Breakdown of the brain's functional network modularity with awareness

The subcortical brain regions influence the cortical areas during resting-state: an fMRI study

Introduction

Numerous modes or patterns of neural activity can be seen in the brain of individuals during the resting state. However, those functions do not persist long, and they are continuously altering in the brain. We have hypothesized that the brain activations during the resting state should themselves be responsible for this alteration of the activities.

Methods

Using the resting-state fMRI data of 63 healthy young individuals, we estimated the causality effects of each resting-state activation map on all other networks. The resting-state networks were identified, their causality effects on the other components were extracted, the networks with the top 20% of the causality were chosen, and the networks which were under the influence of those causal networks were also identified.

Results

Our results showed that the influence of each activation component over other components is different. The brain areas which showed the highest causality coefficients were subcortical regions, such as the brain stem, thalamus, and amygdala. On the other hand, nearly all the areas which were mostly under the causal effects were cortical regions.

Discussion

In summary, our results suggest that subcortical brain areas exert a higher influence on cortical regions during the resting state, which could help in a better understanding the dynamic nature of brain functions.

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.

Neuronal connected burst cascades bridge macroscale adaptive signatures across arousal states

The human brain displays a rich repertoire of states that emerge from the microscopic interactions of cortical and subcortical neurons. Difficulties inherent within large-scale simultaneous neuronal recording limit our ability to link biophysical processes at the microscale to emergent macroscopic brain states. Here we introduce a microscale biophysical network model of layer-5 pyramidal neurons that display graded coarse-sampled dynamics matching those observed in macroscale electrophysiological recordings from macaques and humans. We invert our model to identify the neuronal spike and burst dynamics that differentiate unconscious, dreaming, and awake arousal states and provide insights into their functional signatures. We further show that neuromodulatory arousal can mediate different modes of neuronal dynamics around a low-dimensional energy landscape, which in turn changes the response of the model to external stimuli. Our results highlight the promise of multiscale modelling to bridge theories of consciousness across spatiotemporal scales.

Strong intercorrelations among global graph-theoretic indices of structural connectivity in the human brain

Graph-theoretic metrics derived from neuroimaging data have been heralded as powerful tools for uncovering neural mechanisms of psychological traits, psychiatric disorders, and neurodegenerative diseases. In N = 8,185 human structural connectomes from UK Biobank, we examined the extent to which 11 commonly-used global graph-theoretic metrics index distinct versus overlapping information with respect to interindividual differences in brain organization. Using unthresholded, FA-weighted networks we found that all metrics other than Participation Coefficient were highly intercorrelated, both with each other (mean |r| = 0.788) and with a topologically-naïve summary index of brain structure (mean edge weight; mean |r| = 0.873). In a series of sensitivity analyses, we found that overlap between metrics is influenced by the sparseness of the network and the magnitude of variation in edge weights. Simulation analyses representing a range of population network structures indicated that individual differences in global graph metrics may be intrinsically difficult to separate from mean edge weight. In particular, Closeness, Characteristic Path Length, Global Efficiency, Clustering Coefficient, and Small Worldness were nearly perfectly collinear with one another (mean |r| = 0.939) and with mean edge weight (mean |r| = 0.952) across all observed and simulated conditions. Global graph-theoretic measures are valuable for their ability to distill a high-dimensional system of neural connections into summary indices of brain organization, but they may be of more limited utility when the goal is to index separable components of interindividual variation in specific properties of the human structural connectome.

Understanding the neural bases of bodily self-consciousness: recent achievements and main challenges

The last two decades have seen a surge of interest in the mechanisms underpinning bodily self-consciousness (BSC). Studies showed that BSC relies on several bodily experiences (i.e., self-location, body ownership, agency, first-person perspective) and multisensory integration. The aim of this literature review is to summarize new insights and novel developments into the understanding of the neural bases of BSC, such as the contribution of the interoceptive signals to the neural mechanisms of BSC, and the overlap with the neural bases of conscious experience in general and of higher-level forms of self (i.e., the cognitive self). We also identify the main challenges and propose future perspectives that need to be conducted to progress into the understanding of the neural mechanisms of BSC. In particular, we point the lack of crosstalk and cross-fertilization between subdisciplines of integrative neuroscience to better understand BSC, especially the lack of research in animal models to decipher the neural networks and systems of neurotransmitters underpinning BSC. We highlight the need for more causal evidence that specific brain areas are instrumental in generating BSC and the need for studies tapping into interindividual differences in the phenomenal experience of BSC and their underlying mechanisms.

Influence of goals on modular brain network organization during working memory

Introduction

Top-down control underlies our ability to attend relevant stimuli while ignoring irrelevant, distracting stimuli and is a critical process for prioritizing information in working memory (WM). Prior work has demonstrated that top-down biasing signals modulate sensory-selective cortical areas during WM, and that the large-scale organization of the brain reconfigures due to WM demands alone; however, it is not yet understood how brain networks reconfigure between the processing of relevant versus irrelevant information in the service of WM.

Methods

Here, we investigated the effects of task goals on brain network organization while participants performed a WM task that required participants to detect repetitions (e.g., 0-back or 1-back) and had varying levels of visual interference (e.g., distracting, irrelevant stimuli). We quantified changes in network modularity-a measure of brain sub-network segregation-that occurred depending on overall WM task difficulty as well as trial-level task goals for each stimulus during the task conditions (e.g., relevant or irrelevant).

Results

First, we replicated prior work and found that whole-brain modularity was lower during the more demanding WM task conditions compared to a baseline condition. Further, during the WM conditions with varying task goals, brain modularity was selectively lower during goal-directed processing of task-relevant stimuli to be remembered for WM performance compared to processing of distracting, irrelevant stimuli. Follow-up analyses indicated that this effect of task goals was most pronounced in default mode and visual sub-networks. Finally, we examined the behavioral relevance of these changes in modularity and found that individuals with lower modularity for relevant trials had faster WM task performance.

Discussion

These results suggest that brain networks can dynamically reconfigure to adopt a more integrated organization with greater communication between sub-networks that supports the goal-directed processing of relevant information and guides WM.

Local Brain Network Alterations and Olfactory Impairment in Alzheimer's Disease: An fMRI and Graph-Based Study

Alzheimer's disease (AD) is associated with the abnormal connection of functional networks. Olfactory impairment occurs in early AD; therefore, exploring alterations in olfactory-related regions is useful for early AD diagnosis. We combined the graph theory of local brain network topology with olfactory performance to analyze the differences in AD brain network characteristics. A total of 23 patients with AD and 18 normal controls were recruited for resting-state functional magnetic resonance imaging (fMRI), clinical neuropsychological examinations and the University of Pennsylvania Smell Identification Test (UPSIT). Between-group differences in the topological properties of the local network were compared. Pearson correlations were explored based on differential brain regions and olfactory performance. Statistical analysis revealed a correlation of the degree of cognitive impairment with olfactory recognition function. Local node topological properties were significantly altered in many local brain regions in the AD group. The nodal clustering coefficients of the bilateral temporal pole: middle temporal gyrus (TPOmid), degree centrality of the left insula (INS.L), degree centrality of the right middle temporal gyrus (MTG.R), and betweenness centrality of the left middle temporal gyrus (MTG.L) were related to olfactory performance. Alterations in local topological properties combined with the olfactory impairment can allow early identification of abnormal olfactory-related regions, facilitating early AD screening.

