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Machine-learning defined precision tDCS for improving cognitive function
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DOI：10.1016/j.brs.2023.05.020.png)
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摘要
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Background: Transcranial direct current stimulation (tDCS) paired with cognitive training (CT) is widely inves-tigated as a therapeutic tool to enhance cognitive function in older adults with and without neurodegenerative disease. Prior research demonstrates that the level of benefit from tDCS paired with CT varies from person to person, likely due to individual differences in neuroanatomical structure.Objective: The current study aims to develop a method to objectively optimize and personalize current dosage to maximize the functional gains of non-invasive brain stimulation.Methods: A support vector machine (SVM) model was trained to predict treatment response based on compu-tational models of current density in a sample dataset (n = 14). Feature weights of the deployed SVM were used in a weighted Gaussian Mixture Model (GMM) to maximize the likelihood of converting tDCS non-responders to responders by finding the most optimum electrode montage and applied current intensity (optimized models).Results: Current distributions optimized by the proposed SVM-GMM model demonstrated 93% voxel-wise coherence within target brain regions between the originally non-responders and responders. The optimized current distribution in original non-responders was 3.38 standard deviations closer to the current dose of re-sponders compared to the pre-optimized models. Optimized models also achieved an average treatment response likelihood and normalized mutual information of 99.993% and 91.21%, respectively. Following tDCS dose optimization, the SVM model successfully predicted all tDCS non-responders with optimized doses as responders. Conclusions: The results of this study serve as a foundation for a custom dose optimization strategy towards precision medicine in tDCS to improve outcomes in cognitive decline remediation for older adults.
Keyword:
tES
Aging
Machine-learning
MRI
Finite element model
Precision medicine
期刊
IF:
8.4
论文数:
3.3K
被引数:
1.3W
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