Resting-state EEG power predicts conflict-related brain activity in internally guided but not in externally guided decision-making

Decision-making ability limitations and brain neural activity changes in healthcare workers after mild COVID-19

Studies have demonstrated that the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) extensively affects brain function. Although cognitive dysfunction is considered a common manifestation in COVID-19 patients during the recovery period, the potential changes in decision-making ability, are not yet clear. Decision-making functions are essential to the work of healthcare workers. However, there is a lack of a multidimensional assessment of its functioning in COVID-19 cases. Here, we used tests combined with the resting-state functional magnetic resonance imaging (rs-fMRI) stabilization feature amplitude of low-frequency fluctuations (ALFF) to explore decision-making behavior and brain neural activity changes in healthcare workers after mild COVID-19. Participants were divided into the SARS-CoV-2 infected group (SI, n = 41) and healthy controls (HC, n = 42). All participants underwent a series of neuropsychological tests. They performed the Iowa Gambling Task (IGT) and the Game of Dice Task (GDT), followed by fMRI (n = 20) to assess their decision-making ability under ambiguous and risky conditions and changes in brain neural activity. The SI group performed worse in verbal memory than the HC group. Furthermore, the SI group performed worse in the IGT, whereas no significant difference was observed in the GDT. In addition, rs-fMRI showed enhanced spontaneous neural activity in the postcentral gyrus and inferior parietal lobe in the SI group compared to the HC group.

Internally Formed Preferences for Options only Influence Initial Decisions in Gambling Tasks, while the Gambling Outcomes do not Alter these Preferences

All humans must engage in decision-making. Decision-making processes can be broadly classified into internally guided decision-making (IDM), which is determined by individuals' internal value criteria, such as preference, or externally guided decision-making (EDM), which is determined by environmental external value criteria, such as monetary rewards. However, real-life decisions are never made simply using one kind of decision-making, and the relationship between IDM and EDM remains unclear. This study had individuals perform gambling tasks requiring the EDM using stimuli that formed preferences through the preference judgment task as the IDM. Computational model analysis revealed that strong preferences in the IDM affected initial choice behavior in the EDM. Moreover, through the analysis of the subjective preference evaluation after the gambling tasks, we found that even when stimuli that were preferred in the IDM were perceived as less valuable in the EDM, the preference for IDM was maintained after EDM. These results indicate that although internal criteria, such as preferences, influence EDM, the results show that internal and external criteria differ.

Machine learning-based classifying of risk-takers and risk-aversive individuals using resting-state EEG data: A pilot feasibility study

BACKGROUND

Decision-making is vital in interpersonal interactions and a country's economic and political conditions. People, especially managers, have to make decisions in different risky situations. There has been a growing interest in identifying managers' personality traits (i.e., risk-taking or risk-averse) in recent years. Although there are findings of signal decision-making and brain activity, the implementation of an intelligent brain-based technique to predict risk-averse and risk-taking managers is still in doubt.

METHODS

This study proposes an electroencephalogram (EEG)-based intelligent system to distinguish risk-taking managers from risk-averse ones by recording the EEG signals from 30 managers. In particular, wavelet transform, a time-frequency domain analysis method, was used on resting-state EEG data to extract statistical features. Then, a two-step statistical wrapper algorithm was used to select the appropriate features. The support vector machine classifier, a supervised learning method, was used to classify two groups of managers using chosen features.

RESULTS

Intersubject predictive performance could classify two groups of managers with 74.42% accuracy, 76.16% sensitivity, 72.32% specificity, and 75% F1-measure, indicating that machine learning (ML) models can distinguish between risk-taking and risk-averse managers using the features extracted from the alpha frequency band in 10 s analysis window size.

CONCLUSIONS

The findings of this study demonstrate the potential of using intelligent (ML-based) systems in distinguish between risk-taking and risk-averse managers using biological signals.

Midfrontal theta as an index of conflict strength in approach-approach vs avoidance-avoidance conflicts

The seminal theory of motivational conflicts distinguishes between approach-approach (AP-AP) conflicts, in which a decision is made between desirable alternatives, and avoidance-avoidance (AV-AV) conflicts, in which a decision is made between undesirable alternatives. The behavioral differences between AP-AP and AV-AV conflicts are well documented: abundant research showed that AV-AV conflicts are more difficult to resolve than AP-AP ones. However, there is little to no research looking into the neural underpinnings of the differences between the two conflict types. Here, we show that midfrontal theta, an established neural marker of conflict, distinguished between the two conflict types such that midfrontal theta power was higher in AV-AV conflicts than in AP-AP conflicts. We further demonstrate that higher midfrontal theta power was associated with shorter decision times on a single-trial basis, indicating that midfrontal theta played a role in promoting successful controlled behavior. Taken together, our results show that AP-AP and AV-AV conflicts are distinguishable on the neural level. The implications of these results go beyond motivational conflicts, as they establish midfrontal theta as a measure of the continuous degree of conflict in subjective decisions.

Neuromodulatory feasibility of a current limiter-based tDCS device: a resting-state electroencephalography study

Recently, we introduced a current limiter-based novel transcranial direct-current stimulation (tDCS) device that does not generate significant tDCS-induced electrical artifacts, thereby facilitating simultaneous electroencephalography (EEG) measurement during tDCS application. In this study, we investigated the neuromodulatory effect of the tDCS device using resting-state EEG data measured during tDCS application in terms of EEG power spectral densities (PSD) and brain network indices (clustering coefficient and path length). Resting-state EEG data were recorded from 10 healthy subjects during both eyes-open (EO) and eyes-closed (EC) states for each of five different conditions (baseline, sham, post-sham, tDCS, and post-tDCS). In the tDCS condition, tDCS was applied for 12 min with a current intensity of 1.5 mA, whereas tDCS was applied only for the first 30 s in the sham condition. EEG PSD and brain network indices were computed for the alpha frequency band most closely associated with resting-state EEG. Both alpha PSD and network indices were found to significantly increase during and after tDCS application compared to those of the baseline condition in the EO state, but not in the EC state owing to the ceiling effect. Our results demonstrate the neuromodulatory effect of the tDCS device that does not generate significant tDCS-induced electrical artifacts, thereby allowing simultaneous measurement of electrical brain activity. We expect our novel tDCS device to be practically useful in exploring the impact of tDCS on neuromodulation more precisely using ongoing EEG data simultaneously measured during tDCS application.

