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.
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.
In practical medicine, a diagnostic procedure is used only when it can be interpreted at the individual level. The aim of this work was to systematically investigate the relative and absolute reliability of different TMS motor maps parameters. 18 young healthy male right-handed volunteers were enrolled. Two TMS motor mapping sessions of three right-hand muscles were separated by 6-10 days. The analysis was performed using TMSmap software (http://tmsmap.ru). For reliability assessment, intra-class correlation coefficient (ICC) and smallest detectable changes (SDC) were calculated, while for quantitative comparison of the excitability profiles we used a novel earth mover's distance metrics (EMD), the convergence of the parameters depending on the number of stimuli was estimated.
The anterior cingulate cortex (ACC) is a key structure implicated in the regulation of cognitive control (CC). Previous studies suggest that variability in the ACC sulcal pattern—a neurodevelopmental marker unaffected by maturation or plasticity after birth—is associated with intersubject differences in CC performance. Here, we investigated whether bilingual experience modulates the effects of ACC sulcal variability on CC performance across the lifespan. Using structural MRI, we first established the distribution of the ACC sulcal patterns in a large sample of healthy individuals (N = 270) differing on gender and ethnicity. Second, a participants’ subsample (N = 157) was selected to test whether CC performance was differentially affected by ACC sulcation in bilinguals and monolinguals across age. A prevalent leftward asymmetry unaffected by gender or ethnicity was reported. Sulcal variability in the ACC predicted CC performance differently in bilinguals and monolinguals, with a reversed pattern of structure–function relationship: asymmetrical versus symmetrical ACC sulcal patterns were associated with a performance advantage in monolinguals and a performance detriment to bilinguals and vice versa. Altogether, these findings provide novel insights on the dynamic interplay between early neurodevelopment, environmental background and cognitive efficiency across age.
Competition for resources is a fundamental characteristic of evolution. Auctions have been widely used to model competition of individuals for resources, and bidding behaviour plays a major role in social competition. Yet, how humans learn to bid efficiently remains an open question. We used model‐based neuroimaging to investigate the neural mechanisms of bidding behaviour under different types of competition. Twenty‐seven subjects (nine male) played a prototypical bidding game: a double action, with three “market” types, which differed in the number of competitors. We compared different computational learning models of bidding: directional learning models (DL), where the model bid is “nudged” depending on whether it was accepted or rejected, along with standard reinforcement learning models (RL). We found that DL fit the behaviour best and resulted in higher payoffs. We found the binary learning signal associated with DL to be represented by neural activity in the striatum distinctly posterior to a weaker reward prediction error signal. We posited that DL is an efficient heuristic for valuation when the action (bid) space is continuous. Indeed, we found that the posterior parietal cortex represents the continuous action space of the task, and the frontopolar prefrontal cortex distinguishes among conditions of social competition. Based on our findings, we proposed a conceptual model that accounts for a sequence of processes that are required to perform successful and flexible bidding under different types of competition.
Agency is the attribution of an action to the self and is a prerequisite for experiencing responsibility over its consequences. Here we investigated agency and responsibility by studying the control of movements of an embodied avatar, via brain computer interface (BCI) technology, in immersive virtual reality. After induction of virtual body ownership by visuomotor correlations, healthy participants performed a motor task with their virtual body. We compared the passive observation of the subject's 'own' virtual arm performing the task with (1) the control of the movement through activation of sensorimotor areas (motor imagery) and (2) the control of the movement through activation of visual areas (steady-state visually evoked potentials). The latter two conditions were carried out using a brain-computer interface (BCI) and both shared the intention and the resulting action. We found that BCI-control of movements engenders the sense of agency, which is strongest for sensorimotor areas activation. Furthermore, increased activity of sensorimotor areas, as measured using EEG, correlates with levels of agency and responsibility. We discuss the implications of these results for the neural basis of agency.
