Psychophysiology最新文献

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.

In a complicated social context, outcome evaluation involves not only oneself but also others in relation to the self (i.e., social comparison). Previous event-related potential (ERP) studies have investigated the processing of social comparison-related outcomes when one's interests are independent of the interests of others (i.e., noncompetition circumstances). However, it is unclear how social comparison-related outcomes are processed in the brain when there are conflicts of interest between oneself and others (i.e., competition circumstances). To address this issue, participants in the current study were asked to perform an attentional task with several peers and were subsequently presented with self-related outcomes (i.e., the performance difference between the current trial and several preceding trials) and social comparison-related outcomes (i.e., the performance difference between oneself and their peer). Importantly, rewards and punishments were based on social comparison-related outcomes in the competition condition and on self-related outcomes in the noncompetition condition. ERP results revealed that in the competition condition, positive outcomes involving social comparison elicited a greater P300 response than negative outcomes, whereas this effect was not observed in the noncompetition condition. Additionally, there was generally a larger late positive potential (LPP) response to negative outcomes involving social comparison than to positive outcomes only when one obtained a self-related positive outcome in the competition condition. These findings suggest that competition might strengthen outcome processing involving social comparison at late time ranges relying on self-related outcomes to some extent.

The neurocognitive mechanism underlying negation processing remains controversial. While negation is suggested to modulate the access of word meaning, no such evidence has been observed in the event-related potential (ERP) literature on sentence processing. In the current study, we applied both univariate ERP and multivariate pattern analysis (MVPA) methods to examine the processing of sentence negation. We investigated two types of negative congruent/incongruent sentence pairs with truth-value evaluation (e.g., "A robin is a/not a bird") and without (e.g., "The woman reads a/no book"). In the N400 time window, ERPs consistently showed increased negativity for negative and incongruent conditions. MVPA, on the other hand, revealed nuanced interactions between polarity and congruency. In the later P600 time window, MVPA but not the ERPs revealed an effect of congruency, which may be functionally distinct from the N400 window. We further used cross-decoding to show that the cognitive processes underlying the N400 window for both affirmative and negative sentences are comparable, whereas in the P600 window, only for the truth sentences, negative sentences showed a distinct pattern from their affirmative counterparts. Our results thus speak for a more interactive, but nevertheless serial and biphasic, and potentially construction-specific processing account of negation. We also discuss the advantage of applying MVPA in addition to the classical univariate methods for a better understanding of the neurobiology of negation processing and language comprehension alike.

Prenatal opioid exposure has been associated with developmental problems, including autonomic nervous system dysregulation. However, little is known about the effects of prenatal opioid exposure on the autonomic nervous system beyond the first days of life, particularly across both the parasympathetic and sympathetic branches, and when accounting for exposure to other substances. The present study examined the effects of prenatal exposure to opioid agonist therapy (OAT, e.g., methadone) and other opioids on infant autonomic nervous system activity at rest and in response to a social stressor (the Still-Face Paradigm) at six months among 86 infants varying in prenatal opioid and other substance exposure. Results indicated that OAT and other opioids have unique effects on the developing autonomic nervous system that may further depend on subtype (i.e., methadone versus buprenorphine) and timing in gestation. Results are discussed in the context of theoretical models of the developing stress response system.

