Network analysis reveals increased integration during emotional and motivational processing

Identification of functional dynamic brain states based on graph attention networks

Investigation of the functional dynamics of the human brain can help to unveil inherent cognitive systems. In this study, we adopted a graph attention network-based anomaly detection technique to identify abrupt changes in functional time series. We used the resting-state functional magnetic resonance imaging data of 1010 participants from the Human Connectome Project. By applying multivariate time series anomaly detection using the graph attention network approach, we identified three distinct brain states, termed S1, S2, and S3. We further generated low-dimensional representations of functional connectivity (i.e., gradients) for each brain state and compared these gradients among brain states. S1 and S3 exhibited segregated network patterns, whereas S2 displayed more integrated patterns. A topological analysis based on the graph measures revealed that the integrated state (S2) exhibited strong inter-regional connectivity. Further, the two segregated states exhibited distinct patterns, with S1 being more involved in the somatomotor network and S3 being related to higher-order association areas. When we assessed the transitions between brain states, transitions between the low-level sensory (S1) and higher-order default mode states (S3), as well as between the sensory-focused segregated state (S1) and integrated state (S2), were associated with sensory/motor and memory-related tasks. In contrast, the transitions between the integrated (S2) and segregated states with higher centrality in the default mode region (S3) were found to be related to language and reward tasks. These findings indicate that the proposed approach captures changes in individual participant-level brain dynamics, thereby enabling the assessment of inherently dynamic brain systems.

Dynamical changes of interaction across functional brain communities during propofol-induced sedation

It is crucial to understand how anesthetics disrupt information transmission within the whole-brain network and its hub structure to gain insight into the network-level mechanisms underlying propofol-induced sedation. However, the influence of propofol on functional integration, segregation, and community structure of whole-brain networks were still unclear. We recruited 12 healthy subjects and acquired resting-state functional magnetic resonance imaging data during 5 different propofol-induced effect-site concentrations (CEs): 0, 0.5, 1.0, 1.5, and 2.0 μg/ml. We constructed whole-brain functional networks for each subject under different conditions and identify community structures. Subsequently, we calculated the global and local topological properties of whole-brain network to investigate the alterations in functional integration and segregation with deepening propofol sedation. Additionally, we assessed the alteration of key nodes within the whole-brain community structure at each effect-site concentrations level. We found that global participation was significantly increased at high effect-site concentrations, which was mediated by bilateral postcentral gyrus. Meanwhile, connector hubs appeared and were located in posterior cingulate cortex and precentral gyrus at high effect-site concentrations. Finally, nodal participation coefficients of connector hubs were closely associated to the level of sedation. These findings provide valuable insights into the relationship between increasing propofol dosage and enhanced functional interaction within the whole-brain networks.

Distinct Patterns of Connectivity between Brain Regions Underlie the Intra-Modal and Cross-Modal Value-Driven Modulations of the Visual Cortex

Past reward associations may be signaled from different sensory modalities; however, it remains unclear how different types of reward-associated stimuli modulate sensory perception. In this human fMRI study (female and male participants), a visual target was simultaneously presented with either an intra- (visual) or a cross-modal (auditory) cue that was previously associated with rewards. We hypothesized that, depending on the sensory modality of the cues, distinct neural mechanisms underlie the value-driven modulation of visual processing. Using a multivariate approach, we confirmed that reward-associated cues enhanced the target representation in early visual areas and identified the brain valuation regions. Then, using an effective connectivity analysis, we tested three possible patterns of connectivity that could underlie the modulation of the visual cortex: a direct pathway from the frontal valuation areas to the visual areas, a mediated pathway through the attention-related areas, and a mediated pathway that additionally involved sensory association areas. We found evidence for the third model demonstrating that the reward-related information in both sensory modalities is communicated across the valuation and attention-related brain regions. Additionally, the superior temporal areas were recruited when reward was cued cross-modally. The strongest dissociation between the intra- and cross-modal reward-driven effects was observed at the level of the feedforward and feedback connections of the visual cortex estimated from the winning model. These results suggest that, in the presence of previously rewarded stimuli from different sensory modalities, a combination of domain-general and domain-specific mechanisms are recruited across the brain to adjust the visual perception.SIGNIFICANCE STATEMENT Reward has a profound effect on perception, but it is not known whether shared or disparate mechanisms underlie the reward-driven effects across sensory modalities. In this human fMRI study, we examined the reward-driven modulation of the visual cortex by visual (intra-modal) and auditory (cross-modal) reward-associated cues. Using a model-based approach to identify the most plausible pattern of inter-regional effective connectivity, we found that higher-order areas involved in the valuation and attentional processing were recruited by both types of rewards. However, the pattern of connectivity between these areas and the early visual cortex was distinct between the intra- and cross-modal rewards. This evidence suggests that, to effectively adapt to the environment, reward signals may recruit both domain-general and domain-specific mechanisms.

Controversies and progress on standardization of large-scale brain network nomenclature

Progress in scientific disciplines is accompanied by standardization of terminology. Network neuroscience, at the level of macroscale organization of the brain, is beginning to confront the challenges associated with developing a taxonomy of its fundamental explanatory constructs. The Workgroup for HArmonized Taxonomy of NETworks (WHATNET) was formed in 2020 as an Organization for Human Brain Mapping (OHBM)-endorsed best practices committee to provide recommendations on points of consensus, identify open questions, and highlight areas of ongoing debate in the service of moving the field toward standardized reporting of network neuroscience results. The committee conducted a survey to catalog current practices in large-scale brain network nomenclature. A few well-known network names (e.g., default mode network) dominated responses to the survey, and a number of illuminating points of disagreement emerged. We summarize survey results and provide initial considerations and recommendations from the workgroup. This perspective piece includes a selective review of challenges to this enterprise, including (1) network scale, resolution, and hierarchies; (2) interindividual variability of networks; (3) dynamics and nonstationarity of networks; (4) consideration of network affiliations of subcortical structures; and (5) consideration of multimodal information. We close with minimal reporting guidelines for the cognitive and network neuroscience communities to adopt.

Persistent Frustration-Induced Reconfigurations of Brain Networks Predict Individual Differences in Irritability

OBJECTIVE

Aberrant responses to frustration are central mechanisms of pediatric irritability, which is a common reason for psychiatric consultation and a risk factor for affective disorders and suicidality. This pilot study aimed to characterize brain network configuration during and after frustration and test whether characteristics of networks formed during or after frustration relate to irritability.

METHOD

During functional magnetic resonance imaging, a transdiagnostic sample enriched for irritability (N = 66, mean age = 14.0 years, 50% female participants) completed a frustration-induction task flanked by pretask and posttask resting-state scans. We first tested whether and how the organization of brain regions (ie, nodes) into networks (ie, modules) changes during and after frustration. Then, using a train/test/held-out procedure, we aimed to predict past-week irritability from global efficiency (Eglob) (ie, capacity for parallel information processing) of these modules.

RESULTS

Two modules present in the baseline pretask resting-state scan (one encompassing anterior default mode and temporolimbic regions and one consisting of frontoparietal regions) contributed most to brain circuit reorganization during and after frustration. Only Eglob of modules in the posttask resting-state scans (ie, after frustration) predicted irritability symptoms. Self-reported irritability was predicted by Eglob of a frontotemporal-limbic module. Parent-reported irritability was predicted by Eglob of ventral-prefrontal-subcortical and somatomotor-parietal modules.

