Background: Researchers have endeavored to ascertain the network dysfunction associated with behavioral addiction (BA) through the utilization of resting-state functional connectivity (rsFC). Nevertheless, the identification of aberrant patterns within large-scale networks pertaining to BA has proven to be challenging.
Methods: Whole-brain seed-based rsFC studies comparing subjects with BA and healthy controls (HC) were collected from multiple databases. Multilevel kernel density analysis was employed to ascertain brain networks in which BA was linked to hyper-connectivity or hypo-connectivity with each prior network.
Results: Fifty-six seed-based rsFC publications (1755 individuals with BA and 1828 HC) were included in the meta-analysis. The present study indicate that individuals with BAs exhibit (1) hypo-connectivity within the fronto-parietal network (FN) and hypo- and hyper-connectivity within the ventral attention network (VAN); (2) hypo-connectivity between the FN and regions of the VAN, hypo-connectivity between the VAN and regions of the FN and default mode network (DMN), hyper-connectivity between the DMN and regions of the FN; (3) hypo-connectivity between the reward system and regions of the sensorimotor network (SS), DMN and VAN; (4) hypo-connectivity between the FN and regions of the SS, hyper-connectivity between the VAN and regions of the SS.
Conclusions: These findings provide impetus for a conceptual framework positing a model of BA characterized by disconnected functional coordination among large-scale networks.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jad.2024.03.034 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Large-scale network abnormality in behavioral addiction.
J Affect Disord
June 2024
Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou 510630, China. Electronic address:
Background: Researchers have endeavored to ascertain the network dysfunction associated with behavioral addiction (BA) through the utilization of resting-state functional connectivity (rsFC). Nevertheless, the identification of aberrant patterns within large-scale networks pertaining to BA has proven to be challenging.
Methods: Whole-brain seed-based rsFC studies comparing subjects with BA and healthy controls (HC) were collected from multiple databases.
Large-scale network abnormality in bipolar disorder: A multimodal meta-analysis of resting-state functional and structural magnetic resonance imaging studies.
J Affect Disord
September 2021
Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China; Institute of Molecular and Functional Imaging, Jinan University, Guangzhou, 510630, China. Electronic address:
Background: Bipolar disorder (BD) has been linked to abnormalities in the communication and gray matter volume (GMV) of large-scale brain networks, as reflected by impaired resting-state functional connectivity (rs-FC) and aberrant voxel-based morphometry (VBM). However, identifying patterns of large-scale network abnormality in BD has been elusive.
Methods: Whole-brain seed-based rs-FC and VBM studies comparing individuals with BD and healthy controls (HCs) were retrieved from multiple databases.
Dysfunction of Large-Scale Brain Networks in Schizophrenia: A Meta-analysis of Resting-State Functional Connectivity.
Schizophr Bull
January 2018
Key Laboratory for NeuroInformation of Ministry of Education, High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
Schizophrenia is a complex mental disorder with disorganized communication among large-scale brain networks, as demonstrated by impaired resting-state functional connectivity (rsFC). Individual rsFC studies, however, vary greatly in their methods and findings. We searched for consistent patterns of network dysfunction in schizophrenia by using a coordinate-based meta-analysis.
View Article and Find Full Text PDFAtypical developmental of dorsal and ventral attention networks in autism.
Dev Sci
July 2016
Department of Psychology, University of Miami, USA.
Individuals with autism spectrum disorders (ASD) exhibit early and lifelong impairments in attention across multiple domains. While the disorder is known to affect attention processes, very little is currently known about the brain networks underlying attention in ASD, and even less is known about whether these atypicalities persist across the lifespan. We used functional connectivity analysis applied to resting state functional magnetic resonance imaging (fMRI) data to explore the dorsal (DAN) and ventral (VAN) attention networks in two separate age cohorts of children and adults with and without ASD.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!