We present a publicly available dataset of 227 healthy participants comprising a young (N=153, 25.1±3.1 years, range 20–35 years, 45 female) and an elderly group (N=74, 67.6±4.7 years, range 59–77 years, 37 female) acquired cross-sectionally in Leipzig, Germany, between 2013 and 2015 to study mind-body-emotion interactions. During a two-day assessment, participants completed MRI at 3 Tesla (resting-state fMRI, quantitative T1 (MP2RAGE), T2-weighted, FLAIR, SWI/QSM, DWI) and a 62-channel EEG experiment at rest. During task-free resting-state fMRI, cardiovascular measures (blood pressure, heart rate, pulse, respiration) were continuously acquired. Anthropometrics, blood samples, and urine drug tests were obtained. Psychiatric symptoms were identified with Standardized Clinical Interview for DSM IV (SCID-I), Hamilton Depression Scale, and Borderline Symptoms List. Psychological assessment comprised 6 cognitive tests as well as 21 questionnaires related to emotional behavior, personality traits and tendencies, eating behavior, and addictive behavior. We provide information on study design, methods, and details of the data. This dataset is part of the larger MPI Leipzig Mind-Brain-Body database.
The article is an overview of modern studies of brain organization
and genetic correlates of emotional intelligence. Emotional intelligence is
becoming the subject of more and more attentive study of psychologists
due to the fact that it influences the mental development of humans, plays
an important role in many professions, and its impairment is a marker of
some disorders. Nevertheless, the brain organization and genetic correlates
of emotional intelligence have not been studied enough – first studies
appeared only in the early 2000s. A review of the literature on the enceph-
alographic showed that in rest, people with higher emotional intelligence
show greater excitation of the left anterior regions of the brain. When per-
ceiving affective stimuli, participants with high emotional intelligence show
stronger synchronization of some EEG rhythms. Brain mapping technique
made it possible to identify the areas of the brain involved in activities
related to emotional intelligence. In regard to genetic correlates of emotional
intelligence, some genes of neurotransmitter systems have been associated
to this trait: the catechol-O-methyltransferase gene COMT, the dopamine
DRD2 receptor gene, the serotonin receptor gene HTR2A, and the BDNF
brain neurotrophic factor gene.
The neural underpinnings of subjective experience during resting state remain elusive. Dynamic features of EEG oscillations may provide more understanding of the relationship between the content of inner conscious experience and electrical brain activity. We tested a correlation of rating on the Amsterdam Resting-State Questionnaire (ARSQ) with dynamic parameters of EEG recorded in 49 healthy volunteers during the 10-min resting session. The participants filled ARSQ immediately after the rest. We investigated both linear (1 Hz-band power spectral density - PSD) and dynamic features (standard deviation and frequency of Hilbert envelope) of EEG averaged for the whole resting-state segment. Besides, we conducted a procedure of k-mean clustering based on PSD, localization of components retrieved by independent component analysis for 10-sec EEG epochs to assess spectral and temporal variability of EEG. The correlation analysis showed that the increase of PSD and cluster duration of the high-frequency alpha rhythm (12–13 Hz) in central and frontal areas was positively associated with the rating of experienced thoughts related to Planning (r = 0.44). The time of the presence of low amplitude delta oscillations correlated negatively with Planning (r = -0.52). The participants with higher ARSQ scores of Visual Thoughts had a higher standard deviation of the wideband (1–30 Hz) Hilbert envelope. Our data suggest that the dynamic properties of EEG reflect cognitive states assessed by ARSQ.
In the past decade, several studies have examined the effects of transcranial direct current stimulation (tDCS) on long-term episodic memory formation and retrieval. These studies yielded conflicting results, likely due to differences in stimulation parameters, experimental design and outcome measures.
In this work we aimed to assess the robustness of tDCS effects on long-term episodic memory using a meta-analytical approach.
We conducted four meta-analyses to analyse the effects of anodal and cathodal tDCS on memory accuracy and response times. We also used a moderator analysis to examine whether the size of tDCS effects varied as a function of specific stimulation parameters and experimental conditions.
