Author

Amalia McDonald

University of Virginia

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Neuroscience· 3

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Research Focus:benchmarking· 2 Organism:human· 2 Assay:imaging· 2 Research Focus:clustering· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Richard Cameron CraddockiD· 3 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

2016-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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3 shared papersNeuroscience

Richard Cameron CraddockORCID

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3 papers

Recent·Most cited

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

Amelie Haugg, Fabian M. Renz, Andrew A. Nicholson, Cindy Sumaly Lor, Sebastian J. Götzendorfer, Ronald Sladky, Stavros Skouras, Amalia McDonald, Richard Cameron Craddock, Lydia Hellrung, Matthias Kirschner, Marcus Herdener, Yury Koush, Marina Papoutsi, Jackob Nimrod Keynan, , 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.

bioRxiv · Oct 21, 2020 · PreprintNeuroscience

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

Amelie Haugg, Ronald Sladky, Stavros Skouras, Amalia McDonald, Cameron Craddock, Matthias Kirschner, Marcus Herdener, Yury Koush, Marina Papoutsi, Jackob Nimrod Keynan, Talma Hendler, Kathrin Cohen Kadosh, Catharina Zich, Jeff MacInnes, R. Alison Adcock, , 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.

Human Brain Mapping · Jul 30, 2020Peer-reviewedNeuroscience

The Real-time fMRI Neurofeedback Based Stratification of Default Network Regulation Neuroimaging Data Repository.

Amalia McDonald, Jordan Muraskin, Nicholas T. Van Dam, Caroline Froehlich, Benjamin Puccio, John Pellman, Clemens C. C. Bauer, Alexis Akeyson, Melissa M. Breland, Vince D. Calhoun, Steven Carter, Tiffany Chang, Chelsea Gessner, Alyssa Gianonne, Steven Giavasis, , Richard Cameron Craddock

The paper describes a repository of fMRI data assessing inter-individual differences in default mode network activity and reports technical validation of the dataset.

NeuroImage · Nov 9, 2016Peer-reviewedNeuroscience