Modular Brain Networks

Memory systems 2018 - Towards a new paradigm

The multiple memory systems theory (MMS) postulates that the brain stores information based on the independent and parallel activity of a number of modules, each with distinct properties, dynamics, and neural basis. Much of the evidence for this theory comes from dissociation studies indicating that damage to restricted brain areas cause selective types of memory deficits. MMS has been the prevalent paradigm in memory research for more than thirty years, even as it has been adjusted several times to accommodate new data. However, recent empirical results indicating that the memory systems are not always dissociable constitute a challenge to fundamental tenets of the current theory because they suggest that representations formed by individual memory systems can contribute to more than one type of memory-driven behavioral strategy. This problem can be addressed by applying a dynamic network perspective to memory architecture. According to this view, memory networks can reconfigure or transiently couple in response to environmental demands. Within this context, the neural network underlying a specific memory system can act as an independent unit or as an integrated component of a higher order meta-network. This dynamic network model proposes a way in which empirical evidence that challenges the idea of distinct memory systems can be incorporated within a modular memory architecture. The model also provides a framework to account for the complex interactions among memory systems demonstrated at the behavioral level. Advances in the study of dynamic networks can generate new ideas to experimentally manipulate and control memory in basic or clinical research.

Connectome development and a novel extension to the neurodevelopmental model of schizophrenia

The brain is the ultimate adaptive system, a complex network organized across multiple levels of spatial and temporal resolution that is sculpted over several decades via its interactions with the environment. This review sets out to examine how fundamental biological processes in early and late neurodevelopment, in interaction with environmental inputs, guide the formation of the brain's network and its ongoing reorganization throughout the course of development. Moreover, we explore how disruptions in these processes could lead to abnormal brain network architecture and organization and thereby give rise to schizophrenia. Arguing that the neurodevelopmental trajectory leading up to the manifestation of psychosis may best be understood from the sequential trajectory of connectome formation and maturation, we propose a novel extension to the neurodevelopmental model of the illness that posits that schizophrenia is a disorder of connectome development.

Graph theory methods: applications in brain networks

Network neuroscience is a thriving and rapidly expanding field. Empirical data on brain networks, from molecular to behavioral scales, are ever increasing in size and complexity. These developments lead to a strong demand for appropriate tools and methods that model and analyze brain network data, such as those provided by graph theory. This brief review surveys some of the most commonly used and neurobiologically insightful graph measures and techniques. Among these, the detection of network communities or modules, and the identification of central network elements that facilitate communication and signal transfer, are particularly salient. A number of emerging trends are the growing use of generative models, dynamic (time-varying) and multilayer networks, as well as the application of algebraic topology. Overall, graph theory methods are centrally important to understanding the architecture, development, and evolution of brain networks.

Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification

High-order correlation has recently been proposed to model brain functional connectivity network (FCN) for identifying neurological disorders, such as mild cognitive impairment (MCI) and autism spectrum disorder (ASD). In practice, the high-order FCN (HoFCN) can be derived from multiple low-order FCNs that are estimated separately in a series of sliding windows, and thus it in fact provides a way of integrating dynamic information encoded in a sequence of low-order FCNs. However, the estimation of low-order FCN may be unreliable due to the fact that the use of limited volumes/samples in a sliding window can significantly reduce the statistical power, which in turn affects the reliability of the resulted HoFCN. To address this issue, we propose to enhance HoFCN based on a regularized learning framework. More specifically, we first calculate an initial HoFCN using a recently developed method based on maximum likelihood estimation. Then, we learn an optimal neighborhood network of the initially estimated HoFCN with sparsity and modularity priors as regularizers. Finally, based on the improved HoFCNs, we conduct experiments to identify MCI and ASD patients from their corresponding normal controls. Experimental results show that the proposed methods outperform the baseline methods, and the improved HoFCNs with modularity prior consistently achieve the best performance.

Comparison of Methods to Identify Modules in Noisy or Incomplete Brain Networks

Community structure, or "modularity," is a fundamentally important aspect in the organization of structural and functional brain networks, but their identification with community detection methods is confounded by noisy or missing connections. Although several methods have been used to account for missing data, the performance of these methods has not been compared quantitatively so far. In this study, we compared four different approaches to account for missing connections when identifying modules in binary and weighted networks using both Louvain and Infomap community detection algorithms. The four methods are "zeros," "row-column mean," "common neighbors," and "consensus clustering." Using Lancichinetti-Fortunato-Radicchi benchmark-simulated binary and weighted networks, we find that "zeros," "row-column mean," and "common neighbors" approaches perform well with both Louvain and Infomap, whereas "consensus clustering" performs well with Louvain but not Infomap. A similar pattern of results was observed with empirical networks from stereotactical electroencephalography data, except that "consensus clustering" outperforms other approaches on weighted networks with Louvain. Based on these results, we recommend any of the four methods when using Louvain on binary networks, whereas "consensus clustering" is superior with Louvain clustering of weighted networks. When using Infomap, "zeros" or "common neighbors" should be used for both binary and weighted networks. These findings provide a basis to accounting for noisy or missing connections when identifying modules in brain networks.

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.

Cardiovascular Risk Factors and Brain Health: Impact on Long-Range Cortical Connections and Cognitive Performance

Background Cardiovascular risk factor burden in the absence of clinical or radiological "events" is associated with mild cognitive impairment. Magnetic resonance imaging techniques exploring the integrity of neuronal fiber connectivity within white matter networks supporting cognitive processing could be used to measure the impact of cardiovascular disease on brain health and be used beyond bedside neuropsychological tests to detect subclinical changes and select or stratify participants for entry into clinical trials. Methods and Results We assessed the relationship between verbal IQ and brain network integrity and the effect of cardiovascular risk factors on network integrity by constructing whole-brain structural connectomes from magnetic resonance imaging diffusion images (N=60) from people with various degrees of cardiovascular risk factor burden. We measured axonal integrity by calculating network density and determined the effect of fiber loss on network topology and efficiency, using graph theory. Multivariate analyses were used to evaluate the relationship between cardiovascular risk factor burden, physical activity, age, education, white matter integrity, and verbal IQ . Reduced network density, resulting from a disproportionate loss of long-range white matter fibers, was associated with white matter network fragmentation ( r=-0.52, P<10-4), lower global efficiency ( r=0.91, P<10-20), and decreased verbal IQ (adjusted R2=0.23, P<10-4). Conclusions Cardiovascular risk factors may mediate negative effects on brain health via loss of energy-dependent long-range white matter fibers, which in turn leads to disruption of the topological organization of the white matter networks, lowered efficiency, and reduced cognitive function.

