Frank Scharnowski

University College London, London, United Kingdom 
"Frank Scharnowski"
Mean distance: 13.61 (cluster 29)
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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
Manoliu A, Sladky R, Scherpiet S, et al. (2020) Dopaminergic neuromodulation has no detectable effect on visual-cue induced haemodynamic response function in the visual cortex: A double-blind, placebo-controlled functional magnetic resonance imaging study. Journal of Psychopharmacology (Oxford, England). 269881120972341
Haugg A, Sladky R, Skouras S, et al. (2020) Can we predict real-time fMRI neurofeedback learning success from pretraining brain activity? Human Brain Mapping
Pamplona GSP, Heldner J, Langner R, et al. (2020) Network-based fMRI-neurofeedback training of sustained attention. Neuroimage. 117194
Scharnowski F, Nicholson AA, Pichon S, et al. (2020) The role of the subgenual anterior cingulate cortex in dorsomedial prefrontal-amygdala neural circuitry during positive-social emotion regulation. Human Brain Mapping
Ros T, Enriquez-Geppert S, Zotev V, et al. (2020) Consensus on the reporting and experimental design of clinical and cognitive-behavioural neurofeedback studies (CRED-nf checklist). Brain : a Journal of Neurology
Sitaram R, Ros T, Stoeckel L, et al. (2019) Author Correction: Closed-loop brain training: the science of neurofeedback. Nature Reviews. Neuroscience
Doerig A, Scharnowski F, Herzog MH. (2019) Building perception block by block: a response to Fekete Neuroscience of Consciousness. 2019: niy012
Koush Y, Pichon S, Eickhoff SB, et al. (2018) Brain networks for engaging oneself in positive-social emotion regulation. Neuroimage
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