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Functional magnetic resonance imaging (fMRI) studies on the dynamic representation of task content focus preferentially on the cerebral cortex. However, neurophysiological studies report coding of task-relevant features also by neurons in the striatum, suggesting basal ganglia involvement in cognitive decision-making. Here we use fMRI data to show that also in humans the striatum is an integrated part of the cognitive brain network. Twelve participants performed 3 cognitive tasks in the scanner, i.e., the Eriksen flanker task, a 2-back matching spatial working memory task, and a response scheme switching task. First, we use region of interest-based multivariate pattern classification to demonstrate that each task reliably induces a unique activity pattern in the striatum and in the lateral prefrontal cortex. We show that the three tasks can also be distinguished in putamen, caudate nucleus and ventral striatum alone. We additionally establish that the contribution of striatum to cognition is not sensitive to habituation or learning. Secondly, we use voxel-to-voxel functional connectivity to establish that voxels in the lateral prefrontal cortex and in the striatum that prefer the same task show significantly stronger functional coupling than voxel pairs in these remote structures that prefer different tasks. These results suggest that striatal neurons form subnetworks with cognition-related regions of the prefrontal cortex. These remote neuron populations are interconnected via functional couplings that exceed the time of execution of the specific tasks.

Structural connections between cortical areas form an intricate network with a high degree of specificity. Many aspects of this complex network organization in the adult mammalian cortex are captured by an architectonic type principle, which relates structural connections to the architectonic differentiation of brain regions. In particular, the laminar patterns of projection origins are a prominent feature of structural connections that varies in a graded manner with the relative architectonic differentiation of connected areas in the adult brain. Here we show that the architectonic type principle is already apparent for the laminar origins of cortico-cortical projections in the immature cortex of the macaque monkey. We find that prenatal and neonatal laminar patterns correlate with cortical architectonic differentiation, and that the relation of laminar patterns to architectonic differences between connected areas is not substantially altered by the complete loss of visual input. Moreover, we find that the degree of change in laminar patterns that projections undergo during development varies in proportion to the relative architectonic differentiation of the connected areas. Hence, it appears that initial biases in laminar projection patterns become progressively strengthened by later developmental processes. These findings suggest that early neurogenetic processes during the formation of the brain are sufficient to establish the characteristic laminar projection patterns. This conclusion is in line with previously suggested mechanistic explanations underlying the emergence of the architectonic type principle and provides further constraints for exploring the fundamental factors that shape structural connectivity in the mammalian brain. Supplementary Information The online version contains supplementary material available at 10.1007/s00429-021-02219-6.

Cognitive abilities of the human brain, including language, have expanded dramatically in the course of our recent evolution from nonhuman primates, despite only minor apparent changes at the gene level. The hypothesis we propose for this paradox relies upon fundamental features of human brain connectivity, which contribute to a characteristic anatomical, functional, and computational neural phenotype, offering a parsimonious framework for connectomic changes taking place upon the human-specific evolution of the genome. Many human connectomic features might be accounted for by substantially increased brain size within the global neural architecture of the primate brain, resulting in a larger number of neurons and areas and the sparsification, increased modularity, and laminar differentiation of cortical connections. The combination of these features with the developmental expansion of upper cortical layers, prolonged postnatal brain development, and multiplied nongenetic interactions with the physical, social, and cultural environment gives rise to categorically human-specific cognitive abilities including the recursivity of language. Thus, a small set of genetic regulatory events affecting quantitative gene expression may plausibly account for the origins of human brain connectivity and cognition.

Uncovering organizational principles of the cerebral cortex is essential for proper understanding of this prominent structure of the mammalian brain. The theory of the dual origin of the cerebral cortex offers such organizational principle. Here, we demonstrate that a duality pertains to the connectional architecture of the cerebral cortex of different mammals. This dual structure also constitutes a major axis of organization of the transcriptional architecture of the cortex and reflects the expression of different morphogens stemming from distinct patterning centers in the developing pallium. The duality of the cortex is also reflected in its spatial dimension, highlighting cortical areas as spatially ordered constellations that are centered around the paleocortex and archicortex, with the later primordial moieties reminiscent of antipodal points in the cortical sheet. The ontogeny of the uncovered dual connectional structure might be rooted in heterochronous neurodevelopmental gradients in the developing pallium, a suggestion corroborated by computational modeling. In all, the current results exemplify the duality of the cerebral cortex as an overarching organizational principle, reflected across the different levels of cortical architecture of different mammalian species, defining a natural axis of mammalian cortical organization.

