Information processing architecture of functionally defined clusters in the macaque cortex

It's a matter of time: Reframing the development of cognitive control as a modification of the brain's temporal dynamics

Cognitive control is a process that unfolds over time and regulates thought and action in the service of achieving goals and managing unanticipated challenges. Prevailing accounts attribute the protracted development of this mental process to incremental changes in the functional organization of a cognitive control network. Here, we challenge the notion that cognitive control is linked to a topologically static network, and argue that the capacity to manage unanticipated challenges and its development should instead be characterized in terms of inter-regional functional coupling dynamics. Ongoing changes in temporal coupling have long represented a fundamental pillar in both empirical and theoretical-based accounts of brain function, but have been largely ignored by traditional neuroimaging methods that assume a fixed functional architecture. There is, however, a growing recognition of the importance of temporal coupling dynamics for brain function, and this has led to rapid innovations in analytic methods. Results in this new frontier of neuroimaging suggest that time-varying changes in connectivity strength and direction exist at the large scale and further, that network patterns, like cognitive control process themselves, are transient and dynamic.

Comparative Connectomics

We introduce comparative connectomics, the quantitative study of cross-species commonalities and variations in brain network topology that aims to discover general principles of network architecture of nervous systems and the identification of species-specific features of brain connectivity. By comparing connectomes derived from simple to more advanced species, we identify two conserved themes of wiring: the tendency to organize network topology into communities that serve specialized functionality and the general drive to enable high topological integration by means of investment of neural resources in short communication paths, hubs, and rich clubs. Within the space of wiring possibilities that conform to these common principles, we argue that differences in connectome organization between closely related species support adaptations in cognition and behavior.

Linking Functional Connectivity and Structural Connectivity Quantitatively: A Comparison of Methods

Structural connectivity in the brain is the basis of functional connectivity. Quantitatively linking the two, however, remains a challenge. For a pair of regions of interest (ROIs), anatomical connections derived from diffusion-weighted imaging are often quantified by fractional anisotropy (FA) or edge weight, whereas functional connections, derived from resting-state functional magnetic resonance imaging, can be characterized by non-time-series measures such as zero-lag cross correlation and partial correlation, as well as by time-series measures such as coherence and Granger causality. In this study, we addressed the question of linking structural connectivity and functional connectivity quantitatively by considering two pairs of ROIs, one from the default mode network (DMN) and the other from the central executive network (CEN), using two different data sets. Selecting (1) posterior cingulate cortex and medial prefrontal cortex of the DMN as the first pair of ROIs and (2) left dorsal lateral prefrontal cortex and left inferior parietal lobule of the CEN as the second pair of ROIs, we show that (1) zero-lag cross correlation, partial correlation, and pairwise Granger causality were not significantly correlated with either mean FA or edge weight and (2) conditional Granger causality (CGC) was significantly correlated with edge weight but not with mean FA. These results suggest that (1) edge weight may be a more appropriate measure to quantify the strength of the anatomical connection between ROIs and (2) CGC, which statistically removes common input and the indirect influences between a given ROI pair, may be a more appropriate measure to quantify the strength of the functional interaction enabled by the fibers linking the two ROIs.

The modular and integrative functional architecture of the human brain

Network-based analyses of brain imaging data consistently reveal distinct modules and connector nodes with diverse global connectivity across the modules. How discrete the functions of modules are, how dependent the computational load of each module is to the other modules' processing, and what the precise role of connector nodes is for between-module communication remains underspecified. Here, we use a network model of the brain derived from resting-state functional MRI (rs-fMRI) data and investigate the modular functional architecture of the human brain by analyzing activity at different types of nodes in the network across 9,208 experiments of 77 cognitive tasks in the BrainMap database. Using an author-topic model of cognitive functions, we find a strong spatial correspondence between the cognitive functions and the network's modules, suggesting that each module performs a discrete cognitive function. Crucially, activity at local nodes within the modules does not increase in tasks that require more cognitive functions, demonstrating the autonomy of modules' functions. However, connector nodes do exhibit increased activity when more cognitive functions are engaged in a task. Moreover, connector nodes are located where brain activity is associated with many different cognitive functions. Connector nodes potentially play a role in between-module communication that maintains the modular function of the brain. Together, these findings provide a network account of the brain's modular yet integrated implementation of cognitive functions.

