Fig. 5: fMRI responses predict optimal DBS parameters. | Nature Communications

Fig. 5: fMRI responses predict optimal DBS parameters.

From: Predicting optimal deep brain stimulation parameters for Parkinson’s disease using functional MRI and machine learning

Fig. 5: fMRI responses predict optimal DBS parameters.The alternative text for this image may have been generated using AI.

Confusion matrices depicting the performance of classifiers trained to identify optimal DBS settings using features from A contact and voltage cohorts, C contact cohort alone, and D voltage cohort alone in an independent test set (n = 9 a priori clinically optimized patients). B Confusion matrix depicting the performance of the classifier trained to identify optimal DBS settings using features from contact and voltage cohorts in an independent test set (n = 9 stimulation naïve patients). E Summary of performance (overall accuracy) for classifiers in A–D. Bars from dataset 1 depict classifier test accuracy on n = 9 a priori clinically optimized patients. Bars from dataset 2 depict classifier test accuracy on n = 9 stimulation naïve patients. Dashed line indicates chance at 50% accuracy. Source data are provided as a Source Data file. DBS deep brain stimulation, fMRI functional magnetic resonance imaging, NOpt non-optimal, Opt optimal.

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