From connections to function: the mouse brain connectome atlas.

Mapping synaptic connections and projections is crucial for understanding brain dynamics and function. In a recent issue of Nature, Oh et al. present a wiring diagram of the whole mouse brain, where standardized labeling, tracing, and imaging of axonal connections reveal new details in the network organization of neuronal connectivity.

Brain network communication: concepts, models and applications.

Understanding communication and information processing in nervous systems is a central goal of neuroscience. Over the past two decades, advances in connectomics and network neuroscience have opened new avenues for investigating polysynaptic communication in complex brain networks. Recent work has brought into question the mainstay assumption that connectome signalling occurs exclusively via shortest paths, resulting in a sprawling constellation of alternative network communication models. This Review surveys the latest developments in models of brain network communication. We begin by drawing a conceptual link between the mathematics of graph theory and biological aspects of neural signalling such as transmission delays and metabolic cost. We organize key network communication models and measures into a taxonomy, aimed at helping researchers navigate the growing number of concepts and methods in the literature. The taxonomy highlights the pros, cons and interpretations of different conceptualizations of connectome signalling. We showcase the utility of network communication models as a flexible, interpretable and tractable framework to study brain function by reviewing prominent applications in basic, cognitive and clinical neurosciences. Finally, we provide recommendations to guide the future development, application and validation of network communication models.

Relation of connectome topology to brain volume across 103 mammalian species.

The brain connectome is an embedded network of anatomically interconnected brain regions, and the study of its topological organization in mammals has become of paramount importance due to its role in scaffolding brain function and behavior. Unlike many other observable networks, brain connections incur material and energetic cost, and their length and density are volumetrically constrained by the skull. Thus, an open question is how differences in brain volume impact connectome topology. We address this issue using the MaMI database, a diverse set of mammalian connectomes reconstructed from 201 animals, covering 103 species and 12 taxonomy orders, whose brain size varies over more than 4 orders of magnitude. Our analyses focus on relationships between volume and modular organization. After having identified modules through a multiresolution approach, we observed how connectivity features relate to the modular structure and how these relations vary across brain volume. We found that as the brain volume increases, modules become more spatially compact and dense, comprising more costly connections. Furthermore, we investigated how spatial embedding shapes network communication, finding that as brain volume increases, nodes' distance progressively impacts communication efficiency. We identified modes of variation in network communication policies, as smaller and bigger brains show higher efficiency in routing- and diffusion-based signaling, respectively. Finally, bridging network modularity and communication, we found that in larger brains, modular structure imposes stronger constraints on network signaling. Altogether, our results show that brain volume is systematically related to mammalian connectome topology and that spatial embedding imposes tighter restrictions on larger brains.

Network architecture of intrinsic connectivity in a mammalian spinal cord (the central nervous system's caudal sector).

The vertebrate spinal cord (SP) is the long, thin extension of the brain forming the central nervous system's caudal sector. Functionally, the SP directly mediates motor and somatic sensory interactions with most parts of the body except the face, and it is the preferred model for analyzing relatively simple reflex behaviors. Here, we analyze the organization of axonal connections between the 50 gray matter regions forming the bilaterally symmetric rat SP. The assembled dataset suggests that there are about 385 of a possible 2,450 connections between the 50 regions for a connection density of 15.7%. Multiresolution consensus cluster analysis reveals a hierarchy of structure-function subsystems in this neural network, with 4 subsystems at the top level and 12 at the bottom-level. The top-level subsystems include a) a bilateral subsystem related most clearly to somatic and autonomic motor functions and centered in the ventral horn and intermediate zone; b) a bilateral subsystem associated with general somatosensory functions and centered in the base, neck, and head of the dorsal horn; and c) a pair of unilateral, bilaterally symmetric subsystems associated with nociceptive information processing and occupying the apex of the dorsal horn. The intrinsic SP network displayed no hubs, rich club, or small-world attributes, which are common measures of global functionality. Advantages and limitations of our methodology are discussed in some detail. The present work is part of a comprehensive project to assemble and analyze the neurome of a mammalian nervous system and its interactions with the body.

Neural network architecture of a mammalian brain.

