Literature DB >> 25740690

Abnormal white matter structural networks characterize heroin-dependent individuals: a network analysis.

Ruibin Zhang1, Guihua Jiang2, Junzhang Tian2, Yingwei Qiu2, Xue Wen1, Andrew Zalesky3, Meng Li2, Xiaofen Ma2, Junjing Wang1, Shumei Li2, Tianyue Wang2, Changhong Li1, Ruiwang Huang1.   

Abstract

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.
© 2015 Society for the Study of Addiction.

Entities:  

Keywords:  Addiction; diffusion tensor imaging; graph theory; network-based statistic

Mesh:

Year:  2015        PMID: 25740690     DOI: 10.1111/adb.12234

Source DB:  PubMed          Journal:  Addict Biol        ISSN: 1355-6215            Impact factor:   4.280


  9 in total

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2.  Aberrant brain structural-functional connectivity coupling in euthymic bipolar disorder.

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5.  Abnormal white matter within brain structural networks is associated with high-impulse behaviour in codeine-containing cough syrup dependent users.

Authors:  Yunfan Wu; Zhihua Zhou; Meng Li; Xiaofen Ma; Zhihong Lan; Jin Fang; Shishun Fu; Kanghui Yu; Yi Yin; Shoujun Xu; Cuihua Gao; Jianneng Li; Guihua Jiang
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7.  Random topology organization and decreased visual processing of internet addiction: Evidence from a minimum spanning tree analysis.

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8.  Identification of Tendency to Alcohol Misuse From the Structural Brain Networks.

Authors:  Sujung Yoon; Jungyoon Kim; Gahae Hong; Tammy D Kim; Haejin Hong; Eunji Ha; Jiyoung Ma; In Kyoon Lyoo
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9.  Recovery of superior frontal gyrus cortical thickness and resting-state functional connectivity in abstinent heroin users after 8 months of follow-up.

Authors:  Wenhan Yang; Min Zhang; Fei Tang; Yanyao Du; Li Fan; Jing Luo; Cui Yan; Shicong Wang; Jun Zhang; Kai Yuan; Jun Liu
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  9 in total

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