Biological neuronal networks (BNNs) are a source of inspiration and analogy making for researchers that focus on artificial neuronal networks (ANNs). Moreover, neuroscientists increasingly use ANNs as a model for the brain. Despite certain similarities between these two types of networks, important differences can be discerned. First, biological neural networks are sculpted by evolution and the constraints that it entails, whereas artificial neural networks are engineered to solve particular tasks. Second, the network topology of these systems, apart from some analogies that can be drawn, exhibits pronounced differences. Here, we examine strategies to construct recurrent neural networks (RNNs) that instantiate the network topology of brains of different species. We refer to such RNNs as bio-instantiated. We investigate the performance of bio-instantiated RNNs in terms of: i) the prediction performance itself, that is, the capacity of the network to minimize the desired function at hand in test data, and ii) speed of training, that is, how fast during training the network reaches its optimal performance. We examine bio-instantiated RNNs in working memory tasks where task-relevant information must be tracked as a sequence of events unfolds in time. We highlight the strategies that can be used to construct RNNs with the network topology found in BNNs, without sacrificing performance. Despite that we observe no enhancement of performance when compared to randomly wired RNNs, our approach demonstrates how empirical neural network data can be used for constructing RNNs, thus, facilitating further experimentation with biologically realistic network topologies, in contexts where such aspect is desired.

The connectivity of Artificial Neural Networks (ANNs) is different from the one observed in Biological Neural Networks (BNNs). Can the wiring of actual brains help improve ANNs architectures? Can we learn from ANNs about what network features support computation in the brain when solving a task?ANNs’ architectures are carefully engineered and have crucial importance in many recent performance improvements. On the other hand, BNNs’ exhibit complex emergent connectivity patterns. At the individual level, BNNs connectivity results from brain development and plasticity processes, while at the species level, adaptive reconfigurations during evolution also play a major role shaping connectivity.Ubiquitous features of brain connectivity have been identified in recent years, but their role in the brain’s ability to perform concrete computations remains poorly understood. Computational neuroscience studies reveal the influence of specific brain connectivity features only on abstract dynamical properties, although the implications of real brain networks topologies on machine learning or cognitive tasks have been barely explored.Here we present a cross-species study with a hybrid approach integrating real brain connectomes and Bio-Echo State Networks, which we use to solve concrete memory tasks, allowing us to probe the potential computational implications of real brain connectivity patterns on task solving.We find results consistent across species and tasks, showing that biologically inspired networks perform as well as classical echo state networks, provided a minimum level of randomness and diversity of connections is allowed. We also present a framework, bio2art, to map and scale up real connectomes that can be integrated into recurrent ANNs. This approach also allows us to show the crucial importance of the diversity of interareal connectivity patterns, stressing the importance of stochastic processes determining neural networks connectivity in general.

Significance Communication between cells in the brain relies on different types of transmitter receptors. Can we uncover organizational principles that harness the diversity of such signatures across the brain? We focus on the human cerebral cortex and demonstrate that the distribution of receptors forms a natural axis that stretches from association to sensory areas. Moreover, traversing this axis entails changes in the diversity, excitability, and mirrored density that reflect a basic division in receptor types, that is, ionotropic and metabotropic receptors. The unraveled principles offer explanatory depth for diverse phenomena and entail concrete, testable predictions., Transmitter receptors constitute a key component of the molecular machinery for intercellular communication in the brain. Recent efforts have mapped the density of diverse transmitter receptors across the human cerebral cortex with an unprecedented level of detail. Here, we distill these observations into key organizational principles. We demonstrate that receptor densities form a natural axis in the human cerebral cortex, reflecting decreases in differentiation at the level of laminar organization and a sensory-to-association axis at the functional level. Along this natural axis, key organizational principles are discerned: progressive molecular diversity (increase of the diversity of receptor density); excitation/inhibition (increase of the ratio of excitatory-to-inhibitory receptor density); and mirrored, orderly changes of the density of ionotropic and metabotropic receptors. The uncovered natural axis formed by the distribution of receptors aligns with the axis that is formed by other dimensions of cortical organization, such as the myelo- and cytoarchitectonic levels. Therefore, the uncovered natural axis constitutes a unifying organizational feature linking multiple dimensions of the cerebral cortex, thus bringing order to the heterogeneity of cortical organization.

