Precision Functional Mapping of Individual Human Brains
Evan M. Gordon,Timothy O. Laumann,Adrian W. Gilmore,Adrian W. Gilmore,Dillan J. Newbold,Deanna J. Greene,Jeffrey J. Berg,Mario Ortega,Catherine Hoyt-Drazen,Caterina Gratton,Haoxin Sun,Jacqueline M. Hampton,Rebecca S. Coalson,Annie L. Nguyen,Kathleen B. McDermott,Joshua S. Shimony,Abraham Z. Snyder,Bradley L. Schlaggar,Steven E. Petersen,Steven M. Nelson,Steven M. Nelson,Nico U.F. Dosenbach +21 more
TLDR
A novel MRI dataset containing 5 hr of RSFC data, 6 hour of task fMRI, multiple structural MRIs, and neuropsychological tests from each of ten adults generated ten high-fidelity, individual-specific functional connectomes, revealing several new types of spatial and organizational variability in brain networks.About:
This article is published in Neuron.The article was published on 2017-08-16 and is currently open access. It has received 869 citations till now. The article focuses on the topics: Connectomics & Brain mapping.read more
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fMRIPrep: a robust preprocessing pipeline for functional MRI
Oscar Esteban,Christopher J. Markiewicz,Ross Blair,Craig A. Moodie,Ayse Ilkay Isik,Asier Erramuzpe,James D. Kent,Mathias Goncalves,Elizabeth DuPre,Snyder M,Hiroyuki Oya,Satrajit S. Ghosh,Satrajit S. Ghosh,Jessey Wright,Joke Durnez,Russell A. Poldrack,Krzysztof J. Gorgolewski +16 more
TL;DR: fMRIPrep is a robust and easy-to-use pipeline for preprocessing of diverse fMRI data that dispenses of manual intervention, thereby ensuring the reproducibility of the results.
Journal ArticleDOI
The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences.
TL;DR: The very reason such tasks produce robust and easily replicable experimental effects – low between-participant variability – makes their use as correlational tools problematic, and it is demonstrated that taking reliability estimates into account has the potential to qualitatively change theoretical conclusions.
Posted ContentDOI
FMRIPrep: a robust preprocessing pipeline for functional MRI
Oscar Esteban,Christopher J. Markiewicz,Ross Blair,Craig A. Moodie,Ayse Ilkay Isik,Asier Erramuzpe,James D. Kent,Mathias Goncalves,Elizabeth DuPre,Snyder M,Hiroyuki Oya,Satrajit S. Ghosh,Jessey Wright,Joke Durnez,Russell A. Poldrack,Krzysztof J. Gorgolewski +15 more
TL;DR: FMRIPrep has the potential to transform fMRI research by equipping neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow which can help ensure the validity of inference and the interpretability of their results.
Journal ArticleDOI
Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation
Caterina Gratton,Timothy O. Laumann,Ashley N. Nielsen,Deanna J. Greene,Evan M. Gordon,Evan M. Gordon,Adrian W. Gilmore,Steven M. Nelson,Rebecca S. Coalson,Abraham Z. Snyder,Bradley L. Schlaggar,Nico U.F. Dosenbach,Steven E. Petersen +12 more
TL;DR: It is concluded that functional networks are suited to measuring stable individual characteristics, suggesting utility in personalized medicine.
Journal ArticleDOI
Reproducible brain-wide association studies require thousands of individuals
Scott Marek,Brenden Tervo-Clemmens,Finnegan J. Calabro,David F. Montez,Benjamin P Kay,Alexander S. Hatoum,Meghan Rose Donohue,Will Foran,Ryland L. Miller,Timothy Hendrickson,Stephen M. Malone,Sridhar Kandala,Eric Feczko,Oscar Miranda-Dominguez,Alice M. Graham,Eric Earl,Anders Perrone,Michaela Cordova,Olivia Doyle,Lucille A. Moore,Gregory Mark Conan,Johnny Uriarte,Katherine Allene Snider,Benjamin J. Lynch,James C. Wilgenbusch,Thomas Pengo,Angela Tam,Jianzhong Chen,Dillan J. Newbold,Annie Zheng,Nicole A Seider,Andrew N. Van,Athanasia Metoki,Roselyne Chauvin,Timothy O. Laumann,Deanna J. Greene,Steven E. Petersen,Hugh Garavan,Wesley K. Thompson,Thomas E. Nichols,B.T. Thomas Yeo,Deanna M. Barch,Beatriz Luna,Damien A. Fair,Nico U.F. Dosenbach +44 more
TL;DR: In this article , the authors used three of the largest neuroimaging datasets currently available, with a total sample size of around 50,000 individuals, to quantify brain-wide association studies effect sizes and reproducibility as a function of sample size.
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