Psychophysiology

BackgroundAutonomous sensory meridian response (ASMR) and music-induced frisson are sensory-affective phenomena characterized by tingling, chills, and pronounced emotional responses. Previous research has mainly focused on physiological changes during these experiences, whereas much less is known about whether baseline physiological state is associated with subsequent susceptibility. ObjectiveTo examine whether baseline autonomic flexibility, indexed primarily by heart rate variability (HRV), is associated with later ASMR/frisson responsiveness. Resting EEG measures were included as secondary exploratory markers. MethodsFifteen participants were recruited by convenience sampling; after artifact-based exclusion, 10 participants were included in the analyses. A 5-minute resting baseline EEG and ECG was recorded prior to stimulus presentation. Participants were then exposed to auditory and audiovisual ASMR stimuli, classical music excerpts, and a control stimulus, and reported whether they had experienced ASMR-typical sensations or frisson. Main analyses examined associations between baseline physiological parameters and a combined response-positive outcome. Exploratory analyses included participant-level correlations, comparisons between susceptible and non-susceptible participants, and stimulus-specific effect sizes. ResultsHRV-related measures showed the clearest and most consistent pattern of association with responsiveness. Higher baseline total HRV power was associated with a greater number of response-positive stimuli (r = 0.756, p = 0.011), with similar positive associations for high-frequency HRV (HF; r = 0.672, p = 0.033) and baseline heart rate slope (r = 0.751, p = 0.012). Stimulus-specific analyses likewise showed the most consistent positive baseline effects for total HRV power, with HF and heart rate slope pointing in the same direction. Frontal alpha asymmetry (FAA) was negatively associated with responsiveness ({rho} = -0.862, p = 0.001), but EEG findings overall were less consistent than the HRV-related pattern and are best interpreted as secondary exploratory observations. ConclusionsIn this exploratory pilot sample, baseline HRV, particularly total HRV power, showed the most coherent physiological association with susceptibility to ASMR and music-induced frisson. The findings are consistent with the possibility that these experiences depend not only on stimulus properties, but also on pre-existing physiological state. Given the small sample and exploratory design, the results should be interpreted as hypothesis-generating and require replication in larger confirmatory studies.

Both episodic memory and word retrieval have been linked to power decreases in the alpha and beta oscillatory bands, but these patterns have rarely been related to each other, partly due to a lack of methodological approaches available. In this explorative study, we investigate the similarities and dissimilarities in the oscillatory fingerprints of the retrieval of words and episodes by directly comparing the activity patterns across time, frequency, and space. We acquired electroencephalography (EEG) data of participants performing a language and an episodic memory task based on the same stimulus material. With a newly developed approach, we directly compared the source-reconstructed oscillatory activity using mutual information and a feature-impact analysis. While left temporal and frontal regions showed dissimilarities between the tasks, right-hemispheric parietal regions exhibited similarities. We speculate that this could indicate a homologous function of these regions, potentially sharing less-specific representations between the tasks. We further uncovered a dissociation of the alpha and beta bands regarding the similarity across tasks. While the beta band was dissimilar between word and episodic memory retrieval, the alpha band seemed to contribute to the similarity we observed in right parietal regions. Whether this points to a task-unspecific function of the alpha band or a functional role in the retrieval process of the presumed representations, remains to be determined. In summary, we present an approach to study similarity across tasks using the temporal, spectral, and spatial dimensions of EEG data, and present results of exploring the shared oscillatory fingerprints between episodic memory and word retrieval.

Selective attention enables the prioritization of task-relevant information while managing distractors, and steady-state visual evoked potentials (SSVEPs) are widely used to track this process by tagging different visual objects at distinct flicker frequencies. However, whether the choice of tagging frequency itself influences other neural and cognitive measures remains unclear. Here, 27 participants performed detection and 1-back working memory tasks while a central target and peripheral distractors flickered at either 8.6 Hz or 12 Hz. The working memory task produced slower responses, more errors, and greater perceived difficulty than detection. Tagging frequency strongly shaped neural responses, with 8.6 Hz eliciting higher SSVEP signal-to-noise ratios than 12 Hz regardless of stimulus location. Nevertheless, stronger SSVEP responses for centrally attended stimuli were associated with fewer working memory errors and larger early visual ERP responses, while SSVEPs for attended and distractor stimuli were negatively correlated. In addition, the working memory task produced a larger P1-N1 peak-to-peak difference, and tagging frequency altered the timing and amplitude of early ERP effects. Together, these findings show that tagging frequency is not a neutral methodological parameter, but one that shapes both neural indices of attention and their relationship to cognitive performance.

