Simultaneous acquisition of functional magnetic resonance images and impedance cardiography

Impedance cardiogram based exploration of cardiac mechanisms in post-traumatic stress disorder during trauma recall

Post-traumatic stress disorder (PTSD) is an independent risk factor for developing heart failure; however, the underlying cardiac mechanisms are still elusive. This study aims to evaluate the real-time effects of experimentally induced PTSD symptom activation on various cardiac contractility and autonomic measures. We recorded synchronized electrocardiogram and impedance cardiogram from 137 male veterans (17 PTSD, 120 non-PTSD; 48 twin pairs, 41 unpaired singles) during a laboratory-based traumatic reminder stressor. To identify the parameters describing the cardiac mechanisms by which trauma reminders can create stress on the heart, we utilized a feature selection mechanism along with a random forest classifier distinguishing PTSD and non-PTSD. We extracted 99 parameters, including 76 biosignal-based and 23 sociodemographic, medical history, and psychiatric diagnosis features. A subject/twin-wise stratified nested cross-validation procedure was used for parameter tuning and model assessment to identify the important parameters. The identified parameters included biomarkers such as pre-ejection period, acceleration index, velocity index, Heather index, and several physiology-agnostic features. These identified parameters during trauma recall suggested a combination of increased sympathetic nervous system (SNS) activity and deteriorated cardiac contractility that may increase the heart failure risk for PTSD. This indicates that the PTSD symptom activation associates with real-time reductions in several cardiac contractility measures despite SNS activation. This finding may be useful in future cardiac prevention efforts.

Combining Multiple Psychophysiological Measures of Listening Effort: Challenges and Recommendations

About one-third of all recently published studies on listening effort have used at least one physiological measure, providing evidence of the popularity of such measures in listening effort research. However, the specific measures employed, as well as the rationales used to justify their inclusion, vary greatly between studies, leading to a literature that is fragmented and difficult to integrate. A unified approach that assesses multiple psychophysiological measures justified by a single rationale would be preferable because it would advance our understanding of listening effort. However, such an approach comes with a number of challenges, including the need to develop a clear definition of listening effort that links to specific physiological measures, customized equipment that enables the simultaneous assessment of multiple measures, awareness of problems caused by the different timescales on which the measures operate, and statistical approaches that minimize the risk of type-I error inflation. This article discusses in detail the various obstacles for combining multiple physiological measures in listening effort research and provides recommendations on how to overcome them.

Subjective value then confidence in human ventromedial prefrontal cortex

Two fundamental goals of decision making are to select actions that maximize rewards while minimizing costs and to have strong confidence in the accuracy of a judgment. Neural signatures of these two forms of value: the subjective value (SV) of choice alternatives and the value of the judgment (confidence), have both been observed in ventromedial prefrontal cortex (vmPFC). However, the relationship between these dual value signals and their relative time courses are unknown. Twenty-eight men and women underwent fMRI while performing a two-phase approach-avoidance (Ap-Av) task with mixed-outcomes of monetary rewards paired with painful shock stimuli. Neural responses were measured during offer valuation (offer phase) and choice valuation (commit phase) and analyzed with respect to observed decision outcomes, model-estimated SV and confidence. During the offer phase, vmPFC tracked SV and the decision but not confidence. During the commit phase, vmPFC tracked confidence, computed as the quadratic extension of SV, but not the offer valuation nor the decision. In fact, vmPFC responses from the commit phase were selective for confidence even for reject decisions wherein confidence and SV are inversely related. Conversely, activation of the cognitive control network, including within lateral prefrontal cortex (lPFC) and dorsal anterior cingulate cortex (dACC) was associated with ambivalence, during both the offer and commit phases. Taken together, our results reveal complementary representations in vmPFC during value-based decision making that temporally dissociate such that offer valuation (SV) emerges before decision valuation (confidence).

Automatic Artifact Detection in Impedance Cardiogram Using Pulse Similarity Index

Impedance cardiography (ICG) is a noninvasive technique for evaluation of cardiac hemodynamic parameters such as cardiac output and pre-ejection period. However, the sensitivity of the technique to motion artifact, electrode displacement, and cardiovascular pathologies can severely impact the accuracy of hemodynamic parameter estimates. In this paper, we proposed a new algorithm for the automatic detection and exclusion of corrupted ICG cardiac cycles by defining a pulse similarity index that quantifies the level of pulse corruption and its diversion from a typical-shaped pulse. The index considers different features (activity, structure, shape, and pattern) of the ICG cardiac cycles. The algorithm is compared on sleep data collected from 20 participants against expert identified corrupted cycles. The artifact rejection algorithm achieved a high accuracy of 96% in detection of expert-identified corrupted ICG cycles, including those with normal amplitude as well as out-of-range values, and was robust to different types and levels of artifact. The algorithm shows promise toward applications requiring accurate and reliable automatic measurement of cardiac hemodynamic parameters from prolonged data sets.

