M
Michael Hanke
Researcher at University of Düsseldorf
Publications - 182
Citations - 6639
Michael Hanke is an academic researcher from University of Düsseldorf. The author has contributed to research in topics: Molecular beam epitaxy & Diffraction. The author has an hindex of 32, co-authored 164 publications receiving 5284 citations. Previous affiliations of Michael Hanke include Leibniz Institute for Neurobiology & Karolinska Institutet.
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Journal ArticleDOI
The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments.
Krzysztof J. Gorgolewski,Tibor Auer,Vince D. Calhoun,R. Cameron Craddock,Samir Das,Eugene P. Duff,Guillaume Flandin,Satrajit S. Ghosh,Tristan Glatard,Yaroslav O. Halchenko,Daniel A. Handwerker,Michael Hanke,David Keator,Xiangrui Li,Zachary Michael,Camille Maumet,B. Nolan Nichols,Thomas E. Nichols,John Pellman,Jean-Baptiste Poline,Jean-Baptiste Poline,Ariel Rokem,Gunnar Schaefer,Vanessa Sochat,William Triplett,Jessica A. Turner,Gaël Varoquaux,Russell A. Poldrack +27 more
TL;DR: The Brain Imaging Data Structure (BIDS) is developed, a standard for organizing and describing MRI datasets that uses file formats compatible with existing software, unifies the majority of practices already common in the field, and captures the metadata necessary for most common data processing operations.
Journal ArticleDOI
A common, high-dimensional model of the representational space in human ventral temporal cortex.
James V. Haxby,James V. Haxby,J. Swaroop Guntupalli,Andrew C. Connolly,Yaroslav O. Halchenko,Bryan Conroy,M. Ida Gobbini,M. Ida Gobbini,Michael Hanke,Peter J. Ramadge +9 more
TL;DR: A high-dimensional model of the representational space in human ventral temporal (VT) cortex in which dimensions are response-tuning functions that are common across individuals and patterns of response are modeled as weighted sums of basis patterns associated with these response tunings is presented.
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
PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data
Michael Hanke,Yaroslav O. Halchenko,Yaroslav O. Halchenko,Per B. Sederberg,Stephen José Hanson,James V. Haxby,Stefan Pollmann +6 more
TL;DR: A Python-based, cross-platform, and open-source software toolbox, called PyMVPA, for the application of classifier-based analysis techniques to fMRI datasets, which makes use of Python’s ability to access libraries written in a large variety of programming languages and computing environments to interface with the wealth of existing machine learning packages.
Journal ArticleDOI
The Representation of Biological Classes in the Human Brain
Andrew C. Connolly,J. Swaroop Guntupalli,Jason Gors,Michael Hanke,Michael Hanke,Yaroslav O. Halchenko,Yu-Chien Wu,Hervé Abdi,James V. Haxby,James V. Haxby +9 more
TL;DR: FMRI is used to explore brain activity for a set of categories within the animate domain, including six animal species—two each from three very different biological classes: primates, birds, and insects, which reveals partial engagement of brain systems active normally for inanimate objects in addition to animate regions.