The global neuronal workspace as a broadcasting network

A new strategy for moving forward in the characterization of the global neuronal workspace (GNW) is proposed. According to Dehaene, Changeux, and colleagues (Dehaene, 2014, pp. 304, 312; Dehaene & Changeux, 2004, 2005), broadcasting is the main function of the GNW. However, the dynamic network properties described by recent graph theoretic GNW models are consistent with many large-scale communication processes that are different from broadcasting. We propose to apply a different graph theoretic approach, originally developed for optimizing information dissemination in communication networks, which can be used to identify the pattern of frequency and phase-specific directed functional connections that the GNW would exhibit only if it were a broadcasting network.

Brain Functional Connectivity Asymmetry: Left Hemisphere Is More Modular

Graph-theoretical approaches are increasingly used to study the brain and may enhance our understanding of its asymmetries. In this paper, we hypothesize that the structure of the left hemisphere is, on average, more modular. To this end, we analyzed resting-state functional magnetic resonance imaging data of 90 healthy subjects. We computed functional connectivity by Pearson’s correlation coefficient, turned the matrix into an unweighted graph by keeping a certain percentage of the strongest connections, and quantified modularity separately for the subgraph formed by each hemisphere. Our results show that the left hemisphere is more modular. The result is consistent across a range of binarization thresholds, regardless of whether the two hemispheres are thresholded together or separately. This illustrates that graph-theoretical analysis can provide a robust characterization of lateralization of brain functional connectivity.

Network dynamics scale with levels of awareness

Small world topologies are thought to provide a valuable insight into human brain organisation and consciousness. However, functional magnetic resonance imaging studies in consciousness have not yielded consistent results. Given the importance of dynamics for both consciousness and cognition, here we investigate how the diversity of small world dynamics (quantified by sample entropy; dSW-E¹) scales with decreasing levels of awareness (i.e., sedation and disorders of consciousness). Paying particular attention to result reproducibility, we show that dSW-E is a consistent predictor of levels of awareness even when controlling for the underlying functional connectivity dynamics. We find that dSW-E of subcortical and cortical areas are predictive, with the former showing higher and more robust effect sizes across analyses. We find that the network dynamics of intermodular communication in the cerebellum also have unique predictive power for levels of awareness. Consequently, we propose that the dynamic reorganisation of the functional information architecture, in particular of the subcortex, is a characteristic that emerges with awareness and has explanatory power beyond that of the complexity of dynamic functional connectivity.

Effect of neuromorphic transcutaneous electrical nerve stimulation (nTENS) of cortical functional networks on tactile perceptions: An event-related electroencephalogram study

BACKGROUND

Transcutaneous electrical nerve stimulation (TENS) is generally applied for tactile feedback in the field of prosthetics. The distinct mechanisms of evoked tactile perception between stimulus patterns in conventional TENS (cTENS) and neuromorphic TENS (nTENS) are relatively unknown. This is the first study to investigate the neurobiological effect of nTENS for cortical functional mechanism in evoked tactile perception.

METHODS

Twenty-one healthy participants were recruited in this study. Electroencephalogram (EEG) was recorded while the participants underwent a tactile discrimination task. One cTENS pattern (square pattern) and two nTENS patterns (electromyography and single motor unit patterns) were applied to evoke tactile perception in four fingers, including the right and left index and little fingers. EEG was preprocessed and somatosensory-evoked potentials (SEPs) were determined. Then, source-level functional networks based on graph theory were evaluated, including clustering coefficient, path length, global efficiency, and local efficiency in six frequency bands.

RESULTS

Behavioral results suggested that the single motor units (SMU) pattern of nTENS was the most natural tactile perception. SEPs results revealed that SMU pattern exhibited significant shorter latency in P1 and N1 components than the other patterns, while nTENS patterns have significantly longer latency in P3 component than cTENS pattern. Cortical functional networks showed that the SMU pattern had the lowest short path and highest efficiency in beta and gamma bands.

CONCLUSION

This study highlighted that distinct TENS patterns could affect brain activities. The new characteristics in tactile manifestation of nTENS would provide insights for the application of tactile perception restoration.

Dynamic network impairments underlie cognitive fluctuations in Lewy body dementia

Cognitive fluctuations are a characteristic and distressing disturbance of attention and consciousness seen in patients with Dementia with Lewy bodies and Parkinson's disease dementia. It has been proposed that fluctuations result from disruption of key neuromodulatory systems supporting states of attention and wakefulness which are normally characterised by temporally variable and highly integrated functional network architectures. In this study, patients with DLB (n = 25) and age-matched controls (n = 49) were assessed using dynamic resting state fMRI. A dynamic network signature of reduced temporal variability and integration was identified in DLB patients compared to controls. Reduced temporal variability correlated significantly with fluctuation-related measures using a sustained attention task. A less integrated (more segregated) functional network architecture was seen in DLB patients compared to the control group, with regions of reduced integration observed across dorsal and ventral attention, sensorimotor, visual, cingulo-opercular and cingulo-parietal networks. Reduced network integration correlated positively with subjective and objective measures of fluctuations. Regions of reduced integration and unstable regional assignments significantly matched areas of expression of specific classes of noradrenergic and cholinergic receptors across the cerebral cortex. Correlating topological measures with maps of neurotransmitter/neuromodulator receptor gene expression, we found that regions of reduced integration and unstable modular assignments correlated significantly with the pattern of expression of subclasses of noradrenergic and cholinergic receptors across the cerebral cortex. Altogether, these findings demonstrate that cognitive fluctuations are associated with an imaging signature of dynamic network impairment linked to specific neurotransmitters/neuromodulators within the ascending arousal system, highlighting novel potential diagnostic and therapeutic approaches for this troubling symptom.

A mixed-modeling framework for whole-brain dynamic network analysis

The emerging area of dynamic brain network analysis has gained considerable attention in recent years. However, development of multivariate statistical frameworks that allow for examining the associations between phenotypic traits and dynamic patterns of system-level properties of the brain, and drawing statistical inference about such associations, has largely lagged behind. To address this need we developed a mixed-modeling framework that allows for assessing the relationship between any desired phenotype and dynamic patterns of whole-brain connectivity and topology. This novel framework also allows for simulating dynamic brain networks with respect to desired covariates. Unlike current tools, which largely use data-driven methods, our model-based method enables aligning neuroscientific hypotheses with the analytic approach. We demonstrate the utility of this model in identifying the relationship between fluid intelligence and dynamic brain networks by using resting-state fMRI (rfMRI) data from 200 participants in the Human Connectome Project (HCP) study. We also demonstrate the utility of this model to simulate dynamic brain networks at both group and individual levels. To our knowledge, this approach provides the first model-based statistical method for examining dynamic patterns of system-level properties of the brain and their relationships to phenotypic traits as well as simulating dynamic brain networks.