Working memory (WM) supports the temporary maintenance of goal-relevant information and is disrupted across many neuropsychiatric disorders. We examined whether scalp electroencephalography (EEG) data features beyond spectral power, including waveform shape, broadband spectral structure, and signal complexity, provide complementary information for predicting cognitive and clinical outcomes. EEG was recorded from 200 adults spanning a broad range of neuropsychiatric symptom severity while they completed three WM task paradigms: Sternberg spatial WM (SWM), delayed face recognition (DFR), and dot pattern expectancy (DPX). Separate machine learning models were trained on EEG features from the encoding, delay, and probe phase of each task to predict participants task accuracy, reaction time (RT) variability, WM capacity, and psychopathology scores (Brief Psychiatric Rating Scale). A split-half analytic framework was used, with cross-validated model development in an exploratory dataset (N=100) and evaluation of statistically significant models in a held-out validation dataset (N=100). In the exploratory dataset, SWM task data best predicted WM capacity, DPX task data predicted RT variability, and DFR task data predicted psychopathology, suggesting that these three WM paradigms engage distinct neural processes relevant to different outcomes. No models reliably predicted task accuracy. Models incorporating features beyond spectral power generally outperformed power-only models, and task-derived features outperformed resting-state-derived features. However, only those models predicting WM capacity and RT variability generalized to the validation dataset; models predicting psychopathology did not. These findings demonstrate functional heterogeneity across WM paradigms, show that complementary EEG features enhance predictive modeling, and highlight the importance of rigorous validation for identifying robust brain-behavior relationships.

Reaction time is a measure of the speed of our response to stimuli in the environment. Even for a well-trained task, a subjects reaction time varies. One source of this variability is internal state fluctuations (such as changes in arousal). There are few studies that systematically quantify the extent to which reaction time varies across different timescales and link this to measures of systemic physiology associated with arousal. In much of the literature, it is assumed but not demonstrated that behavioral and systemic measurements associated with arousal will be consistently linked because both estimate a common underlying arousal process. In this work, we examined this assumption by simultaneously measuring reaction time, heart rate, and pupil diameter in rhesus macaque monkeys performing several visual tasks over hours and across hundreds of sessions. We found a portion of the variability in reaction time could be linked to systemic physiological signatures of arousal on fast timescales from second to second and slower timescales from minute to minute. This link between reaction time and systemic physiology was also present for different biomarkers of arousal (heart rate and pupil). However, the strength of this relationship varied depending on the arousal biomarker. Our findings support the conclusion that there are multiple arousal mechanisms that act simultaneously to influence behavior and multiple timescales at which they operate.

Human attention is inherently transient and limited in span to only a few moments without lapsing. The intrinsic dynamics of large-scale neurocognitive networks are thought to contribute to these lapses and result in the unavoidable fluctuations in attention that constrain its span. However, it remains unclear how the millisecond temporal dynamics of specific electrophysiological brain states contribute to the endogenous maintenance of attention or the onset of attentional lapses. In the present study, we investigated whether the strength and millisecond dynamics of brain electric microstates differentiate states of focus from inattention and contribute to the endogenous maintenance of attention over short and long timescales. We recorded 128-channel EEG while participants maintained their attention during the wait time delay of trials in the Sustained Attention to Cue Task (SACT) and segmented the EEG into a categorized time series of microstates based on data-driven clustering of topographic voltage patterns. The findings revealed that the prevalence and rate of occurrence of microstates C and E in the wait time delay of trials differentiated trials in which the target stimulus was correctly detected from incorrectly detected. These same microstates were also implicated in the maintenance of attention over short and long timescales, with their time-varying dynamics changing systematically during the wait time delay of trials and over the course of the task session. Together, these findings demonstrate the sensitivity of microstates to variation in attentional states and suggest that the millisecond dynamics of these brain states contribute to the maintenance of attention over time.

