. 12, 723.
Krönke, K.M., Wolff, M., Mohr, H., Kräplin, A., Smolka, M.N., Bühringer, G., and Goschke, T. (2018). Monitor yourself! Deficient error-related brain activity predicts real-life self-control failures. Cogn. Affect. Behav. Neurosci. 18, 622-637.
Mohr, H., Wolfensteller, U., and Ruge, H. (2018). Large-scale coupling dynamics of instructed reversal learning. NeuroImage 167, 237-246.
Mohr, H., Wolfensteller, U., Betzel, R.F., Misic, B., Sporns, O., Richiardi, J., and Ruge, H. (2016). Integration and segregation of large-scale brain networks during short-term task automatization. Nature Comm. 7, 13217.
Frimmel, S., Wolfensteller, U., Mohr, H., and Ruge, H. (2016). The neural basis of integrating pre- and post-response information for goal-directed actions. Neuropsychologia 80, 56-70.
Trost, S., Diekhof, E.K., Mohr, H., Vieker, H., Kramer, B., Wolf, C., Keil, M., Dechent, P., Binder, E.B., and Gruber, O. (2016). Investigating the Impact of a Genome-Wide Supported Bipolar Risk Variant of MAD1L1 on the Human Reward System. Neuropsychopharmacology 41, 2679-2687.
Mohr, H., Wolfensteller, U., Frimmel, S., and Ruge, H. (2015). Sparse regularization techniques provide novel insights into outcome integration processes. NeuroImage 104, 163-176.
Wolf, C., Mohr, H., Diekhof, E.K., Vieker, H., Goya-Maldonado, R., Trost, S., Kramer, B., Keil, M., Binder, E.B., and Gruber, O. (2015). CREB1 Genotype Modulates Adaptive Reward-Based Decisions in Humans. Cereb. Cortex. 26, 2970-2981.
Wolf, C., Mohr, H., Schneider-Axmann, T., Reif, A., Wobrock, T., Scherk, H., Kraft, S., Schmitt, A., Falkai, P., and Gruber, O. (2014). CACNA1C genotype explains interindividual differences in amygdala volume among patients with schizophrenia. Eur. Arch. Psychiatry Clin. Neurosci. 264, 93-102.