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Cross-validation and permutations in MVPA: validity of permutation strategies and power of cross-validation schemes

Giancarlo Valente, Agustin Lage Castellanos, Lars Hausfeld, Federico De Martino, Elia Formisano
2021 NeuroImage  
In this work we focus on the false positive control of different permutation strategies and on the statistical power of different cross-validation schemes.  ...  Furthermore, using both simulations and real data from the HCP WU-Minn 3T fMRI dataset, we show that, among the different cross-validation schemes, a repeated split-half cross-validation is the most powerful  ...  EF is partially supported by the Dutch province of Limburg. References  ... 
doi:10.1016/j.neuroimage.2021.118145 pmid:33961999 fatcat:ctvrt76oabav5iu5isolqo4z6u

MVPA Permutation Schemes: Permutation Testing in the Land of Cross-Validation

Joset A. Etzel, Todd S. Braver
2013 2013 International Workshop on Pattern Recognition in Neuroimaging  
In the first, which we call the "dataset-wise" scheme, the examples are relabeled prior to conducting the crossvalidation, while in the second, the "fold-wise" scheme, each fold of the cross-validation  ...  Permutation tests are widely used for significance testing in classification-based fMRI analyses, but the precise manner of relabeling varies, and is generally non-trivial for MVPA because of the complex  ...  Fold-Wise Permutation Schemes The fold-wise permutation scheme also draws new task labels at random from the twenty listed in Fig. 3 , but the labels are drawn independently on each cross-validation fold  ... 
doi:10.1109/prni.2013.44 dblp:conf/prni/EtzelB13 fatcat:jringzeehnfv7ma7inzrvwjrne

Inter-subject pattern analysis: a straightforward and powerful scheme for group-level MVPA [article]

Qi Wang, Bastien Cagna, Thierry Chaminade, Sylvain Takerkart
2019 bioRxiv   pre-print
AbstractMultivariate pattern analysis (MVPA) has become vastly popular for analyzing functional neuroimaging data. At the group level, two main strategies are used in the literature.  ...  This study provides a thorough comparison of these two group-level decoding schemes, using both a large number of artificial datasets where the size of the multivariate effect and the amount of inter-individual  ...  Foundation (40463) and the Agence Nationale de la Recherche (ANR-15-CE23-0026).  ... 
doi:10.1101/587899 fatcat:64aoikfhjvgote42ht2k3od3ea

Inter-subject pattern analysis: A straightforward and powerful scheme for group-level MVPA

Qi Wang, Bastien Cagna, Thierry Chaminade, Sylvain Takerkart
2019 NeuroImage  
Multivariate pattern analysis (MVPA) has become vastly popular for analyzing functional neuroimaging data. At the group level, two main strategies are used in the literature.  ...  This study provides a thorough comparison of these two group-level decoding schemes, using both a large number of artificial datasets where the size of the multivariate effect and the amount of inter-individual  ...  The acquisition of the data was made  ... 
doi:10.1016/j.neuroimage.2019.116205 pmid:31546047 fatcat:ji2yem7igvbmtkuffkx4xuadcm

Optimizing fMRI experimental design for MVPA-based BCI control: Combining the strengths of block and event-related designs

Giancarlo Valente, Amanda L. Kaas, Elia Formisano, Rainer Goebel
2019 NeuroImage  
Much attention has been devoted to developing and validating data analysis strategies, but relatively little guidance is available on the choice of experimental design, even less so in the context of BCI-MVPA  ...  Our findings suggest that the blocked fast event-related design could be a viable alternative to block designs in the context of BCI-MVPA, when expectations, strategies and adaptation make blocking of  ...  This work was also supported by European Research Council (ERC; advanced grant #269853 awarded to RG) and by The Netherlands Organisation for Scientific Research (NWO) (VICI Grant 453-12-002 to EF).  ... 
doi:10.1016/j.neuroimage.2018.10.080 fatcat:4zseekvnnjeivdzvcl77abkssm

