Machine learning for neuroimaging with scikit-learn.
Alexandre Abraham,Alexandre Abraham,Fabian Pedregosa,Fabian Pedregosa,Michael Eickenberg,Michael Eickenberg,Philippe Gervais,Philippe Gervais,Andreas Mueller,Jean Kossaifi,Alexandre Gramfort,Alexandre Gramfort,Alexandre Gramfort,Bertrand Thirion,Bertrand Thirion,Gaël Varoquaux,Gaël Varoquaux +16 more
TLDR
It is illustrated how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps and its application to neuroimaging data provides a versatile tool to study the brain.Abstract:
Statistical machine learning methods are increasingly used for neuroimaging data analysis. Their main virtue is their ability to model high-dimensional datasets, e.g. multivariate analysis of activation images or resting-state time series. Supervised learning is typically used in decoding or encoding settings to relate brain images to behavioral or clinical observations, while unsupervised learning can uncover hidden structures in sets of images (e.g. resting state functional MRI) or find sub-populations in large cohorts. By considering different functional neuroimaging applications, we illustrate how scikit-learn, a Python machine learning library, can be used to perform some key analysis steps. Scikit-learn contains a very large set of statistical learning algorithms, both supervised and unsupervised, and its application to neuroimaging data provides a versatile tool to study the brain.read more
Citations
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Statistical Parametric Mapping The Analysis Of Functional Brain Images
TL;DR: As you may know, people have search numerous times for their chosen novels like this statistical parametric mapping the analysis of functional brain images, but end up in malicious downloads.
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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.
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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,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.
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Assessing and tuning brain decoders: cross-validation, caveats, and guidelines
Gaël Varoquaux,Pradeep Reddy Raamana,Denis A. Engemann,Andrés Hoyos-Idrobo,Yannick Schwartz,Bertrand Thirion +5 more
TL;DR: T theory and experiments outline that the popular “leave‐one‐out” strategy leads to unstable and biased estimates, and a repeated random splits method should be preferred, and it can be favorable to use sane defaults, in particular for non‐sparse decoders.
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Variability in the analysis of a single neuroimaging dataset by many teams
Rotem Botvinik-Nezer,Rotem Botvinik-Nezer,Felix Holzmeister,Colin F. Camerer,Anna Dreber,Anna Dreber,Juergen Huber,Magnus Johannesson,Michael Kirchler,Roni Iwanir,Jeanette A. Mumford,R. Alison Adcock,Paolo Avesani,Paolo Avesani,Blazej M. Baczkowski,Aahana Bajracharya,Leah Bakst,Sheryl B. Ball,Marco Barilari,Nadège Bault,Derek Beaton,Julia Beitner,Julia Beitner,Roland G. Benoit,Ruud Berkers,Jamil P. Bhanji,Bharat B. Biswal,Bharat B. Biswal,Sebastian Bobadilla-Suarez,Tiago Bortolini,Katherine L. Bottenhorn,Alexander Bowring,Senne Braem,Senne Braem,Hayley R. Brooks,Emily G. Brudner,Cristian Buc Calderon,Julia