Intracortical Causal Information Flow of Oscillatory Activity (Effective Connectivity) at the Sleep Onset Transition

We investigated the sleep onset transition in humans from an effective connectivity perspective in a baseline condition (approx. 16 h of wakefulness) and after sleep deprivation (40 h of sustained wakefulness). Using EEG recordings (27 derivations), source localization (LORETA) allowed us to reconstruct the underlying patterns of neuronal activity in various brain regions, e.g., the default mode network (DMN), dorsolateral prefrontal cortex and hippocampus, which were defined as regions of interest (ROI). We applied isolated effective coherence (iCOH) to assess effective connectivity patterns at the sleep onset transition [2 min prior to and 10 min after sleep onset (first occurrence of stage 2)]. ICOH reveals directionality aspects and resolves the spectral characteristics of information flow in a given network of ROIs. We observed an anterior-posterior decoupling of the DMN, and moreover, a prominent role of the posterior cingulate cortex guiding the process of the sleep onset transition, particularly, by transmitting information in the low frequency range (delta and theta bands) to other nodes of DMN (including the hippocampus). In addition, the midcingulate cortex appeared as a major cortical relay station for spindle synchronization (originating from the thalamus; sigma activity). The inclusion of hippocampus indicated that this region might be functionally involved in sigma synchronization observed in the cortex after sleep onset. Furthermore, under conditions of increased homeostatic pressure, we hypothesize that an anterior-posterior decoupling of the DMN occurred at a faster rate compared to baseline overall indicating weakened connectivity strength within the DMN. Finally, we also demonstrated that cortico-cortical spindle synchronization was less effective after sleep deprivation than in baseline, thus, reflecting the reduction of spindles under increased sleep pressure.

Low rank and sparsity constrained method for identifying overlapping functional brain networks

Analysis of functional magnetic resonance imaging (fMRI) data has revealed that brain regions can be grouped into functional brain networks (fBNs) or communities. A community in fMRI analysis signifies a group of brain regions coupled functionally with one another. In neuroimaging, functional connectivity (FC) measure can be utilized to quantify such functionally connected regions for disease diagnosis and hence, signifies the need of devising novel FC estimation methods. In this paper, we propose a novel method of learning FC by constraining its rank and the sum of non-zero coefficients. The underlying idea is that fBNs are sparse and can be embedded in a relatively lower dimension space. In addition, we propose to extract overlapping networks. In many instances, communities are characterized as combinations of disjoint brain regions, although recent studies indicate that brain regions may participate in more than one community. In this paper, large-scale overlapping fBNs are identified on resting state fMRI data by employing non-negative matrix factorization. Our findings support the existence of overlapping brain networks.

Cortical Thickness and Surface Area Networks in Healthy Aging, Alzheimer's Disease and Behavioral Variant Fronto-Temporal Dementia

Models of the human brain as a complex network of inter-connected sub-units are important in helping to understand the structural basis of the clinical features of neurodegenerative disorders. The aim of this study was to characterize in a systematic manner the differences in the structural correlation networks in cortical thickness (CT) and surface area (SA) in Alzheimer's disease (AD) and behavioral variant Fronto-Temporal Dementia (bvFTD). We have used the baseline magnetic resonance imaging (MRI) data available from a large population of patients from three clinical trials in mild to moderate AD and mild bvFTD and compared this to a well-characterized healthy aging cohort. The study population comprised 202 healthy elderly subjects, 213 with bvFTD and 213 with AD. We report that both CT and SA network architecture can be described in terms of highly correlated networks whose positive and inverse links map onto the intrinsic modular organization of the four cortical lobes. The topology of the disturbance in structural network is different in the two disease conditions, and both are different from normal aging. The changes from normal are global in character and are not restricted to fronto-temporal and temporo-parietal lobes, respectively, in bvFTD and AD, and indicate an increase in both global correlational strength and in particular nonhomologous inter-lobar connectivity defined by inverse correlations. These inverse correlations appear to be adaptive in character, reflecting coordinated increases in CT and SA that may compensate for corresponding impairment in functionally linked nodes. The effects were more pronounced in the cortical thickness atrophy network in bvFTD and in the surface area network in AD. Although lobar modularity is preserved in the context of neurodegenerative disease, the hub-like organization of networks differs both from normal and between the two forms of dementia. This implies that hubs may be secondary features of the connectivity adaptation to neurodegeneration and may not be an intrinsic property of the brain. However, analysis of the topological differences in hub-like organization CT and SA networks, and their underlying positive and negative correlations, may provide a basis for assisting in the differential diagnosis of bvFTD and AD.

Impact of modular organization on dynamical richness in cortical networks

As in many naturally formed networks, the brain exhibits an inherent modular architecture that is the basis of its rich operability, robustness, and integration-segregation capacity. However, the mechanisms that allow spatially segregated neuronal assemblies to swiftly change from localized to global activity remain unclear. Here, we integrate microfabrication technology with in vitro cortical networks to investigate the dynamical repertoire and functional traits of four interconnected neuronal modules. We show that the coupling among modules is central. The highest dynamical richness of the network emerges at a critical connectivity at the verge of physical disconnection. Stronger coupling leads to a persistently coherent activity among the modules, while weaker coupling precipitates the activity to be localized solely within the modules. An in silico modeling of the experiments reveals that the advent of coherence is mediated by a trade-off between connectivity and subquorum firing, a mechanism flexible enough to allow for the coexistence of both segregated and integrated activities. Our results unveil a new functional advantage of modular organization in complex networks of nonlinear units.

Comprehensive computational modelling of the development of mammalian cortical connectivity underlying an architectonic type principle

The architectonic type principle relates patterns of cortico-cortical connectivity to the relative architectonic differentiation of cortical regions. One mechanism through which the observed close relation between cortical architecture and connectivity may be established is the joint development of cortical areas and their connections in developmental time windows. Here, we describe a theoretical exploration of the possible mechanistic underpinnings of the architectonic type principle, by performing systematic computational simulations of cortical development. The main component of our in silico model was a developing two-dimensional cortical sheet, which was gradually populated by neurons that formed cortico-cortical connections. To assess different explanatory mechanisms, we varied the spatiotemporal trajectory of the simulated neurogenesis. By keeping the rules governing axon outgrowth and connection formation constant across all variants of simulated development, we were able to create model variants which differed exclusively by the specifics of when and where neurons were generated. Thus, all differences in the resulting connectivity were due to the variations in spatiotemporal growth trajectories. Our results demonstrated that a prescribed targeting of interareal connection sites was not necessary for obtaining a realistic replication of the experimentally observed relation between connection patterns and architectonic differentiation. Instead, we found that spatiotemporal interactions within the forming cortical sheet were sufficient if a small number of empirically well-grounded assumptions were met, namely planar, expansive growth of the cortical sheet around two points of origin as neurogenesis progressed, stronger architectonic differentiation of cortical areas for later neurogenetic time windows, and stochastic connection formation. Thus, our study highlights a potential mechanism of how relative architectonic differentiation and cortical connectivity become linked during development. We successfully predicted connectivity in two species, cat and macaque, from simulated cortico-cortical connection networks, which further underscored the general applicability of mechanisms through which the architectonic type principle can explain cortical connectivity in terms of the relative architectonic differentiation of cortical regions.