Connectomics research is making rapid advances, although models revealing general principles of connectional architecture are far from complete. Our analysis of 106 published connection reports indicates that the adult rat brain interregional connectome has about 76,940 of a possible 623,310 axonal connections between its 790 gray matter regions mapped in a reference atlas, equating to a network density of 12.3%. We examined the sexually dimorphic network using multiresolution consensus clustering that generated a nested hierarchy of interconnected modules/subsystems with three first-order modules and 157 terminal modules in females. Top-down hierarchy analysis suggests a mirror-image primary module pair in the central nervous system's rostral sector (forebrain-midbrain) associated with behavior control, and a single primary module in the intermediate sector (rhombicbrain) associated with behavior execution; the implications of these results are considered in relation to brain development and evolution. Bottom-up hierarchy analysis reveals known and unfamiliar modules suggesting strong experimentally testable hypotheses. Global network analyses indicate that all hubs are in the rostral module pair, a rich club extends through all three primary modules, and the network exhibits small-world attributes. Simulated lesions of all regions individually enabled ranking their impact on global network organization, and the visual path from the retina was used as a specific example, including the effects of cyclic connection weight changes from the endogenous circadian rhythm generator, suprachiasmatic nucleus. This study elucidates principles of interregional neuronal network architecture for a mammalian brain and suggests a strategy for modeling dynamic structural connectivity.

Intrinsic circuitry of the rhombicbrain (central nervous system's intermediate sector) in a mammal.

The rhombicbrain (rhombencephalon or intermediate sector) is the vertebrate central nervous system part between the forebrain-midbrain (rostral sector) and spinal cord (caudal sector), and it has three main divisions: pons, cerebellum, and medulla. Using a data-driven approach, here we examine intrinsic rhombicbrain (intrarhombicbrain) network architecture that in rat consists of 52,670 possible axonal connections between 230 gray matter regions (115 bilaterally symmetrical pairs). Our analysis indicates that only 8,089 (15.4%) of these connections exist. Multiresolution consensus cluster analysis yields a nested hierarchy model of rhombicbrain subsystems that at the top level are associated with 1) the cerebellum and vestibular nuclei, 2) orofacial-pharyngeal-visceral integration, and 3) auditory connections; the bottom level has 68 clusters, ranging in size from 2 to 11 regions. The model provides a basis for functional hypothesis development and interrogation. More granular network analyses performed on the intrinsic connectivity of individual and combined main rhombicbrain divisions (pons, cerebellum, medulla, pons + cerebellum, and pons + medulla) demonstrate the mutability of network architecture in response to the addition or subtraction of connections. Clear differences between the structure-function network architecture of the rhombicbrain and forebrain-midbrain are discussed, with a stark comparison provided by the subsystem and small-world organization of the cerebellar cortex and cerebral cortex. Future analysis of the connections within and between the forebrain-midbrain and rhombicbrain will provide a model of brain neural network architecture in a mammal.

Partial entropy decomposition reveals higher-order information structures in human brain activity.

The standard approach to modeling the human brain as a complex system is with a network, where the basic unit of interaction is a pairwise link between two brain regions. While powerful, this approach is limited by the inability to assess higher-order interactions involving three or more elements directly. In this work, we explore a method for capturing higher-order dependencies in multivariate data: the partial entropy decomposition (PED). Our approach decomposes the joint entropy of the whole system into a set of nonnegative atoms that describe the redundant, unique, and synergistic interactions that compose the system's structure. PED gives insight into the mathematics of functional connectivity and its limitation. When applied to resting-state fMRI data, we find robust evidence of higher-order synergies that are largely invisible to standard functional connectivity analyses. Our approach can also be localized in time, allowing a frame-by-frame analysis of how the distributions of redundancies and synergies change over the course of a recording. We find that different ensembles of regions can transiently change from being redundancy-dominated to synergy-dominated and that the temporal pattern is structured in time. These results provide strong evidence that there exists a large space of unexplored structures in human brain data that have been largely missed by a focus on bivariate network connectivity models. This synergistic structure is dynamic in time and likely will illuminate interesting links between brain and behavior. Beyond brain-specific application, the PED provides a very general approach for understanding higher-order structures in a variety of complex systems.

Information-processing dynamics in neural networks of macaque cerebral cortex reflect cognitive state and behavior.

One of the essential functions of biological neural networks is the processing of information. This includes everything from processing sensory information to perceive the environment, up to processing motor information to interact with the environment. Due to methodological limitations, it has been historically unclear how information processing changes during different cognitive or behavioral states and to what extent information is processed within or between the network of neurons in different brain areas. In this study, we leverage recent advances in the calculation of information dynamics to explore neural-level processing within and between the frontoparietal areas AIP, F5, and M1 during a delayed grasping task performed by three macaque monkeys. While information processing was high within all areas during all cognitive and behavioral states of the task, interareal processing varied widely: During visuomotor transformation, AIP and F5 formed a reciprocally connected processing unit, while no processing was present between areas during the memory period. Movement execution was processed globally across all areas with predominance of processing in the feedback direction. Furthermore, the fine-scale network structure reconfigured at the neuron level in response to different grasping conditions, despite no differences in the overall amount of information present. These results suggest that areas dynamically form higher-order processing units according to the cognitive or behavioral demand and that the information-processing network is hierarchically organized at the neuron level, with the coarse network structure determining the behavioral state and finer changes reflecting different conditions.