Although all selected studies reported a significant effect of tDCS in at least one condition in the published paper, the results of the four meta-analyses showed only statistically non-significant close-to-zero effects. A moderator analysis suggested that for anodal tDCS, the duration of the stimulation and the task used to probe memory moderated the effectiveness of tDCS. For cathodal tDCS, site of stimulation was a significant moderator, although this result was based on only a few observations.
To warrant theoretical advancement and practical implications, more rigorous research is needed to fully understand whether tDCS reliably modulates episodic memory, and the specific circumstances under which this modulation does, and does not, occur.
Many studies suggest that social punishment is beneficial for cooperation and consequently maintaining the social norms in society. Neuroimaging and brain stimulation studies show that the brain regions which respond to violations of social norms, the understanding of the mind of others and the executive functions, are involved during social punishment. Despite the rising number of studies on social punishment, the concordant map of activations - the set of key regions responsible for the general brain response to social punishment - is still unknown. By using coordinate-based fMRI meta-analysis, the present study examined the concordant map of neural activations associated with various social punishment tasks. A total of 17 articles with 18 contrasts including 383 participants, equalling 191 foci were included in activation likelihood estimation (ALE) analysis. The majority of the studies (61%) employed the widely used neuroeconomic paradigms, such as fairness-related norm tasks (Ultimatum Game, third-party punishment game), while the remaining tasks reported included criminal scenarios evaluation and social rejection tasks. The analysis presented revealed concordant activation in the bilateral claustrum, right interior frontal and left superior frontal gyri. This study provides an integrative view on brain responses to social punishment.
Synchronization between oscillatory signals is considered to be one of the main mechanisms through which neuronal populations interact with each other. It is conventionally studied with mass-bivariate measures utilizing either sensor-to-sensor or voxel-to-voxel signals. However, none of these approaches aims at maximizing synchronization, especially when two multichannel datasets are present. Examples include cortico-muscular coherence (CMC), cortico-subcortical interactions or hyperscanning (where electroencephalographic EEG/magnetoencephalographic MEG activity is recorded simultaneously from two or more subjects). For all of these cases, a method which could find two spatial projections maximizing the strength of synchronization would be desirable. Here we present such method for the maximization of coherence between two sets of EEG/MEG/EMG (electromyographic)/LFP (local field potential) recordings. We refer to it as canonical Coherence (caCOH). caCOH maximizes the absolute value of the coherence between the two multivariate spaces in the frequency domain. This allows very fast optimization for many frequency bins. Apart from presenting details of the caCOH algorithm, we test its efficacy with simulations using realistic head modelling and focus on the application of caCOH to the detection of cortico-muscular coherence. For this, we used diverse multichannel EEG and EMG recordings and demonstrate the ability of caCOH to extract complex patterns of CMC distributed across spatial and frequency domains. Finally, we indicate other scenarios where caCOH can be used for the extraction of neuronal interactions.
Items presented in large font are rated with higher judgments of learning (JOLs) than those presented in small font. According to current explanations of this phenomenon in terms of processing fluency or implicit beliefs, this effect should be present no matter the type of material under study. However, we hypothesized that the linguistic cues present in sentences may prevent using font size as a cue for JOLs. Experiment 1, with short sentences, showed the standard font-size effect on JOLs, and Experiment 2, with pairs of longer sentences, showed a reduced effect. These results suggest that linguistic factors do not prevent font size from being used for JOLs. However, Experiment 3, with both short and long sentences, showed an effect of font size only for the former and not the latter condition, suggesting that the greater amount of to-be-remembered information eliminated the font-size effect. In Experiment 4, we tested a mechanism to explain this result and manipulated cognitive load using the dot-memory task. The short sentences from Experiments 1 and 3 were used, and the results replicated the font-size effect only in the low-cognitive load condition. Our results are consistent with the idea that perceptual information is used to make JOLs only with materials such as words, word pairs, or short sentences, and that the increased cognitive load required to process longer sentences prevents using font size as a cue for JOLs.
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.