Functional Clusters, Hubs, and Communities in the Cortical Microconnectome

Although relationships between networks of different scales have been observed in macroscopic brain studies, relationships between structures of different scales in networks of neurons are unknown. To address this, we recorded from up to 500 neurons simultaneously from slice cultures of rodent somatosensory cortex. We then measured directed effective networks with transfer entropy, previously validated in simulated cortical networks. These effective networks enabled us to evaluate distinctive nonrandom structures of connectivity at 2 different scales. We have 4 main findings. First, at the scale of 3-6 neurons (clusters), we found that high numbers of connections occurred significantly more often than expected by chance. Second, the distribution of the number of connections per neuron (degree distribution) had a long tail, indicating that the network contained distinctively high-degree neurons, or hubs. Third, at the scale of tens to hundreds of neurons, we typically found 2-3 significantly large communities. Finally, we demonstrated that communities were relatively more robust than clusters against shuffling of connections. We conclude the microconnectome of the cortex has specific organization at different scales, as revealed by differences in robustness. We suggest that this information will help us to understand how the microconnectome is robust against damage.

Comparison of diffusion tractography and tract-tracing measures of connectivity strength in rhesus macaque connectome

With the mapping of macroscale connectomes by means of in vivo diffusion-weighted MR Imaging (DWI) rapidly gaining in popularity, one of the necessary steps is the examination of metrics of connectivity strength derived from these reconstructions. In the field of human macroconnectomics the number of reconstructed fiber streamlines (NOS) is more and more used as a metric of cortico-cortical interareal connectivity strength, but the link between DWI NOS and in vivo animal tract-tracing measurements of anatomical connectivity strength remains poorly understood. In this technical report, we communicate on a comparison between DWI derived metrics and tract-tracing metrics of projection strength. Tract-tracing information on projection strength of interareal pathways was extracted from two commonly used macaque connectome datasets, including (1) the CoCoMac database of collated tract-tracing experiments of the macaque brain and (2) the high-resolution tract-tracing dataset of Markov and Kennedy and coworkers. NOS and density of reconstructed fiber pathways derived from DWI data acquired across 10 rhesus macaques was found to positively correlate to tract-tracing based measurements of connectivity strength across both the CoCoMac and Markov dataset (both P < 0.001), suggesting DWI NOS to form a valid method of assessment of the projection strength of white matter pathways. Our findings provide confidence of in vivo DWI connectome reconstructions to represent fairly realistic estimates of the wiring strength of white matter projections. Our cross-modal comparison supports the notion of in vivo DWI to be a valid methodology for robust description and interpretation of brain wiring.

Age of Childhood Onset in Type 1 Diabetes and Functional Brain Connectivity in Midlife

OBJECTIVES

The development of Type 1 diabetes mellitus (T1DM) within the first 7 years of life has been linked to poorer cognitive performance. Adults with T1DM have altered functional brain connectivity, but no studies have examined whether earlier age of T1DM onset is associated with functional connectivity later in life. Accordingly, we tested the relationship between age of onset and resting state functional connectivity in a cohort of middle-aged adults with childhood-onset T1DM.

METHODS

Participants were from a subsample of the Pittsburgh Epidemiology of Diabetes Complications cohort and included 66 adults (mean age = 47.54 years, 32 men). Resting state blood oxygen level-dependent activity was used to calculate mean connectivity for eight functional brain networks. A multivariate analysis of variance examined associations between age of onset and network connectivity. Diffusion tensor and fluid-attenuated inversion recovery images were analyzed to identify microstructural alterations and white-matter hyperintensity volumes.

RESULTS

Later childhood onset of T1DM was associated with lower connectivity (F(8,57) = 2.40, p = .026). A significant interaction was present for current age such that an inverse association with age of onset for functional connectivity was present in older individuals (F(8,55) = 2.88, p = .035). Lower connectivity was associated with older age, increased white-matter hyperintensity volume, and lower microstructural integrity.

CONCLUSIONS

Diagnosis of T1DM later in childhood may be associated with lower brain functional connectivity, particularly in those surviving into older ages. These alterations may be an early marker for subsequent cognitive decrements. Future studies are warranted to understand the pathways underlying these associations.

Network structure shapes spontaneous functional connectivity dynamics

The structural organization of the brain constrains the range of interactions between different regions and shapes ongoing information processing. Therefore, it is expected that large-scale dynamic functional connectivity (FC) patterns, a surrogate measure of coordination between brain regions, will be closely tied to the fiber pathways that form the underlying structural network. Here, we empirically examined the influence of network structure on FC dynamics by comparing resting-state FC (rsFC) obtained using BOLD-fMRI in macaques (Macaca fascicularis) to structural connectivity derived from macaque axonal tract tracing studies. Consistent with predictions from simulation studies, the correspondence between rsFC and structural connectivity increased as the sample duration increased. Regions with reciprocal structural connections showed the most stable rsFC across time. The data suggest that the transient nature of FC is in part dependent on direct underlying structural connections, but also that dynamic coordination can occur via polysynaptic pathways. Temporal stability was found to be dependent on structural topology, with functional connections within the rich-club core exhibiting the greatest stability over time. We discuss these findings in light of highly variable functional hubs. The results further elucidate how large-scale dynamic functional coordination exists within a fixed structural architecture.

