Meta-analytic clustering dissociates brain activity and behavior profiles across reward processing paradigms
Jessica S Flannery1, Michael C Riedel2, Katherine L Bottenhorn1
1Department of Psychology, Florida International University, AHC-4, RM 312, 11299 S.W. 8th St, Miami, FL, 33199, USA.
Cognitive, Affective & Behavioral Neuroscience
|December 25, 2019
Summary
This study reveals distinct brain networks involved in reward processing, identifying seven clusters associated with predicting value and processing various internal and external influences. These findings enhance our understanding of reward learning mechanisms.
Area of Science:
- Neuroscience
- Cognitive Science
- Psychology
Background:
- Reward learning is crucial for adaptive behavior, and its impairment is linked to mental health issues.
- Functional neuroimaging studies have identified brain networks involved in reward processing, considering factors like risk, preference, delay, and social context.
Purpose of the Study:
- To synthesize existing neuroimaging findings and provide a comprehensive neurocognitive perspective on reward processing.
- To elucidate specific brain regions and networks engaged in distinct aspects of reward processing using advanced meta-analytic techniques.
Main Methods:
- A data-driven, meta-analytic k-means clustering approach was applied to neuroimaging results from 749 experimental contrasts across 176 studies.
- Seven meta-analytic groupings (MAGs) of brain activity were identified from data involving 13,358 healthy participants.
- Exploratory functional decoding was used to determine the putative functions of each MAG.
Main Results:
- A seven-MAG clustering solution revealed dissociable patterns of brain activity across reward processing tasks.
- Functional decoding indicated that each MAG mapped onto distinct behavioral profiles.
- Specific MAGs were associated with predicting value (MAG-1 & MAG-2) and processing emotional, external, and internal influences (MAG-3 through MAG-7).
Conclusions:
- The findings support and extend existing reward learning theories.
- Large-scale brain network activity is associated with distinct facets of reward processing.
- This research highlights specialized neural roles in value prediction and the integration of diverse influences during reward-based decision-making.
