Background

Working memory decline has been associated with normal aging. The frontal brain structure responsible for this decline is primarily located in the prefrontal cortex (PFC). Our previous neuroimaging study demonstrated a significant change in functional connectivity between the left dorsolateral PFC (DLPFC) and left ventrolateral PFC (VLPFC) when applying 2 mA tDCS in MRI scanner during an N-Back task. These regions were part of the working memory network. The present study is the first study that utilizes individualized finite element models derived from older adults’ MRI to predict significant changes of functional connectivity observed from an acute tDCS application.

Methods

Individualized head models from 15 healthy older adults (mean age = 71.3 years) were constructed to create current density maps. Each head model was segmented into 11 tissue types: white matter, gray matter, CSF, muscle, blood vessels, fat, eyes, air, skin, cancellous, and cortical bone. Electrodes were segmented from T1-weighted images and added to the models. Computed median and maximum current density values in the left DLPFC and left VLPFC regions of interest (ROIs) were correlated with beta values as functional connectivity metrics measured in different timepoint (baseline, during stimulation) and stimulation condition (active and sham).

Main results

Positive significant correlations (R² = 0.523 for max J, R² = 0.367 for median J, p < 0.05) were found between the beta values and computed current densities in the left DLPFC ROIs for active stimulation, but no significant correlation was found during sham stimulation. We found no significant correlation between connectivity and current densities computed in the left VLPFC for both active and sham stimulation.

Conclusions

The amount of current within the left DLPFC ROIs was found positively correlated with changes in functional connectivity between left DLPFC and left VLPFC during active 2 mA stimulation. Future work may include expansion of number of participants to further test the accuracy of tDCS models used to predict tDCS-induced functional connectivity changes within the working memory network.

中文翻译：

---

个性化 tDCS 模型预测老年人工作记忆网络中的功能连接变化

背景

工作记忆衰退与正常衰老有关。导致这种下降的大脑额叶结构主要位于前额叶皮层 (PFC)。我们之前的神经影像学研究表明，在 N-Back 任务期间，在 MRI 扫描仪中应用 2 mA tDCS 时，左背外侧 PFC (DLPFC) 和左腹外侧 PFC (VLPFC) 之间的功能连接发生了显着变化。这些区域是工作记忆网络的一部分。本研究是第一项利用来自老年人 MRI 的个性化有限元模型来预测从急性 tDCS 应用中观察到的功能连接的显着变化的研究。

方法

构建了来自 15 名健康老年人（平均年龄 = 71.3 岁）的个性化头部模型以创建电流密度图。每个头部模型被分割成 11 种组织类型：白质、灰质、脑脊液、肌肉、血管、脂肪、眼睛、空气、皮肤、松质骨和皮质骨。电极从 T1 加权图像中分割出来并添加到模型中。左侧 DLPFC 和左侧 VLPFC 感兴趣区域 (ROI) 中计算的中值和最大电流密度值与 beta 值相关，作为在不同时间点（基线、刺激期间）和刺激条件（活动和假）测量的功能连接性指标。

主要结果

 对于主动刺激，左侧 DLPFC ROI 的 β 值和计算的电流密度之间存在正显着相关性（最大 J 的 R ²  = 0.523，中位数 J 的 R ² = 0.367，p < 0.05），但在活动刺激期间未发现显着相关性假刺激。我们发现在左侧 VLPFC 中计算的主动和假刺激的连接性和电流密度之间没有显着相关性。

结论

发现左侧 DLPFC ROI 内的电流量与主动 2 mA 刺激期间左侧 DLPFC 和左侧 VLPFC 之间的功能连接变化呈正相关。未来的工作可能包括扩大参与者的数量，以进一步测试用于预测工作记忆网络内 tDCS 引起的功能连接性变化的 tDCS 模型的准确性。