Addiction Biology
Zhang, R. B., Jiang, G. H., Tian, J. Z., Qiu, Y. W., Wen, X., Zalesky, A., Li, M., Ma, X. F., Wang, J. J., Li, S. M., Wang, T. Y., Li, C. H., Huang, R. W.
Neuroimaging studies suggested that drug addiction is linked to abnormal brain functional connectivity. However, little is known about the alteration of brain white matter (WM) connectivity in addictive drug users and nearly no study has been performed to examine the alterations of brain WM connectivity in heroin-dependent individuals (HDIs). Diffusion tensor imaging (DTI) offers a comprehensive technique to map the whole brain WM connectivity in vivo. In this study, we acquired DTI datasets from 20 HDIs and 18 healthy controls and constructed their brain WM structural networks using a deterministic fibre tracking approach. Using graph theoretical analysis, we explored the global and nodal topological parameters of brain network for both groups and adopted a network-based statistic (NBS) approach to assess between-group differences in inter-regional WM connections. Statistical analysis indicated the global efficiency and network strength were significantly increased, but the characteristic path length was significantly decreased in the HDIs compared with the controls. We also found that in the HDIs, the nodal efficiency was significantly increased in the left prefrontal cortex, bilateral orbital frontal cortices and left anterior cingulate gyrus. Moreover, the NBS analysis revealed that in the HDIs, the significant increased connections were located in the paralimbic, orbitofrontal, prefrontal and temporal regions. Our results may reflect the disruption of whole brain WM structural networks in the HDIs. Our findings suggest that mapping brain WM structural network may be helpful for better understanding the neuromechanism of heroin addiction.
Citation : Zhang, R. B., Jiang, G. H., Tian, J. Z., Qiu, Y. W., Wen, X., Zalesky, A., Li, M., Ma, X. F., Wang, J. J., Li, S. M., Wang, T. Y., Li, C. H., Huang, R. W.; Abnormal white matter structural networks characterize heroin-dependent individuals: a network analysis, Addict. Biol. 2016 May; 21:667-678