Edge-centric functional network predicts risk propensity in economic decision-making: evidence from a resting-state fMRI study

Despite node-centric studies revealing an association between resting-state functional connectivity and individual risk propensity, the prediction of future risk decisions remains undetermined. Herein, we applied a recently emerging edge-centric method, the edge community similarity network (ECSN), to alternatively describe the community structure of resting-state brain activity and to probe its contribution to predicting risk propensity during gambling. Results demonstrated that inter-individual variability of risk decisions correlates with the inter-subnetwork couplings spanning the visual network (VN) and default mode network (DMN), cingulo-opercular task control network, and sensory/somatomotor hand network (SSHN). Particularly, participants who have higher community similarity of these subnetworks during the resting state tend to choose riskier and higher yielding bets. And in contrast to low-risk propensity participants, those who behave high-risky show stronger couplings spanning the VN and SSHN/DMN. Eventually, based on the resting-state ECSN properties, the risk rate during the gambling task is effectively predicted by the multivariable linear regression model at the individual level. These findings provide new insights into the neural substrates of the inter-individual variability in risk propensity and new neuroimaging metrics to predict individual risk decisions in advance.

Towards a systematization of brain oscillatory activity in actions

Information processing in the brain is governed by oscillatory activity. Activity oscillations in specific frequency bands (theta, alpha, beta and gamma) have been associated with various cognitive functions. A drawback of this is that the plethora of findings led to considerable uncertainty as to the functional relevance of activity in different frequency bands and their interrelation. Here, we use a novel cognitive-science theoretical framework to better understand and conceptually harmonize neurophysiological research on human action control. We outline how this validated starting point can systematize and probably reframe the functional relevance of oscillatory activity relevant for action control and beyond.

Augmenting Human Selves Through Artificial Agents – Lessons From the Brain

Much of current artificial intelligence (AI) and the drive toward artificial general intelligence (AGI) focuses on developing machines for functional tasks that humans accomplish. These may be narrowly specified tasks as in AI, or more general tasks as in AGI – but typically these tasks do not target higher-level human cognitive abilities, such as consciousness or morality; these are left to the realm of so-called “strong AI” or “artificial consciousness.” In this paper, we focus on how a machine can augment humans rather than do what they do, and we extend this beyond AGI-style tasks to augmenting peculiarly personal human capacities, such as wellbeing and morality. We base this proposal on associating such capacities with the “self,” which we define as the “environment-agent nexus”; namely, a fine-tuned interaction of brain with environment in all its relevant variables. We consider richly adaptive architectures that have the potential to implement this interaction by taking lessons from the brain. In particular, we suggest conjoining the free energy principle (FEP) with the dynamic temporo-spatial (TSD) view of neuro-mental processes. Our proposed integration of FEP and TSD – in the implementation of artificial agents – offers a novel, expressive, and explainable way for artificial agents to adapt to different environmental contexts. The targeted applications are broad: from adaptive intelligence augmenting agents (IA’s) that assist psychiatric self-regulation to environmental disaster prediction and personal assistants. This reflects the central role of the mind and moral decision-making in most of what we do as humans.

Ongoing Brain Activity and Its Role in Cognition: Dual versus Baseline Models

What is the role of the brain's ongoing activity for cognition? The predominant perspectives associate ongoing brain activity with resting state, the default-mode network (DMN), and internally oriented mentation. This triad is often contrasted with task states, non-DMN brain networks, and externally oriented mentation, together comprising a "dual model" of brain and cognition. In opposition to this duality, however, we propose that ongoing brain activity serves as a neuronal baseline; this builds upon Raichle's original search for the default mode of brain function that extended beyond the canonical default-mode brain regions. That entails what we refer to as the "baseline model." Akin to an internal biological clock for the rest of the organism, the ongoing brain activity may serve as an internal point of reference or standard by providing a shared neural code for the brain's rest as well as task states, including their associated cognition. Such shared neural code is manifest in the spatiotemporal organization of the brain's ongoing activity, including its global signal topography and dynamics like intrinsic neural timescales. We conclude that recent empirical evidence supports a baseline model over the dual model; the ongoing activity provides a global shared neural code that allows integrating the brain's rest and task states, its DMN and non-DMN, and internally and externally oriented cognition.

A developmental examination of medial frontal theta dynamics and inhibitory control

Medial frontal theta-band oscillations are a robust marker of action-outcome monitoring. In a large developmental sample (n = 432, 9-16 years), we examined whether phase and non-phase locked medial frontal theta power were related to inhibitory control among children and adolescents. Our results showed that the well-established increase in medial frontal theta power during inhibitory control was captured largely by non-phase locked dynamics, which partially mediated the positive effect of age on task performance. A person-centered approach also revealed latent classes of individuals based on their multivariate theta power dynamics (phase locked/non-phase locked, GO/NOGO). The class of individuals showing low phase locked and high non-phase locked medial frontal theta were significantly older, had better inhibitory control, scored higher on measures of general cognitive function, and were more efficient in their behavioural responses. The functional significance of phase and non-phase locked theta dynamics, and their potential changes, could have important implications for action-outcome monitoring and cognitive function in both typical and atypical development, as well as related psychopathology .

