Psychophysiology

Numerous studies have demonstrated rapid (< 100 ms) visuo-cortical differentiation of threat-associated faces. This may be due to low-spatial frequency (LSF) visual information originating from magnocellular pathways. Yet it remains unclear whether potentially magnocellular fear signals extend beyond evolutionarily prepared emotional faces and whether they are subject to short-term neuroplasticity. If so, spatial frequency characteristics should modulate processing of faces with newly acquired threat-relevance. Furthermore, it is unknown whether sub-bands of the visual spectrum are associated with autonomic arousal. Using a differential fear-conditioning paradigm, this study tested whether early visual attentional capture, indicated by the P1 event-related potential component, prioritizes LSF information of threat-associated faces with neutral expressions. Additionally, it was tested whether such effects would be paralleled by threat differentiation in the skin conductance response (SCR). For contingency aware participants, stimulus ratings confirmed successful fear conditioning and participants showed a selective left-hemispheric enhancement of the P1 in response to LSF threat-faces. By contrast, CS differentiation in the SCR was not modulated by spatial frequencies but by stimulus duration, with longer CS presentations resulting in larger SCR to threat compared to neutral faces. For contingency unaware participants, trial-by-trial amplitudes of P1 and SCR were positively correlated. Data support the notion that magnocellular-cortical pathways adapt quickly to novel threat-associations and facilitate rapid threat retrieval even for perceptually neutral faces. However, at least in the short term, these signals do not necessarily associate with anticipatory arousal in SCR. Impact statementOur electroencephalography (EEG) study provides evidence for distinct contributions of subcortical signals during early visual perception of fear-conditioned faces (P1 event-related potential) but not autonomic arousal (skin conductance response). Instead, skin conductance responses reflected conscious anticipatory arousal irrespective of the visual pathway. Together, these results reveal parallel but dissociable mechanisms of fear perception that are differentially sensitive to visual properties of threat-associated faces.

Several aspects of parameterised neural activity, including the aperiodic exponent and individual peak alpha frequency, have emerged as promising biomarkers for ageing, pathology, and cognitive decline. Their potential clinical application is tempered by a lack of evidence on long-term temporal stability. Existing investigations have largely relied on cross-sectional designs or considered stability for up to 90 days. Here, we examined five-year reliability, stability, and age-related changes in periodic and aperiodic neural activity using electroencephalography in adults aged 20-70 years. Resting-state EEG was recorded in two sessions, approximately five years apart. We extracted the aperiodic exponent, aperiodic offset, peak alpha power, and individual alpha peak frequency from each channel and examined test-retest reliability at both the channel and cluster levels. All parameters demonstrated fair to excellent test-retest reliability (intraclass correlations = 0.51-0.88). Linear mixed models revealed that individual peak alpha frequency decreased, the aperiodic exponent flattened, and parameterized alpha power remained unchanged. There were no interactions between time and age. Our findings suggest that parameterized activity is reliable over long timeframes and likely captures neural ageing. Spectral parameterization may provide a means of characterizing gradual, normative neurophysiological ageing. Future research should explore the utility of identifying deviations that may indicate pathology.

The balance perturbation-evoked N1 potential is a reliable cortical response during reactive balance control that is correlated to a variety of cognitive and motor functions. Although the supplementary motor area (SMA) has been identified as the primary source of the N1, it is less understood whether other brain regions contribute to N1 recorded at the scalp. We used source localization on electroencephalography (EEG) data from 25 younger adults recorded during backward whole-body perturbations during stance. We identified the sources that contribute to channel-based N1 recordings and quantified their impact on N1 amplitude and latency. In younger adults, N1 amplitudes can be explained by one single source in a central midline cortical region covering the SMA. When reconstructing N1 signals using backprojections with one versus all independent components (IC) identified as brain sources there was no difference in peak amplitudes and a small but significant difference in N1 peak latencies. Parallel brain sources thus deflect the time course of the N1, but not its magnitude. Brain areas associated with ICs contributing to the shift in N1 latency varied between participants. Our results emphasize the dominant influence of central cortical areas on the N1 response, informing hypothesizes regarding the nature of the signal and its functional role. Importantly, the extent and location of other cortical structures that influence N1 timing, such as parietal cortex areas and the anterior cingulate cortex, may further elucidate cortical contributions to balance. These markers could be crucial for the early detection of balance problems in clinical populations. NEW & NOTEWORTHYWe demonstrate that channel-level amplitudes of the balance perturbation-evoked N1 in younger adults primarily reflect neural activity originating from cortical central midline regions, particularly the SMA. In contrast, contributions from parallel active brain regions evoked by balance perturbations are indicated by an influence on N1 peak latencies. Our findings imply that the perturbation-evoked N1, unlike other evoked potentials, is not a mixture of multiple neural sources in younger adults.

