Background: It is unknown whether impaired coupling among 3 core large-scale brain networks (salience [SN], default mode [DMN] and executive control networks [ECN]) is associated with relapse behaviour in treated heroin-dependent patients. Methods: We conducted a prospective resting-state functional MRI study comparing the functional connectivity strength among healthy controls and heroin-dependent men who had either relapsed or were in early remission. Men were considered to be either relapsed or in early remission based on urine drug screens during a 3-month follow-up period. We also examined how the coupling of large-scale networks correlated with relapse behaviour among heroin-dependent men. Results: We included 20 controls and 50 heroin-dependent men (26 relapsed and 24 early remission) in our analyses. The relapsed men showed greater connectivity than the early remission and control groups between the dorsal anterior cingulate cortex (key node of the SN) and the dorsomedial prefrontal cortex (included in the DMN). The relapsed men and controls showed lower connectivity than the early remission group between the left dorsolateral prefrontal cortex (key node of the left ECN) and the dorsomedial prefrontal cortex. The percentage of positive urine drug screens positively correlated with the coupling between the dorsal anterior cingulate cortex and dorsomedial prefrontal cortex, but negatively correlated with the coupling between the left dorsolateral prefrontal cortex and dorsomedial prefrontal cortex. Limitations: We examined deficits in only 3 core networks leading to relapse behaviour. Other networks may also contribute to relapse. Conclusion: Greater coupling between the SN and DMN and lower coupling between the left ECN and DMN is associated with relapse behaviour. These findings may shed light on the development of new treatments for heroin addiction.

The triple‐network model has been widely applied in neuropsychiatric disorders including autism spectrum disorder (ASD). However, the mechanism of causal regulations within the triple‐network and their relations with symptoms of ASD remains unclear. 81 male ASD and 80 well matched typically developing control (TDC) were included in this study, recruited from Autism Brain Image Data Exchange‐I datasets. Spatial reference‐based independent component analysis was used to identify the anterior and posterior part of default‐mode network (aDMN and pDMN), salience network (SN), and bilateral executive‐control network (ECN) from resting‐state functional magnetic resonance imaging data. Spectral dynamic causal model and parametric empirical Bayes with Bayesian model reduction/average were adopted to explore the effective connectivity (EC) within triple‐network and the relationship between EC and autism diagnostic observation schedule (ADOS) scores. After adjusting for age and site effect, ASD and TDC groups both showed inhibition patterns. Compared with TDC, ASD group showed weaker self‐inhibition in aDMN and pDMN, stronger inhibition in pDMN→aDMN, weaker inhibition in aDMN→LECN, pDMN→SN, LECN→SN, and LECN→RECN. Furthermore, negative relationships between ADOS scores and pDMN self‐inhibition strength, as well as with the EC of pDMN→aDMN were observed in ASD group. The present study reveals imbalanced effective connections within triple‐networks in ASD children. More attentions should be focused at the pDMN, which modulates the core symptoms of ASD and may serve as an important region for ASD diagnosis and the target region for ASD treatments.

Background: Based on hubs of neural circuits associated with addiction and their degree centrality (DC), this study aimed to construct the addiction-related brain networks for patients diagnosed with heroin dependence undertaking stable methadone maintenance treatment (MMT) and further prospectively identify the ones at high risk for relapse with cluster analysis.Methods: Sixty-two male MMT patients and 30 matched healthy controls (HC) underwent brain resting-state functional MRI data acquisition. The patients received 26-month follow-up for the monthly illegal-drug-use information. Ten addiction-related hubs were chosen to construct a user-defined network for the patients. Then the networks were discriminated with K-means-clustering-algorithm into different groups and followed by comparative analysis to the groups and HC. Regression analysis was used to investigate the brain regions significantly contributed to relapse.Results: Sixty MMT patients were classified into two groups according to their brain-network patterns calculated by the best clustering-number-K. The two groups had no difference in the demographic, psychological indicators and clinical information except relapse rate and total heroin consumption. The group with high-relapse had a wider range of DC changes in the cortical−striatal−thalamic circuit relative to HC and a reduced DC in the mesocorticolimbic circuit relative to the low-relapse group. DC activity in NAc, vACC, hippocampus and amygdala were closely related with relapse.Conclusion: MMT patients can be identified and classified into two subgroups with significantly different relapse rates by defining distinct brain-network patterns even if we are blind to their relapse outcomes in advance. This may provide a new strategy to optimize MMT.