Evolution provides an important window into how cortical organization shapes function and vice versa. The complex mosaic of changes in brain morphology and functional organization that have shaped the mammalian cortex during evolution, complicates attempts to chart cortical differences across species. It limits our ability to fully appreciate how evolution has shaped our brain, especially in systems associated with unique human cognitive capabilities that lack anatomical homologues in other species. Here, we develop a function-based method for cross-species alignment that enables the quantification of homologous regions between humans and rhesus macaques, even when their location is decoupled from anatomical landmarks. Critically, we find cross-species similarity in functional organization reflects a gradient of evolutionary change that decreases from unimodal systems and culminates with the most pronounced changes in posterior regions of the default mode network (angular gyrus, posterior cingulate and middle temporal cortices). Our findings suggest that the establishment of the default mode network, as the apex of a cognitive hierarchy, has changed in a complex manner during human evolution - even within subnetworks.

A key component of current theories of brain structure and function is the layer-specific origin of structural connections of the cerebral cortex. This fundamental connectional feature pertains to different mammalian cortices, and recent neuroimaging advancements have started to pave the way for its function-based mapping in humans. Here, we propose a framework that systematically explains the characteristic layer-specific origin of structural connections and its graded variation across the cortical sheet and across mammalian species. The framework unifies seemingly dispersed observations on multiple levels of cortical organization, including the cellular, connectional, and functional level. Moreover, the framework allows the prediction of the layer-specific origin of connections in a spectrum of mammals, from rodents to humans.

Common principles and developmental mechanisms characterize the brain connectome of diverse species, from flies to humans., The wiring of vertebrate and invertebrate brains provides the anatomical skeleton for cognition and behavior. Connections among brain regions are characterized by heterogeneous strength that is parsimoniously described by the wiring cost and homophily principles. Moreover, brains exhibit a characteristic global network topology, including modules and hubs. However, the mechanisms resulting in the observed interregional wiring principles and network topology of brains are unknown. Here, with the aid of computational modeling, we demonstrate that a mechanism based on heterochronous and spatially ordered neurodevelopmental gradients, without the involvement of activity-dependent plasticity or axonal guidance cues, can reconstruct a large part of the wiring principles (on average, 83%) and global network topology (on average, 80%) of diverse adult brain connectomes, including fly and human connectomes. In sum, space and time are key components of a parsimonious, plausible neurodevelopmental mechanism of brain wiring with a potential universal scope, encompassing vertebrate and invertebrate brains.

The cerebral cortex of mammals exhibits intricate interareal wiring. Moreover, mammalian cortices differ vastly in size, cytological composition, and phylogenetic distance. Given such complexity and pronounced species differences, it is a considerable challenge to decipher organizational principles of mammalian connectomes. Here, we demonstrate species-specific and species-general unifying principles linking the physical, cytological, and connectional dimensions of architecture in the mouse, cat, marmoset, and macaque monkey. The existence of connections is related to the cytology of cortical areas, in addition to the role of physical distance, but this relation is attenuated in mice and marmoset monkeys. The cytoarchitectonic cortical gradients, and not the rostrocaudal axis of the cortex, are closely linked to the laminar origin of connections, a principle that allows the extrapolation of this connectional feature to humans. Lastly, a network core, with a central role under different modes of network communication, characterizes all cortical connectomes. We observe a displacement of the network core in mammals, with a shift of the core of cats and macaque monkeys toward the less neuronally dense areas of the cerebral cortex. This displacement has functional ramifications but also entails a potential increased degree of vulnerability to pathology. In sum, our results sketch out a blueprint of mammalian connectomes consisting of species-specific and species-general links between the connectional, physical, and cytological dimensions of the cerebral cortex, possibly reflecting variations and persistence of evolutionarily conserved mechanisms and cellular phenomena. Our framework elucidates organizational principles that encompass but also extend beyond the wiring economy principle imposed by the physical embedding of the cerebral cortex.

