Prediction of clinical and language changes after a single session of repetitive transcranial magnetic stimulation in primary progressive aphasia: A machine learning approach.

Background: Repetitive transcranial magnetic stimulation (rTMS) offers a non-invasive therapeutic option for primary progressive aphasia (PPA) and Alzheimer's disease among other neurological conditions. Objective: This study aims to enhance the personalization of rTMS therapy by predicting patient-specific changes after one session of rTMS across multiple clinical assessments, based on the targeted stimulation area. Methods: We studied 20 PPA patients, each receiving rTMS targeting in three distinct brain regions. Using machine learning, we developed predictive models for eight clinical outcome measures, incorporating features extracted from structural MRI, resting-state EEG, and individualized electric field modeling via SimNIBS. These features were used to estimate each patient's likely response to rTMS in terms of cognitive and language function. Results: Of the outcome measures, 'Clinical impression of change' emerged as the most reliable indicator of perceived improvement, achieving a top F1-score of 0.80 using a Decision Tree model based solely on sociodemographic features. Meanwhile, 'Words per minute' reached an F1-score of 0.77 with a Random Forest model utilizing EEG-derived features. Conclusions: The predictive models developed in this study underscore the feasibility of using EEG and SimNIBS data to anticipate patient outcomes from rTMS therapy. This data-driven framework can aid in customizing rTMS protocols, paving the way for more targeted and effective interventions for PPA and related neurodegenerative conditions.