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Abstract Quadratic Phase Coupling (QPC) serves as an essential statistical instrument for evaluating nonlinear synchronization within multivariate time series data, especially in signal processing and neuroscience fields. This study explores the precision of QPC detection using numerical estimates derived from cross-bicoherence and bivariate Granger causality within a straightforward, yet noisy, instantaneous multiplier model. It further assesses the impact of accidental statistically significant bifrequency interactions, introducing new metrics such as the ratio of bispectral quadratic phase coupling and the ratio of bivariate Granger causality quadratic phase coupling. Ratios nearing 1 signify a high degree of accuracy in detecting QPC. The coupling strength between interacting channels is identified as a key element that introduces nonlinearities, influencing the signal-to-noise ratio in the output channel. The model is tested across 59 experimental conditions of simulated recordings, with each condition evaluated against six coupling strength values, covering a wide range of carrier frequencies to examine a broad spectrum of scenarios. The findings demonstrate that the bispectral method outperforms bivariate Granger causality, particularly in identifying specific QPC under conditions of very weak couplings and in the presence of noise. The detection of specific QPC is crucial for neuroscience applications aimed at better understanding the temporal and spatial coordination between different brain regions.

Background: In the oddball paradigm, the dorsolateral prefrontal cortex (DLPFC) is often associated with active cognitive responses, such as maintaining information in working memory or adapting response strategies. While some evidence points to the DLPFC’s role in passive auditory deviance perception, a detailed understanding of the spatiotemporal neurodynamics involved remains unclear. Methods: In this study, event-related optical signals (EROS) and event-related potentials (ERPs) were simultaneously recorded for the first time over the prefrontal cortex using a 64-channel electroencephalography (EEG) system, during passive auditory deviance perception in 12 right-handed young adults (7 women and 5 men). In this oddball paradigm, deviant stimuli (a 1500 Hz pure tone) elicited a negative shift in the N1 ERP component, related to mismatch negativity (MMN), and a significant positive deflection associated with the P300, compared to standard stimuli (a 1000 Hz tone). Results: We hypothesize that the DLPFC not only participates in active tasks but also plays a critical role in processing deviant stimuli in passive conditions, shifting from pre-attentive to attentive processing. We detected enhanced neural activity in the left middle frontal gyrus (MFG), at the same timing of the MMN component, followed by later activation at the timing of the P3a ERP component in the right MFG. Conclusions: Understanding these dynamics will provide deeper insights into the DLPFC’s role in evaluating the novelty or unexpectedness of the deviant stimulus, updating its cognitive value, and adjusting future predictions accordingly. However, the small number of subjects could limit the generalizability of the observations, in particular with respect to the effect of handedness, and additional studies with larger and more diverse samples are necessary to validate our conclusions.

Abstract Altered functioning of GABAergic interneurons expressing parvalbumin (PV) in the basal ganglia-thalamo-cortical circuit are likely to be involved in several human psychiatric disorders characterized by deficits in attention and sensory gating with dysfunctional decision-making behavior. However, the contribution of these interneurons in the ability to acquire demanding learning tasks remains unclear. Here, we combine an operant conditioning task with local field potentials simultaneously recorded in several nuclei involved in reward circuits of wild-type (WT) and PV-deficient (PVKO) mice, which are characterized by changes in firing activity of PV-expressing interneurons. In comparison with WT mice, PVKO animals presented significant deficits in the acquisition of the selected learning task. Recordings from prefrontal cortex, nucleus accumbens (NAc) and hippocampus showed significant decreases of the spectral power in beta and gamma bands in PVKO compared with WT mice particularly during the performance of the operant conditioning task. From the first to the last session, at all frequency bands the spectral power in NAc tended to increase in WT and to decrease in PVKO. Results indicate that PV deficiency impairs signaling necessary for instrumental learning and the recognition of natural rewards.