Mapping human brain networks with cortico-cortical evoked potentials

The cerebral cortex forms a sheet of neurons organized into a network of interconnected modules that is highly expanded in humans and presumably enables our most refined sensory and cognitive abilities. The links of this network form a fundamental aspect of its organization, and a great deal of research is focusing on understanding how information flows within and between different regions. However, an often-overlooked element of this connectivity regards a causal, hierarchical structure of regions, whereby certain nodes of the cortical network may exert greater influence over the others. While this is difficult to ascertain non-invasively, patients undergoing invasive electrode monitoring for epilepsy provide a unique window into this aspect of cortical organization. In this review, we highlight the potential for cortico-cortical evoked potential (CCEP) mapping to directly measure neuronal propagation across large-scale brain networks with spatio-temporal resolution that is superior to traditional neuroimaging methods. We first introduce effective connectivity and discuss the mechanisms underlying CCEP generation. Next, we highlight how CCEP mapping has begun to provide insight into the neural basis of non-invasive imaging signals. Finally, we present a novel approach to perturbing and measuring brain network function during cognitive processing. The direct measurement of CCEPs in response to electrical stimulation represents a potentially powerful clinical and basic science tool for probing the large-scale networks of the human cerebral cortex.

Functional subdivisions of medial parieto-occipital cortex in humans and nonhuman primates using resting-state fMRI

Based on its diverse and wide-spread patterns of connectivity, primate posteromedial cortex (PMC) is well positioned to support roles in several aspects of sensory-, cognitive- and motor-related processing. Previous work in both humans and non-human primates (NHPs) using resting-state functional MRI (rs-fMRI) suggests that a subregion of PMC, the medial parieto-occipital cortex (mPOC), by virtue of its intrinsic functional connectivity (FC) with visual cortex, may only play a role in higher-order visual processing. Recent neuroanatomical tracer studies in NHPs, however, demonstrate that mPOC also has prominent cortico-cortical connections with several frontoparietal structures involved in movement planning and control, a finding consistent with increasing observations of reach- and grasp-related activity in the mPOC of both NHPs and humans. To reconcile these observations, here we used rs-fMRI data collected from both awake humans and anesthetized macaque monkeys to more closely examine and compare parcellations of mPOC across species and explore the FC patterns associated with these subdivisions. Seed-based and voxel-wise hierarchical cluster analyses revealed four broad spatially separated functional boundaries that correspond with graded differences in whole-brain FC patterns in each species. The patterns of FC observed are consistent with mPOC forming a critical hub of networks involved in action planning and control, spatial navigation, and working memory. In addition, our comparison between species indicates that while there are several similarities, there may be some species-specific differences in functional neural organization. These findings and the associated theoretical implications are discussed.

Stable long-range interhemispheric coordination is supported by direct anatomical projections

The functional interaction between the brain's two hemispheres includes a unique set of connections between corresponding regions in opposite hemispheres (i.e., homotopic regions) that are consistently reported to be exceptionally strong compared with other interhemispheric (i.e., heterotopic) connections. The strength of homotopic functional connectivity (FC) is thought to be mediated by the regions' shared functional roles and their structural connectivity. Recently, homotopic FC was reported to be stable over time despite the presence of dynamic FC across both intrahemispheric and heterotopic connections. Here we build on this work by considering whether homotopic FC is also stable across conditions. We additionally test the hypothesis that strong and stable homotopic FC is supported by the underlying structural connectivity. Consistent with previous findings, interhemispheric FC between homotopic regions were significantly stronger in both humans and macaques. Across conditions, homotopic FC was most resistant to change and therefore was more stable than heterotopic or intrahemispheric connections. Across time, homotopic FC had significantly greater temporal stability than other types of connections. Temporal stability of homotopic FC was facilitated by direct anatomical projections. Importantly, temporal stability varied with the change in conductive properties of callosal axons along the anterior-posterior axis. Taken together, these findings suggest a notable role for the corpus callosum in maintaining stable functional communication between hemispheres.

