Author

Kathryn C. Dickerson

Duke University

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

Biology Disciplines

Click to filter author papers

Neuroscience· 4

Signature Assays, Organisms & Methods

Click to filter · or open tag

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

Frequent Co-Authors

R. Alison AdcockiD· 4 shared Jeff MacInnes· 3 shared 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

Activity by Year

2018-2020 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

4 shared papersNeuroscience

R. Alison AdcockORCID

Mutual co-authors in cluster

Author profile →

4 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

Expected reward value and reward uncertainty have temporally dissociable effects on memoryformation

Jessica K. Stanek, Kathryn C. Dickerson, Kimberly S. Chiew, Nathaniel J. Clement, R. Alison Adcock

This study shows that expected reward value and reward uncertainty affect memory formation at different times during reward anticipation.

Journal of Cognitive Neuroscience · Apr 16, 2019Peer-reviewedNeuroscience

Single Session Real-time fMRI Neurofeedback has a Lasting Impact on Cognitive Behavioral Therapy Strategies

Katherine E. MacDuffie, Jeff MacInnes, Kathryn C. Dickerson, Kari Merrill Eddington, Timothy J. Strauman, R. Alison Adcock

This paper shows that real-time fMRI neurofeedback during CBT helps patients better use and retain mood regulation strategies long after the session.

NeuroImage Clinical · Jan 1, 2018Peer-reviewedNeuroscience