In recent years, a growing body of studies have aimed at analyzing the brain as a complex dynamic system by using various neuroimaging data. This has opened new avenues to answer compelling questions about the brain function in health and disease. However, methods that allow for providing statistical inference about how the complex interactions of the brain are associated with desired phenotypes are to be developed for a more profound insight. This study introduces a promising regression-based model to relate dynamic brain networks to desired phenotypes and provide statistical inference. Moreover, it can be used for simulating dynamic brain networks with respect to desired phenotypes at the group and individual levels.

Mixed Modeling Frameworks for Analyzing Whole-Brain Network Data

Brain network analyses have exploded in recent years and hold great potential in helping us understand normal and abnormal brain function. Network science approaches have facilitated these analyses and our understanding of how the brain is structurally and functionally organized. However, the development of statistical methods that allow relating this organization to health outcomes has lagged behind. We have attempted to address this need by developing mixed modeling frameworks that allow relating system-level properties of brain networks to outcomes of interest. These frameworks serve as a synergistic fusion of multivariate statistical approaches with network science, providing a needed analytic (modeling and inferential) foundation for whole-brain network data. In this chapter we delineate these approaches that have been developed for single-task and multitask (longitudinal) brain network data, illustrate their utility with data applications, detail their implementation with a user-friendly Matlab toolbox, and discuss ongoing work to adapt the methods to (within-task) dynamic network analysis.

State-related neural influences on fMRI connectivity estimation

The spatiotemporal structure of functional magnetic resonance imaging (fMRI) signals has provided a valuable window into the network underpinnings of human brain function and dysfunction. Although some cross-regional temporal correlation patterns (functional connectivity; FC) exhibit a high degree of stability across individuals and species, there is growing acknowledgment that measures of FC can exhibit marked changes over a range of temporal scales. Further, FC can covary with experimental task demands and ongoing neural processes linked to arousal, consciousness and perception, cognitive and affective state, and brain-body interactions. The increased recognition that such interrelated neural processes modulate FC measurements has raised both challenges and new opportunities in using FC to investigate brain function. Here, we review recent advances in the quantification of neural effects that shape fMRI FC and discuss the broad implications of these findings in the design and analysis of fMRI studies. We also discuss how a more complete understanding of the neural factors that shape FC measurements can resolve apparent inconsistencies in the literature and lead to more interpretable conclusions from fMRI studies.

Implicit-explicit gradient of nondual awareness or consciousness as such

Consciousness is multi-dimensional but is most often portrayed with a two-dimensional (2D) map that has global levels or states on one axis and phenomenal contents on the other. On this map, awareness is conflated either with general alertness or with phenomenal content. This contributes to ongoing difficulties in the scientific understanding of consciousness. Previously, I have proposed that consciousness as such or nondual awareness-a basic non-conceptual, non-propositional awareness in itself free of subject-object fragmentation-is a unique kind that cannot be adequately specified by this 2D map of states and contents. Here, I propose an implicit-explicit gradient of nondual awareness to be added as the z-axis to the existing 2D map of consciousness. This gradient informs about the degree to which nondual awareness is manifest in any experience, independent of the specifics of global state or local content. Alternatively, within the multi-dimensional state space model of consciousness, nondual awareness can be specified by several vectors, each representing one of its properties. In the first part, I outline nondual awareness or consciousness as such in terms of its phenomenal description, its function and its neural correlates. In the second part, I explore the implicit-explicit gradient of nondual awareness and how including it as an additional axis clarifies certain features of everyday dualistic experiences and is especially relevant for understanding the unitary and nondual experiences accessed via different contemplative methods, mind-altering substances or spontaneously.

Age differences in functional network reconfiguration with working memory training

Demanding cognitive functions like working memory (WM) depend on functional brain networks being able to communicate efficiently while also maintaining some degree of modularity. Evidence suggests that aging can disrupt this balance between integration and modularity. In this study, we examined how cognitive training affects the integration and modularity of functional networks in older and younger adults. Twenty three younger and 23 older adults participated in 10 days of verbal WM training, leading to performance gains in both age groups. Older adults exhibited lower modularity overall and a greater decrement when switching from rest to task, compared to younger adults. Interestingly, younger but not older adults showed increased task-related modularity with training. Furthermore, whereas training increased efficiency within, and decreased participation of, the default-mode network for younger adults, it enhanced efficiency within a task-specific salience/sensorimotor network for older adults. Finally, training increased segregation of the default-mode from frontoparietal/salience and visual networks in younger adults, while it diffusely increased between-network connectivity in older adults. Thus, while younger adults increase network segregation with training, suggesting more automated processing, older adults persist in, and potentially amplify, a more integrated and costly global workspace, suggesting different age-related trajectories in functional network reorganization with WM training.

Dynamic reconfiguration of functional brain networks supporting response inhibition in a stop-signal task

Response inhibition is the ability to suppress automatic actions or behaviors that are not appropriate or are no longer adaptive to the situation. Although many studies have suggested regional brain activation, the nature of the reconfiguration of functional brain networks involved in response inhibition remains unknown. Here, we assessed brain changes associated with response inhibition using graph theoretical analysis applied to functional connectivity data acquired while subjects performed a simple stop-signal task. We identified several ways in which global network organization shifted to meet the demand for response inhibition. Increased demand for response inhibition was associated with a global network configuration with more efficient communication across the network (functional integration) and more specialized processing (functional segregation). Regions distributed in the frontoparietal network and attention networks were found to be highly efficient in the stop condition. Nodal efficiency was significantly associated with reaction time and showed a different pattern between the go and stop conditions. In addition, the conditional differences (stop vs. go) in nodal efficiency and regional task activation were common in the postcentral gyrus (PoCG) and superior temporal lobe gyrus (STG), and a negative correlation between these differences was found in the frontal and parietal lobes. These results provide compelling evidence that response inhibition is associated with truly global changes in brain functional connectivity and additional insights into how defects in response inhibition are associated with neurological or psychiatric difficulties.

Cerebral functional networks during sleep in young and older individuals

Even though sleep modification is a hallmark of the aging process, age-related changes in functional connectivity using functional Magnetic Resonance Imaging (fMRI) during sleep, remain unknown. Here, we combined electroencephalography and fMRI to examine functional connectivity differences between wakefulness and light sleep stages (N1 and N2 stages) in 16 young (23.1 ± 3.3y; 7 women), and 14 older individuals (59.6 ± 5.7y; 8 women). Results revealed extended, distributed (inter-between) and local (intra-within) decreases in network connectivity during sleep both in young and older individuals. However, compared to the young participants, older individuals showed lower decreases in connectivity or even increases in connectivity between thalamus/basal ganglia and several cerebral regions as well as between frontal regions of various networks. These findings reflect a reduced ability of the older brain to disconnect during sleep that may impede optimal disengagement for loss of responsiveness, enhanced lighter and fragmented sleep, and contribute to age effects on sleep-dependent brain plasticity.