Electroencephalography (EEG) data are inherently contaminated by non-neuronal noise, including eye movements, muscle activity, cardiac signals, electrical interference, and technical issues such as poorly connected electrodes. Preprocessing to remove these artefacts is essential, yet the optimal method remains unclear due to the vast number of available techniques, their combinatorial use in pipelines, and adjustable parameters. Consequently, most studies adopt ad hoc preprocessing strategies based on dataset characteristics, study goals, and researcher expertise, with little justification for their choices. Such variability can influence downstream results, potentially determining whether effects are detected, and introduces risks of questionable analytical practices. Here, we present a method to objectively evaluate and compare preprocessing pipelines. Our approach uses realistically simulated signals injected into real EEG data as "ground truth", enabling the assessment of a pipelines ability to remove noise without distorting neuronal signals. This evaluation is independent of the studys main analyses, ensuring that pipeline selection does not bias results. By applying this procedure, researchers can select preprocessing strategies that maximize signal-to-noise ratio while maintaining the integrity of the neural signal, improving both reproducibility and interpretability of EEG studies. Although the data presented here focuses on processing and analysis most relevant for ERP research, the method can be flexibly expanded to other types of analyses or signals.

Brain activity comprises both rhythmic (periodic) and arrhythmic (aperiodic) components. These signal elements vary across healthy aging, and disease, and may make distinct contributions to conscious perception. Despite pioneering techniques to parameterize rhythmic and arrhythmic neural components based on power spectra, the methodology for quantifying rhythmic activity remains in its infancy. Previous work has relied on parametric estimates of rhythmic power extracted from specparam, or estimates of rhythmic power obtained after detrending neural spectra. Variation in analytical choices for isolating brain rhythms from background arrhythmic activity makes interpreting findings across studies difficult. Whether these current approaches can accurately recover the independent contribution of these neural signal elements remains to be established. Here, using simulation and parameter recovery approaches, we show that power estimates obtained from detrended spectra conflate these two neurophysiological components, yielding spurious correlations between spectral model parameters. In contrast, modelled rhythmic power obtained from specparam, which detrends the power spectra and parametrizes brain rhythms, independently recovers the rhythmic and arrhythmic components in simulated neural time series, minimising spurious relationships. We validate these methods using resting-state recordings from a large cohort. Based on our findings, we recommend modelled rhythmic power estimates from specparam for the robust independent quantification of rhythmic and arrhythmic signal components for cognitive neuroscience.

Interpersonal neural synchrony (INS) in mother-child dyads is often interpreted as a neural marker of relational quality and sensitive caregiving, yet findings on its predictors remain heterogeneous. One possible source of this variability is the diversity of interactional paradigms used in hyperscanning research. This study examined how maternal personality, child temperament, and affective states relate to INS across interaction contexts varying in social interactivity. Thirty-three mother-child dyads (n = 20 female children) participated in a functional near-infrared spectroscopy hyperscanning experiment involving passive video co-exposure, a structured cooperative task, and free interaction. Fronto-temporal activity was recorded simultaneously, and INS was computed using wavelet transform coherence. Above-chance levels of INS emerged in inter-brain region combinations primarily involving the mothers left inferior frontal gyrus (IFG) and the childs right IFG (adjusted ps < 0.030, Cohens d range = 0.14-0.31). Maternal neuroticism was the only significant predictor of INS, with higher levels associated with increased synchrony during passive video co-exposure (adjusted p = 0.012) and free interaction (adjusted p = 0.021), but not during the structured game. These findings indicate that maternal dispositional traits shape INS in a context-dependent manner. Notably, the positive association between neuroticism and INS suggests that heightened neural synchrony may reflect over-attunement in more anxious caregivers, rather than optimal coordination. Excessive synchrony may therefore index tightly coupled, over-monitoring interaction dynamics, consistent with models of affiliative vigilance in anxious parenting. Overall, INS may follow a non-linear pattern in which moderate levels are most adaptive, highlighting its flexible, dynamic, and context-sensitive nature.

