Randomized parcellation based inference. Da Mota, B.; Fritsch, V.; Varoquaux, G.; Banaschewski, T.; Barker, G., J.; Bokde, A., L.; Bromberg, U.; Conrod, P.; Gallinat, J.; Garavan, H.; Martinot, J., L.; Nees, F.; Paus, T.; Pausova, Z.; Rietschel, M.; Smolka, M., N.; Ströhle, A.; Frouin, V.; Poline, J., B.; and Thirion, B. NeuroImage, 89:203-215, Elsevier Inc., 2014.
Randomized parcellation based inference [link]Website  abstract   bibtex   
Neuroimaging group analyses are used to relate inter-subject signal differences observed in brain imaging with behavioral or genetic variables and to assess risks factors of brain diseases. The lack of stability and of sensitivity of current voxel-based analysis schemes may however lead to non-reproducible results. We introduce a new approach to overcome the limitations of standard methods, in which active voxels are detected according to a consensus on several random parcellations of the brain images, while a permutation test controls the false positive risk. Both on synthetic and real data, this approach shows higher sensitivity, better accuracy and higher reproducibility than state-of-the-art methods. In a neuroimaging-genetic application, we find that it succeeds in detecting a significant association between a genetic variant next to the COMT gene and the BOLD signal in the left thalamus for a functional Magnetic Resonance Imaging contrast associated with incorrect responses of the subjects from a Stop Signal Task protocol. © 2013 Elsevier Inc.
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 title = {Randomized parcellation based inference},
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 year = {2014},
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 keywords = {Group analysis,Multiple comparisons,Parcellation,Permutations,Reproducibility},
 pages = {203-215},
 volume = {89},
 websites = {http://dx.doi.org/10.1016/j.neuroimage.2013.11.012},
 publisher = {Elsevier Inc.},
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 abstract = {Neuroimaging group analyses are used to relate inter-subject signal differences observed in brain imaging with behavioral or genetic variables and to assess risks factors of brain diseases. The lack of stability and of sensitivity of current voxel-based analysis schemes may however lead to non-reproducible results. We introduce a new approach to overcome the limitations of standard methods, in which active voxels are detected according to a consensus on several random parcellations of the brain images, while a permutation test controls the false positive risk. Both on synthetic and real data, this approach shows higher sensitivity, better accuracy and higher reproducibility than state-of-the-art methods. In a neuroimaging-genetic application, we find that it succeeds in detecting a significant association between a genetic variant next to the COMT gene and the BOLD signal in the left thalamus for a functional Magnetic Resonance Imaging contrast associated with incorrect responses of the subjects from a Stop Signal Task protocol. © 2013 Elsevier Inc.},
 bibtype = {article},
 author = {Da Mota, Benoit and Fritsch, Virgile and Varoquaux, Gaël and Banaschewski, Tobias and Barker, Gareth J. and Bokde, Arun L.W. and Bromberg, Uli and Conrod, Patricia and Gallinat, Jürgen and Garavan, Hugh and Martinot, Jean Luc and Nees, Frauke and Paus, Tomas and Pausova, Zdenka and Rietschel, Marcella and Smolka, Michael N. and Ströhle, Andreas and Frouin, Vincent and Poline, Jean Baptiste and Thirion, Bertrand},
 journal = {NeuroImage}
}
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