Alzheimer Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam UMC location VUmc, Amsterdam, The Netherlands.
Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands.
PLoS Comput Biol. 2024 Jan 17;20(1):e1011164. doi: 10.1371/journal.pcbi.1011164. eCollection 2024 Jan.
Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation technique with potential for counteracting disrupted brain network activity in Alzheimer's disease (AD) to improve cognition. However, the results of tDCS studies in AD have been variable due to different methodological choices such as electrode placement. To address this, a virtual brain network model of AD was used to explore tDCS optimization. We compared a large, representative set of virtual tDCS intervention setups, to identify the theoretically optimized tDCS electrode positions for restoring functional network features disrupted in AD. We simulated 20 tDCS setups using a computational dynamic network model of 78 neural masses coupled according to human structural topology. AD network damage was simulated using an activity-dependent degeneration algorithm. Current flow modeling was used to estimate tDCS-targeted cortical regions for different electrode positions, and excitability of the pyramidal neurons of the corresponding neural masses was modulated to simulate tDCS. Outcome measures were relative power spectral density (alpha bands, 8-10 Hz and 10-13 Hz), total spectral power, posterior alpha peak frequency, and connectivity measures phase lag index (PLI) and amplitude envelope correlation (AEC). Virtual tDCS performance varied, with optimized strategies improving all outcome measures, while others caused further deterioration. The best performing setup involved right parietal anodal stimulation, with a contralateral supraorbital cathode. A clear correlation between the network role of stimulated regions and tDCS success was not observed. This modeling-informed approach can guide and perhaps accelerate tDCS therapy development and enhance our understanding of tDCS effects. Follow-up studies will compare the general predictions to personalized virtual models and validate them with tDCS-magnetoencephalography (MEG) in a clinical AD patient cohort.
经颅直流电刺激（tDCS）是一种非侵入性脑刺激技术，具有对抗阿尔茨海默病（AD）中大脑网络活动紊乱以改善认知的潜力。然而，由于电极放置等不同的方法学选择，tDCS 在 AD 中的研究结果一直存在差异。为了解决这个问题，使用 AD 的虚拟大脑网络模型来探索 tDCS 的优化。我们比较了大量具有代表性的虚拟 tDCS 干预方案，以确定理论上优化的 tDCS 电极位置，以恢复 AD 中功能网络特征的破坏。我们使用耦合根据人类结构拓扑的 78 个神经质量的计算动态网络模型模拟了 20 个 tDCS 方案。使用依赖于活动的退化算法模拟 AD 网络损伤。使用电流流动建模来估计不同电极位置的 tDCS 靶向皮质区域，并调制相应神经质量的锥体神经元的兴奋性以模拟 tDCS。结果衡量标准是相对功率谱密度（alpha 频段，8-10 Hz 和 10-13 Hz）、总光谱功率、后 alpha 峰频率以及连接性衡量标准相位滞后指数（PLI）和幅度包络相关（AEC）。虚拟 tDCS 性能各不相同，优化策略可改善所有结果衡量标准，而其他策略则导致进一步恶化。表现最佳的方案涉及右顶叶阳极刺激，对侧眶上阴极。刺激区域的网络作用与 tDCS 成功之间没有明显的相关性。这种基于建模的方法可以指导并可能加速 tDCS 治疗的发展，并增强我们对 tDCS 效果的理解。后续研究将比较一般预测与个性化虚拟模型，并在临床 AD 患者队列中用 tDCS-脑磁图（MEG）对其进行验证。