CONCLUSION

These pilot results suggest the importance of the postfrustration recovery period in the pathophysiology of irritability. Eglob in 3 specific posttask modules, involved in emotion processing, reward processing, or motor function, predicted irritability. These findings, if replicated, could represent specific intervention targets for irritability.

Dynamic segregation and integration of brain functional networks associated with emotional arousal

The organization of brain functional networks dynamically changes with emotional stimuli, but its relationship to emotional behaviors is still unclear. In the DEAP dataset, we used the nested-spectral partition approach to identify the hierarchical segregation and integration of functional networks and investigated the dynamic transitions between connectivity states under different arousal conditions. The frontal and right posterior parietal regions were dominant for network integration whereas the bilateral temporal, left posterior parietal, and occipital regions were responsible for segregation and functional flexibility. High emotional arousal behavior was associated with stronger network integration and more stable state transitions. Crucially, the connectivity states of frontal, central, and right parietal regions were closely related to arousal ratings in individuals. Besides, we predicted the individual emotional performance based on functional connectivity activities. Our results demonstrate that brain connectivity states are closely associated with emotional behaviors and could be reliable and robust indicators for emotional arousal.

Irritability in early to middle childhood: Cross-sectional and longitudinal associations with resting state amygdala and ventral striatum connectivity

BACKGROUND

Irritability is a common symptom that may affect children's brain development. This study aims to (1) characterize age-dependent and age-independent neural correlates of irritability in a sample of 4-8 year old children, and (2) examine early irritability as a predictor of change in brain connectivity over time.

METHODS

Typically developing children, ages 4-8 years, with varying levels of irritability were included. Resting state fMRI and parent-rated irritability (via Child Behavior Checklist; CBCL) were collected at up to three time points, resulting in a cross-sectional sample at baseline (N = 176, M = 6.27, SD = 1.49), and two subsamples consisting of children who were either 4 or 6 years old at baseline that were followed longitudinally for two additional timepoints, one- and two-years post-baseline. That is, a "younger" cohort (age 4 at baseline, n = 34, M age = 4.44, SD = 0.25) and an "older" cohort (age 6 at baseline, n = 29, M age = 6.50, SD = 0.30). Across our exploratory analyses, we examined how irritability related to seed-based intrinsic connectivity via whole-brain connectivity ANCOVAs using the left and right amygdala, and left and right ventral striatum as seed regions.

RESULTS

Cross-sectionally, higher levels of irritability were associated with greater amygdala connectivity with the posterior cingulate, controlling for child age. No age-dependent effects were observed in the cross-sectional analyses. Longitudinal analyses in the younger cohort revealed that early higher vs. lower levels of irritability, controlling for later irritability, were associated with decreases in amygdala and ventral striatum connectivity with multiple frontal and parietal regions over time. There were no significant findings in the older cohort.

CONCLUSIONS

Findings suggest that irritability is related to altered neural connectivity during rest regardless of age in early to middle childhood and that early childhood irritability may be linked to altered changes in neural connectivity over time. Understanding how childhood irritability interacts with neural processes can inform pathophysiological models of pediatric irritability and the development of targeted mechanistic interventions.

The Entangled Brain

The Entangled Brain (Pessoa, L., 2002. MIT Press) promotes the idea that we need to understand the brain as a complex, entangled system. Why does the complex systems perspective, one that entails emergent properties, matter for brain science? In fact, many neuroscientists consider these ideas a distraction. We discuss three principles of brain organization that inform the question of the interactional complexity of the brain: (1) massive combinatorial anatomical connectivity; (2) highly distributed functional coordination; and (3) networks/circuits as functional units. To motivate the challenges of mapping structure and function, we discuss neural circuits illustrating the high anatomical and functional interactional complexity typical in the brain. We discuss potential avenues for testing for network-level properties, including those relying on distributed computations across multiple regions. We discuss implications for brain science, including the need to characterize decentralized and heterarchical anatomical-functional organization. The view advocated has important implications for causation, too, because traditional accounts of causality provide poor candidates for explanation in interactionally complex systems like the brain given the distributed, mutual, and reciprocal nature of the interactions. Ultimately, to make progress understanding how the brain supports complex mental functions, we need to dissolve boundaries within the brain-those suggested to be associated with perception, cognition, action, emotion, motivation-as well as outside the brain, as we bring down the walls between biology, psychology, mathematics, computer science, philosophy, and so on.

Considering dynamic nature of the brain: the clinical importance of connectivity variability in machine learning classification and prediction

The brain connectivity of resting-state fMRI (rs-fMRI) represents an intrinsic state of brain architecture, and it has been used as a useful neural marker for detecting psychiatric conditions as well as for predicting psychosocial characteristics. However, most studies using brain connectivity have focused more on the strength of functional connectivity over time (static-FC) but less attention to temporal characteristics of connectivity changes (FC-variability). The primary goal of the current study was to investigate the effectiveness of using the FC-variability in classifying an individual's pathological characteristics from others and predicting psychosocial characteristics. In addition, the current study aimed to prove that benefits of the FC-variability are reliable across various analysis procedures. To this end, three open public large resting-state fMRI datasets including individuals with Autism Spectrum Disorder (ABIDE; N = 1249), Schizophrenia disorder (COBRE; N = 145), and typical development (NKI; N = 672) were utilized for the machine learning (ML) classification and prediction based on their static-FC and the FC-variability metrics. To confirm the robustness of FC-variability utility, we benchmarked the ML classification and prediction with various brain parcellations and sliding window parameters. As a result, we found that the ML performances were significantly improved when the ML included FC-variability features in classifying pathological populations from controls (e.g., individuals with autism spectrum disorder vs. typical development) and predicting psychiatric severity (e.g., score of autism diagnostic observation schedule), regardless of parcellation selection and sliding window size. Additionally, the ML performance deterioration was significantly prevented with FC-variability features when excessive features were inputted into the ML models, yielding more reliable results. In conclusion, the current finding proved the usefulness of the FC-variability and its reliability.

Representational Coding of Overt and Covert Orienting of Visuospatial Attention in the Frontoparietal Network

Orienting of visuospatial attention refers to reallocation of attentional focus from one target or location to another and can occur either with (overt) or without (covert) eye movement. Although it has been demonstrated that both types of orienting commonly involve frontal and parietal brain regions as the frontoparietal network (FPN), the underlying representational coding of these two types of orienting remains unclear. In this functional magnetic resonance imaging study, participants performed a task that elicited overt and covert orienting to endogenously or exogenously cued targets with eye-tracking to monitor eye movement. Although the FPN was commonly activated for both overt and covert orienting, multivariate patterns of the activation of voxels in the FPN accurately predicted whether eye movements were involved or not during orienting. These overt- and covert-preferred voxels were topologically distributed as distinct and interlaced clusters in a millimeter scale. Inclusion of the two types of clusters predicted orienting type more accurately than one type of clusters alone. These findings suggest that overt and covert orienting are represented by interdependent functional clusters of neuronal populations in regions of the FPN, which might reflect a generalizable principle in the nervous system for functional organization of closely associated processes.