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.

Emotion is key to human communication, inferring emotion in a speakers voice is a cross-cultural and cross-linguistic capability. Electroencephalography (EEG) studies of neural mechanisms supporting emotion perception have reported that early components of the event-related potential (ERP) are modulated by emotion. However, the nature of emotions effect, especially on the P200 component, is disputed. We hypothesised that early acoustic features of emotional utterances might account for ERP modulations previously attributed to emotion. We recorded multi-channel EEG from healthy participants (n = 30) tasked with recognising the emotion of utterances. We used fifty vocalisations in five emotions - anger, happiness, neutral, sadness and pleasure - drawn from the Montreal Affective Voices dataset. We statistically quantified instantaneous associations between ERP amplitudes, emotion categories, and acoustic features, specifically, intensity, pitch, first formant, and second formant. We found that shortly after utterance onset (120-250 ms, i.e., P200, early P300) ERP amplitude for sad vocalisations was less than for other emotional categories. Moreover, ERP amplitude at around 180 ms for happy vocalisation was less than for anger, sadness, and pleasure. Our analysis showed that acoustic intensity explains most of these early-latency effects. We also found that, at longer latency (220-500 ms; late P200, P300) ERP amplitude for neutral vocalisations was less than for other emotional categories. Furthermore, there were also ERP differences between anger and happiness, anger and pleasure, anger and sadness, happiness and pleasure, as well as happiness and sadness in shorter windows during this late period. Acoustic pitch and, to a lesser degree, acoustic intensity explain most of these later effects. We conclude that acoustic features can account for early ERP modulations evoked by emotional utterances. Because previous studies used a variety of stimuli, our result likely resolves previous disputes on emotions effect on P200.

Sleep supports offline information processing and is essential for cognitive and emotional functioning. Abnormal sleep patterns are a hallmark of affective disorders. We hypothesized that affective symptoms occur with maladaptive neural processing during offline periods. To test this idea, we used multivariate EEG decoding with cross-state classification. A model trained on EEG data acquired while participants (N = 52) viewed emotional images was used to classify stimulus valence. Applying this model to data collected during a nap revealed the re-emergence of affective neural patterns. Critically, offline reinstatement of patterns reflecting negatively valenced processing predicted greater depressive symptoms across participants. These associations reflected both cognitive-affective and somatic-performance depression subscales, and they generalized across sleep stages and wakeful rest. The finding that offline neural information processing is linked with emotional well-being supports a model whereby maladaptive negative biases can be perpetuated during rest, potentially shaping the progression of affective disorders.

Voluntary action shapes our perception through sensory predictions of its consequences. These predictions are thought to inhibit perceptual processing of predicted outcomes, leading to sensory attenuation. However, some studies have reported findings that contrast with this effect, suggesting that the influence of predictions may reflect, or interact with, attentional processes. Here, we investigated the interplay of action-based prediction and top-down attention on early and late perceptual processing, where prediction and attention refer to the likelihood of a sensory event and its behavioral relevance, respectively. Electroencephalography (EEG) was recorded while participants viewed two sequential gratings. The orientation of the first was either predicted and self-generated or unpredicted and externally generated. Participants judged the tilt direction of the second grating relative to the first but responded only when the grating appeared in a task-relevant color. EEG analyses revealed no modulation of early visual potentials (N1a), but modulations in later processing stages (P3b). Specifically, the P3b exhibited reduced amplitude for predicted and self-generated stimuli compared to unpredicted and externally generated ones, but only when they were task-relevant. Multivariate pattern analysis further showed that the significant temporal cluster supporting decoding of the first gratings orientation was largest for relevant, predicted and self-generated stimuli. Our results suggest an optimization process whereby action-based prediction, when aligned with task goals, reduces the amount of evidence needed and increases its accumulation speed, while preserving sensory representations as accurate as in the absence of prediction/self-generation. HighlightsO_LIFeature-based attention and prediction were orthogonally manipulated in an agency context. C_LIO_LINo early sensory modulation by attention or action-based prediction was observed. C_LIO_LIReduction of both P3b amplitude and latency emerged for predicted/self-generated and task-relevant stimuli. C_LIO_LIMultivariate analysis and behavior results support an interpretation whereby relevant action-based predictions optimize evidence accumulation process. C_LI