Depressive rumination is correlated with brain responses during self-related processing

BACKGROUND

Rumination, a tendency to focus on negative self-related thoughts, is a central symptom of depression. Studying the self-related aspect of such symptoms is challenging because of the need to distinguish self effects from the emotional content of task stimuli. This study employed an emotionally neutral self-related paradigm to investigate possible altered self-processing in depression and its link to rumination.

METHODS

People with major depressive disorder (n = 25) and controls (n = 25) underwent task-based electro-encephalogram recording. We studied late event-related potentials, along with low-frequency oscillatory power. We compared electroencephalogram metrics between groups and correlated them with depressive symptoms and reported rumination.

RESULTS

Participants with major depressive disorder displayed a difference in late positive potentials across frontocentral electrodes between self-related and non-self-related conditions. We found no such difference in controls. The magnitude of this difference was positively correlated with depressive symptoms and reported rumination. Participants with major depressive disorder also had elevated theta oscillation power at central electrodes in self-related conditions, a finding that we did not see in controls.

LIMITATIONS

Patients with major depressive disorder were medicated at the time of the study. The group studied was primarily female, so the observed effects may have been sex-specific.

CONCLUSION

Rumination appears to be linked to altered self-related processing in depression, independent of stimuli-related emotional confounds. This connection between self-related processing and depression may point to a self disorder as a core component of depression.

Prediction of dispositional dialectical thinking from resting-state electroencephalography

This study aims to explore the possibility of predicting the dispositional level of dialectical thinking using resting-state electroencephalography signals. Thirty-four participants completed a self-reported measure of dialectical thinking, and their resting-state electroencephalography was recorded. After wave filtration and eye movement removal, time-frequency electroencephalography signals were converted into four frequency domains: delta (1-4 Hz), theta (4-7 Hz), alpha (7-13 Hz), and beta (13-30 Hz). Functional principal component analysis with B-spline approximation was then applied for feature reduction. Five machine learning methods (support vector regression, least absolute shrinkage and selection operator, K-nearest neighbors, random forest, and gradient boosting decision tree) were applied to the reduced features for prediction. The model ensemble technique was used to create the best performing final model. The results showed that the alpha wave of the electroencephalography signal in the early period (12-15 s) contributed most to the prediction of dialectical thinking. With data-driven electrode selection (FC1, FCz, Fz, FC3, Cz, AFz), the prediction model achieved an average coefficient of determination of 0.45 on 200 random test sets. Furthermore, a significant positive correlation was found between the alpha value of standardized low-resolution electromagnetic tomography activity in the right dorsal anterior cingulate cortex and dialectical self-scale score. The prefrontal and midline alpha oscillations of resting electroencephalography are good predictors of the dispositional level of dialectical thinking, possibly reflecting these brain structures' involvement in dialectical thinking.

Using conflict negativity to index psychological tension between impartiality and status-upholding principles

People often endorse the moral principle that all human lives are equally valuable. At the same time, people often privilege high-status individuals over low-status individuals. These two inclinations come into conflict in a scenario involving the potential killing of a high-status person to save the lives of multiple low-status people. In the present study, participants viewed a series of sacrificial dilemmas in which the social status of the victims and beneficiaries was varied. We measured participants' choice (sacrifice vs. don't sacrifice), response time, and electroencephalographic activity, with an emphasis on conflict negativity (CN). Overall, we found no effects of victim/beneficiaries status on choice and response time. However, participants displayed a more pronounced CN effect when contemplating a high-status victim/low-status beneficiaries tradeoff than a low-status-victim/high-status beneficiaries tradeoff. Further analyses revealed that this effect was primarily driven by participants who endorsed deontological principles (e.g., "Some rules must never be broken, no matter the consequences"). In contrast, those who endorsed utilitarian principles displayed equivalent levels of conflict negativity, regardless of the social status of victims and beneficiaries. These findings shed light on the role of conflict in the phenomenology of moral decision making.

The interplay of resting and inhibitory control-related theta-band activity depends on age

Resting‐state neural activity plays an important role for cognitive control processes. Regarding response inhibition processes, an important facet of cognitive control, especially theta‐band activity has been the focus of research. Theoretical considerations suggest that the interrelation of resting and task‐related theta activity is subject to maturational effects. To investigate whether the relationship between resting theta activity and task‐related theta activity during a response inhibition task changes even in young age, we tested N = 166 healthy participants between 8 and 30 years of age. We found significant correlations between resting and inhibitory control‐related theta activity as well as behavioral inhibition performance. Importantly, these correlations were moderated by age. The moderation analysis revealed that higher resting theta activity was associated with stronger inhibition‐related theta activity in individuals above the age of ~10.7 years. The EEG beamforming analysis showed that this activity is associated with superior frontal region function (BA6). The correlation between resting and superior frontal response inhibition‐related theta activity became stronger with increasing age. A similar pattern was found for response inhibition performance, albeit only evident from the age of ~19.5 years. The results suggest that with increasing age, resting theta activity becomes increasingly important for processing the alarm/surprise signals in superior frontal brain regions during inhibitory control. Possible causes for these developmental changes are discussed.

The relationship of resting-state EEG oscillations to executive functions in middle childhood

Executive functions (EFs) play important roles in children's development, but their neural mechanisms are rarely investigated, especially for the different components of EFs in middle childhood. Therefore, this study aimed to explore the links between resting-state EEG in the frontal scalp region and EFs in children aged 7-9 years. Fifty-nine typically developing children from the second and third grades performed two core EF tasks, i.e., inhibition and working memory, and a high-level EF task, i.e., planning, followed by the recording of EEG signals during eyes-open and eyes-closed resting states. The results showed that distinct EEG activities in the frontal scalp region predicted different EF components. More specifically, after controlling for age and verbal ability, alpha to theta power ratio (ATR) and beta to theta power ratio (BTR) during the eyes-open resting state positively predicted inhibition, and beta to theta power ratio (BTR) during the eyes-open resting state positively predicted planning. However, we did not find any EEG features related to working memory. Our results contributed to the understanding of inter-individual differences in EFs and provided insights into the regulation of corresponding EEG activities through EEG neurofeedback for enhancing children's EFs.

