Frank Scharnowski

University College London, London, United Kingdom 
"Frank Scharnowski"
Mean distance: 13.61 (cluster 29)
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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
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