Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations
| العنوان: | Back-to-back regression: Disentangling the influence of correlated factors from multivariate observations |
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| المؤلفون: | David Lopez-Paz, Jean-Rémi King, Maxime Oquab, François Charton |
| المساهمون: | Laboratoire des systèmes perceptifs (LSP), Département d'Etudes Cognitives - ENS Paris (DEC), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS), Facebook AI Research [Paris] (FAIR), Facebook |
| المصدر: | NeuroImage NeuroImage, Elsevier, 2020, 220, ⟨10.1016/j.neuroimage.2020.117028⟩ NeuroImage, Vol 220, Iss, Pp 117028-(2020) |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | Multivariate statistics, Computer science, Decoding, Cognitive Neuroscience, 050105 experimental psychology, lcsh:RC321-571, 03 medical and health sciences, 0302 clinical medicine, Partial least squares regression, Image Processing, Computer-Assisted, Humans, 0501 psychology and cognitive sciences, lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry, Cross-Decomposition, Cerebral Cortex, Brain Mapping, MEG, business.industry, [SCCO.NEUR]Cognitive science/Neuroscience, fMRI, 05 social sciences, Magnetoencephalography, Pattern recognition, Feature Discovery, Magnetic Resonance Imaging, Regression, [STAT]Statistics [stat], Neurology, Reading, Encoding, Multivariate Analysis, Regression Analysis, Artificial intelligence, business, Canonical correlation, 030217 neurology & neurosurgery |
| الوصف: | International audience; Identifying causes solely from observations can be particularly challenging when i) the factors under investigation are difficult to manipulate independently from one-another and ii) observations are high-dimensional. To address this issue, we introduce "Back-to-Back" regression (B2B), a linear method designed to efficiently estimate, from a set of correlated factors, those that most plausibly account for multidimensional observations. First, we prove the consistency of B2B, its links to other linear approaches, and show how it can provide a robust, unbiased and interpretable scalar estimate for each factor. Second, we use a variety of simulated data to show that B2B can outperform forward modeling ("encoding"), backward modeling ("decoding") as well as crossdecomposition modeling (i.e.. canonical correlation analysis and partial least squares) on causal identification when the factors and the observations are not orthogonal. Finally, we apply B2B to a hundred magneto-encephalography recordings and to a hundred functional Magnetic Resonance Imaging recordings acquired while subjects performed a one hour reading task. B2B successfully disentangles the respective contribution of collinear factors such as word length, word frequency in the early visual and late associative cortical responses respectively. B2B compared favorably to other standard techniques on this disentanglement. We discuss how the speed and the generality of B2B sets promising foundations to help identify the causal contributions of covarying factors from high-dimensional observations. |
| تدمد: | 1095-9572 1053-8119 |
| DOI: | 10.1016/j.neuroimage.2020.117028⟩ |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85e37e83bc4bcfa3decab7dd324d5cd7 https://pubmed.ncbi.nlm.nih.gov/32603859 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....85e37e83bc4bcfa3decab7dd324d5cd7 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 10959572 10538119 |
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| DOI: | 10.1016/j.neuroimage.2020.117028⟩ |