Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

Objective: Executive function correlates with the parasympathetic nervous system (PNS) based on static heart rate variability (HRV) measurements. Our study advances this understanding by employing dynamic assessments of the PNS to explore and quantify its relationship with inhibitory control (IC). Methods: We recruited 31 men aged 20–35 years. We monitored their electrocardiogram (ECG) signals during the administration of the Conners’ Continuous Performance Test-II (CCPT-II) on a weekly basis over 2 weeks. HRV analysis was performed on ECG-derived RR intervals using 5-minute windows, each overlapping for the next 4 minutes to establish 1-minute intervals. For each time window, the HRV metrics extracted were: mean RR intervals, standard deviation of NN intervals (SDNN), low-frequency power with logarithm (lnLF), and high-frequency power with logarithm (lnHF). Each value was correlated with detectability and compared to the corresponding baseline value at t0. Results: Compared with the baseline level, SDNN and lnLF showed marked decreases during CCPT-II. The mean values of HRV showed significant correlation with d’, including mean SDNN (R=0.474, p=0.012), mean lnLF (R=0.390, p=0.045), and mean lnHF (R=0.400, p=0.032). In the 14th time window, the significant correlations included SDNN (R=0.578, p=0.002), lnLF (R=0.493, p=0.012), and lnHF (R=0.432, p=0.031). Significant correlation between d’ and HRV parameters emerged only during the initial CCPT-II. Conclusion A significant correlation between PNS and IC was observed in the first session alone. The IC in the repeated CCPT-II needs to consider the broader neural network.

OBJECTIVE: Decreased cardiac vagal control (CVC) has been proposed in posttraumatic stress disorder (PTSD), but the results are mixed. Analyses with larger sample sizes and better methodology are needed. METHODS: Thirty-two drug-naive survivors with current PTSD, 32 survivors without PTSD and 192 matched controls were recruited for a case-control analysis. We used the PTSD checklist-civilian version (PCL-C) to assess posttraumatic symptoms severity. Cardiac autonomic function was evaluated by measuring heart rate variability (HRV) parameters. Frequency-domain indices of HRV were obtained. The obtained results were evaluated in association with personality traits assessed by the Tridimensional Personality Questionnaire (TPQ). RESULTS: PTSD patients exhibited decreased LF-HRV and HF-HRV as compared to survivors without PTSD and to matched controls. The PTSD symptoms severity was associated with reduced mean RR intervals, Var-HRV, LF-HRV and HF-HRV. The harm avoidance score (which has been suggested to be associated with serotonergic activity) was negatively correlated with Var-HRV, LF-HRV and HF-HRV. CONCLUSION: These data suggest that PTSD is accompanied by decreased CVC, highlighting the importance of assessing HRV in PTSD patients. In view of the increased risk for cardiovascular diseases in these vulnerable individuals, one might consider the treatment to restore their autonomic function while reducing PTSD symptoms.
Cardiovascular Diseases ; Case-Control Studies ; Heart Rate ; Humans ; Surveys and Questionnaires ; Sample Size ; Stress Disorders, Post-Traumatic ; Survivors

OBJECTIVE: Decreased heart rate variability (HRV) has been reported in generalized anxiety disorder (GAD), but the results are mixed. Little is known about the impact of comorbid major depression (MD) on HRV in GAD patients. Both issues necessitate further investigation. METHODS: Twenty unmedicated, physically healthy GAD patients, 20 GAD patients with a secondary diagnosis of MD, 40 MD patients and 60 matched controls were recruited. We used the Hamilton Anxiety Rating Scale and the Hamilton Depression Rating Scale to assess anxiety and depression severity, respectively. Cardiac autonomic function was evaluated by measuring HRV parameters. Frequency-domain indices of HRV were obtained. RESULTS: Three patient groups had more anxiety and depression symptoms than control subjects, but heart rates (HRs) were significantly elevated only in GAD patients with comorbid depression. Relative to controls, GAD patients had reduced HRV while GAD patients with comorbid depression displayed the greatest reductions in HRV among three patients groups. Correlation analyses revealed anxiety/depression severity significantly associated with HRs, variance, LF-HRV and HF-HRV. However, separately analyzing among individual groups and adjusting for HRV-associated covariables rendered the correlations non-significant. CONCLUSION: Our results suggest that reduction in HRV is a psychophysiological marker of GAD and individuals with comorbid GAD and MD may be distinguished based on psychophysiological correlates (for example, HF-HRV) from non-comorbid GAD patients. Taken into account that comorbid depression may confer increased risks for cardiovascular events in GAD patients, this subgroup of GAD patients may benefit better from cardiovascular risk reduction strategies.
Anxiety Disorders* ; Anxiety* ; Case-Control Studies* ; Depression* ; Diagnosis ; Heart Rate* ; Heart* ; Humans ; Risk Reduction Behavior ; Taiwan*

OBJECTIVE: Anxiety trait, anxiety and depression states have all been reported to increase risks for cardiovascular disease (CVD), possibly through altering cardiac autonomic regulation. Our aim was to investigate whether the relationship between harm avoidance (HA, an anxiety-related personality trait) and cardiac autonomic regulation is independent of anxiety and depression states in healthy adults. METHODS: We recruited 535 physically and mentally healthy volunteers. Participants completed the Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI) and Tri-dimensional Personality Questionnaire. Participants were divided into high or low HA groups as discriminated by the quartile value. Cardiac autonomic function was evaluated by measuring heart rate variability (HRV). We obtained the time and frequency-domain indices of HRV including variance (total HRV), the low-frequency power (LF; 0.05-0.15 Hz), which may reflect baroreflex function, the high-frequency power (HF; 0.15-0.40 Hz), which reflects cardiac parasympathetic activity, as well as the LF/HF ratio. RESULTS: The BDI and HA scores showed associations with HRV parameters. After adjustment for the BDI scores and other control variables, HA is still associated with reduced variance, LF and HF power. Compared with the participants with low HA, those with high HA displayed significant reductions in variance, LF and HF power and a significant increase in their LF/HF ratio. CONCLUSION: This study highlights the independent role of HA in contributing to decreased autonomic cardiac regulation in healthy adults and provides a potential underlying mechanism for anxiety trait to confer increased risk for CVD.
Adult* ; Anxiety ; Baroreflex ; Cardiovascular Diseases ; Depression ; Healthy Volunteers ; Heart Rate* ; Heart* ; Humans