Detection of quadratic phase coupling by cross-bicoherence and spectral Granger causality in bifrequencies interactions.

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.

On the semantics of ecoacoustic codes.

Ecological codes have been defined as every biological code integrated by factors originated by the environmental context that participates in the codepoiesis process. Ecological codes create a strict relationship between the inner world of organsims and the external relational world, and represent the mechanism with which the vivo-scape is realized. Acoustic codes are used in nature to decode acoustic signals between individuals of the same or different species and belong to the category of biological codes. Ecoacoustic codes are the outcome of the evolution of acoustic codes, and results as the interplay between acoustic codes and environmental factors. Soundtope codes represent the results of emerging properties of the acoustic communities.

Electrophysiological Markers of Fairness and Selfishness Revealed by a Combination of Dictator and Ultimatum Games.

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.

Operant conditioning deficits and modified local field potential activities in parvalbumin-deficient mice.

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.

Attention Networks in ADHD Adults after Working Memory Training with a Dual n-Back Task.

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.

Early Attentional Modulation by Working Memory Training in Young Adult ADHD Patients during a Risky Decision-Making Task.

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.

Complex temporal patterns processing by a neural mass model of a cortical column.

It is well known that neuronal networks are capable of transmitting complex spatiotemporal information in the form of precise sequences of neuronal discharges characterized by recurrent patterns. At the same time, the synchronized activity of large ensembles produces local field potentials that propagate through highly dynamic oscillatory waves, such that, at the whole brain scale, complex spatiotemporal dynamics of electroencephalographic (EEG) signals may be associated to sensorimotor decision making processes. Despite these experimental evidences, the link between highly temporally organized input patterns and EEG waves has not been studied in detail. Here, we use a neural mass model to investigate to what extent precise temporal information, carried by deterministic nonlinear attractor mappings, is filtered and transformed into fluctuations in phase, frequency and amplitude of oscillatory brain activity. The phase shift that we observe, when we drive the neural mass model with specific chaotic inputs, shows that the local field potential amplitude peak appears in less than one full cycle, thus allowing traveling waves to encode temporal information. After converting phase and amplitude changes obtained into point processes, we quantify input-output similarity following a threshold-filtering algorithm onto the amplitude wave peaks. Our analysis shows that the neural mass model has the capacity for gating the input signal and propagate selected temporal features of that signal. Finally, we discuss the effect of local excitatory/inhibitory balance on these results and how excitability in cortical columns, controlled by neuromodulatory innervation of the cerebral cortex, may contribute to set a fine tuning and gating of the information fed to the cortex.

Response Adaptation in Barrel Cortical Neurons Facilitates Stimulus Detection during Rhythmic Whisker Stimulation in Anesthetized Mice.

Rodents use rhythmic whisker movements at frequencies between 4 and 12 Hz to sense the environment that will be disturbed when the animal touches an object. The aim of this work is to study the response adaptation to rhythmic whisker stimulation trains at 4 Hz in the barrel cortex and the sensitivity of cortical neurons to changes in the timing of the stimulation pattern. Longitudinal arrays of four iridium oxide electrodes were used to obtain single-unit recordings in supragranular, granular, and infragranular neurons in urethane anesthetized mice. The stimulation protocol consisted in a stimulation train of three air puffs (20 ms duration each) in which the time interval between the first and the third stimuli was fixed (500 ms) and the time interval between the first and the second stimuli changed (regular: 250 ms; "accelerando": 375 ms; or "decelerando" stimulation train: 125 ms interval). Cortical neurons adapted strongly their response to regular stimulation trains. Response adaptation was reduced when accelerando or decelerando stimulation trains were applied. This facilitation of the shifted stimulus was mediated by activation of NMDA receptors because the effect was blocked by AP5. The facilitation was not observed in thalamic nuclei. Facilitation increased during periods of EEG activation induced by systemic application of IGF-I, probably by activation of NMDA receptors, as well. We suggest that response adaptation is the outcome of an intrinsic cortical information processing aimed at contributing to improve the detection of "unexpected" stimuli that disturbed the rhythmic behavior of exploration.

Attractor dynamics of a Boolean model of a brain circuit controlled by multiple parameters.

Studies of Boolean recurrent neural networks are briefly introduced with an emphasis on the attractor dynamics determined by the sequence of distinct attractors observed in the limit cycles. We apply this framework to a simplified model of the basal ganglia-thalamocortical circuit where each brain area is represented by a "neuronal" node in a directed graph. Control parameters ranging from neuronal excitability that affects all cells to targeted local connections modified by a new adaptive plasticity rule, and the regulation of the interactive feedback affecting the external input stream of information, allow the network dynamics to switch between stable domains delimited by highly discontinuous boundaries and reach very high levels of complexity with specific configurations. The significance of this approach with regard to brain circuit studies is briefly discussed.

