Nikolaus Weiskopf

Affiliations: 
University College London, London, United Kingdom 
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"Nikolaus Weiskopf"
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Publications

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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
Clark IA, Callaghan MF, Weiskopf N, et al. (2021) The relationship between hippocampal-dependent task performance and hippocampal grey matter myelination and iron content. Brain and Neuroscience Advances. 5: 23982128211011923
Papoutsi M, Magerkurth J, Josephs O, et al. (2020) Activity or connectivity? A randomized controlled feasibility study evaluating neurofeedback training in Huntington's disease. Brain Communications. 2: fcaa049
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
Lee Y, Kettinger AO, Wilm BJ, et al. (2019) A comprehensive approach for correcting voxel-wise b-value errors in diffusion MRI. Magnetic Resonance in Medicine
Betts MJ, Kirilina E, Otaduy MCG, et al. (2019) Locus coeruleus imaging as a biomarker for noradrenergic dysfunction in neurodegenerative diseases. Brain : a Journal of Neurology
Callaghan MF, Lutti A, Ashburner J, et al. (2019) Example dataset for the hMRI toolbox. Data in Brief. 25: 104132
Papazoglou S, Streubel T, Ashtarayeh M, et al. (2019) Biophysically motivated efficient estimation of the spatially isotropic component from a single gradient-recalled echo measurement. Magnetic Resonance in Medicine
Karolis VR, Callaghan MF, Tseng CJ, et al. (2019) Spatial gradients of healthy aging: a study of myelin-sensitive maps. Neurobiology of Aging. 79: 83-92
Sitaram R, Ros T, Stoeckel L, et al. (2019) Author Correction: Closed-loop brain training: the science of neurofeedback. Nature Reviews. Neuroscience
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