BackgroundGrowing evidence suggests that sleep plays an important role in PTSD outcomes, potentially due to its influence on emotional memory consolidation, though these mechanisms remain unknown. This study sought to test the hypotheses that sleep neurophysiology, PTSD status, and sex moderates the degree to which the late positive potential (LPP) mediates memory accuracy for affective visual stimuli. MethodsN = 39 participants (18 female) viewed 75 negative and 75 neutral IAPS images while EEG was recorded. After viewing the images, participants took a two-hour long nap which was followed by a memory assessment. Memory accuracy was measured using d = Z(hit rate) - Z(false alarm rate), where hit rate refers to the proportion of images seen during the memory assessment that are correctly identified as being previously seen, false alarm rate refers to the proportion of images seen during the memory assessment that are incorrectly identified as being previously seen, and Z() is the inverse cumulative distribution function of the standard normal distribution function. ResultsThe early (300 - 1000 ms) and late (1000 - 1500 ms) LPP mediated enhanced discrimination accuracy for emotional compared to neural stimuli (d) (ps < 0.001). The association between the late LPP and d was moderated by sleep such that the association was stronger when participants spent proportionately more time in N3 and REM (p = 0.02). The differences in reactivity between emotional and neutral images for both the early and late LPP were attenuated in PTSD+ individuals vs. controls (ps < 0.001). Despite mediation results showing greater d for emotional compared to neutral stimuli, women showed overall worse memory accuracy for negative compared to neutral stimuli (p < 0.001) whereas men showed no difference (p = 0.64). ConclusionsN3 and REM sleep play a critical role for memory of stimuli that produce large and sustained neural responses. PTSD is marked by a diminished ability to distinguish between negative and neutral information. More research is critical to understand sex effects on emotional memory.

Meningeal lymphatic vessels (mLV) play essential roles in draining cerebrospinal fluid (CSF) into peripheral blood. The mLVs are hypothesized to be supportive structures to the glymphatic system, which is thought to remove metabolic byproducts from brain parenchyma and has been most directly studied in rodent models. Previous rodent studies have indicated a correlation between mLV function and cognitive performance, but this relationship in humans remains unexplored. Age-related declines in glymphatic system efficiency in humans and cognitive performance have been observed separately. This study investigates age- and sex-related differences in CSF production via choroid plexus volumes, mLV characteristics, and glymphatic system efficiency, overall elucidating the implication of cerebral lymphatic function on cognition. We recruited 26 healthy adults from Dallas-Fort Worth and acquired magnetic resonance images. mLVs along the sagittal sinus were visualized and segmented from T2-FLAIR images. The glymphatic system was evaluated by measuring diffusivity along the perivascular space. Choroid plexus volume and brain volume were estimated from T1-MPRAGE. Neuropsychological tests were conducted to assess cognitive function. Our findings indicate that glymphatic function diminishes with age, while mLV and choroid plexus volumes increase. Males displayed greater mLV volume than females, yet no sex differences were found in glymphatic function or choroid plexus volume. Notably, mLV volume increased as glymphatic function declined, independent of age. Moreover, a glymphatic-mLV latent variable significantly predicted processing speed, underscoring the influence of cerebral lymphatics on cognition. In conclusion, this study highlights a decline in glymphatic function with age, accompanied by increased mLV volumes and altered processing speed. These lymphatic system changes may underlie or contribute to the cognitive declines observed in healthy and pathological aging. Significance StatementThe glymphatic system and meningeal lymphatic vessels play crucial roles in removing brain cell waste. The relationship between these systems and their effect on human cognition, particularly processing speed, is unknown. We demonstrate that these systems change with advancing age. Variations in cerebral lymphatic function contribute to differences in processing speed independent of age, ultimately affecting higher-order cognitive function. The findings presented have implications for cognitive function in both healthy and diseased states.

