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

Ficco, L., Mancuso, L., Manuello, J., Teneggi, A., Liloia, D., Duca, S., et al. (2021). Disentangling predictive processing in the brain: A meta-analytic study in favour of a predictive network. Scientific Reports, 11: 16258. doi:10.1038/s41598-021-95603-5.

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Open Access Tis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. Te images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2021
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 Creators:
Ficco, Linda1, Author           
Mancuso, Lorenzo1, Author
Manuello, Jordi1, Author
Teneggi, Alessia1, Author
Liloia, Donato1, Author
Duca, Sergio1, Author
Costa, Tommaso1, Author
Kovacs, Gyula Zoltán1, Author
Cauda, Franco1, Author
Affiliations:
1External Organizations, ou_persistent22              

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 Abstract: According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its
upcoming states and refning these predictions through error signals. Despite extensive research
investigating the neural bases of this theory, to date no previous study has systematically attempted
to defne 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 frst 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 taskdriven attention and execution. In sum, we fnd that: (i) predictive processing seems to occur more in
certain brain regions than others, when considering diferent sensory modalities at a time; (ii) there is
no evidence, at the network level, for a distinction between error and prediction processing.

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Language(s): eng - English
 Dates: 2021-08-102021
 Publication Status: Issued
 Pages: -
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41598-021-95603-5
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Title: Scientific Reports
Source Genre: Journal
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Publ. Info: Nature
Pages: - Volume / Issue: 11 Sequence Number: 16258 Start / End Page: - Identifier: ISSN: 2045-2322