Author

Jeff MacInnes

University of Washington

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· 4

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 3 Assay:imaging· 2 Approach:statistical model· 2 Research Focus:clustering· 1 Research Focus:differential expression· 1 Approach:experimental assay· 1 Organism:human· 1 Approach:machine learning (classical)· 1

Frequent Co-Authors

Kathryn C. Dickerson· 3 shared R. Alison AdcockiD· 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

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3 shared papersNeuroscience

Kathryn C. Dickerson

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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

Wearable Eye-tracking for Research: Automated dynamic gaze mapping and accuracy/precision comparisons across devices

Jeff MacInnes, Shariq Iqbal, John Pearson, Elizabeth N. Johnson

An automated pipeline maps head-mounted gaze onto a fixed 2D stimulus without tethering participants, plus a task built to compare calibration accuracy and precision across 3 commercial headsets.

bioRxiv · Jun 28, 2018 · PreprintNeuroscience

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