Machine learning and individual variability in electric field characteristics predict tDCS treatment response

Transcranial direct current stimulation (tDCS) is widely investigated as a therapeutic tool to enhance cognitive function in older adults with and without neurodegenerative disease. Prior research demonstrates that electric current delivery to the brain can vary significantly across individuals. Qua...

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Bibliographic Details
Published in:Brain stimulation 2020-11, Vol.13 (6), p.1753-1764
Main Authors: Albizu, Alejandro, Fang, Ruogu, Indahlastari, Aprinda, O’Shea, Andrew, Stolte, Skylar E., See, Kyle B., Boutzoukas, Emanuel M., Kraft, Jessica N., Nissim, Nicole R., Woods, Adam J.
Format: Article
Language:English
Subjects:
Aged
Aged, 80 and over
Brain - diagnostic imaging
Brain - physiology
Cognition - physiology
Cognitive aging
Double-Blind Method
Female
Finite element modeling
Forecasting
Humans
Individuality
Machine learning
Machine Learning - trends
Magnetic Resonance Imaging - methods
Male
Memory, Short-Term - physiology
Pilot Projects
tDCS
Transcranial direct current stimulation
Transcranial Direct Current Stimulation - methods
Transcranial Direct Current Stimulation - trends
Treatment Outcome
Treatment response
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Summary:Transcranial direct current stimulation (tDCS) is widely investigated as a therapeutic tool to enhance cognitive function in older adults with and without neurodegenerative disease. Prior research demonstrates that electric current delivery to the brain can vary significantly across individuals. Quantification of this variability could enable person-specific optimization of tDCS outcomes. This pilot study used machine learning and MRI-derived electric field models to predict working memory improvements as a proof of concept for precision cognitive intervention. Fourteen healthy older adults received 20 minutes of 2 mA tDCS stimulation (F3/F4) during a two-week cognitive training intervention. Participants performed an N-back working memory task pre-/post-intervention. MRI-derived current models were passed through a linear Support Vector Machine (SVM) learning algorithm to characterize crucial tDCS current components (intensity and direction) that induced working memory improvements in tDCS responders versus non-responders. SVM models of tDCS current components had 86% overall accuracy in classifying treatment responders vs. non-responders, with current intensity producing the best overall model differentiating changes in working memory performance. Median current intensity and direction in brain regions near the electrodes were positively related to intervention responses (r=0.811,p
ISSN:1935-861X
1876-4754
DOI:10.1016/j.brs.2020.10.001