Structural connectivity of the developing human amygdala

A large corpus of research suggests that there are changes in the manner and degree to which the amygdala supports cognitive and emotional function across development. One possible basis for these developmental differences could be the maturation of amygdalar connections with the rest of the brain. Recent functional connectivity studies support this conclusion, but the structural connectivity of the developing amygdala and its different nuclei remains largely unstudied. We examined age related changes in the DWI connectivity fingerprints of the amygdala to the rest of the brain in 166 individuals of ages 5-30. We also developed a model to predict age based on individual-subject amygdala connectivity, and identified the connections that were most predictive of age. Finally, we segmented the amygdala into its four main nucleus groups, and examined the developmental changes in connectivity for each nucleus. We observed that with age, amygdalar connectivity becomes increasingly sparse and localized. Age related changes were largely localized to the subregions of the amygdala that are implicated in social inference and contextual memory (the basal and lateral nuclei). The central nucleus’ connectivity also showed differences with age but these differences affected fewer target regions than the basal and lateral nuclei. The medial nucleus did not exhibit any age related changes. These findings demonstrate increasing specificity in the connectivity patterns of amygdalar nuclei across age.

The nature of individual differences in inhibited temperament and risk for psychiatric disease: A review and meta-analysis

What makes us different from one another? Why does one person jump out of airplanes for fun while another prefers to stay home and read? Why are some babies born with a predisposition to become anxious? Questions about individual differences in temperament have engaged the minds of scientists, psychologists, and philosophers for centuries. Recent technological advances in neuroimaging and genetics provide an unprecedented opportunity to answer these questions. Here we review the literature on the neurobiology of one of the most basic individual differences-the tendency to approach or avoid novelty. This trait, called inhibited temperament, is innate, heritable, and observed across species. Importantly, inhibited temperament also confers risk for psychiatric disease. Here, we provide a comprehensive review of inhibited temperament, including neuroimaging and genetic studies in human and non-human primates. We conducted a meta-analysis of neuroimaging findings in inhibited humans that points to alterations in a fronto-limbic-basal ganglia circuit; these findings provide the basis of a model of inhibited temperament neurocircuitry. Lesion and neuroimaging studies in non-human primate models of inhibited temperament highlight roles for the amygdala, hippocampus, orbitofrontal cortex, and dorsal prefrontal cortex. Genetic studies highlight a role for genes that regulate neurotransmitter function, such as the serotonin transporter polymorphisms (5-HTTLPR), as well as genes that regulate stress response, such as corticotropin-releasing hormone (CRH). Together these studies provide a foundation of knowledge about the genetic and neural substrates of this most basic of temperament traits. Future studies using novel imaging methods and genetic approaches promise to expand upon these biological bases of inhibited temperament and inform our understanding of risk for psychiatric disease.

Signature of consciousness in the dynamics of resting-state brain activity

At rest, the brain is traversed by spontaneous functional connectivity patterns. Two hypotheses have been proposed for their origins: they may reflect a continuous stream of ongoing cognitive processes as well as random fluctuations shaped by a fixed anatomical connectivity matrix. Here we show that both sources contribute to the shaping of resting-state networks, yet with distinct contributions during consciousness and anesthesia. We measured dynamical functional connectivity with functional MRI during the resting state in awake and anesthetized monkeys. Under anesthesia, the more frequent functional connectivity patterns inherit the structure of anatomical connectivity, exhibit fewer small-world properties, and lack negative correlations. Conversely, wakefulness is characterized by the sequential exploration of a richer repertoire of functional configurations, often dissimilar to anatomical structure, and comprising positive and negative correlations among brain regions. These results reconcile theories of consciousness with observations of long-range correlation in the anesthetized brain and show that a rich functional dynamics might constitute a signature of consciousness, with potential clinical implications for the detection of awareness in anesthesia and brain-lesioned patients.

Functional network alterations and their structural substrate in drug-resistant epilepsy

The advent of MRI has revolutionized the evaluation and management of drug-resistant epilepsy by allowing the detection of the lesion associated with the region that gives rise to seizures. Recent evidence indicates marked chronic alterations in the functional organization of lesional tissue and large-scale cortico-subcortical networks. In this review, we focus on recent methodological developments in functional MRI (fMRI) analysis techniques and their application to the two most common drug-resistant focal epilepsies, i.e., temporal lobe epilepsy related to mesial temporal sclerosis and extra-temporal lobe epilepsy related to focal cortical dysplasia. We put particular emphasis on methodological developments in the analysis of task-free or “resting-state” fMRI to probe the integrity of intrinsic networks on a regional, inter-regional, and connectome-wide level. In temporal lobe epilepsy, these techniques have revealed disrupted connectivity of the ipsilateral mesiotemporal lobe, together with contralateral compensatory reorganization and striking reconfigurations of large-scale networks. In cortical dysplasia, initial observations indicate functional alterations in lesional, peri-lesional, and remote neocortical regions. While future research is needed to critically evaluate the reliability, sensitivity, and specificity, fMRI mapping promises to lend distinct biomarkers for diagnosis, presurgical planning, and outcome prediction.

