
MUSC researchers used machine learning on fMRI scans to predict which smokers would benefit from repetitive transcranial magnetic stimulation (rTMS) for quitting smoking.
Key Details
- 1MUSC study combines machine learning and fMRI to personalize rTMS for smoking cessation.
- 242 participants took part in an earlier study, split into real vs sham TMS groups.
- 3The salience network's connectivity in the brain, analyzed by AI, correlated best with positive rTMS outcomes.
- 4Machine learning enabled predictions of individual responsiveness to rTMS based on brain network analysis.
- 5Study published in Brain Connectivity; NIH grant support cited.
- 6The research establishes groundwork for precision neuromodulation and larger future trials.
Why It Matters

Source
EurekAlert
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