BackgroundFunctional motor disorder (FMD) is a common and disabling condition with incompletely understood pathophysiology. Eye-tracking offers a method to objectively examine cognitive and motor control processes and their underlying neural pathways. We aimed to quantify saccade, blink and pupil responses in FMD and healthy controls performing an interleaved pro-/anti-saccade task, and to investigate the relationships between oculomotor measures and motor and non-motor symptom severity. MethodsWe conducted video-based eye-tracking in 104 patients with clinically definite FMD and 115 age- and sex-matched healthy controls performing the saccade task. Patients completed questionnaires on depressive, pain-related, dissociative, non-motor somatic symptoms. Clinician-rated motor severity and centrally acting medication was recorded in FMD patients. ResultsCompared to controls, FMD patients showed increased anti-saccade error rates (p < 0.001), anticipatory saccades (p [&le;] 0.003), altered blink distribution (p < 0.001), and reduced pupil dilation velocity (p < 0.001). However, reduced pupil dilation velocity was not significant in subsample of unmedicated patients. Higher anti-saccade error rates were significantly associated with depressive symptoms, pain severity, dissociative symptoms, non-motor somatic symptom burden, and motor severity (all p < 0.05). ConclusionsWe hypothesize that the altered saccade and blink responses result from altered processing in the frontal cortex and basal ganglia which provide critical input to brainstem oculomotor control areas in FMD. These results support neurobiological models proposing altered predictive and attentional processing underlying FMD. Association between oculomotor measures and symptom severity suggests that specific cognitive abnormalities may play a role in the pathophysiology of these symptoms in FMD. WHAT IS ALREADY KNOWN ON THIS TOPICFMD is increasingly interpreted through predictive coding models suggesting abnormalities in predictions about motor and sensory states driven by abnormally focused attention. Yet the underlying neurobiology remains poorly defined. Empirical studies directly probing basic predictive processes in FMD are scarce, and implicit cognitive-motor interactions, particularly those involving motor learning and adaptation, have been insufficiently explored. WHAT THIS STUDY ADDSOnly two previous studies have used eye-tracking in FMD, focusing mainly on diagnostic saccadic markers. Using time-series analyses of saccadic, blink, and pupillary data, we show abnormalities in inhibitory control, predictive processing, and implicit learning. Due to strong homology between human and primate neurophysiology and neuroimaging findings in oculomotor control, the findings can be linked to dysfunction within cortico-basal ganglia circuits. HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICYOculomotor abnormalities correlated with motor and non-motor symptom severity, indicating mechanistic relevance. The findings provide empirical support for predictive coding accounts and point to involvement of subcortical structures including projections from the frontal cortex to the basal ganglia. This highlights the value of studying cortico-basal ganglia circuits with implications for treatment and of developing oculomotor measures as potential biomarkers in FMD.

Prolonged esports play induces cognitive fatigue that is not fully captured by subjective awareness, motivating practical, non-stimulant nutritional strategies supported by objective physiological markers. We here tested whether acute milk protein intake attenuates fatigue-related physiological responses during prolonged esports play and supports subjective state, executive control, and in-game performance. In a randomized, single-blind (assessor-blind), energy-matched controlled crossover study, 15 healthy young adults with esports experience completed two sessions in which they consumed either a milk protein drink or an energy-matched apple juice control before a 3-h virtual soccer task. Physiological measures included pupillometry during gameplay, salivary cortisol, continuous interstitial glucose monitoring, and heart rate. Subjective ratings (VAS) and executive function (flanker task) were assessed across post-ingestion time points, and in-game performance metrics were aggregated within hourly gameplay blocks. Milk protein intake was associated with a coherent pattern of physiological advantages, including larger pupil diameter during gameplay, smoother interstitial glucose dynamics, and lower salivary cortisol, while heart rate showed time-dependent changes without a clear condition effect. These physiological changes co-occurred with higher enjoyment and lower hunger, improved flanker performance, and condition-dependent improvements in in-game performance, most notably higher shot success rate. Additionally, pupil diameter during gameplay was associated with inhibitory-control efficiency on the flanker task. These findings suggest that acute milk protein intake may serve as a practical, non-stimulant nutritional strategy to sustain physiological state and cognitive-behavioral performance during prolonged esports (virtual soccer) play. Highlights- Prolonged esports play models modern digital cognitive activity and cognitive fatigue. - Acute milk protein intake increases pupil diameter during prolonged esports play. - Interstitial glucose dynamics are smoother and salivary cortisol is lower with milk protein. - Enjoyment increases and hunger decreases during 3 h of virtual soccer play. - Executive function and in-game performance improve, most notably shot success rate.

