Neurocognitive networks: Findings, models, and theory

Can repetitive transcranial magnetic stimulation promote recovery of consciousness in patients with disorders of consciousness? A randomized controlled trial

BACKGROUND

Disorders of consciousness (DoC) are characterized by a broad decline in background excitatory synaptic activity and varying levels of cerebral network disruption. Repetitive transcranial magnetic stimulation (rTMS), a neuromodulatory technique, is anticipated to assist the recovery of consciousness. Nonetheless, ongoing debates persist regarding its effectiveness, in light of the inconsistent results of recent research.

OBJECTIVE

The purpose of this study is to investigate the efficacy of rTMS in promoting recovery of consciousness in patients with DoC and to probe its impact on activity of cerebral functional networks.

METHODS

Forty-eight patients with DoC were included in this randomized controlled trial (Chinese Clinical Trial Registry: ChiCTR2100044930). Twenty-four patients in the control group accepted conventional therapy. Another 24 patients in the rTMS group received extra rTMS over the dorsolateral prefrontal cortex (DLPFC) once per workday during a 4-week intervention phase. Primary outcome was the proportion of patients emerging improvement on level of consciousness (LOC) based on coma recovery scale- revised (CRS-R) at the end of intervention. Furthermore, other behavioral scales such as the clinical global impression-improvement (CGI-I) and resting state-electroencephalography (rs-EEG) microstate were employed as secondary outcomes. Different microstates served as tools to detect the activity of respective corresponding resting state cerebral functional networks.

RESULTS

In comparison to the control group, the rTMS group exhibited a higher proportion of patients emerging improvement on LOC at post-intervention, with a risk ratio of 3.06 (95 % CI 1.54 to 6.09, P = 0.001). The distribution of patients with each grade of CGI-I across the groups also implied a trend that favored the rTMS group (common odds ratio:0.20, 95 % CI 0.065 to 0.63, P = 0.006). With respect to microstate E, the rTMS group had a significantly reduced global explained variance (GEV) was observed in the rTMS group (Z = -2.61, Pbonf = 0.027).

CONCLUSION

High-frequency rTMS over the DLPFC could promote recovery of consciousness in patients with DoC. It might get involved in modulating the balance among cerebral functional networks and facilitating consciousness recovery.

Neural mechanisms of spatial navigation in ASD and TD children: insights from EEG microstate and functional connectivity analysis

Introduction

Autism Spectrum Disorder (ASD) is associated with atypical neural dynamics, affecting spatial navigation and information integration. EEG microstates and functional connectivity (FC) are useful tools for investigating these differences. This study examines alterations in EEG microstates and theta-band FC during map-reading tasks in children with ASD (n = 12) compared to typically developing (TD) peers (n = 12), aiming to uncover neural mechanisms underlying spatial processing deficits in ASD.

Methods

EEG data were collected from children with ASD (n = 12) and TD controls (n = 12) aged 6-10 years during a map-reading task. Microstate analysis quantified the temporal dynamics of four canonical microstates (A, B, C, and D). Theta-band (4-8 Hz) FC was analyzed to assess interregional neural communication during the task. Statistical tests identified group differences in microstate metrics and FC patterns.

Results

Children with ASD showed significant differences in EEG microstate dynamics compared to TD controls. The ASD group showed reduced occurrence, but longer duration and greater coverage in microstate A, indicating abnormal temporal and spatial brain activity. For microstate B, the ASD group displayed shorter durations and lower coverage, suggesting impairments in cognitive control. In microstate C, the ASD group exhibited reduced duration, coverage, and steady-state distribution, pointing to disruptions in spatial attention. Conversely, microstate D showed increased occurrence and greater coverage in the ASD group, reflecting atypical spatial attention allocation. Theta-band FC analysis revealed significantly reduced connectivity in key brain networks involved in spatial navigation, particularly between fronto-parietal and occipito-temporal regions. This suggests disrupted integration of spatial and cognitive processes in children with ASD.

Discussion

The alterations in EEG microstate patterns and theta-band FC highlight differences in the neural mechanisms underlying spatial navigation and cognitive integration in ASD. These findings suggest that microstate and FC analyses could serve as biomarkers for understanding visual spatial navigation in ASD, related to perceptual abnormalities. This research provides a basis for individualized rehabilitation plans for children with ASD, using qEEG biomarkers to guide neuromodulation techniques, such as transcranial direct current stimulation (tDCS). Future studies should investigate longitudinal changes and intervention effects on these neural dynamics.

Cognitive load during driving: EEG microstate metrics are sensitive to task difficulty and predict safety outcomes

Engaging in phone conversations or other cognitively challenging tasks while driving detrimentally impacts cognitive functions and has been associated with increased risk of accidents. Existing EEG methods have been shown to differentiate between load and no load, but not between different levels of cognitive load. Furthermore, it has not been investigated whether EEG measurements of load can be used to predict safety outcomes in critical events. EEG microstates analysis, categorizing EEG signals into a concise set of prototypical functional states, has been used in other task contexts with good results, but has not been applied in the driving context. Here, this gap is addressed by means of a driving simulation experiment. Three phone use conditions (no phone use, hands-free, and handheld), combined with two task difficulty levels (single- or double-digit addition and subtraction), were tested before and during a rear-end collision conflict. Both conventional EEG spectral power and EEG microstates were analyzed. The results showed that different levels of cognitive load influenced EEG microstates differently, while EEG spectral power remained unaffected. A distinct EEG pattern emerged when drivers engaged in phone tasks while driving, characterized by a simultaneous increase and decrease in two of the EEG microstates, suggesting a heightened focus on auditory information, potentially at a cost to attention reorientation ability. The increase and decrease in these two microstates follow a monotonic sequence from baseline to hands-free simple, hands-free complex, handheld simple, and finally handheld complex, showing sensitivity to task difficulty. This pattern was found both before and after the lead vehicle braked. Furthermore, EEG microstates prior to the lead vehicle braking improved predictions of safety outcomes in terms of minimum time headway after the lead vehicle braked, clearly suggesting that these microstates measure brain states which are indicative of impaired driving. Additionally, EEG microstates are more predictive of safety outcomes than task difficulty, highlighting individual differences in task effects. These findings enhance our understanding of the neural dynamics involved in distracted driving and can be used in methods for evaluating the cognitive load induced by in-vehicle systems.

Bridging the Gap between Psychophysiological and Audiological Factors in the Assessment of Tinnitus: An EEG Investigation in the Beta Band

BACKGROUND

Despite substantial progress in investigating its psychophysical complexity, tinnitus remains a scientific and clinical enigma. The present study, through an ecological and multidisciplinary approach, aims to identify associations between electroencephalographic (EEG) and psycho-audiological variables.

METHODS

EEG beta activity, often related to stress and anxiety, was acquired from 12 tinnitus patients (TIN group) and 7 controls (CONT group) during an audio cognitive task and at rest. We also investigated psychological (SCL-90-R; STAI-Y; BFI-10) and audiological (THI; TQ12-I; Hyperacusis) variables using non-parametric statistics to assess differences and relationships between and within groups.

RESULTS

In the TIN group, frontal beta activity positively correlated with hyperacusis, parietal activity, and trait anxiety; the latter is also associated with depression in CONT. Significant differences in paranoid ideation and openness were found between groups.

CONCLUSIONS

The connection between anxiety trait, beta activity in the fronto-parietal cortices and hyperacusis provides insights into brain functioning in tinnitus patients, offering quantitative descriptions for clinicians and new multidisciplinary treatment hypotheses.

