. This paper presents a novel rodent avoidance test. We have devel-oped a specialized device and procedures that expand the possibilities for exploration of the processes of learning and memory in a psychophysiological experiment. The device consists of a current stimulating electrode-platform and custom software that allows to control and record real-time experimental pro-tocols as well as reconstructs animal movement paths. The device can be used to carry out typical footshock-avoidance tests, such as passive, active, modified active and pedal-press avoidance tasks. It can also be utilized in the studies of prosocial behavior, including cooperation, competition, emotional contagion and empathy. This novel footshock-avoidance test procedure allows flexible current-stimulating settings. In our work, we have used slow-rising current. A test animal can choose between the current rise and time-out intervals as a signal for action in footshock avoidable tasks. This represents a choice between escape and avoid-ance. This method can be used to explore individual differences in decision-making and choice of avoidance strategies. It has been shown previously that a behavioral act, for example, pedal-pressing is ensured by motivation-dependent brain activity (avoidance or approach). We have created an experimental design based on tasks of instrumental learning: pedal-pressing in an operant box results in a reward, which is either a piece of food in a feeder (food-acquisition behavior) or an escape-platform (footshock-avoidance behavior). Data recording and analysis were performed using custom software, the open source Accord.NET Framework was used for real-time object detection and tracking.
Variability of neural activity is regarded as a crucial feature of healthy brain function, and several neuroimaging approaches have been employed to assess it noninvasively. Studies on the variability of both evoked brain response and spontaneous brain signals have shown remarkable changes with aging but it is unclear if the different measures of brain signal variability – identified with either hemodynamic or electrophysiological methods – reflect the same underlying physiology. In this study, we aimed to explore age differences of spontaneous brain signal variability with two different imaging modalities (EEG, fMRI) in healthy younger (25 ± 3 years, N = 135) and older (67 ± 4 years, N = 54) adults. Consistent with the previous studies, we found lower blood oxygenation level dependent (BOLD) variability in the older subjects as well as less signal variability in the amplitude of low-frequency oscillations (1–12 Hz), measured in source space. These age-related reductions were mostly observed in the areas that overlap with the default mode network. Moreover, age-related increases of variability in the amplitude of beta-band frequency EEG oscillations (15–25 Hz) were seen predominantly in temporal brain regions. There were significant sex differences in EEG signal variability in various brain regions while no significant sex differences were observed in BOLD signal variability. Bivariate and multivariate correlation analyses revealed no significant associations between EEG- and fMRI-based variability measures. In summary, we show that both BOLD and EEG signal variability reflect aging-related processes but are likely to be dominated by different physiological origins, which relate differentially to age and sex.
Stochastic Resonance (SR) is a well-known noise-induced phenomenon widely reported in dynamical systems with a threshold, while Inverse Stochastic Resonance (ISR) is an opposing phenomenon observed in the dynamical systems which exhibit bistability between a stable node and a stable limit cycle. This study shows a co-occurrence of SR and ISR, in a minimal circuit of synaptically coupled spiking neurons that is designed to show bistability between quiescence and a persistent firing mode. We identify noise, synaptic and intrinsic parameters ranges that allow for ISR. The minimal computational model, is investigated for a range of parameters, and our simulations indicate that the main features of SR, are the direct results of dynamical properties which lead to ISR.
Neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream. Here, neurofeedback researchers present a consensus-derived checklist that aims to improve the reporting and experimental design standards in the field.
While much is known about motor control during simple movements, corticomuscular communication profiles during compound movement control remain largely unexplored. Here, we aimed at examining frequency band related interactions between brain and muscles during different movement periods of a bipedal squat (BpS) task utilizing regression corticomuscular coherence (rCMC), as well as partial directed coherence (PDC) analyses. Participants performed 40 squats, divided into three successive movement periods (Eccentric (ECC), Isometric (ISO) and Concentric (CON)) in a standardized manner. EEG was recorded from 32 channels specifically-tailored to cover bilateral sensorimotor areas while bilateral EMG was recorded from four main muscles of BpS. We found both significant CMC and PDC (in beta and gamma bands) during BpS execution, where CMC was significantly elevated during ECC and CON when compared to ISO. Further, the dominant direction of information flow (DIF) was most prominent in EEG-EMG direction for CON and EMG-EEG direction for ECC. Collectively, we provide novel evidence that motor control during BpS is potentially achieved through central motor commands driven by a combination of directed inputs spanning across multiple frequency bands. These results serve as an important step toward a better understanding of brain-muscle relationships during multi joint compound movements.
