Objective To investigate the correlation between vertebral artery tortuosity and posterior circulation ischemia (PCI). Methods The patients with PCI aged ≥50 years old and the controls without PCI at the same time were enrolled. CT angiography was performed in all patients. The cervical vertebral artery tortuosity was observed and rated, and the related risk factors for influencing PCI were analyzed. Results A total of 112 patients with PCI and 90 controls were enrolled. Univariate analysis showed that the proportions of patients with hypertension (80. 36% vs. 54. 44% ; χ2 = 15. 613, P < 0. 001), smoking (35. 71% vs. 18. 89% ; χ2 = 6. 974, P = 0. 008), alcohol consumption (25. 89% vs. 10. 00% ; χ2 = 8. 253, P = 0. 004), posterior circulation vascular stenosis (54. 46% vs. 24. 44% ; χ2 = 18. 578, P < 0. 001), and vertebral artery tortuosity (71. 43% vs. 48. 89% ; χ2 = 10. 695, P = 0. 001), as well as the levels of the total cholesterol (4. 96 ± 1. 26 mmol/L vs. 4. 61 ± 1. 04 mmol/L; t = - 2. 110, P = 0. 036 ), low-density lipoprotein cholesterol (3. 02 ± 0. 90 mmol/L vs. 2. 69 ± 0. 78 mmol/L; t = - 2. 671, P = 0. 008 ), and fibrinogen (3. 67 ± 1. 69 mg/L vs. 3. 25 ± 0. 97 mg/L; t = - 2. 002, P = 0. 047) in the PCI group were significantly higher than those in the control group. The proportion of bilateral vertebral artery tortuosity in the PCI group was significantly higher that in the control group (30. 36% vs. 12. 22% ; χ2 = 9. 478, P =0. 002). The proportion of grade 3 vertebral artery tortuosity in the PCI group was significantly higher than that in the control group (43. 75% vs. 26. 67% ; χ2 = 6. 310, P = 0. 012). Multivariate logistic regression analysis showed that smoking (odds ratio [OR] 2. 339, 95% confidence interval [CI] 1. 037-5. 278; P =0. 041), low-density lipoprotein cholesterol (OR 1. 580,95% CI 1. 050-2. 377; P = 0. 028), hypertension (OR 2. 631, 95% CI 1. 237-5. 596; P = 0. 012), posterior circulation vascular stenosis (OR 3. 419, 95% CI 1. 638-7. 134; P = 0. 001), and vertebral artery tortuosity (OR 2. 413, 95% CI 1. 212-4. 803; P = 0. 012) were the independent risk factors for PCI. Conclusion The vertebral artery tortuosity is an independent risk factor for PCI in the middle-aged and elderly people.

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.

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.

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.

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.

Objective To observe whether there was difference of inferior glenohumeral joint capsule thickness(ICT)measured on coronal and transverse axillary section with high-frequency ultrasound.Methods ICT of 56 patients with frozen shoulder(FS group)and 115 healthy controls(HC group)were measured on coronal and transverse axillary sections with high-frequency ultrasound.The ultrasonic findings were compared between groups,while ICT measured on different sections were compared within groups.Results In FS group,ICT thickened,presented as low echo with poor boundary clarity,with reduced and uneven internal echo.No echo areas could be detected when there was fluid accumulation,and concomitant blood flow signal could be observed.In HC group,the inferior glenohumeral joint capsule presented as moderate echo with clear boundary,with uniform low or equal echo.No significant difference of ICT values measured on coronal or transverse section was found within both groups(both P＞0.05).Conclusion ICT measured on coronal and transverse axillary section with high-frequency ultrasound were not significantly different.