Effects of Bumetanide on Neurocognitive Functioning in Children with Autism Spectrum Disorder: Secondary Analysis of a Randomized Placebo-Controlled Trial

We present the secondary-analysis of neurocognitive tests in the 'Bumetanide in Autism Medication and Biomarker' (BAMBI;EUDRA-CT-2014-001560-35) study, a randomized double-blind placebo-controlled (1:1) trial testing 3-months bumetanide treatment (≤ 1 mg twice-daily) in unmedicated children 7-15 years with ASD. Children with IQ ≥ 70 were analyzed for baseline deficits and treatment-effects on the intention-to-treat-population with generalized-linear-models, principal component analysis and network analysis. Ninety-two children were allocated to treatment and 83 eligible for analyses. Heterogeneous neurocognitive impairments were found that were unaffected by bumetanide treatment. Network analysis showed higher modularity after treatment (mean difference:-0.165, 95%CI:-0.317 to - 0.013,p = .034) and changes in the relative importance of response inhibition in the neurocognitive network (mean difference:-0.037, 95%CI:-0.073 to - 0.001,p = .042). This study offers perspectives to include neurocognitive tests in ASD trials.

Creative therapy in health and disease: Inner vision

Can we better understand the unique mechanisms of de novo abilities in light of our current knowledge of the psychological and neuroscientific literature on creativity? This review outlines the state-of-the-art in the neuroscience of creativity and points out crucial aspects that still demand further exploration, such as brain plasticity. The progressive development of current neuroscience research on creativity presents a multitude of prospects and potentials for furnishing efficacious therapy in the context of health and illness. Therefore, we discuss directions for future studies, identifying a focus on pinpointing the neglected beneficial practices for creative therapy. We emphasize the neglected neuroscience perspective of creativity on health and disease and how creative therapy could offer limitless possibilities to improve our well-being and give hope to patients with neurodegenerative diseases to compensate for their brain injuries and cognitive impairments by expressing their hidden creativity.

Evidence for modulation of EEG microstates by mental workload levels and task types

Electroencephalography (EEG) microstate analysis has become a popular tool for studying the spatial and temporal dynamics of large-scale electrophysiological activities in the brain in recent years. Four canonical topographies of the electric field (classes A, B, C, and D) have been widely identified, and changes in microstate parameters are associated with several psychiatric disorders and cognitive functions. Recent studies have reported the modulation of EEG microstate by mental workload (MWL). However, the common practice of evaluating MWL is in a specific task. Whether the modulation of microstate by MWL is consistent across different types of tasks is still not clear. Here, we studied the topographies and dynamics of microstate in two independent MWL tasks: NBack and the multi-attribute task battery (MATB) and showed that the modulation of MWL on microstate topographies and parameters depended on tasks. We found that the parameters of microstates A and C, and the topographies of microstates A, B, and D were significantly different between the two tasks. Meanwhile, all four microstate topographies and parameters of microstates A and C were different during the NBack task, but no significant difference was found during the MATB task. Furthermore, we employed a support vector machine recursive feature elimination procedure to investigate whether microstate parameters were suitable for MWL classification. An averaged classification accuracy of 87% for within-task and 78% for cross-task MWL discrimination was achieved with at least 10 features. Collectively, our findings suggest that topographies and parameters of microstates can provide valuable information about neural activity patterns with a dynamic temporal structure at different levels of MWL, but the modulation of MWL depends on tasks and their corresponding functional systems. Moreover, as a potential indicator, microstate parameters could be used to distinguish MWL.

From neurotransmitters to networks: Transcending organisational hierarchies with molecular-informed functional imaging

The human brain exhibits complex interactions across micro, meso-, and macro-scale organisational principles. Recent synergistic multi-modal approaches have begun to link micro-scale information to systems level dynamics, transcending organisational hierarchies and offering novel perspectives into the brain's function and dysfunction. Specifically, the distribution of micro-scale properties (such as receptor density or gene expression) can be mapped onto macro-scale measures from functional MRI to provide novel neurobiological insights. Methodological approaches to enrich functional imaging analyses with molecular information are rapidly evolving, with several streams of research having developed relatively independently, each offering unique potential to explore the trans-hierarchical functioning of the brain. Here, we address the three principal streams of research - spatial correlation, molecular-enriched network, and in-silico whole brain modelling analyses - to provide a critical overview of the different sources of molecular information, how this information can be utilised within analyses of fMRI data, the merits and pitfalls of each methodology, and, through the use of key examples, highlight their promise to shed new light on key domains of neuroscientific inquiry.

Antisocial behavior is associated with reduced frontoparietal network efficiency in youth

Youth antisocial behavior (AB) is associated with deficits in socioemotional processing, reward and threat processing and executive functioning. These deficits are thought to emerge from differences in neural structure, functioning and connectivity, particularly within the default, salience and frontoparietal networks. However, the relationship between AB and the organization of these networks remains unclear. To address this gap, the current study applied unweighted, undirected graph analyses to resting-state functional magnetic resonance imaging data in a cohort of 161 adolescents (95 female) enriched for exposure to poverty, a risk factor for AB. As prior work indicates that callous-unemotional (CU) traits may moderate the neurocognitive profile of youth AB, we examined CU traits as a moderator. Using multi-informant latent factors, AB was found to be associated with less efficient frontoparietal network topology, a network associated with executive functioning. However, this effect was limited to youth at low or mean levels of CU traits, indicating that these neural differences were specific to those high on AB but not CU traits. Neither AB, CU traits nor their interaction was significantly related to default or salience network topologies. Results suggest that AB, specifically, may be linked with shifts in the architecture of the frontoparietal network.

Microstate analysis in infancy

BACKGROUND

Microstate analysis is an emerging method for investigating global brain connections using electroencephalography (EEG). Microstates have been colloquially referred to as the "atom of thought," meaning that from these underlying networks comes coordinated neural processing and cognition. The present study examined microstates at 6-, 8-, and 10-months of age. It was hypothesized that infants would demonstrate distinct microstates comparable to those identified in adults that also parallel resting-state networks using fMRI. An additional exploratory aim was to examine the relationship between microstates and temperament, assessed via parent reports, to further demonstrate microstate analysis as a viable tool for examining the relationship between neural networks, cognitive processes as well as emotional expression embodied in temperament attributes.

METHODS

The microstates analysis was performed with infant EEG data when the infant was either 6- (n = 12), 8- (n = 16), or 10-months (n = 6) old. The resting-state task involved watching a 1-minute video segment of Baby Einstein while listening to the accompanying music. Parents completed the IBQ-R to assess infant temperament.

RESULTS

Four microstate topographies were extracted. Microstate 1 had an isolated posterior activation; Microstate 2 had a symmetric occipital to prefrontal orientation; Microstate 3 had a left occipital to right frontal orientation; and Microstate 4 had a right occipital to left frontal orientation. At 10-months old, Microstate 3, thought to reflect auditory/language processing, became activated more often, for longer periods of time, covering significantly more time across the task and was more likely to be transitioned into. This finding is interpreted as consistent with language acquisition and phonological processing that emerges around 10-months. Microstate topographies and parameters were also correlated with differing temperament broadband and narrowband scales on the IBQ-R.

CONCLUSION

Three microstates emerged that appear comparable to underlying networks identified in adult and infant microstate literature and fMRI studies. Each of the temperament domains was related to specific microstates and their parameters. These networks also correspond with auditory and visual processing as well as the default mode network found in prior research and can lead to new investigations examining differences across stimulus presentations to further explain how infants begin to recognize, respond to, and engage with the world around them.

