%0 Journal Article
%A Ficco, Linda
%A Mancuso, Lorenzo
%A Manuello, Jordi
%A Teneggi, Alessia
%A Liloia, Donato
%A Duca, Sergio
%A Costa, Tommaso
%A Kovacs, Gyula Zoltán
%A Cauda, Franco
%+ External Organizations
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%T Disentangling predictive processing in the brain: A meta-analytic study in favour of a predictive network : 
%G eng
%U https://hdl.handle.net/21.11116/0000-0008-FD51-0
%R 10.1038/s41598-021-95603-5
%7 2021-08-10
%D 2021
%* Review method: peer-reviewed
%X According to the predictive coding (PC) theory, the brain is constantly engaged in predicting its<br>upcoming states and refning these predictions through error signals. Despite extensive research<br>investigating the neural bases of this theory, to date no previous study has systematically attempted<br>to defne the neural mechanisms of predictive coding across studies and sensory channels, focussing<br>on functional connectivity. In this study, we employ a coordinate-based meta-analytical approach<br>to address this issue. We frst use the Activation Likelihood Estimation (ALE) algorithm to detect<br>spatial convergence across studies, related to prediction error and encoding. Overall, our ALE results<br>suggest the ultimate role of the left inferior frontal gyrus and left insula in both processes. Moreover,<br>we employ a meta-analytic connectivity method (Seed-Voxel Correlations Consensus). This technique<br>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<br>certain brain regions than others, when considering diferent sensory modalities at a time; (ii) there is<br>no evidence, at the network level, for a distinction between error and prediction processing.
%J Scientific Reports
%V 11
%] 16258
%I Nature
%@ 2045-2322