Process-Specific Alliances (PSAs) in Cognitive Neuroscience

Most cognitive neuroscience theories have focused on the functions of individual brain regions, but cognitive abilities depend also on functional interactions among multiple regions. Many recent studies on these interactions have examined large-scale, resting-state networks, but these networks are difficult to link to theories about specific cognitive processes. Cognitive theories are easier to link to the mini-networks we call process specific alliances (PSAs). A PSA is a small team of brain regions that rapidly assemble to mediate a cognitive process in response to task demands but quickly disassemble when the process is no longer needed. We compare PSAs to resting-state networks and to other connectivity-based, task-related networks, and we characterize the advantages and disadvantages of each type of network.

Modeling and interpreting mesoscale network dynamics

Recent advances in brain imaging techniques, measurement approaches, and storage capacities have provided an unprecedented supply of high temporal resolution neural data. These data present a remarkable opportunity to gain a mechanistic understanding not just of circuit structure, but also of circuit dynamics, and its role in cognition and disease. Such understanding necessitates a description of the raw observations, and a delineation of computational models and mathematical theories that accurately capture fundamental principles behind the observations. Here we review recent advances in a range of modeling approaches that embrace the temporally-evolving interconnected structure of the brain and summarize that structure in a dynamic graph. We describe recent efforts to model dynamic patterns of connectivity, dynamic patterns of activity, and patterns of activity atop connectivity. In the context of these models, we review important considerations in statistical testing, including parametric and non-parametric approaches. Finally, we offer thoughts on careful and accurate interpretation of dynamic graph architecture, and outline important future directions for method development.

Classification and characterisation of brain network changes in chronic back pain: A multicenter study

Background. Chronic pain is a common, often disabling condition thought to involve a combination of peripheral and central neurobiological factors. However, the extent and nature of changes in the brain is poorly understood.

Methods. We investigated brain network architecture using resting-state fMRI data in chronic back pain patients in the UK and Japan (41 patients, 56 controls), as well as open data from USA. We applied machine learning and deep learning (conditional variational autoencoder architecture) methods to explore classification of patients/controls based on network connectivity. We then studied the network topology of the data, and developed a multislice modularity method to look for consensus evidence of modular reorganisation in chronic back pain.

Results. Machine learning and deep learning allowed reliable classification of patients in a third, independent open data set with an accuracy of 63%, with 68% in cross validation of all data. We identified robust evidence of network hub disruption in chronic pain, most consistently with respect to clustering coefficient and betweenness centrality. We found a consensus pattern of modular reorganisation involving extensive, bilateral regions of sensorimotor cortex, and characterised primarily by negative reorganisation - a tendency for sensorimotor cortex nodes to be less inclined to form pairwise modular links with other brain nodes. Furthermore, these regions were found to display increased connectivity with the pregenual anterior cingulate cortex, a region known to be involved in endogenous pain control. In contrast, intraparietal sulcus displayed a propensity towards positive modular reorganisation, suggesting that it might have a role in forming modules associated with the chronic pain state.

Conclusion. The results provide evidence of consistent and characteristic brain network changes in chronic pain, characterised primarily by extensive reorganisation of the network architecture of the sensorimotor cortex.

The struggle for existence in the world market ecosystem

The global trade system can be viewed as a dynamic ecosystem in which exporters struggle for resources: the markets in which they export. We can think that the aim of an exporter is to gain the entirety of a market share (say, car imports from the United States). This is similar to the objective of an organism in its attempt to monopolize a given subset of resources in an ecosystem. In this paper, we adopt a multilayer network approach to describe this struggle. We use longitudinal, multiplex data on trade relations, spanning several decades. We connect two countries with a directed link if the source country’s appearance in a market correlates with the target country’s disappearing, where a market is defined as a country-product combination in a given decade. Each market is a layer in the network. We show that, by analyzing the countries’ network roles in each layer, we are able to classify them as out-competing, transitioning or displaced. This classification is a meaningful one: when testing the future export patterns of these countries, we show that out-competing countries have distinctly stronger growth rates than the other two classes.

Structural covariance across the lifespan: Brain development and aging through the lens of inter-network relationships

Recent studies have revealed that brain development is marked by morphological synchronization across brain regions. Regions with shared growth trajectories form structural covariance networks (SCNs) that not only map onto functionally identified cognitive systems, but also correlate with a range of cognitive abilities across the lifespan. Despite advances in within-network covariance examinations, few studies have examined lifetime patterns of structural relationships across known SCNs. In the current study, we used a big-data framework and a novel application of covariate-adjusted restricted cubic spline regression to identify volumetric network trajectories and covariance patterns across 13 networks (n = 5,019, ages = 7-90). Our findings revealed that typical development and aging are marked by significant shifts in the degree that networks preferentially coordinate with one another (i.e., modularity). Specifically, childhood showed higher modularity of networks compared to adolescence, reflecting a shift over development from segregation to desegregation of inter-network relationships. The shift from young to middle adulthood was marked by a significant decrease in inter-network modularity and organization, which continued into older adulthood, potentially reflecting changes in brain organizational efficiency with age. This study is the first to characterize brain development and aging in terms of inter-network structural covariance across the lifespan.

The cortical structure of functional networks associated with age-related cognitive abilities in older adults

Age and cortical structure are both associated with cognition, but characterizing this relationship remains a challenge. A popular approach is to use functional network organization of the cortex as an organizing principle for post-hoc interpretations of structural results. In the current study, we introduce two complimentary approaches to structural analyses that are guided by a-priori functional network maps. Specifically, we systematically investigated the relationship of cortical structure (thickness and surface area) of distinct functional networks to two cognitive domains sensitive to age-related decline thought to rely on both common and distinct processes (executive function and episodic memory) in older adults. We quantified the cortical structure of individual functional network's predictive ability and spatial extent (i.e., number of significant regions) with cognition and its mediating role in the age-cognition relationship. We found that cortical thickness, rather than surface area, predicted cognition across the majority of functional networks. The default mode and somatomotor network emerged as particularly important as they appeared to be the only two networks to mediate the age-cognition relationship for both cognitive domains. In contrast, thickness of the salience network predicted executive function and mediated the age-cognition relationship for executive function. These relationships remained significant even after accounting for global cortical thickness. Quantifying the number of regions related to cognition and mediating the age-cognition relationship yielded similar patterns of results. This study provides a potential approach to organize and describe the apparent widespread regional cortical structural relationships with cognition and age in older adults.

Normative pathways in the functional connectome

Functional connectivity is frequently derived from fMRI data to reduce a complex image of the brain to a graph, or "functional connectome". Often shortest-path algorithms are used to characterize and compare functional connectomes. Previous work on the identification and measurement of semi-metric (shortest circuitous) pathways in the functional connectome has discovered cross-sectional differences in major depressive disorder (MDD), autism spectrum disorder (ASD), and Alzheimer's disease. However, while measurements of shortest path length have been analyzed in functional connectomes, less work has been done to investigate the composition of the pathways themselves, or whether the edges composing pathways differ between individuals. Developments in this area would help us understand how pathways might be organized in mental disorders, and if a consistent pattern can be found. Furthermore, studies in structural brain connectivity and other real-world graphs suggest that shortest pathways may not be as important in functional connectivity studies as previously assumed. In light of this, we present a novel measurement of the consistency of pathways across functional connectomes, and an algorithm for improvement by selecting the most frequently occurring "normative pathways" from the k shortest paths, instead of just the shortest path. We also look at this algorithm's effect on various graph measurements, using randomized matrix simulations to support the efficacy of this method and demonstrate our algorithm on the resting-state fMRI (rs-fMRI) of a group of 34 adolescent control participants. Additionally, a comparison of normative pathways is made with a group of 82 age-matched participants, diagnosed with MDD, and in doing so we find the normative pathways that are most disrupted. Our results, which are carried out with estimates of connectivity derived from correlation, partial correlation, and normalized mutual information connectomes, suggest disruption to the default mode, affective, and ventral attention networks. Normative pathways, especially with partial correlation, make greater use of critical anatomical pathways through the striatum, cingulum, and the cerebellum. In summary, MDD is characterized by a disruption of normative pathways of the ventral attention network, increases in alternative pathways in the frontoparietal network in MDD, and a mixture of both in the default mode network. Additionally, within- and between-groups findings depend on the estimate of connectivity.