After a protracted history, neurofeedback has begun to attract the attention and scrutiny of the scientific and medical mainstream (Kamiya, 2011; Linden, 2014; Sitaram et al., 2017). A debate now centres on the extent to which neurofeedback alters brain function and behaviour, and the mechanisms through which neurofeedback operates (e.g., neurofeedback-specific versus nonspecific). A series of correspondences in Lancet Psychiatry (Micoulaud-Franchi & Fovet, 2016; Pigott et al., 2017; Schönenberg et al., 2017b, 2017a; Thibault & Raz, 2016a, 2016b) and Brain (Fovet et al., 2017; Schabus, 2017, 2018; Schabus et al., 2017; Thibault, Lifshitz, & Raz, 2017, 2018; Witte, Kober, & Wood, 2018) discusses the theoretical arguments and empirical data backing the involvement of these two mechanisms.
Intracortical microstimulation (ICMS) of the primary somatosensory cortex (S1) can produce percepts that mimic somatic sensation and, thus, has potential as an approach to sensorize prosthetic limbs. However, it is not known whether ICMS could recreate active texture exploration-the ability to infer information about object texture by using one's fingertips to scan a surface. Here, we show that ICMS of S1 can convey information about the spatial frequencies of invisible virtual gratings through a process of active tactile exploration. Two rhesus monkeys scanned pairs of visually identical screen objects with the fingertip of a hand avatar-controlled first via a joystick and later via a brain-machine interface-to find the object with denser virtual gratings. The gratings consisted of evenly spaced ridges that were signaled through individual ICMS pulses generated whenever the avatar's fingertip crossed a ridge. The monkeys learned to interpret these ICMS patterns, evoked by the interplay of their voluntary movements and the virtual textures of each object, to perform a sensory discrimination task. Discrimination accuracy followed Weber's law of just-noticeable differences (JND) across a range of grating densities; a finding that matches normal cutaneous sensation. Moreover, 1 monkey developed an active scanning strategy where avatar velocity was integrated with the ICMS pulses to interpret the texture information. We propose that this approach could equip upper-limb neuroprostheses with direct access to texture features acquired during active exploration of natural objects.
Although many real-time neural decoding algorithms have been proposed for brain-machine interface (BMI) applications over the years, an optimal, consensual approach remains elusive. Recent advances in deep learning algorithms provide new opportunities for improving the design of BMI decoders, including the use of recurrent artificial neural networks to decode neuronal ensemble activity in real time. Here, we developed a long-short term memory (LSTM) decoder for extracting movement kinematics from the activity of large (N = 134-402) populations of neurons, sampled simultaneously from multiple cortical areas, in rhesus monkeys performing motor tasks. Recorded regions included primary motor, dorsal premotor, supplementary motor, and primary somatosensory cortical areas. The LSTM's capacity to retain information for extended periods of time enabled accurate decoding for tasks that required both movements and periods of immobility. Our LSTM algorithm significantly outperformed the state-of-the-art unscented Kalman filter when applied to three tasks: center-out arm reaching, bimanual reaching, and bipedal walking on a treadmill. Notably, LSTM units exhibited a variety of well-known physiological features of cortical neuronal activity, such as directional tuning and neuronal dynamics across task epochs. LSTM modeled several key physiological attributes of cortical circuits involved in motor tasks. These findings suggest that LSTM-based approaches could yield a better algorithm strategy for neuroprostheses that employ BMIs to restore movement in severely disabled patients.
According to dual process theories, depletion of executive resources may amplify decision-making biases. Psychological studies investigating the influence of executive control on risky decision mak- ing typically employ dual task paradigms, e.g. a risky decision-making task in parallel with an exec- utive task. However, these paradigms often reveal relatively weak to null effects. In this study, we designed a novel task to determine the influence of executive control on risky decision making di- rectly, and simultaneously separating gains and losses using a block design. Contrary to other tasks, risk taking, and executive control occurred during the same decision. When risky decisions were conditioned on high executive control, participants demonstrated a reflection effect: higher risk taking for loss blocks, compared to gain blocks. Further exploration revealed that the gain-domain specific influence of executive control on risky decisions occurred due to the influence of trial-by- trial decision-making strategies.
