Author

Fukuda Megumi

RIKEN Center for Brain ScienceORCID 0000-0002-8608-4756Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Neuroscience· 2

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Research Focus:clustering· 1 Research Focus:differential expression· 1 Organism:human· 1 Assay:imaging· 1 Approach:machine learning (classical)· 1 Approach:statistical model· 1

Frequent Co-Authors

Amalia McDonald· 2 shared Amelie HauggiD· 2 shared Benjamin BeckeriD· 2 shared Bettina SorgeriD· 2 shared Catharina ZichiD· 2 shared Dimitri Van De VilleiD· 2 shared Dong‐Youl KimiD· 2 shared Frank ScharnowskiiD· 2 shared

Activity by Year

2020-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersNeuroscience

Amalia McDonald

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2 papers

Recent·Most cited

NeurosciencebioRxivPreprintOct 21, 2020

Determinants of Real-Time fMRI Neurofeedback Performance and Improvement: a Machine Learning Mega-Analysis

Amelie Haugg, Fabian M. Renz, Andrew A. Nicholson…·David Steyrl

This study uses machine learning to analyze 608 participants and identifies two factors improving neurofeedback performance: pre-training no-feedback runs and training patients over healthy participants.

5 citations

machine learning (classical)benchmarking

𝕏 PostBluesky

NeuroscienceHuman Brain MappingPeer-reviewedJul 30, 2020

Can we predict real-time fMRI neurofeedback learning success from pre-training brain activity?

Amelie Haugg, Ronald Sladky, Stavros Skouras…·Frank Scharnowski

This study investigates whether pre-training brain activity can predict success in real-time fMRI neurofeedback learning across 24 studies and 401 participants, finding a weak correlation but no consistent predictors.

48 citations

humanimaging

𝕏 PostBluesky