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, Leder H, Mikuni J, et al. (2024) Using machine learning to predict judgments on Western visual art along content-representational and formal-perceptual attributes. Plos One. 19: e0304285 |
| Mikuni J, Spee BTM, Forlani G, et al. (2024) Cross-cultural comparison of beauty judgments in visual art using machine learning analysis of art attribute predictors among Japanese and German speakers. Scientific Reports. 14: 15948 |
| Karner A, Zhang M, Lor CS, et al. (2024) The "SpiDa" dataset: self-report questionnaires and ratings of spider images from spider-fearful individuals. Frontiers in Psychology. 15: 1327367 |
| Popovova J, Mazloum R, Macauda G, et al. (2024) Enhanced attention-related alertness following right anterior insular cortex neurofeedback training. Iscience. 27: 108915 |
| Watve A, Haugg A, Frei N, et al. (2024) Facing emotions: real-time fMRI-based neurofeedback using dynamic emotional faces to modulate amygdala activity. Frontiers in Neuroscience. 17: 1286665 |
| Lieberman JM, Rabellino D, Densmore M, et al. (2023) A tale of two targets: examining the differential effects of posterior cingulate cortex- and amygdala-targeted fMRI-neurofeedback in a PTSD pilot study. Frontiers in Neuroscience. 17: 1229729 |
| Park AH, Patel H, Mirabelli J, et al. (2023) Machine learning models predict PTSD severity and functional impairment: A personalized medicine approach for uncovering complex associations among heterogeneous symptom profiles. Psychological Trauma : Theory, Research, Practice and Policy |
| 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 |