Integration and segregation across large-scale intrinsic brain networks as a marker of sustained attention and task-unrelated thought

Sustained attention is a fundamental cognitive process that can be decoupled from distinct external events, and instead emerges from ongoing intrinsic large-scale network interdependencies fluctuating over seconds to minutes. Lapses of sustained attention are commonly associated with the subjective experience of mind wandering and task-unrelated thoughts. Little is known about how fluctuations in information processing underpin sustained attention, nor how mind wandering undermines this information processing. To overcome this, we used fMRI to investigate brain activity during subjects' performance (n=29) of a cognitive task that was optimized to detect and isolate continuous fluctuations in both sustained attention (via motor responses) and task-unrelated thought (via subjective reports). We then investigated sustained attention with respect to global attributes of communication throughout the functional architecture, i.e., by the segregation and integration of information processing across large scale-networks. Further, we determined how task-unrelated thoughts related to these global information processing markers of sustained attention. The results show that optimal states of sustained attention favor both enhanced segregation and reduced integration of information processing in several task-related large-scale cortical systems with concurrent reduced segregation and enhanced integration in the auditory and sensorimotor systems. Higher degree of mind wandering was associated with losses of the favored segregation and integration of specific subsystems in our sustained attention model. Taken together, we demonstrate that intrinsic ongoing neural fluctuations are characterized by two converging communication modes throughout the global functional architecture, which give rise to optimal and suboptimal attention states. We discuss how these results might potentially serve as neural markers for clinically abnormal attention. SIGNIFICANCE STATEMENT: Most of our brain activity unfolds in an intrinsic manner, i.e., is unrelated to immediate external stimuli or tasks. Here we use a gradual continuous performance task to map this intrinsic brain activity to both fluctuations of sustained attention and mind wandering. We show that optimal sustained attention is associated with concurrent segregation and integration of information processing within many large-scale brain networks, while task-unrelated thought is related to sub-optimal information processing in specific subsystems of this sustained attention network model. These findings provide a novel information processing framework for investigating the neural basis of sustained attention, by mapping attentional fluctuations to genuinely global features of intra-brain communication.

Computational animal welfare: towards cognitive architecture models of animal sentience, emotion and wellbeing

To understand animal wellbeing, we need to consider subjective phenomena and sentience. This is challenging, since these properties are private and cannot be observed directly. Certain motivations, emotions and related internal states can be inferred in animals through experiments that involve choice, learning, generalization and decision-making. Yet, even though there is significant progress in elucidating the neurobiology of human consciousness, animal consciousness is still a mystery. We propose that computational animal welfare science emerges at the intersection of animal behaviour, welfare and computational cognition. By using ideas from cognitive science, we develop a functional and generic definition of subjective phenomena as any process or state of the organism that exists from the first-person perspective and cannot be isolated from the animal subject. We then outline a general cognitive architecture to model simple forms of subjective processes and sentience. This includes evolutionary adaptation which contains top-down attention modulation, predictive processing and subjective simulation by re-entrant (recursive) computations. Thereafter, we show how this approach uses major characteristics of the subjective experience: elementary self-awareness, global workspace and qualia with unity and continuity. This provides a formal framework for process-based modelling of animal needs, subjective states, sentience and wellbeing.

Increase in internetwork functional connectivity in the human brain with attention capture

Attention is often extolled for its selective neural properties. Yet, when powerfully captured by a salient unexpected event, attention can give rise to a broad cascade of systemic effects for evaluating and adaptively responding to the event. Using graph theory analysis combined with fMRI, we show here that the extensive psychophysiological and cognitive changes associated with such attention capture are related to large-scale distributed changes in the brain's functional connectivity. Novel task-irrelevant "oddball" stimuli presented to subjects during the performance of a target-search task triggered an increase in internetwork functional connectivity that degraded the brain's network modularity, thereby facilitating the integration of information. Furthermore, this phenomenon habituated with repeated oddball presentations, mirroring the behavior. These functional network connectivity changes are remarkably consistent with those previously obtained with conscious target perception, thus raising the possibility that large-scale internetwork connectivity changes triggered by attentional capture and awareness rely on common neural network dynamics.NEW & NOTEWORTHY The selective properties of attention have been extensively studied. There are some circumstances in which attention can have widespread and systemic effects, however, such as when it is captured by an unexpected, salient stimulus or event. How are such effects propagated in the human brain? Using graph theory analysis of fMRI data, we show here that salient task-irrelevant events produced a global increase in the functional integration of the brain's neural networks.

Neural correlates of conscious tactile perception: An analysis of BOLD activation patterns and graph metrics

Theories of human consciousness substantially vary in the proposed spatial extent of brain activity associated with conscious perception as well as in the assumed functional alterations within the involved brain regions. Here, we investigate which local and global changes in brain activity accompany conscious somatosensory perception following electrical finger nerve stimulation, and whether there are whole-brain functional network alterations by means of graph metrics. Thirty-eight healthy participants performed a somatosensory detection task and reported their decision confidence during fMRI. For conscious tactile perception in contrast to undetected near-threshold trials (misses), we observed increased BOLD activity in the precuneus, the intraparietal sulcus, the insula, the nucleus accumbens, the inferior frontal gyrus and the contralateral secondary somatosensory cortex. For misses compared to correct rejections, bilateral secondary somatosensory cortices, supplementary motor cortex and insula showed greater activations. The analysis of whole-brain functional network topology for hits, misses and correct rejections, did not result in any significant differences in modularity, participation, clustering or path length, which was supported by Bayes factor statistics. In conclusion, for conscious somatosensory perception, our results are consistent with an involvement of (probably) domain-general brain areas (precuneus, insula, inferior frontal gyrus) in addition to somatosensory regions; our data do not support the notion of specific changes in graph metrics associated with conscious experience. For the employed somatosensory submodality of fine electrical current stimulation, this speaks for a global broadcasting of sensory content across the brain without substantial reconfiguration of the whole-brain functional network resulting in an integrative conscious experience.