Modelling brain dynamics by Boolean networks

Understanding the relationship between brain architecture and brain function is a central issue in neuroscience. We modeled realistic spatio-temporal patterns of brain activity on a human connectome with a Boolean networks model with the aim of computationally replicating certain cognitive functions as they emerge from the standardization of many fMRI studies, identified as patterns of human brain activity. Results from the analysis of simulation data, carried out for different parameters and initial conditions identified many possible paths in the space of parameters of these network models, with normal (ordered asymptotically constant patterns), chaotic (oscillating or disordered) but also highly organized configurations, with countless spatial–temporal patterns. We interpreted these results as routes to chaos, permanence of the systems in regimes of complexity, and ordered stationary behavior, associating these dynamics to cognitive processes. The most important result of this work is the study of emergent neural circuits, i.e., configurations of areas that synchronize over time, both locally and globally, determining the emergence of computational analogues of cognitive processes, which may or may not be similar to the functioning of biological brain. Furthermore, results put in evidence the creation of how the brain creates structures of remote communication. These structures have hierarchical organization, where each level allows for the emergence of brain organizations which behave at the next superior level. Taken together these results allow the interplay of dynamical and topological roots of the multifaceted brain dynamics to be understood.

Pre-Surgery Demographic, Clinical, and Symptom Characteristics Associated with Different Self-Reported Cognitive Processes in Patients with Breast Cancer

Cancer related cognitive impairment (CRCI) is a common and persistent symptom in breast cancer patients. The Attentional Function Index (AFI) is a self-report measure that assesses CRCI. AFI includes three subscales, namely effective action, attentional lapses, and interpersonal effectiveness, that are based on working memory, inhibitory control, and cognitive flexibility. Previously, we identified three classes of patients with distinct CRCI profiles using the AFI total scores. The purpose of this study was to expand our previous work using latent class growth analysis (LCGA), to identify distinct cognitive profiles for each of the AFI subscales in the same sample (i.e., 397 women who were assessed seven times from prior to through to 6 months following breast cancer surgery). For each subscale, parametric and non-parametric statistics were used to determine differences in demographic, clinical, and pre-surgical psychological and physical symptoms among the subgroups. Three-, four-, and two-classes were identified for the effective action, attentional lapses, and interpersonal effectiveness subscales, respectively. Across all three subscales, lower functional status, higher levels of anxiety, depression, fatigue, and sleep disturbance, and worse decrements in energy were associated with worse cognitive performance. These and other modifiable characteristics may be potential targets for personalized interventions for CRCI.

Neural network involvement for religious experiences in worship measured by EEG microstate analysis

To date, not much is known about large-scale brain activation patterns in religious states of mind and previous studies have not set an emphasis on experience. The present study investigated the phenomenon of religious experiences through microstate analysis, and it was the first neurocognitive research to tackle the dimension of experience directly. Hence, a total of 60 evangelical Christians participated in an experiment where they were asked to engage in worship and try to connect with God. With a bar slider, people were able to continuously rate how strongly they sensed God's presence at any given moment. A selection of songs was used to help in the induction of the desired experience. With 64 electroencephalography (EEG) electrodes, the brain activity was assessed and analyzed with five clusters of microstate classes. First, we hypothesized that the neural network for multisensory integration was involved in the religious experience. Second, we hypothesized that the same was true for the Default Mode Network (DMN). Our results suggested an association between the auditory network and the religious experience, and an association with the salience network as well as with the DMN. No associations with the network thought to be involved with multisensory integration was detected.

EEG microstate dynamics indicate a U-shaped path to propofol-induced loss of consciousness

Evidence suggests that the stream of consciousness is parsed into transient brain states manifesting themselves as discrete spatiotemporal patterns of global neuronal activity. Electroencephalographical (EEG) microstates are proposed as the neurophysiological correlates of these transiently stable brain states that last for fractions of seconds. To further understand the link between EEG microstate dynamics and consciousness, we continuously recorded high-density EEG in 23 surgical patients from their awake state to unconsciousness, induced by step-wise increasing concentrations of the intravenous anesthetic propofol. Besides the conventional parameters of microstate dynamics, we introduce a new implementation of a method to estimate the complexity of microstate sequences. The brain activity under the surgical anesthesia showed a decreased sequence complexity of the stereotypical microstates, which became sparser and longer-lasting. However, we observed an initial increase in microstates' temporal dynamics and complexity with increasing depth of sedation leading to a distinctive "U-shape" that may be linked to the paradoxical excitation induced by moderate levels of propofol. Our results support the idea that the brain is in a metastable state under normal conditions, balancing between order and chaos in order to flexibly switch from one state to another. The temporal dynamics of EEG microstates indicate changes of this critical balance between stability and transition that lead to altered states of consciousness.

Altered Resting-State Functional Connectivity in the Default Mode Network in Male Juvenile Violent Offenders

Young males are often associated with more violence, leading to some serious negative consequences. However, the physiology and the neuroimaging patterns underlying juvenile violence remain unclear. Of the limited knowledge on juvenile violence, the default mode network has been known to be associated with its pathophysiology. This study aimed to investigate functional connectivity alterations of the default mode network in male juvenile violent offenders. 31 juvenile violent offenders in a high-security facility, who were convicted of aggressive behaviors by court, and 28 normal controls from a middle school were recruited as participants. They underwent a resting-state functional magnetic resonance imaging scan. And independent component analysis approaches were used to analyze their data. Compared to the normal controls, the juvenile violent offenders showed a different default mode network pattern, with the functional connectivity increased in the posterior cingulate, and decreased in the right middle temporal, left angular, right precuneus and right middle frontal cortex. Our findings revealed that the male juvenile violent offenders were associated with abnormal default mode network functional connectivity, which might be a neuroimaging basis for their tendency to violence.

Exploring the neurocognome: Neurocognitive network organization in healthy young adults

Conventional neurocognitive assessment does not account for the complex interplay between neurocognitive functions that gives rise to (goal-directed) behavior. This study aims to explore the value of the application of network analysis to individual neurocognitive data, in order to investigate neurocognitive network organization (i.e., the neurocognome). Participants were healthy young adults (N = 51, average age: 26 years [range: 18-34], 49% female) that underwent a single comprehensive neurocognitive assessment. To allow implementation of network analysis, we developed a new measure of connectivity between neurocognitive functions that can be calculated on a single neurocognitive assessment. Connectivity values between all possible pairs of neurocognitive functions were used to reconstruct individual neurocognitive networks. Graph theory was applied to extract measures of global and local network organization from neurocognitive networks at the individual level. The results confirmed the expectation that neurocognitive connectivity values should be higher for connections between neurocognitive functions that are more closely related (i.e., within neurocognitive domains) than for connections between neurocognitive functions that are less closely related (i.e., across neurocognitive domains). The results further showed that reconstruction of the neurocognitive network at the individual level has considerable agreement with a group-based approach, providing preliminary evidence for the validity of our approach. The reconstructed neurocognitive network also showed considerable consistency among (randomly selected) independent subgroups, supporting the stability of our approach. Lastly, neurocognitive network parameters were related to intelligence and behavior problems, reflecting relevance of neurocognitive network organization for other important domains of functioning. Moreover, local network parameters (i.e., the relative importance of neurocognitive functions in the network) may have stronger relevance for behavioral functioning than conventional measures of neurocognitive functioning (i.e., z-scores reflecting performance on individual neurocognitive tests). Taken together, this study indicates that analysis of individual neurocognitive network organization has potential value for neurocognitive assessment in research and clinical practice.