Research in the macaque monkey suggests that cortical areas with similar microstructure are more likely to be connected. Here, we examine this link in the human cerebral cortex using 2 magnetic resonance imaging (MRI) measures: quantitative T1 maps, which are sensitive to intracortical myelin content and provide an in vivo proxy for cortical microstructure, and resting-state functional connectivity. Using ultrahigh-resolution MRI at 7 T and dedicated image processing tools, we demonstrate a systematic relationship between T1-based intracortical myelin content and functional connectivity. This effect is independent of the proximity of areas. We employ nonlinear dimensionality reduction to characterize connectivity components and identify specific aspects of functional connectivity that are linked to myelin content. Our results reveal a consistent spatial pattern throughout different analytic approaches. While functional connectivity and myelin content are closely linked in unimodal areas, the correspondence is lower in transmodal areas, especially in posteromedial cortex and the angular gyrus. Our findings are in agreement with comprehensive reports linking histologically assessed microstructure and connectivity in different mammalian species and extend them to the human cerebral cortex in vivo.

Structural connectivity among cortical areas provides the substrate for information exchange in the cerebral cortex and is characterized by systematic patterns of presence or absence of connections. What principles govern this cortical wiring diagram? Here, we investigate the relation of physical distance and cytoarchitecture with the connectional architecture of the mouse cortex. Moreover, we examine the relation between patterns of ipsilateral and contralateral connections. Our analysis reveals a mirrored and attenuated organization of contralateral connections when compared with ipsilateral connections. Both physical distance and cytoarchitectonic similarity of cortical areas are related to the presence or absence of connections. Notably, our analysis demonstrates that the combination of these factors relates better to cortico-cortical connectivity than each factor in isolation and that the two factors relate differently to ipsilateral and contralateral connectivity. Physical distance is more tightly related to the presence or absence of ipsilateral connections, but its relevance greatly diminishes for contralateral connections, while the contribution of cytoarchitectonic similarity remains relatively stable. Our results, together with similar findings in the cat and macaque cortex, suggest that a common set of principles underlies the macroscale wiring of the mammalian cerebral cortex.

The wiring of the brain provides the anatomical skeleton for cognition and behavior. Connections among brain regions have a diverse and characteristic strength. This strength heterogeneity is captured by the wiring cost and homophily principles. Moreover, brains have a characteristic global network topology, including modularity and short path lengths. However, the mechanisms underlying the inter-regional wiring principles and global network topology of brains are unknown. Here, we address this issue by modeling the ontogeny of brain connectomes. We demonstrate that spatially embedded and heterochronous neurogenetic gradients, without the need of axonal-guidance molecules or activity-dependent plasticity, can reconstruct the wiring principles and shape the global network topology observed in adult brain connectomes. Thus, two fundamental dimensions, that is, space and time, are key components of a plausible neurodevelopmental mechanism with a universal scope, encompassing vertebrate and invertebrate brains.

Reconstructing the anatomical pathways of the brain to study the human connectome has become an important endeavour for understanding brain function and dynamics. Reconstruction of the cortico-cortical connectivity matrix in vivo often relies on noninvasive diffusion-weighted imaging (DWI) techniques but the extent to which they can accurately represent the topological characteristics of structural connectomes remains unknown. We explored this question by constructing connectomes using DWI data collected from macaque monkeys in vivo and with data from published invasive tracer studies. We found the strength of fiber tracts was well estimated from DWI and topological properties like degree and modularity were captured by tractography-based connectomes. Rich-club/core-periphery type architecture could also be detected but the classification of hubs using betweenness centrality, participation coefficient and core-periphery identification techniques was inaccurate. Our findings indicate that certain aspects of cortical topology can be faithfully represented in noninvasively-obtained connectomes while other network analytic measures warrant cautionary interpretations.

