Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network

Abstract According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically atte...

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Autores principales:	Linda Ficco, Lorenzo Mancuso, Jordi Manuello, Alessia Teneggi, Donato Liloia, Sergio Duca, Tommaso Costa, Gyula Zoltán Kovacs, Franco Cauda
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8b52951a386140cab42a0747f6ad0eb3
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spelling	oai:doaj.org-article:8b52951a386140cab42a0747f6ad0eb32021-12-02T15:08:39ZDisentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network10.1038/s41598-021-95603-52045-2322https://doaj.org/article/8b52951a386140cab42a0747f6ad0eb32021-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-95603-5https://doaj.org/toc/2045-2322Abstract According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in task-driven attention and execution. In sum, we find that: (i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; (ii) there is no evidence, at the network level, for a distinction between error and prediction processing.Linda FiccoLorenzo MancusoJordi ManuelloAlessia TeneggiDonato LiloiaSergio DucaTommaso CostaGyula Zoltán KovacsFranco CaudaNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Linda Ficco Lorenzo Mancuso Jordi Manuello Alessia Teneggi Donato Liloia Sergio Duca Tommaso Costa Gyula Zoltán Kovacs Franco Cauda Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network
description	Abstract According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its upcoming states and refining these predictions through error signals. Despite extensive research investigating the neural bases of this theory, to date no previous study has systematically attempted to define the neural mechanisms of predictive coding across studies and sensory channels, focussing on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach to address this issue. We first use the Activation Likelihood Estimation (ALE) algorithm to detect spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover, we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique reveals a large, bilateral predictive network, which resembles large-scale networks involved in task-driven attention and execution. In sum, we find that: (i) predictive processing seems to occur more in certain brain regions than others, when considering different sensory modalities at a time; (ii) there is no evidence, at the network level, for a distinction between error and prediction processing.
format	article
author	Linda Ficco Lorenzo Mancuso Jordi Manuello Alessia Teneggi Donato Liloia Sergio Duca Tommaso Costa Gyula Zoltán Kovacs Franco Cauda
author_facet	Linda Ficco Lorenzo Mancuso Jordi Manuello Alessia Teneggi Donato Liloia Sergio Duca Tommaso Costa Gyula Zoltán Kovacs Franco Cauda
author_sort	Linda Ficco
title	Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network
title_short	Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network
title_full	Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network
title_fullStr	Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network
title_full_unstemmed	Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network
title_sort	disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8b52951a386140cab42a0747f6ad0eb3
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Disentangling predictive processing in the brain: a meta-analytic study in favour of a predictive network

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