Network topology of symbolic and nonsymbolic number comparison

Studies of brain activity during number processing suggest symbolic and nonsymbolic numerical stimuli (e.g., Arabic digits and dot arrays) engage both shared and distinct neural mechanisms. However, the extent to which number format influences large-scale functional network organization is unknown. In this study, using 7 Tesla MRI, we adopted a network neuroscience approach to characterize the whole-brain functional architecture supporting symbolic and nonsymbolic number comparison in 33 adults. Results showed the degree of global modularity was similar for both formats. The symbolic format, however, elicited stronger community membership among auditory regions, whereas for nonsymbolic, stronger membership was observed within and between cingulo-opercular/salience network and basal ganglia communities. The right posterior inferior temporal gyrus, left intraparietal sulcus, and two regions in the right ventromedial occipital cortex demonstrated robust differences between formats in terms of their community membership, supporting prior findings that these areas are differentially engaged based on number format. Furthermore, a unified fronto-parietal/dorsal attention community in the nonsymbolic condition was fractionated into two components in the symbolic condition. Taken together, these results reveal a pattern of overlapping and distinct network architectures for symbolic and nonsymbolic number processing.

Combining local and global evolutionary trajectories of brain-behaviour relationships through game theory

The study of the evolution of brain-behaviour relationships concerns understanding the causes and repercussions of cross- and within-species variability. Understanding such variability is a main objective of evolutionary and cognitive neuroscience, and it may help explaining the appearance of psychopathological phenotypes. Although brain evolution is related to the progressive action of selection and adaptation through multiple paths (e.g. mosaic vs. concerted evolution, metabolic vs. structural and functional constraints), a coherent, integrative framework is needed to combine evolutionary paths and neuroscientific evidence. Here, we review the literature on evolutionary pressures focusing on structural-functional changes and developmental constraints. Taking advantage of recent progress in neuroimaging and cognitive neuroscience, we propose a twofold hypothetical model of brain evolution. Within this model, global and local trajectories imply rearrangements of neural subunits and subsystems and of behavioural repertoires of a species, respectively. We incorporate these two processes in a game in which the global trajectory shapes the structural-functional neural substrates (i.e. players), while the local trajectory shapes the behavioural repertoires (i.e. stochastic payoffs).

Altered large-scale functional brain organization in posttraumatic stress disorder: A comprehensive review of univariate and network-level neurocircuitry models of PTSD

Classical neural circuitry models of posttraumatic stress disorder (PTSD) are largely derived from univariate activation studies and implicate the fronto-limbic circuit as a main neural correlate of PTSD symptoms. Though well-supported by human neuroimaging literature, these models are limited in their ability to explain the widely distributed neural and behavioral deficits in PTSD. Emerging interest in the application of large-scale network methods to functional neuroimaging provides a new opportunity to overcome such limitations and conceptualize the neural circuitry of PTSD in the context of network patterns. This review aims to evaluate both the classical neural circuitry model and a new, network-based model of PTSD neural circuitry using a breadth of functional brain organization research in subjects with PTSD. Taken together, this literature suggests global patterns of reduced functional connectivity (FC) in PTSD groups as well as altered FC targets that reside disproportionately in canonical functional networks, especially the default mode network. This provides evidence for an integrative model that includes elements of both the classical models and network-based models to characterize the neural circuitry of PTSD.

The Prospective Sense of Agency is Rooted in Local and Global Properties of Intrinsic Functional Brain Networks

The sense of agency (SoA) refers to a constitutional aspect of the self describing the extent to which individuals feel in control over their actions and consequences thereof. Although the SoA has been associated with mental health and well-being, it is still unknown how interindividual variability in the SoA is embedded in the intrinsic brain organization. We hypothesized that the prospective component of an implicit SoA is associated with brain networks related to SoA and sensorimotor predictions on multiple spatial scales. We replicated previous findings by showing a significant prospective SoA as indicated by intentional binding effects. Then, using task-free fMRI and graph analysis, we analyzed associations between intentional binding effects and the intrinsic brain organization at regional, modular, and whole-brain scales. The results showed that intermodular connections of a frontoparietal module including the premotor cortex, supramarginal gyrus, and dorsal precuneus are associated with individual differences in prospective intentional binding. Notably, prospective intentional binding effects were also related to global brain modularity within a specific structural resolution range. These findings suggest that an implicit SoA generated through sensorimotor predictions relies on the intrinsic organization of the brain connectome on both local and global scales.

Dynamic functional connectivity between nucleus accumbens and the central executive network relates to chronic cannabis use

The neural mechanisms of drug cue‐reactivity regarding the temporal fluctuations of functional connectivity, namely the dynamic connectivity, are sparsely studied. Quantifying the task‐modulated variability in dynamic functional connectivity at cue exposure can aid the understanding. We analyzed changes in dynamic connectivity in 54 adult cannabis users and 90 controls during a cannabis cue exposure task. The variability was measured as standard deviation in the (a) connectivity weights of the default mode, the central executive, and the salience networks and two reward loci (amygdalae and nuclei accumbens); and (b) topological indexes of the whole brain (global efficiency, modularity and network resilience). These were compared for the main effects of task conditions and the group (users vs. controls), and correlated with pre‐ and during‐scan subjective craving. The variability of connectivity weights between the central executive network and nuclei accumbens was increased in users throughout the cue exposure task, and, was positively correlated with during‐scan craving for cannabis. The variability of modularity was not different by groups, but positively correlated with prescan craving. The variability of dynamic connectivity during cannabis cue exposure task between the central executive network and the nuclei accumbens, and, the level of modularity, seem to relate to the neural underpinning of cannabis use and the subjective craving.

Evolving complexity: how tinkering shapes cells, software and ecological networks

A common trait of complex systems is that they can be represented by means of a network of interacting parts. It is, in fact, the network organization (more than the parts) that largely conditions most higher-level properties, which are not reducible to the properties of the individual parts. Can the topological organization of these webs provide some insight into their evolutionary origins? Both biological and artificial networks share some common architectural traits. They are often heterogeneous and sparse, and most exhibit different types of correlations, such as nestedness, modularity or hierarchical patterns. These properties have often been attributed to the selection of functionally meaningful traits. However, a proper formulation of generative network models suggests a rather different picture. Against the standard selection-optimization argument, some networks reveal the inevitable generation of complex patterns resulting from reuse and can be modelled using duplication-rewiring rules lacking functionality. These give rise to the observed heterogeneous, scale-free and modular architectures. Here, we examine the evidence for tinkering in cellular, technological and ecological webs and its impact in shaping their architecture. Our analysis suggests a serious consideration of the role played by selection as the origin of network topology. Instead, we suggest that the amplification processes associated with reuse might shape these graphs at the topological level. In biological systems, selection forces would take advantage of emergent patterns. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.