Abstract reasoning is associated with the ability to detect relations among objects, ideas, events. It underlies the understanding of other individuals’ thoughts and intentions. In natural settings, individuals have to infer relevant associations that have proven to be reliable or precise predictors. Salience theory suggests that the attribution of meaning to stimulus depends on their contingency, saliency, and relevance to adaptation. So far, subjective estimates of relevance have mostly been explored in motivation and implicit learning. Mechanisms underlying formation of associations in abstract thinking with regard to their subjective relevance, or salience, are not clear. Applying novel computational methods, we investigated relevance detection in categorization tasks in 17 healthy individuals. Two models of relevance detection were developed: a conventional one with nouns from the same semantic category, an aberrant one based on an insignificant common feature. Control condition introduced non-related words. The participants were to detect either a relevant principle or an insignificant feature to group presented words. In control condition they inferred that the stimuli were irrelevant to any grouping idea. Cross-frequency phase coupling analysis revealed statistically distinct patterns of synchronization representing search and decision in the models of normal and aberrant relevance detection. Significantly distinct frontotemporal functional networks with central and parietal components in the theta and alpha frequency bands may reflect differences in relevance detection.
Objective. The rapidly developing paradigm of closed-loop neuroscience has extensively employed brain rhythms as the signal forming real-time neurofeedback, triggering brain stimulation, or governing stimulus selection. However, the efficacy of brain rhythm contingent paradigms suffers from significant delays related to the process of extraction of oscillatory parameters from broad-band neural signals with conventional methods. To this end, real-time algorithms are needed that would shorten the delay while maintaining an acceptable speed-accuracy trade-off. Approach. Here we evaluated a family of techniques based on the application of the least-squares complex-valued filter (LSCF) design to real-time quantification of brain rhythms. These techniques allow for explicit optimization of the speed-accuracy trade-off when quantifying oscillatory patterns. We used EEG data collected from 10 human participants to systematically compare LSCF approach to the other commonly used algorithms. Each method being evaluated was optimized by scanning through the grid of its hyperparameters using independent data samples. Main results. When applied to the task of estimating oscillatory envelope and phase, the LSCF techniques outperformed in speed and accuracy both conventional Fourier transform and rectification based methods as well as more advanced techniques such as those that exploit autoregressive extrapolation of narrow-band filtered signals. When operating at zero latency, the weighted LSCF approach yielded 75\% accuracy when detecting alpha-activity episodes, as defined by the amplitude crossing of the 95th-percentile threshold. Significance. The LSCF approaches are easily applicable to low-delay quantification of brain rhythms. As such, these methods are useful in a variety of neurofeedback, brain-computer-interface and other experimental paradigms that require rapid monitoring of brain rhythms.
The addictive component of tobacco, nicotine, acts via nicotinic acetylcholine receptors (nAChRs). The β2 subunit-containing nAChRs (β2-nAChRs) play a crucial role in the rewarding properties of nicotine and are particularly densely expressed in the mesolimbic dopamine (DA) system. Specifically, nAChRs directly and indirectly affect DA neurons in the ventral tegmental area (VTA). The understanding of ACh and nicotinic regulation of DA neuron activity is incomplete. By computational modeling, we provide mechanisms for several apparently contradictory experimental results. First, systemic knockout of β2-containing nAChRs drastically reduces DA neurons bursting, although the major glutamatergic (Glu) afferents that have been shown to evoke this bursting stay intact. Second, the most intuitive way to rescue this bursting—by re-expressing the nAChRs on VTA DA neurons—fails. Third, nAChR re-expression on VTA GABA neurons rescues bursting in DA neurons and increases their firing rate under the influence of ACh input, whereas nicotinic application results in the opposite changes in firing. Our model shows that, first, without ACh receptors, Glu excitation of VTA DA and GABA neurons remains balanced and GABA inhibition cancels the direct excitation. Second, re-expression of ACh receptors on DA neurons provides an input that impedes membrane repolarization and is ineffective in restoring firing of DA neurons. Third, the distinct responses to ACh and nicotine occur because of distinct temporal patterns of these inputs: pulsatile versus continuous. Altogether, this study highlights how β2-nAChRs influence coactivation of the VTA DA and GABA neurons required for motivation and saliency signals carried by DA neuron activity.
