Carlos M. Gómez

Background: Predictive-processing accounts propose altered prediction-error signaling in psychosis. However, event-related potential (ERP) findings in first-episode psychosis (FEP) are inconsistent and focused predominantly on mismatch negativity (MMN). Examining anticipatory and later post-stimulus ERP responses alongside MMN within a single passive paradigm may broaden the assessment of predictive processing in FEP. Methods: 34 participants with non-affective FEP and 34 healthy controls (HC) underwent EEG during a passive auditory pattern-based paradigm. Mean amplitudes of contingent negative variation (CNV), MMN, and post-imperative negative variation (PINV) were quantified at five frontocentral electrodes. Exploratory analyses examined N1 and P2 mean amplitudes. Mixed models tested group, electrode, and group-by-electrode effects adjusting for age, sex and years of education. Regressions examined ERP associations with psychotic-like experiences, neurocognition, and social cognition across groups, and with symptoms, functioning, and antipsychotic dose within FEP. Results: FEP participants reported more psychotic-like experiences and showed poorer global and social cognition than HC (all p-FDR ≤.042). Standardized adjusted FEP–HC differences at FCz were nonsignificant for CNV (β = −0.46), MMN (β = −0.30), and PINV (β = −0.10; all p-FDR ≥.265). Exploratory analyses identified lower P2 amplitude in FEP at FCz (β = −0.73; p-FDR =.019) and in the averaged frontocentral ROI (β = −0.64; p-FDR =.027). No ERP-phenotype associations survived FDR correction. Conclusions: Despite phenotypic differences between FEP and HC, no significant differences emerged in the primary ERP indices of predictive processing examined. The exploratory P2 finding and its possible relationship to predictive processing and frontal dysfunction in FEP warrant further investigation.

The defense reflex has been extensively characterized across sensory modalities; however, the integration of electrical, neurovascular, motor, and peripheral autonomic responses involving the central and autonomic nervous systems remains poorly understood. This study investigated defensive responses to high-intensity auditory stimulation using a multimodal approach combining electroencephalography (EEG), functional near-infrared spectroscopy (fNIRS), and peripheral physiological measures. Participants were exposed to twenty white-noise bursts (100 dB, 500 ms, instantaneous rise time) separated by silent intervals (50 ± 10 s), grouped into five blocks to assess initial responses and habituation.Hemodynamic responses revealed widespread activation of bilateral auditory and frontal regions following stimulus onset, with a progressive reduction in response amplitude across blocks. In late blocks, the centrality of frontal nodes increases, suggesting a pivotal role of frontal regions for habituation. At the peripheral level, habituation was observed in phasic electrodermal activity, vascular indices, motor response, blinking, and parasympathetic cardiac modulation, primarily after the first block, whereas respiratory measures remained stable. Cardiac defense-like responses show a multi-temporal component structure, with habituation restricted to the late decelerative phase. Multivariate analyses revealed a structured yet partially independent organization of neural and autonomic responses to habituation. Together, these findings provide a comprehensive characterization of the defense response to intense auditory stimulation, highlighting selective habituation across central, neurovascular, motor and autonomic systems, and offering new insights into how neurovascular and peripheral processes adapt to repeated auditory threats.

Predictive coding is a theory that tries to account for how the brain processes in an anticipatory manner the expected stimuli, and reorganizes the underlying neural networks as a consequence of the outcome of predictions: Correct or incorrect. EEG has the advantage of making a continuous and almost instantaneous record of brain activity. The present report summarizes work on Event-Related Potentials (ERPs) and reviews the neural validity of Predictive processing as a mechanism to predict future events, assess the validity of predictions, and then update the probabilities associated with future events. Using two experimental models: predictive tone sequences and central cue Posner paradigms and Bayesian modelling, the report suggests that Contingent Negative Variation (CNV) would be related to prior expectation, Mismatch negativity (MMN) and P300 to Bayesian surprise and/or prediction error, and Post Imperative Negative Variation (PINV) to the assessment of trial outcome in uncertainty situations. The review tends to support predictive coding as a theory consistent with brain operations indexed by ERPs.

Background. This study examines a competition based model (Cmodel) designed to capture the temporal dynamics of successive brain microstates derived from electroencephalography (EEG) recordings during eyes-open conditions. The analyzed data were obtained from a public repository comprising microstate sequences from 60 sessions of a single subject . When applied to microstate dynamics, the Cmodel posits a stochastic competition among neural circuits underlying the expression of individual microstates. Methods.The model is formulated at a conceptual level (computational level in Marr framework) and employs a geometric distribution to account for the long right tail of microstate duration distributions, interpreted as the probability of failure of the currently active microstate to persist. To account for the short-lived left tail, the model incorporates a transient increase in the stability of the currently active network, or equivalently, a temporary decrease in the activation probability of competing microstates (refractory period). Results. The model provides a good fit to the microstate duration distributions across all 60 sessions. One third of sessions showed microstate identity sequential dependency with respect to the previous microstates. Discussion. These results suggest that the Cmodel captures key aspects of microstate temporal structure. Moreover, because microstate probabilities can be modulated by psychophysiological conditions, including the influence of previously active networks, the model may serve as a building block for more comprehensive neurobiological frameworks of neural and behavioral dynamics. In such frameworks, microstate sequences could emerge from structured competition and flow among neural networks supporting microstate expression.