Remote firing propagation in the neural network of C. elegans

Understanding the mechanisms of firing propagation in brain networks has been a long-standing problem in the fields of nonlinear dynamics and network science. In general, it is believed that a specific firing in a brain network may be gradually propagated from a source node to its neighbors and then to the neighbors' neighbors and so on. Here, we explore firing propagation in the neural network of Caenorhabditis elegans and surprisingly find an abnormal phenomenon, i.e., remote firing propagation between two distant and indirectly connected nodes with the intermediate nodes being inactivated. This finding is robust to source nodes but depends on the topology of network such as the unidirectional couplings and heterogeneity of network. Further, a brief theoretical analysis is provided to explain its mechanism and a principle for remote firing propagation is figured out. This finding provides insights for us to understand how those cognitive subnetworks emerge in a brain network.

Neurocognitive Functions in Bipolar Disorder in Relation to Comorbid ADHD

Introduction

Bipolar disorder (BD) and attention deficit hyperactivity disorder (ADHD) often co-occur in adult population. Both conditions present various neurocognitive and behavioral problems. We aimed to examine neurocognitive functions in adult patients with comorbid BD and ADHD (BD+ADHD) in comparison to patients with only BD, only ADHD and healthy controls (HCs).

Method

An extensive cognitive battery which evaluates verbal learning and memory, visual memory, processing speed, attention, executive functions, working memory and verbal fluency, was used to assess neurocognitive functions respectively in adult (age 18-65 years) patients with BD (n=37), ADHD (n=43), BD+ADHD (n=20) in comparison to HCs (n=51). The Multivariate Analysis of Covariance models, where age, level of education and total BIS-11 scores were included as covariates, were used for comparing neurocognitive scores among groups.

Results

Both BD and BD+ADHD groups showed significantly poorer performance than HCs in processing speed, attention, executive functions, and verbal fluency domains. The BD group had additional significant deficits in executive functions, verbal learning and memory domains. There were no significant differences between BD and BD+ADHD groups with regards to verbal learning and memory, visual memory, processing speed, attention, executive functions, working memory and verbal fluency domains. Patients with only ADHD showed significantly poorer performance than HCs in verbal fluency domain.

Conclusions

Our results show similarities in the neurocognitive functions of adults with BD and BD+ADHD across a wide range of cognitive domains. The findings point to the need for further exploration of diverging and converging neurodevelopmental trajectories of BD and ADHD.

Capturing the Forest but Missing the Trees: Microstates Inadequate for Characterizing Shorter-Scale EEG Dynamics

The brain is known to be active even when not performing any overt cognitive tasks, and often it engages in involuntary mind wandering. This resting state has been extensively characterized in terms of fMRI-derived brain networks. However, an alternate method has recently gained popularity: EEG microstate analysis. Proponents of microstates postulate that the brain discontinuously switches between four quasi-stable states defined by specific EEG scalp topologies at peaks in the global field potential (GFP). These microstates are thought to be “atoms of thought,” involved with visual, auditory, salience, and attention processing. However, this method makes some major assumptions by excluding EEG data outside the GFP peaks and then clustering the EEG scalp topologies at the GFP peaks, assuming that only one microstate is active at any given time. This study explores the evidence surrounding these assumptions by studying the temporal dynamics of microstates and its clustering space using tools from dynamical systems analysis, fractal, and chaos theory to highlight the shortcomings in microstate analysis. The results show evidence of complex and chaotic EEG dynamics outside the GFP peaks, which is being missed by microstate analysis. Furthermore, the winner-takes-all approach of only one microstate being active at a time is found to be inadequate since the dynamic EEG scalp topology does not always resemble that of the assigned microstate, and there is competition among the different microstate classes. Finally, clustering space analysis shows that the four microstates do not cluster into four distinct and separable clusters. Taken collectively, these results show that the discontinuous description of EEG microstates is inadequate when looking at nonstationary short-scale EEG dynamics.

General Principles of Neurorobotic Models Employing Entrainment and Chaos Control

Neurorobotic models are perfect candidates for proving the validity of embodied/dynamical approaches to cognition. In order to control bodies of arbitrary complexity in complex environments, it is necessary to coordinate vast numbers of sensory and motor components. In this review, we took at several studies of neurorobotics and generalize common principles of how they achieve this massive feat, relying on the key concepts of entrainment and chaos control. We discuss current limitations and ways that these techniques could be expanded to cover more wide ranges of behavior, for example by taking inspiration from ecological psychology.

Markov Model-Based Method to Analyse Time-Varying Networks in EEG Task-Related Data

The dynamic nature of functional brain networks is being increasingly recognized in cognitive neuroscience, and methods to analyse such time-varying networks in EEG/MEG data are required. In this work, we propose a pipeline to characterize time-varying networks in single-subject EEG task-related data and further, evaluate its validity on both simulated and experimental datasets. Pre-processing is done to remove channel-wise and trial-wise differences in activity. Functional networks are estimated from short non-overlapping time windows within each “trial,” using a sparse-MVAR (Multi-Variate Auto-Regressive) model. Functional “states” are then identified by partitioning the entire space of functional networks into a small number of groups/symbols via k-means clustering.The multi-trial sequence of symbols is then described by a Markov Model (MM). We show validity of this pipeline on realistic electrode-level simulated EEG data, by demonstrating its ability to discriminate “trials” from two experimental conditions in a range of scenarios. We then apply it to experimental data from two individuals using a Brain-Computer Interface (BCI) via a P300 oddball task. Using just the Markov Model parameters, we obtain statistically significant discrimination between target and non-target trials. The functional networks characterizing each ‘state’ were also highly similar between the two individuals. This work marks the first application of the Markov Model framework to infer time-varying networks from EEG/MEG data. Due to the pre-processing, results from the pipeline are orthogonal to those from conventional ERP averaging or a typical EEG microstate analysis. The results provide powerful proof-of-concept for a Markov model-based approach to analyzing the data, paving the way for its use to track rapid changes in interaction patterns as a task is being performed. MATLAB code for the entire pipeline has been made available.

Discrete Sequential Information Coding: Heteroclinic Cognitive Dynamics

Discrete sequential information coding is a key mechanism that transforms complex cognitive brain activity into a low-dimensional dynamical process based on the sequential switching among finite numbers of patterns. The storage size of the corresponding process is large because of the permutation capacity as a function of control signals in ensembles of these patterns. Extracting low-dimensional functional dynamics from multiple large-scale neural populations is a central problem both in neuro- and cognitive- sciences. Experimental results in the last decade represent a solid base for the creation of low-dimensional models of different cognitive functions and allow moving toward a dynamical theory of consciousness. We discuss here a methodology to build simple kinetic equations that can be the mathematical skeleton of this theory. Models of the corresponding discrete information processing can be designed using the following dynamical principles: (i) clusterization of the neural activity in space and time and formation of information patterns; (ii) robustness of the sequential dynamics based on heteroclinic chains of metastable clusters; and (iii) sensitivity of such sequential dynamics to intrinsic and external informational signals. We analyze sequential discrete coding based on winnerless competition low-frequency dynamics. Under such dynamics, entrainment, and heteroclinic coordination leads to a large variety of coding regimes that are invariant in time.