This study explored the effect of the perceived social content of affective pictures on the subjective evaluation of affective valence and arousal. For this purpose, we established three categories of social content (pictures without people, with one person and with two or more people). A sample of 161 subjects rated 200 pictures varying in affective valence (unpleasant, neutral, and pleasant), arousal and social content. Results of two-factor analysis of variance, F(4, 157) = 71.7, p < .001, ηp2 = .31, showed that perceived social content influenced the ratings of affective valence, specially for unpleasant pictures, with the greatest social content (two or more people) leading subjects to rate unpleasant pictures with the lowest ratings (all pairwise comparisons’ p < .001). Regarding arousal, F(4, 157) = 64.0, p < .001, ηp2 = .29), the higher the social content, the higher the arousal ratings, but only for pleasant (all pairwise comparisons’ p < .007) and unpleasant (all pairwise comparisons’ p < .001) pictures. Overall, this study demonstrated an effect of the perceived social content on the subjective evaluation of affective valence and arousal of emotional stimuli.
Even though humans are mostly not aware of their heartbeats, several heartbeat-related effects have been reported to influence conscious perception. It is not clear whether these effects are distinct or related phenomena, or whether they are early sensory effects or late decisional processes. Combining electroencephalography and electrocardiography, along with signal detection theory analyses, we identify two distinct heartbeat-related influences on conscious perception differentially related to early vs. late somatosensory processing. First, an effect on early sensory processing was found for the heartbeat-evoked potential (HEP), a marker of cardiac interoception. The amplitude of the prestimulus HEP negatively correlated with localization and detection of somatosensory stimuli, reflecting a more conservative detection bias (criterion). Importantly, higher HEP amplitudes were followed by decreases in early (P50) as well as late (N140, P300) somatosensory-evoked potential (SEP) amplitudes. Second, stimulus timing along the cardiac cycle also affected perception. During systole, stimuli were detected and correctly localized less frequently, relating to a shift in perceptual sensitivity. This perceptual attenuation was accompanied by the suppression of only late SEP components (P300) and was stronger for individuals with a more stable heart rate. Both heart-related effects were independent of alpha oscillations’ influence on somatosensory processing. We explain cardiac cycle timing effects in a predictive coding account and suggest that HEP-related effects might reflect spontaneous shifts between interoception and exteroception or modulations of general attentional resources. Thus, our results provide a general conceptual framework to explain how internal signals can be integrated into our conscious perception of the world.
Much of our behaviour is driven by two motivational dimensions—approach and avoidance. These have been related to frontal hemispheric asymmetries in clinical and resting‐state EEG studies: Approach was linked to higher activity of the left relative to the right hemisphere, while avoidance was related to the opposite pattern. Increased approach behaviour, specifically towards unhealthy foods, is also observed in obesity and has been linked to asymmetry in the framework of the right‐brain hypothesis of obesity. Here, we aimed to replicate previous EEG findings of hemispheric asymmetries for self‐reported approach/avoidance behaviour and to relate them to eating behaviour. Further, we assessed whether resting fMRI hemispheric asymmetries can be detected and whether they are related to approach/avoidance, eating behaviour and BMI. We analysed three samples: Sample 1 (n = 117) containing EEG and fMRI data from lean participants, and Samples 2 (n = 89) and 3 (n = 152) containing fMRI data from lean, overweight and obese participants. In Sample 1, approach behaviour in women was related to EEG, but not to fMRI hemispheric asymmetries. In Sample 2, approach/avoidance behaviours were related to fMRI hemispheric asymmetries. Finally, hemispheric asymmetries were not related to either BMI or eating behaviour in any of the samples. Our study partly replicates previous EEG findings regarding hemispheric asymmetries and indicates that this relationship could also be captured using fMRI. Our findings suggest that eating behaviour and obesity are likely to be mediated by mechanisms not directly relating to frontal asymmetries in neuronal activation quantified with EEG and fMRI.
Muscarinic acetylcholine receptors (mAChRs) are critically involved in hippocampal theta generation, but much less is known about the role of nicotinic AChRs (nAChRs). Here we provide evidence that α7 nAChRs expressed on interneurons, particularly those in oriens lacunosum moleculare (OLM), also regulate hippocampal theta generation. Local hippocampal infusion of a selective α7 nAChR antagonist significantly reduces hippocampal theta power and impairs Y-maze spontaneous alternation performance in freely moving mice. By knocking out receptors in different neuronal subpopulations, we find that α7 nAChRs expressed in OLM interneurons regulate theta generation. Our in vitro slice studies indicate that α7 nAChR activation increases OLM neuron activity that, in turn, enhances pyramidal cell excitatory postsynaptic currents (EPSCs). Our study also suggests that mAChR activation promotes transient theta generation, while α7 nAChR activation facilitates future theta generation by similar stimulations, revealing a complex mechanism whereby cholinergic signaling modulates different aspects of hippocampal theta oscillations through different receptor subtypes.
