Altered white matter functional network in nicotine addiction. (March 2023)

• Record Type: Journal Article
• Authors: Fan, Chuan; Zha, Rujing; liu, Yan; Wei, Zhengde; Wang, Ying; Song, Hongwen; Lv, Wanwan; Ren, Jiecheng; Hong, Wei; Gou, Huixing; Zhang, Pengyu; Chen, Yucan; Zhou, Yi; Pan, Yu; Zhang, Xiaochu
• Is Part Of: Psychiatry research. Volume 321(2023)
• Publication Date: 2023-03
• Subjects: Functional connectivity -- Graph theory -- White matter -- Nicotine addiction -- Small-world; Psychiatry -- Periodicals; Psychiatry -- periodicals; Psychiatrie -- Périodiques; 616.89
• DOI: 10.1016/j.psychres.2023.115073
• Languages: English
• ISSNs: 0165-1781

Abstract

Highlights: For the first time, the present study explored the topological properties of the WM functional network in the case of addiction, highlighting decreased small-world topological properties in nicotine addiction participants compared with healthy controls. The clinical application based on the abnormal small-world topological properties of WM function indicated potential biomarkers for clinical diagnosis and severity identification in the case of nicotine addiction. These findings indicated that smokers reflected decreased segregated function while maintaining a relatively normal integrated function for the WM functional network. Abnormal small-world properties in WM functional connectivity may expand our understanding of neuropsychiatric disorders. Thus, the small-world topological properties of the WM functional network could be qualified as potential biomarkers in the addiction field. Abstract: Nicotine addiction is a neuropsychiatric disorder with dysfunction in cortices as well as white matter (WM). The nature of the functional alterations in WM remains unclear. The small-world model can well characterize the structure and function of the human brain. In this study, we utilized the small-world model to compare the WM functional connectivity between 62 nicotine addiction participants (called the discovery sample) and 66 matched healthy controls (called the control sample). We also recruited an independent sample comprising 32 nicotine addicts (called the validation sample) for clinical application. The WM functional network data at the network level showed that the nicotine addiction group revealed decreased small-worldness index (σ) and normalized clustering coefficient (γ) compared with healthy controls. For clinical application, the small-world topology of WM functional connectivity could distinguish nicotine addicts from healthy controls (classification accuracy=0.59323, p = 0.0464). We trained abnormal small-world properties on the discovery sample to identify the severity of nicotine addiction, and the identification was successfully applied to the validation sample (classification accuracy=0.65625, p = 0.0106). Our neuroimaging findings provide direct evidence for WM functional changes in nicotine addiction and suggest that the small-world properties of WM function could be qualified as potential biomarkers in nicotine addiction.