Deriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
Alexandre Abraham,Michael P. Milham,Adriana Di Martino,R. Cameron Craddock,Dimitris Samaras,Bertrand Thirion,Gaël Varoquaux +6 more
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TLDR
The feasibility of inter‐site classification of neuropsychiatric status, with an application to the Autism Brain Imaging Data Exchange (ABIDE) database, a large (N=871) multi‐site autism dataset is demonstrated.About:
This article is published in NeuroImage.The article was published on 2017-02-15 and is currently open access. It has received 516 citations till now. The article focuses on the topics: Autism & Resting state fMRI.read more
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Identification of autism spectrum disorder using deep learning and the ABIDE dataset.
Anibal Sólon Heinsfeld,Alexandre Rosa Franco,R. Cameron Craddock,Augusto Buchweitz,Felipe Meneguzzi,Felipe Meneguzzi +5 more
TL;DR: The results improved the state-of-the-art by achieving 70% accuracy in identification of ASD versus control patients in the dataset, and identified the areas of the brain that contributed most to differentiating ASD from typically developing controls as per the deep learning model.
Journal ArticleDOI
Best practices in data analysis and sharing in neuroimaging using MRI.
Thomas E. Nichols,Samir Das,Samir Das,Simon B. Eickhoff,Simon B. Eickhoff,Alan C. Evans,Alan C. Evans,Tristan Glatard,Tristan Glatard,Michael Hanke,Nikolaus Kriegeskorte,Michael P. Milham,Michael P. Milham,Russell A. Poldrack,Jean-Baptiste Poline,Erika Proal,Bertrand Thirion,David C. Van Essen,Tonya White,B.T. Thomas Yeo +19 more
TL;DR: Intentions from developing a set of recommendations on behalf of the Organization for Human Brain Mapping are described and barriers that impede these practices are identified, including how the discipline must change to fully exploit the potential of the world's neuroimaging data.
Journal ArticleDOI
MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites.
Oscar Esteban,Daniel Birman,Marie Schaer,Oluwasanmi Koyejo,Russell A. Poldrack,Krzysztof J. Gorgolewski +5 more
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.
Journal ArticleDOI
Enhancing studies of the connectome in autism using the autism brain imaging data exchange II
Adriana Di Martino,David H. O’Connor,David H. O’Connor,Bosi Chen,Kaat Alaerts,Jeffrey S. Anderson,Michal Assaf,Michal Assaf,Joshua H. Balsters,Leslie C. Baxter,Anita Beggiato,Sylvie Bernaerts,Laura M. E. Blanken,Susan Y. Bookheimer,B. Blair Braden,B. Blair Braden,Lisa Byrge,F. Xavier Castellanos,F. Xavier Castellanos,Mirella Dapretto,Richard Delorme,Damien A. Fair,Inna Fishman,Jacqueline Fitzgerald,Louise Gallagher,R. Joanne Jao Keehn,Daniel P. Kennedy,Janet E. Lainhart,Janet E. Lainhart,Beatriz Luna,Stewart H. Mostofsky,Ralph-Axel Müller,Ralph-Axel Müller,Mary Beth Nebel,Joel T. Nigg,Joel T. Nigg,Kirsten O'Hearn,Marjorie Solomon,Roberto Toro,Chandan J. Vaidya,Nicole Wenderoth,Nicole Wenderoth,Tonya White,R. Cameron Craddock,Catherine Lord,Bennett L. Leventhal,Michael P. Milham,Michael P. Milham +47 more
TL;DR: This new multisite open-data resource is an aggregate of resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets and includes a range of psychiatric variables to inform the understanding of the neural correlates of co-occurring psychopathology.
Journal ArticleDOI
Cross-validation failure: Small sample sizes lead to large error bars.
TL;DR: In this article, the authors raise awareness on error bars of cross-validation, which are often underestimated and propose solutions to increase sample size, tackling possible increases in heterogeneity of the data.
References
More filters
Journal Article
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +15 more
TL;DR: Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems, focusing on bringing machine learning to non-specialists using a general-purpose high-level language.
Posted Content
Scikit-learn: Machine Learning in Python
Fabian Pedregosa,Gaël Varoquaux,Alexandre Gramfort,Vincent Michel,Bertrand Thirion,Olivier Grisel,Mathieu Blondel,Andreas Müller,Joel Nothman,Gilles Louppe,Peter Prettenhofer,Ron Weiss,Vincent Dubourg,Jake Vanderplas,Alexandre Passos,David Cournapeau,Matthieu Brucher,Matthieu Perrot,Edouard Duchesnay +18 more
TL;DR: Scikit-learn as mentioned in this paper is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems.
Journal ArticleDOI
An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest.
Rahul S. Desikan,Florent Ségonne,Bruce Fischl,Bruce Fischl,Brian T. Quinn,Bradford C. Dickerson,Deborah Blacker,Randy L. Buckner,Randy L. Buckner,Anders M. Dale,R. Paul Maguire,Bradley T. Hyman,Marilyn S. Albert,Ronald J. Killiany +13 more
TL;DR: An automated labeling system for subdividing the human cerebral cortex into standard gyral-based neuroanatomical regions is both anatomically valid and reliable and may be useful for both morphometric and functional studies of the cerebral cortex.
Journal ArticleDOI
Statistical parametric maps in functional imaging: A general linear approach
Karl J. Friston,Andrew P. Holmes,Keith J. Worsley,J-B. Poline,Chris D. Frith,Richard S. J. Frackowiak +5 more
TL;DR: In this paper, the authors present a general approach that accommodates most forms of experimental layout and ensuing analysis (designed experiments with fixed effects for factors, covariates and interaction of factors).
Journal Article
LIBLINEAR: A Library for Large Linear Classification
TL;DR: LIBLINEAR is an open source library for large-scale linear classification that supports logistic regression and linear support vector machines and provides easy-to-use command-line tools and library calls for users and developers.
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