Therapeutic Effects of Bilateral Anodal Transcranial Direct Current Stimulation on Prefrontal and Motor Cortical Areas in Children with Autism Spectrum Disorders: A Pilot Study

Dysfunctional frontal cortical areas associated with clinical features are observed in children with autism spectrum disorder (ASD). This study attempted to identify any potential therapeutic effects of bilateral anodal transcranial direct current stimulation (tDCS) applied over the left and right prefrontal and motor areas on the clinical characteristics of children with ASD. Fifty children with confirmed ASD medical diagnoses were divided equally and randomly into a tDCS treatment group and a control group. The tDCS treatment group underwent 10 sessions (20-min durations, five per week) of bilateral anodal tDCS stimulation applied simultaneously over the left and right prefrontal and motor areas, whereas the control group underwent the same procedures but with the use of sham tDCS stimulation. Total scores and sub-scores of autism treatment evaluation checklist (ATEC) (language and communication; sociability; sensory awareness; and behavioral, health, and physical conditions) were measured before and after the tDCS treatment sessions of both groups. There were significant decreases in total ATEC scores (P = 0.014), sociability sub-scores (P = 0.021), and behavioral, health, and physical condition sub-scores (P = 0.011) in the tDCS treatment group. No significant changes were observed in total ATEC scores and sub-scores in the control group. In conclusion, compared to the control group, bilateral anodal tDCS showed potential therapeutic effects on children with ASD in terms of improvements in sociability, behavior, health, and physical conditions with no reported side effects. Autism Res 2020, 13: 828-836. © 2020 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Dysfunctional frontal cortical areas are associated with clinical features in children with autism spectrum disorder (ASD). Transcranial direct current stimulation (tDCS) is found to be a safe, noninvasive method to stimulate cortical regions and thus have therapeutic effects on children with ASD. © 2020 International Society for Autism Research, Wiley Periodicals, Inc.

Dopamine Signaling Modulates the Stability and Integration of Intrinsic Brain Networks

Dopaminergic projections are hypothesized to stabilize neural signaling and neural representations, but how they shape regional information processing and large-scale network interactions remains unclear. Here we investigated effects of lowered dopamine levels on within-region temporal signal variability (measured by sample entropy) and between-region functional connectivity (measured by pairwise temporal correlations) in the healthy brain at rest. The acute phenylalanine and tyrosine depletion (APTD) method was used to decrease dopamine synthesis in 51 healthy participants who underwent resting-state functional MRI (fMRI) scanning. Functional connectivity and regional signal variability were estimated for each participant. Multivariate partial least squares (PLS) analysis was used to statistically assess changes in signal variability following APTD as compared with the balanced control treatment. The analysis captured a pattern of increased regional signal variability following dopamine depletion. Changes in hemodynamic signal variability were concomitant with changes in functional connectivity, such that nodes with greatest increase in signal variability following dopamine depletion also experienced greatest decrease in functional connectivity. Our results suggest that dopamine may act to stabilize neural signaling, particularly in networks related to motor function and orienting attention towards behaviorally-relevant stimuli. Moreover, dopamine-dependent signal variability is critically associated with functional embedding of individual areas in large-scale networks.

Using Low-Dimensional Manifolds to Map Relationships Between Dynamic Brain Networks

As the field of dynamic brain networks continues to expand, new methods are needed to allow for optimal handling and understanding of this explosion in data. We propose here a novel approach that embeds dynamic brain networks onto a two-dimensional (2D) manifold based on similarities and differences in network organization. Each brain network is represented as a single point on the low dimensional manifold with networks of similar topology being located in close proximity. The rich spatio-temporal information has great potential for visualization, analysis, and interpretation of dynamic brain networks. The fact that each network is represented by a single point makes it possible to switch between the low-dimensional space and the full connectivity of any given brain network. Thus, networks in a specific region of the low-dimensional space can be examined to identify network features, such as the location of brain network hubs or the interconnectivity between brain circuits. In this proof-of-concept manuscript, we show that these low dimensional manifolds contain meaningful information, as they were able to successfully discriminate between cognitive tasks and study populations. This work provides evidence that embedding dynamic brain networks onto low dimensional manifolds has the potential to help us better visualize and understand dynamic brain networks with the hope of gaining a deeper understanding of normal and abnormal brain dynamics.

Pain-Evoked Reorganization in Functional Brain Networks

Recent studies indicate that a significant reorganization of cerebral networks may occur in patients with chronic pain, but how immediate pain experience influences the organization of large-scale functional networks is not yet well characterized. To investigate this question, we used functional magnetic resonance imaging in 106 participants experiencing both noxious and innocuous heat. Painful stimulation caused network-level reorganization of cerebral connectivity that differed substantially from organization during innocuous stimulation and standard resting-state networks. Noxious stimuli increased somatosensory network connectivity with (a) frontoparietal networks involved in context representation, (b) "ventral attention network" regions involved in motivated action selection, and (c) basal ganglia and brainstem regions. This resulted in reduced "small-worldness," modularity (fewer networks), and global network efficiency and in the emergence of an integrated "pain supersystem" (PS) whose activity predicted individual differences in pain sensitivity across 5 participant cohorts. Network hubs were reorganized ("hub disruption") so that more hubs were localized in PS, and there was a shift from "connector" hubs linking disparate networks to "provincial" hubs connecting regions within PS. Our findings suggest that pain reorganizes the network structure of large-scale brain systems. These changes may prioritize responses to painful events and provide nociceptive systems privileged access to central control of cognition and action during pain.

Optimized Configuration of Functional Brain Network for Processing Semantic Audiovisual Stimuli Underlying the Modulation of Attention: A Graph-Based Study

Semantic audiovisual stimuli have a facilitatory effect on behavioral performance and influence the integration of multisensory inputs across sensory modalities. Many neuroimaging and electrophysiological studies investigated the neural mechanisms of multisensory semantic processing and reported that attention modulates the response to multisensory semantic inputs. In the present study, we designed an functional magnetic resonance imaging (fMRI) experiment of semantic discrimination using the unimodal auditory, unimodal visual and bimodal audiovisual stimuli with semantic information. By manipulating the stimuli present on attended and unattended position, we recorded the task-related fMRI data corresponding to the unimodal auditory, unimodal visual and bimodal audiovisual stimuli in attended and unattended conditions. We also recorded the fMRI data in resting state. Then the fMRI method was used together with a graph theoretical analysis to construct the functional brain networks in task-related and resting states and quantitatively characterize the topological network properties. The aim of our present study is to explore the characteristics of functional brain networks that process semantic audiovisual stimuli in attended and unattended conditions, revealing the neural mechanism of multisensory processing and the modulation of attention. The behavioral results showed that the audiovisual stimulus presented simultaneously promoted the performance of semantic discrimination task. And the analyses of network properties showed that compared with the resting-state condition, the functional networks of processing semantic audiovisual stimuli (both in attended and unattended conditions) had greater small-worldness, global efficiency, and lower clustering coefficient, characteristic path length, global efficiency and hierarchy. In addition, the hubs were concentrated in the bilateral temporal lobes, especially in the anterior temporal lobes (ATLs), which were positively correlated to reaction time (RT). Moreover, attention significantly altered the degree of small-worldness and the distribution of hubs in the functional network for processing semantic audiovisual stimuli. Our findings suggest that the topological structure of the functional brain network for processing semantic audiovisual stimulus is modulated by attention, and has the characteristics of high efficiency and low wiring cost, which maintains an optimized balance between functional segregation and integration for multisensory processing efficiently.

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.