Individual behavior during financial decision making is motivated by fairness, but an unanswered question from previous studies is whether particular patterns of brain activity correspond to different profiles of fairness. Event Related Potentials (ERPs) were recorded from 39 participants who played the role of allocators in a Dictator Game (DG) and responders in an Ultimatum Game (UG). Two very homogeneous groups were formed by fair and selfish individuals. At fronto-central cortical sites, the latency of ERP early negativity (N1) was 10 ms shorter in selfish participants than in fair participants. In fair DG players, the subsequent positive wave P2 suggested that more cognitive resources were required when they allocated the least gains to the other party. P2 latency and amplitude in the selfish group supported the hypothesis that these participants tended to maximize their profit. During UG, we observed that medial frontal negativity (MFN) occurred earlier and with greater amplitude when selfish participants rejected less favorable endowment shares. In this case, all players received zero payoffs, which showed that MFN in selfish participants was associated with a spiteful punishment. At posterior-parietal sites, we found that the greater the selfishness, the greater the amplitude of the late positive component (LPC). Our results bring new evidence to the existence of specific somatic markers associated with the activation of distinct cerebral circuits by the evaluation of fair and unfair proposals in participants characterized by different expressions of perceived fairness, thus suggesting that a particular brain dynamics could be associated with moral decisions.

Patients affected by Attention-Deficit/Hyperactivity Disorder (ADHD) are characterized by impaired executive functioning and/or attention deficits. Our study aim is to determine whether the outcomes measured by the Attention Network Task (ANT), i.e., the reaction times (RTs) to specific target and cue conditions and alerting, orienting, and conflict (or executive control) effects are affected by cognitive training with a Dual n-back task. We considered three groups of young adult participants: ADHD patients without medication (ADHD), ADHD with medication (MADHD), and age/education-matched controls. Working memory training consisted of a daily practice of 20 blocks of Dual n-back task (approximately 30 min per day) for 20 days within one month. Participants of each group were randomly assigned into two subgroups, the first one with an adaptive mode of difficulty (adaptive training), while the second was blocked at the level 1 during the whole training phase (1-back task, baseline training). Alerting and orienting effects were not modified by working memory training. The dimensional analysis showed that after baseline training, the lesser the severity of the hyperactive-impulsive symptoms, the larger the improvement of reaction times on trials with high executive control/conflict demand (i.e., what is called Conflict Effect), irrespective of the participants’ group. In the categorical analysis, we observed the improvement in such Conflict Effect after the adaptive training in adult ADHD patients irrespective of their medication, but not in controls. The ex-Gaussian analysis of RT and RT variability showed that the improvement in the Conflict Effect correlated with a decrease in the proportion of extreme slow responses. The Dual n-back task in the adaptive mode offers as a promising candidate for a cognitive remediation of adult ADHD patients without pharmaceutical medication.

Background: Working memory (WM) deficits and impaired decision making are among the characteristic symptoms of patients affected by attention deficit/hyperactivity disorder (ADHD). The inattention associated with the disorder is likely to be due to functional deficits of the neural networks inhibiting irrelevant sensory input. In the presence of unnecessary information, a good decisional process is impaired and ADHD patients tend to take risky decisions. This study is aimed to test the hypothesis that the level of difficulty of a WM training (WMT) is affecting the top-down modulation of the attentional processes in a probabilistic gambling task. Methods: Event-related potentials (ERP) triggered by the choice of the amount wagered in the gambling task were recorded, before and after WMT with a the dual n-back task, in young ADHD adults and matched controls. For each group of participants, randomly assigned individuals were requested to perform WMT with a fixed baseline level of difficulty. The remaining participants were trained with a performance-dependent adaptive n-level of difficulty. Results: We compared the ERP recordings before and after 20 days of WMT in each subgroup. The analysis was focused on the time windows with at least three recording sites showing differences before and after training, after Bonferroni correction ( p < 0.05 ). In ADHD, the P1 wave component was selectively affected at frontal sites and its shape was recovered close to controls’ only after adaptive training. In controls, the strongest contrast was observed at parietal level with a left hemispheric dominance at latencies near 900 ms, more after baseline than after adaptive training. Conclusion: Partial restoration of early selective attentional processes in ADHD patients might occur after WMT with a high cognitive load. Modified frontal sites’ activities might constitute a neural marker of this effect in a gambling task. In controls, conversely, an increase in late parietal negativity might rather be a marker of an increase in transfer effects to fluid intelligence.