NeuroCave: A web-based immersive visualization platform for exploring connectome datasets

We introduce NeuroCave, a novel immersive visualization system that facilitates the visual inspection of structural and functional connectome datasets. The representation of the human connectome as a graph enables neuroscientists to apply network-theoretic approaches in order to explore its complex characteristics. With NeuroCave, brain researchers can interact with the connectome-either in a standard desktop environment or while wearing portable virtual reality headsets (such as Oculus Rift, Samsung Gear, or Google Daydream VR platforms)-in any coordinate system or topological space, as well as cluster brain regions into different modules on-demand. Furthermore, a default side-by-side layout enables simultaneous, synchronized manipulation in 3D, utilizing modern GPU hardware architecture, and facilitates comparison tasks across different subjects or diagnostic groups or longitudinally within the same subject. Visual clutter is mitigated using a state-of-the-art edge bundling technique and through an interactive layout strategy, while modular structure is optimally positioned in 3D exploiting mathematical properties of platonic solids. NeuroCave provides new functionality to support a range of analysis tasks not available in other visualization software platforms.

On the nature and use of models in network neuroscience

Network theory provides an intuitively appealing framework for studying relationships among interconnected brain mechanisms and their relevance to behaviour. As the space of its applications grows, so does the diversity of meanings of the term network model. This diversity can cause confusion, complicate efforts to assess model validity and efficacy, and hamper interdisciplinary collaboration. In this Review, we examine the field of network neuroscience, focusing on organizing principles that can help overcome these challenges. First, we describe the fundamental goals in constructing network models. Second, we review the most common forms of network models, which can be described parsimoniously along the following three primary dimensions: from data representations to first-principles theory; from biophysical realism to functional phenomenology; and from elementary descriptions to coarse-grained approximations. Third, we draw on biology, philosophy and other disciplines to establish validation principles for these models. We close with a discussion of opportunities to bridge model types and point to exciting frontiers for future pursuits.

Understanding the Emergence of Neuropsychiatric Disorders With Network Neuroscience

Major neuropsychiatric disorders such as psychosis are increasingly acknowledged to be disorders of brain connectivity. Yet tools to map, model, predict, and change connectivity are difficult to develop, largely because of the complex, dynamic, and multivariate nature of interactions between brain regions. Network neuroscience (NN) provides a theoretical framework and mathematical toolset to address these difficulties. Building on areas of mathematics such as graph theory, NN in its simplest form summarizes neuroimaging data by treating brain regions as nodes in a graph and by treating interactions or connections between nodes as edges in the graph. Network metrics can then be used to quantitatively describe the architecture of the graph, which in turn reflects the network's function. We review evidence supporting the utility of NN in understanding psychiatric disorders, with a focus on normative brain network development and abnormalities associated with psychosis. We also emphasize relevant methodological challenges, such as motion artifact correction, which are particularly important to consider when applying network tools to developmental neuroimaging data. We close with a discussion of several emerging frontiers of NN in psychiatry, including generative network modeling and network control theory. We aim to offer an accessible introduction to this emerging field and motivate further work that uses NN to better understand the normative development of brain networks and alterations in that development that accompany or foreshadow psychiatric disease.

Brain network connectivity associated with anticipatory postural control in children and adults

Internal models provide a coherent framework for understanding motor behavior. Examples for the use of internal models include anticipatory postural adjustments (APAs), where the individual anticipates and cancels out the destabilizing effect of movement on body posture. Yet little is known about the functional changes in the brain supporting the development of APAs. Here, we addressed this issue by relating individual differences in APAs as assessed during bimanual load lifting to interindividual variation in brain network interactions at rest. We showed that the strength of the connectivity between three main canonical brain networks, namely the cingulo-opercular, the fronto-parietal and the somatosensory-motor networks, is an index of the ability to implement APAs from late childhood (9- to 11-year-old children). We also found an effect of age on the relationship between APAs and coupling strength between these networks, consistent with the notion that APAs are near but not yet fully mature in children. We discuss the implications of these findings for our understanding of learning disorders with impairment in predictive motor control.

Weighted Stochastic Block Models of the Human Connectome across the Life Span

The human brain can be described as a complex network of anatomical connections between distinct areas, referred to as the human connectome. Fundamental characteristics of connectome organization can be revealed using the tools of network science and graph theory. Of particular interest is the network's community structure, commonly identified by modularity maximization, where communities are conceptualized as densely intra-connected and sparsely inter-connected. Here we adopt a generative modeling approach called weighted stochastic block models (WSBM) that can describe a wider range of community structure topologies by explicitly considering patterned interactions between communities. We apply this method to the study of changes in the human connectome that occur across the life span (between 6-85 years old). We find that WSBM communities exhibit greater hemispheric symmetry and are spatially less compact than those derived from modularity maximization. We identify several network blocks that exhibit significant linear and non-linear changes across age, with the most significant changes involving subregions of prefrontal cortex. Overall, we show that the WSBM generative modeling approach can be an effective tool for describing types of community structure in brain networks that go beyond modularity.

The impact of long-term abacus training on modular properties of functional brain network

Training induces cognitive and neural plasticity, and understanding of the neural mechanisms of training-induced brain plasticity has significant implications for improving children's academic achievement. Previous studies have indicated that training in abacus-based mental calculation (AMC) improves arithmetical capacities and results in brain plasticity within visuospatial brain regions. However, previous studies have reported alterations within distributed brain regions. Thus, it remains unclear whether and how AMC training influences the functional integration and separation between and/or within networks. The current study aimed to address these questions using graph theory, engaging 162 children, 90 of whom were given long-term AMC training. The AMC group exhibited greater local efficiency and intra-module connections within the visual network and less local efficiency and intra-module connections in the cingulo-opercular network (CON). Interestingly, in the AMC group, negative correlations were found between local efficiency and intra-module connections across the two networks. Furthermore, both network characteristics of the CON were negatively correlated with math ability in the AMC group. No such correlations were found in the control group. The current study delineated the enhanced neural mechanisms of visuospatial-related brain regions at an intermediate level and highlighted the intrinsic association between different brain ensembles in neural plasticity, thus furthering the understanding of the effects of AMC training on brain network reconfiguration.

