Comprehensive investigation of predictive processing: A cross- and within-cognitive domains fMRI meta-analytic approach

作     者：Costa, Cristiano Pezzetta, Rachele Masina, Fabio Lago, Sara Gastaldon, Simone Frangi, Camilla Genon, Sarah Arcara, Giorgio Scarpazza, Cristina 

作者机构：Padova Neurosci Ctr Padua Italy IRCCS Osped San Camillo Venice Italy Univ Padua Dipartimento Psicol Sviluppo & Socializzaz Padua Italy Univ Padua Dipartimento Psicol Gen Padua Italy Heinrich Heine Univ Dusseldorf Inst Syst Neurosci Dusseldorf Germany Res Ctr Julich Inst Neurosci & Med Brain & Behav INM 7 Julich Germany 

出 版 物：《HUMAN BRAIN MAPPING》 (人脑图像描记)

年 卷 期：2024年第45卷第12期

页      面：e26817-e26817页

核心收录：

学科分类：0710[理学-生物学] 1001[医学-基础医学(可授医学、理学学位)] 07[理学] 1010[医学-医学技术（可授医学、理学学位）] 09[农学] 1009[医学-特种医学] 

基　　金：Fondo per il Programma Nazionale di Ricerca eProgetti di Rilevante Interesse Nazionale delPiano Nazionale di Ripresa e Resilienza (PRIN 2022 PNRR) [P2022LC5AK] Fondo per il ProgrammaNazionale di Ricerca e Progetti di Rilevante Interesse Nazionale (PRIN 2022) [2022XKZBFC] Italian Ministry of Health (Ricerca Corrente) 

主　　题：ALE meta-analysis cognitive functions domain-general encoding network predictive processing violation 

摘      要：Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain-general network perspective, suggesting the existence of a prediction network, these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain-general predictive network that encompasses all well-established cognitive domains exists remains unanswered. The present meta-analysis aims to address this gap by testing the hypothesis that PP relies on a large-scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta-analytic approach was employed, along with Meta-Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena-independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross-domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large-scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross-dom