ObjectivesChronic insomnia (INS) is particularly prevalent in older adults and females. Sex-and age-related differences in neurophysiological markers of sleep quality (sleep spindles and slow-wave activity [SWA]) may underlie differential vulnerability to INS. This study investigated the effects of sex and insomnia on spindle and SWA beyond aging, to better understand the mechanistic differences contributing to the higher prevalence of INS in females. MethodsAfter a habituation night, one night of sleep assessed with polysomnography was analyzed in 222 adults (aged 18-82) including 119 INS (71% female) and 103 healthy sleepers (HS; 61% female). Spindle density, slow oscillation (SO) density, relative sigma power and SWA were derived during NREM sleep. Age, group, sex, and group-by-sex interactions were examined, with age as a covariate. ResultsAge, insomnia, and sex each contributed uniquely to NREM oscillatory activity. INS primarily reduced spindle and SO density, while sex accounted for differences in SWA. While SWA was higher in females overall, sex differences were not significant within the INS or HS groups. Female INS reported highest rates of insomnia severity as well as lower sigma power than males in the INS group. Spindle and SO density deficits were also present in female INS relative to female HS, as well as male INS relative to male HS. ConclusionsThe combination of reduced sigma power in females with insomnia relative to their male counterparts, as well as less spindle and SO density compared to female healthy sleepers may contribute to greater insomnia severity in females. Statement of SignificanceInsomnia is a growing public health concern that is more commonly reported in females, yet the neural mechanisms underlying this sex difference remain poorly understood. Our findings suggest that specific markers of sleep quality are disproportionately disrupted in females with insomnia, potentially contributing to greater vulnerability and symptom severity. These results provide new insight into how sex influences the neurophysiology of insomnia disorder and identify oscillatory markers that could serve as targets for personalized interventions. Future research should investigate whether these alterations represent persistent dysfunction or reversible changes, which could advance understanding of the biological basis of insomnia and inform strategies to improve sleep health in at-risk populations.

Abstract Background: Poor sleep quality is associated with increased cardiovascular risk, although its relationship with left ventricle (LV) structure is poorly understood and ethnic differences in the relationship between sleep and LV structure have not been studied. We investigated the association between poor sleep quality and LV structure in a tri-ethnic cohort. Methods: A total of 1284 participants were analysed from the Southall and Brent Revisited (SABRE) study (age=49.9{+/-} 6.2y; male 75.9%, Europeans (EU)=615, South Asians (SA)=457, African/African-Caribbean (AC)=212). A composite sleep quality score was calculated, and LV structure was measured using echocardiography. Associations between sleep quality and LV mass indexed to height1.7 (LVMi), relative wall thickness (RWT) and LV end-diastolic volume indexed to height1.7 (LVEDVi) were estimated using multivariable linear regression with adjustment for demographic and lifestyle factors across three models. Analyses were performed in the whole cohort and stratified by ethnicity. Results: Compared with those who reported very good sleep quality, participants with poorer sleep quality had higher LVMi (4.8 (95% CI 1.4; 8.2)g/(m1.7*unit sleep score); p=0.006). When stratifying by ethnicity, the association between sleep quality and LVMi was unconvincing in EU (1.9(-3.5, 7.3)g/(m1.7*unit sleep score); p=0.493), whereas poor sleep was associated with higher LVMi in AC and SA participants (9.1(1.3;16.8)g/(m1.7*unit sleep score); p=0.023 and 5.8(0.5;11.0)g/(m1.7*unit sleep score); p=0.031 respectively). Conclusions: Poor sleep quality is associated with higher LVMi in older African/African-Caribbeans and South Asians, but not in Europeans. This may contribute to cardiovascular risk. Keywords: sleep, left ventricle, hypertrophy, remodelling