A subset of regions in the lateral and medial prefrontal cortex and the anterior insula increase their activity level whenever a cognitive task becomes more demanding, regardless of the specific nature of this demand. During execution of a task, these areas and the surrounding cortex temporally encode aspects of the task context in spatially distributed patterns of activity. It is not clear whether these patterns reflect underlying anatomical subnetworks that still exist when task execution has finished. We use fMRI in 12 participants performing alternating blocks of three cognitive tasks to address this question. A first data set is used to define multiple demand regions in each participant. A second dataset from the same participants is used to determine multiple demand voxel assemblies with a preference for one task over the others. We then show that these voxels remain functionally coupled during execution of non-preferred tasks and that they exhibit stronger functional connectivity during rest. This indicates that the assemblies of task preference sharing voxels reflect patterns of underlying anatomical connections. Moreover, we show that voxels preferring the same task have more similar whole brain functional connectivity profiles that are consistent across participants. This suggests that voxel assemblies differ in patterns of input–output connections, most likely reflecting task demand-specific information exchange. Electronic supplementary material The online version of this article (10.1007/s00429-018-1638-9) contains supplementary material, which is available to authorized users.

It is commonly assumed that the brain is a small-world network (e.g., Sporns and Honey 2006). Indeed, one of the present authors claimed as much 15 years ago (Hilgetag et al. 2000). The small-worldness is believed to be a crucial aspect of efficient brain organization that confers significant advantages in signal processing (e.g., LagoFernandez et al. 2000). Correspondingly, the small-world organization is deemed essential for healthy brain function, as alterations of small-world features are observed in patient groups with Alzheimer’s disease (Stam et al. 2007), autism (Barttfeld et al. 2011) or schizophrenia spectrum diseases (Liu et al. 2008; Wang et al. 2012; Zalesky et al. 2011). While the colloquial idea of a small, interconnected world has a long tradition (e.g., Klemperer 1938), the present concept of small-world features of networks is frequently associated with the Milgram experiment (Milgram 1967) that demonstrated surprisingly short paths across social networks (‘six degrees of separation’). The concept was formalized by Watts and Strogatz (1998), who derived small-world networks from regular networks by including a small proportion of random network shortcuts. Such an organization results in short paths across the whole network—almost as small as in random networks—combined with local ‘cliquishness’ (or clustering) of neighboring nodes, due to dense local interconnections. These features can be mathematically summarized by the smallworld coefficient (Humphries et al. 2006), which is defined as the clustering coefficient of a given network (normalized by the clustering coefficient of a same-size random network) divided by the network’s normalized average shortest pathlength. While any network that has a smallworld coefficient larger than one is formally a small-world network, for many researchers, the term has become associated with the specific Watts and Strogatz model that is based on the partial random rewiring of a regular network (Fig. 1a). Indeed, the estimation of the rewiring probability has been used to directly associate real-world networks with the Watts and Strogatz model (Humphries and Gurney 2008). Incidentally, the small-world coefficient might not faithfully capture the small-world property as originally described by Watts and Strogatz (1998). Therefore, an alternative coefficient has been proposed that compares the clustering of the network to a lattice instead of a random network (Telesford et al. 2011). A large number of empirical network data conform to the small-world features of short paths combined with high clustering, including many neural networks—but do these Electronic supplementary material The online version of this article (doi:10.1007/s00429-015-1035-6) contains supplementary material, which is available to authorized users.

The orbital and medial prefrontal cortex (OMPFC) has been implicated in decision-making, reward and emotion processing, and psychopathology, such as depression and obsessive–compulsive disorder. Human and monkey anatomical studies indicate the presence of various cortical subdivisions and suggest that these are organized in two extended networks, a medial and an orbital one. Attempts have been made to replicate these neuroanatomical findings in vivo using MRI techniques for imaging connectivity. These revealed several consistencies, but also many inconsistencies between reported results. Here, we use fMRI resting-state functional connectivity (FC) and data-driven modularity optimization to parcellate the OMPFC to investigate replicability of in vivo parcellation more systematically. By collecting two resting-state data sets per participant, we were able to quantify the reliability of the observed modules and their boundaries. Results show that there was significantly more than chance overlap in modules and their boundaries at the level of individual data sets. Moreover, some of these consistent boundaries significantly co-localized across participants. Hierarchical clustering showed that the whole-brain FC profiles of the OMPFC subregions separate them in two networks, a medial and orbital one, which overlap with the organization proposed by Barbas and Pandya (J Comp Neurol 286:353–375, 1989) and Ongür and Price (Cereb Cortex 10:206–219, 2000). We conclude that in vivo resting-state FC can delineate reliable and neuroanatomically plausible subdivisions that agree with established cytoarchitectonic trends and connectivity patterns, while other subdivisions do not show the same consistency across data sets and studies. Electronic supplementary material The online version of this article (doi:10.1007/s00429-017-1378-2) contains supplementary material, which is available to authorized users.