What COVID-19 tells us about the self: The deep intersubjective and cultural layers of our brain

The COVID-19 crisis is affecting our sense of self and touches upon our existential fears. This extends to the self-other relationship, as there is both being infected and infecting the other. What does this pandemic crisis tell us about our self and relatedness, its cultural differences, and how these are rooted in the brain's relation to the world? First, we discuss the psychological and neuronal features of self and self-other relation and how they are rooted in a deeper layer of the brain's neural activity complementing its cognitive surface layer. Second, we demonstrate cultural differences of Eastern and Western concepts of the self (i.e., independency and interdependency) and how these reflect the manifestation of the brain's neuro-social and neuro-ecological alignment. Finally, we highlight the intersubjective and cultural nature of the self and its surface in the COVID-19 crisis. Discussing various lines of empirical data showing the brain's intimate alignment to both social and ecological environmental contexts, our results support the assumption of the brain's deep layer features by laying bare a continuum of different degrees of neuro-social and neuro-ecological alignment. This entails a two-stage model of self with neuro-social-ecological and psychological levels that extends the previously suggested basis model of self-specificity. We conclude that the current pandemic shows the importance of the deeper intersubjective and cultural layers of both the self and brain; their neglect can be life-threatening for the self and others and, paradoxically, might reduce, rather than enlarge, the self's sense of freedom and independence.

Association between long-range temporal correlations in intrinsic EEG activity and subjective sense of identity

The long-range temporal correlation (LRTC) in resting-state intrinsic brain activity is known to be associated with temporal behavioral patterns, including decision making based on internal criteria such as self-knowledge. However, the association between the neuronal LRTC and the subjective sense of identity remains to be explored; in other words, whether our subjective sense of consistent self across time relates to the temporal consistency of neural activity. The present study examined the relationship between the LRTC of resting-state scalp electroencephalography (EEG) and a subjective sense of identity measured by the Erikson Psychosocial Stage Inventory (EPSI). Consistent with our prediction based on previous studies of neuronal-behavioral relationships, the frontocentral alpha LRTC correlated negatively with identity confusion. Moreover, from the descriptive analyses, centroparietal beta LRTC showed negative correlations with identity confusion, and frontal theta LRTC showed positive relationships with identity synthesis. These results suggest that more temporal consistency (reversely, less random noise) in intrinsic brain activity is associated with less confused and better-synthesized identity. Our data provide further evidence that the LRTC of intrinsic brain activity might serve as a noise suppression mechanism at the psychological level.

Computational modeling of choice-induced preference change: A Reinforcement-Learning-based approach

The value learning process has been investigated using decision-making tasks with a correct answer specified by the external environment (externally guided decision-making, EDM). In EDM, people are required to adjust their choices based on feedback, and the learning process is generally explained by the reinforcement learning (RL) model. In addition to EDM, value is learned through internally guided decision-making (IDM), in which no correct answer defined by external circumstances is available, such as preference judgment. In IDM, it has been believed that the value of the chosen item is increased and that of the rejected item is decreased (choice-induced preference change; CIPC). An RL-based model called the choice-based learning (CBL) model had been proposed to describe CIPC, in which the values of chosen and/or rejected items are updated as if own choice were the correct answer. However, the validity of the CBL model has not been confirmed by fitting the model to IDM behavioral data. The present study aims to examine the CBL model in IDM. We conducted simulations, a preference judgment task for novel contour shapes, and applied computational model analyses to the behavioral data. The results showed that the CBL model with both the chosen and rejected value’s updated were a good fit for the IDM behavioral data compared to the other candidate models. Although previous studies using subjective preference ratings had repeatedly reported changes only in one of the values of either the chosen or rejected items, we demonstrated for the first time both items’ value changes were based solely on IDM choice behavioral data with computational model analyses.

Resting theta activity is associated with specific coding levels in event-related theta activity during conflict monitoring

Brain electrical activity in the theta frequency band is essential for cognitive control (e.g., during conflict monitoring), but is also evident in the resting state. The link between resting state theta activity and its relevance for theta-related neural mechanisms during cognitive control is still undetermined. Yet, theoretical considerations suggest that there may be a connection. To examine the link between resting state theta activity and conflict-related theta activity, we combined temporal EEG signal decomposition methods with time-frequency decomposition and beamforming methods in N = 86 healthy participants. Results indicate that resting state theta activity is closely associated with the strength of conflict-related neural activity at the level of ERPs and total theta power (consisting of phase-locked and nonphase-locked aspects of theta activity). The data reveal that resting state theta activity is related to a specific aspect of conflict-related theta activity, mainly in superior frontal regions and in the supplemental motor area (SMA, BA6) in particular. The signal decomposition showed that only stimulus-related, but not motor-response-related coding levels in the EEG signal and the event-related total theta activity were associated with resting theta activity. This specificity of effects may explain why the association between resting state theta activity and overt conflict monitoring performance may not be as strong as often assumed. The results suggest that resting state theta activity is particularly important to consider for input integration processes during cognitive control.