Oxytocin is a hypothalamic hormone and neuromodulator that has been linked to a variety of different functions, including parturition, social behavior, and cognitive processing. More recently, oxytocin has also been associated with metabolism and energy balance. However, evidence to date in this field has been inconsistent, especially in human research. To address this, we performed a preregistered systematic review and meta-analysis, which synthesized existing literature on the effect of exogenous oxytocin administration compared to a placebo on caloric intake and appetite in humans, using a living meta-analysis approach. Results indicated a significant, reductive effect of oxytocin administration on appetite in participants belonging to certain patient groups (e.g., obesity or type II diabetes; Hedges' g = -0.21). A separate moderator analysis evaluating oxytocin's effect on caloric intake revealed a conditional effect depending on the patient group, with the obesity group showing a significant effect. We did not find any statistically significant effects in healthy participants. However, further analyses revealed that these effects were also not equivalent, indicating that the data are currently too insensitive to draw clear conclusions. Taken together, the results provide some evidence for the role of oxytocin in regulating appetite in an anorexigenic, possibly homeostatic fashion. Future updates in this living meta-analysis may lead to more definitive conclusions.

Heart rate variability (HRV) is widely used to assess autonomic regulation, but its interpretation in older adults is influenced by age, sex, body composition, and hemodynamic status, particularly in underrepresented populations living in geographically extreme environments. We analyzed 530 community-dwelling older adults from the Magallanes region in southern Chile using an integrated framework that combined HRV indices with demographic, anthropometric, and cardiovascular descriptors. After quality-controlled preprocessing, we characterized the distribution and association structure of autonomic and physiological variables and then performed a large-scale unsupervised clustering benchmark across multiple feature spaces, dimensionality-reduction strategies, and clustering algorithms. Conventional descriptors explained only a limited proportion of HRV variability, whereas integrated multivariate analysis revealed a structured continuum of autonomic heterogeneity. A six-cluster solution provided the best compromise between separation, balance, and physiological interpretability, identifying profiles that differed in HRV magnitude, blood pressure burden, body composition, sex distribution, and age structure. These findings indicate that autonomic regulation in older adults cannot be adequately summarized by isolated descriptors such as age, body mass index, or blood pressure alone. Instead, it is better represented as a multidimensional physiological organization that supports future hypothesis generation for risk stratification and longitudinal monitoring in aging populations.

AO_SCPLOWBSTRACTC_SCPLOWDecoding imagined speech from electroencephalography (EEG) recordings is potentially useful for brain-computer interfaces. Previous studies have focused on decoding semantic information from EEG, leaving the decoding of emotion - an important component of human communication - largely unexplored. Here, we report two experiments involving participants tasked with overt (n = 14) or imagined (n = 21) emotional vocalisation in five different categories: anger, happiness, neutral, sadness, and pleasure. Throughout, we recorded 64-channel EEG; we computed time-frequency features and used a logistic-regression classifier to evaluate emotion decoding accuracy. In five participants, we also recorded facial surface electromyography (sEMG) during imagined vocalisation, and studied the contamination of EEG by sEMG. Our results show that emotion can be decoded from single-trial EEG recordings of both overt (78.1%, chance = 20%) and imagined vocalisation (36.4%). The high-gamma band (50 to 100 Hz) and lateral EEG channels (T7, T8, and proximal) were important for decoding. sEMG analysis indicated that involuntary facial muscle activity contributed to these spectral and spatial patterns during imagined vocalisation, especially during happy vocalisations. We conclude that involuntary facial muscle activity is associated with certain emotion categories (i.e., happiness), and drives above-chance decoding of emotion from single-trial EEG recordings of imagined vocalisation.