The aim of our work was to study the influence of the different brain rhythms (i.e. theta, beta, gamma ranges with frequencies from 5 Hz to 80 Hz) on the ultra slow oscillations (USOs with frequency of 0.5 Hz and below), where high and low activity states alternate. The USOs is usually observed within neural activity in the human brain and in the prefrontal cortex in particular during rest. The USOs are considered to be generated by the local cortical circuitry together with pulse-like inputs and neuronal noise. Structure of the USOs shows specific statistics and their characteristics has been connected with cognitive abilities, such as working memory performance and capacity. In our study we used the previously constructed computational model describing activity of a cortical circuit consisting of the populations of pyramidal cells and interneurons. This model was developed to mimic global input impinging on the local PFC circuit from other cortical areas or subcortical structures. The studied the model dynamics numerically. We found that frequency increase deferentially lengthens the up states and therefore increases stability of self-sustained activity with oscillations in the gamma band. We argue that such effects would be beneficial to information processing and transfer in cortical networks with hierarchical inhibition.
Aim of the work was to study the influence of different brain rhythms (i.e. theta, beta, gamma ranges with frequencies from 5 to 80 Hz) on the ultraslow oscillations with frequency of 0.5 Hz and below, where high and low activity states alternate. Ultraslow oscillations are usually observed within neural activity in the human brain and in the prefrontal cortex in particular during rest. Ultraslow oscillations are considered to be generated by local cortical circuitry together with pulse-like inputs and neuronal noise. Structure of ultraslow oscillations shows specific statistics and their characteristics has been connected with cognitive abilities, such as working memory performance and capacity. Methods. In the study we used previously constructed computational model describing activity of a cortical circuit consisting of the populations of pyramidal cells and interneurons. This model was developed to mimic global input impinging on the local prefrontal cortex circuit from other cortical areas or subcortical structures. The model dynamics was studied numerically. Results. We found that frequency increase deferentially lengthens the up states and therefore increases stability of self-sustained activity with oscillations in the gamma band. Discussion. We argue that such effects would be beneficial to information processing and transfer in cortical networks with hierarchical inhibition.
The history of research in ﬁnance and economics has been widely impacted by the ﬁeld of Agent-based Computational Economics (ACE). While at the same time being popular among natural science researchers for its proximity to the successful methods of physics and chemistry for example, the ﬁeld of ACE has also received critics by a part of the social science community for its lack of empiricism. Yet recent trends have shifted the weights of these general arguments and poten- tially given ACE a whole new range of realism. At the base of these trends are found two present-day major scientiﬁc breakthroughs: the steady shift of psychology towards a hard science due to the advances of neuropsychology, and the progress of reinforcement learning due to increasing computational power and big data. We outline here the main lines of a computational research study where each agent would trade by reinforcement learning.
Gamma rhythm (20-100 Hz) plays a key role in numerous cognitive tasks: working memory, sensory processing and in routing of information across neural circuits. In comparison with lower frequency oscillations in the brain, gamma-rhythm associated firing of the individual neurons is sparse and the activity is locally distributed in the cortex. Such “weak” gamma rhythm results from synchronous firing of pyramidal neurons in an interplay with the local inhibitory interneurons in a "pyramidal-interneuron gamma" or PING. Experimental evidence shows that individual pyramidal neurons during such oscillations tend to fire at rates below gamma, with the population showing clear gamma oscillations and synchrony. One possible way to describe such features is that this gamma oscillation is generated within local synchronous neuronal clusters. The number of such synchronous clusters defines the overall coherence of the rhythm and its spatial structure. The number of clusters in turn depends on the properties of the synaptic coupling and the intrinsic properties of the constituent neurons. We previously showed that a slow spike frequency adaptation current in the pyramidal neurons can effectively control cluster numbers. These slow adaptation currents are modulated by endogenous brain neuromodulators such as dopamine, whose level is in turn related to cognitive task requirements. Hence we postulate that dopaminergic modulation can effectively control the clustering of weak gamma and its coherence. In this paper we study how dopaminergic modulation of the network and cell properties impacts the cluster formation process in a PING network model.
