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Jonathan D. Power

Researcher at Cornell University

Publications -  62
Citations -  27694

Jonathan D. Power is an academic researcher from Cornell University. The author has contributed to research in topics: Resting state fMRI & Brain mapping. The author has an hindex of 36, co-authored 56 publications receiving 22586 citations. Previous affiliations of Jonathan D. Power include Cornell College & NewYork–Presbyterian Hospital.

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Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

TL;DR: The results suggest the need for greater care in dealing with subject motion, and the need to critically revisit previous rs-fcMRI work that may not have adequately controlled for effects of transient subject movements.
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Functional network organization of the human brain

TL;DR: In this article, the authors studied functional brain organization in healthy adults using resting state functional connectivity MRI and proposed two novel brain wide graphs, one of 264 putative functional areas, the other a modification of voxelwise networks that eliminates potentially artificial short-distance relationships.
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Methods to detect, characterize, and remove motion artifact in resting state fMRI

TL;DR: It is found that motion-induced signal changes are often complex and variable waveforms, often shared across nearly all brain voxels, and often persist more than 10s after motion ceases, which increase observed RSFC correlations in a distance-dependent manner.
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Prediction of Individual Brain Maturity Using fMRI

TL;DR: Support vector machine-based multivariate pattern analysis extracts sufficient information from fcMRI data to make accurate predictions about individuals’ brain maturity across development, and prediction of individual brain maturity as a functional connectivity maturation index is allowed.
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Multi-task connectivity reveals flexible hubs for adaptive task control

TL;DR: It was found that the FPN's brain-wide functional connectivity pattern shifted more than those of other networks across a variety of task states and that these connectivity patterns could be used to identify the current task.