Resting-state EEG Dynamics Reveals Differences in Network Organization and its Fluctuation between Frequency Bands

Functional connectivity in EEG resting-state is not stable but fluctuates considerably. The aim of this study was to investigate how efficient information flows through a network, i.e. how resting-state EEG networks are organized and whether this organization it also subject to fluctuations. Differences of the network organization (small-worldness), degree of clustered connectivity, and path length as an indicator of how information is integrated into the network across time was compared between theta, alpha and beta bands. We show robust differences in network organization (small-worldness) between frequency bands. Fluctuations in network organization were larger in the theta, compared to the alpha and beta frequency. Variation in network organization and not the frequency of fluctuations differs between frequency bands. Furthermore, the degree of clustered connectivity and its modulation across time is the same across frequency bands, but the path length revealed the same modulatory pattern as the small-world metric. It is therefore the interplay of local processing efficiency and global information processing efficiency in the brain that fluctuates in a frequency-specific way. Properties of how information can be integrated is subject to fluctuations in a frequency-specific way in the resting-state. The possible relevance of these resting-state EEG properties is discussed including its clinical relevance.

Low-frequency neural activity at rest is correlated with the movement-related cortical potentials elicited during both real and imagined movements

Ongoing low-frequency activity in the brain has been shown to indicate an inhibitory neural state; however, the effects of this low-frequency activity on event-related neural processes associated with movement preparation, including movement-related cortical potentials (MRCPs) or more specifically, the motor potential (MP), and event-related desynchronization (ERD) have not been assessed. Using data from 48 participants, the current study examined how ongoing mu and beta frequency activity at rest relates to the MP and mu and beta ERD during real or imagined movement of the fingers. Resting state EEG activity was collected for 1 min, prior to the real and imagined finger movement trials. 20 real and 20 imagined movement trials were collected for each hand. Resting beta activity correlated with MP amplitude during movement trials for both the right (r(47) = -0.304, p = 0.035) and left (r(47) = -0.468, p < 0.001) hands, whereas resting mu correlated with MP amplitude during motor imagery trials of both the right (r(47) = -0.289, p = 0.046) and left (r(47) = -0.330, p = 0.020) hands. Ongoing mu and beta activity was not significantly correlated with mu or beta ERD for both the movement and imagery trials. A connection between low-frequency activity and MP could inform biofeedback procedures that promote a reduction of this activity, ultimately allowing for easier identification of the intent to move.

Temporal integration as "common currency" of brain and self-scale-free activity in resting-state EEG correlates with temporal delay effects on self-relatedness

The self is a multifaceted phenomenon that integrates information and experience across multiple time scales. How temporal integration on the psychological level of the self is related to temporal integration on the neuronal level remains unclear. To investigate temporal integration on the psychological level, we modified a well-established self-matching paradigm by inserting temporal delays. On the neuronal level, we indexed temporal integration in resting-state EEG by two related measures of scale-free dynamics, the power law exponent and autocorrelation window. We hypothesized that the previously established self-prioritization effect, measured as decreased response times or increased accuracy for self-related stimuli, would change with the insertion of different temporal delays between the paired stimuli, and that these changes would be related to temporal integration on the neuronal level. We found a significant self-prioritization effect on accuracy in all conditions with delays, indicating stronger temporal integration of self-related stimuli. Further, we observed a relationship between temporal integration on psychological and neuronal levels: higher degrees of neuronal integration, that is, higher power-law exponent and longer autocorrelation window, during resting-state EEG were related to a stronger increase in the self-prioritization effect across longer temporal delays. We conclude that temporal integration on the neuronal level serves as a template for temporal integration of the self on the psychological level. Temporal integration can thus be conceived as the "common currency" of neuronal and psychological levels of self.

Stronger resting-state neural oscillations associated with wiser advising from the 2nd- but not the 3rd-person perspective

This is the first electroencephalogram study exploring the personal perspective effect on wise advising. Participants advised hypothetical protagonists in life dilemmas from both the 2nd- and 3rd-person perspective. Their advice for each dilemma was rated by two independent raters on wisdom criteria, i.e., metacognitive humility, metacognitive flexibility, and perspective taking. The results revealed that participants felt a significantly shorter psychological distance from protagonists when advising from the 2nd- (vs. the 3rd-) person perspective, p < 0.001. However, there was no significant effect of perspective condition on the wisdom score. Nevertheless, stronger resting-state absolute EEG powers in the frontal lobe were associated with wiser advising from the 2nd-, but not the 3rd-person perspective. Moreover, Z tests revealed that the correlations between the resting-state absolute EEG powers and wisdom scores were significantly stronger during advising from the 2nd- than the 3rd-person perspective. These results suggest that advising from the 2nd-person perspective was more self-related, and mental activities during rest contributed to advising from the 2nd- but not the 3rd-person perspective.

Lessons From Astronomy and Biology for the Mind-Copernican Revolution in Neuroscience

Neuroscience made major progress in unravelling the neural basis of mental features like self, consciousness, affect, etc. However, we nevertheless lack what recently has been described as "missing ingredient" or "common currency" in the relationship between neuronal and mental activity. Rather than putting forward yet another theory of the neural basis of mental features, I here suggest a change in our methodological strategy how to approach the brain, that is, our view or vantage point of the brain. Learning from astronomy (Copernicus) and biology (Darwin), I suggest that we may want to change our currently pre-Copernican vantage point from within brain to a post-Copernican vantage point from beyond brain. Such post-Copernican vantage point from beyond brain allows us taking into view that what happens beyond the brain itself, e.g., the world, and how that shapes the brain and its neural activity, e.g., world-brain relation. We then lend empirical support to the world-brain relation by converging it with Karl Friston's free energy principle that, as we see it, provides a neuro-ecological and therefore post-Copernican view of the brain. That, in turn, allows us taking into view that mental features are shaped by both world and brain and are therefore truly neuro-ecological rather than merely neuronal. This raises the question for the link, e.g., the "missing ingredient" or "common currency" of world brain relation and mental features. Recent empirical evidence suggests that temporo-spatial dynamics may provide such link as it characterizes both the world-brain relation's free energy and mental features, e.g., their spatiotemporality as described in philosophy. Taken together, I here advocate a change in our methodological strategy on how to approach the brain, that is, a shift from a pre-Copernican vantage point from within brain to a post-Copernican vantage point from beyond brain. The latter allows us taking into view that what happens beyond the brain in the world and how that shapes the brain in such a way that it can yield mental features. This amounts to nothing less than a Copernican turn or revolution in neuroscience akin to the ones in astronomy (Copernicus) and biology (Darwin).

