R
Russell A. Poldrack
Researcher at Stanford University
Publications - 481
Citations - 70423
Russell A. Poldrack is an academic researcher from Stanford University. The author has contributed to research in topics: Cognition & Functional neuroimaging. The author has an hindex of 125, co-authored 452 publications receiving 58695 citations. Previous affiliations of Russell A. Poldrack include University of Illinois at Urbana–Champaign & University of Texas at Austin.
Papers
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Journal ArticleDOI
Momentary Influences on Self-Regulation in Two Populations With Health Risk Behaviors: Adults Who Smoke and Adults Who Are Overweight and Have Binge-Eating Disorder
Emily A. Scherer,Stephen A. Metcalf,Cady Lauren Whicker,Sophia M. Bartels,Michael J. Grabinski,Sunny Jung Kim,Mary Ann Sweeney,Shea M. Lemley,Hannah Lavoie,Haiyi Xie,Patrick G. Bissett,Jesse Dallery,Michaela Kiernan,Michael R. Lowe,Lisa Simon Onken,Judith J. Prochaska,Luke E. Stoeckel,Russell A. Poldrack,David P. MacKinnon,Lisa A. Marsch +19 more
TL;DR: In this article , the authors used ecological momentary assessment (EMA) to assess if self-regulation can be engaged and manipulated on a momentary basis in naturalistic, non-laboratory settings.
Posted ContentDOI
ASLPrep: A Generalizable Platform for Processing of Arterial Spin Labeled MRI and Quantification of Regional Brain Perfusion
Azeez Adebimpe,Maxwell A. Bertolero,Sudipto Dolui,Matthew Cieslak,Kristin Murtha,Erica B. Baller,B. F. Boeve,Adam L. Boxer,Ellyn R. Butler,Cook Pa,Stanley J. Colcombe,Covitz S,Christos Davatzikos,Diego Davila,Mark A. Elliott,Matthew W Flounders,Alexandre Rosa Franco,R.E. Gur,R.C. Gur,Jaber B,McMillian C,Michael P. Milham,Oathe Dj,Christopher Olm,Jeffrey S. Phillips,William Tackett,David R. Roalf,Howard J. Rosen,Tinashe M. Tapera,Tisdall,Oscar Esteban,Russell A. Poldrack,John A. Detre,Theodore D. Satterthwaite +33 more
TL;DR: ASLPrep as discussed by the authors is a suite of software pipelines that ensure the reproducible and generalizable processing of ASL MRI data, which is the primary method for non-invasively measuring regional brain perfusion in humans.
Proceedings ArticleDOI
Learning Predictive Cognitive Structure from fMRI Using Supervised Topic Models
TL;DR: An experimental study of topic models applied to the analysis of functional magnetic resonance images finds that the supervised models are more effective than unsupervised approaches for analyzing cognitive function with fMRI.