A consensus on the prefrontal cortex (PFC) holds that it is pivotal for flexible behavior and the integration of the cognitive, affective, and motivational domains. Certain models have been put forth and a dominant model postulates a hierarchical anterior-posterior gradient. The structural connectivity principles of this model dictate that increasingly anterior PFC regions exhibit more efferent connections toward posterior ones than vice versa. Such hierarchical asymmetry principles are thought to pertain to the macaque PFC. Additionally, the laminar patterns of the connectivity of PFC regions can be used for defining hierarchies. In the current study, we formally tested the asymmetry-based hierarchical principles of the anterior-posterior model by employing an exhaustive dataset on macaque PFC connectivity and tools from network science. On the one hand, the asymmetry-based principles and predictions of the hierarchical anterior-posterior model were not confirmed. The wiring of the macaque PFC does not fully correspond to the principles of the model, and its asymmetry-based hierarchical layout does not follow a strict anterior-posterior gradient. On the other hand, our results suggest that the laminar-based hierarchy seems a more tenable working hypothesis for models advocating an anterior-posterior gradient. Our results can inform models of the human PFC.

Structural connectivity among cortical areas provides the substrate for information exchange in the brain and is characterized by the presence or absence of connections between specific areas. What principles govern this cortical wiring diagram? Here, we investigate the relation of physical distance and cytoarchitecture with the connectional architecture of the mouse cortex. Moreover, we examine the relation between patterns of ipsilateral and contralateral connections. Our analysis reveals a mirrored and attenuated organization of contralateral connections when compared to ipsilateral connections. Both spatial proximity and cytoarchitectonic similarity of cortical areas are related to the presence or absence of connections. Notably, our analysis demonstrated that these factors conjointly relate better to cortico-cortical connectivity than each factor in isolation, and that the two factors contribute differently to ipsilateral and contralateral connectivity. Distance is more tightly related to the presence or absence of ipsilateral connections, but its contribution greatly diminishes for contralateral connections, while the contribution of cytoarchitectonic similarity remains stable. Our results, conjointly with similar findings in the cat and macaque cortex, suggest that a common set of principles underlies the macroscale wiring of mammalian brains.

Understanding how the structure of cognition arises from the to-pographical organization of the cortex is a primary goal in neuroscience. Previous work has described local functional gradients extending from perceptual and motor regions to cortical areas representing more abstract functions, yet an overarching framework for the association between structure and function is still lacking. Here we show that the principal gradient revealed by the decomposition of connectivity data in humans and the macaque monkey is anchored, at one end, by regions serving primary sensory/motor functions and, at the other, by transmodal regions that, in humans, are known as the default-mode network (DMN). These DMN regions exhibit the greatest geodesic distance along the cortical surface — and are precisely equidistant — from primary sensory/motor morphological landmarks. The principal gradient also provides an organizing spatial framework for multiple large-scale networks and characterizes a spectrum from unimodal to heteromodal activity in a functional meta-analysis. Together these observations provide a novel characterization of the topographical organization of cortex and indicate that the role of the DMN in cognition might arise from its position at one extreme of a hierarchy, allowing it to process transmodal information that is unrelated to immediate sensory input.

Understanding the wiring diagram of the human cerebral cortex is a fundamental challenge in neuroscience. Several topological properties of this intricate network have been uncovered, yet elemental aspects of its organization remain elusive. Here we explore wiring principles of the human connectome by examining which structural traits of cortical regions, particularly their characteristic cytoarchitecture and thickness, relate to the existence and strength of inter-regional connections. To this end, we use the comprehensive data from the classic work of von Economo and Koskinas in conjuction with diffusion data from the Human Connectome Project. Our results reveal a prominent role of the cytoarchitectonic similarity of upper cortical layers for predicting the existence of connections. In contrast, cortical thickness similarity was not systematically related to the existence of connections. Our findings are in line with recent findings in non-human mammalian cerebral cortices, suggesting that the cytoarchitectonic similarity of cortical regions underlies an overarching wiring principle of the mammalian cerebral cortex. The present results invite hypotheses about potentially evolutionary conserved neurobiological mechanisms that give rise to the observed relation of cytoarchitecture and connectivity in the human cerebral cortex.

