Frank Scharnowski
Affiliations: | University College London, London, United Kingdom |
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"Frank Scharnowski"Mean distance: 13.61 (cluster 29) | S | N | B | C | P |
Parents
Sign in to add mentor| Volker H. Franz | research assistant | University of Giessen | |
| Thomas Kammer | research assistant | MPI Biological Cybernetics | |
| Michael H. Herzog | grad student | Swiss Institute of Technology | |
| Nikolaus Weiskopf | grad student | MPI Tuebingen | |
| Patrik O. Vuilleumier | post-doc | University of Geneva | |
| Geraint Rees | post-doc | 2008-2011 | UCL |
Children
Sign in to add trainee| Ronald Sladky | post-doc | ||
| David Steyrl | post-doc | 2019- | University of Vienna |
Collaborators
Sign in to add collaborator| Frouke Hermens | collaborator | 2004- | EPFL |
| Johannes Rueter | collaborator | 2006-2008 | EPFL |
BETA: Related publications
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Publications
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| Spee BTM, Mikuni J, Leder H, et al. (2023) Machine learning revealed symbolism, emotionality, and imaginativeness as primary predictors of creativity evaluations of western art paintings. Scientific Reports. 13: 12966 |
| Lieberman JM, Rabellino D, Densmore M, et al. (2023) Posterior cingulate cortex targeted real-time fMRI neurofeedback recalibrates functional connectivity with the amygdala, posterior insula, and default-mode network in PTSD. Brain and Behavior. e2883 |
| Lor CS, Zhang M, Karner A, et al. (2023) Pre- and post-task resting-state differs in clinical populations. Neuroimage. Clinical. 37: 103345 |
| Hellrung L, Kirschner M, Sulzer J, et al. (2022) Analysis of individual differences in neurofeedback training illuminates successful self-regulation of the dopaminergic midbrain. Communications Biology. 5: 845 |
| Krylova M, Skouras S, Razi A, et al. (2021) Progressive modulation of resting-state brain activity during neurofeedback of positive-social emotion regulation networks. Scientific Reports. 11: 23363 |
| Eder SJ, Nicholson AA, Stefanczyk MM, et al. (2021) Securing Your Relationship: Quality of Intimate Relationships During the COVID-19 Pandemic Can Be Predicted by Attachment Style. Frontiers in Psychology. 12: 647956 |
| Haugg A, Manoliu A, Sladky R, et al. (2021) Disentangling craving- and valence-related brain responses to smoking cues in individuals with nicotine use disorder. Addiction Biology. e13083 |
| Aue T, Hoeppli ME, Scharnowski F, et al. (2021) Contributions of diagnostic, cognitive, and somatovisceral information to the prediction of fear ratings in spider phobic and non-spider-fearful individuals. Journal of Affective Disorders. 294: 296-304 |
| Haugg A, Renz FM, Nicholson AA, et al. (2021) Predictors of real-time fMRI neurofeedback performance and improvement - A machine learning mega-analysis. Neuroimage. 237: 118207 |
| Eder SJ, Steyrl D, Stefanczyk MM, et al. (2021) Predicting fear and perceived health during the COVID-19 pandemic using machine learning: A cross-national longitudinal study. Plos One. 16: e0247997 |