Features and Extra-Striate Body Area Representations of Diagnostic Body Parts in Anger and Fear Perception

Social species perceive emotion via extracting diagnostic features of body movements. Although extensive studies have contributed to knowledge on how the entire body is used as context for decoding bodily expression, we know little about whether specific body parts (e.g., arms and legs) transmit enough information for body understanding. In this study, we performed behavioral experiments using the Bubbles paradigm on static body images to directly explore diagnostic body parts for categorizing angry, fearful and neutral expressions. Results showed that subjects recognized emotional bodies through diagnostic features from the torso with arms. We then conducted a follow-up functional magnetic resonance imaging (fMRI) experiment on body part images to examine whether diagnostic parts modulated body-related brain activity and corresponding neural representations. We found greater activations of the extra-striate body area (EBA) in response to both anger and fear than neutral for the torso and arms. Representational similarity analysis showed that neural patterns of the EBA distinguished different bodily expressions. Furthermore, the torso with arms and whole body had higher similarities in EBA representations relative to the legs and whole body, and to the head and whole body. Taken together, these results indicate that diagnostic body parts (i.e., torso with arms) can communicate bodily expression in a detectable manner.

Life Habits and Mental Health: Behavioural Addiction, Health Benefits of Daily Habits, and the Reward System

In this review, the underlying mechanisms of health benefits and the risk of habitual behaviours such as internet use and media multitasking were explored, considering their associations with the reward/motivation system. The review highlights that several routines that are beneficial when undertaken normally may evolve into excessive behaviour and have a negative impact, as represented by "the inverted U-curve model". This is especially critical in the current era, where technology like the internet has become mainstream despite the enormous addictive risk. The understanding of underlying mechanisms of behavioural addiction and optimal level of habitual behaviours for mental health benefits are deepened by shedding light on some findings of neuroimaging studies to have hints to facilitate better management and prevention strategies of addictive problems. With the evolution of the world, and the inevitable use of some technologies that carry the risk of addiction, more effective strategies for preventing and managing addiction are in more demand than before, and the insights of this study are also valuable foundations for future research.

Neurodevelopment of the incentive network facilitates motivated behaviour from adolescence to adulthood

The ability to enhance motivated performance through incentives is crucial to guide and ultimately optimise the outcome of goal-directed behaviour. It remains largely unclear how motivated behaviour and performance develops particularly across adolescence. Here, we used computational fMRI to assess how response speed and its underlying neural circuitry are modulated by reward and loss in a monetary incentive delay paradigm. We demonstrate that maturational fine-tuning of functional coupling within the cortico-striatal incentive circuitry from adolescence to adulthood facilitates the ability to enhance performance selectively for higher subjective values. Additionally, during feedback, we found developmental sex differences of striatal representations of reward prediction errors in an exploratory analysis. Our findings suggest that a reduced capacity to utilise subjective value for motivated behaviour in adolescence is rooted in immature information processing in the incentive system. This indicates that the neurocircuitry for coordination of incentivised, motivated cognitive control acts as a bottleneck for behavioural adjustments in adolescence.

Prenatal exposure to maternal depression is related to the functional connectivity organization underlying emotion perception in 8-10-month-old infants - Preliminary findings

Emotion perception is critical for infant's social development. Mother's mood during pregnancy has been associated with infants' emotional developmental risks. Graphtheory analysis was applied on EEG data recorded from 35, 8-to-10-month-old-infants prenatally exposed to high or low depressed symptoms, while viewing happy and sad faces. We found an interaction between group and emotion such that infants exposed to high-depressed-symptoms showed higher modularity - reflecting reduced perceptual-dynamics - for viewing happy emotions compared to sad. The opposite was observed for infants exposed to low-depressive-symptoms. These preliminary findings suggest that prenatal depressive mood may shape early functional organization for viewing emotional faces.

Neural profiles of observing acceptance and rejection decisions in human mate choice copying

Mate choice copying refers to an agent copying the choice for a potential sexual/romantic partner made by a relevant model and has been observed across many species. This study investigated the neural profiles of two copying strategies in humans - acceptance and rejection copying - using functional magnetic resonance imaging (fMRI). Female participants observed female models accepting, rejecting, or being undecided about (control), males as potential romantic partners before and after rating their own willingness to choose the same males. We found that observing acceptance shifted participants' own choices towards acceptance, while observing rejection shifted participants' choices towards rejection. A network of motivation-, conflict- and reinforcement learning related brain regions was activated for observing the models' decisions. The rostral anterior cingulate gyrus (rACCg) and the caudate in particular were activated more strongly when observing acceptance. Activation in the inferior parietal lobe directly scaled with the magnitude of changes in choices after observing acceptance, while activation in the ACCg also scaled with changes after observing rejection. These findings point to partly dissociable neural profiles for copying strategies that might be linked to different contributions of incentive-driven and vicarious motivation, potentially reflecting the presence or absence of internalised reward experiences.

Anxiety during abstinence from alcohol: A systematic review of rodent and human evidence for the anterior insula's role in the abstinence network

Alcohol Use Disorder (AUD) is a chronic, relapsing disease that impacts almost a third of Americans. Despite effective treatments for attaining sobriety, the majority of patients relapse within a year, making relapse a substantial barrier to long-term treatment success. A major factor contributing to relapse is heightened negative affect that results from the combination of abstinence-related increases in stress-reactivity and decreases in reward sensitivity. Substantial research has contributed to the understanding of reward-related changes in AUD. However, less is known about anxiety during abstinence, a critical component of understanding addiction as anxiety during abstinence can trigger relapse. Most of what we know about abstinence-related negative affect comes from rodent studies which have identified key brain regions responsible for abstinence-related behaviors. This abstinence network is composed of brain regions that make up the extended amygdala: the nucleus accumbens (NAcc), the central nucleus of the amygdala (CeA), and the bed nucleus of the stria terminalis (BNST). More recently, emerging evidence from rodent and human studies suggests a fourth brain region, the anterior insula, might be part of the abstinence network. Here, we review current rodent and human literature on the extended amygdala's role in alcohol abstinence and anxiety, present evidence for the anterior insula's role in the abstinence network, and provide future directions for research to further elucidate the neural underpinnings of abstinence in humans. A better understanding of the abstinence network is critical toward understanding and possibly preventing relapse in AUD.

Large-Scale Morphological Network Efficiency of Human Brain: Cognitive Intelligence and Emotional Intelligence

Network efficiency characterizes how information flows within a network, and it has been used to study the neural basis of cognitive intelligence in adolescence, young adults, and elderly adults, in terms of the white matter in the human brain and functional connectivity networks. However, there were few studies investigating whether the human brain at different ages exhibited different underpins of cognitive and emotional intelligence (EI) from young adults to the middle-aged group, especially in terms of the morphological similarity networks in the human brain. In this study, we used 65 datasets (aging 18–64), including sMRI and behavioral measurements, to study the associations of network efficiency with cognitive intelligence and EI in young adults and the middle-aged group. We proposed a new method of defining the human brain morphological networks using the morphological distribution similarity (including cortical volume, surface area, and thickness). Our results showed inverted age × network efficiency interactions in the relationship of surface-area network efficiency with cognitive intelligence and EI: a negative age × global efficiency (nodal efficiency) interaction in cognitive intelligence, while a positive age × global efficiency (nodal efficiency) interaction in EI. In summary, this study not only proposed a new method of morphological similarity network but also emphasized the developmental effects on the brain mechanisms of intelligence from young adult to middle-aged groups and may promote mental health study on the middle-aged group in the future.

Activation of the cognitive control network associated with information uncertainty

The cognitive control network (CCN) that comprises regions of the frontoparietal network, the cingulo-opercular network, and other sub-cortical regions as core structures is commonly activated by events with an increase in information uncertainty. However, it is not clear whether this CCN activation is associated with both information entropy that represents the information conveyed by the context formed by a sequence of events and the surprise that quantifies the information conveyed by a specific type of event in the context. We manipulated entropy and surprise in this functional magnetic resonance imaging study by varying the probability of occurrence of two types of events in both the visual and auditory modalities and measured brain response as a function of entropy and surprise. We found that activation in regions of the CCN increased as a function of entropy and surprise in both the visual and auditory tasks. The frontoparietal network and additional structures in the CCN mediated the relationship between these information measures and behavioral response. These results suggest that the CCN is a high-level modality-general neural entity for the control of the processing of information conveyed by both context and event.