Auditory-prefrontal axonal connectivity in the macaque cortex: quantitative assessment of processing streams

Primate sensory systems subserve complex neurocomputational functions. Consequently, these systems are organised anatomically in a distributed fashion, commonly linking areas to form specialised processing streams. Each stream is related to a specific function, as evidenced from studies of the visual cortex, which features rather prominent segregation into spatial and non-spatial domains. It has been hypothesised that other sensory systems, including auditory, are organised in a similar way on the cortical level. Recent studies offer rich qualitative evidence for the dual stream hypothesis. Here we provide a new paradigm to quantitatively uncover these patterns in the auditory system, based on an analysis of multiple anatomical studies using multivariate techniques. As a test case, we also apply our assessment techniques to more ubiquitously-explored visual system. Importantly, the introduced framework opens the possibility for these techniques to be applied to other neural systems featuring a dichotomised organisation, such as language or music perception.

Identification of optimal structural connectivity using functional connectivity and neural modeling

The complex network dynamics that arise from the interaction of the brain's structural and functional architectures give rise to mental function. Theoretical models demonstrate that the structure-function relation is maximal when the global network dynamics operate at a critical point of state transition. In the present work, we used a dynamic mean-field neural model to fit empirical structural connectivity (SC) and functional connectivity (FC) data acquired in humans and macaques and developed a new iterative-fitting algorithm to optimize the SC matrix based on the FC matrix. A dramatic improvement of the fitting of the matrices was obtained with the addition of a small number of anatomical links, particularly cross-hemispheric connections, and reweighting of existing connections. We suggest that the notion of a critical working point, where the structure-function interplay is maximal, may provide a new way to link behavior and cognition, and a new perspective to understand recovery of function in clinical conditions.

Understanding Structural-Functional Relationships in the Human Brain

Relating the brain’s structural connectivity (SC) to its functional connectivity (FC) is a fundamental goal in neuroscience because it is capable of aiding our understanding of how the relatively fixed SC architecture underlies human cognition and diverse behaviors. With the aid of current noninvasive imaging technologies (e.g., structural MRI, diffusion MRI, and functional MRI) and graph theory methods, researchers have modeled the human brain as a complex network of interacting neuronal elements and characterized the underlying structural and functional connectivity patterns that support diverse cognitive functions. Specifically, research has demonstrated a tight SC-FC coupling, not only in interregional connectivity strength but also in network topologic organizations, such as community, rich-club, and motifs. Moreover, this SC-FC coupling exhibits significant changes in normal development and neuropsychiatric disorders, such as schizophrenia and epilepsy. This review summarizes recent progress regarding the SC-FC relationship of the human brain and emphasizes the important role of large-scale brain networks in the understanding of structural-functional associations. Future research directions related to this topic are also proposed.

Structure and function of complex brain networks

An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a "rich club," centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed.

Bridging the gap between the human and macaque connectome: a quantitative comparison of global interspecies structure-function relationships and network topology

Resting state functional connectivity MRI (rs-fcMRI) may provide a powerful and noninvasive "bridge" for comparing brain function between patients and experimental animal models; however, the relationship between human and macaque rs-fcMRI remains poorly understood. Here, using a novel surface deformation process for species comparisons in the same anatomical space (Van Essen, 2004, 2005), we found high correspondence, but also unique hub topology, between human and macaque functional connectomes. The global functional connectivity match between species was moderate to strong (r = 0.41) and increased when considering the top 15% strongest connections (r = 0.54). Analysis of the match between functional connectivity and the underlying anatomical connectivity, derived from a previous retrograde tracer study done in macaques (Markov et al., 2012), showed impressive structure-function correspondence in both the macaque and human. When examining the strongest structural connections, we found a 70-80% match between structural and functional connectivity matrices in both species. Finally, we compare species on two widely used metrics for studying hub topology: degree and betweenness centrality. The data showed topological agreement across the species, with nodes of the posterior cingulate showing high degree and betweenness centrality. In contrast, nodes in medial frontal and parietal cortices were identified as having high degree and betweenness in the human as opposed to the macaque. Our results provide: (1) a thorough examination and validation for a surface-based interspecies deformation process, (2) a strong theoretical foundation for making interspecies comparisons of rs-fcMRI, and (3) a unique look at topological distinctions between the species.