"Average is good, extremes are bad" - Non-linear inverted U-shaped relationship between neural mechanisms and functionality of mental features

Traditionally, studies emphasize differences in neural measures between pathological and healthy groups, assuming a binary distinction between the groups, and a linear relationship between neural measures and symptoms. Here, we present four examples that show a continuous relation across the divide of normal and pathological states between neural measures and mental functions. This relation can be characterized by a non-linear inverted-U shaped curve. Along this curve, mid-range or average expression of a neural measure is associated with optimal function of a mental feature (in healthy states), whereas extreme expression, either high or low, is associated with sub-optimal function, and occurs in different neural disorders. Neural expression between the optimal or intermediate and pathological or extreme values is associated with sub-optimal function and at-risk mental states. Thus, this model of neuro-mental relationship can be summarized as "average is good, extremes are bad". By focussing on neuro-mental relationships, this model can facilitate the transition of psychiatry from a categorical to a dimensional and individualized approach needed in the era of precision medicine.

On the relevance of EEG resting theta activity for the neurophysiological dynamics underlying motor inhibitory control

The modulation of theta frequency activity plays a major role in inhibitory control processes. However, the relevance of resting theta band activity and of the ability to spontaneously modulate this resting theta activity for neural mechanisms underlying inhibitory control is elusive. Various theoretical conceptions suggest to take these aspects into consideration. In the current study, we examine whether the strength of resting theta band activity or the ability to modulate the resting state theta activity affects response inhibition. We combined EEG-time frequency decomposition and beamforming in a conflict-modulated Go/Nogo task. A sample of N = 66 healthy subjects was investigated. We show that the strength of resting state theta activity modulates the effects of conflicts during motor inhibitory control. Especially when resting theta activity was low, conflicts strongly affected response inhibition performance and total theta band activity during Nogo trials. These effects were associated with theta-related activity differences in the superior (BA7) and inferior parietal cortex (BA40). The results were very specific for total theta band activity since evoked theta activity and measures of intertrial phase coherency (phase-locking factor) were not affected. The data suggest that the strength of resting state theta activity modulates processing of a theta-related alarm or surprise signal during inhibitory control. The ability to voluntarily modulate theta band activity did not affect conflict-modulated inhibitory control. These findings have important implications for approaches aiming to optimize human cognitive control.

Is temporo-spatial dynamics the "common currency" of brain and mind? In Quest of "Spatiotemporal Neuroscience"

Neuroscience has made considerable progress in unraveling the neural correlates of mental phenomena like self, consciousness, and perception. However, the "common currency" shared between neuronal and mental activity, brain and mind, remains yet unclear. In this article, we propose that the dynamics of time and space provides a "common currency" that connects neuronal and mental features. Time and space are here understood in a dynamic context (as in contemporary physics): that is, in terms of the way the brain's spontaneous activity constructs its spatial and temporal relationships, for instance in terms of functional connectivity and different frequencies of fluctuations. Recruiting recent empirical evidence, we show that the different ways in which the spontaneous activity constructs its "inner time and space" are manifested in distinct mental features. Specifically, we demonstrate how spatiotemporal mechanisms like spatiotemporal repertoire, integration, and speed yield mental features like consciousness, self, and time speed perception. The focus on the brain's spatiotemporal mechanisms entails what we describe as "Spatiotemporal Neuroscience". Spatiotemporal Neuroscience conceives neuronal activity in terms of its temporo-spatial dynamics rather than its various functions (e.g., cognitive, affective, social, etc.) as in other branches of neuroscience (as distinguished from Cognitive, Affective, Cultural, Social, etc. Neuroscience). That allows Spatiotemporal Neuroscience to take into view the so-called 'spatio-temporality' of mental features including their non-causal, intrinsic and transformative relationship with neuronal features. In conclusion, Spatiotemporal Neuroscience opens the door to investigate and ultimately reveal the brain's own temporo-spatial dynamics as the hitherto missing "common currency" of neuronal and mental features.

Frequency of helping friends and helping strangers is explained by different neural signatures

Acts of helping friends and strangers are part of everyday life. However, people vary significantly with respect to how often they help others and with respect to whom they actually help on a day-to-day basis. Despite everyday helping being so pervasive, these individual differences are poorly understood. Here, we used source-localized resting electroencephalography to measure objective and stable individual differences in neural baseline activation in combination with an ecologically valid method that allows assessment of helping behavior in the field. Results revealed that neural baseline activation in the right dorsolateral prefrontal cortex (DLPFC) - a brain region associated with self-control and strategic social behavior - predicts the daily frequency of helping friends, whereas the daily frequency of helping strangers was predicted by neural baseline activation in the dorsomedial prefrontal cortex (DMPFC) - a brain region associated with social cognition processes. These findings offer evidence that distinct neural signatures and associated psychological and cognitive processes may underlie the propensity to help friends and strangers in daily life.