Structured AbstractO_ST_ABSObjectiveC_ST_ABSTo facilitate the scalability of EEG research, this paper compares the data quality and evaluates the absolute agreement of EEG features between laboratory and clinic settings. MethodsResting state EEG recordings were obtained from 36 participants (11 infants, 10 children, and 15 adults) from the waiting room of a primary care clinic and a laboratory. Intraclass correlation coefficients (ICC(2,1)) quantified the absolute agreement between laboratory and clinic settings for periodic power bands, alpha peak characteristics, and aperiodic components. The mean absolute difference (MAD) between laboratory and clinic recorded EEGs were calculated to describe signal consistency across settings. ResultsMore components were rejected from clinic-recorded EEGs, though data quality otherwise did not differ between settings. The ICC (2,1) for all EEG measures were generally in the good-to-excellent range across ages and regions of interest. The MAD decreased with age and was largest in the alpha frequency range. ConclusionsHigh quality EEG data can be collected from outpatient clinic settings among infants, children, and adults. There is high reliability in the parameterized periodic and aperiodic EEG features between laboratory and clinic settings. SignificanceFuture research may collect EEG datasets from naturalistic settings with confidence in their reliability relative to laboratory recordings.

The identification of objective, dimensional indices of mental health is of central importance in the pursuit of transdiagnostic multi-dimensional frameworks of psychopathology. Altered visual processing occupies a specific domain of interest and motivated the present investigation aimed to quantify the visuocortical impact of affective naturalistic distractor cues on limited capacity attentional resources in obsessive-compulsive disorder (OCD). The current investigation examined the extent to which attentional resources are allocated toward task cues under affective and disorder-relevant distraction in participants with OCD (N = 33) and control participants (N = 31). Steady-state visual evoked potentials (ssVEPs) in response to task-relevant cues were examined using a foreground task where participants detected coherent motion in a flickering random dot kinematogram (RDK) overlaid on naturalistic distractor pictures ranging in emotional content (pleasant, neutral, unpleasant, and OCD-evoking pictures). Amplitude envelopes of ssVEPs in response to the motion stimulus served as an index of visuocortical engagement with task-relevant cues. Data were also fitted to the distraction under competition model (DUC), a computational framework of attention selection. Group differences emerged with stronger visuocortical competition effects (attenuated task engagement) for the OCD group, driven largely by the unpleasant pictures, followed by the OCD-evoking pictures. Furthermore, the DUC model fit well in both groups, demonstrated the dominance of the visuocortical competition observed in response to the unpleasant pictures, and revealed the presence of substantial competition in response to the OCD-evoking pictures in the OCD group.