Context Matters: Situational Stress Impedes Functional Reorganization of Intrinsic Brain Connectivity during Problem-Solving

Extensive research has established a relationship between individual differences in brain activity in a resting state and individual differences in behavior. Conversely, when individuals are engaged in various tasks, certain task-evoked reorganization occurs in brain functional connectivity, which can consequently influence individuals' performance as well. Here, we show that resting state and task-dependent state brain patterns interact as a function of contexts engendering stress. Findings revealed that when the resting state connectome was examined during performance, the relationship between connectome strength and performance only remained for participants under stress (who also performed worse than all other groups on the math task), suggesting that stress preserved brain patterns indicative of underperformance whereas non-stressed individuals spontaneously transitioned out of these patterns. Results imply that stress may impede the reorganization of a functional network in task-evoked brain states. This hypothesis was subsequently verified using graph theory measurements on a functional network, independent of behavior. For participants under stress, the functional network showed less topological alterations compared to non-stressed individuals during the transition from resting state to task-evoked state. Implications are discussed for network dynamics as a function of context.

High-Expanding Regions in Primate Cortical Brain Evolution Support Supramodal Cognitive Flexibility

Primate cortical evolution has been characterized by massive and disproportionate expansion of a set of specific regions in the neocortex. The associated increase in neocortical neurons comes with a high metabolic cost, thus the functions served by these regions must have conferred significant evolutionary advantage. In the present series of analyses, we show that evolutionary high-expanding cortex - as estimated from patterns of surface growth from several primate species - shares functional connections with different brain networks in a context-dependent manner. Specifically, we demonstrate that high-expanding cortex is characterized by high internetwork functional connectivity; is recruited flexibly over many different cognitive tasks; and changes its functional coupling pattern between rest and a multimodal task-state. The capacity of high-expanding cortex to connect flexibly with various specialized brain networks depending on particular cognitive requirements suggests that its selective growth and sustainment in evolution may have been linked to an involvement in supramodal cognition. In accordance with an evolutionary-developmental view, we find that this observed ability of high-expanding regions - to flexibly modulate functional connections as a function of cognitive state - emerges gradually through childhood, with a prolonged developmental trajectory plateauing in young adulthood.

Psychological mechanisms and functions of 5-HT and SSRIs in potential therapeutic change: Lessons from the serotonergic modulation of action selection, learning, affect, and social cognition

Uncertainty regarding which psychological mechanisms are fundamental in mediating SSRI treatment outcomes and wide-ranging variability in their efficacy has raised more questions than it has solved. Since subjective mood states are an abstract scientific construct, only available through self-report in humans, and likely involving input from multiple top-down and bottom-up signals, it has been difficult to model at what level SSRIs interact with this process. Converging translational evidence indicates a role for serotonin in modulating context-dependent parameters of action selection, affect, and social cognition; and concurrently supporting learning mechanisms, which promote adaptability and behavioural flexibility. We examine the theoretical basis, ecological validity, and interaction of these constructs and how they may or may not exert a clinical benefit. Specifically, we bridge crucial gaps between disparate lines of research, particularly findings from animal models and human clinical trials, which often seem to present irreconcilable differences. In determining how SSRIs exert their effects, our approach examines the endogenous functions of 5-HT neurons, how 5-HT manipulations affect behaviour in different contexts, and how their therapeutic effects may be exerted in humans - which may illuminate issues of translational models, hierarchical mechanisms, idiographic variables, and social cognition.

Functional Connectome Analyses Reveal the Human Olfactory Network Organization

The olfactory system is uniquely heterogeneous, performing multifaceted functions (beyond basic sensory processing) across diverse, widely distributed neural substrates. While knowledge of human olfaction continues to grow, it remains unclear how the olfactory network is organized to serve this unique set of functions.

The olfactory system is uniquely heterogeneous, performing multifaceted functions (beyond basic sensory processing) across diverse, widely distributed neural substrates. While knowledge of human olfaction continues to grow, it remains unclear how the olfactory network is organized to serve this unique set of functions. Leveraging a large and high-quality resting-state functional magnetic resonance imaging (rs-fMRI) dataset of nearly 900 participants from the Human Connectome Project (HCP), we identified a human olfactory network encompassing cortical and subcortical regions across the temporal and frontal lobes. Highlighting its reliability and generalizability, the connectivity matrix of this olfactory network mapped closely onto that extracted from an independent rs-fMRI dataset. Graph theoretical analysis further explicated the organizational principles of the network. The olfactory network exhibits a modular composition of three (i.e., the sensory, limbic, and frontal) subnetworks and demonstrates strong small-world properties, high in both global integration and local segregation (i.e., circuit specialization). This network organization thus ensures the segregation of local circuits, which are nonetheless integrated via connecting hubs [i.e., amygdala (AMY) and anterior insula (INSa)], thereby enabling the specialized, yet integrative, functions of olfaction. In particular, the degree of local segregation positively predicted olfactory discrimination performance in the independent sample, which we infer as a functional advantage of the network organization. In sum, an olfactory functional network has been identified through the large HCP dataset, affording a representative template of the human olfactory functional neuroanatomy. Importantly, the topological analysis of the olfactory network provides network-level insights into the remarkable functional specialization and spatial segregation of the olfactory system.

Supramodal Mechanisms of the Cognitive Control Network in Uncertainty Processing

Information processing under conditions of uncertainty requires the involvement of cognitive control. Despite behavioral evidence of the supramodal function (i.e., independent of sensory modality) of cognitive control, the underlying neural mechanism needs to be directly tested. This study used functional magnetic imaging together with visual and auditory perceptual decision-making tasks to examine brain activation as a function of uncertainty in the two stimulus modalities. The results revealed a monotonic increase in activation in the cortical regions of the cognitive control network (CCN) as a function of uncertainty in the visual and auditory modalities. The intrinsic connectivity between the CCN and sensory regions was similar for the visual and auditory modalities. Furthermore, multivariate patterns of activation in the CCN predicted the level of uncertainty within and across stimulus modalities. These findings suggest that the CCN implements cognitive control by processing uncertainty as abstract information independent of stimulus modality.

Differential color tuning of the mesolimbic reward system

Visual wavelengths are not only associated with the subjective experience of color but also have long been thought to regulate affect. Here we examined the attracting rewarding properties of opposite ends of the wavelength spectrum, as well as their individual variation. As reward is multifaceted, we sought convergent evidence from subjective and objective behavioral and attentional indices, as well as its neural reward system bases. On average, short (blue) relative to long (red) wavelengths were judged subjectively more pleasant and had objectively greater behavioral and attentional salience, regulating speed of simple color discriminations and perception of temporal order. Consistent with reward, these color effects were magnified following monetary reinforcement. Pronounced individual differences in color effects were related to reward but not punishment sensitivity, with blue relative to red preference associated with high relative to low reward sensitivity. An fMRI study revealed these individual differences were supported by color-dependent functional coupling between the visual cortices and mesolimbic reward circuitry. Our findings reveal the reward bases of color, demonstrating color is a potent regulator of perception, action, and neural dynamics.