Metacognitive monitoring is a powerful tool that supports our ongoing cognitive processes (Flavell, 1976). In applied settings, such as when we are trying to learn a new language, monitoring the learning progress may determine the difference between success and failure. One way to measure metacognitive monitoring in relation to learning new material is the so-called Judgments of Learning (JOLs). JOLs are estimations of future success in recalling recently learned information. Depending on the confidence that we have in remembering the new information later, we may decide to keep rehearsing it or just move on. Existing research shows that several variables can mislead our JOLs in relation to the subsequent recall accuracy; at the same time, other variables that influence the recall itself do not affect JOLs. Perceptual fluency, manipulated in different sensory modalities by e.g. font size or presentation volume, leads to differences in JOLs (e.g., higher JOLs for bigger font size), although recall accuracy remains the same regardless of the manipulation. On the other and, the animacy manipulation (e.g., dog vs. table) does not affect JOLs but animate words are remembered better. Our main aim was to study JOL brain correlates for variables that differently affect JOLs and memory. Participants were presented with words in an easy- or difficult to-read font that referred to animate or inanimate objects while EEG was recorded. For each word, participants had to choose on a 0-100% scale the confidence they had in remembering it in near future. We found a higher P2 response for high- (70–100%) than to medium- JOLs (40–60%) ratings, which may reflect attentional recruitment resulting in modulation of perceptual processing. Furthermore, we found a greater P600 response for medium- than high-JOLs, suggesting a deeper reanalysis of these type of “less confident” answers. When animacy and perceptual fluency are split between medium and high-JOLs, we found LPC (late positive component) only for animacy, being showing a higher amplitude for the high- than medium-JOLs.. This might indicate a higher involvement of memory processes during the processing of animacy-related information. Finally, when comparing difficult type font words rated with medium and high-JOLs, we obtained larger P3b for high-JOLs rated words, which may attributed to their deeper evaluation. This is the first evidence of differential brain signatures for JOLs depending on their ratings level and different experimental manipulations. Our results highlight the relevance of metacognitive evaluations in the cognitive processing.
The work was supported by the Russian Science Foundation (project No. 19-18-00534).
Human memory is not a literal record of our experiences but a fallible and malleable cognitive process. Because of the reconstructive nature of memory, we are often prone to accept false events and recall them as truthful (Bartlett, 1932). One easy and reliable method to create and study false memories in the laboratory is the misinformation paradigm. In this paradigm participants are presented with a story (original information). After some time, parts of this story are presented again but now including some modifications (misinformation). Finally, the memory is measured for the original information, the misinformation, and, as control, some other incorrect information never presented before. The misinformation effect occurs when the percentage of misinformation accepted is higher than the acceptance of control incorrect information. This effect has been largely studied in relation to its applied relevance in eyewitness testimony research. Yet, the neural substrates and temporal dynamics of processing correct and false information remain scarcely studied. In this study the neural activity was recorded using EEG while participants performed a memory recognition test which comprised misinformation, true, and simply incorrect items. The only previous EEG study on neural correlates focused on misinformation pointed to the P3b and LPC (late positive component) ERPs components as the key to distinguishing between memories for correct and false memories. High P3b is linked with a strong match between the expectation and the stimuli presented. LPC is a late component around 400 to 800 ms after the stimulus presentation, associated with the recollection of accurate information. Our results show that for the contrasts of misinformation accepted vs rejected, and false information accepted vs rejected (correct rejections), P3b was significantly more positive when the inaccurate information was accepted. These differences suggest a larger cognitive workload on accepting this type of information than when it is correctly rejected. Furthermore, in both contrasts we found differences in P600 which is linked to reprocessing of detected anomalies in the input. Here, we found a more expressed P600 for accepted than for rejected misinformation. P600 was also stronger for correct rejections than false alarms. In this latter case, the higher P600 amplitude may reflect the detection and reanalysis of the rejection of this false information. Interestingly, in the case of acceptance of misinformation, the higher P600 amplitude suggest that participants are not totally blind to the inaccuracy of the misinformation, though still they accept it.
The work was supported by the Russian Science Foundation (project №19-18-00534).