BackgroundChronobiology research has historically focused on circadian rhythms; however, longer infradian rhythms are prevalent in human physiology and may have important implications for health and wellbeing. Previous studies have identified widespread infradian rhythms across human physiology, often in the context of hormonal regulation and disease. Despite growing evidence of their ubiquity, the mechanisms, significance, and clinical relevance of these rhythms remain poorly understood, largely due to lack of longitudinal datasets and robust detection methods. The emergence of new wearable technologies enables rich, continuous data capture within individuals, allowing physiological rhythms to be studied at scale. MethodsThis study analyzed a cohort of healthy, young adults (N=623), with up to four years of wearable and questionnaire data collected through the University of Notre Dames (USA) NetHealth project. Participants who recorded at least three months of continuous (>80% adherence) heart rate data were included and significant infradian rhythms were identified using wavelet analysis. Unsupervised non-negative matrix factorization was performed to cluster similar wavelet power spectrum distributions. Individuals heart rate rhythms were compared to known environmental cycles (day-of-week, lunar, seasonal) and considering demographics and social networks. A second, smaller cohort (N=70) with heart rate and menstrual timing were included to analyze the interplay of hormonal regulation on monthly cycles. Multinomial logistic regression, and statistical tests (i.e., one-way ANOVA) were applied to quantify the effects of environmental, behavioral and demographic factors on heart rate rhythms. FindingsSignificant infradian rhythms of heart rate were detected in 69.7% (365/523) of the cohort and 35.9% (188/523) had two or more rhythms. Annual, biannual and 10-week rhythms were the most common. Within the 4-45-day band, individuals clustered into four multiday chronotypes based on dominant periodicities in their wavelet power spectra: weekly ([~]7 days), shorter-monthly ([~]25 days), longer-monthly ([~]35 days), and multi-month (>35 days). Heart rate rhythms were influenced by environmental cycles (day-of-week and seasonality) but were not tightly correlated to external cues. Additionally, heart rate rhythms were synchronized to the menstrual cycle in most menstruating females, although monthly rhythms were also observed in males and menopausal women. InterpretationThe prevalence of infradian, or multiday heart rate rhythms in healthy young people motivates further scientific investigation to understand the mechanisms of these rhythms and their potential association with autonomic function, and risk of disease or disease-specific symptoms. Characterizing physiological rhythms can drive new insights into how multiscale fluctuations modulate disease symptoms across neurological, psychiatric, and broader health conditions.

When participants are engaged with auditory narratives, physiological and neural signals exhibit temporal correlations between subjects. The intersubject correlation (ISC) increases when attention is directed to the stories, suggesting that shared neural and bodily dynamics arise from a similar processing of the narratives. Identifying the factors that drive these common responses is clinically relevant for interpreting EEG ISC exhibited in unresponsive patients. In this study, we investigated whether the ISC of the EEG elicited by auditory narratives is driven by low-level acoustic (envelope, spectrogram) and/or higher-level linguistic information (word onset, word surprisal) in two groups of healthy participants during passive, attentive and distracted listening. We use temporal response functions (TRFs) for acoustic, and linguistic features to assess the contribution of each feature to the ISC, measured using correlated component analysis (CorrCA). TRFs derived for acoustic features explained a larger fraction of variance in the EEG than linguistic features and were the main contributors to the ISC. The attention-related increase in ISC was driven by all features. Importantly, word surprisal had an effect on ISC only during active story engagement, with timing and scalp distribution consistent with language processing. Notably, the linear responses captured by TRFs only explained a small amount of the overall ISC, suggesting that ISC is largely driven by nonlinear responses to the narratives. We propose that the combined use of ISC and TRFs has the potential to provide meaningful markers of language processing in patients with disorders of consciousness, and we suggest practical recommendations for their implementation.

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.

Several studies suggest that neural oscillations play a role in cognition, including predictive processing. Recent models propose that alpha-band traveling waves reflect prediction (top-down, frontal to occipital), while bottom-up waves reflect prediction errors. We tested this hypothesis using a visual statistical learning task in which participants (N=31) detected target shapes presented with varying levels of predictability. EEG and pupillometry were recorded throughout the task. Behavioral results showed faster responses for predictable targets, and pupil dilation increased for unexpected shapes. ERP analyses revealed predictability effects on the P300 component across occipital, central, and frontal electrodes, along with modulation of alpha oscillations. However, traveling-wave analyses did not show a clear effect of predictability between conditions. Instead, wave patterns varied with participants cognitive strategies: individuals who relied more on statistical regularities showed stronger top-down alpha-band waves. These findings suggest that alpha traveling waves may reflect general cognitive strategies rather than specific predictive processes.

Historically, neural variability observed during task was interpreted as "noise," assumed to obscure meaningful signal and thus something to be minimized both analytically by researchers and functionally by the brain. Changes to this signal-to-noise ratio have been proposed as a possible neural mechanism behind the increased reaction-time variability (RTV) in attention deficit hyperactivity disorder (ADHD). However, not all variability is the same - in some cases, variability can have some underlying "statistical structure" that can be beneficial to information processing. The challenge lies in distinguishing meaningful variability from random noise. The edge-of-synchrony critical point, which describes a system poised between synchronous and asynchronous regimes, could be a good theoretical framework to study these different types of neural variability. In this study, we investigate whether changes in criticality and oscillatory dynamics preceded slower behavioral responses during a bimodal continuous performance task in ADHD. We find evidence that, prior to slower responses, neural dynamics shift toward criticality in both ADHD and control groups, suggesting that increase variability in ADHD and during attention lapses are related to structured variability and not necessarily random noise. Notably, these findings run counter predictions based on the proposed model and previous literature on neural noise in this population, challenging predictions of edge-of-synchrony criticality as a unifying account of neural variability and behavioral performance. Furthermore, this effect did not emerge at the between-subject level, underscoring the limitations of relying on between-subject correlations to infer neural mechanisms. Impact StatementOur findings add new perspective to the hypothesis that links neural variability to reaction time variability in adults with and without ADHD. We found that neural dynamics shift towards criticality prior to slow reaction times in adults with and without ADHD, but in ADHD, dynamics lie closer to criticality regardless of response type, suggesting a different "attractor" state.