Bispectral analysis of spontaneous EEG activity from patients with moderate dementia due to Alzheimer's disease

Dementia due to Alzheimer's disease (AD) is a common disorder with a great impact on the patients' quality of life. The aim of this pilot study was to characterize spontaneous electroencephalography (EEG) activity in dementia due to AD using bispectral analysis. Five minutes of EEG activity were recorded from 17 patients with moderate dementia due to AD and 19 age-matched controls. Bispectrum results revealed that AD patients are characterized by an increase of phase coupling at low frequencies in comparison with controls. Additionally, some bispectral features calculated from the bispectrum showed significant differences between both groups (p <; 0.05, Mann-Whitney U test with Bonferroni's correction). Finally, a stepwise logistic regression analysis with a leave-one-out cross-validation procedure was used for classification purposes. An accuracy of 86.11% (sensitivity = 88.24%; specificity =84.21%) was achieved. This study suggests the usefulness of bispectral analysis to provide further insights into the underlying brain dynamics associated with AD.

From swing to cane: Sex differences of EEG resting-state temporal patterns during maturation and aging

While many insights on brain development and aging have been gained by studying resting-state networks with fMRI, relating these changes to cognitive functions is limited by the temporal resolution of fMRI. In order to better grasp short-lasting and dynamically changing mental activities, an increasing number of studies utilize EEG to define resting-state networks, thereby often using the concept of EEG microstates. These are brief (around 100 ms) periods of stable scalp potential fields that are influenced by cognitive states and are sensitive to neuropsychiatric diseases. Despite the rising popularity of the EEG microstate approach, information about age changes is sparse and nothing is known about sex differences. Here we investigated age and sex related changes of the temporal dynamics of EEG microstates in 179 healthy individuals (6-87 years old, 90 females, 204-channel EEG). We show strong sex-specific changes in microstate dynamics during adolescence as well as at older age. In addition, males and females differ in the duration and occurrence of specific microstates. These results are of relevance for the comparison of studies in populations of different age and sex and for the understanding of the changes in neuropsychiatric diseases.

Top-down beta oscillatory signaling conveys behavioral context in early visual cortex

Top-down modulation of sensory processing is a critical neural mechanism subserving numerous important cognitive roles, one of which may be to inform lower-order sensory systems of the current ‘task at hand’ by conveying behavioral context to these systems. Accumulating evidence indicates that top-down cortical influences are carried by directed interareal synchronization of oscillatory neuronal populations, with recent results pointing to beta-frequency oscillations as particularly important for top-down processing. However, it remains to be determined if top-down beta-frequency oscillations indeed convey behavioral context. We measured spectral Granger Causality (sGC) using local field potentials recorded from microelectrodes chronically implanted in visual areas V1/V2, V4, and TEO of two rhesus macaque monkeys, and applied multivariate pattern analysis to the spatial patterns of top-down sGC. We decoded behavioral context by discriminating patterns of top-down (V4/TEO-to-V1/V2) beta-peak sGC for two different task rules governing correct responses to identical visual stimuli. The results indicate that top-down directed influences are carried to visual cortex by beta oscillations, and differentiate task demands even before visual stimulus processing. They suggest that top-down beta-frequency oscillatory processes coordinate processing of sensory information by conveying global knowledge states to early levels of the sensory cortical hierarchy independently of bottom-up stimulus-driven processing.

EEG microstates as a tool for studying the temporal dynamics of whole-brain neuronal networks: A review

The present review discusses a well-established method for characterizing resting-state activity of the human brain using multichannel electroencephalography (EEG). This method involves the examination of electrical microstates in the brain, which are defined as successive short time periods during which the configuration of the scalp potential field remains semi-stable, suggesting quasi-simultaneity of activity among the nodes of large-scale networks. A few prototypic microstates, which occur in a repetitive sequence across time, can be reliably identified across participants. Researchers have proposed that these microstates represent the basic building blocks of the chain of spontaneous conscious mental processes, and that their occurrence and temporal dynamics determine the quality of mentation. Several studies have further demonstrated that disturbances of mental processes associated with neurological and psychiatric conditions manifest as changes in the temporal dynamics of specific microstates. Combined EEG-fMRI studies and EEG source imaging studies have indicated that EEG microstates are closely associated with resting-state networks as identified using fMRI. The scale-free properties of the time series of EEG microstates explain why similar networks can be observed at such different time scales. The present review will provide an overview of these EEG microstates, available methods for analysis, the functional interpretations of findings regarding these microstates, and their behavioral and clinical correlates.

The electrophysiology of neuroHIV: A systematic review of EEG and MEG studies in people with HIV infection since the advent of highly-active antiretroviral therapy

OBJECTIVE

The Human Immunodeficiency Virus (HIV) has an impact on the brain, even when the infection is well-controlled with modern highly-active antiretroviral therapy (HAART). While dementia is rare in those on HAART, milder cognitive impairment is common. The causes, patterns, and evolution of brain dysfunction in people living with HIV remain uncertain. We evaluate whether electrophysiological methods provide informative measures of brain dysfunction in this population.

METHODS

A systematic literature search identified studies that used EEG or MEG to evaluate persons living with HIV published between 1996 (when HAART became available) and 2016.

RESULTS

Twenty-eight studies were identified. Most involved small samples, and all but four were cross-sectional. Reduced amplitude of Event Related Potentials and decreased power in the alpha band at rest were the most frequent differences between people with and without HIV infection. Of the 16 studies that also assessed cognitive ability, 13 found a significant relationship between cognition and electrophysiological changes in the HIV+ groups. Five of those studies also reported a significant relationship with current immunosuppression, suggesting a direct effect of HIV on the brain. There were few longitudinal studies; whether these electrophysiological changes progress over time, or respond to treatment, remains unclear.

CONCLUSIONS

EEG and MEG can provide useful information about brain dysfunction in people with HIV infection, but more consistent assessments of both cognition and EEG patterns, as well as longitudinal studies with larger, better characterized samples are needed.

SIGNIFICANCE

This is the first systematic review of electrophysiological findings in HIV since the availability of HAART. EEG and MEG measures are sensitive to brain dysfunction in this population, and could complement other approaches in improving the assessment, understanding and treatment of neurocognitive disorders in HIV.

Coordination Dynamics in Cognitive Neuroscience

Many researchers and clinicians in cognitive neuroscience hold to a modular view of cognitive function in which the cerebral cortex operates by the activation of areas with circumscribed elementary cognitive functions. Yet an ongoing paradigm shift to a dynamic network perspective is underway. This new viewpoint treats cortical function as arising from the coordination dynamics within and between cortical regions. Cortical coordination dynamics arises due to the unidirectional influences imposed on a cortical area by inputs from other areas that project to it, combined with the projection reciprocity that characterizes cortical connectivity and gives rise to reentrant processing. As a result, cortical dynamics exhibits both segregative and integrative tendencies and gives rise to both cooperative and competitive relations within and between cortical areas that are hypothesized to underlie the emergence of cognition in brains.

Hyperglycemia Reduces Efficiency of Brain Networks in Subjects with Type 2 Diabetes

Previous research has shown that the brain is an important target of diabetic complications. Since brain regions are interconnected to form a large-scale neural network, we investigated whether severe hyperglycemia affects the topology of the brain network in people with type 2 diabetes. Twenty middle-aged (average age: 54 years) individuals with poorly controlled diabetes (HbA1c: 8.9−14.6%, 74−136 mmol/mol) and 20 age-, sex-, and education-matched healthy volunteers were recruited. Graph theoretic network analysis was performed with axonal fiber tractography and tract-based spatial statistics (TBSS) using diffusion tensor imaging. Associations between the blood glucose level and white matter network characteristics were investigated. Individuals with diabetes had lower white matter network efficiency (P<0.001) and longer white matter path length (P<0.05) compared to healthy individuals. Higher HbA1c was associated with lower network efficiency (r = −0.53, P = 0.001) and longer network path length (r = 0.40, P<0.05). A disruption in local microstructural integrity was found in the multiple white matter regions and associated with higher HbA1c and fasting plasma glucose levels (corrected P<0.05). Poorer glycemic control is associated with lower efficiency and longer connection paths of the global brain network in individuals with diabetes. Chronic hyperglycemia in people with diabetes may disrupt the brain’s topological integration, and lead to mental slowing and cognitive impairment.