Structure-function subsystem model and computational lesions of the central nervous system's rostral sector (forebrain and midbrain).

The craniote central nervous system has been divided into rostral, intermediate, and caudal sectors, with the rostral sector containing the vertebrate forebrain and midbrain. Here, network science tools were used to create and analyze a rat hierarchical structure-function subsystem model of intrarostral sector neural connectivity between gray matter regions. The hierarchy has 109 bottom-level subsystems and three upper-level subsystems corresponding to voluntary behavior control, cognition, and affect; instinctive survival behaviors and homeostasis; and oculomotor control. As in previous work, subsystems identified based on their coclassification as network communities are revealed as functionally related. We carried out focal perturbations of neural structural connectivity comprehensively by computationally lesioning each region of the network, and the resulting effects on the network's modular (subsystem) organization were systematically mapped and measured. The pattern of changes was found to be correlated with three structural attributes of the lesioned region: region centrality (degree, strength, and betweenness), region position in the hierarchy, and subsystem distribution of region neural outputs and inputs. As expected, greater region centrality results, on average, in stronger lesion impact and more distributed lesion effects. In addition, our analysis suggests that strongly functionally related regions, belonging to the same bottom-level subsystem, exhibit similar effects after lesioning. These similarities account for coherent patterns of disturbances that align with subsystem boundaries and propagate through the network. These systematic lesion effects and their similarity across functionally related regions are of potential interest for theoretical, experimental, and clinical studies.

Neuroscience Needs Network Science.

The brain is a complex system comprising a myriad of interacting neurons, posing significant challenges in understanding its structure, function, and dynamics. Network science has emerged as a powerful tool for studying such interconnected systems, offering a framework for integrating multiscale data and complexity. To date, network methods have significantly advanced functional imaging studies of the human brain and have facilitated the development of control theory-based applications for directing brain activity. Here, we discuss emerging frontiers for network neuroscience in the brain atlas era, addressing the challenges and opportunities in integrating multiple data streams for understanding the neural transitions from development to healthy function to disease. We underscore the importance of fostering interdisciplinary opportunities through workshops, conferences, and funding initiatives, such as supporting students and postdoctoral fellows with interests in both disciplines. By bringing together the network science and neuroscience communities, we can develop novel network-based methods tailored to neural circuits, paving the way toward a deeper understanding of the brain and its functions, as well as offering new challenges for network science.

Structural-functional brain network coupling predicts human cognitive ability.

Individual differences in general cognitive ability (GCA) have a biological basis within the structure and function of the human brain. Network neuroscience investigations revealed neural correlates of GCA in structural as well as in functional brain networks. However, whether the relationship between structural and functional networks, the structural-functional brain network coupling (SC-FC coupling), is related to individual differences in GCA remains an open question. We used data from 1030 adults of the Human Connectome Project, derived structural connectivity from diffusion weighted imaging, functional connectivity from resting-state fMRI, and assessed GCA as a latent g-factor from 12 cognitive tasks. Two similarity measures and six communication measures were used to model possible functional interactions arising from structural brain networks. SC-FC coupling was estimated as the degree to which these measures align with the actual functional connectivity, providing insights into different neural communication strategies. At the whole-brain level, higher GCA was associated with higher SC-FC coupling, but only when considering path transitivity as neural communication strategy. Taking region-specific variations in the SC-FC coupling strategy into account and differentiating between positive and negative associations with GCA, allows for prediction of individual cognitive ability scores in a cross-validated prediction framework (correlation between predicted and observed scores: r = 0.25, p < .001). The same model also predicts GCA scores in a completely independent sample (N = 567, r = 0.19, p < .001). Our results propose structural-functional brain network coupling as a neurobiological correlate of GCA and suggest brain region-specific coupling strategies as neural basis of efficient information processing predictive of cognitive ability.

A cross-disorder connectome landscape of brain dysconnectivity.

Many human brain disorders are associated with characteristic alterations in the structural and functional connectivity of the brain. In this article, we explore how commonalities and differences in connectome alterations can reveal relationships across disorders. We survey recent literature on connectivity changes in neurological and psychiatric disorders in the context of key organizational principles of the human connectome and observe that several disturbances to network properties of the human brain have a common role in a wide range of brain disorders and point towards potentially shared network mechanisms underpinning disorders. We hypothesize that the distinct dimensions along which connectome networks are organized (for example, 'modularity' and 'integration') provide a general coordinate system that allows description and categorization of relationships between seemingly disparate disorders. We outline a cross-disorder 'connectome landscape of dysconnectivity' along these principal dimensions of network organization that may place shared connectome alterations between brain disorders in a common framework.

