Author

Kristi R. Griffiths

The University of SydneyORCID 0000-0002-7108-2272Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

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

Frequent Co-Authors

Amir DezfouliiD· 2 shared Bernard W. BalleineiD· 2 shared Fabio Tozeto RamosiD· 1 shared Jacqueline F. SaadiD· 1 shared Leanne Maree WilliamsiD· 1 shared Mayuresh S. KorgaonkariD· 1 shared Michael R. KohniD· 1 shared Mike E. Le PelleyiD· 1 shared

Activity by Year

2017-2021 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 17 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

2 shared papersBioinformatics

Amir DezfouliORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

No support for white matter microstructural properties and connectivity differences in the combined and inattentive adhd presentations.

Jacqueline F. Saad, Kristi R. Griffiths, Michael R. Kohn, Taylor A. Braund, Simon D. Clarke, Leanne Maree Williams, Mayuresh S. Korgaonkar

This study examines white matter microstructural properties and connectivity differences between ADHD combined and inattentive subtypes but finds no significant differences.

PLoS ONE · May 5, 2021Peer-reviewedNeuroscience

Models that learn how humans learn: the case of decision-making and its disorders

Amir Dezfouli, Kristi R. Griffiths, Fabio Tozeto Ramos, Peter Dayan, Bernard W. Balleine

The paper introduces a recurrent neural network approach to model human decision-making and shows it outperforms traditional methods in predicting behavior in depression and bipolar disorder.

PLoS Computational Biology · Jun 11, 2019Peer-reviewedBioinformatics

The Algorithmic Neuroanatomy Of Action-Outcome Learning

Richard W. Morris, Amir Dezfouli, Kristi R. Griffiths, Mike E. Le Pelley, Bernard W. Balleine

The paper uses fMRI and computational modeling to show that a Kalman filter best explains action-outcome learning, with the medial prefrontal cortex distinguishing action effects from background noise.

bioRxiv · May 14, 2017 · PreprintNeuroscience