Decoding the Formation of New Semantics: MVPA Investigation of Rapid Neocortical Plasticity during Associative Encoding through Fast Mapping

Tali Atir-Sharon, Asaf Gilboa, Hananel Hazan, Ester Koilis, Larry M. Manevitz
2015 Neural Plasticity  
In healthy adults, this is better captured by unique anterior and lateral temporal lobe activity patterns, while hippocampal involvement is less predictive of this kind of learning.  ...  By contrast, successful explicit encoding could be classified by activity in medial and dorsolateral prefrontal and parahippocampal cortices.  ...  A process of feature selection was separately performed, anew for each classification experiment in -fold cross-validation scheme.  ... 
doi:10.1155/2015/804385 pmid:26257961 pmcid:PMC4519547 fatcat:topfz7rijvhmncqb4rxulzkfby

Recent developments in multivariate pattern analysis for functional MRI

Zhi Yang, Fang Fang, Xuchu Weng
2012 Neuroscience Bulletin  
In this review, we introduce several significant advances in MVPA applications and summarize various combinations of algorithms and parameters in different problem settings.  ...  The limitations of MVPA and some critical questions that need to be addressed in future research are also discussed.  ...  Acknowledgements: We thank Professor Georg Northoff for helpful comments in the preparation of the manuscript.  ... 
doi:10.1007/s12264-012-1253-3 pmid:22833038 pmcid:PMC5561894 fatcat:b7w5olxdbbdyhap26whh6lj6p4

Permutations of functional magnetic resonance imaging classification may not be normally distributed

Mohammed S Al-Rawi, Adelaide Freitas, João V Duarte, Joao P Cunha, Miguel Castelo-Branco
2017 Statistical Methods in Medical Research  
Overall, the results showed a strong dependence across the folds of the cross-validation and across fMRI runs, and that may hinder the reliability of using cross-validation.  ...  The method usually builds a permutation distribution (PD) of classification accuracies/errors, typically deployed in cross-validation manner, that is centered around theoretical chance-level.  ...  approach and/or learning strategies.  ... 
doi:10.1177/0962280215601707 pmid:29251253 fatcat:67zur723vzdoxg5mxlg77kp73a

Multivariate classification of neuroimaging data with nested subclasses: Biased accuracy and implications for hypothesis testing

Hamidreza Jamalabadi, Sarah Alizadeh, Monika Schönauer, Christian Leibold, Steffen Gais, Saad Jbabdi
2018 PLoS Computational Biology  
A powerful method for detecting systematic differences between experimental conditions in such multivariate data sets is multivariate pattern analysis (MVPA), particularly pattern classification.  ...  To do so, we propose to use blocked permutation testing on subclass levels and show that it can confine the false positive rate to the predefined α-levels.  ...  Both cross-validation and permutation are applied independently.  ... 
doi:10.1371/journal.pcbi.1006486 fatcat:scvyvfxvbvhche4du7e6ne2e7e

Better-Than-Chance Classification for Signal Detection [article]

Jonathan D. Rosenblatt, Yuval Benjamini, Roee Gilron, Roy Mukamel, Jelle J. Goeman
2017 arXiv   pre-print
In particular, to replace V-fold cross validation with the Leave-One-Out Bootstrap.  ...  This method of signal detection is particularly popular in neuroimaging and genetics.  ...  Hemerik, Yakir Brechenko, Omer Shamir, Joshua Vogelstein, Gilles Blanchard, and Jason Stein for their valuable inputs.  ... 
arXiv:1608.08873v2 fatcat:tzikewfskvf6vdhb6gzdmzfy3i

