Paweł Krukow

Background: Early adversity is associated with long-term alterations in learning and decision-making under uncertainty, yet its impact on implicit predictive processing remains insufficiently characterized. This study investigated implicit statistical learning (ISL) in relation to exposure to adverse childhood experiences, operationalized using Childhood Trauma Questionnaire (CTQ) scores, in a non-clinical sample of young adults. Methods: Fifty-four participants were classified into High-CTQ and Low-CTQ groups and completed a visual ISL task in which stimulus predictability varied across conditions. Behavioral indices included reaction time and response accuracy as measures of sensitivity to probabilistic structure. Results: Both groups exhibited an implicit learning effect, indicated by faster responses in high- versus low-predictability conditions. However, this effect was attenuated in the High-CTQ group, suggesting reduced behavioral differentiation across probabilistic contingencies. Although the group × condition interaction for reaction time was not statistically significant, a significant main effect of group emerged for accuracy, with lower performance in the High-CTQ group, particularly in the low-predictability condition. In addition, individuals with higher CTQ scores subjectively reported perceiving stimulus sequences as more random despite equivalent task engagement across groups. Conclusions: These findings suggest that early adversity is associated with altered utilization of probabilistic environmental structure during statistical learning. Rather than reflecting a generalized deficit in learning or processing speed, the observed pattern indicates moderately reduced sensitivity to weaker predictive signals and a biased subjective representation of environmental randomness in the High-CTQ group.

Introduction: Disturbances in predictive coding are increasingly recognized as core mechanisms shaping perception, cognition, and language in mental disorders. However, the linguistic manifestations of prediction error responses have not been systematically integrated with neurophysiological findings. This review aims to synthesize how different forms of predictive error reactivity are expressed in language and how these patterns correspond to neural signatures. Material and methods: A scoping review was conducted following PRISMAScR guidelines, covering empirical and theoretical studies from PubMed and Scopus from 2010 up to 2026. Studies were included if they examined (1) a psychiatric population, (2) predictive coding or predictionerror mechanisms, (3) linguistic behavior at any structural level, and (4) behavioral and/or neurophysiological data. Extracted information underwent thematic synthesis using open, axial, and selective coding. Results: Thirteen publications met the inclusion criteria. Six recurring mechanisms of predictive error responses were identified: heightened prediction error precision, weakened prediction error signalling, impaired sensory-motor prediction, disruptions in hierarchical semantic-pragmatic prediction, deficits in predictive source monitoring, and global predictive instability. Each mechanism was associated with specific linguistic manifestations such as semantic entropy, discourse incoherence, and atypical lexical activation. Concurrently, corresponding neurophysiological indices were observed, including reduced N1/N400 modulation, converging with electrophysiological and neurodynamic evidence. Conclusions: Language provides a sensitive behavioural window into predictive processing disturbances in mental disorders. The correspondence between linguistic patterns and neurophysiological responses highlights the value of integrative, multilevel approaches for characterizing predictive coding dysfunction in clinical populations. These results suggest that predictive processing deficits occur hierarchically across language levels, producing cascading effects paralleling the clinical course of psychotic illness.

Adverse childhood experiences, such as violence, abuse, and neglect, are increasingly recognized as significant modifiers of brain development. Here, we tested whether adults with histories of childhood trauma, but without psychiatric comorbidities exhibit altered excitation/inhibition (E/I) balance, as indicated by electroencephalography (EEG) signatures. Participants, divided into low- trauma and high-trauma groups, underwent two experimental conditions: eyes-closed resting-state recording and a reaction-time task with visual stimuli. From these data, we computed 1/f spectral slopes, a widely used electrophysiological marker of E/I balance; we complemented these analyses with a leaky integrate-and-fire (LIF) microcircuit model combined with a biophysically grounded forward-modeling approach to simulate realistic brain signals and derive E/I balance estimates. Group comparisons for both slopes and E/I estimates revealed significant resting-state differences, characterized by a shift toward increased neuronal excitation in the high-trauma group. The high- trauma group exhibited altered stimulus-related 1/f slope dynamics relative to the pre-stimulus baseline, reflecting attenuated neuronal inhibition. E/I ratio measures were not significantly correlated with participants’ transient affective states. Together, these findings suggest that childhood trauma is associated with enduring, trait-like alterations in cortical E/I balance that extend beyond affective state and manifest across both resting and task-related brain dynamics. Significance Statement: Childhood trauma is a major risk factor for mental illness, yet its lasting effects on basic brain physiology remain poorly understood. Using electroencephalography combined with biophysically grounded neural circuit modeling, we show that young adults with a history of childhood interpersonal trauma exhibit a persistent shift toward cortical hyperexcitation at rest and a reduced ability to engage inhibitory control during cognitive processing. These effects occur even in individuals without psychiatric diagnoses and are independent of current anxiety or depression, indicating a trait-like neurophysiological footprint of early adversity. This excitation-inhibition imbalance may represent a transdiagnostic vulnerability linking childhood trauma to later psychopathology.