The complexity of physiological signals provides insight into the maturation and functional organization of the developing brain. This study investigated age-related changes in electroencephalographic (EEG) signal complexity and their association with behavioral variability in 240 participants aged 6 to 29 years. EEG signals were recorded during the resting state, and Multiscale Entropy (MSE) was computed across 34 temporal scales, grouped into fine, medium, and coarse scales. Behavioral variability was assessed using measures from Oddball and Delayed Match-to-Sample tasks. Quadratic regression analyses characterized age-related changes in MSE across scalp regions, and Pearson correlations evaluated associations between age-adjusted residuals of MSE and behavioral variability. The results showed that MSE changed with age across temporal scales in all cortical regions. Developmentally, MSE showed a significant age-related increase at fine scales across the entire scalp, region-specific decreases at medium scales, and a generalized decrease at coarse scales. Behavioral variability decreased with age across both tasks. Notably, fine-scale age residual MSE in central and posterior regions was negatively correlated with the coefficient of variation in the Oddball task, indicating that higher neural complexity supports more stable performance. These findings suggest scale- and region-specific age-related changes in neural complexity and suggest that fine-scale MSE captures aspects of brain maturation related to behavioral stability beyond traditional variability measures.

Inter-saccadic times or eye fixation durations (EFDs) are relatively stable at around 250 ms, equivalent to four saccades per second. However, the mean and standard deviation are not sufficient to describe the frequency histogram distribution of EFD. The exGaussian has been proposed for fitting the EFD histograms. The present report tries to adjust a competition model (C model) between the saccadic and the fixation network to the EFD histograms. This model is at a rather conceptual level (computational level in Marr’s classification). Both models were adjusted to EFD from an open database with data of 179,473 eye fixations. The C model showed to be able, along with exGaussian model, to be compatible with explaining the EFD distributions. The two parameters of the C model can be ascribed to (i) a refractory period for new saccades modeled by a sigmoid equation (A parameter), while (ii) the ps parameter would be related to the continuous competition between the saccadic network related to the saliency map and the eye fixation network, and would be modeled through a geometric probability density function. The model suggests that competition between neural networks would be an organizational property of brain neural networks to facilitate the decision process for action and perception. In the visual scene scanning, the C model dynamic justifies the early post-saccadic stability of the foveated image, and the subsequent exploration of a broad space in the observed image. The code to extract the data and to run the model is added in the ^{Supplementary Materials}. Additionally, entropy of EFD is reported.

Specific Language Impairment (SLI) is a persistent difficulty in the acquisition and use of expressive and/or receptive language, which negatively impacts academic and social development. The present study evaluated the validity of the statistical learning model proposed to explain language difficulties in children with SLI. To this end, two auditory paradigms of varying complexity, framed within predictive coding theory, were passively presented to children diagnosed with SLI and without neurological impairments. The paradigms consisted of stimulus sequences with decreasing or increasing frequencies, interspersed with the sporadic occurrence of unexpected tone endings. The psychophysiological response was recorded using EEG, focusing on the P1, Mismatch Negativity (MMN), Post-imperative Negative Variation PINV, and Contingent Negative Variation (CNV) components. Results showed an absent MMN and a higher P1 response to deviant tones in children with SLI, suggesting an impaired development of frontal MMN generators, possibly compensated by the primary auditory cortex. SLI participants also showed increased PINV and CNV responses during the most complex paradigm, which could imply greater cognitive effort and resource allocation to the reassessment of stimulus patterns in this group. Finally, the incomplete maturation of frontal areas in children of this age range (3 to 11 years old) was proposed to explain the increased activity observed in both groups for the N1/MMN elicited in the simplest paradigm. These findings support statistical learning as a valid model for understanding the possible neural basis of SLI, identifying this type of predictive EEG design as a potential early detection protocol.

Recent studies combining electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) have shown promising results linking neural and vascular responses. This study analyzes the topographical effect of auditory stimulus intensity on cortical activation and explores neurovascular coupling between fNIRS hemodynamic signals and auditory-evoked potentials (AEPs), extracted from EEG. Forty healthy volunteers (13 males, 27 females; mean age = 22.27 ± 3.96 years) listened to complex tones of varying intensities (50-, 70-, and 90-dB SPL) across seven frequencies (range of 400–2750 Hz) in blocks of five, while EEG and fNIRS were recorded. PERMANOVA analysis revealed that increasing intensity modulated hemodynamic activity, leading to amplitude changes and enhanced recruitment of auditory and prefrontal cortices. To isolate stimulus-specific activity, Spearman correlations were computed on residuals—components of AEPs and fNIRS responses with individual trends removed. The N1 amplitude increase was correlated with higher superior temporal gyrus (STG) and superior frontal gyrus (SFG) activity, and reduced activity in inferior frontal gyrus (IFG) for the oxygenated hemoglobin (HbO), while the deoxygenated hemoglobin (HbR) was associated with increased activity in one channel near the Supramarginal Gyrus (SMG). P2 amplitude increase was associated with higher activation in SFG and IFG for HbO, while for HbR with the activity in SMG, angular gyrus (AnG), SFG, and IFG. Additionally, internal correlations between fNIRS channels revealed strong associations within auditory and frontal regions. These findings provide insights into existing models of neurovascular coupling by showing how stimulus properties, such as intensity, modulate the relationship between neural activity and vascular responses.