Limbic dysregulation is associated with lowered heart rate variability and increased trait anxiety in healthy adults.

OBJECTIVES: We tested whether dynamic interaction between limbic regions supports a control systems model of excitatory and inhibitory components of a negative feedback loop, and whether dysregulation of those dynamics might correlate with trait differences in anxiety and their cardiac characteristics among healthy adults. EXPERIMENTAL DESIGN: Sixty-five subjects received fMRI scans while passively viewing angry, fearful, happy, and neutral facial stimuli. Subjects also completed a trait anxiety inventory, and were monitored using ambulatory wake ECG. The ECG data were analyzed for heart rate variability, a measure of autonomic regulation. The fMRI data were analyzed with respect to six limbic regions (bilateral amygdala, bilateral hippocampus, Brodmann Areas 9, 45) using limbic time-series cross-correlations, maximum BOLD amplitude, and BOLD amplitude at each point in the time-series. PRINCIPAL OBSERVATIONS: Diminished coupling between limbic time-series in response to the neutral, fearful, and happy faces was associated with greater trait anxiety, greater sympathetic activation, and lowered heart rate variability. Individuals with greater levels of trait anxiety showed delayed activation of Brodmann Area 45 in response to the fearful and happy faces, and lowered Brodmann Area 45 activation with prolonged left amygdala activation in response to the neutral faces. CONCLUSIONS: The dynamics support limbic regulation as a control system, in which dysregulation, as assessed by diminished coupling between limbic time-series, is associated with increased trait anxiety and excitatory autonomic outputs. Trait-anxious individuals showed delayed inhibitory activation in response to overt-affect stimuli and diminished inhibitory activation with delayed extinction of excitatory activation in response to ambiguous-affect stimuli.

Analysis of nonstationarity in renal autoregulation mechanisms using time-varying transfer and coherence functions.

The extent to which renal blood flow dynamics vary in time and whether such variation contributes substantively to dynamic complexity have emerged as important questions. Data from Sprague-Dawley rats (SDR) and spontaneously hypertensive rats (SHR) were analyzed by time-varying transfer functions (TVTF) and time-varying coherence functions (TVCF). Both TVTF and TVCF allow quantification of nonstationarity in the frequency ranges associated with the autoregulatory mechanisms. TVTF analysis shows that autoregulatory gain in SDR and SHR varies in time and that SHR exhibit significantly more nonstationarity than SDR. TVTF gain in the frequency range associated with the myogenic mechanism was significantly higher in SDR than in SHR, but no statistical difference was found with tubuloglomerular (TGF) gain. Furthermore, TVCF analysis revealed that the coherence in both strains is significantly nonstationary and that low-frequency coherence was negatively correlated with autoregulatory gain. TVCF in the frequency range from 0.1 to 0.3 Hz was significantly higher in SDR (7 out of 7, >0.5) than in SHR (5 out of 6, <0.5), and consistent for all time points. For TGF frequency range (0.03-0.05 Hz), coherence exhibited substantial nonstationarity in both strains. Five of six SHR had mean coherence (<0.5), while four of seven SDR exhibited coherence (<0.5). Together, these results demonstrate substantial nonstationarity in autoregulatory dynamics in both SHR and SDR. Furthermore, they indicate that the nonstationarity accounts for most of the dynamic complexity in SDR, but that it accounts for only a part of the dynamic complexity in SHR.

Representation of time-varying nonlinear systems with time-varying principal dynamic modes.

System identification of nonlinear time-varying (TV) systems has been a daunting task, as the number of parameters required for accurate identification is often larger than the number of data points available, and scales with the number of data points. Further, a 3-D graphical representation of TV second-order nonlinear dynamics without resorting to taking slices along one of the four axes has been a significant challenge to date. In this paper, we present a TV principal dynamic mode (TVPDM) method which overcomes these deficiencies. The TVPDM, by design, reduces one dimension, and by projecting PDM coefficients onto a set of basis functions, both nonstationary and nonlinear dynamics can be characterized. Another significant advantage of the TVPDM is its ability to discriminate the signal from noise dynamics, and provided that signal dynamics are orthogonal to each other, it has the capability to separate them. The efficacy of the proposed method is demonstrated with computer simulation examples comprised of various forms of nonstationarity and nonlinearity. The application of the TVPDM to the human heart rate and arterial blood pressure data during different postures is also presented and the results reveal significant nonstationarity even for short-term data recordings. The newly developed method has the potential to be a very useful tool for characterizing nonlinear TV systems, which has been a significant, challenging problem to date.

Autonomic nervous nonlinear interactions lead to frequency modulation between low- and high-frequency bands of the heart rate variability spectrum.

Cardiac sympathetic and parasympathetic neural activities have been found to interact with each other to efficiently regulate the heart rate and maintain homeostasis. Quantitative and noninvasive methods used to detect the presence of interactions have been lacking, however. This may be because interactions among autonomic nervous systems are nonlinear and nonstationary. The goal of this work was to identify nonlinear interactions between the sympathetic and parasympathetic nervous systems in the form of frequency and amplitude modulations in human heart rate data. To this end, wavelet analysis was performed, followed by frequency analysis of the resultant wavelet decomposed signals in several frequency brackets defined as very low frequency (f < 0.04 Hz), low frequency (LF; 0.04-0.15 Hz), and high frequency (HF; 0.15-0.4 Hz). Our analysis suggests that the HF band is significantly modulated by the LF band in the heart rate data obtained in both supine and upright body positions. The strength of modulations is stronger in the upright than supine position, which is consistent with elevated sympathetic nervous activities in the upright position. Furthermore, significantly stronger frequency modulation than in the control condition was also observed with the cold pressor test. The results with the cold pressor test, as well as the body position experiments, further demonstrate that the frequency modulation between LF and HF is most likely due to sympathetic and parasympathetic nervous interactions during sympathetic activations. The modulation phenomenon suggests that the parasympathetic nervous system is frequency modulated by the sympathetic nervous system. In this study, there was no evidence of amplitude modulation among these frequencies.