Abnormal dynamic properties of functional connectivity in disorders of consciousness

Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to research abnormal functional connectivity (FC) in patients with disorders of consciousness (DOC). However, most studies assumed steady spatial-temporal signal interactions between distinct brain regions during the scan period. The aim of this study was to explore abnormal dynamic functional connectivity (dFC) in DOC patients. After excluding 26 patients' data that failed to meet the requirements of imaging quality, we retained 19 DOC patients (12 with unresponsive wakefulness syndrome and 7 in a minimally conscious state, diagnosed with the Coma Recovery Scale-Revised [CRS-R]) for the dFC analysis. We used the sliding windows approach to construct dFC matrices. Then these matrices were clustered into distinct states using the k-means clustering algorithm. We found that the DOC patients showed decreased dFC in the sensory and somatomotor networks compared with the healthy controls. There were also significant differences in temporal properties, the mean dwell time (MDT) and the number of transitions (NT), between the DOC patients and the healthy controls. In addition, we also used a hidden Markov model (HMM) to test the robustness of the results. With the connectome-based predictive modeling (CPM) approach, we found that the properties of abnormal dynamic network can be used to predict the CRS-R scores of the patients after severe brain injury. These findings may contribute to a better understanding of the abnormal brain networks in DOC patients.

I am Me: Brain systems integrate and segregate to establish a multidimensional sense of self

Humans experience a sense of self, which is proposed to emerge from the integration of intrinsic and extrinsic self-processing through the propagation of information across brain systems. Using a novel functional magnetic resonance imaging (fMRI) paradigm, we tested this hypothesis in a non-clinical sample by modulating the intrinsic and extrinsic self-relatedness of auditory action consequences in terms of identity and agency, respectively. In addition, the relevance of individual traits associated with altered self-experiences (e.g., psychosis-like experiences) was examined. The task-evoked fMRI results showed distinctive associations between the neural coding of identity and negative affect traits, and between agency and psychosis-like experiences. Most importantly, regarding the functional connectivity analysis, graph theoretical measures demonstrated that the simultaneous processing of identity and agency relies on the functional integration and segregation of default mode, sensorimotor, language, and executive brain networks. Finally, cross-network interactions mediated by executive and sensorimotor regions were negatively associated with psychosis-like experiences when the intrinsic and extrinsic self-relatedness of action consequences conflicted. These findings provide evidence that the self is a multidimensional phenomenon rooted in the functional interactions between large-scale neuronal networks. Such interactions may have particular relevance for self-experience alterations.

Higher Cortical Visual Disorders

PURPOSE OF REVIEW

This article reviews the disorders that result from disruption of extrastriate regions of the cerebral cortex responsible for higher visual processing. For each disorder, a historical perspective is offered and relevant neuroscientific studies are reviewed.

RECENT FINDINGS

Careful analysis of the consequences of lesions that disrupt visual functions such as facial recognition and written language processing has improved understanding of the role of key regions in these networks. In addition, modern imaging techniques have built upon prior lesion studies to further elucidate the functions of these cortical areas. For example, functional MRI (fMRI) has identified and characterized the response properties of ventral regions that contribute to object recognition and dorsal regions that subserve motion perception and visuospatial attention. Newer network-based functional imaging studies have shed light on the mechanisms behind various causes of spontaneous visual hallucinations.

SUMMARY

Understanding the regions and neural networks responsible for higher-order visual function helps the practicing neurologist to diagnose and manage associated disorders of visual processing and to identify and treat responsible underlying disease.

Neuromodulatory Influences on Integration and Segregation in the Brain

Cognitive function relies on the dynamic cooperation of specialized regions of the brain; however, the elements of the system responsible for coordinating this interaction remain poorly understood. In this Opinion article I argue that this capacity is mediated in part by competitive and cooperative dynamic interactions between two prominent metabotropic neuromodulatory systems - the cholinergic basal forebrain and the noradrenergic locus coeruleus (LC). I assert that activity in these projection nuclei regulates the amount of segregation and integration within the whole brain network by modulating the activity of a diverse set of specialized regions of the brain on a timescale relevant for cognition and attention.

Network Pharmacology in Research of Chinese Medicine Formula: Methodology, Application and Prospective

Chinese medicine (CM) is usually prescribed as CM formula to treat disease. The lack of effective research approach makes it difficult to elucidate the molecular mechanisms of CM formula owing to its complicated chemical compounds. Network pharmacology is increasingly applied in CM formula research in recent years, which is identified suitable for the study of CM formula. In this review, we summarized the methodology of network pharmacology, including network construction, network analysis and network verification. The aim of constructing a network is to achieve the interaction between the bioactive compounds and targets and the interaction between various targets, and then find out and validate the key nodes via network analysis and network verification. Besides, we reviewed the application in CM formula research, mainly including targets discovery, bioactive compounds screening, toxicity evaluation, mechanism research and quality control research. Finally, we proposed prospective in the future and limitations of network pharmacology, expecting to provide new strategy and thinking on study for CM formula.

Functional Brain Network Topology Discriminates between Patients with Minimally Conscious State and Unresponsive Wakefulness Syndrome

Consciousness arises from the functional interaction of multiple brain structures and their ability to integrate different complex patterns of internal communication. Although several studies demonstrated that the fronto-parietal and functional default mode networks play a key role in conscious processes, it is still not clear which topological network measures (that quantifies different features of whole-brain functional network organization) are altered in patients with disorders of consciousness. Herein, we investigate the functional connectivity of unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS) patients from a topological network perspective, by using resting-state EEG recording. Network-based statistical analysis reveals a subnetwork of decreased functional connectivity in UWS compared to in the MCS patients, mainly involving the interhemispheric fronto-parietal connectivity patterns. Network topological analysis reveals increased values of local-community-paradigm correlation, as well as higher clustering coefficient and local efficiency in UWS patients compared to in MCS patients. At the nodal level, the UWS patients showed altered functional topology in several limbic and temporo-parieto-occipital regions. Taken together, our results highlight (i) the involvement of the interhemispheric fronto-parietal functional connectivity in the pathophysiology of consciousness disorders and (ii) an aberrant connectome organization both at the network topology level and at the nodal level in UWS patients compared to in the MCS patients.

Applying a network framework to the neurobiology of reading and dyslexia

Background

There is a substantial literature on the neurobiology of reading and dyslexia. Differences are often described in terms of individual regions or individual cognitive processes. However, there is a growing appreciation that the brain areas subserving reading are nested within larger functional systems, and new network analysis methods may provide greater insight into how reading difficulty arises. Yet, relatively few studies have adopted a principled network-based approach (e.g., connectomics) to studying reading. In this study, we combine data from previous reading literature, connectomics studies, and original data to investigate the relationship between network architecture and reading.

Methods

First, we detailed the distribution of reading-related areas across many resting-state networks using meta-analytic data from NeuroSynth. Then, we tested whether individual differences in modularity, the brain’s tendency to segregate into resting-state networks, are related to reading skill. Finally, we determined whether brain areas that function atypically in dyslexia, as identified by previous meta-analyses, tend to be concentrated in hub regions.