Acute White-Matter Abnormalities in Sports-Related Concussion: A Diffusion Tensor Imaging Study from the NCAA-DoD CARE Consortium

Sports-related concussion (SRC) is an important public health issue. Although standardized assessment tools are useful in the clinical management of acute concussion, the underlying pathophysiology of SRC and the time course of physiological recovery after injury remain unclear. In this study, we used diffusion tensor imaging (DTI) to detect white matter alterations in football players within 48 h after SRC. As part of the NCAA-DoD CARE Consortium study of SRC, 30 American football players diagnosed with acute concussion and 28 matched controls received clinical assessments and underwent advanced magnetic resonance imaging scans. To avoid selection bias and partial volume effects, whole-brain skeletonized white matter was examined by tract-based spatial statistics to investigate between-group differences in DTI metrics and their associations with clinical outcome measures. Mean diffusivity was significantly higher in brain white matter of concussed athletes, particularly in frontal and subfrontal long white matter tracts. In the concussed group, axial diffusivity was significantly correlated with the Brief Symptom Inventory and there was a similar trend with the symptom severity score of the Sport Concussion Assessment Tool. In addition, concussed athletes with higher fractional anisotropy performed better on the cognitive component of the Standardized Assessment of Concussion. Overall, the results of this study are consistent with the hypothesis that SRC is associated with changes in white matter tracts shortly after injury, and these differences are correlated clinically with acute symptoms and functional impairments.

A Blind Module Identification Approach for Predicting Effective Connectivity Within Brain Dynamical Subnetworks

Model-based network discovery measures, such as the brain effective connectivity, require fitting of generative process models to measurements obtained from key areas across the network. For distributed dynamic phenomena, such as generalized seizures and slow-wave sleep, studying effective connectivity from real-time recordings is significantly complicated since (i) outputs from only a subnetwork can be practically measured, and (ii) exogenous subnetwork inputs are unobservable. Model fitting, therefore, constitutes a challenging blind module identification or model inversion problem for finding both the parameters and the many unknown inputs of the subnetwork. We herein propose a novel estimation framework for identifying nonlinear dynamic subnetworks in the case of slowly-varying, otherwise unknown local inputs. Starting with approximate predictions obtained using Cubature Kalman filtering, residuals of local output predictions are utilized to improve upon local input estimates. The algorithm performance is tested on both simulated and clinical EEG of induced seizures under electroconvulsive therapy (ECT). For the simulated network, the algorithm significantly boosted the estimation accuracy for inputs and connections from noisy EEG. For the clinical data, the algorithm predicted increased subnetwork inputs during the pre-stimulus anesthesia condition. Importantly, it predicted an increased frontocentral connectivity during the generalized seizure that is commensurate with electrode placement and that corroborates the clinical hypothesis of increased frontal focality of therapeutic ECT seizures. The proposed framework can be extended to account for several input configurations and can in principle be applied to study effective connectivity within brain subnetworks defined at the microscale (cortical lamina interaction) or at the macroscale (sensory integration).

A Network-Based Perspective in Alzheimer's Disease: Current State and an Integrative Framework

A major rise in the prevalence and impact of Alzheimer's disease (AD) is projected in the coming decades, resulting from increasing life expectancy, thus leading to substantially increased healthcare costs. While brain disfunctions at the time of diagnosis are irreversible, it is widely accepted that AD pathology develops decades before clinical symptoms onset. If incipient processes can be detected early in the disease progression, prospective intervention for preventing or slowing the disease can be designed. Currently, there is no noninvasive biomarker available to detect and monitor early stages of disease progression. The complex etiology of AD warrants a systems-based approach supporting the integration of multimodal and multilevel data, while network-based modeling provides the scaffolding for methods revealing complex systems-level disruptions initiated by the disease. In this work, we review current state-of-the-art, focusing on network-based biomarkers at molecular and brain functional connectivity levels. Particular emphasis is placed on outlining recent trends, which highlight the functional importance of modular substructures in molecular and connectivity networks and their potential biomarker value. Our perspective is rooted in network medicine and summarizes the pipelines for identifying network-based biomarkers, as well as the benefits of integrating genotype and brain phenotype information for a comprehensively noninvasive approach in the early diagnosis of AD. Finally, we propose a framework for integrating knowledge from molecular and brain connectivity levels, which has the potential to enable noninvasive diagnosis, provide support for monitoring therapies, and help understand heretofore unexamined deep level relations between genotype and brain phenotype.

Adolescent Tuning of Association Cortex in Human Structural Brain Networks

Motivated by prior data on local cortical shrinkage and intracortical myelination, we predicted age-related changes in topological organization of cortical structural networks during adolescence. We estimated structural correlation from magnetic resonance imaging measures of cortical thickness at 308 regions in a sample of N = 297 healthy participants, aged 14–24 years. We used a novel sliding-window analysis to measure age-related changes in network attributes globally, locally and in the context of several community partitions of the network. We found that the strength of structural correlation generally decreased as a function of age. Association cortical regions demonstrated a sharp decrease in nodal degree (hubness) from 14 years, reaching a minimum at approximately 19 years, and then levelling off or even slightly increasing until 24 years. Greater and more prolonged age-related changes in degree of cortical regions within the brain network were associated with faster rates of adolescent cortical myelination and shrinkage. The brain regions that demonstrated the greatest age-related changes were concentrated within prefrontal modules. We conclude that human adolescence is associated with biologically plausible changes in structural imaging markers of brain network organization, consistent with the concept of tuning or consolidating anatomical connectivity between frontal cortex and the rest of the connectome.

Dynamic reshaping of functional brain networks during visual object recognition

OBJECTIVE

Emerging evidence shows that the modular organization of the human brain allows for better and efficient cognitive performance. Many of these cognitive functions are very fast and occur in a sub-second time scale such as the visual object recognition.

APPROACH

Here, we investigate brain network modularity while controlling stimuli meaningfulness and measuring a participant's reaction time. We particularly raised two questions: i) does the dynamic brain network modularity change during the recognition of meaningful and meaningless visual images? And (ii) is there a correlation between network modularity and the reaction time of the participants? To tackle these issues, we collected dense-electroencephalography (EEG, 256 channels) data from 20 healthy human subjects performing a cognitive task consisting of naming meaningful (tools, animals…) and meaningless (scrambled) images. Functional brain networks in both categories were estimated at the sub-second time scale using the EEG source connectivity method. By using multislice modularity algorithms, we tracked the reconfiguration of functional networks during the recognition of both meaningful and meaningless images.

MAIN RESULTS

Results showed a difference in the module's characteristics of both conditions in term of integration (interactions between modules) and occurrence (probability on average of any two brain regions to fall in the same module during the task). Integration and occurrence were greater for meaningless than for meaningful images. Our findings revealed also that the occurrence within the right frontal regions and the left occipito-temporal can help to predict the ability of the brain to rapidly recognize and name visual stimuli.

SIGNIFICANCE

We speculate that these observations are applicable not only to other fast cognitive functions but also to detect fast disconnections that can occur in some brain disorders.