The locus coeruleus-norepinephrine (LC-NE) system, which regulates arousal levels, is important for cognitive control, including emotional conflict resolution. Additionally, the LC-NE system is implicated in P300 generation. If the P300 is mediated by the LC-NE system, and considering the established correlations between LC activity and pupil dilation, P300 amplitude should correlate with task-evoked (phasic) pupil dilation on a trial-by-trial basis. However, prior studies, predominantly utilizing oddball-type paradigms, have not demonstrated correlations between concurrently recorded task-evoked pupil dilation and P300 responses. Using a recently developed emotional face-word Stroop task that links pupil dilation to the LC-NE system, here, we examined both intra- and inter-individual correlations between task-evoked pupil dilation and P300 amplitude. We found that lower accuracy, slower reaction times, and larger task-evoked pupil dilation were obtained in the incongruent compared to the congruent condition. Furthermore, we observed intra-individual correlations between task-evoked pupil dilation and P300 amplitude, with larger pupil dilation correlating with a greater P300 amplitude. In contrast, pupil dilation did not exhibit consistent correlations with N450 and N170 amplitudes. Baseline (tonic) pupil size also showed correlations with P300 and N170 amplitudes, with smaller pupil size corresponding to larger amplitude. Moreover, inter-individual differences in task-evoked pupil dilation between the congruent and incongruent conditions correlated with differences in reaction time and P300 amplitude, though these effects only approached significance. To summarize, our study provides evidence for a connection between task-evoked pupil dilation and P300 amplitude at the single-trial level, suggesting the involvement of the LC-NE system in P300 generation.

Imagery has been associated with cardiovascular and psychological responses to stress; however, the mechanisms underlying this association are not fully understood. The present study examined if the ability to image mastering challenging or difficult situations moderated the relationship between heart rate reactivity and perceptions of stress and physiological arousal experienced during acute stress. Four hundred and fifty-eight participants completed a standardized laboratory stress protocol with heart rate being measured throughout. After completing an acute psychological stress task, participants rated how stressed and physiologically aroused they felt (i.e., intensity) and whether they perceived the stress and physiological arousal as being helpful/unhelpful to performance (i.e., interpretation). Mastery imagery ability was assessed by questionnaire. Moderation analyses controlling for gender demonstrated that imagery ability moderated the relationship between heart rate reactivity and interpretation of stress (β = 0.015, p = .003) and perceived physiological arousal (β = 0.013, p = .004). Simple slope analysis indicated that in those with higher imagery ability, heart rate reactivity was associated with stress and arousal being perceived as more positive toward performance. Imagery ability did not moderate the relationship between heart rate reactivity and perceived stress intensity or physiological arousal intensity (p's > .05), but imagery ability did predict lower perceived stress intensity (β = -0.217, p < .001) and perceived physiological arousal intensity (β = -0.172, p < .001). Higher mastery imagery ability may possibly help individuals perceive responses to stress as more beneficial for performance and thus be an effective coping technique.

While physical performance has long been thought to be limited only by physiological factors, many experiments denote that psychological ones can also influence it. Specifically, the deception paradigm investigates the effect of psychological factors on performance by manipulating a psychological variable unbeknownst to the subjects. For example, during a physical exercise performed to failure, previous results revealed an improvement in performance (i.e., holding time) when the clock shown to the subjects was deceptively slowed down. However, the underlying neurophysiological changes supporting this performance improvement due to deceptive time manipulation remain unknown. Here, we addressed this issue by investigating from a neuromuscular perspective the effect of a deceptive clock manipulation on a single-joint isometric task conducted to failure in 24 healthy participants (11 females). Neuromuscular fatigue was assessed by pre- to post-exercise changes in quadriceps maximal voluntary torque (T_max ), voluntary activation level (VAL), and potentiated twitch (T_TW ). Our main results indicated a significant performance improvement when the clock was slowed down (Biased: 356 ± 118 s vs. Normal: 332 ± 112 s, p = .036) but, surprisingly, without any difference in the associated neuromuscular fatigue (p > .05 and BF < 0.3 for T_max , VAL, and T_TW between both sessions). Computational modeling showed that, when observed, the holding time improvement was explained by a neuromuscular fatigue accumulation based on subjective rather than actual time. These results support a psychological influence on neuromuscular processes and contribute significantly to the literature on the mind-body influence, by challenging our understanding of fatigue.

