Over the past two decades, functional magnetic resonance imaging (fMRI) has become the primary tool for exploring neural correlates of emotion. To enhance the reliability of results in understanding the complex nature of emotional experiences, researchers combine findings from multiple fMRI studies using coordinate-based meta-analysis (CBMA). As one of the most widely employed CBMA methods worldwide, activation likelihood estimation (ALE) is of great importance in affective neuroscience and neuropsychology. This comprehensive review provides an introductory guide for implementing the ALE method in emotion research, outlining the experimental steps involved. By presenting a case study about the emotion of disgust, with regard to both its core and social processing, we offer insightful commentary as to how ALE can enable researchers to produce consistent results and, consequently, fruitfully investigate the neural mechanisms underpinning emotions, facilitating further progress in this field.

Activation Likelihood Estimation Neuroimaging Meta-Analysis: a Powerful Tool for Emotion Research

Costa, Tommaso;Ferraro, Mario;Manuello, Jordi
;
Camasio, Alessia;Nani, Andrea;Mancuso, Lorenzo;Cauda, Franco;Liloia, Donato
2024-01-01

Abstract

Over the past two decades, functional magnetic resonance imaging (fMRI) has become the primary tool for exploring neural correlates of emotion. To enhance the reliability of results in understanding the complex nature of emotional experiences, researchers combine findings from multiple fMRI studies using coordinate-based meta-analysis (CBMA). As one of the most widely employed CBMA methods worldwide, activation likelihood estimation (ALE) is of great importance in affective neuroscience and neuropsychology. This comprehensive review provides an introductory guide for implementing the ALE method in emotion research, outlining the experimental steps involved. By presenting a case study about the emotion of disgust, with regard to both its core and social processing, we offer insightful commentary as to how ALE can enable researchers to produce consistent results and, consequently, fruitfully investigate the neural mechanisms underpinning emotions, facilitating further progress in this field.
2024
Volume 17
2331
2345
affective mapping, BrainMap, quantitative synthesis, coordinate-based meta-analysis, fMRI, affective neuroscience
Costa, Tommaso; Ferraro, Mario; Manuello, Jordi; Camasio, Alessia; Nani, Andrea; Mancuso, Lorenzo; Cauda, Franco; Fox, Peter; Liloia, Donato
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1984370
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