Anesthetics fragment hippocampal network activity, alter spine dynamics, and affect memory consolidation.

General anesthesia is characterized by reversible loss of consciousness accompanied by transient amnesia. Yet, long-term memory impairment is an undesirable side effect. How different types of general anesthetics (GAs) affect the hippocampus, a brain region central to memory formation and consolidation, is poorly understood. Using extracellular recordings, chronic 2-photon imaging, and behavioral analysis, we monitor the effects of isoflurane (Iso), medetomidine/midazolam/fentanyl (MMF), and ketamine/xylazine (Keta/Xyl) on network activity and structural spine dynamics in the hippocampal CA1 area of adult mice. GAs robustly reduced spiking activity, decorrelated cellular ensembles, albeit with distinct activity signatures, and altered spine dynamics. CA1 network activity under all 3 anesthetics was different to natural sleep. Iso anesthesia most closely resembled unperturbed activity during wakefulness and sleep, and network alterations recovered more readily than with Keta/Xyl and MMF. Correspondingly, memory consolidation was impaired after exposure to Keta/Xyl and MMF, but not Iso. Thus, different anesthetics distinctly alter hippocampal network dynamics, synaptic connectivity, and memory consolidation, with implications for GA strategy appraisal in animal research and clinical settings.

Uncovering individual differences in fine-scale dynamics of functional connectivity.

Functional connectivity (FC) profiles contain subject-specific features that are conserved across time and have potential to capture brain-behavior relationships. Most prior work has focused on spatial features (nodes and systems) of these FC fingerprints, computed over entire imaging sessions. We propose a method for temporally filtering FC, which allows selecting specific moments in time while also maintaining the spatial pattern of node-based activity. To this end, we leverage a recently proposed decomposition of FC into edge time series (eTS). We systematically analyze functional magnetic resonance imaging frames to define features that enhance identifiability across multiple fingerprinting metrics, similarity metrics, and data sets. Results show that these metrics characteristically vary with eTS cofluctuation amplitude, similarity of frames within a run, transition velocity, and expression of functional systems. We further show that data-driven optimization of features that maximize fingerprinting metrics isolates multiple spatial patterns of system expression at specific moments in time. Selecting just 10% of the data can yield stronger fingerprints than are obtained from the full data set. Our findings support the idea that FC fingerprints are differentially expressed across time and suggest that multiple distinct fingerprints can be identified when spatial and temporal characteristics are considered simultaneously.

Subsystem macroarchitecture of the intrinsic midbrain neural network and its tectal and tegmental subnetworks.

The midbrain is the smallest of three primary vertebrate brain divisions. Here we use network science tools to reveal the global organizing principles of intramidbrain axonal circuitry before adding extrinsic connections with the remaining nervous system. Curating the experimental neuroanatomical literature yielded 17,248 connection reports for 8,742 possible connections between the 94 gray matter regions forming the right and left midbrain. Evidence for the existence of 1,676 connections suggests a 19.2% connection density for this network, similar to that for the intraforebrain network [L. W. Swanson et al., Proc. Natl. Acad. Sci. U.S.A. 117, 31470-31481 (2020)]. Multiresolution consensus cluster analysis parceled this network into a hierarchy with 6 top-level and 30 bottom-level subsystems. A structure-function model of the hierarchy identifies midbrain subsystems that play specific functional roles in sensory-motor mechanisms, motivation and reward, regulating complex reproductive and agonistic behaviors, and behavioral state control. The intramidbrain network also contains four bilateral region pairs designated putative hubs. One pair contains the superior colliculi of the tectum, well known for participation in visual sensory-motor mechanisms, and the other three pairs form spatially compact right and left units (the ventral tegmental area, retrorubral area, and midbrain reticular nucleus) in the tegmentum that are implicated in motivation and reward mechanisms. Based on the core hypothesis that subsystems form functionally cohesive units, the results provide a theoretical framework for hypothesis-driven experimental analysis of neural circuit mechanisms underlying behavioral responses mediated in part by the midbrain.

Modular origins of high-amplitude cofluctuations in fine-scale functional connectivity dynamics.