Multi-Level Clustering of Dynamic Directional Brain Network Patterns and Their Behavioral Relevance

Dynamic functional connectivity (DFC) obtained from resting state functional magnetic resonance imaging (fMRI) data has been shown to provide novel insights into brain function which may be obscured by static functional connectivity (SFC). Further, DFC, and by implication how different brain regions may engage or disengage with each other over time, has been shown to be behaviorally relevant and more predictive than SFC of behavioral performance and/or diagnostic status. DFC is not a directional entity and may capture neural synchronization. However, directional interactions between different brain regions is another putative mechanism by which neural populations communicate. Accordingly, static effective connectivity (SEC) has been explored as a means of characterizing such directional interactions. But investigation of its dynamic counterpart, i.e., dynamic effective connectivity (DEC), is still in its infancy. Of particular note are methodological insufficiencies in identifying DEC configurations that are reproducible across time and subjects as well as a lack of understanding of the behavioral relevance of DEC obtained from resting state fMRI. In order to address these issues, we employed a dynamic multivariate autoregressive (MVAR) model to estimate DEC. The method was first validated using simulations and then applied to resting state fMRI data obtained in-house (N = 21), wherein we performed dynamic clustering of DEC matrices across multiple levels [using adaptive evolutionary clustering (AEC)] – spatial location, time, and subjects. We observed a small number of directional brain network configurations alternating between each other over time in a quasi-stable manner akin to brain microstates. The dominant and consistent DEC network patterns involved several regions including inferior and mid temporal cortex, motor and parietal cortex, occipital cortex, as well as part of frontal cortex. The functional relevance of these DEC states were determined using meta-analyses and pertained mainly to memory and emotion, but also involved execution and language. Finally, a larger cohort of resting-state fMRI and behavioral data from the Human Connectome Project (HCP) (N = 232, Q1–Q3 release) was used to demonstrate that metrics derived from DEC can explain larger variance in 70 behaviors across different domains (alertness, cognition, emotion, and personality traits) compared to SEC in healthy individuals.

Effective connectivity modulations related to win and loss outcomes

Previous studies have characterized the brain regions involved in encoding monetary reward and punishment outcomes. The question of how this information is integrated across brain regions has received less attention. Here, we investigated changes in effective connectivity related to the processing of positive and negative monetary outcomes using functional magnetic resonance imaging data from the Human Connectome Project. Specifically, subjects engaged in a card guessing game which could yield win, loss, or neutral outcomes. A general linear model was used to define a network of regions involved in win and loss outcome processing, including anterior insula, anterior cingulate cortex, and ventral striatum. Dynamic causal modelling (DCM) was implemented to study between-region couplings and outcome-related modulations thereof within this network. In addition, we explored the relation between effective connectivity patterns and choice behavior in the gambling task. Parametric empirical Bayesian modelling was conducted for group-level inferences of both DCM and the choice behavior. Behaviorally, both win and loss outcomes increased the probability of choice switches in subsequent gambles. In terms of connectivity, win outcomes were associated with increased extrinsic connectivity across the network, while loss outcomes featured a balance between increased and decreased extrinsic connectivity. Moreover, self-inhibitory connections tended to decrease for both win and loss outcomes. Interestingly, a substantial discrepancy was observed for occipital cortex connectivity, which was characterized by intrinsic disinhibition in loss but not in win trials. The observed differences in effective connectivity during the processing of positive and negative outcomes, despite similarities in average regional activity and choice behavior, highlight the value of exploring network dynamics in the context of incentive manipulations.

Increased Global Interaction Across Functional Brain Modules During Cognitive Emotion Regulation

Cognitive emotion regulation (CER) enables humans to flexibly modulate their emotions. While local theories of CER neurobiology suggest interactions between specialized local brain circuits underlying CER, e.g., in subparts of amygdala and medial prefrontal cortices (mPFC), global theories hypothesize global interaction increases among larger functional brain modules comprising local circuits. We tested the global CER hypothesis using graph-based whole-brain network analysis of functional MRI data during aversive emotional processing with and without CER. During CER, global between-module interaction across stable functional network modules increased. Global interaction increase was particularly driven by subregions of amygdala and cuneus-nodes of highest nodal participation-that overlapped with CER-specific local activations, and by mPFC and posterior cingulate as relevant connector hubs. Results provide evidence for the global nature of human CER, complementing functional specialization of embedded local brain circuits during successful CER.

Exploring EEG Effective Connectivity Network in Estimating Influence of Color on Emotion and Memory

Color is a perceptual stimulus that has a significant impact on improving human emotion and memory. Studies have revealed that colored multimedia learning materials (MLMs) have a positive effect on learner's emotion and learning where it was assessed by subjective/objective measurements. This study aimed to quantitatively assess the influence of colored MLMs on emotion, cognitive processes during learning, and long-term memory (LTM) retention using electroencephalography (EEG). The dataset consisted of 45 healthy participants, and MLMs were designed in colored or achromatic illustrations to elicit emotion and that to assess its impact on LTM retention after 30-min and 1-month delay. The EEG signal analysis was first started to estimate the effective connectivity network (ECN) using the phase slope index and expand it to characterize the ECN pattern using graph theoretical analysis. EEG results showed that colored MLMs had influences on theta and alpha networks, including (1) an increased frontal-parietal connectivity (top-down processing), (2) a larger number of brain hubs, (3) a lower clustering coefficient, and (4) a higher local efficiency, indicating that color influences information processing in the brain, as reflected by ECN, together with a significant improvement in learner's emotion and memory performance. This is evidenced by a more positive emotional valence and higher recall accuracy for groups who learned with colored MLMs than that of achromatic MLMs. In conclusion, this paper demonstrated how the EEG ECN parameters could help quantify the influences of colored MLMs on emotion and cognitive processes during learning.

Individualized functional networks reconfigure with cognitive state

There is extensive evidence that functional organization of the human brain varies dynamically as the brain switches between task demands, or cognitive states. This functional organization also varies across subjects, even when engaged in similar tasks. To date, the functional network organization of the brain has been considered static. In this work, we use fMRI data obtained across multiple cognitive states (task-evoked and rest conditions) and across multiple subjects, to measure state- and subject-specific functional network parcellation (the assignment of nodes to networks). Our parcellation approach provides a measure of how node-to-network assignment (NNA) changes across states and across subjects. We demonstrate that the brain's functional networks are not spatially fixed, but that many nodes change their network membership as a function of cognitive state. Such reconfigurations are highly robust and reliable to the extent that they can be used to predict cognitive state with up to 97% accuracy. Our findings suggest that if functional networks are to be defined via functional clustering of nodes, then it is essential to consider that such definitions may be fluid and cognitive-state dependent.

Anterior insular cortex is a bottleneck of cognitive control

Cognitive control, with a limited capacity, is a core process in human cognition for the coordination of thoughts and actions. Although the regions involved in cognitive control have been identified as the cognitive control network (CCN), it is still unclear whether a specific region of the CCN serves as a bottleneck limiting the capacity of cognitive control (CCC). Here, we used a perceptual decision-making task with conditions of high cognitive load to challenge the CCN and to assess the CCC in a functional magnetic resonance imaging study. We found that the activation of the right anterior insular cortex (AIC) of the CCN increased monotonically as a function of cognitive load, reached its plateau early, and showed a significant correlation to the CCC. In a subsequent study of patients with unilateral lesions of the AIC, we found that lesions of the AIC were associated with a significant impairment of the CCC. Simulated lesions of the AIC resulted in a reduction of the global efficiency of the CCN in a network analysis. These findings suggest that the AIC, as a critical hub in the CCN, is a bottleneck of cognitive control.

