Data from: A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula
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| Název: | Data from: A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula |
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| Autoři: | Ince, Robin A. A., Giordano, Bruno L., Kayser, Christoph, Rousselet, Guillaume A., Gross, Joachim, Schyns, Philippe G., Ince, Robin A.A. |
| Zdroj: | oai:easy.dans.knaw.nl:easy-dataset:92256 ; 10.5061/dryad.8b146 ; oai:services.nod.dans.knaw.nl:Products/dans:oai:easy.dans.knaw.nl:easy-dataset:92256 ; 10|re3data_____::84e123776089ce3c7a33db98d9cd15a8 ; 10|openaire____::9e3be59865b2c1c335d32dae2fe7b254 ; re3data_____::r3d100000044 ; 10|eurocrisdris::fe4903425d9040f680d8610d9079ea14 ; 10|re3data_____::94816e6421eeb072e7742ce6a9decc5f ; 10|opendoar____::8b6dd7db9af49e67306feb59a8bdc52c |
| Informace o vydavateli: | Data Archiving and Networked Services (DANS) |
| Rok vydání: | 2017 |
| Témata: | Life sciences, medicine and health care, face, EEG, multivariate, planar gradient, mutual information, meg, n170, statistics, neuroimaging, stat, psy |
| Popis: | We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Figure source dataSource data for results figures.figure_data.zipeeg_face_vs_noiseThis is an EEG data set used in Section 4.1. Two classes of stimulus images were presented (faces or noise). Data are from a single subject.meg_speechThis is an MEG data set used in Section 4.2. MEG was recorded during a 7.5 minute auditory presentation of speech. Data are from a single subject.eeg_eye_visibilityThis is an EEG data set used in ... |
| Druh dokumentu: | article in journal/newspaper dataset |
| Jazyk: | unknown |
| Relation: | http://dx.doi.org/10.5061/dryad.8b146; https://dx.doi.org/10.5061/dryad.8b146 |
| DOI: | 10.5061/dryad.8b146 |
| Dostupnost: | https://doi.org/10.5061/dryad.8b146 |
| Rights: | lic_creative-commons ; undefined |
| Přístupové číslo: | edsbas.1B33540F |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Figure source dataSource data for results figures.figure_data.zipeeg_face_vs_noiseThis is an EEG data set used in Section 4.1. Two classes of stimulus images were presented (faces or noise). Data are from a single subject.meg_speechThis is an MEG data set used in Section 4.2. MEG was recorded during a 7.5 minute auditory presentation of speech. Data are from a single subject.eeg_eye_visibilityThis is an EEG data set used in ... |
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| DOI: | 10.5061/dryad.8b146 |