Abstract：
BACKGROUND: Heart rate variability (HRV) is a useful tool for evaluating cardiovascular autonomic nervous system (ANS) functions. This systematic review and meta-analysis examined the potential effects of transcranial direct current stimulation (tDCS) protocols on HRV parameters.
METHODS: This study acquired 97 comparisons from 24 qualified studies for data synthesis. Using standardized mean difference (SMD), individual and overall effect sizes were estimated to show differences in HRV variables between active tDCS and sham stimulation conditions. More positive effect size values indicated that active tDCS caused greater increases in HRV than sham stimulation. Furthermore, moderator variable analyses were performed to determine whether changes in HRV variables differed depending on (a) task types (physical stress versus psychological stress versus resting condition), (b) targeted brain regions, (c) stimulation polarity, (d) characteristics of participants, and (e) specific HRV variables. Finally, we used meta-regression analyses to determine whether different tDCS parameters (i.e., the number of tDCS sessions, stimulation duration, and density) were associated with changes in HRV patterns.
RESULTS: The random-effects model meta-analysis showed that tDCS protocols significantly improved HRV variables (SMD = 0.400; P < 0.001). Moreover, for increasing HRV during the physical stress task (SMD = 1.352; P = 0.001), anodal stimulation on the M1 was effective, while combined polarity stimulation on the PFC improved HRV during the psychological stress task (SMD = 0.550; P < 0.001) and resting condition (SMD = 0.192; P = 0.012). Additional moderator variables and meta-regression analyses failed to show that tDCS protocols had positive effects in certain conditions, such as different stimulus polarity, characteristics of participants, specific HRV variables, and tDCS parameters.
CONCLUSIONS: These findings tentatively suggest that using tDCS protocols to stimulate optimal targeted brain areas may be effective in improving HRV patterns potentially related to cardiovascular ANS functions.
METHODS: This study acquired 97 comparisons from 24 qualified studies for data synthesis. Using standardized mean difference (SMD), individual and overall effect sizes were estimated to show differences in HRV variables between active tDCS and sham stimulation conditions. More positive effect size values indicated that active tDCS caused greater increases in HRV than sham stimulation. Furthermore, moderator variable analyses were performed to determine whether changes in HRV variables differed depending on (a) task types (physical stress versus psychological stress versus resting condition), (b) targeted brain regions, (c) stimulation polarity, (d) characteristics of participants, and (e) specific HRV variables. Finally, we used meta-regression analyses to determine whether different tDCS parameters (i.e., the number of tDCS sessions, stimulation duration, and density) were associated with changes in HRV patterns.
RESULTS: The random-effects model meta-analysis showed that tDCS protocols significantly improved HRV variables (SMD = 0.400; P < 0.001). Moreover, for increasing HRV during the physical stress task (SMD = 1.352; P = 0.001), anodal stimulation on the M1 was effective, while combined polarity stimulation on the PFC improved HRV during the psychological stress task (SMD = 0.550; P < 0.001) and resting condition (SMD = 0.192; P = 0.012). Additional moderator variables and meta-regression analyses failed to show that tDCS protocols had positive effects in certain conditions, such as different stimulus polarity, characteristics of participants, specific HRV variables, and tDCS parameters.
CONCLUSIONS: These findings tentatively suggest that using tDCS protocols to stimulate optimal targeted brain areas may be effective in improving HRV patterns potentially related to cardiovascular ANS functions.
摘要：
背景：心率变异性（HRV）是评估心血管自主神经系统（ANS）功能的有用工具。这项系统评价和荟萃分析研究了经颅直流电刺激（tDCS）方案对HRV参数的潜在影响。
方法：本研究从24个合格的数据综合研究中获得了97个比较。使用标准化平均差(SMD)，估计个体和总体效应大小,以显示活动性tDCS和假刺激条件之间HRV变量的差异.更积极的效应大小值表明，与假刺激相比，活性tDCS引起的HRV增加更大。此外，进行调节变量分析以确定HRV变量的变化是否取决于（a）任务类型（身体压力与心理压力与静息状态），(b)目标大脑区域，(c)刺激极性，(d)参与者的特点，和(e)特定的HRV变量。最后,我们使用元回归分析来确定不同的tDCS参数(即，tDCS会话的数量，刺激持续时间,和密度）与HRV模式的变化有关。
结果：随机效应模型荟萃分析表明，tDCS方案显著改善了HRV变量（SMD=0.400；P<0.001）。此外，在物理压力任务期间增加HRV(SMD=1.352；P=0.001)，M1的阳极刺激是有效的，而PFC上的联合极性刺激改善了心理应激任务（SMD=0.550；P<0.001）和静息状态（SMD=0.192；P=0.012）期间的HRV。其他调节变量和荟萃回归分析未能表明tDCS协议在某些条件下具有积极作用，例如不同的刺激极性，参与者的特点，特定的HRV变量，和tDCS参数。
结论：这些发现初步表明，使用tDCS方案刺激最佳目标脑区可能有效改善与心血管ANS功能相关的HRV模式。
方法：本研究从24个合格的数据综合研究中获得了97个比较。使用标准化平均差(SMD)，估计个体和总体效应大小,以显示活动性tDCS和假刺激条件之间HRV变量的差异.更积极的效应大小值表明，与假刺激相比，活性tDCS引起的HRV增加更大。此外，进行调节变量分析以确定HRV变量的变化是否取决于（a）任务类型（身体压力与心理压力与静息状态），(b)目标大脑区域，(c)刺激极性，(d)参与者的特点，和(e)特定的HRV变量。最后,我们使用元回归分析来确定不同的tDCS参数(即，tDCS会话的数量，刺激持续时间,和密度）与HRV模式的变化有关。
结果：随机效应模型荟萃分析表明，tDCS方案显著改善了HRV变量（SMD=0.400；P<0.001）。此外，在物理压力任务期间增加HRV(SMD=1.352；P=0.001)，M1的阳极刺激是有效的，而PFC上的联合极性刺激改善了心理应激任务（SMD=0.550；P<0.001）和静息状态（SMD=0.192；P=0.012）期间的HRV。其他调节变量和荟萃回归分析未能表明tDCS协议在某些条件下具有积极作用，例如不同的刺激极性，参与者的特点，特定的HRV变量，和tDCS参数。
结论：这些发现初步表明，使用tDCS方案刺激最佳目标脑区可能有效改善与心血管ANS功能相关的HRV模式。