Tracking mood fluctuations with functional network patterns

Subjective mood is a psychophysiological property that depends on complex interactions among the central and peripheral nervous systems. How network interactions in the brain drive temporal fluctuations in mood is unknown. Here we investigate how functional network configuration relates to mood profiles in a single individual over the course of 1 year. Using data from the 'MyConnectome Project', we construct a comprehensive mapping between resting-state functional connectivity (FC) patterns and subjective mood scales using an associative multivariate technique (partial least squares). We report three principal findings. First, FC patterns reliably tracked daily fluctuations in mood. Second, positive mood was marked by an integrated architecture, with prominent interactions between canonical resting-state networks. Finally, one of the top-ranked nodes in mood-related network reconfiguration was the subgenual anterior cingulate cortex, an area commonly associated with mood regulation and dysregulation. Altogether, these results showcase the utility of highly sampled individual-focused data sets for affective neuroscience.

An integrative Bayesian approach to matrix-based analysis in neuroimaging

Understanding the correlation structure associated with brain regions is a central goal in neuroscience, as it informs about interregional relationships and network organization. Correlation structure can be conveniently captured in a matrix that indicates the relationships among brain regions, which could involve electroencephalogram sensors, electrophysiology recordings, calcium imaging data, or functional magnetic resonance imaging (FMRI) data-We call this type of analysis matrix-based analysis, or MBA. Although different methods have been developed to summarize such matrices across subjects, including univariate general linear models (GLMs), the available modeling strategies tend to disregard the interrelationships among the regions, leading to "inefficient" statistical inference. Here, we develop a Bayesian multilevel (BML) modeling framework that simultaneously integrates the analyses of all regions, region pairs (RPs), and subjects. In this approach, the intricate relationships across regions as well as across RPs are quantitatively characterized. The adoption of the Bayesian framework allows us to achieve three goals: (a) dissolve the multiple testing issue typically associated with seeking evidence for the effect of each RP under the conventional univariate GLM; (b) make inferences on effects that would be treated as "random" under the conventional linear mixed-effects framework; and (c) estimate the effect of each brain region in a manner that indexes their relative "importance". We demonstrate the BML methodology with an FMRI dataset involving a cognitive-emotional task and compare it to the conventional GLM approach in terms of model efficiency, performance, and inferences. The associated program MBA is available as part of the AFNI suite for general use.

Martial Arts "Kendo" and the Motivation Network During Attention Processing: An fMRI Study

Japanese martial arts, Budo, have been reported to improve cognitive function, especially attention. However, the underlying neural mechanisms of the effect of Budo on attention processing has not yet been investigated. Kendo, a type of fencing using bamboo swords, is one of the most popular forms of Budo worldwide. We investigated the difference in functional connectivity (FC) between Kendo players (KPs) and non-KPs (NKPs) during an attention-related auditory oddball paradigm and during rest. The analyses focused on the brain network related to “motivation.” Resting-state functional magnetic resonance imaging (rs-fMRI) and task-based fMRI using the oddball paradigm were performed in healthy male volunteers (14 KPs and 11 NKPs). Group differences in FC were tested using CONN-software within the motivation network, which consisted of 22 brain regions defined by a previous response-conflict task-based fMRI study with a reward cue. Daily general physical activities were assessed using the International Physical Activity Questionnaire (IPAQ). We also investigated the impact of major confounders, namely, smoking habits, alcohol consumption, IPAQ score, body mass index (BMI), and reaction time (RT) in the oddball paradigm. Resting-state fMRI revealed that KPs had a significantly lower FC than NKPs between the right nucleus accumbens and right frontal eye field (FEF) within the motivation network. Conversely, KPs exhibited a significantly higher FC than NKPs between the left intraparietal sulcus (IPS) and the left precentral gyrus (PCG) within the network during the auditory oddball paradigm [statistical thresholds, False Discovery Rate (FDR) < 0.05]. These results remained significant after controlling for major covariates. Our results suggest that attenuated motivation network integrity at rest together with enhanced motivation network integrity during attentional demands might underlie the instantaneous concentration abilities of KPs.

What drives prioritized visual processing? A motivational relevance account

Emotion is fundamental to our being, and an essential aspect guiding behavior when rapid responding is required. This includes whether we approach or avoid a stimulus, and the accompanying physiological responses. A common tenet is that threat-related content drives stimulus processing and biases visual attention, so that rapid responding can be initiated. In this paper, it will be argued instead that prioritization of threatening stimuli should be encompassed within a motivational relevance framework. To more fully understand what is, or is not, prioritized for visual processing one must, however, additionally consider: (i) stimulus ambiguity and perceptual saliency; (ii) task demands, including both perceptual load and cognitive load; and (iii) endogenous/affective states of the individual. Combined with motivational relevance, this then leads to a multifactorial approach to understanding the drivers of prioritized visual processing. This accords with current recognition that the brain basis allowing for visual prioritization is also multifactorial, including transient, dynamic and overlapping networks. Taken together, the paper provides a reconceptualization of how "emotional" information prioritizes visual processing.

Bed nucleus of the stria terminalis regulates fear to unpredictable threat signals

The bed nucleus of the stria terminalis (BNST) has been implicated in conditioned fear and anxiety, but the specific factors that engage the BNST in defensive behaviors are unclear. Here we examined whether the BNST mediates freezing to conditioned stimuli (CSs) that poorly predict the onset of aversive unconditioned stimuli (USs) in rats. Reversible inactivation of the BNST selectively reduced freezing to CSs that poorly signaled US onset (e.g., a backward CS that followed the US), but did not eliminate freezing to forward CSs even when they predicted USs of variable intensity. Additionally, backward (but not forward) CSs selectively increased Fos in the ventral BNST and in BNST-projecting neurons in the infralimbic region of the medial prefrontal cortex (mPFC), but not in the hippocampus or amygdala. These data reveal that BNST circuits regulate fear to unpredictable threats, which may be critical to the etiology and expression of anxiety.

Consistent Gradient of Performance and Decoding of Stimulus Type and Valence From Local and Network Activity

The individual differences approach focuses on the variation of behavioral and neural signatures across subjects. In this context, we searched for intracranial neural markers of performance in three individuals with distinct behavioral patterns (efficient, borderline, and inefficient) in a dual-valence task assessing facial and lexical emotion recognition. First, we performed a preliminary study to replicate well-established evoked responses in relevant brain regions. Then, we examined time series data and network connectivity, combined with multivariate pattern analyses and machine learning, to explore electrophysiological differences in resting-state versus task-related activity across subjects. Next, using the same methodological approach, we assessed the neural decoding of performance for different dimensions of the task. The classification of time series data mirrored the behavioral gradient across subjects for stimulus type but not for valence. However, network-based measures reflected the subjects' hierarchical profiles for both stimulus types and valence. Therefore, this measure serves as a sensitive marker for capturing distributed processes such as emotional valence discrimination, which relies on an extended set of regions. Network measures combined with classification methods may offer useful insights to study single subjects and understand inter-individual performance variability. Promisingly, this approach could eventually be extrapolated to other neuroscientific techniques.

