Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe University Frankfurt, Frankfurt am Main 60528, Germany
Institute of Medical Psychology and Medical Sociology, University Medical Center Schleswig Holstein, Kiel University, Kiel 24105, Germany.
J Neurosci. 2024 May 29;44(22):e1372232024. doi: 10.1523/JNEUROSCI.1372-23.2024.
Transcranial direct current stimulation (tDCS) is a noninvasive neuromodulation technique gaining more attention in neurodevelopmental disorders (NDDs). Due to the phenotypic heterogeneity of NDDs, tDCS is unlikely to be equally effective in all individuals. The present study aimed to establish neuroanatomical markers in typically developing (TD) individuals that may be used for the prediction of individual responses to tDCS. Fifty-seven male and female children received 2 mA anodal and sham tDCS, targeting the left dorsolateral prefrontal cortex (DLPFC), right inferior frontal gyrus, and bilateral temporoparietal junction. Response to tDCS was assessed based on task performance differences between anodal and sham tDCS in different neurocognitive tasks (-back, flanker, Mooney faces detection, attentional emotional recognition task). Measures of cortical thickness (CT) and surface area (SA) were derived from 3 Tesla structural MRI scans. Associations between neuroanatomy and task performance were assessed using general linear models (GLM). Machine learning (ML) algorithms were employed to predict responses to tDCS. Vertex-wise estimates of SA were more closely linked to differences in task performance than measures of CT. Across ML algorithms, highest accuracies were observed for the prediction of -back task performance differences following stimulation of the DLPFC, where 65% of behavioral variance was explained by variability in SA. Lower accuracies were observed for all other tasks and stimulated regions. This suggests that it may be possible to predict individual responses to tDCS for some behavioral measures and target regions. In the future, these models might be extended to predict treatment outcome in individuals with NDDs.
经颅直流电刺激（tDCS）是一种非侵入性的神经调节技术，在神经发育障碍（NDD）中越来越受到关注。由于 NDD 的表型异质性，tDCS 不太可能对所有个体都同样有效。本研究旨在确定在正常发育（TD）个体中的神经解剖学标志物，这些标志物可能用于预测个体对 tDCS 的反应。57 名男性和女性儿童接受了 2mA 的阳极和假 tDCS 刺激，刺激部位为左背外侧前额叶皮层（DLPFC）、右额下回和双侧颞顶联合区。根据不同神经认知任务（-back、flanker、Mooney 面孔检测、注意力情绪识别任务）中阳极和假 tDCS 之间的任务表现差异来评估 tDCS 的反应。从 3T 结构 MRI 扫描中得出皮质厚度（CT）和表面积（SA）的测量值。使用一般线性模型（GLM）评估神经解剖结构与任务表现之间的关联。采用机器学习（ML）算法来预测 tDCS 的反应。SA 的顶点估计与任务表现差异的相关性比 CT 测量值更为密切。在所有 ML 算法中，对于刺激 DLPFC 后 -back 任务表现差异的预测，观察到最高的准确性，其中 65%的行为变异由 SA 的变异性解释。对于所有其他任务和刺激区域，观察到的准确性较低。这表明，对于某些行为测量和目标区域，可能有可能预测个体对 tDCS 的反应。在未来，这些模型可能会扩展到预测 NDD 个体的治疗效果。