A. Camilleri,Jaime J. Castrellon,Luca Cecchetti,Edna C. Cieslik,Zachary J. Cole,Olivier Collignon,Olivier Collignon,Robert W. Cox,William A. Cunningham,Stefan Czoschke,Kamalaker Dadi,Charles P. Davis,Alberto De Luca,Mauricio R. Delgado,Lysia Demetriou,Lysia Demetriou,Jeffrey B. Dennison,Xin Di,Xin Di,Erin W. Dickie,Ekaterina Dobryakova,Claire Donnat,Juergen Dukart,Niall W. Duncan,Joke Durnez,Amr Eed,Simon B. Eickhoff,Andrew Erhart,Laura Fontanesi,G. Matthew Fricke,Fu Shiguang,Adriana Galván,Remi Gau,Sarah Genon,Tristan Glatard,Enrico Glerean,Jelle J. Goeman,Sergej A.E. Golowin,Carlos González-García,Krzysztof J. Gorgolewski,Cheryl L. Grady,Mikella A. Green,João F. Guassi Moreira,Olivia Guest,Shabnam Hakimi,J. Paul Hamilton,Roeland Hancock,Giacomo Handjaras,Bronson Harry,Colin Hawco,Peer Herholz,Gabrielle Herman,Stephan Heunis,Felix Hoffstaedter,Jeremy Hogeveen,Susan Holmes,Chuan-Peng Hu,Scott A. Huettel,Matthew Hughes,Vittorio Iacovella,Alexandru D. Iordan,Peder M. Isager,Ayse Ilkay Isik,Andrew Jahn,Matthew R. Johnson,Tom Johnstone,Michael Joseph,Anthony C. Juliano,Joseph W. Kable,Michalis Kassinopoulos,Cemal Koba,Xiangzhen Kong,Timothy R. Koscik,Nuri Erkut Kucukboyaci,Nuri Erkut Kucukboyaci,Brice A. Kuhl,Sebastian Kupek,Angela R. Laird,Claus Lamm,Robert Langner,Nina Lauharatanahirun,Hongmi Lee,Sangil Lee,Alexander Leemans,Andrea Leo,Elise Lesage,Flora Li,Monica Y.C. Li,Monica Y.C. Li,Phui Cheng Lim,Evan N. Lintz,Schuyler W. Liphardt,Annabel B. Losecaat Vermeer,Bradley C. Love,Bradley C. Love,Michael Mack,Norberto Malpica,Theo Marins,Camille Maumet,Kelsey McDonald,Joseph T. McGuire,Helena Melero,Helena Melero,Adriana S. Méndez Leal,Benjamin Meyer,Benjamin Meyer,Kristin N. Meyer,Glad Mihai,Glad Mihai,Georgios D. Mitsis,Jorge Moll,Dylan M. Nielson,Gustav Nilsonne,Gustav Nilsonne,Michael Notter,Emanuele Olivetti,Emanuele Olivetti,Adrian I. Onicas,Paolo Papale,Paolo Papale,Kaustubh R. Patil,Jonathan E. Peelle,Alexandre Pérez,Doris Pischedda,Jean-Baptiste Poline,Jean-Baptiste Poline,Yanina Prystauka,Shruti Ray,Patricia A. Reuter-Lorenz,Richard C. Reynolds,Emiliano Ricciardi,Jenny R. Rieck,Anais Rodriguez-Thompson,Anthony Romyn,Taylor Salo,Gregory R. Samanez-Larkin,Emilio Sanz-Morales,Margaret L. Schlichting,Douglas H. Schultz,Qiang Shen,Margaret A. Sheridan,Jennifer A. Silvers,Kenny Skagerlund,Alec Smith,David V. Smith,Peter Sokol-Hessner,Simon R. Steinkamp,Sarah M. Tashjian,Bertrand Thirion,John Thorp,Gustav Tinghög,Loreen Tisdall,Loreen Tisdall,Steven H. Tompson,Claudio Toro-Serey,Juan Carlos de la Torre,Leonardo Tozzi,Vuong Truong,Luca Turella,Anna van 't Veer,Tom Verguts,Jean M. Vettel,Jean M. Vettel,Jean M. Vettel,Sagana Vijayarajah,Khoi Vo,Matthew B. Wall,Matthew B. Wall,Wouter D. Weeda,Susanne Weis,David J. White,David Wisniewski,Alba Xifra-Porxas,Emily A. Yearling,Sangsuk Yoon,Rui Yuan,Kenneth S. L. Yuen,Kenneth S. L. Yuen,Lei Zhang,Xu Zhang,Joshua E. Zosky,Thomas E. Nichols,Russell A. Poldrack,Tom Schonberg +220 more
TL;DR: The results obtained by seventy different teams analysing the same functional magnetic resonance imaging dataset show substantial variation, highlighting the influence of analytical choices and the importance of sharing workflows publicly and performing multiple analyses.
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