Altered segregation between task-positive and task-negative regions in mild traumatic brain injury

Changes in large-scale brain networks that accompany mild traumatic brain injury (mTBI) were investigated using functional magnetic resonance imaging (fMRI) during the N-back working memory task at two cognitive loads (1-back and 2-back). Thirty mTBI patients were examined during the chronic stage of injury and compared to 28 control participants. Demographics and behavioral performance were matched across groups. Due to the diffuse nature of injury, we hypothesized that there would be an imbalance in the communication between task-positive and Default Mode Network (DMN) regions in the context of effortful task execution. Specifically, a graph-theoretic measure of modularity was used to quantify the extent to which groups of brain regions tended to segregate into task-positive and DMN sub-networks. Relative to controls, mTBI patients showed reduced segregation between the DMN and task-positive networks, but increased functional connectivity within the DMN regions during the more cognitively demanding 2-back task. Together, our findings reveal that patients exhibit alterations in the communication between and within neural networks during a cognitively demanding task. These findings reveal altered processes that persist through the chronic stage of injury, highlighting the need for longitudinal research to map the neural recovery of mTBI patients.

Neural Correlates of Non-clinical Internet Use in the Motivation Network and Its Modulation by Subclinical Autistic Traits

Background: Increasing evidence regarding the neural correlates of excessive or pathological internet use (IU) has accumulated in recent years, and comorbidity with depression and autism has been reported in multiple studies. However, psychological and neural correlates of non-clinical IU in healthy individuals remain unclear.

Objectives: The aim of the current study was to investigate the relationships between non-clinical IU and functional connectivity (FC), focusing on the brain’s motivation network. We sought to clarify the influence of depression and autistic traits on these relationships in healthy individuals.

Methods: Resting-state functional magnetic resonance imaging (fMRI) was performed in 119 healthy volunteers. IU, depression, and autistic traits were assessed using the Generalized Problematic Internet Use Scale 2 (GPIUS2), Beck Depression Inventory-II (BDI-II), and the autism spectrum quotient (AQ) scale, respectively. Correlational analyses were performed using CONN-software within the motivation-related network, which consisted of 22 brain regions defined by a previous response-conflict task-based fMRI study with a reward cue. We also performed mediation analyses via the bootstrap method.

Results: Total GPIUS2 scores were positively correlated with FC between the (a) left middle frontal gyrus (MFG) and bilateral medial prefrontal cortex; (b) left MFG and right supplementary motor area (SMA); (c) left MFG and right anterior insula, and (d) right MFG and right insula. The “Mood Regulation” subscale of the GPIUS2 was positively correlated with FC between left MFG and right SMA. The “Deficient Self-Regulation” subscale was positively correlated with FC between right MFG and right anterior insula (statistical thresholds, FDR < 0.05). Among these significant correlations, those between GPIUS2 (total and “Mood Regulation” subscale) scores and FC became stronger after controlling for AQ scores (total and “Attention Switching” subscale), indicating significant mediation by AQ (95% CI < 0.05). In contrast, BDI-II had no mediating effect.

Conclusion: Positive correlations between IU and FC in the motivation network may indicate health-promoting effects of non-clinical IU. However, this favorable association is attenuated in individuals with subclinical autistic traits, suggesting the importance of a personalized educational approach for these individuals in terms of adequate IU.

Disrupted topological organization of human brain connectome in diabetic retinopathy patients

OBJECTIVE

There is increasing neuroimaging evidence that type 2 diabetes patients with retinal microvascular complications show abnormal brain functional and structural architecture and are at an increased risk of cognitive decline and dementia. However, changes in the topological properties of the functional brain connectome in diabetic retinopathy (DR) patients remain unknown. The aim of this study was to explore the topological organization of the brain connectome in DR patients using graph theory approaches.

METHODS

Thirty-five DR patients (18 males and 17 females) and 38 healthy controls (HCs) (18 males and 20 females), matched for age, sex, and education, underwent resting-state magnetic resonance imaging scans. Graph theory analysis was performed to investigate the topological properties of brain functional connectome at both global and nodal levels.

RESULTS

Both DR and HC groups showed high-efficiency small-world network in their brain functional networks. Notably, the DR group showed reduction in the clustering coefficient (P=0.0572) and local efficiency (P=0.0151). Furthermore, the DR group showed reduced nodal centralities in the default-mode network (DMN) and increased nodal centralities in the visual network (VN) (P<0.01, Bonferroni-corrected). The DR group also showed abnormal functional connections among the VN, DMN, salience network (SN), and sensorimotor network (SMN). Altered network metrics and nodal centralities were significantly correlated with visual acuity and fasting blood glucose level in DR patients.

CONCLUSION

DR patients showed abnormal topological organization of the human brain connectome. Specifically, the DR group showed reduction in the clustering coefficient and local efficiency, relative to HC group. Abnormal nodal centralities and functional disconnections were mainly located in the DMN, VN, SN, and SMN in DR patients. Furthermore, the disrupted topological attributes showed correlations with clinical variables. These findings offer important insight into the neural mechanism of visual loss and cognitive deficits in DR patients.

The intersection of stress and reward: BNST modulation of aversive and appetitive states

The bed nucleus of the stria terminalis (BNST) is widely acknowledged as a brain structure that regulates stress and anxiety states, as well as aversive and appetitive behaviours. The diverse roles of the BNST are afforded by its highly modular organisation, neurochemical heterogeneity, and complex intrinsic and extrinsic circuitry. There has been growing interest in the BNST in relation to psychopathologies such as anxiety and addiction. Although research on the human BNST is still in its infancy, there have been extensive preclinical studies examining the molecular signature and hodology of the BNST and their involvement in stress and reward seeking behaviour. This review examines the neurochemical phenotype and connectivity of the BNST, as well as electrophysiological correlates of plasticity in the BNST mediated by stress and/or drugs of abuse.

Graph measures in task-based fMRI: Functional integration during read-out of visual and auditory information

This study investigated how attending to auditory and visual information systematically changes graph theoretical measures of integration and functional connectivity between three network modules: auditory, visual, and a joint task core. Functional MRI BOLD activity was recorded while healthy volunteers attended to colour and/or pitch information presented within an audiovisual stimulus sequence. Network nodes and modules were based on peak voxels of BOLD contrasts, including colour and pitch sensitive brain regions as well as the dorsal attention network. Network edges represented correlations between nodes' activity and were computed separately for each condition. Connection strength was increased between the task and the visual module when participants attended to colour, and between the task and the auditory module when they attended to pitch. Moreover, several nodal graph measures showed consistent changes to attentional modulation in form of stronger integration of sensory regions in response to attention. Together, these findings corroborate dynamical adjustments of both modality-specific and modality-independent functional brain networks in response to task demands and their representation in graph theoretical measures.

Informativeness of Auditory Stimuli Does Not Affect EEG Signal Diversity

Brain signal diversity constitutes a robust neuronal marker of the global states of consciousness. It has been demonstrated that, in comparison to the resting wakefulness, signal diversity is lower during unconscious states, and higher during psychedelic states. A plausible interpretation of these findings is that the neuronal diversity corresponds to the diversity of subjective conscious experiences. Therefore, in the present study we varied an information rate processed by the subjects and hypothesized that greater information rate will be related to richer and more differentiated phenomenology and, consequently, to greater signal diversity. To test this hypothesis speech recordings (excerpts from an audio-book) were presented to subjects at five different speeds (65, 83, 100, 117, and 135% of the original speed). By increasing or decreasing speed of the recordings we were able to, respectively, increase or decrease the presented information rate. We also included a backward (unintelligible) speech presentation and a resting-state condition (no auditory stimulation). We tested 19 healthy subjects and analyzed the recorded EEG signal (64 channels) in terms of Lempel-Ziv diversity (LZs). We report the following findings. First, our main hypothesis was not confirmed, as Bayes Factor indicates evidence for no effect when comparing LZs among five presentation speeds. Second, we found that LZs during the resting-state was greater than during processing of both meaningful and unintelligible speech. Third, an additional analysis uncovered a gradual decrease of diversity over the time-course of the experiment, which might reflect a decrease in vigilance. We thus speculate that higher signal diversity during the unconstrained resting-state might be due to a greater variety of experiences, involving spontaneous attention switching and mind wandering.

