Disrupted white matter structural connectivity in heroin abusers.

Neurocognitive impairment is one of the factors that put heroin abusers at greater risk for relapse, and deficits in related functional brain connections have been found. However, the alterations in structural brain connections that may underlie these functional and neurocognitive impairments remain largely unknown. In the present study, we investigated topological organization alterations in the structural network of white matter in heroin abusers and examined the relationships between the network changes and clinical measures. We acquired diffusion tensor imaging datasets from 76 heroin abusers and 78 healthy controls. Network-based statistic was applied to identify alterations in interregional white matter connectivity, and graph theory methods were used to analyze the properties of global networks. The participants also completed a battery of neurocognitive measures. One increased subnetwork characterizing widespread abnormalities in structural connectivity was present in heroin users, which mainly composed of default-mode, attentional and visual systems. The connection strength was positively correlated with increases in fractional anisotropy in heroin abusers. Intriguingly, the changes in within-frontal and within-temporal connections in heroin abusers were significantly correlated with daily heroin dosage and impulsivity scores, respectively. These findings suggest that heroin abusers have extensive abnormal white matter connectivity, which may mediate the relationship between heroin dependence and clinical measures. The increase in white matter connectivity may be attributable to the inefficient microstructure integrity of white matter. The present findings extend our understanding of cerebral structural disruptions that underlie neurocognitive and functional deficits in heroin addiction and provide circuit-level markers for this chronic disorder.

ZNF804A variants confer risk for heroin addiction and affect decision making and gray matter volume in heroin abusers.

Drug addiction shares common neurobiological pathways and risk genes with other psychiatric diseases, including psychosis. One of the commonly identified risk genes associated with broad psychosis has been ZNF804A. We sought to test whether psychosis risk variants in ZNF804A increase the risk of heroin addiction by modulating neurocognitive performance and gray matter volume (GMV) in heroin addiction. Using case-control genetic analysis, we compared the distribution of ZNF804A variants (genotype and haplotype) in 1035 heroin abusers and 2887 healthy subjects. We also compared neurocognitive performance (impulsivity, global cognitive ability and decision-making ability) in 224 subjects and GMV in 154 subjects based on the ZNF804A variants. We found significant differences in the distribution of ZNF804A intronic variants (rs1344706 and rs7597593) allele and haplotype frequencies between the heroin and control groups. Decision-making impairment was worse in heroin abusers who carried the ZNF804A risk allele and haplotype. Subjects who carried more risk alleles and haplotypes of ZNF804A had greater GMV in the bilateral insular cortex, right temporal cortex and superior parietal cortex. The interaction between heroin addiction and ZNF804A variants affected GMV in the left sensorimotor cortex. Our findings revealed several ZNF804A variants that were significantly associated with the risk of heroin addiction, and these variants affected decision making and GMV in heroin abusers compared with controls. The precise neural mechanisms that underlie these associations are unknown, which requires future investigations of the effects of ZNF804A on both dopamine neurotransmission and the relative increases in the volume of various brain areas.

Test-retest reliability of graph metrics in high-resolution functional connectomics: a resting-state functional MRI study.

BACKGROUND: The combination of resting-state functional MRI (R-fMRI) technique and graph theoretical approaches has emerged as a promising tool for characterizing the topological organization of brain networks, that is, functional connectomics. In particular, the construction and analysis of high-resolution brain connectomics at a voxel scale are important because they do not require prior regional parcellations and provide finer spatial information about brain connectivity. However, the test-retest reliability of voxel-based functional connectomics remains largely unclear. AIMS: This study tended to investigate both short-term (∼20 min apart) and long-term (6 weeks apart) test-retest (TRT) reliability of graph metrics of voxel-based brain networks. METHODS: Based on graph theoretical approaches, we analyzed R-fMRI data from 53 young healthy adults who completed two scanning sessions (session 1 included two scans 20 min apart; session 2 included one scan that was performed after an interval of ∼6 weeks). RESULTS: The high-resolution networks exhibited prominent small-world and modular properties and included functional hubs mainly located at the default-mode, salience, and executive control systems. Further analysis revealed that test-retest reliabilities of network metrics were sensitive to the scanning orders and intervals, with fair to excellent long-term reliability between Scan 1 and Scan 3 and lower reliability involving Scan 2. In the long-term case (Scan 1 and Scan 3), most network metrics were generally test-retest reliable, with the highest reliability in global metrics in the clustering coefficient and in the nodal metrics in nodal degree and efficiency. CONCLUSION: We showed high test-retest reliability for graph properties in the high-resolution functional connectomics, which provides important guidance for choosing reliable network metrics and analysis strategies in future studies.