Frequency modulation between low- and high-frequency components of the heart rate variability spectrum.

Interactions among physiological mechanisms are abundant in biomedical signals, and they may exist to maintain efficient homeostasis. For example, sympathetic and parasympathetic neural activities interact to either elevate or depress the heart rate to maintain homeostasis. There has been considerable effort devoted to developing algorithms that can detect interactions between various physiological mechanisms. However, methods used to detect the presence of interactions between the sympathetic and parasympathetic nervous systems, to take one example, have had limited success. This may be because interactions in physiological systems are non-linear and non-stationary. The goal of this work was to identify non-linear interactions between the sympathetic and parasympathetic nervous systems in the form of frequency and amplitude modulations in human heart-rate data (n=6). To this end, wavelet analysis was performed, followed by frequency analysis of the resultant wavelet decomposed signals in several frequency brackets we define as: very low frequency (f<0.04 Hz), low frequency (0.04-0.15 Hz) and high frequency (0.15-0.4 Hz). Our analysis suggests that the high-frequency bracket is modulated by the low-frequency bracket in the heart rate data obtained in both upright and sitting positions. However, there was no evidence of amplitude modulation among these frequencies.

Quantifying cardiac sympathetic and parasympathetic nervous activities using principal dynamic modes analysis of heart rate variability.

The ratio between low-frequency (LF) and high-frequency (HF) spectral power of heart rate has been used as an approximate index for determining the autonomic nervous system (ANS) balance. An accurate assessment of the ANS balance can only be achieved if clear separation of the dynamics of the sympathetic and parasympathetic nervous activities can be obtained, which is a daunting task because they are nonlinear and have overlapping dynamics. In this study, a promising nonlinear method, termed the principal dynamic mode (PDM) method, is used to separate dynamic components of the sympathetic and parasympathetic nervous activities on the basis of ECG signal, and the results are compared with the power spectral approach to assessing the ANS balance. The PDM analysis based on the 28 subjects consistently resulted in a clear separation of the two nervous systems, which have similar frequency characteristics for parasympathetic and sympathetic activities as those reported in the literature. With the application of atropine, in 13 of 15 supine subjects there was an increase in the sympathetic-to-parasympathetic ratio (SPR) due to a greater decrease of parasympathetic than sympathetic activity (P=0.003), and all 13 subjects in the upright position had a decrease in SPR due to a greater decrease of sympathetic than parasympathetic activity (P<0.001) with the application of propranolol. The LF-to-HF ratio calculated by the power spectral density is less accurate than the PDM because it is not able to separate the dynamics of the parasympathetic and sympathetic nervous systems. The culprit is equivalent decreases in both the sympathetic and parasympathetic activities irrespective of the pharmacological blockades. These findings suggest that the PDM shows promise as a noninvasive and quantitative marker of ANS imbalance, which has been shown to be a factor in many cardiac and stress-related diseases.

Frequency modulation between low- and high-frequency components of the heart rate variability spectrum may indicate sympathetic-parasympathetic nonlinear interactions.

Interactions among physiological mechanisms are abundant in biomedical signals, and they may exist to maintain efficient homeostasis. For example, sympathetic and parasympathetic neural activities interact to either elevate or depress the heart rate, to maintain homeostasis. There has been considerable effort devoted to developing algorithms that can detect interactions between various physiological mechanisms. However, methods used to detect the presence of interactions between the sympathetic and parasympathetic nervous systems, to take one example, have had limited success. This may be because interactions in physiological systems are nonlinear and nonstationary. The goal of this work was to identify nonlinear interactions between the sympathetic and parasympathetic nervous systems in the form of frequency and amplitude modulations in human heart rate data. To this end, wavelet analysis was performed, followed by frequency analysis of the resultant wavelet decomposed signals in several frequency brackets we define as: very low frequency (f<0.04 Hz), low frequency (0.04-0.15 Hz) and high frequency (0.15-0.4 Hz). Our analysis suggests that the HF bracket is significantly modulated by the LF bracket in the heart rate data obtained in both supine and upright body positions. Furthermore, the strength of modulations is stronger in the upright than supine position, which is consistent with elevated sympathetic nervous activities in the upright position. However, there was no evidence of amplitude modulation among these frequencies.

Nonlinear analysis of the separate contributions of autonomic nervous systems to heart rate variability using principal dynamic modes.

This paper introduces a modified principal dynamic modes (PDM) method, which is able to separate the dynamics of sympathetic and parasympathetic nervous activities. The PDM is based on the principle that among all possible choices of expansion bases, there are some that require the minimum number of basis functions to achieve a given mean-square approximation of the system output. Such a minimum set of basis functions is termed PDMs of the nonlinear system. We found that the first two dominant PDMs have similar frequency characteristics for parasympathetic and sympathetic activities, as reported in the literature. These results are consistent for all nine of our healthy human subjects using our modified PDM approach. Validation of the purported separation of parasympathetic and sympathetic activities was performed by the application of the autonomic nervous system blocking drugs atropine and propranolol. With separate applications of the respective drugs, we found a significant decrease in the amplitude of the waveforms that correspond to each nervous activity. Furthermore, we observed near complete elimination of these dynamics when both drugs were given to the subjects. Comparison of our method to the conventional low-frequency/high-frequency ratio shows that our proposed approach provides more accurate assessment of the autonomic nervous balance. Our nonlinear PDM approach allows a clear separation of the two autonomic nervous activities, the lack of which has been the main reason why heart rate variability analysis has not had wide clinical acceptance.