Automatic analysis of pre-ejection period during sleep using impedance cardiogram

The pre-ejection period (PEP) is a valid index of myocardial contractility and beta-adrenergic sympathetic control of the heart defined as the time between electrical systole (ECG Q wave) to the initial opening of the aortic valve, estimated as the B point on the impedance cardiogram (ICG). B-point detection accuracy can be severely impacted if ICG cardiac cycles corrupted by motion artifact, noise, or electrode displacement are included in the analyses. Here, we developed new algorithms to detect and exclude corrupted ICG cycles by analyzing their level of activity. PEP was then estimated and analyzed on ensemble-averaged clean ICG cycles using an automatic algorithm previously developed by the authors for the detection of B point in awake individuals. We investigated the algorithms' performance relative to expert visual scoring on long-duration data collected from 20 participants during overnight recordings, where the quality of ICG could be highly affected by movement artifacts and electrode displacements and the signal could also vary according to sleep stage and time of night. The artifact rejection algorithm achieved a high accuracy of 87% in detection of expert-identified corrupted ICG cycles, including those with normal amplitude as well as out-of-range values, and was robust to different types and levels of artifact. Intraclass correlations for concurrent validity of the B-point detection algorithm in different sleep stages and in-bed wakefulness exceeded 0.98, indicating excellent agreement with the expert. The algorithms show promise toward sleep applications requiring accurate and reliable automatic measurement of cardiac hemodynamic parameters.

The psychological and physiological effects of interacting with an anti-fat peer

This experiment tested whether interacting with a peer who holds explicitly anti-fat attitudes leads to cognitive performance deficits and poorer psychological and cardiovascular outcomes among higher body weight women by increasing anticipated rejection. One hundred and forty six higher body weight women were randomly assigned to interact in a non-romantic context with a same-sex peer who endorsed explicit anti-fat or unbiased attitudes. All women showed greater heart rate reactivity and anger when interacting with an anti-fat peer. The heavier women were, and the more they thought they were overweight, the more they anticipated rejection when interacting with an anti-fat peer. This anticipated rejection was in turn associated with poorer cognitive performance, lower state self-esteem, and increased negative emotions, rumination, compensatory efforts, and thoughts related to anxiety and evaluation. These effects were not observed among women in our sample categorized as slightly "overweight" or who perceived themselves as only slightly overweight.

Toward a better noninvasive assessment of preejection period: A novel automatic algorithm for B-point detection and correction on thoracic impedance cardiogram

Impedance cardiography is the most common clinically validated, noninvasive method for determining the timing of the opening of the aortic valve, an important event used for measuring preejection period, which reflects sympathetic beta-adrenergic influences on the heart. Automatic detection of the exact time of the opening of the aortic valve (B point on the impedance cardiogram) has proven to be challenging as its appearance varies between and within individuals and may manifest as a reversal, inflection, or rapid slope change of the thoracic impedance derivative's (dZ/dt) rapid rise. Here, a novel automatic algorithm is proposed for the detection of the B point by finding the main rapid rise of the dZ/dt signal, which is due to blood ejection. Several conditions based on zero crossings, minima, and maxima of the dZ/dt signal and its derivatives are considered to reject any unwanted noise and artifacts and select the true B-point location. The detected B-point locations are then corrected by modeling the B-point time data using forward and reverse autoregressive models. The proposed algorithm is validated against expert-detected B points and is compared with different conventional methods; it significantly outperforms them by at least 54% in mean error, 30% in mean absolute error, and 27% in standard deviation of error. This algorithm can be adopted in ambulatory studies requiring beat-to-beat evaluation of cardiac hemodynamic parameters over extended time periods where expert scoring is not feasible.