Brain Network Connectivity and Executive Function in Long-Term Survivors of Childhood Acute Lymphoblastic Leukemia

Chemotherapeutic agents used to treat acute lymphoblastic leukemia (ALL), the most common cancer affecting young children, have been associated with long-term cognitive impairments that reduce quality of life. Executive dysfunction is one of the most consistently observed deficits and can have substantial and pervasive effects on academic success, occupational achievement, psychosocial function, and psychiatric status. We examined the neural mechanisms of executive dysfunction by measuring structural and functional connectomes in 161 long-term survivors of pediatric ALL, age 8-21 years, who were treated on a single contemporary chemotherapy-only protocol for standard/high- or low-risk disease. Lower global efficiency, a measure of information exchange and network integration, of both structural and functional connectomes was found in survivors with executive dysfunction compared with those without dysfunction (p < 0.046). Patients with standard/high- versus low-risk disease and those who received greater number of intrathecal treatments containing methotrexate had the lowest network efficiencies. Patients with executive dysfunction also showed hyperconnectivity in sensorimotor, visual, and auditory-processing regions (p = 0.037) and poor separation between sensorimotor, executive/attention, salience, and default mode networks (p < 0.0001). Connectome disruption was consistent with a pattern of delayed neurodevelopment that may be associated with reduced resilience, adaptability, and flexibility of the brain network. These findings highlight the need for interventions that will prevent or manage cognitive impairment in survivors of pediatric acute lymphoblastic leukemia.

Fragility and volatility of structural hubs in the human connectome

Brain structure reflects the influence of evolutionary processes that pit the costs of its anatomical wiring against the computational advantages conferred by its complexity. We show that cost-neutral 'mutations' of the human connectome almost inevitably degrade its complexity and disconnect high-strength connections to prefrontal network hubs. Conversely, restoring the peripheral location and strong connectivity of empirically observed hubs confers a wiring cost that the brain appears to minimize. Progressive cost-neutral randomization yields daughter networks that differ substantially from one another and results in a topologically unstable phenomenon consistent with a phase transition in complex systems. The fragility of hubs to disconnection shows a significant association with the acceleration of gray matter loss in schizophrenia. Together with effects on wiring cost, we suggest that fragile prefrontal hub connections and topological volatility act as evolutionary influences on brain networks whose optimal set point may be perturbed in neuropsychiatric disorders.

GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures

BACKGROUND

We previously presented GraphVar as a user-friendly MATLAB toolbox for comprehensive graph analyses of functional brain connectivity. Here we introduce a comprehensive extension of the toolbox allowing users to seamlessly explore easily customizable decoding models across functional connectivity measures as well as additional features.

NEW METHOD

GraphVar 2.0 provides machine learning (ML) model construction, validation and exploration. Machine learning can be performed across any combination of graph measures and additional variables, allowing for a flexibility in neuroimaging applications.

RESULTS

In addition to previously integrated functionalities, such as network construction and graph-theoretical analyses of brain connectivity with a high-speed general linear model (GLM), users can now perform customizable ML across connectivity matrices, graph measures and additionally imported variables. The new extension also provides parametric and nonparametric testing of classifier and regressor performance, data export, figure generation and high quality export.

COMPARISON WITH EXISTING METHODS

Compared to other existing toolboxes, GraphVar 2.0 offers (1) comprehensive customization, (2) an all-in-one user friendly interface, (3) customizable model design and manual hyperparameter entry, (4) interactive results exploration and data export, (5) automated queue system for modelling multiple outcome variables within the same session, (6) an easy to follow introductory review.

CONCLUSIONS

GraphVar 2.0 allows comprehensive, user-friendly exploration of encoding (GLM) and decoding (ML) modelling approaches on functional connectivity measures making big data neuroscience readily accessible to a broader audience of neuroimaging investigators.

Neural Mechanisms of Early-Life Social Stress as a Developmental Risk Factor for Severe Psychiatric Disorders

BACKGROUND

To explore the domain-general risk factor of early-life social stress in mental illness, rearing rodents in persistent postweaning social isolation has been established as a widely used animal model with translational relevance for neurodevelopmental psychiatric disorders such as schizophrenia. Although changes in resting-state brain connectivity are a transdiagnostic key finding in neurodevelopmental diseases, a characterization of imaging correlates elicited by early-life social stress is lacking.

METHODS

We performed resting-state functional magnetic resonance imaging of postweaning social isolation rats (N = 23) 9 weeks after isolation. Addressing well-established transdiagnostic connectivity changes of psychiatric disorders, we focused on altered frontal and posterior connectivity using a seed-based approach. Then, we examined changes in regional network architecture and global topology using graph theoretical analysis.

RESULTS

Seed-based analyses demonstrated reduced functional connectivity in frontal brain regions and increased functional connectivity in posterior brain regions of postweaning social isolation rats. Graph analyses revealed a shift of the regional architecture, characterized by loss of dominance of frontal regions and emergence of nonfrontal regions, correlating to our behavioral results, and a reduced modularity in isolation-reared rats.

CONCLUSIONS

Our result of functional connectivity alterations in the frontal brain supports previous investigations postulating social neural circuits, including prefrontal brain regions, as key pathways for risk for mental disorders arising through social stressors. We extend this knowledge by demonstrating more widespread changes of brain network organization elicited by early-life social stress, namely a shift of hubness and dysmodularity. Our results highly resemble core alterations in neurodevelopmental psychiatric disorders such as schizophrenia, autism, and attention-deficit/hyperactivity disorder in humans.

Concurrent Changes of Brain Functional Connectivity and Motor Variability When Adapting to Task Constraints

In behavioral neuroscience, the adaptability of humans facing different constraints has been addressed on one side at the brain level, where a variety of functional networks dynamically support the same performance, and on the other side at the behavioral level, where fractal properties in sensorimotor variables have been considered as a hallmark of adaptability. To bridge the gap between the two levels of observation, we have jointly investigated the changes of network connectivity in the sensorimotor cortex assessed by modularity analysis and the properties of motor variability assessed by multifractal analysis during a prolonged tapping task. Four groups of participants had to produce the same tapping performance while being deprived from 0, 1, 2, or 3 sensory feedbacks simultaneously (auditory and/or visual and/or tactile). Whereas tapping performance was not statistically different across groups, the number of brain networks involved and the degree of multifractality of the inter-tap interval series were significantly correlated, increasing as a function of feedback deprivation. Our findings provide first evidence that concomitant changes in brain modularity and multifractal properties characterize adaptations underlying unchanged performance. We discuss implications of our findings with respect to the degeneracy properties of complex systems, and the entanglement of adaptability and effective adaptation.