The Semantic Network at Work and Rest: Differential Connectivity of Anterior Temporal Lobe Subregions

The anterior temporal lobe (ATL) makes a critical contribution to semantic cognition. However, the functional connectivity of the ATL and the functional network underlying semantic cognition has not been elucidated. In addition, subregions of the ATL have distinct functional properties and thus the potential differential connectivity between these subregions requires investigation. We explored these aims using both resting-state and active semantic task data in humans in combination with a dual-echo gradient echo planar imaging (EPI) paradigm designed to ensure signal throughout the ATL. In the resting-state analysis, the ventral ATL (vATL) and anterior middle temporal gyrus (MTG) were shown to connect to areas responsible for multimodal semantic cognition, including bilateral ATL, inferior frontal gyrus, medial prefrontal cortex, angular gyrus, posterior MTG, and medial temporal lobes. In contrast, the anterior superior temporal gyrus (STG)/superior temporal sulcus was connected to a distinct set of auditory and language-related areas, including bilateral STG, precentral and postcentral gyri, supplementary motor area, supramarginal gyrus, posterior temporal cortex, and inferior and middle frontal gyri. Complementary analyses of functional connectivity during an active semantic task were performed using a psychophysiological interaction (PPI) analysis. The PPI analysis highlighted the same semantic regions suggesting a core semantic network active during rest and task states. This supports the necessity for semantic cognition in internal processes occurring during rest. The PPI analysis showed additional connectivity of the vATL to regions of occipital and frontal cortex. These areas strongly overlap with regions found to be sensitive to executively demanding, controlled semantic processing.

SIGNIFICANCE STATEMENT

Previous studies have shown that semantic cognition depends on subregions of the anterior temporal lobe (ATL). However, the network of regions functionally connected to these subregions has not been demarcated. Here, we show that these ventrolateral anterior temporal subregions form part of a network responsible for semantic processing during both rest and an explicit semantic task. This demonstrates the existence of a core functional network responsible for multimodal semantic cognition regardless of state. Distinct connectivity is identified in the superior ATL, which is connected to auditory and language areas. Understanding the functional connectivity of semantic cognition allows greater understanding of how this complex process may be performed and the role of distinct subregions of the anterior temporal cortex.

Cognitive Correlates of Basal Forebrain Atrophy and Associated Cortical Hypometabolism in Mild Cognitive Impairment

Degeneration of basal forebrain (BF) cholinergic nuclei is associated with cognitive decline, and this effect is believed to be mediated by neuronal dysfunction in the denervated cortical areas. MRI-based measurements of BF atrophy are increasingly being used as in vivo surrogate markers for cholinergic degeneration, but the functional implications of reductions in BF volume are not well understood. We used high-resolution MRI, fluorodeoxyglucose-positron emission tomography (PET), and neuropsychological test data of 132 subjects with mild cognitive impairment (MCI) and 177 cognitively normal controls to determine associations between BF atrophy, cortical hypometabolism, and cognitive deficits. BF atrophy in MCI correlated with both impaired memory function and attentional control deficits, whereas hippocampus volume was more specifically associated with memory deficits. BF atrophy was also associated with widespread cortical hypometabolism, and path analytic models indicated that hypometabolism in domain-specific cortical networks mediated the association between BF volume and cognitive dysfunction. The presence of cortical amyloid pathology, as assessed using AV45-PET, did not significantly interact with the observed associations. These data underline the potential of multimodal imaging markers to study structure-function-cognition relationships in the living human brain and provide important in vivo evidence for an involvement of the human BF in cortical activity and cognitive function.

MEG Analysis of Neural Interactions in Attention-Deficit/Hyperactivity Disorder

The aim of the present study was to explore the interchannel relationships of resting-state brain activity in patients with attention-deficit/hyperactivity disorder (ADHD), one of the most common mental disorders that develop in children. Magnetoencephalographic (MEG) signals were recorded using a 148-channel whole-head magnetometer in 13 patients with ADHD (range: 8–12 years) and 14 control subjects (range: 8–13 years). Three complementary measures (coherence, phase-locking value, and Euclidean distance) were calculated in the conventional MEG frequency bands: delta, theta, alpha, beta, and gamma. Our results showed that the interactions among MEG channels are higher for ADHD patients than for control subjects in all frequency bands. Statistically significant differences were observed for short-distance values within right-anterior and central regions, especially at delta, beta, and gamma-frequency bands (p < 0.05; Mann-Whitney U test with false discovery rate correction). These frequency bands also showed statistically significant differences in long-distance interactions, mainly among anterior and central regions, as well as among anterior, central, and other areas. These differences might reflect alterations during brain development in children with ADHD. Our results support the role of frontal abnormalities in ADHD pathophysiology, which may reflect a delay in cortical maturation in the frontal cortex.

Analysis of spontaneous EEG activity in Alzheimer's disease using cross-sample entropy and graph theory

The aim of this pilot study was to analyze spontaneous electroencephalography (EEG) activity in Alzheimer's disease (AD) by means of Cross-Sample Entropy (Cross-SampEn) and two local measures derived from graph theory: clustering coefficient (CC) and characteristic path length (PL). Five minutes of EEG activity were recorded from 37 patients with dementia due to AD and 29 elderly controls. Our results showed that Cross-SampEn values were lower in the AD group than in the control one for all the interactions among EEG channels. This finding indicates that EEG activity in AD is characterized by a lower statistical dissimilarity among channels. Significant differences were found mainly for fronto-central interactions (p <; 0.01, permutation test). Additionally, the application of graph theory measures revealed diverse neural network changes, i.e. lower CC and higher PL values in AD group, leading to a less efficient brain organization. This study suggests the usefulness of our approach to provide further insights into the underlying brain dynamics associated with AD.

Cognitive Effort and Schizophrenia Modulate Large-Scale Functional Brain Connectivity

Schizophrenia (SZ) is characterized by cognitive dysfunction and disorganized thought, in addition to hallucinations and delusions, and is regarded a disorder of brain connectivity. Recent efforts have been made to characterize the underlying brain network organization and interactions. However, to which degree connectivity alterations in SZ vary across different levels of cognitive effort is unknown. Utilizing independent component analysis (ICA) and methods for delineating functional connectivity measures from functional magnetic resonance imaging (fMRI) data, we investigated the effects of cognitive effort, SZ and their interactions on between-network functional connectivity during 2 levels of cognitive load in a large and well-characterized sample of SZ patients (n = 99) and healthy individuals (n = 143). Cognitive load influenced a majority of the functional connections, including but not limited to fronto-parietal and default-mode networks, reflecting both decreases and increases in between-network synchronization. Reduced connectivity in SZ was identified in 2 large-scale functional connections across load conditions, with a particular involvement of an insular network. The results document an important role of interactions between insular, default-mode, and visual networks in SZ pathophysiology. The interplay between brain networks was robustly modulated by cognitive effort, but the reduced functional connectivity in SZ, primarily related to an insular network, was independent of cognitive load, indicating a relatively general brain network-level dysfunction.