Noise-induced organized slow fluctuations in networks of neural areas with interarea feed-forward excitation and inhibition

Slow coherent spontaneous fluctuations (<0.1 Hz) in functional magnetic resonance imaging blood-oxygen-level-dependent signals have been observed for a resting state of the human brain. In this paper, considering feed-forward inhibition in addition to excitation between brain areas, which we assume to be in up (active) or down (quiescent) states, we propose a model for the generation and organization of the slow fluctuations. Connectivity with feed-forward excitation and inhibition between the areas makes the system have multiple stable states and organized slow fluctuations manifest as noise-induced slow transitions between the states. With various connectivities, we observe slow fluctuations and various organizations, including anticorrelated clusters, through numerical simulations.

Fine-grained mapping of mouse brain functional connectivity with resting-state fMRI

Understanding the intrinsic circuit-level functional organization of the brain has benefited tremendously from the advent of resting-state fMRI (rsfMRI). In humans, resting-state functional network has been consistently mapped and its alterations have been shown to correlate with symptomatology of various neurological or psychiatric disorders. To date, deciphering the mouse brain functional connectivity (MBFC) with rsfMRI remains a largely underexplored research area, despite the plethora of human brain disorders that can be modeled in this specie. To pave the way from pre-clinical to clinical investigations we characterized here the intrinsic architecture of mouse brain functional circuitry, based on rsfMRI data acquired at 7T using the Cryoprobe technology. High-dimensional spatial group independent component analysis demonstrated fine-grained segregation of cortical and subcortical networks into functional clusters, overlapping with high specificity onto anatomical structures, down to single gray matter nuclei. These clusters, showing a high level of stability and reliability in their patterning, formed the input elements for computing the MBFC network using partial correlation and graph theory. Its topological architecture conserved the fundamental characteristics described for the human and rat brain, such as small-worldness and partitioning into functional modules. Our results additionally showed inter-modular interactions via "network hubs". Each major functional system (motor, somatosensory, limbic, visual, autonomic) was found to have representative hubs that might play an important input/output role and form a functional core for information integration. Moreover, the rostro-dorsal hippocampus formed the highest number of relevant connections with other brain areas, highlighting its importance as core structure for MBFC.

Contributions and challenges for network models in cognitive neuroscience

The confluence of new approaches in recording patterns of brain connectivity and quantitative analytic tools from network science has opened new avenues toward understanding the organization and function of brain networks. Descriptive network models of brain structural and functional connectivity have made several important contributions; for example, in the mapping of putative network hubs and network communities. Building on the importance of anatomical and functional interactions, network models have provided insight into the basic structures and mechanisms that enable integrative neural processes. Network models have also been instrumental in understanding the role of structural brain networks in generating spatially and temporally organized brain activity. Despite these contributions, network models are subject to limitations in methodology and interpretation, and they face many challenges as brain connectivity data sets continue to increase in detail and complexity.

Resting-state fMRI reveals functional connectivity between face-selective perirhinal cortex and the fusiform face area related to face inversion

Studies examining the neural correlates of face perception and recognition in humans have revealed multiple brain regions that appear to play a specialized role in face processing. These include an anterior portion of perirhinal cortex (PrC) that appears to be homologous to the face-selective 'anterior face patch' recently reported in non-human primates. Electrical stimulation studies in the macaque indicate that the anterior face patch is strongly connected with other face-selective patches of cortex, even in the absence of face stimuli. The intrinsic functional connectivity of face-selective PrC and other regions of the face-processing network in humans are currently less well understood. Here, we examined resting-state fMRI connectivity across five face-selective regions in the right hemisphere that were identified with separate functional localizer scans: the PrC, amygdala (Amg), superior temporal sulcus, fusiform face area (FFA), and occipital face area. A partial correlation technique, controlling for fluctuations in occipitotemporal cortex that were not face specific, revealed connectivity between the PrC and the FFA, as well as the Amg. When examining the 'unique' connectivity of PrC within this face processing network, we found that the connectivity between the PrC and the FFA as well as that between the PrC and the Amg persisted even after controlling for potential mediating effects of other face-selective regions. Lastly, we examined the behavioral relevance of PrC connectivity by examining inter-individual differences in resting-state fluctuations in relation to differences in behavioral performance for a forced-choice recognition memory task that involved judgments on upright and inverted faces. This analysis revealed a significant correlation between the increased accuracy for upright faces (i.e., the face inversion effect) and the strength of connectivity between the PrC and the FFA. Together, these findings point to a high degree of functional integration of face-selective aspects of PrC in the face processing network with notable behavioral relevance.