The topology of structural brain networks shapes brain dynamics, including the correlation structure of brain activity (functional connectivity) as estimated from functional neuroimaging data. Empirical studies have shown that functional connectivity fluctuates over time, exhibiting patterns that vary in the spatial arrangement of correlations among segregated functional systems. Recently, an exact decomposition of functional connectivity into frame-wise contributions has revealed fine-scale dynamics that are punctuated by brief and intermittent episodes (events) of high-amplitude cofluctuations involving large sets of brain regions. Their origin is currently unclear. Here, we demonstrate that similar episodes readily appear in silico using computational simulations of whole-brain dynamics. As in empirical data, simulated events contribute disproportionately to long-time functional connectivity, involve recurrence of patterned cofluctuations, and can be clustered into distinct families. Importantly, comparison of event-related patterns of cofluctuations to underlying patterns of structural connectivity reveals that modular organization present in the coupling matrix shapes patterns of event-related cofluctuations. Our work suggests that brief, intermittent events in functional dynamics are partly shaped by modular organization of structural connectivity.

Co-evolving dynamics and topology in a coupled oscillator model of resting brain function.

Dynamic models of ongoing BOLD fMRI brain dynamics and models of communication strategies have been two important approaches to understanding how brain network structure constrains function. However, dynamic models have yet to widely incorporate one of the most important insights from communication models: the brain may not use all of its connections in the same way or at the same time. Here we present a variation of a phase delayed Kuramoto coupled oscillator model that dynamically limits communication between nodes on each time step. An active subgraph of the empirically derived anatomical brain network is chosen in accordance with the local dynamic state on every time step, thus coupling dynamics and network structure in a novel way. We analyze this model with respect to its fit to empirical time-averaged functional connectivity, finding that, with the addition of only one parameter, it significantly outperforms standard Kuramoto models with phase delays. We also perform analyses on the novel time series of active edges it produces, demonstrating a slowly evolving topology moving through intermittent episodes of integration and segregation. We hope to demonstrate that the exploration of novel modeling mechanisms and the investigation of dynamics of networks in addition to dynamics on networks may advance our understanding of the relationship between brain structure and function.

Few temporally distributed brain connectivity states predict human cognitive abilities.

Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Rare states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture and to be highly subject-specific. However, it is unclear whether such network-defining states also contribute to individual variations in cognitive abilities - which strongly rely on the interactions among distributed brain regions. By introducing CMEP, a new eigenvector-based prediction framework, we show that as few as 16 temporally separated time frames (< 1.5% of 10 min resting-state fMRI) can significantly predict individual differences in intelligence (N = 263, p < .001). Against previous expectations, individual's network-defining time frames of particularly high cofluctuation do not predict intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest connectivity, temporally distributed information is necessary to extract information about cognitive abilities. This information is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.

Modular community structure of the face network supports face recognition.

Face recognition is dependent on computations conducted in specialized brain regions and the communication among them, giving rise to the face-processing network. We examined whether modularity of this network may underlie the vast individual differences found in human face recognition abilities. Modular networks, characterized by strong within and weaker between-network connectivity, were previously suggested to promote efficacy and reduce interference among cognitive systems and also correlated with better cognitive abilities. The study was conducted in a large sample (n = 409) with diffusion-weighted imaging, resting-state fMRI, and a behavioral face recognition measure. We defined a network of face-selective regions and derived a novel measure of communication along with structural and functional connectivity among them. The modularity of this network was positively correlated with recognition abilities even when controlled for age. Furthermore, the results were specific to the face network when compared with the place network or to spatially permuted null networks. The relation to behavior was also preserved at the individual-edge level such that a larger correlation to behavior was found within hemispheres and particularly within the right hemisphere. This study provides the first evidence of modularity-behavior relationships in the domain of face processing and more generally in visual perception.

Multitask brain network reconfiguration is inversely associated with human intelligence.

Intelligence describes the general cognitive ability level of a person. It is one of the most fundamental concepts in psychological science and is crucial for the effective adaption of behavior to varying environmental demands. Changing external task demands have been shown to induce reconfiguration of functional brain networks. However, whether neural reconfiguration between different tasks is associated with intelligence has not yet been investigated. We used functional magnetic resonance imaging data from 812 subjects to show that higher scores of general intelligence are related to less brain network reconfiguration between resting state and seven different task states as well as to network reconfiguration between tasks. This association holds for all functional brain networks except the motor system and replicates in two independent samples (n = 138 and n = 184). Our findings suggest that the intrinsic network architecture of individuals with higher intelligence scores is closer to the network architecture as required by various cognitive demands. Multitask brain network reconfiguration may, therefore, represent a neural reflection of the behavioral positive manifold - the essence of the concept of general intelligence. Finally, our results support neural efficiency theories of cognitive ability and reveal insights into human intelligence as an emergent property from a distributed multitask brain network.