The ability to accurately identify and interpret others' emotions is critical for social and emotional functioning during adolescence. Indeed, previous research has identified that laboratory-based indices of facial emotion recognition and engagement with emotional faces predict adolescent mood states. Whether socioemotional information processing relates to real-world affective dynamics using an ecologically sensitive approach, however, has rarely been assessed. In the present study, adolescents (N = 62; ages 13-18) completed a Facial Recognition Task, including happy, angry, and sad stimuli, while EEG data were acquired. Participants also provided ecological momentary assessment (EMA) data probing their current level of happiness, anger, and sadness for 1-week, resulting in indices of emotion (mean-level, inertia, instability). Analyses focused on relations between (1) accuracy for and (2) prolonged engagement with (LPP) emotional faces and EMA-reported emotions. Greater prolonged engagement with happy faces was related to less resistance to changes in happiness (i.e., less happiness inertia), whereas greater prolonged engagement with angry faces associated with more resistance to changes in anger (i.e., greater anger inertia). Results suggest that socioemotional processes captured by laboratory measures have real-world implications for adolescent affective states and highlight potentially actionable targets for novel treatment approaches (e.g., just-in-time interventions). Future studies should continue to assess relations among socioemotional informational processes and dynamic fluctuations in adolescent affective states.

Machine learning techniques have proven to be a useful tool in cognitive neuroscience. However, their implementation in scalp-recorded electroencephalography (EEG) is relatively limited. To address this, we present three analyses using data from a previous study that examined event-related potential (ERP) responses to a wide range of naturally-produced speech sounds. First, we explore which features of the EEG signal best maximize machine learning accuracy for a voicing distinction, using a support vector machine (SVM). We manipulate three dimensions of the EEG signal as input to the SVM: number of trials averaged, number of time points averaged, and polynomial fit. We discuss the trade-offs in using different feature sets and offer some recommendations for researchers using machine learning. Next, we use SVMs to classify specific pairs of phonemes, finding that we can detect differences in the EEG signal that are not otherwise detectable using conventional ERP analyses. Finally, we characterize the timecourse of phonetic feature decoding across three phonological dimensions (voicing, manner of articulation, and place of articulation), and find that voicing and manner are decodable from neural activity, whereas place of articulation is not. This set of analyses addresses both practical considerations in the application of machine learning to EEG, particularly for speech studies, and also sheds light on current issues regarding the nature of perceptual representations of speech.

In this study, we conducted research on a deep learning-based blood pressure (BP) estimation model suitable for wearable environments. To measure BP while wearing a wearable watch, it needs to be considered that computing power for signal processing is limited and the input signals are subject to noise interference. Therefore, we employed a convolutional neural network (CNN) as the BP estimation model and utilized time-series electrocardiogram (ECG) and photoplethysmogram (PPG) signals, which are quantifiable in a wearable context. We generated periodic input signals and used differential and thresholding methods to decrease noise in the preprocessing step. We then applied a max-pooling technique with filter sizes of 2 × 1 and 5 × 1 within a 3-layer convolutional neural network to estimate BP. Our method was trained, validated, and tested using 2.4 million data samples from 49 patients in the intensive care unit. These samples, totaling 3.1 GB were obtained from the publicly accessible MIMIC database. As a result of a test with 480,000 data samples, the average root mean square error in BP estimation was 3.41, 5.80, and 2.78 mm Hg in the prediction of pulse pressure, systolic BP (SBP), and diastolic BP (DBP), respectively. The cumulative error percentage less than 5 mm Hg was 68% and 93% for SBP and DBP, respectively. In addition, the cumulative error percentage less than 15 mm Hg was 98% and 99% for SBP and DBP. Subsequently, we evaluated the impact of changes in input signal length (1 cycle vs. 30 s) and the introduction of noise on BP estimation results. The experimental results revealed that the length of the input signal did not significantly affect the performance of CNN-based analysis. When estimating BP using noise-added ECG signals, the mean absolute error (MAE) for SBP and DBP was 9.72 and 6.67 mm Hg, respectively. Meanwhile, when using noise-added PPG signals, the MAE for SBP and DBP was 26.85 and 14.00 mm Hg, respectively. Therefore, this study confirmed that using ECG signals rather than PPG signals is advantageous for noise reduction in a wearable environment. Besides, short sampling frames without calibration can be effective as input signals. Furthermore, it demonstrated that using a model suitable for information extraction rather than a specialized deep learning model for sequential data can yield satisfactory results in BP estimation.