Effects of unilateral deactivations of dorsolateral prefrontal cortex and anterior cingulate cortex on saccadic eye movements

The dorsolateral prefrontal cortex (dlPFC) and anterior cingulate cortex (ACC) have both been implicated in the cognitive control of saccadic eye movements by single neuron recording studies in nonhuman primates and functional imaging studies in humans, but their relative roles remain unclear. Here, we reversibly deactivated either dlPFC or ACC subregions in macaque monkeys while the animals performed randomly interleaved pro- and antisaccades. In addition, we explored the whole-brain functional connectivity of these two regions by applying a seed-based resting-state functional MRI analysis in a separate cohort of monkeys. We found that unilateral dlPFC deactivation had stronger behavioral effects on saccades than unilateral ACC deactivation, and that the dlPFC displayed stronger functional connectivity with frontoparietal areas than the ACC. We suggest that the dlPFC plays a more prominent role in the preparation of pro- and antisaccades than the ACC.

Structural and functional brain networks: from connections to cognition

How rich functionality emerges from the invariant structural architecture of the brain remains a major mystery in neuroscience. Recent applications of network theory and theoretical neuroscience to large-scale brain networks have started to dissolve this mystery. Network analyses suggest that hierarchical modular brain networks are particularly suited to facilitate local (segregated) neuronal operations and the global integration of segregated functions. Although functional networks are constrained by structural connections, context-sensitive integration during cognition tasks necessarily entails a divergence between structural and functional networks. This degenerate (many-to-one) function-structure mapping is crucial for understanding the nature of brain networks. The emergence of dynamic functional networks from static structural connections calls for a formal (computational) approach to neuronal information processing that may resolve this dialectic between structure and function.

Imaging structural and functional brain networks in temporal lobe epilepsy

Early imaging studies in temporal lobe epilepsy (TLE) focused on the search for mesial temporal sclerosis, as its surgical removal results in clinically meaningful improvement in about 70% of patients. Nevertheless, a considerable subgroup of patients continues to suffer from post-operative seizures. Although the reasons for surgical failure are not fully understood, electrophysiological and imaging data suggest that anomalies extending beyond the temporal lobe may have negative impact on outcome. This hypothesis has revived the concept of human epilepsy as a disorder of distributed brain networks. Recent methodological advances in non-invasive neuroimaging have led to quantify structural and functional networks in vivo. While structural networks can be inferred from diffusion MRI tractography and inter-regional covariance patterns of structural measures such as cortical thickness, functional connectivity is generally computed based on statistical dependencies of neurophysiological time-series, measured through functional MRI or electroencephalographic techniques. This review considers the application of advanced analytical methods in structural and functional connectivity analyses in TLE. We will specifically highlight findings from graph-theoretical analysis that allow assessing the topological organization of brain networks. These studies have provided compelling evidence that TLE is a system disorder with profound alterations in local and distributed networks. In addition, there is emerging evidence for the utility of network properties as clinical diagnostic markers. Nowadays, a network perspective is considered to be essential to the understanding of the development, progression, and management of epilepsy.

Structure and function of complex brain networks

An increasing number of theoretical and empirical studies approach the function of the human brain from a network perspective. The analysis of brain networks is made feasible by the development of new imaging acquisition methods as well as new tools from graph theory and dynamical systems. This review surveys some of these methodological advances and summarizes recent findings on the architecture of structural and functional brain networks. Studies of the structural connectome reveal several modules or network communities that are interlinked by hub regions mediating communication processes between modules. Recent network analyses have shown that network hubs form a densely linked collective called a "rich club," centrally positioned for attracting and dispersing signal traffic. In parallel, recordings of resting and task-evoked neural activity have revealed distinct resting-state networks that contribute to functions in distinct cognitive domains. Network methods are increasingly applied in a clinical context, and their promise for elucidating neural substrates of brain and mental disorders is discussed.

Rich club organization and intermodule communication in the cat connectome

Macroscopic brain networks have been shown to display several properties of an efficient communication architecture. In light of global communication, the formation of a densely connected neural "rich club" of hubs is of particular interest, because brain hubs have been suggested to play a key role in enabling short communication pathways within neural networks. Here, analyzing the cat connectome as reconstructed from tract tracing data (Scannell et al., 1995), we provide several lines of evidence of an important role of the structural rich club to interlink functional domains. First, rich club hub nodes were found to be mostly present at the boundaries between functional communities and well represented among intermodule hubs, displaying a diverse connectivity profile. Second, rich club connections, linking nodes of the rich club, and feeder connections, linking non-rich club nodes to rich club nodes, were found to comprise 86% of the intermodule connections, whereas local connections between peripheral nodes mostly spanned between nodes of the same functional community. Third, almost 90% of all intermodule communication paths were found to follow a sequence or "path motif" that involved rich club or feeder edges and thus traversed a rich club node. Together, our findings provide evidence of the structural rich club to form a central infrastructure for intermodule communication in the brain.