Extended cessation (EC) is a rare, non-ordinary meditative endpoint characterized by a temporary absence of reportable phenomenal experience, followed by an extraordinary perceptual vividness, openness, equanimity and affective balance. EC thus offers a unique, non-pharmacological window into the brain dynamics underlying suspension of conscious experience and the subsequent psychological transformations. The present study investigated whole-brain electrophysiological changes induced by EC using a dense-sampling electroencephalographic microstate analysis, in five highly trained meditators. Temporal parameters and transition probabilities of canonical microstates during EC were compared with two control conditions (counting and memory tasks) across six frequency bands (broadband, delta, theta, alpha, beta, gamma). EC was characterized by alterations in global explained variance and coverage of microstates B and C, both associated with self-referential processing. Specifically, EC involved less frequent and shorter occurrences of microstate B, and more frequent and longer occurrences of microstate C. Transition probabilities also reconfigured: transitions from A and B to C increased, whereas transitions from A to B decreased. These broadband effects were distributed across delta, theta, and beta frequency sub-bands. Additional band-specific changes emerged for microstate A and D. Delta band showed longer microstate A and increased B-to-A transitions during EC, while beta band showed less frequent and shorter D and decreased bidirectional B-to-D transitions. These scalp-level findings support a precision re-weighting account of EC, reflecting self-referential reconfigurations with enhanced sensory-anchored inflow. This study provides initial evidence for the neurophysiological correlates of EC, with potential implications for human wellbeing. SIGNIFICANCE STATEMENTExtended cessation is a rare meditative state involving a voluntarily, temporary suspension of conscious experience, offering an exceptional opportunity to study how the brain supports and disrupts conscious awareness without pharmacological intervention. By applying EEG microstate analysis, this study identifies distinct large-scale neural reconfigurations during EC, particularly within microstates linked to self-referential processing. These findings suggest that EC involves a dynamic redistribution of precision and sensory-anchored processing, providing initial neurophysiological evidence for how advanced meditation may reshape conscious experience. This work advances the scientific understanding of non-ordinary states of consciousness and highlights their potential relevance for human well-being.

Observing touch activates brain regions similar to those activated by experiencing actual touch, suggesting that visual information can cross-modally influence tactile perception. In this electroencephalography (EEG) study, we investigated how viewing visual displays of an arm being touched may affect the perception and processing of digitally rendered touch patterns designed to resemble either stroking or tapping. Thirty-one participants experienced touch patterns delivered to their left forearm via a wearable sleeve while viewing either a photo of an arm or spatiotemporally aligned videos of an arm being touched in synchrony with either of the two touch patterns. Continuity and pleasantness ratings of touch stimuli were higher for stroking than for tapping. Correlations between continuity and pleasantness ratings were stronger when touch was accompanied by videos of touch than by the photo of an arm. Analysis of evoked brain responses revealed visual modulation of touch processing at centroparietal electrodes beginning at around 0.9 s, with the cross-modal effects diverging between stroking and tapping at about 1.6 s. Furthermore, the interaction effects of cross-modal influences between stroking and tapping at the neural level positively correlated with the visual modulation of pleasantness ratings in two right frontal clusters at around 1.4 s and 1.8 s. These results suggest that observing touch influences the perception and processing of touch through initial sensory integration at centroparietal sites, followed by later frontal valuation processes. This extends previous findings on affective touch by demonstrating that visual inputs can cross-modally shape the hedonic evaluation of digitally actuated touch.