Network Approaches to Understand Individual Differences in Brain Connectivity: Opportunities for Personality Neuroscience

Over the past decade, advances in the interdisciplinary field of network science have provided a framework for understanding the intrinsic structure and function of human brain networks. A particularly fruitful area of this work has focused on patterns of functional connectivity derived from non-invasive neuroimaging techniques such as functional magnetic resonance imaging (fMRI). An important subset of these efforts has bridged the computational approaches of network science with the rich empirical data and biological hypotheses of neuroscience, and this research has begun to identify features of brain networks that explain individual differences in social, emotional, and cognitive functioning. The most common approach estimates connections assuming a single configuration of edges that is stable across the experimental session. In the literature, this is referred to as a static network approach, and researchers measure static brain networks while a subject is either at rest or performing a cognitively demanding task. Research on social and emotional functioning has primarily focused on linking static brain networks with individual differences, but recent advances have extended this work to examine temporal fluctuations in dynamic brain networks. Mounting evidence suggests that both the strength and flexibility of time-evolving brain networks influence individual differences in executive function, attention, working memory, and learning. In this review, we first examine the current evidence for brain networks involved in cognitive functioning. Then we review some preliminary evidence linking static network properties to individual differences in social and emotional functioning. We then discuss the applicability of emerging dynamic network methods for examining individual differences in social and emotional functioning. We close with an outline of important frontiers at the intersection between network science and neuroscience that will enhance our understanding of the neurobiological underpinnings of social behavior.

Unpacking the polarization of workplace skills

Economic inequality is one of the biggest challenges facing society today. Inequality has been recently exacerbated by growth in high- and low-wage occupations at the expense of middle-wage occupations, leading to a "hollowing" of the middle class. Yet, our understanding of how workplace skills drive this process is limited. Specifically, how do skill requirements distinguish high- and low-wage occupations, and does this distinction constrain the mobility of individuals and urban labor markets? Using unsupervised clustering techniques from network science, we show that skills exhibit a striking polarization into two clusters that highlight the specific social-cognitive skills and sensory-physical skills of high- and low-wage occupations, respectively. The connections between skills explain various dynamics: how workers transition between occupations, how cities acquire comparative advantage in new skills, and how individual occupations change their skill requirements. We also show that the polarized skill topology constrains the career mobility of individual workers, with low-skill workers "stuck" relying on the low-wage skill set. Together, these results provide a new explanation for the persistence of occupational polarization and inform strategies to mitigate the negative effects of automation and offshoring of employment. In addition to our analysis, we provide an online tool for the public and policy makers to explore the skill network: skillscape.mit.edu.

Similarity in functional brain architecture between rest and specific task modes: A model of genetic and environmental contributions to episodic memory

The ability to keep a mental record of specific past events, dubbed episodic memory (EM), is key to lifespan adaptation. Nonetheless, the neural mechanisms underlying its typical inter-individual variability remain poorly understood. To address this issue, we tested whether individual differences in EM could be predicted from levels of functional brain re-organization between rest and task modes relevant to the transformation of perceptual information into mental representations (relational processing, meaning extraction, online maintenance versus updating of bound perceptual features). To probe the trait specificity of our model, we included three additional core mental functions, processing speed, abstract reasoning, and cognitive control. Finally, we investigated the extent to which our proposed model reflected genetic versus environmental contributions to EM variability. Hypotheses were tested by applying graph theoretical analysis and structural equation modeling to resting state and task fMRI data from two samples of participants in the Human Connectome Project (Sample 1: N = 338 unrelated individuals; Sample 2: N = 268 monozygotic vs. dizygotic twins [134 same-sex pairs]). Levels of functional brain reorganization between rest and the scrutinized task modes, particularly relational processing and online maintenance of bound perceptual features, contributed substantially to variations in both EM and abstract reasoning (but not in cognitive control or processing speed) among the younger adults in our sample, implying a substantial neurofunctional overlap, at least during this life stage. Similarity in functional organization between rest and each of the scrutinized task modes drew on distinguishable neural resources and showed differential susceptibility to genetic versus environmental influences. Our results suggest that variability on complex traits, such as EM, is supported by neural mechanisms comprising multiple components, each reflecting a distinct pattern of genetic versus environmental contributions and whose relative importance may vary across typical versus psychopathological development.

Prenatal Maternal Cortisol Has Sex-Specific Associations with Child Brain Network Properties

Elevated maternal cortisol concentrations have the potential to alter fetal development in a sex-specific manner. Female brains are known to show adaptive behavioral and anatomical flexibility in response to early-life exposure to cortisol, but it is not known how these sex-specific effects manifest at the whole-brain structural networks. A prospective longitudinal study of 49 mother child dyads was conducted with serial assessments of maternal cortisol levels from 15 to 37 gestational weeks. We modeled the structural network of typically developing children (aged 6-9 years) and examined its global connectome properties, rich-club organization, and modular architecture. Network segregation was susceptible only for girls to variations in exposure to maternal cortisol during pregnancy. Girls generated more connections than boys to maintain topologically capable and efficient neural circuits, and this increase in neural cost was associated with higher levels of internalizing problems. Maternal cortisol concentrations at 31 gestational weeks gestation were most strongly associated with altered neural connectivity in girls, suggesting a sensitive period for the maternal cortisol-offspring brain associations. Our data suggest that girls exhibit an adaptive response by increasing the neural network connectivity necessary for maintaining homeostasis and efficient brain function across the lifespan.

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.

Stimulus Dependent Dynamic Reorganization of the Human Face Processing Network

Using the "face inversion effect", a hallmark of face perception, we examined network mechanisms supporting face representation by tracking functional magnetic resonance imaging (fMRI) stimulus-dependent dynamic functional connectivity within and between brain networks associated with the processing of upright and inverted faces. We developed a novel approach adapting the general linear model (GLM) framework classically used for univariate fMRI analysis to capture stimulus-dependent fMRI dynamic connectivity of the face network. We show that under the face inversion manipulation, the face and non-face networks have complementary roles that are evident in their stimulus-dependent dynamic connectivity patterns as assessed by network decomposition into components or communities. Moreover, we show that connectivity patterns are associated with the behavioral face inversion effect. Thus, we establish "a network-level signature" of the face inversion effect and demonstrate how a simple physical transformation of the face stimulus induces a dramatic functional reorganization across related brain networks. Finally, we suggest that the dynamic GLM network analysis approach, developed here for the face network, provides a general framework for modeling the dynamics of blocked stimulus-dependent connectivity experimental designs and hence can be applied to a host of neuroimaging studies.

Estimating the impact of structural directionality: How reliable are undirected connectomes?

Directionality is a fundamental feature of network connections. Most structural brain networks are intrinsically directed because of the nature of chemical synapses, which comprise most neuronal connections. Because of the limitations of noninvasive imaging techniques, the directionality of connections between structurally connected regions of the human brain cannot be confirmed. Hence, connections are represented as undirected, and it is still unknown how this lack of directionality affects brain network topology. Using six directed brain networks from different species and parcellations (cat, mouse, C. elegans, and three macaque networks), we estimate the inaccuracies in network measures (degree, betweenness, clustering coefficient, path length, global efficiency, participation index, and small-worldness) associated with the removal of the directionality of connections. We employ three different methods to render directed brain networks undirected: (a) remove unidirectional connections, (b) add reciprocal connections, and (c) combine equal numbers of removed and added unidirectional connections. We quantify the extent of inaccuracy in network measures introduced through neglecting connection directionality for individual nodes and across the network. We find that the coarse division between core and peripheral nodes remains accurate for undirected networks. However, hub nodes differ considerably when directionality is neglected. Comparing the different methods to generate undirected networks from directed ones, we generally find that the addition of reciprocal connections (false positives) causes larger errors in graph-theoretic measures than the removal of the same number of directed connections (false negatives). These findings suggest that directionality plays an essential role in shaping brain networks and highlight some limitations of undirected connectomes.

