Salience Network Connectivity Predicts Response to Repetitive Transcranial Magnetic Stimulation in Smoking Cessation: A Preliminary Machine Learning Study.

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Abstract

<b><i>Background:</i></b> Combining functional magnetic resonance imaging (fMRI) and machine learning (ML) can be used to identify therapeutic targets and evaluate the effect of repetitive transcranial magnetic stimulation (rTMS) in neural networks in tobacco use disorder. We investigated whether large-scale network connectivity can predict the rTMS effect on smoking cessation. <b><i>Methods:</i></b> Smoking cue exposure task-fMRI (T-fMRI) and resting-state fMRI (Rs-fMRI) scans were acquired before and after the 10 sessions of active or sham rTMS (10 Hz, 3000 pulses per session) over the left dorsal lateral prefrontal cortex in 42 treatment-seeking smokers. Five large-scale networks (default model network, central executive network, dorsal attention network, salience network [SN], and reward network) were compared before and after 10 sessions of rTMS, as well as between active and sham rTMS conditions. We performed neural network and regression analysis on the average connectivity of large-scale networks and the effectiveness of rTMS induced by rTMS. <b><i>Results:</i></b> Regression analyses indicated higher salience connectivity in T-fMRI and lower reward connectivity in Rs-fMRI, predicting a better outcome of TMS treatment for smoking cessation (<i>p</i> < 0.01, Bonferroni corrected). Neural Network analyses suggested that SN was the most important predictor of rTMS effectiveness in both T-fMRI (0.33 of feature importance) and Rs-fMRI (0.37 feature importance). <b><i>Conclusions:</i></b> Both T-fMRI and Rs-fMRI connectivity in SN predict a better outcome of TMS treatment for smoking cessation, but in opposite directions. The work shows that ML models can be used to target TMS treatment. Given the small sample size, all ML findings should be replicated in a larger cohort to ensure their validity.

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