Excessive self-blaming emotions are commonly observed in anxiety disorders, with qualitatively similar symptomatology reported in subclinical populations. Interpretation of moral information requires assessing the social conceptual information, a process overseen by the superior anterior temporal lobe (sATL). Feelings of self-blame evoke interactions of sATL and socio-affective regions, and previous research shows that subclinical anxiety modulates the organisation of the self-blame circuitry. This study aimed to extend these findings by exploring links of trait-anxiety with (i) self-blaming emotions and associated behaviours in an experimental task, and (ii) self-blame-dependent neural activity and connectivity, as observed during reliving of autobiographical guilt memories. We also explored the role of resting-state fMRI in linking these phenomena. Increased anxiety was linked to stronger self-blaming emotions, and more pronounced self-attacking and hiding. When experiencing negative emotions about themselves (i.e. shame and self-anger), anxious individuals were also less likely to disengage from self-focused thoughts. These behavioural findings were paralleled by enhanced self-blame-related connectivity between the left sATL and bilateral posterior subgenual cingulate cortex. Distinct patterns of activity and connectivity within the ATL-related circuitry were furthermore linked to individual differences in intensity of the self-blaming emotions and approach-avoidance motivation towards the guilt memories. As such, the results of the current study link stronger self-blaming emotions in anxious individuals with specific maladaptive patterns of behaviour. Furthermore, the work provides robust evidence for the important role of ATL-related circuitry in self-blame processing, supporting its broader involvement in social conceptual processing and its alterations in subclinical anxiety.

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.

Functional brain network (FBN) dysconnectivity has been repeatedly reported in bipolar disorder (BD). However, it remains unclear how this dysconnectivity manifests from the perspective of oscillatory FBNs, that is, which network measures and frequency bands most reliably capture this alteration. Moreover, it is unknown whether this dysconnection is predominantly expressed through linear or nonlinear interactions. Here, we investigated properties of oscillatory FBNs in individuals with euthymic BD. Networks were constructed using linear and nonlinear connectivity measures applied to source-localized resting-state electroencephalography (EEG) current density signals. We then quantified whole-FBN and nodal features using conventional and spectral graph theory methods to characterize disorder-related network mechanisms and evaluate their potential as biomarkers. Significant group differences between BD and control groups were observed in the theta and alpha1 bands. Dynamical whole-FBN alterations were detected primarily in linear oscillatory FBNs, with reduced Shannon entropy and energy in the BD group. These effects were replicated using machine learning, achieving 85% classification accuracy with entropy and energy as the most informative features. In contrast, nodal-level differences emerged mainly in nonlinear FBNs, revealing increased centrality in frontal, and decreased centrality across temporal, and limbic regions. These findings emphasize distinct, frequency-specific roles of linear and nonlinear oscillatory FBNs in BD, with global dysconnectivity reflected in linear FBNs and local alterations captured by nonlinear connectivity. Moreover, network measures related to synchronization stability and complexity more effectively capture BD-related dysconnectivity, suggesting that dynamic features of oscillatory FBNs may serve as potential biomarkers for BD.

Human intelligence is essential to understand complex ideas, to engage in various forms of reasoning, to learn from experience, and to adapt to new situations by taking thought. The P300 event-related brain potential has been related to intelligence scores and thus represents as a promising biomarker of general cognitive ability. However, empirical results are characterized by enormous heterogeneity, and a quantitative assessment of this literature is lacking. This preregistered meta-analysis provides the first systematic overview of neuroscientific studies that have examined associations between general cognitive ability and the P300 in healthy adult participants. Out of 5641 articles screened, 49 studies with up to 381 effects were eligible for PRISMA-based meta-analytic comparison. Study quality was evaluated using a novel Study Design and Implementation Assessment Device, which we developed particularly for Individual Difference Research (DIAD-ID) and provide together with our analysis code as free online resources to support future meta-analyses. Confirming our hypotheses, a small but significant positive across-study association was observed for general cognitive ability and P300 amplitudes (r = .13; 95%-CI [.06, .19]), while a significant negative across-study association was revealed for P300 latencies (r = -.18; 95%-CI [-.24, -.13]). Study heterogeneity was substantial, and sub-analyses highlighted potential moderators such as type of the task during EEG recording. We discuss limitations, open questions, and provide concrete guidelines for future research on the neurobiological underpinnings of individual differences in cognitive ability.