Event-Related Potentials during a Gambling Task in Young Adults with Attention-Deficit/Hyperactivity Disorder.

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 topology of the directed clique complex as a network invariant.

We introduce new algebro-topological invariants of directed networks, based on the topological construction of the directed clique complex. The shape of the underlying directed graph is encoded in a way that can be studied mathematically to obtain network invariants such as the Euler characteristic and the Betti numbers. Two different cases illustrate the application of the Euler characteristic. We investigate how the evolution of a Boolean recurrent artificial neural network is influenced by its topology in a dynamics involving pruning and strengthening of the connections, and to show that the topological features of the directed clique complex influence the dynamical evolution of the network. The second application considers the directed clique complex in a broader framework, to define an invariant of directed networks, the network degree invariant, which is constructed by computing the topological invariant on a sequence of sub-networks filtered by the minimum in- or out-degree of the nodes. The application of the Euler characteristic presented here can be extended to any directed network and provides a new method for the assessment of specific functional features associated with the network topology.

Synchronization-based computation through networks of coupled oscillators.

The mesoscopic activity of the brain is strongly dynamical, while at the same time exhibits remarkable computational capabilities. In order to examine how these two features coexist, here we show that the patterns of synchronized oscillations displayed by networks of neural mass models, representing cortical columns, can be used as substrates for Boolean-like computations. Our results reveal that the same neural mass network may process different combinations of dynamical inputs as different logical operations or combinations of them. This dynamical feature of the network allows it to process complex inputs in a very sophisticated manner. The results are reproduced experimentally with electronic circuits of coupled Chua oscillators, showing the robustness of this kind of computation to the intrinsic noise and parameter mismatch of the coupled oscillators. We also show that the information-processing capabilities of coupled oscillations go beyond the simple juxtaposition of logic gates.

Mesoscopic segregation of excitation and inhibition in a brain network model.

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.

An attractor-based complexity measurement for Boolean recurrent neural 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.

Cooperative behavior in a jump diffusion model for a simple network of spiking neurons.

The distribution of time intervals between successive spikes generated by a neuronal cell --the interspike intervals (ISI)-- may reveal interesting features of the underlying dynamics. In this study we analyze the ISI sequence --the spike train-- generated by a simple network of neurons whose output activity is modeled by a jump-diffusion process. We prove that, when specific ranges of the involved parameters are chosen, it is possible to observe multimodal ISI distributions which reveal that the modeled network fires with more than one single preferred time interval. Furthermore, the system exhibits resonance behavior, with modulation of the spike timings by the noise intensity. We also show that inhibition helps the signal transmission between the units of the simple network.

Visual thalamocortical circuits in parvalbumin-deficient mice.

The dorsal lateral geniculate nucleus (dLGN) is considered as the visual gateway to the visual cortex (VC) and sends collaterals to the thalamic reticular nucleus (RTN) that in turn receives collaterals of the corticofugal feedback projections. At all levels of this thalamocortical circuit there are GABAergic neurons expressing the calcium-buffer parvalbumin (PV). The present study reports for the first time the analysis of in vivo extracellular electrophysiological recordings performed simultaneously in dLGN, RTN and VC of anesthetized wild-type (WT) and parvalbumin-deficient (PVKO) mice. The firing rates of VC and RTN cells were increased in PVKO during spontaneous activity as well as in the presence of a photic stimulation (strobe flash at 2.5Hz). Interestingly, dLGN cells in PVKO did not show significant changes in the rate of firing in comparison to WT. dLGN responses to the light flashes were characterized by ripples of inhibition and phasic excitation/rebound. We have analyzed the pattern of functional interactions between pairs of neighboring cells in VC, dLGN and RTN and across these areas in simultaneously recorded thalamocortical triplets, with one neuron from each area. We found that in PVKO the strength of the interactions tended to decrease locally, between neighboring cells, but tended to increase across the areas. The combination of these analyses provides new evidence on the important role played by PV-expression in regulating information processing in the central visual pathway suggesting that the ability to process information along parallel channels is decreased in the thalamocortical pathway of PV-deficient mice. This article is part of a Special Issue entitled Neural Coding 2012.

The calcium-binding protein parvalbumin modulates the firing 1 properties of the reticular thalamic nucleus bursting neurons.