Quantitative finance has had a long tradition of a bottom-up approach to complex systems inference via multi-agent systems (MAS). These statistical tools are based on modelling agents trading via a centralised order book, in order to emulate complex and diverse market phenomena. These past financial models have all relied on so-called zero-intelligence agents, so that the crucial issues of agent information and learning, central to price formation and hence to all market activity, could not be properly assessed. In order to address this, we designed a next-generation MAS stock market simulator, in which each agent learns to trade autonomously via reinforcement learning. We calibrate the model to real market data from the London Stock Exchange over the years 2007 to 2018, and show that it can faithfully reproduce key market microstructure metrics, such as various price autocorrelation scalars over multiple time intervals. Agent learning thus enables accurate emulation of the market microstructure as an emergent property of the MAS.
Narratives, in the form of, e.g., written stories, mouth-to-mouth accounts, audiobooks, fiction movies, and media-feeds, powerfully shape the perception of reality and widely influence human decision-making. In this review, we describe findings from recent neuroimaging studies unraveling how narratives influence the human brain, thus shaping perception, cognition, emotions, and decision-making. It appears that narrative sense-making relies on default-mode network (DMN) structures of the brain, especially precuneus. Activity in precuneus further seems to differ for fictitious vs. real narratives. Notably, high inter-subject correlation (ISC) of brain activity during narrative processing seems to predict the efficacy of a narrative. Factors that enhance the ISC of brain activity during narratives include higher levels of attention, emotional arousal, and negative emotional valence. Higher levels of attentional suspense seem to co-vary with activity in the temporoparietal junction, emotional arousal with activity in dorsal attention network, and negative emotional valence with activity in DMN. Lingering after-effects of emotional narratives have been further described in DMN, amygdala, and sensory cortical areas. Finally, inter-individual differences in personality, and cultural-background related analytical and holistic thinking styles, shape ISC of brain activity during narrative perception. Together, these findings offer promising leads for future studies elucidating the effects of narratives on the human brain, and how such effects might predict the efficacy of narratives in modulating decision-making.
Inhibition of return (IOR) represents a delay in responding to a previously inspected location and is viewed as a crucial mechanism that sways attention toward novelty in visual search. Although most visual processing occurs in retinotopic, eye-centered, coordinates, IOR must be coded in spatiotopic, environmental, coordinates to successfully serve its role as a foraging facilitator. Early studies supported this suggestion but recent results have shown that both spatiotopic and retinotopic reference frames of IOR may co-exist. The present study tested possible sources for IOR at the retinotopic location including being part of the spatiotopic IOR gradient, part of hemifield inhibition and being an independent source of IOR. We conducted four experiments that alternated the cue-target spatial distance (discrete and contiguous) and the response modality (manual and saccadic). In all experiments, we tested spatiotopic, retinotopic and neutral (neither spatiotopic nor retinotopic) locations. We did find IOR at both the retinotopic and spatiotopic locations but no evidence for an independent source of retinotopic IOR for either of the response modalities. In fact, we observed the spread of IOR across entire validly cued hemifield including at neutral locations. We conclude that these results indicate a strategy to inhibit the whole cued hemifield or suggest a large horizontal gradient around the spatiotopically cued location.
Cross-frequency coupling (CFC) between neuronal oscillations reflects an integration of spatially and spectrally distributed information in the brain. Here, we propose a novel framework for detecting such interactions in Magneto- and Electroencephalography (MEG/EEG), which we refer to as Nonlinear Interaction Decomposition (NID). In contrast to all previous methods for separation of cross-frequency (CF) sources in the brain, we propose that the extraction of nonlinearly interacting oscillations can be based on the statistical properties of their linear mixtures. The main idea of NID is that nonlinearly coupled brain oscillations can be mixed in such a way that the resulting linear mixture has a non-Gaussian distribution. We evaluate this argument analytically for amplitude-modulated narrow-band oscillations which are either phase-phase or amplitude-amplitude CF coupled. We validated NID extensively with simulated EEG obtained with realistic head modelling. The method extracted nonlinearly interacting components reliably even at SNRs as small as −15 dB. Additionally, we applied NID to the resting-state EEG of 81 subjects to characterize CF phase-phase coupling between alpha and beta oscillations. The extracted sources were located in temporal, parietal and frontal areas, demonstrating the existence of diverse local and distant nonlinear interactions in resting-state EEG data. All codes are available publicly via GitHub.