Network supporting contextual fear learning after dorsal hippocampal damage has increased dependence on retrosplenial cortex

Hippocampal damage results in profound retrograde, but no anterograde amnesia in contextual fear conditioning (CFC). Although the content learned in the latter have been discussed, alternative regions supporting CFC learning were seldom proposed and never empirically addressed. Here, we employed network analysis of pCREB expression quantified from brain slices of rats with dorsal hippocampal lesion (dHPC) after undergoing CFC session. Using inter-regional correlations of pCREB-positive nuclei between brain regions, we modelled functional networks using different thresholds. The dHPC network showed small-world topology, equivalent to SHAM (control) network. However, diverging hubs were identified in each network. In a direct comparison, hubs in both networks showed consistently higher centrality values compared to the other network. Further, the distribution of correlation coefficients was different between the groups, with most significantly stronger correlation coefficients belonging to the SHAM network. These results suggest that dHPC network engaged in CFC learning is partially different, and engage alternative hubs. We next tested if pre-training lesions of dHPC and one of the new dHPC network hubs (perirhinal, Per; or disgranular retrosplenial, RSC, cortices) would impair CFC. Only dHPC-RSC, but not dHPC-Per, impaired CFC. Interestingly, only RSC showed a consistently higher centrality in the dHPC network, suggesting that the increased centrality reflects an increased functional dependence on RSC. Our results provide evidence that, without hippocampus, the RSC, an anatomically central region in the medial temporal lobe memory system might support CFC learning and memory.

Neural activity to threat in ventromedial prefrontal cortex correlates with individual differences in anxiety and reward processing

Emotion studies show that ventromedial prefrontal cortex (vmPFC) plays a critical role in negative affect evaluation. Here we investigated two questions: Does the neural sensitivity to threat of bodily harm in vmPFC alter as anxiety levels increase? If the neural sensitivity to threat in vmPFC reflects a kind of general emotional processing, does it predict reward processing? To address these questions, we first recorded participants' self-reported anxiety. In an investigation of neural responses in vmPFC (Session 1), we measured brain activity (fMRI) associated with the anticipation of threat, using a sphere based ROI approach. In a behavioral experiment (Session 2), participants' reward processing efficiency was evaluated when they performed a visual discrimination task in which they had the opportunity to earn cash rewards. We found that across participants, there were tightly coupled associations between signal changes in the vmPFC and self-reported state anxiety. Specifically, participants who showed more activation in vmPFC to threat also exhibited greater behavioral efficiency in reward processing. Path analysis revealed a closely interconnected network of vmPFC (cortical) and VS (ventral striatum, subcortical) which predicted reward processing. Therefore, in addition to negative affect evaluation, neural sensitivity in vmPFC correlated with both anxiety and reward-related metrics. These results support an emerging model in which the vmPFC functions to defend the organism from acute stress and facilitate reward processes.

Hick-Hyman Law is Mediated by the Cognitive Control Network in the Brain

The Hick-Hyman law describes a linear increase in reaction time (RT) as a function of the information entropy of response selection, which is computed as the binary logarithm of the number of response alternatives. While numerous behavioral studies have provided evidence for the Hick-Hyman law, its neural underpinnings have rarely been examined and are still unclear. In this functional magnetic resonance imaging study, by utilizing a choice reaction time task to manipulate the entropy of response selection, we examined brain activity mediating the input and the output, as well as the connectivity between corresponding regions in human participants. Beyond confirming the Hick-Hyman law in RT performance, we found that activation of the cognitive control network (CCN) increased and activation of the default mode network (DMN) decreased, both as a function of entropy. However, only the CCN, but not the DMN, was involved in mediating the relationship between entropy and RT. The CCN was involved in both stages of uncertainty representation and response generation, while the DMN was mainly involved at the stage of uncertainty representation. These findings indicate that the CCN serves as a core entity underlying the Hick-Hyman law by coordinating uncertainty representation and response generation in the brain.

Adolescent Development of Value-Guided Goal Pursuit

Adolescents are challenged to orchestrate goal-directed actions in increasingly independent and consequential ways. In doing so, it is advantageous to use information about value to select which goals to pursue and how much effort to devote to them. Here, we examine age-related changes in how individuals use value signals to orchestrate goal-directed behavior. Drawing on emerging literature on value-guided cognitive control and reinforcement learning, we demonstrate how value and task difficulty modulate the execution of goal-directed action in complex ways across development from childhood to adulthood. We propose that the scope of value-guided goal pursuit expands with age to include increasingly challenging cognitive demands, and scaffolds on the emergence of functional integration within brain networks supporting valuation, cognition, and action.

Subgenual anterior cingulate cortex controls sadness-induced modulations of cognitive and emotional network hubs

The regulation of cognitive and emotional processes is critical for proper executive functions and social behavior, but its specific mechanisms remain unknown. Here, we addressed this issue by studying with functional magnetic resonance imaging the changes in network topology that underlie competitive interactions between emotional and cognitive networks in healthy participants. Our behavioral paradigm contrasted periods with high emotional and cognitive demands by including a sadness provocation task followed by a spatial working memory task. The sharp contrast between successive tasks was designed to enhance the separability of emotional and cognitive networks and reveal areas that regulate the flow of information between them (hubs). By applying graph analysis methods on functional connectivity between 20 regions of interest in 22 participants we identified two main brain network modules, one dorsal and one ventral, and their hub areas: the left dorsolateral prefrontal cortex (dlPFC) and the left medial frontal pole (mFP). These hub areas did not modulate their mutual functional connectivity following sadness but they did so through an interposed area, the subgenual anterior cingulate cortex (sACC). Our results identify dlPFC and mFP as areas regulating interactions between emotional and cognitive networks, and suggest that their modulation by sadness experience is mediated by sACC.

Anger Modulates Influence Hierarchies Within and Between Emotional Reactivity and Regulation Networks

Emotion regulation is hypothesized to be mediated by the interactions between emotional reactivity and regulation networks during the dynamic unfolding of the emotional episode. Yet, it remains unclear how to delineate the effective relationships between these networks. In this study, we examined the aforementioned networks’ information flow hierarchy during viewing of an anger provoking movie excerpt. Anger regulation is particularly essential for averting individuals from aggression and violence, thus improving prosocial behavior. Using subjective ratings of anger intensity we differentiated between low and high anger periods of the film. We then applied the Dependency Network Analysis (D_EPNA), a newly developed graph theory method to quantify networks’ node importance during the two anger periods. The D_EPNA analysis revealed that the impact of the ventromedial prefrontal cortex (vmPFC) was higher in the high anger condition, particularly within the regulation network and on the connections between the reactivity and regulation networks. We further showed that higher levels of vmPFC impact on the regulation network were associated with lower subjective anger intensity during the high-anger cinematic period, and lower trait anger levels. Supporting and replicating previous findings, these results emphasize the previously acknowledged central role of vmPFC in modulating negative affect. We further show that the impact of the vmPFC relies on its correlational influence on the connectivity between reactivity and regulation networks. More importantly, the hierarchy network analysis revealed a link between connectivity patterns of the vmPFC and individual differences in anger reactivity and trait, suggesting its potential therapeutic role.