The reticular thalamic nucleus (RTN) of the mouse is characterized by an overwhelming majority of GABAergic neurons receiving afferences from both the thalamus and the cerebral cortex and sending projections mainly on thalamocortical neurons. The RTN neurons express high levels of the "slow Ca(2+) buffer" parvalbumin (PV) and are characterized by low-threshold Ca(2+) currents, I(T). We performed extracellular recordings in ketamine/xylazine anesthetized mice in the rostromedial portion of the RTN. In the RTN of wild-type and PV knockout (PVKO) mice we distinguished four types of neurons characterized on the basis of their firing pattern: irregular firing (type I), medium bursting (type II), long bursting (type III), and tonically firing (type IV). Compared with wild-type mice, we observed in the PVKOs the medium bursting (type II) more frequently than the long bursting type and longer interspike intervals within the burst without affecting the number of spikes. This suggests that PV may affect the firing properties of RTN neurons via a mechanism associated with the kinetics of burst discharges. Ca(v)3.2 channels, which mediate the I(T) currents, were more localized to the somatic plasma membrane of RTN neurons in PVKO mice, whereas Ca(v)3.3 expression was similar in both genotypes. The immunoelectron microscopy analysis showed that Ca(v)3.2 channels were localized at active axosomatic synapses, thus suggesting that the differential localization of Ca(v)3.2 in the PVKOs may affect bursting dynamics. Cross-correlation analysis of simultaneously recorded neurons from the same electrode tip showed that about one-third of the cell pairs tended to fire synchronously in both genotypes, independent of PV expression. In summary, PV deficiency does not affect the functional connectivity between RTN neurons but affects the distribution of Ca(v)3.2 channels and the dynamics of burst discharges of RTN cells, which in turn regulate the activity in the thalamocortical circuit.

Dopamine deficiency increases synchronized activity in the rat subthalamic nucleus.

Abnormal neuronal activity in the subthalamic nucleus (STN) plays a crucial role in the pathophysiology of Parkinson's disease (PD). In this study we investigated changes in rat STN neuronal activity after 28days following the injection of 6-OHDA in the substantia nigra pars compacta (SNc). This drug provoked a lesion of SNc that induced a dopamine (DA) depletion assessed by changes in rotating capacity in response to apomorphine injection and by histological analysis. By means of extracellular recordings and waveshape spike sorting it was possible to analyze simultaneous spike trains and compute the crosscorrelations. Based on the analysis of the autocorrelograms we classified four types of firing patterns: regular (Poissonian-like), oscillatory (in the range 4-12Hz), bursty and cells characterized by a long refractoriness. The distribution of unit types in the control (n=61) and lesioned (n=83) groups was similar, as well as the firing rate. In 6-OHDA treated rats we observed a significant increase (from 26% to 48%) in the number of pairs with synchronous firing. These data suggest that the synchronous activity of STN cells, provoked by loss of DA cells in SNc, is likely to be among the most significant dysfunctions in the basal ganglia of Parkinsonian patients. We raise the hypothesis that in normal conditions, DA maintains a balance between funneling information via the hyperdirect cortico-subthalamic pathway and parallel processing through the parallel cortico-basal ganglia-subthalamic pathways, both of which are necessary for selected motor behaviors. This article is part of a Special Issue entitled 'Neural Coding'.

Integration and transmission of distributed deterministic neural activity in feed-forward networks.

A ten layer feed-forward network characterized by diverging/converging patterns of projection between successive layers of regular spiking (RS) neurons is activated by an external spatiotemporal input pattern fed to Layer 1 in presence of stochastic background activities fed to all layers. We used three dynamical systems to derive the external input spike trains including the temporal information, and three types of neuron models for the network, i.e. either a network formed either by neurons modeled by exponential integrate-and-fire dynamics (RS-EIF, Fourcaud-Trocmé et al., 2003), or by simple spiking neurons (RS-IZH, Izhikevich, 2004) or by multiple-timescale adaptive threshold neurons (RS-MAT, Kobayashi et al., 2009), given five intensities for the background activity. The assessment of the temporal structure embedded in the output spike trains was carried out by detecting the preferred firing sequences for the reconstruction of de-noised spike trains (Asai and Villa, 2008). We confirmed that the RS-MAT model is likely to be more efficient in integrating and transmitting the temporal structure embedded in the external input. We observed that this structure could be propagated not only up to the 10th layer but in some cases it was retained better beyond the 4th downstream layers. This study suggests that diverging/converging network structures, by the propagation of synfire activity, could play a key role in the transmission of complex temporal patterns of discharges associated to deterministic nonlinear activity. This article is part of a Special Issue entitled Neural Coding.