Examination of the cortico-cortical network of mammals has unraveled key topological features and their role in the function of the healthy and diseased brain. Recent findings from social and biological networks pinpoint the significant role of weak connections in network coherence and mediation of information from segregated parts of the network. In the current study, inspired by such findings and proposed architectures pertaining to social networks, we examine the structure of weak connections in the macaque cortico-cortical network by employing a tract-tracing dataset. We demonstrate that the cortico-cortical connections as a whole, as well as connections between segregated communities of brain areas, comply with the architecture suggested by the so-called strength-of-weak-ties hypothesis. However, we find that the wiring of these connections is not optimal with respect to the aforementioned architecture. This configuration is not attributable to a trade-off with factors known to constrain brain wiring, i.e., wiring cost and efficiency. Lastly, weak connections, but not strong ones, appear important for network cohesion. Our findings relate a topological property to the strength of cortico-cortical connections, highlight the prominent role of weak connections in the cortico-cortical structural network and pinpoint their potential functional significance. These findings suggest that certain neuroimaging studies, despite methodological challenges, should explicitly take them into account and not treat them as negligible.

Release of dopamine in the nucleus accumbens (NAcc) is essential for acute drug reward. The present study was designed to trace the reinforcing effect of dopamine release by measuring the functional connectivity (FC) between the NAcc and brain regions involved in a limbic cortical-subcortical circuit during a dopaminergic challenge. Twenty healthy volunteers received single doses of methylphenidate (40 mg) and placebo on separate test days according to a double-blind, cross-over study design. Resting state functional magnetic resonance imaging (fMRI) was measured between 1.5 and 2 h postdosing. FC between regions of interest (ROI) in the NAcc, the medial dorsal nucleus (MDN) of the thalamus and remote areas within the limbic circuit was explored. Methylphenidate significantly reduced FC between the NAcc and the basal ganglia (i.e., subthalamic nucleus and ventral pallidum (VP)), relative to placebo. Methylphenidate also decreased FC between the NAcc and the medial prefrontal cortex (mPFC) as well as the temporal cortex. Methylphenidate did not affect FC between MDN and the limbic circuit. It is concluded that methylphenidate directly affects the limbic reward circuit. Drug-induced changes in FC of the NAcc may serve as a useful marker of drug activity in in the brain reward circuit.

Human and nonhuman primates exhibit flexible behavior. Functional, anatomical, and lesion studies indicate that the lateral fronta cortex (LFC) plays a pivotal role in such behavior. LFC consists of distinct subregions exhibiting distinct connectivity patterns that possibly relate to functional specializations. Inference about the border of each subregion in the human brain is performed with the aid of macroscopic landmarks and/or cytoarchitectonic parcellations extrapolated in a stereotaxic system. However, the high interindividual variability, the limited availability of cytoarchitectonic probabilistic maps, and the absence of robust functional localizers render the in vivo delineation and examination of the LFC subregions challenging. In this study, we use resting state fMRI for the in vivo parcellation of the human LFC on a subjectwise and data-driven manner. This approach succeeds in uncovering neuroanatomically realistic subregions, with potential anatomical substrates including BA46, 44, 45, 9 and related (sub)divisions. Ventral LFC subregions exhibit different functional connectivity (FC), which can account for different contributions in the language domain, while more dorsal adjacent subregions mark a transition to visuospatial/sensorimotor networks. Dorsal LFC subregions participate in known large-scale networks obeying an external/internal information processing dichotomy. Furthermore, we raced "families" of LFC subregions organized along the dorsal-ventral and anterior-posterior axis with distinct functional networks also encompassing specialized cingulate divisions. Similarities with the connectivity of macaque candidate homologs were observed, such as the premotor affiliation of presumed BA 46. Thecurrent findings partially support dominant LFC models.