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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.

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Analyses of regional-average activation and multivoxel pattern information tell complementary stories

TL;DR: Comparison of sensitivity showed a general trend towards greater sensitivity to task manipulations by MVPA compared to univariate analysis, which demonstrates that MVPA methods may provide a different view of the functional organization of mental processing compared tounivariate analysis.
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The impact of study design on pattern estimation for single-trial multivariate pattern analysis.

TL;DR: This work focuses on how the combination of study design and pattern estimator impacts the Type I error rate of the subsequent pattern analysis, and shows that collinearities in the models, along with temporal autocorrelation, can cause false positive correlations between activation pattern estimates that adversely impact the false positive rates of pattern similarity and classification analyses.
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Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention.

TL;DR: The presence of two distinct temporal states that fluctuated over the course of 18 mo were identified, associated with distinct patterns of time-resolved blood oxygen level dependent (BOLD) connectivity within individual scanning sessions and related to significant alterations in global efficiency of brain connectivity as well as differences in self-reported attention.
Posted ContentDOI

MRIQC: Advancing the Automatic Prediction of Image Quality in MRI from Unseen Sites

TL;DR: The MRI Quality Control tool (MRIQC), a tool for extracting quality measures and fitting a binary (accept/exclude) classifier, is introduced, which performs with high accuracy in intra-site prediction, but performance on unseen sites leaves space for improvement.
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Mapping Mental Function to Brain Structure: How Can Cognitive Neuroimaging Succeed?

TL;DR: The Cognitive Atlas Project is outlined, which is developing formal ontologies that describe the structure of mental processes, and it is shown how this knowledge could be used in conjunction with data-mining approaches to more directly relate mental processes and brain function.