The temporal signature of self: Temporal measures of resting-state EEG predict self-consciousness

The self is the core of our mental life. Previous investigations have demonstrated a strong neural overlap between self-related activity and resting state activity. This suggests that information about self-relatedness is encoded in our brain's spontaneous activity. The exact neuronal mechanisms of such "rest-self containment," however, remain unclear. The present EEG study investigated temporal measures of resting state EEG to relate them to self-consciousness. This was obtained with the self-consciousness scale (SCS) which measures Private, Public, and Social dimensions of self. We demonstrate positive correlations between Private self-consciousness and three temporal measures of resting state activity: scale-free activity as indexed by the power-law exponent (PLE), the auto-correlation window (ACW), and modulation index (MI). Specifically, higher PLE, longer ACW, and stronger MI were related to higher degrees of Private self-consciousness. Finally, conducting eLORETA for spatial tomography, we found significant correlation of Private self-consciousness with activity in cortical midline structures such as the perigenual anterior cingulate cortex and posterior cingulate cortex. These results were reinforced with a data-driven analysis; a machine learning algorithm accurately predicted an individual as having a "high" or "low" Private self-consciousness score based on these measures of the brain's spatiotemporal structure. In conclusion, our results demonstrate that Private self-consciousness is related to the temporal structure of resting state activity as featured by temporal nestedness (PLE), temporal continuity (ACW), and temporal integration (MI). Our results support the hypothesis that self-related information is temporally contained in the brain's resting state. "Rest-self containment" can thus be featured by a temporal signature.

Relation between choice-induced preference change and depression

Most experimental studies of depressive symptom effects on decision-making have examined situations in which a single correct answer exists based on external circumstances (externally guided decision-making, e.g., gambling task). In addition to such decision-making, for decision-making of other types, no correct answer exists based on external circumstances (internally guided decision-making, e.g., preference judgment). For internally guided decision-making, a phenomenon is known by which preference for the chosen item increases and preference for the rejected item is decreased after choosing between two equally preferred items which is designated as choice-induced preference change. Recent reports suggest that this phenomenon is explainable by reinforcement learning theory just as it is with externally guided decision-making. Although many earlier studies have revealed the effects of depression in externally guided decision-making, the relation between depressive symptoms and choice-induced preference change remains unclear. This study investigated the relation between depressive symptoms and choice-induced preference change using the blind choice paradigm. Results show that depressive symptoms are correlated with change in preference of rejected items (Spearman's r = .28, p = .04): depressed individuals tend to show less decreased preference of rejected items. These results indicate that individual differences of depressive symptoms affect choice-induced preference change. We discuss the mechanisms underlying the relation between depression and choice-induced preference change.

How voluntary orienting of attention and alerting modulate costs of conflict processing

There is evidence that pre-cued valid orienting of attention to competing information diminishes costs of brain processing of conflict. Still unclear, because scantily addressed by neuroimaging studies and mostly analyzed by means of behavioral indexing, it is whether conflict undergoes an equivalent modulation by tonic and pre-cued phasic alerting. Here, we investigated the functional relationships between attention orienting, alerting and executive systems using the Attention Network Test (ANT). Both reaction times (RTs) and ERPs were recorded. In line with previous literature, results showed that both RTs and a so-called ERPs conflict negativity (CN), prominent at anterior scalp and indexing conflict processing, were positively modulated by a prior valid orienting of attention onto the location of conflicting stimuli. Indeed, in this condition both kinds of markers showed faster latencies, while CN also reached higher amplitude values than in both alerting conditions, and, in turn, in pre-cued phasic alerting than in uncued tonic alerting. Moreover, while CN was larger over the right hemisphere independent of functional conditions, it was strongly modulated by the latter over the left hemisphere. Our ERP findings support the views of conflict modulation by both orienting and phasic alerting and of a functional integration between attentional brain networks.

Post-response βγ power predicts the degree of choice-based learning in internally guided decision-making

Choosing an option increases a person’s preference for that option. This phenomenon, called choice-based learning (CBL), has been investigated separately in the contexts of internally guided decision-making (IDM, e.g., preference judgment), for which no objectively correct answer exists, and externally guided decision making (EDM, e.g., perceptual decision making), for which one objectively correct answer exists. For the present study, we compared decision making of these two types to examine differences of underlying neural processes of CBL. As IDM and EDM tasks, occupation preference judgment and salary judgment were used, respectively. To compare CBL for the two types of decision making, we developed a novel measurement of CBL: decision consistency. When CBL occurs, decision consistency is higher in the last-half trials than in first-half trials. Electroencephalography (EEG) data have demonstrated that the change of decision consistency is positively correlated with the fronto-central beta–gamma power after response in the first-half trials for IDM, but not for EDM. Those results demonstrate for the first time the difference of CBL between IDM and EDM. The fronto-central beta–gamma power is expected to reflect a key process of CBL, specifically for IDM.

How do abnormalities in the brain's spontaneous activity translate into symptoms in schizophrenia? From an overview of resting state activity findings to a proposed spatiotemporal psychopathology

Schizophrenia is a complex neuropsychiatric disorder with a variety of symptoms that include sensorimotor, affective, cognitive, and social changes. The exact neuronal mechanisms underlying these symptoms remain unclear though. Neuroimaging has focused mainly on the brain's extrinsic activity, specifically task-evoked or stimulus-induced activity, as related to the sensorimotor, affective, cognitive, and social functions. Recently, the focus has shifted to the brain's spontaneous activity, otherwise known as its resting state activity. While various spatial and temporal abnormalities have been observed in spontaneous activity in schizophrenia, their meaning and significance for the different psychopathological symptoms in schizophrenia, are yet to be defined. The first aim in this paper is to provide an overview of recent findings concerning changes in the spatial (e.g., functional connectivity) and temporal (e.g., couplings between different frequency fluctuations) properties of spontaneous activity in schizophrenia. The second aim is to link these spatiotemporal changes to the various psychopathological symptoms of schizophrenia, with a specific focus on basic symptoms, formal thought disorder, and ego-disturbances. Based on the various findings described, we postulate that the spatiotemporal changes on the neuronal level of the brain's spontaneous activity transform into corresponding spatiotemporal changes on the psychological level which, in turn, leads to the different kinds of psychopathological symptoms. We consequently suggest a spatiotemporal rather than cognitive or sensory approach to the condition, amounting to what we describe as "Spatiotemporal Psychopathology".