Brain connectivity and visual attention

Emerging hypotheses suggest that efficient cognitive functioning requires the integration of separate, but interconnected cortical networks in the brain. Although task-related measures of brain activity suggest that a frontoparietal network is associated with the control of attention, little is known regarding how components within this distributed network act together or with other networks to achieve various attentional functions. This review considers both functional and structural studies of brain connectivity, as complemented by behavioral and task-related neuroimaging data. These studies show converging results: The frontal and parietal cortical regions are active together, over time, and identifiable frontoparietal networks are active in relation to specific task demands. However, the spontaneous, low-frequency fluctuations of brain activity that occur in the resting state, without specific task demands, also exhibit patterns of connectivity that closely resemble the task-related, frontoparietal attention networks. Both task-related and resting-state networks exhibit consistent relations to behavioral measures of attention. Further, anatomical structure, particularly white matter pathways as defined by diffusion tensor imaging, places constraints on intrinsic functional connectivity. Lastly, connectivity analyses applied to investigate cognitive differences across individuals in both healthy and diseased states suggest that disconnection of attentional networks is linked to deficits in cognitive functioning, and in extreme cases, to disorders of attention. Thus, comprehensive theories of visual attention and their clinical translation depend on the continued integration of behavioral, task-related neuroimaging, and brain connectivity measures.

Comparing connectivity pattern and small-world organization between structural correlation and resting-state networks in healthy adults

In recent years, coordinated variations in brain morphology (e.g. volume, thickness, surface area) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks (SCNs). However, it remains unclear how morphometric correlations relate to functional connectivity between brain regions. Resting-state networks (RSNs), derived from coordinated variations in neural activity at rest, have been shown to reflect connectivity between functionally related regions as well as, to some extent, anatomical connectivity between brain regions. Therefore, it is intriguing to investigate similarities between SCN and RSN to help identify how morphometric correlations relate to connections defined by resting-state connectivity. We investigated the similarities in connectivity patterns and small-world organization between SCN, derived from correlations of regional gray matter volume across individuals, and RSN in 36 healthy individuals. The results showed a significant similarity between SCN and RSN (60% for positive connections and 40% for negative connections) that might be explained by shared experience-related functional connectivity underlying both SCN and RSN. Conversely, the small-world parameters of the networks were significantly different, suggesting that SCN topological parameters cannot be regarded as a substitute for topological organization in resting-state networks. While our data suggest that using structural correlation networks can be useful in understanding alterations in structural associations in various brain disorders, it should be noted that a portion of the observed alterations might be explained by factors other than those reflecting resting-state connectivity.

The history of CoCoMac

CoCoMac, the "Collation of Connectivity Data for the Macaque" is a relational database system which presently constitutes the largest electronic repository of published neuroanatomical connectivity data. Developed since 1996, CoCoMac comprises approximately 40,000 experimental findings on anatomical connections in the macaque brain, as derived from neuroanatomical tract tracing studies. In this historical review, I describe the origin and the history of CoCoMac from a personal perspective, illustrate the principles of its structure and outline the impact it has had on systems neuroscience, in particular as a prelude to the "Human Connectome" research programme.

Functional connectivity patterns of medial and lateral macaque frontal eye fields reveal distinct visuomotor networks

It has been previously shown that small- and large-amplitude saccades have different functions during vision in natural environments. Large saccades are associated with reaching movements toward objects, whereas small saccades facilitate the identification of more detailed object features necessary for successful grasping and manual manipulation. To determine whether these represent dichotomous processing streams, we used resting-state functional MRI to examine the functional connectivity patterns of the medial and lateral frontal eye field (FEF) regions that encode large- and small-amplitude saccades, respectively. We found that the spontaneous blood oxygen level-dependent signals of the medial FEF were functionally correlated with areas known to be involved in reaching movements and executive control processes, whereas lateral FEF was functionally correlated with cortical areas involved in object processing and in grasping, fixation, and manipulation of objects. The results provide strong evidence for two distinct visuomotor network systems in the primate brain that likely reflect the alternating phases of vision for action in natural environments.