Prediction Of Smoking Cessation Based On Resting State Functional Magnetic Resonance Imaging

Objectives:to explore baseline clinical data and radiological data difference between quiters and retainers in smokers,and try to predict the smoking cessation outcome by these materials.Data and methods:78 smokers who attempt to quit smoking were investigated for the baseline clinical data,and conducted T1 phase and resting state fMRI scan.All subjects received a standard smoking cessation cure for 12 weeks with varenicline,at the end time we called them back to verify the final smoking state.Using Rest v1.8 software,we extract all subjects' MRI data available to calculate the ReHo value,and the ALFF/fALFF value.Select about 2/3 of all subjects available to compare the clinical data and images between the quiters and retainer,and find the difference,base on which the logistic regression was established.Predict the outcome of the rest smokers,and test its prediction efficiency.Results:10 subjects were removed from the research due to lost the follow-up or the image quality is not qualified.Finally 48 subjects were included in analysis,and 19 of them were quiters,29 fails.The clinical and fALFF data didn't differ significantly between groups,nevertheless,the ReHo reveal 4 significant different brain region and the ALFF reveal 6.Extracting the data,2 logistic regression equations were established to predict the outcome of smoking cessation of the rest 20 smokers(8 were quiters).The prediction coincidence rate of ReHo and ALFF is 70%and 55%respectively.Conclusions:There are some brain regions which exhibit significant difference in resting state fMRI images in baseline between quiters and retainers in smokers.Extracting that difference may help predict the smoking cessation outcome,especially in screening out the persons under high risk to be retainer..Change the prediction level can adjust the prediction performance,in order to meet the different needs.