Foundational perspectives on causality in large-scale brain networks

A profusion of recent work in cognitive neuroscience has been concerned with the endeavor to uncover causal influences in large-scale brain networks. However, despite the fact that many papers give a nod to the important theoretical challenges posed by the concept of causality, this explosion of research has generally not been accompanied by a rigorous conceptual analysis of the nature of causality in the brain. This review provides both a descriptive and prescriptive account of the nature of causality as found within and between large-scale brain networks. In short, it seeks to clarify the concept of causality in large-scale brain networks both philosophically and scientifically. This is accomplished by briefly reviewing the rich philosophical history of work on causality, especially focusing on contributions by David Hume, Immanuel Kant, Bertrand Russell, and Christopher Hitchcock. We go on to discuss the impact that various interpretations of modern physics have had on our understanding of causality. Throughout all this, a central focus is the distinction between theories of deterministic causality (DC), whereby causes uniquely determine their effects, and probabilistic causality (PC), whereby causes change the probability of occurrence of their effects. We argue that, given the topological complexity of its large-scale connectivity, the brain should be considered as a complex system and its causal influences treated as probabilistic in nature. We conclude that PC is well suited for explaining causality in the brain for three reasons: (1) brain causality is often mutual; (2) connectional convergence dictates that only rarely is the activity of one neuronal population uniquely determined by another one; and (3) the causal influences exerted between neuronal populations may not have observable effects. A number of different techniques are currently available to characterize causal influence in the brain. Typically, these techniques quantify the statistical likelihood that a change in the activity of one neuronal population affects the activity in another. We argue that these measures access the inherently probabilistic nature of causal influences in the brain, and are thus better suited for large-scale brain network analysis than are DC-based measures. Our work is consistent with recent advances in the philosophical study of probabilistic causality, which originated from inherent conceptual problems with deterministic regularity theories. It also resonates with concepts of stochasticity that were involved in establishing modern physics. In summary, we argue that probabilistic causality is a conceptually appropriate foundation for describing neural causality in the brain.

Nature of functional links in valuation networks differentiates impulsive behaviors between abstinent heroin-dependent subjects and nondrug-using subjects

Advanced neuroimaging studies have identified brain correlates of pathological impulsivity in a variety of neuropsychiatric disorders. However, whether and how these spatially separate and functionally integrated neural correlates collectively contribute to aberrant impulsive behaviors remains unclear. Building on recent progress in neuroeconomics toward determining a biological account of human behaviors, we employed resting-state functional MRI to characterize the nature of the links between these neural correlates and to investigate their impact on impulsivity. We demonstrated that through functional connectivity with the ventral medial prefrontal cortex, the δ-network (regions of the executive control system, such as the dorsolateral prefrontal cortex) and the β-network (regions of the reward system involved in the mesocorticolimbic pathway), jointly influence impulsivity measured by the Barratt impulsiveness scale scores. In control nondrug-using subjects, the functional link between the β- and δ-networks is balanced, and the δ-network competitively controls impulsivity. However, in abstinent heroin-dependent subjects, the link is imbalanced, with stronger β-network connectivity and weaker δ-network connectivity. The imbalanced link is associated with impulsivity, indicating that the β- and δ-networks may mutually reinforce each other in abstinent heroin-dependent subjects. These findings of an aberrant link between the β- and δ-networks in abstinent heroin-dependent subjects may shed light on the mechanism of aberrant behaviors of drug addiction and may serve as an endophenotype to mark individual subjects' self-control capacity.

A comparative study of event-related coupling patterns during an auditory oddball task in schizophrenia

OBJECTIVE

The aim of this research is to explore the coupling patterns of brain dynamics during an auditory oddball task in schizophrenia (SCH).

APPROACH

Event-related electroencephalographic (ERP) activity was recorded from 20 SCH patients and 20 healthy controls. The coupling changes between auditory response and pre-stimulus baseline were calculated in conventional EEG frequency bands (theta, alpha, beta-1, beta-2 and gamma), using three coupling measures: coherence, phase-locking value and Euclidean distance.

MAIN RESULTS

Our results showed a statistically significant increase from baseline to response in theta coupling and a statistically significant decrease in beta-2 coupling in controls. No statistically significant changes were observed in SCH patients.

SIGNIFICANCE

Our findings support the aberrant salience hypothesis, since SCH patients failed to change their coupling dynamics between stimulus response and baseline when performing an auditory cognitive task. This result may reflect an impaired communication among neural areas, which may be related to abnormal cognitive functions.

Transdiagnostic dimensions of anxiety and depression moderate motivation-related brain networks during goal maintenance

BACKGROUND

Advancing research on the etiology, prevention, and treatment of psychopathology requires the field to move beyond modular conceptualizations of neural dysfunction toward understanding disturbance in key brain networks. Although some studies of anxiety and depression have begun doing so, they typically suffer from several drawbacks, including: (1) a categorical approach ignoring transdiagnostic processes, (2) failure to account for substantial anxiety and depression comorbidity, (3) examination of networks at rest, which overlooks disruption manifesting only when networks are challenged. Accordingly, the present study examined relationships between transdiagnostic dimensions of anxiety/depression and patterns of functional connectivity while goal maintenance was challenged.

METHODS

Participants (n = 179, unselected community members and undergraduates selected to be high/low on anxiety/depression) performed a task in which goal maintenance was challenged (color-word Stroop) while fMRI data were collected. Analyses examined moderation by anxiety/depression of condition-dependent coupling between regions of dorsolateral prefrontal cortex (dlPFC) previously associated with approach and avoidance motivation and amygdala/orbitofrontal cortex (OFC).

RESULTS

Anxious arousal was positively associated with amygdala↔right dlPFC coupling. Depression was positively associated with OFC↔right dlPFC coupling and negatively associated with OFC↔left dlPFC coupling.

CONCLUSIONS

Findings advance the field toward an integrative model of the neural instantiation of anxiety/depression by identifying specific, distinct dysfunctions associated with anxiety and depression in networks important for maintaining approach and avoidance goals. Specifically, findings shed light on potential neural mechanisms involved in attentional biases in anxiety and valuation biases in depression and underscore the importance of examining transdiagnostic dimensions of anxiety/depression while networks are challenged.

Reduced fiber integrity and cognitive control in adolescents with internet gaming disorder

The association between the impaired cognitive control and brain regional abnormalities in Internet gaming disorder (IGD) adolescents had been validated in numerous studies. However, few studies focused on the role of the salience network (SN), which regulates dynamic communication among brain core neurocognitive networks to modulate cognitive control. Seventeen IGD adolescents and 17 healthy controls participated in the study. By combining resting-state functional connectivity and diffusion tensor imaging (DTI) tractography methods, we examined the changes of functional and structural connections within SN in IGD adolescents. The color-word Stroop task was employed to assess the impaired cognitive control in IGD adolescents. Correlation analysis was carried out to investigate the relationship between the neuroimaging indices and behavior performance in IGD adolescents. The impaired cognitive control in IGD was validated by more errors during the incongruent condition in color-word Stroop task. The right SN tract showed the decreased fractional anisotropy (FA) in IGD adolescents, though no significant differences of functional connectivity were detected. Moreover, the FA values of the right SN tract were negatively correlated with the errors during the incongruent condition in IGD adolescents. Our results revealed the disturbed structural connectivity within SN in IGD adolescents, which may be related with impaired cognitive control. It is hoped that the brain-behavior relationship from network perspective may enhance the understanding of IGD.