Quantifying rapid changes in cardiovascular state with a moving ensemble average

MEAP, the moving ensemble analysis pipeline, is a new open-source tool designed to perform multisubject preprocessing and analysis of cardiovascular data, including electrocardiogram (ECG), impedance cardiogram (ICG), and continuous blood pressure (BP). In addition to traditional ensemble averaging, MEAP implements a moving ensemble averaging method that allows for the continuous estimation of indices related to cardiovascular state, including cardiac output, preejection period, heart rate variability, and total peripheral resistance, among others. Here, we define the moving ensemble technique mathematically, highlighting its differences from fixed-window ensemble averaging. We describe MEAP's interface and features for signal processing, artifact correction, and cardiovascular-based fMRI analysis. We demonstrate the accuracy of MEAP's novel B point detection algorithm on a large collection of hand-labeled ICG waveforms. As a proof of concept, two subjects completed a series of four physical and cognitive tasks (cold pressor, Valsalva maneuver, video game, random dot kinetogram) on 3 separate days while ECG, ICG, and BP were recorded. Critically, the moving ensemble method reliably captures the rapid cyclical cardiovascular changes related to the baroreflex during the Valsalva maneuver and the classic cold pressor response. Cardiovascular measures were seen to vary considerably within repetitions of the same cognitive task for each individual, suggesting that a carefully designed paradigm could be used to capture fast-acting event-related changes in cardiovascular state.

Preejection Period as a Sympathetic Activity Index: a Role of Confounding Factors

In previous studies, one of the systolic time intervals – preejection period (PEP) – was used as an index of sympathetic activity reflecting the cardiac contractility. However, PEP could be also influenced by several other cardiovascular variables including preload, afterload and diastolic blood pressure (DBP). The aim of this study was to assess the behavior of the PEP together with other potentially confounding cardiovascular system characteristics in healthy humans during mental and orthostatic stress (head-up tilt test – HUT). Forty-nine healthy volunteers (28 females, 21 males, mean age 18.6 years (SD=1.8 years)) participated in the study. We recorded finger arterial blood pressure by volume-clamp method (Finometer Pro, FMS, Netherlands), PEP, thoracic fluid content (TFC) – a measure of preload, and cardiac output (CO) by impedance cardiography (CardioScreen® 2000, Medis, Germany). Systemic vascular resistance (SVR) – a measure of afterload – was calculated as a ratio of mean arterial pressure and CO. We observed that during HUT, an expected decrease in TFC was accompanied by an increase of PEP, an increase of SVR and no significant change in DBP. During mental stress, we observed a decrease of PEP and an increase of TFC, SVR and DBP. Correlating a change in assessed measures (delta values) between mental stress and previous supine rest, we found that ΔPEP correlated negatively with ΔCO and positively with ΔSVR. In orthostasis, no significant correlation between ΔPEP and ΔDBP, ΔTFC, ΔCO, ΔMBP or ΔSVR was found. We conclude that despite an expected increase of sympathetic activity during both challenges, PEP behaved differently indicating an effect of other confounding factors. To interpret PEP values properly, we recommend simultaneously to measure other variables influencing this cardiovascular measure.

Mathematical detection of aortic valve opening (B point) in impedance cardiography: A comparison of three popular algorithms

The preejection period (PEP) is an index of left ventricle contractility widely used in psychophysiological research. Its computation requires detecting the moment when the aortic valve opens, which coincides with the B point in the first derivative of impedance cardiogram (ICG). Although this operation has been traditionally made via visual inspection, several algorithms based on derivative calculations have been developed to enable an automatic performance of the task. However, despite their popularity, data about their empirical validation are not always available. The present study analyzes the performance in the estimation of the aortic valve opening of three popular algorithms, by comparing their performance with the visual detection of the B point made by two independent scorers. Algorithm 1 is based on the first derivative of the ICG, Algorithm 2 on the second derivative, and Algorithm 3 on the third derivative. Algorithm 3 showed the highest accuracy rate (78.77%), followed by Algorithm 1 (24.57%) and Algorithm 2 (13.82%). In the automatic computation of PEP, Algorithm 2 resulted in significantly more missed cycles (48.57%) than Algorithm 1 (6.3%) and Algorithm 3 (3.5%). Algorithm 2 also estimated a significantly lower average PEP (70 ms), compared with the values obtained by Algorithm 1 (119 ms) and Algorithm 3 (113 ms). Our findings indicate that the algorithm based on the third derivative of the ICG performs significantly better. Nevertheless, a visual inspection of the signal proves indispensable, and this article provides a novel visual guide to facilitate the manual detection of the B point.

Recent Advances in Laboratory Assessment of Emotion Regulation

Over the past 15 years, the emotion regulation perspective has been widely integrated into theoretical and applied contexts in clinical psychology and beyond. Recent refinements to behavioral, subjective, psychophysiological and neuroimaging methods allow emotion regulation to be captured and assessed in the laboratory with greater precision. Technological advances enabling investigators to leverage information from multiple modalities are increasingly accessible, and as such, will further efforts to generate testable hypotheses about specific mechanisms implicated in emotion regulation and difficulties therein. In combination with theory-driven design, progressively sophisticated methods for laboratory assessment have potential to further emotion regulation as both a valid scientific construct and a useful paradigm for human emotion and behavior that has applicability to both clinical and non-clinical contexts.