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Objective:To investigate the changes in dynamic functional connectivity density (dFCD) and its relationship with Fagerstr?m test for nicotine dependence (FTND) scores in individuals with smoking addiction based on functional MR.Methods:The clinical and imaging data of 176 volunteers recruited through wechat and other online platforms from September 2019 to December 2020 in the First Affiliated Hospital of Zhengzhou University were retrospectively analyzed. The 176 volunteers were male, aged 20 to 55 years old, and were divided into light smoking addiction group (59 cases), heavy smoking addiction group (61 cases) and control group (56 cases). All subjects underwent resting state functional MR scanning and dFCD was calculated. The dFCD values of three groups were analyzed by ANOVA analysis (GRF corrected, voxel level P<0.005, cluster level P<0.01). Bonferroni correction was used for pairwise comparison. Pearson partial correlation analysis was used to analyze the correlation between dFCD values of brain regions with statistically significant differences and FTND scores. Results:Differences in dFCD among light smoking addiction group, heavy smoking addiction group and control group were mainly distributed in the right orbitofrontal cortex, left caudate nucleus, right putamen, bilateral calcarine sulcus cortex, right cuneus, left parahippocampal gyrus, left precuneus, left middle temporal gyrus and bilateral thalamus (GRF corrected, voxel level P<0.005, cluster level P<0.01). Compared with the control group, both the light and heavy smoking addiction groups showed decreased dFCD in the bilateral calcarine sulcus cortex, right cuneus and left precuneus, as well as increased dFCD in the right orbitofrontal cortex, right putamen, left caudate nucleus and left thalamus (Bonferroni corrected, P<0.05). Compared with the control group, the heavy smoking addiction group showed increased dFCD in the right thalamus, and the light smoking addiction group showed decreased dFCD in the left middle temporal gyrus (Bonferroni corrected, P<0.001). Compared with the light smoking addiction group, the heavy smoking addiction group showed increased dFCD in the left middle temporal gyrus and right thalamus, and decreased dFCD in the left parahippocampal gyrus (Bonferroni corrected, P<0.05). The mean value of dFCD in the right thalamus was positively correlated with FTND scores in smoking addiction patients ( r=0.227, P=0.014), and the mean value of dFCD in the right thalamus of the heavy smoking addiction subgroup was positively correlated with FTND scores ( r=0.323, P=0.013). There was no correlation between FTND scores and dFCD in the right thalamus of the light smoking addiction group ( P>0.05). Conclusion:There are changes of neural activity in brain regions related to smoking behaviors among people with different severity of smoking addiction, and smoking behaviors of people with heavy smoking addiction tend to be habitual compared with those with light smoking addiction.
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Objective:To investigate the alterations of resting-state functional connectivity (RSFC) in ventral tegmental area (VTA) and substantia nigra (SN) among male smokers, and its correlation with clinical characteristics of smoking.Methods:The resting-state functional magnetic resonance data of 131 subjects recruited from January 2014 to December 2018 were analyzed retrospectively, including 76 smokers (smoking group) and 55 non-smokers (control group). VTA/SN was selected as regions of interest (ROI), and then calculated RSFC between VTA/SN and the whole brain.Based on SPM12 software, independent sample t-test was conducted to compare the differences in RSFC between smoking group and control group.Based on SPSS 22.0 software, Pearson correlation analysis was used to investigate the relationships between the RSFC of brain regions with significant differences and Fagerstr?m test for nicotine dependence (FTND) score, pack-year of smokers. Results:Compared with control group, the results showed decreased RSFC between VTA and the brain regions related default mode network (DMN)(including posterior cingulate cortex, right anterior cuneiform lobe, bilateral superior temporal gyrus, right middle temporal gyrus and right inferior parietal lobule), and regions of limbic system(including right marginal lobe and right angular gyrus), right calcarine (MNI: x, y, z=24, -55, -14) and left insula(MNI: x, y, z=-35, -11, 9) in smoking group(GRF corrected, voxel level P<0.005, cluster level P<0.05). Taking SN as the seed, there was no significant difference between smoking group and control group ( P>0.05). RSFC of VTA-left superior temporal gyrus was positively correlated with pack-year( r=0.243, P=0.034) and FTND ( r=0.282, P=0.014). VTA-left insula RSFC was positively correlated with FTND ( r=0.316, P=0.006). Conclusion:The RSFC in the mesolimbic system and the VTA-DMN circuit exist abnormal changes in smokers.To some extent, it may explain the reward deficits and dysfunction of emotion regulation in smokers, which may provide clues for further understanding the mechanism of tobacco addiction.
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Objective:To explore the value of machine learning models based on MRI predict the brain age of smokers and healthy controls, and further to explore the relationship between smoking and brain aging.Methods:This was a retrospective study. Dataset 1 consisted of 95 male smokers [20-50 (34±7) years old] and 49 healthy controls [20-50 (33±7) years old] recruited from August 2014 to October 2017 in First Affiliated Hospital of Zhengzhou University. Dataset 2 contained 114 healthy male volunteers [20-50 (34±11) years old] from the Southwestern University Adult Imaging Database from 2010 to 2015. All subjects underwent high-resolution 3D T 1WI scan. Gaussian process regression (GPR) model and support vector machine model were constructed to predict brain age based on structural MR images of healthy controls in dataset 1 and dataset 2. After the performance of the model was verified by the cross-validation method, the mean absolute error (MAE) between the predicted brain age and the actual age and the correlation ( r-value) between the actual age and the predicted brain age were calculated, and the best model was finally selected. The best models were applied to smokers and healthy controls to predict brain age. Finally, a general linear model was used to compare the differences in brain-predicted age difference (PAD) between smokers and healthy controls with age, taking years of education and total intracranial volume as covariates. Result:The performance of GPR model (MAE=5.334, r=0.747) in predicting brain age was better than support vector machine model (MAE=6.040, r=0.679). The GPR model predicted that PAD value of smokers in dataset 1 (2.19±6.64) was higher than that of healthy controls in dataset 1 (-0.80±8.94), and the difference was statistically significant ( F=8.52, P=0.004). Conclusion:GPR model based MRI has better performance in predicting brain age in smokers and healthy controls, and smokers show increased PAD values, further indicating that smoking accelerates brain aging.