Verbal fluency tests (VFTs) are commonly used tasks for analyzing frontal cortex activation imagined by functional near-infrared spectroscopy (fNIRS). Some new VFT versions have been developed to strengthen the test’s executive character. However, these modified tasks were not used to recognize whether they activate cortical areas significantly differently compared with typical tasks. We introduced a modified VFT version consisting of the necessity to produce words according to one phonemic criterion and exclude others according to another phonemic criterion to establish frontal cortex task-dependent hemodynamic activity for the proposed new task. The analysis was conducted by comparison with a typical initial letter fluency task in a group of 35 students. Behavioral results showed between-tasks differences in productivity during the initial phase of performance. Increased oxygenated hemoglobin (oxy-Hb) concentration in lateral and frontopolar regions was noted in both analyzed tasks, but oxy-Hb suppression has been observed in the middle/superior frontal cortex only for the new one. Such an arrangement of selective metabolic activation and inactivation suggests that the brain’s adaptation to increased processing demands may consist of restraining the oxygen intake of the neuronal region, which activity is not necessary to perform a task.

Background. Schizophrenia is a psychiatric illness with a significant functional impairment. Its diagnosis is based on clinical observation, despite much evidence for its neurobiological basis. More recently, alterations in retinal structure and function were also documented. The retina is a part of the central nervous system, developing embryologically from the same tissue as the brain. Research indicates that retinal morphology measures may be treated as a proxy of brain health. Due to the need to identify schizophrenia biomarkers, we applied a set of automated classifiers encompassing selected retinal and other indirect markers of neuronal system health. Methods. The data came from 59 schizophrenia patients and 61 healthy controls. Interpretable classifiers were also used to differentiate patients with shorter and longer duration of illness. After obtaining the classification results, Choquet integral-based aggregators were adopted to increase discrimination accuracy. Results. Applying Smooth Quadrature-Inspired Choquet aggregators led to a patient vs. control group discrimination accuracy of 93.5%, and accuracy for discrimination between patients with shorter vs. longer duration of illness was 86.25%. In addition, the three-class classification regarding controls and two subgroups of patients reached 88.37% accuracy using 3/8 rule smooth quadrature and triangular norm. Discussion. These results indicate that using measures of retinal morphology and additional variables associated with brain health are useful to classify schizophrenia patients at a level similar to brain imaging-based data. The application of the Choquet integral aggregation function improved the classification results, which is another argument for using this computational method in medical research.

Background and Hypothesis: Given the available findings confirming accelerated brain aging in schizophrenia (SZ), we conducted a study aimed at verifying whether quantitative retinal morphological data enable age prediction and whether schizophrenia patients present with a positive retinal age gap (RAG). Study Design: Two samples of patients and controls were enrolled: one included 59 SZ patients and 60 controls, all of whom underwent optical coherence tomography (OCT) enabling the measurement of 72 variables. A second sample of 65 SZ patients and 70 controls was then combined with the first sample, to generate a database where each subject was represented by 28 morphological variables. Four different machine learning (ML) algorithms were used for age prediction based on z-standardized OCT data. The associations between RAG, demographic, and clinical data were also analyzed. Study Results: Patients from both samples had significantly higher retinal age and positive RAG ranging between 5.88 and 7.44 years depending on the specific sample. Predictions based on the larger group but with fewer OCT variables exhibited higher prediction relative error. All ML algorithms generated similar outcomes regarding retinal age. RAG correlated with the dose of antipsychotic medication and the severity of symptoms. Correlations with chronological age showed that RAG was the highest in younger patients, and from the age of about 45 years, it decreased. Conclusions: ML-based results corroborated accelerated retinal aging in schizophrenia and showed its associations with pharmacological treatment and syndrome severity. The finding of a larger RAG in younger patients is novel and requires replication.

Schizophrenia is a serious mental disorder with a complex neurobiological background and a well-defined psychopathological picture. Despite many efforts, a definitive disease biomarker has still not been identified. One of the promising candidates for a disease-related biomarker could involve retinal morphology , given that the retina is a part of the central nervous system that is known to be affected in schizophrenia and related to multiple illness features. In this study Optical Coherence Tomography (OCT) data is applied to assess the different layers of the retina. OCT data were applied in the process of automatic differentiation of schizophrenic patients from healthy controls. Numerical experiments involved applying several individual 1D Convolutional Neural Network-based models as well as further using the aggregation of classification results to improve the initial classification results. The main goal of the study was to check how methods based on the aggregation of classification results work in classifying neuroanatomical features of schizophrenia. Among over 300, 000 different variants of tested aggregation operators, a few versions provided satisfactory results.