Results

We found that most resting-state networks contributed to the reading network, including those subserving domain-general cognitive skills such as attention and executive function. There was also a positive relationship between the global modularity of an individual’s brain network and reading skill, with the visual, default mode and cingulo-opercular networks showing the highest correlations. Brain areas implicated in dyslexia were also significantly more likely to have a higher participation coefficient (connect to multiple resting-state networks) than other areas.

Conclusions

These results contribute to the growing literature on the relationship between reading and brain network architecture. They suggest that an efficient network organization, i.e., one in which brain areas form cohesive resting-state networks, is important for skilled reading, and that dyslexia can be characterized by abnormal functioning of hub regions that map information between multiple systems. Overall, use of a connectomics framework opens up new possibilities for investigating reading difficulty, especially its commonalities across other neurodevelopmental disorders.

Electronic supplementary material

The online version of this article (10.1186/s11689-018-9251-z) contains supplementary material, which is available to authorized users.

Fundamental computational constraints on the time course of perception and action

A cognitive system faced with contingent events that cause rapid changes in sensory data may (i) incrementally incorporate new data into the ongoing perceptual and motor processing; or (ii) restart processing on each new event; or (iii) sample the data and hold onto the sample until its processing is complete, while disregarding any contingent changes. We offer a set of computational first-principles arguments for a hypothesis, according to which any system that contends with certain classes of perception and behavioral control tasks must include the sample-and-hold option (possibly alongside the other two, which may be useful in other tasks). This hypothesis has implications for understanding the dynamics of perception and action. In particular, a sample-and-hold channel necessarily processes sensory data on some kind of cycle (which does not imply precise periodicity). Further, being prepared to face the world at all times requires that the sampling that initiates each cycle be triggered by every significant action on part of the agent itself, such as saccades. We survey a range of evidence for the sample-and-hold functionality, touching upon diverse phenomena such as attentional blink and backward masking, the yoking of olfaction to respiration, thalamocortical interactions, and metastable brain dynamics in perception and consciousness.

Working Memory and Reasoning Benefit from Different Modes of Large-scale Brain Dynamics in Healthy Older Adults

Researchers have proposed that solving complex reasoning problems, a key indicator of fluid intelligence, involves the same cognitive processes as solving working memory tasks. This proposal is supported by an overlap of the functional brain activations associated with the two types of tasks and by high correlations between interindividual differences in performance. We replicated these findings in 53 older participants but also showed that solving reasoning and working memory problems benefits from different configurations of the functional connectome and that this dissimilarity increases with a higher difficulty load. Specifically, superior performance in a typical working memory paradigm ( n-back) was associated with upregulation of modularity (increased between-network segregation), whereas performance in the reasoning task was associated with effective downregulation of modularity. We also showed that working memory training promotes task-invariant increases in modularity. Because superior reasoning performance is associated with downregulation of modular dynamics, training may thus have fostered an inefficient way of solving the reasoning tasks. This could help explain why working memory training does little to promote complex reasoning performance. The study concludes that complex reasoning abilities cannot be reduced to working memory and suggests the need to reconsider the feasibility of using working memory training interventions to attempt to achieve effects that transfer to broader cognition.

Large-scale network integration in the human brain tracks temporal fluctuations in memory encoding performance

Although activation/deactivation of specific brain regions has been shown to be predictive of successful memory encoding, the relationship between time-varying large-scale brain networks and fluctuations of memory encoding performance remains unclear. Here, we investigated time-varying functional connectivity patterns across the human brain in periods of 30–40 s, which have recently been implicated in various cognitive functions. During functional magnetic resonance imaging, participants performed a memory encoding task, and their performance was assessed with a subsequent surprise memory test. A graph analysis of functional connectivity patterns revealed that increased integration of the subcortical, default-mode, salience, and visual subnetworks with other subnetworks is a hallmark of successful memory encoding. Moreover, multivariate analysis using the graph metrics of integration reliably classified the brain network states into the period of high (vs. low) memory encoding performance. Our findings suggest that a diverse set of brain systems dynamically interact to support successful memory encoding.

Applications of community detection techniques to brain graphs: Algorithmic considerations and implications for neural function

The human brain can be represented as a graph in which neural units such as cells or small volumes of tissue are heterogeneously connected to one another through structural or functional links. Brain graphs are parsimonious representations of neural systems that have begun to offer fundamental insights into healthy human cognition, as well as its alteration in disease. A critical open question in network neuroscience lies in how neural units cluster into densely interconnected groups that can provide the coordinated activity that is characteristic of perception, action, and adaptive behaviors. Tools that have proven particularly useful for addressing this question are community detection approaches, which can identify communities or modules: groups of neural units that are densely interconnected with other units in their own group but sparsely interconnected with units in other groups. In this paper, we describe a common community detection algorithm known as modularity maximization, and we detail its applications to brain graphs constructed from neuroimaging data. We pay particular attention to important algorithmic considerations, especially in recent extensions of these techniques to graphs that evolve in time. After recounting a few fundamental insights that these techniques have provided into brain function, we highlight potential avenues of methodological advancements for future studies seeking to better characterize the patterns of coordinated activity in the brain that accompany human behavior. This tutorial provides a naive reader with an introduction to theoretical considerations pertinent to the generation of brain graphs, an understanding of modularity maximization for community detection, a resource of statistical measures that can be used to characterize community structure, and an appreciation of the usefulness of these approaches in uncovering behaviorally-relevant network dynamics in neuroimaging data.

Fronto-Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue

Fronto-parietal subnetworks were revealed to compensate for cognitive decline due to mental fatigue by community structure analysis. Here, we investigate changes in topology of subnetworks of resting-state fMRI networks due to mental fatigue induced by prolonged performance of a cognitively demanding task, and their associations with cognitive decline. As it is well established that brain networks have modular organization, community structure analyses can provide valuable information about mesoscale network organization and serve as a bridge between standard fMRI approaches and brain connectomics that quantify the topology of whole brain networks. We developed inter- and intramodule network metrics to quantify topological characteristics of subnetworks, based on our hypothesis that mental fatigue would impact on functional relationships of subnetworks. Functional networks were constructed with wavelet correlation and a data-driven thresholding scheme based on orthogonal minimum spanning trees, which allowed detection of communities with weak connections. A change from pre- to posttask runs was found for the intermodule density between the frontal and the temporal subnetworks. Seven inter- or intramodule network metrics, mostly at the frontal or the parietal subnetworks, showed significant predictive power of individual cognitive decline, while the network metrics for the whole network were less effective in the predictions. Our results suggest that the control-type fronto-parietal networks have a flexible topological architecture to compensate for declining cognitive ability due to mental fatigue. This community structure analysis provides valuable insight into connectivity dynamics under different cognitive states including mental fatigue.