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Objective:To explore the differences of static and dynamic spontaneous brain activity between male smoking addicts and healthy controls, and analyze the mechanism of smoking addiction.Methods:Based on static amplitude of low-frequency fluctuation (sALFF) and dynamic amplitude of low frequency fluctuation (dALFF), the differences of static and dynamic spontaneous brain activity were compared between male smoking addicts ( n=63) and healthy controls ( n=30) by independent sample t-test. Pearson correlation analysis was used to investigate the relationships between the altered dALFF values and score of Fagerstr?m test for nicotine dependence(FTND) and pack-years of smoking addicted males. Results:Compared with healthy controls, the values of sALFF in the left superior/middle/inferior orbitofrontal gyrus ( t=5.17, clusters≥108) were increased and the variation of dALFF in the right superior temporal/middle gyrus, left orbitofrontal region, left orbital superior/middle/inferior frontal gyrus, right orbitofrontal gyrus/middle/inferior frontal gyrus and right putamen ( t=4.90, 4.37, 4.91, 4.62, 4.59, clusters≥96) were also increased in the smoking addicted group. It was noteworthy that the dALFF values of the right superior temporal/middle gyrus( r=0.252, P=0.047), left orbital region superior frontal gyrus( r=0.281, P=0.026) and right putamen( r=0.313, P=0.012) were positively correlated with pack-years of male smoking addicts. Conclusion:Male smoking addicts may have abnormal static and dynamics spontaneous neural activity in prefrontal cortex (including orbital frontal lobe), putamen and superior temporal/middle gyrus, which are correlated with pack-years.
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Objective:To investigate the application value of oxygen-challenge MRI in the identification of ischemic penumbra in acute middle cerebral artery occlusion (MCAO) model in rats.Methods:Fifty-eight SD rats were received MCAO processing. Ten MCAO rats were randomly selected. MRI scanning was performed and modified neurological severity score mNSS was evaluated before and after oxygen stimulation at the 1st, 3rd, 6th and 12th hour after MCAO model preparation. The scanning data before and after oxygen stimulation were recorded as air group and oxygen group respectively. T 1 values and T 1 change rate of different brain regions were measured. Twenty-four MCAO rats were selected for oxygen stimulation according to the above four time points. At each time point, 3 MCAO rats were randomly selected for HE staining and recorded as oxygen stimulation group 1 ( n=12). Meanwhile, 3 MCAO rats were randomly selected for immunohistochemical examination at each time points to determine the expression of protein kinase C receptor (RACK1), recorded as oxygen stimulation group 2 ( n = 12). Another 24 MCAO rats were selected without oxygen stimulation. Among them, three MCAO rats were randomly selected at each time point for HE staining, recorded as non-oxygen stimulation group 1 ( n=12); and three MCAO rats were randomly selected at each time point for immunohistochemistry to detect the expression of RACK1, recorded as non-oxygen stimulation group 2 ( n=12). Independent sample t-test was used for the comparison between the two groups. One way ANOVA was used for the comparison in T 1 change rate between different brain regions, and repeated measurement ANOVA was used for the comparison of T 1 change rate in the same brain region at different time points. The correlation between the expression of RACK1 protein in oxygen group 2 and non-oxygen group 2, as well as T 1 values in air group and oxygen group was analyzed by Spearman analysis. Results:At the same time point, the difference of mNSS scores between air group and oxygen group was statistically significant ( P<0.05). At different time points, the difference of mNSS between the two groups was also found statistically significant ( P<0.05). There were significant differences in T 1 values and T 1 change rate between air group and oxygen group in ischemic core area, mismatched area and contralateral normal area, among which the difference was statistically significant (all P<0.05). At different time points, the cellular edema or vacuolation in the cerebral ischemic area of 24 MCAO rats in the oxygen challenge group 1 and the non-oxygen challenge group 1 showed an aggravating trend, and the degree of cellular edema or vacuolation in the cerebral ischemic area of the non oxygen challenge group 1 was slightly less degree than that of the non-oxygen challenge group 1. The expression of RACK1 protein in the cerebral ischemia area of 24 MCAO rats in the oxygen challenge group 2 and the non-oxygen challenge group 2 decreased at different time points, and the expression of RACK1 protein in the non-oxygen challenge group 2 was higher than that in the oxygen challenge group 2 ( P<0.05). Conclusions:Oxygen challenge MRI can be used to determine the oxygen metabolism in the brain infarction tissue of MCAO rats. The oxygen challenge MRI T 1 value and T 1 change rate can help to identify the ischemic penumbra of MCAO rats. Oxygen stimulation may delay the development of cerebral ischemia. The expression of RACK1 plays a protective role in acute cerebral ischemia and is related to the development of hypoxia in brain tissue.