Atypical sensory experiences are highly prevalent in autistic children and include both hyper- and hypo-responsivity, often accompanied by sensory overload. Alpha oscillations (7-13 Hz), which dynamically regulate cortical excitability, represent a plausible neural mechanism underlying these phenomena: reduced alpha activity is associated with enhanced sensory responsiveness, whereas increased alpha supports suppression of external input. Although decreased alpha power has been repeatedly reported in autism, it remains unclear whether this reduction reflects lower oscillatory amplitude or reduced temporal stability of alpha rhythms, two mechanisms with distinct neurophysiological implications. To better characterize alpha activity in autism, we examined resting-state alpha dynamics in non-autistic children (NA; n = 39), autistic children (AU; n = 52), and siblings of autistic children (SIB; n = 26), aged 8-14 years. We combined traditional broadband measures of relative alpha power, parametric separation of periodic and aperiodic activity, and single-event analyses that quantify the temporal structure of alpha oscillations. Both broadband relative alpha power and periodic alpha power were reduced in autism over parietal regions, replicating prior findings. Importantly, ordinal analyses revealed an intermediate profile in siblings, supporting a liability-related gradient of alpha alterations. However, single-event analyses demonstrated that the average amplitude of individual alpha bursts did not differ between groups. Instead, autistic children showed significantly shorter alpha burst duration and reduced alpha abundance (i.e., proportion of time occupied by rhythmic alpha episodes), with siblings again exhibiting intermediate values. Linear regression analyses confirmed that reductions in relative and periodic alpha power were primarily driven by decreased alpha abundance rather than diminished burst amplitude. These findings indicate that altered alpha activity in autism reflects reduced temporal stability and density of alpha events rather than weaker oscillatory amplitude per se. Reduced persistence of alpha rhythms may therefore represent a neural marker of altered cortical excitability and sensory regulation in autism. Lay summaryAutistic children often experience the world differently at the sensory level, including being more easily overwhelmed by sounds, lights, or other stimuli. In this study, we looked at a type of brain activity called alpha rhythms, which help regulate how strongly the brain responds to incoming information. We found that, in autistic children, these alpha rhythms were not weaker when they occurred, but they lasted for a shorter time and happened less often. Siblings of autistic children showed an intermediate pattern. These results suggest that sensory differences in autism may be linked to less stable brain rhythms that normally help control sensory input. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=158 SRC="FIGDIR/small/716324v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1be733dorg.highwire.dtl.DTLVardef@7fea49org.highwire.dtl.DTLVardef@1ee9124org.highwire.dtl.DTLVardef@17af139_HPS_FORMAT_FIGEXP M_FIG C_FIG

Human-dog interaction is widely used to alleviate stress, yet the accompanying cortical and autonomic dynamics during acute stress and recovery remain incompletely characterized. In this study, 70 adult dog owners completed a standardized stress protocol while prefrontal cortex activity was continuously monitored with functional near-infrared spectroscopy (fNIRS), alongside subjective stress and salivary cortisol measures. Participants then underwent a recovery phase involving interaction with a companion dog, manipulating contact type (direct in person vs. indirect video conferencing), and familiarity (own vs. unfamiliar dog). Stress responses were quantified through heart rate (HR), heart rate variability (HRV), low- and high-frequency spectral power (LF, HF, and LF/HF), and prefrontal functional connectivity (FC) based on maximum cross-correlation coefficients between fNIRS channels. As expected, HR, HRV-derived indices, and FC increased from baseline to the stress phase, confirming robust engagement of autonomic and prefrontal networks. During the recovery phase, all dog interaction conditions demonstrated reductions in HR, LF/HF ratio, and FC toward or below baseline, consistent with physiological and neural stress recovery; direct interaction was associated with particularly pronounced parasympathetic enhancement and a drop in FC that fell significantly below baseline in some cases. Across groups, HRV, LF/HF, and FC were the most consistent predictors of subjective stress ratings, whereas associations with cortisol were limited. These findings suggest that human-dog interaction promotes coordinated autonomic and prefrontal recovery from acute stress, and that fNIRS-derived metrics might provide a marker of stress modulation that can distinguish high-cognitive load and low cognitive demand states beyond traditional stress indices.