Chunking dynamics: heteroclinics in mind

Recent results of imaging technologies and non-linear dynamics make possible to relate the structure and dynamics of functional brain networks to different mental tasks and to build theoretical models for the description and prediction of cognitive activity. Such models are non-linear dynamical descriptions of the interaction of the core components—brain modes—participating in a specific mental function. The dynamical images of different mental processes depend on their temporal features. The dynamics of many cognitive functions are transient. They are often observed as a chain of sequentially changing metastable states. A stable heteroclinic channel (SHC) consisting of a chain of saddles—metastable states—connected by unstable separatrices is a mathematical image for robust transients. In this paper we focus on hierarchical chunking dynamics that can represent several forms of transient cognitive activity. Chunking is a dynamical phenomenon that nature uses to perform information processing of long sequences by dividing them in shorter information items. Chunking, for example, makes more efficient the use of short-term memory by breaking up long strings of information (like in language where one can see the separation of a novel on chapters, paragraphs, sentences, and finally words). Chunking is important in many processes of perception, learning, and cognition in humans and animals. Based on anatomical information about the hierarchical organization of functional brain networks, we propose a cognitive network architecture that hierarchically chunks and super-chunks switching sequences of metastable states produced by winnerless competitive heteroclinic dynamics.

Inferring a dual-stream model of mentalizing from associative white matter fibres disconnection

In the field of cognitive neuroscience, it is increasingly accepted that mentalizing is subserved by a complex frontotemporoparietal cortical network. Some researchers consider that this network can be divided into two distinct but interacting subsystems (the mirror system and the mentalizing system per se), which respectively process low-level, perceptive-based aspects and high-level, inference-based aspects of this sociocognitive function. However, evidence for this type of functional dissociation in a given neuropsychological population is currently lacking and the structural connectivities of the two mentalizing subnetworks have not been established. Here, we studied mentalizing in a large sample of patients (n = 93; 46 females; age range: 18-65 years) who had been resected for diffuse low-grade glioma-a rare tumour that migrates preferentially along associative white matter pathways. This neurological disorder constitutes an ideal pathophysiological model in which to study the functional anatomy of associative pathways. We mapped the location of each patient's resection cavity and residual lesion infiltration onto the Montreal Neurological Institute template brain and then performed multilevel lesion analyses (including conventional voxel-based lesion-symptom mapping and subtraction lesion analyses). Importantly, we estimated each associative pathway's degree of disconnection (i.e. the degree of lesion infiltration) and built specific hypotheses concerning the connective anatomy of the mentalizing subnetworks. As expected, we found that impairments in mentalizing were mainly related to the disruption of right frontoparietal connectivity. More specifically, low-level and high-level mentalizing accuracy were correlated with the degree of disconnection in the arcuate fasciculus and the cingulum, respectively. To the best of our knowledge, our findings constitute the first experimental data on the structural connectivity of the mentalizing network and suggest the existence of a dual-stream hodological system. Our results may lead to a better understanding of disorders that affect social cognition, especially in neuropathological conditions characterized by atypical/aberrant structural connectivity, such as autism spectrum disorders.

A time and place for language comprehension: mapping the N400 and the P600 to a minimal cortical network

We propose a new functional-anatomical mapping of the N400 and the P600 to a minimal cortical network for language comprehension. Our work is an example of a recent research strategy in cognitive neuroscience, where researchers attempt to align data regarding the nature and time-course of cognitive processing (from ERPs) with data on the cortical organization underlying it (from fMRI). The success of this "alignment" approach critically depends on the functional interpretation of relevant ERP components. Models of language processing that have been proposed thus far do not agree on these interpretations, and present a variety of complicated functional architectures. We put forward a very basic functional-anatomical mapping based on the recently developed Retrieval-Integration account of language comprehension (Brouwer et al., 2012). In this mapping, the left posterior part of the Middle Temporal Gyrus (BA 21) serves as an epicenter (or hub) in a neurocognitive network for the retrieval of word meaning, the ease of which is reflected in N400 amplitude. The left Inferior Frontal Gyrus (BA 44/45/47), in turn, serves a network epicenter for the integration of this retrieved meaning with the word's preceding context, into a mental representation of what is being communicated; these semantic and pragmatic integrative processes are reflected in P600 amplitude. We propose that our mapping describes the core of the language comprehension network, a view that is parsimonious, has broad empirical coverage, and can serve as the starting point for a more focused investigation into the coupling of brain anatomy and electrophysiology.

Anterobasal temporal lobe lesions alter recurrent functional connectivity within the ventral pathway during naming

An increasing amount of evidence supports a crucial role for the anterior temporal lobe (ATL) in semantic processing. Critically, a selective disruption of the functional connectivity between left and right ATLs in patients with chronic aphasic stroke has been illustrated. The aim of the current study was to evaluate the consequences that lesions on the ATL have on the neurocognitive network supporting semantic cognition. Unlike previous work, in this magnetoencephalography study we selected a group of patients with small lesions centered on the left anteroventral temporal lobe before surgery. We then used an effective connectivity method (i.e., dynamic causal modeling) to investigate the consequences that these lesions have on the functional interactions within the network. This approach allowed us to evaluate the directionality of the causal interactions among brain regions and their associated connectivity strengths. Behaviorally, we found that semantic processing was altered when patients were compared with a strictly matched group of controls. Dynamic causal modeling for event related responses revealed that picture naming was associated with a bilateral frontotemporal network, encompassing feedforward and feedback connections. Comparison of specific network parameters between groups revealed that patients displayed selective network adjustments. Specifically, backward connectivity from anterior to posterior temporal lobe was decreased in the ipsilesional hemisphere, whereas it was enhanced in the contralesional hemisphere. These results reinforce the relevance of ATL in semantic memory, as well as its amodal organization, and highlight the role of feedback connections in enabling the integration of the semantic information.

Hierarchical brain networks active in approach and avoidance goal pursuit

Effective approach/avoidance goal pursuit is critical for attaining long-term health and well-being. Research on the neural correlates of key goal-pursuit processes (e.g., motivation) has long been of interest, with lateralization in prefrontal cortex being a particularly fruitful target of investigation. However, this literature has often been limited by a lack of spatial specificity and has not delineated the precise aspects of approach/avoidance motivation involved. Additionally, the relationships among brain regions (i.e., network connectivity) vital to goal-pursuit remain largely unexplored. Specificity in location, process, and network relationship is vital for moving beyond gross characterizations of function and identifying the precise cortical mechanisms involved in motivation. The present paper integrates research using more spatially specific methodologies (e.g., functional magnetic resonance imaging) with the rich psychological literature on approach/avoidance to propose an integrative network model that takes advantage of the strengths of each of these literatures.

Neural dynamics of attentional cross-modality control

Attentional networks that integrate many cortical and subcortical elements dynamically control mental processes to focus on specific events and make a decision. The resources of attentional processing are finite. Nevertheless, we often face situations in which it is necessary to simultaneously process several modalities, for example, to switch attention between players in a soccer field. Here we use a global brain mode description to build a model of attentional control dynamics. This model is based on sequential information processing stability conditions that are realized through nonsymmetric inhibition in cortical circuits. In particular, we analyze the dynamics of attentional switching and focus in the case of parallel processing of three interacting mental modalities. Using an excitatory-inhibitory network, we investigate how the bifurcations between different attentional control strategies depend on the stimuli and analyze the relationship between the time of attention focus and the strength of the stimuli. We discuss the interplay between attention and decision-making: in this context, a decision-making process is a controllable bifurcation of the attention strategy. We also suggest the dynamical evaluation of attentional resources in neural sequence processing.