Researchers have identified patterns of brain connectivity that could act as potential biomarkers for nicotine addiction. Their findings, published in Psychiatry Research, indicate that nicotine addiction is associated with changes in white matter functional connectivity.
White matter is a type of tissue in the brain that is composed of nerve fibers, called axons, that extend from nerve cells (neurons) and form connections between different regions of the brain. White matter is responsible for transmitting signals between different parts of the brain, as well as between the brain and the rest of the body. It plays a critical role in cognitive functions such as attention, learning, and memory, as well as in the regulation of movement and sensation.
“White matter lesions are the core pathological changes in smoking addiction, and current research has shown that white matter function can also reveal the severity of neuropsychiatric disorders,” said study author Rujing Zha, a special associate researcher at the University of Science and Technology of China. “Therefore, we want to explore whether changes in white matter function in the field of addiction can reflect the severity of addiction and serve as a biological marker for clinical applications.”
In this study, the researchers wanted to see how the brain connectivity of people addicted to nicotine compared to healthy people. They recruited 62 nicotine addicts and 66 healthy people to participate in the study, and also recruited another 32 nicotine addicts to validate their findings.
To be eligible for the study, the smokers had to smoke at least 10 cigarettes a day for at least 2 years, be right-handed, have normal vision, and not have any mental or physical health problems (except for nicotine addiction). Both smokers and healthy controls were excluded if they had any neurological or psychological disorders, drug abuse (except for nicotine), a prior head injury, anxiety disorders or major depression, or any contraindications for MRI scans. The severity of nicotine addiction was measured using a test called the Fagerstrom Test of Nicotine Dependence.
The researchers used a mathematical model called the small-world model to compare the brain connectivity of the two groups. They also used the validation sample of 32 smokers who were recruited separately to confirm their findings. All the participants were recruited through internet advertisements and posters.
“Our study first started with exploring the functional changes related to the pathogenesis of smoking addiction,” Zha told PsyPost. “Secondly, we established cross-dataset classification and prediction models using three independent samples: a smoking group, a healthy control group, and another independent smoking group, to investigate the universality of functional changes. Finally, our study utilized the latest analytical methods, which were applied for the first time in the field of addiction, to promote the understanding and clinical application of the neural mechanisms of substance addiction.”
The researchers found that the nicotine addiction group had lower small-worldness index (σ) and normalized clustering coefficient (γ) compared to the healthy controls.
The small-worldness index describes how efficiently the brain networks communicate and integrate information. A lower small-worldness index means that the brain networks are less efficient in communicating and integrating information. The clustering coefficient describes the degree to which neurons in the brain are connected to each other. A lower normalized clustering coefficient means that there are fewer connections between neurons in the brain.
Overall, these findings suggest that the brain networks of people addicted to nicotine are less efficient and less well-connected compared to healthy individuals.
Additionally, the researchers used the data from the first group (called the discovery sample) to identify abnormal small-world properties that were associated with the severity of nicotine addiction. They then applied this identification process to a second group of nicotine addicts (called the validation sample) and were able to accurately classify 65.62% of these individuals based on their severity of addiction.
“Our most gratifying finding is that the abnormal changes in white matter function in smokers can not only be used to distinguish between addicted and healthy individuals but also for disease classification,” Zha said. “Even in cross-dataset studies, the disease classification model still holds, which to some extent demonstrates the universality of the abnormal changes in white matter function as a model feature.”
But the study, like all research, includes some caveats.
“Our smoking addiction group has a relatively young average age, with all participants being under 40 years old, and none of them having serious physical illnesses caused by smoking,” Zha explained. “Additionally, all participants are male. Therefore, our research results are only applicable to young, smoking male individuals without serious underlying diseases. Further research is needed to determine whether our research results are applicable to smoking-addicted adolescents, women, middle-aged and elderly populations.”
“In the future, the scope of our research on smoking addiction should be expanded to include adolescents, women, and middle-aged and elderly populations,” Zha added. “At the same time, we should also pay attention to the changes in brain function of individuals who have quit smoking.”
The study, “Altered white matter functional network in nicotine addiction“, was authored by Chuan Fan, Rujing Zha, Yan liu, Zhengde Wei, Ying Wang, Hongwen Song, Wanwan Lv, Jiecheng Ren, Wei Hong, Huixing Gou, Pengyu Zhang, Yucan Chen, Yi Zhou, Yu Pan, and Xiaochu Zhang.