Graph theory methods: applications in brain networks

Network neuroscience is a thriving and rapidly expanding field. Empirical data on brain networks, from molecular to behavioral scales, are ever increasing in size and complexity. These developments lead to a strong demand for appropriate tools and methods that model and analyze brain network data, such as those provided by graph theory. This brief review surveys some of the most commonly used and neurobiologically insightful graph measures and techniques. Among these, the detection of network communities or modules, and the identification of central network elements that facilitate communication and signal transfer, are particularly salient. A number of emerging trends are the growing use of generative models, dynamic (time-varying) and multilayer networks, as well as the application of algebraic topology. Overall, graph theory methods are centrally important to understanding the architecture, development, and evolution of brain networks.

Functional and structural connectome properties in the 5XFAD transgenic mouse model of Alzheimer's disease

Neurodegeneration in Alzheimer's disease (AD) is associated with amyloid-beta peptide accumulation into insoluble amyloid plaques. The five-familial AD (5XFAD) transgenic mouse model exhibits accelerated amyloid-beta deposition, neuronal dysfunction, and cognitive impairment. We aimed to determine whether connectome properties of these mice parallel those observed in patients with AD. We obtained diffusion tensor imaging and resting-state functional magnetic resonance imaging data for four transgenic and four nontransgenic male mice. We constructed both structural and functional connectomes and measured their topological properties by applying graph theoretical analysis. We compared connectome properties between groups using both binarized and weighted networks. Transgenic mice showed higher characteristic path length in weighted structural connectomes and functional connectomes at minimum density. Normalized clustering and modularity were lower in transgenic mice across the upper densities of the structural connectome. Transgenic mice also showed lower small-worldness index in higher structural connectome densities and in weighted structural networks. Hyper-correlation of structural and functional connectivity was observed in transgenic mice compared with nontransgenic controls. These preliminary findings suggest that 5XFAD mouse connectomes may provide useful models for investigating the molecular mechanisms of AD pathogenesis and testing the effectiveness of potential treatments.

Functional networks underlying item and source memory: shared and distinct network components and age-related differences

Although the medial temporal lobes (MTLs) are critical for both item memory (IM) and source memory (SM), the lateral prefrontal cortex and posterior parietal cortex play a greater role during SM than IM. It is unclear, however, how these differences translate into shared and distinct IM versus SM network components and how these network components vary with age. Within a sample of younger adults (YAs; n = 15, M_age = 19.5 years) and older adults (OAs; n = 40, M_age = 68.6 years), we investigated the functional networks underlying IM and SM. Before functional MRI scanning, participants encoded nouns while making either pleasantness or size judgments. During functional MRI scanning, participants completed IM and SM retrieval tasks. We found that MTL nodes were similarly interconnected among each other during both IM and SM (shared network components) but maintained more intermodule connections during SM (distinct network components). Also, during SM, OAs (compared to YAs) had MTL nodes with more widespread connections. These findings provide a novel viewpoint on neural mechanism differences underlying IM versus SM in YAs and OAs.

Socioeconomic status moderates age-related differences in the brain's functional network organization and anatomy across the adult lifespan

An individual’s socioeconomic status (SES) is a central feature of their environmental surroundings and has been shown to relate to the development and maturation of their brain in childhood. Here, we demonstrate that an individual’s present (adult) SES relates to their brain function and anatomy across a broad range of middle-age adulthood. In middle-aged adults (35–64 years), lower SES individuals exhibit less organized functional brain networks and reduced cortical thickness compared with higher SES individuals. These relationships cannot be fully explained by differences in health, demographics, or cognition. Additionally, childhood SES does not explain the relation between SES and brain network organization. These observations provide support for a powerful relationship between the environment and the brain that is evident in adult middle age.

An individual’s environmental surroundings interact with the development and maturation of their brain. An important aspect of an individual’s environment is his or her socioeconomic status (SES), which estimates access to material resources and social prestige. Previous characterizations of the relation between SES and the brain have primarily focused on earlier or later epochs of the lifespan (i.e., childhood, older age). We broaden this work to examine the relationship between SES and the brain across a wide range of human adulthood (20–89 years), including individuals from the less studied middle-age range. SES, defined by education attainment and occupational socioeconomic characteristics, moderates previously reported age-related differences in the brain’s functional network organization and whole-brain cortical structure. Across middle age (35–64 years), lower SES is associated with reduced resting-state system segregation (a measure of effective functional network organization). A similar but less robust relationship exists between SES and age with respect to brain anatomy: Lower SES is associated with reduced cortical gray matter thickness in middle age. Conversely, younger and older adulthood do not exhibit consistent SES-related difference in the brain measures. The SES–brain relationships persist after controlling for measures of physical and mental health, cognitive ability, and participant demographics. Critically, an individual’s childhood SES cannot account for the relationship between their current SES and functional network organization. These findings provide evidence that SES relates to the brain’s functional network organization and anatomy across adult middle age, and that higher SES may be a protective factor against age-related brain decline.

Default mode network abnormalities in posttraumatic stress disorder: A novel network-restricted topology approach

Disruption in the default mode network (DMN) has been implicated in numerous neuropsychiatric disorders, including posttraumatic stress disorder (PTSD). However, studies have largely been limited to seed-based methods and involved inconsistent definitions of the DMN. Recent advances in neuroimaging and graph theory now permit the systematic exploration of intrinsic brain networks. In this study, we used resting-state functional magnetic resonance imaging (fMRI), diffusion MRI, and graph theoretical analyses to systematically examine the DMN connectivity and its relationship with PTSD symptom severity in a cohort of 65 combat-exposed US Veterans. We employed metrics that index overall connectivity strength, network integration (global efficiency), and network segregation (clustering coefficient). Then, we conducted a modularity and network-based statistical analysis to identify DMN regions of particular importance in PTSD. Finally, structural connectivity analyses were used to probe whether white matter abnormalities are associated with the identified functional DMN changes. We found decreased DMN functional connectivity strength to be associated with increased PTSD symptom severity. Further topological characterization suggests decreased functional integration and increased segregation in subjects with severe PTSD. Modularity analyses suggest a spared connectivity in the posterior DMN community (posterior cingulate, precuneus, angular gyrus) despite overall DMN weakened connections with increasing PTSD severity. Edge-wise network-based statistical analyses revealed a prefrontal dysconnectivity. Analysis of the diffusion networks revealed no alterations in overall strength or prefrontal structural connectivity. DMN abnormalities in patients with severe PTSD symptoms are characterized by decreased overall interconnections. On a finer scale, we found a pattern of prefrontal dysconnectivity, but increased cohesiveness in the posterior DMN community and relative sparing of connectivity in this region. The DMN measures established in this study may serve as a biomarker of disease severity and could have potential utility in developing circuit-based therapeutics.