How to Link Brain and Experience? Spatiotemporal Psychopathology of the Lived Body

The focus of the present article is on sketching a psychopathology of the body in schizophrenia and linking it to brain activity. This is done providing converging data from psychopathological evidence (phenomenal), phenomenological contructs (trans-phenomenal) and neuroscientific measures (pre-phenomenal). The phenomenal level is the detailed documentation of the patients' subjective anomalous experiences. These phenomena are explicit contents in the patients' field of consciousness. The trans-phenomenal level targets the implicit yet operative matrix that underlies these anomalous subjective experiences. Abnormal phenomena are viewed as expressions of a modification of trans-phenomenal matrix, that is, in terms of an abnormal synthesis or integration through time of intero-, proprio- and extero-ceptive stimuli. Finally, we link the abnormalities of the trans-phenomenal matrix to pre-phenomenal alterations of the brain resting state and of its spatio-temporal organization, as documented by neurobiological methods providing spatial and temporal resolution of intrinsic brain activity (with many features of the resting state remaining yet unclear though). Based on phenomenological research, the body in schizophrenia is typically experienced in an itemized way as an object external to one's self and unrelated to events in the external world. Based on neurobiological data, we tentatively hypothesize that such anomalies of the lived body are related to decreased integration between intero-, extero- and proprioceptive experiences by the brain's spontaneous activity and its temporal structure. Taken all together, this suggests that we view abnormalities of bodily experience in terms of their underlying abnormal spatiotemporal features which, as we suppose, can be traced back to the spatiotemporal features of the brain's spontaneous activity.

Is the Sense of Agency in Schizophrenia Influenced by Resting-State Variation in Self-Referential Regions of the Brain?

Schizophrenia is a disturbance of the self, of which the attribution of agency is a major component. In this article, we review current theories of the Sense of Agency, their relevance to schizophrenia, and propose a novel framework for future research. We explore some of the models of agency, in which both bottom-up and top-down processes are implicated in the genesis of agency. We further this line of inquiry by suggesting that ongoing neurological activity (the brain's resting state) in self-referential regions of the brain can provide a deeper level of influence beyond what the current models capture. Based on neuroimaging studies, we suggest that aberrant activity in regions such as the default mode network of individuals with schizophrenia can lead to a misattribution of internally/externally generated stimuli. This can result in symptoms such as thought insertion and delusions of control. Consequently, neuroimaging can contribute to a more comprehensive conceptualization and measurement of agency and potential treatment implications.

Is the self a higher-order or fundamental function of the brain? The "basis model of self-specificity" and its encoding by the brain's spontaneous activity

What is the self? This is a question that has long been discussed in (Western) philosophy where the self is traditionally conceived a higher-order function at the apex or pinnacle of all functions. This tradition has been transferred to recent neuroscience where the self is often considered to be a higher-order cognitive function reflected in memory and other high-level judgements. However, other lines of research demonstrate a close and intimate relationship between self-specificity and more basic functions like perceptions, emotions and reward. This paper focuses on the relationship between self-specificity and other basic functions relating to emotions, reward and perception. I propose the basis model that conceives self-specificity as a fundamental feature of the brain's spontaneous activity. This is supported by recent findings showing rest-self overlap in midline regions as well as findings demonstrating that the resting state can predict subsequent degrees of self-specificity. I conclude that such self-specificity in the brain's spontaneous activity may be central in linking the self to either internal or external stimuli. This may also provide the basis for coding the self as subject in relation to internal (i.e., self-consciousness) or external (i.e., phenomenal consciousness) mental events.

How does the 'rest-self overlap' mediate the qualitative and automatic features of self-reference?

The target article points out the qualitative and automatic features of self-reference while leaving open the underlying neural mechanisms. Based on empirical evidence about rest-self overlap and rest-stimulus interaction being special for self-related stimuli, I postulate that the resting state shows self-specific organization. The resting state's self-specific organization may be encoded by activity balances between different networks which in turn predispose the qualitative features of subsequent self-related stimulus-induced activity in, for instance, SAN as well as the automatic features of self-reference effects.

The motor cost of telling lies: electrocortical signatures and personality foundations of spontaneous deception

Although universal, lying is generally considered immoral behavior. Most neuroscience studies on lying sanction or instruct deceptive behaviors and thus might fail to acknowledge the significance of lie-related moral conflicts. By combining electroencephalogram (EEG) recordings with a novel paradigm in which participants decided freely whether to deceive another person, we have generated indices of the cognitive (reaction times and stimulus-locked event-related components) and moral (readiness potential and its correlations with deception-related personality traits) cost of spontaneous deception. Our data fail to support the consensus that deception is cognitively more demanding than truth telling, suggesting that spontaneous deception, as opposed to lying out of requirement, might not mandate additional cognitive workload. Interestingly, lying was associated with decreased motor readiness, an event-related potential (ERP) component that is linked to motor preparation of self-determined actions and modulated when we face moral dilemmas. Notably, this reduction was less extensive in manipulative participants and greater in those who cared highly about their impression management. Our study expands on previous findings on deception by associating a cortical marker of reduced preparation to act with individual differences in moral cognition.

Neural correlates of HIV risk feelings

Field studies on HIV risk perception suggest that people rely on impressions they have about the safety of their partner. The present fMRI study investigated the neural correlates of the intuitive perception of risk. First, during an implicit condition, participants viewed a series of unacquainted persons and performed a task unrelated to HIV risk. In the following explicit condition, participants evaluated the HIV risk for each presented person. Contrasting responses for high and low HIV risk revealed that risky stimuli evoked enhanced activity in the anterior insula and medial prefrontal regions, which are involved in salience processing and frequently activated by threatening and negative affect-related stimuli. Importantly, neural regions responding to explicit HIV risk judgments were also enhanced in the implicit condition, suggesting a neural mechanism for intuitive impressions of riskiness. Overall, these findings suggest the saliency network as neural correlate for the intuitive sensing of risk.