| Background and objectSmoking is one of the most important causes of disability and premature death worldwide,as well as one of the largest avoidable causes of disease and death.As the most populous country in the world,China also suffers from the world's largest number of cigarettes and secondhand smoke,with about 1 million deaths caused by tobacco-related diseases each year.Nicotine is the main substance causing addiction in the process of smoking.The interaction between nicotine and Acetylcholine receptor(NAChRs)activates the projection of dopamine pathway.The receptors of?4 and ?2 subunits of acetylcholine are considered to be the main targets of nicotine smoking.Nicotine provides a positive enhancement effect on synaptic connections in neural networks and improves a series of behavioral states that accompany quitting smoking,resulting in varying degrees of nicotine dependence among smokers.In recent years,brain neuroimaging studies of a variety of diseases have shown that a holistic analysis based on the network level may be able to more comprehensively describe the effects of long-term nicotine dependence on brain function in smokers.This study adopts the sliding window correlation method,which is the most widely used in the dynamic functional connection analysis method,by selecting a fixed length time window and using the data points in the window to measure the functional connectivity of the brain,and the correlation matrix calculated on the window segment of the BOLD time series derived by the independent component analysis method.The purpose of this study was to further explore the differences of dynamic functional connectivity indexes of nicotine dependent patients and their correlation with smoking index,in order to explore the internal connection model of nicotine addicts' dynamic functional network,which is helpful to find their potential neuroimaging evidence and provide theoretical basis for clinical smoking cessation treatment.Methods and materialsIn this study,121 nicotine addict(mean age 35.87 ±7.66)and 58 non-smoking volunteers(mean age 34.27±6.47)were collected.All the subjects were scanned on the 3.0T MRI scanner(Siemens,Magnetom Skyra)with a standard 16-channel head coil.Before the magnetic resonance examination,all the subjects were assesed the clinical scale,trained the subjects' attention during the examination,and fixed the subjects' head.First,conventional MRI sequences were scanned to exclude subjects with craniocerebral diseases,and then functional magnetic resonance imaging(fMRI)sequences were scanned.On the Matlab platform,software was used to preprocess the collected fMRI data.The subjects,whose head motion coefficient is greater than 3mm,were excluded.Then,the preprocessed data were divided into 80 components for independent component analysis at the group level.Finally,a total of 27 effective independent components are obtained,and then the dynamic functional connection analysis of 27 effective independent components was carried out by sliding time window technology.The functional connection indexes of nicotine addict group and non-smoking control group were compared by SPSS 21.0software.Then,according to FTND score,the nicotine addict group was divided into mild nicotine addict group(n=76)and severe nicotine addict group(n=39).The functional connection indexes of mild nicotine addict group,severe nicotine addict group and non-smoking control group were compared among the three groups.Age and education level were taken as covariates.SPSS was used to analyze the correlation between dynamic functional connectivity indexs and smoking indexs.Furthermore,the reliability of the dynamic functional connectivity analysis results wsa verified by changing the sliding window size(W)and the clustering number(k).Results1.The dynamic functional connectivity indexes were compared between the nicotine addict group and the non-smoking control group:there was significant difference in the fraction time of the state 2 and state 3 and the mean dwell time of the state 2 between the nicotine addict group and the non-smoking control group(P<0.05),but there was no significant difference in the numbers of transitions between the two groups(P>0.05).There was no significant difference in the three functional connection indexes between the two groups in the state 1 and state 4(P>0.05).There were significant differences in fraction time of state 2,state 3 and mean dwell time of state 2 between non-smoking control group and mild nicotine addict group(P<0.05).There were significant differences in mean dwell time of state 2 between normal control group and severe nicotine addict group(P<0.05).There was no significant difference in three functional connectivity indexes between mild nicotine dependence group and severe nicotine addict group(P>0.05).2.Comparison of the dynamic functional connectivity mode between the nicotine addict group and the non-smoking control group:compared with the control group,the functional network connection between the executive control network and precuneus,the functional network connection between executive control network and salience network were weakened in the smoking group,while the functional network between salience network and precuneus,and the functional network between the visual network and the language network were enhanced in the smoking group.3.Correlation analysis between smoking index and dynamic functional connectivity index:there was a slight negative correlation between smoking age and fraction time and mean dwell time of state 2(r=0.196,P=0.036;r=-0.211,P=0.024).FTND score was positively correlated with mean dwell time of state 4(r=0.266,P=0.004),and negatively correlated with numbers of transitions(r=-0.269,P=0.004).There was no significant correlation between the other two smoking indexes(initial smoking age,number of daily smoking)and dynamic functional connectivity index.ConclusionIn this study,it is found that nicotine addict patients have abnormal whole brain functional network connection,which is mainly manifested by the decrease of overall network connection efficiency and the improvement of partial local network connection efficiency.The length of smoking timeand the degree of nicotine addict have a direct effect on the brain functional connection.the longer the smoking time,the greater the effect on the whole brain weak functional connection.the greater the degree of nicotine addict,the greater the effect on the state of strong functional network connections in the whole brain,and the longer it is maintained in this state. |
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