Attention-deficit hyperactivity disorder (ADHD) is characterized by deficits in executive functions and decision making during childhood and adolescence. Contradictory results exist whether altered event-related potentials (ERPs) in adults are associated with the tendency of ADHD patients toward risky behavior. Clinically diagnosed ADHD patients (n = 18) and healthy controls (n = 18), aged between 18 and 29 (median 22 Yo), were screened with the Conners' Adult ADHD Rating Scales and assessed by the Mini-International Neuropsychiatric Interview, adult ADHD Self-Report Scale, and by the 60-item HEXACO Personality Inventory. The characteristic personality traits of ADHD patients were the high level of impulsiveness associated with lower values of agreeableness. All participants performed a probability gambling task (PGT) with two frequencies of the feedback information of the outcome. For each trial, ERPs were triggered by the self-paced trial onset and by the gamble selection. After trial onset, N2-P3a ERP component associated with the attentional load peaked earlier in the ADHD group than in controls. An N500 component related to the feedback frequency condition after trial onset and an N400-like component after gamble selection suggest a large affective stake of the decision making and an emphasized post-decisional evaluation of the choice made by the ADHD participants. By combining ERPs, related to the emotions associated with the feedback frequency condition, and behavioral analyses during completion of PGT, this study provides new findings on the neural dynamics that differentiate controls and young ADHD adults. In the patients' group, we raise the hypothesis that the activity of frontocentral and centroparietal neural circuits drive the decision-making processes dictated by an impaired cognitive workload followed by the build-up of large emotional feelings generated by the conflict toward the outcome of the gambling choice. Our results can be used for new investigations aimed at studying the fine spatiotemporal distribution of cortical activity, and the neural circuits that underly the generation of that activity, associated with the behavioral deficits characteristic of ADHD.

The mesoscopic activity of the brain is strongly dynamical, while at the sametime exhibiting remarkable computational capabilities. In order to examinehow these two features coexist, here we show that the patterns of synchronizedoscillations displayed by networks of neural mass models, representing cortical columns, can be usedas substrates for Boolean computation. Our results reveal that different logicaloperations can be implemented by the same neural mass network at different timesfollowing the dynamics of the input. The results are reproduced experimentallywith electronic circuits of coupled Chua oscillators, showing the robustness of this kind of computation to the intrinsic noise and parameter mismatch of the oscillators responsible for the functioning of the gates. We also show that theinformation-processing capabilities of coupled oscillations go beyond thesimple juxtaposition of logic gates.

Neurons in the brain are known to operate under a careful balance of excitation and inhibition, which maintains neural microcircuits within the proper operational range. How this balance is played out at the mesoscopic level of neuronal populations is, however, less clear. In order to address this issue, here we use a coupled neural mass model to study computationally the dynamics of a network of cortical macrocolumns operating in a partially synchronized, irregular regime. The topology of the network is heterogeneous, with a few of the nodes acting as connector hubs while the rest are relatively poorly connected. Our results show that in this type of mesoscopic network excitation and inhibition spontaneously segregate, with some columns acting mainly in an excitatory manner while some others have predominantly an inhibitory effect on their neighbors. We characterize the conditions under which this segregation arises, and relate the character of the different columns with their topological role within the network. In particular, we show that the connector hubs are preferentially inhibitory, the more so the larger the node's connectivity. These results suggest a potential mesoscale organization of the excitation-inhibition balance in brain networks.

We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of ω-automata, and then translating the most refined classification of ω-automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits.