Machine learning and individual variability in electric field characteristics predict tDCS treatment response.
Machine learning and individual variability in electric field characteristics predict tDCS treatment response.
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DOI：
10.1016/j.brs.2020.10.001
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发表时间：
2020-11
影响因子：
7.7
通讯作者：
Woods AJ
中科院分区：
文献类型：
--
作者：
Albizu A;Fang R;Indahlastari A;O'Shea A;Stolte SE;See KB;Boutzoukas EM;Kraft JN;Nissim NR;Woods AJ
经颅直流电刺激（tDCS）作为一种治疗工具被广泛研究，以增强患有和不患有神经退行性疾病的老年人的认知功能。先前的研究表明，输送到大脑的电流在个体之间可能会有很大差异。这种变异性的量化可以实现tDCS结果的个人特定优化。这项试点研究使用机器学习和MRI衍生的电场模型来预测工作记忆的改善，作为精确认知干预的概念证明。在为期两周的认知训练干预期间，14名健康老年人接受了20分钟的2 mA tDCS刺激（F3/F4）。参与者在干预前/后进行了N-back工作记忆任务。通过线性支持向量机（SVM）学习算法传递MRI衍生电流模型，以表征诱导tDCS应答者与非应答者工作记忆改善的关键tDCS电流成分（强度和方向）。tDCS电流成分的SVM模型在分类治疗应答者与非应答者方面具有86%的总体准确性，其中电流强度产生区分工作记忆表现变化的最佳总体模型。电极附近脑区的中值电流强度和方向与干预反应呈正相关（r = 0：811，p < 0：001和r = 0：774，p = 0：001）。这项研究提供了第一个证据，即MRI衍生的tDCS当前模型的模式识别分析可以提供tDCS治疗反应的个体预后分类，准确率为86%。电流强度和方向的个体差异在确定对tDCS的治疗反应中起重要作用。这些发现为tDCS反应机制提供了重要见解，并为未来tDCS干预的精确给药模型提供了概念证明。
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 < 0:001 and r = 0:774, p = 0:001). This study provides the first evidence that pattern recognition analyses of MRI-derived tDCS current models can provide individual prognostic classification of tDCS treatment response with 86% accuracy. Individual differences in current intensity and direction play important roles in determining treatment response to tDCS. These findings provide important insights into mechanisms of tDCS response as well as proof of concept for future precision dosing models of tDCS intervention.
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