How do you perceive threat? It's all in your pattern of brain activity

Orlando Fernandes, Liana Catrina Lima Portugal, Rita de Cássia S. Alves, Tiago Arruda-Sanchez, Eliane Volchan, Mirtes Garcia Pereira, Janaina Mourão-Miranda, Letícia Oliveira
2019 Brain Imaging and Behavior  
Whether subtle differences in the emotional context during threat perception can be detected by multi-voxel pattern analysis (MVPA) remains a topic of debate.  ...  In the directed away context, the threat perception was relatively less intense and more variable across individuals, enabling the regression model to successfully capture the individual differences and  ...  Acknowledgments We thank Lucas Rego Ramos for helping during the collection of the fMRI data.  ... 
doi:10.1007/s11682-019-00177-6 pmid:31446554 fatcat:s3uzf6wtajcexgmwkpds4v6kcq

Prediction of trust propensity from intrinsic brain morphology and functional connectome

Chunliang Feng, Zhiyuan Zhu, Zaixu Cui, Vadim Ushakov, Jean‐Claude Dreher, Wenbo Luo, Ruolei Gu, Xia Wu, Frank Krueger
2020 Human Brain Mapping  
Our modular and functional decoding analyses showed that the contributing regions were part of three large-scale networks implicated in calculus-based trust strategy, cost-benefit calculation, and trustworthiness  ...  Here, we applied a prediction framework in two independent samples of healthy participants to examine the relationship between trust propensity and multimodal brain measures.  ...  Joseph Kable for his helpful comments on an earlier version of the manuscript. C.F. was supported by the National Natural Science Foundation of China (31900757, 32 020103008).  ... 
doi:10.1002/hbm.25215 pmid:33001541 pmcid:PMC7721234 fatcat:uwkyotgi7zhknmordkuh45v6ny

Multiple subject learning for inter-subject prediction

Sylvain Takerkart, Liva Ralaivola
2014 2014 International Workshop on Pattern Recognition in Neuroimaging  
We show that MSL outperforms other models in the inter-subject prediction task and we discuss the empirical behavior of this new model.  ...  We here introduce a model called Multiple Subject Learning (MSL) that is designed to effectively combine the information provided by fMRI data from several subjects; in a first stage, a weighting of single-subject  ...  We plan on studying their consistency, both across folds of the cross-validation and across the different MSL classifiers used in a MSL* ensemble.  ... 
doi:10.1109/prni.2014.6858548 dblp:conf/prni/TakerkartR14 fatcat:n7jqwdudknh45dipssoci42hyu

Valid population inference for information-based imaging: From the second-level t -test to prevalence inference

Carsten Allefeld, Kai Görgen, John-Dylan Haynes
2016 NeuroImage  
One method to do so, 'permutation-based information prevalence inference using the minimum statistic', is described in detail and applied to empirical data.  ...  This constraint changes the meaning of the population-level null hypothesis being tested, which becomes equivalent to the global null hypothesis that there is no effect in any subject in the population  ...  Note that this excludes other quantities that may be of interest in MVPA, in particular classifier weights (see Gaonkar and Davatzikos, 2013) .  ... 
doi:10.1016/j.neuroimage.2016.07.040 pmid:27450073 fatcat:54dm5lkxtvbhfhy2vkif6xnsiy

Using fMRI to decode true thoughts independent of intention to conceal

Zhi Yang, Zirui Huang, Javier Gonzalez-Castillo, Rui Dai, Georg Northoff, Peter Bandettini
2014 NeuroImage  
A set of regionsnamely right superior temporal gyrus, left supra-marginal gyrus, and left middle frontal gyrusexhibited high decoding power.  ...  In this study, we focus on examining: (1) whether spatial patterns of the hemodynamic response carry sufficient information to allow reliable decoding of "Yes/No" thoughts; and (2) whether decoding of  ...  This work was supported by the National Science Foundation of China (NSFC, No. 81270023 to ZY), the Foundation of Beijing Key Laboratory of Mental Disorders (2014JSJB03 to ZY), and the NIMH Intramural  ... 
doi:10.1016/j.neuroimage.2014.05.034 pmid:24844742 pmcid:PMC4179453 fatcat:yxk2ux37svbmbgkf7g2vtix444
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