Subsystem organization of axonal connections within and between the right and left cerebral cortex and cerebral nuclei (endbrain)

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.

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.

Connectomes: from a sparsity of networks to large-scale databases

The analysis of whole brain networks started in the 1980s when only a handful of connectomes were available. In these early days, information about the human connectome was absent and one could only dream about having information about connectivity in a single human subject. Thanks to non-invasive methods such as diffusion imaging, we now know about connectivity in many species and, for some species, in many individuals. To illustrate the rapid change in availability of connectome data, the UK Biobank is on track to record structural and functional connectivity in 100,000 human subjects. Moreover, connectome data from a range of species is now available: from Caenorhabditis elegans and the fruit fly to pigeons, rodents, cats, non-human primates, and humans. This review will give a brief overview of what structural connectivity data is now available, how connectomes are organized, and how their organization shows common features across species. Finally, I will outline some of the current challenges and potential future work in making use of connectome information.

Activation-Inhibition Coordination in Neuron, Brain, and Behavior Sequencing/Organization: Implications for Laterality and Lateralization

Activation-inhibition coordination is considered a dynamic process that functions as a common mechanism in the synchronization and functioning of neurons, brain, behavior, and their sequencing/organization, including over these different scales. The concept has broad applicability, for example, in applications to maladaptivity/atypicality. Young developed the hypothesis to help explain the efficacy of right-hand reaching to grasp in 1-month-olds, a study that implicated that the left hemisphere is specialized for activation-inhibition coordination. This underlying left-hemisphere function, noted to characterize the left hemisphere right from birth, can explain equally its language and fine motor skills, for example. The right hemisphere appears specialized for less complex inhibitory skills, such as outright damping/inhibition. The hypotheses related to inhibition and hemispheric specialization that appear in the literature typically refer to right hemisphere skills in these regards. The research to present also refers to excitation/inhibition balance/ratio in synaptic function, but not to coordination in the sense described here. Furthermore, it refers to the inhibitory function widely in neuronal networks. The paper presents a comprehensive literature review, framing the research in terms of the proposed concept. Further, the paper presents a broad model of activation-inhibition coordination that can help better understand neuron, brain, and behavior, generally, and left hemisphere specialization, specifically.

The Connectome and Chemo-Connectome Databases for Mice Brain Connection Analysis

The various brain functions rely on the intricate connection networks and certain molecular characteristics of neurons in the brain. However, the databases for the mouse brain connectome and chemo-connectome are still inadequate, hindering the brain circuital and functional analysis. Here, we created mice brain connectome and chemo-connectome databases based on mouse brain projection data of 295 non-overlapping brain areas and in situ hybridization (ISH) data of 50 representative neurotransmission-related genes from the Allen Brain Institute. Based on this connectome and chemo-connectome databases, functional connection patterns and detailed chemo-connectome for monoaminergic nuclei were analyzed and visualized. These databases will aid in the comprehensive research of the mouse connectome and chemo-connectome in the whole brain and serve as a convenient resource for systematic analysis of the brain connection and function.

The brainstem connectome database

Connectivity data of the nervous system and subdivisions, such as the brainstem, cerebral cortex and subcortical nuclei, are necessary to understand connectional structures, predict effects of connectional disorders and simulate network dynamics. For that purpose, a database was built and analyzed which comprises all known directed and weighted connections within the rat brainstem. A longterm metastudy of original research publications describing tract tracing results form the foundation of the brainstem connectome (BC) database which can be analyzed directly in the framework neuroVIISAS. The BC database can be accessed directly by connectivity tables, a web-based tool and the framework. Analysis of global and local network properties, a motif analysis, and a community analysis of the brainstem connectome provides insight into its network organization. For example, we found that BC is a scale-free network with a small-world connectivity. The Louvain modularity and weighted stochastic block matching resulted in partially matching of functions and connectivity. BC modeling was performed to demonstrate signal propagation through the somatosensory pathway which is affected in Multiple sclerosis.

Measurement(s)	brainstem
Technology Type(s)	tract tracing metastudy
Factor Type(s)	brain region
Sample Characteristic - Organism	Rattus rattus
Sample Characteristic - Environment	Experimental setup
Sample Characteristic - Location	Germany

Functional connectivity of fMRI using differential covariance predicts structural connectivity and behavioral reaction times

Recordings from resting state functional Magnetic Resonance Imaging (rs-fMRI) reflect the influence of pathways between brain areas. A wide range of methods have been proposed to measure this functional connectivity (FC), but the lack of “ground truth” has made it difficult to systematically validate them. Most measures of FC produce connectivity estimates that are symmetrical between brain areas. Differential covariance (dCov) is an algorithm for analyzing FC with directed graph edges. When we applied dCov to rs-fMRI recordings from the human connectome project (HCP) and anesthetized mice, dCov-FC accurately identified strong cortical connections from diffusion Magnetic Resonance Imaging (dMRI) in individual humans and viral tract tracing in mice. In addition, those HCP subjects whose dCov-FCs were more integrated, as assessed by a graph-theoretic measure, tended to have shorter reaction times in several behavioral tests. Thus, dCov-FC was able to identify anatomically verified connectivity that yielded measures of brain integration significantly correlated with behavior.

Sex-related human brain asymmetry in hemispheric functional gradients

The left and right hemispheres of the human brain are two connected but relatively independent functional modules; they show multidimensional asymmetries ranging from particular local brain unit properties to entire hemispheric connectome topology. To date, however, it remains largely unknown whether and how hemispheric functional hierarchical structures differ between hemispheres. In the present study, we adopted a newly developed resting-state (rs) functional connectivity (FC)-based gradient approach to evaluate hemispheric functional hierarchical structures and their asymmetries in right-handed healthy young adults. Our results showed an overall mirrored principal functional gradient between hemispheres, with the sensory cortex and the default-mode network (DMN) anchored at the two opposite ends of the gradient. Interestingly, the left hemisphere showed a significantly larger full range of the principal gradient in both males and females, with males exhibiting greater leftward asymmetry. Similarly, the principal gradient component scores of two regions around the middle temporal gyrus and posterior orbitofrontal cortex exhibited similar hemisphere × sex interaction effects: a greater degree of leftward asymmetry in males than in females. Moreover, we observed significant main hemisphere and sex effects in distributed regions across the entire hemisphere. All these results are reproducible and robust between test-retest rs-fMRI sessions. Our findings provide evidence of functional gradients that enhance the present understanding of human brain asymmetries in functional organization and highlight the impact of sex on hemispheric functional gradients and their asymmetries.

Regional, Layer, and Cell-Type-Specific Connectivity of the Mouse Default Mode Network

The evolutionarily conserved default mode network (DMN) is a distributed set of brain regions coactivated during resting states that is vulnerable to brain disorders. How disease affects the DMN is unknown, but detailed anatomical descriptions could provide clues. Mice offer an opportunity to investigate structural connectivity of the DMN across spatial scales with cell-type resolution. We co-registered maps from functional magnetic resonance imaging and axonal tracing experiments into the 3D Allen mouse brain reference atlas. We find that the mouse DMN consists of preferentially interconnected cortical regions. As a population, DMN layer 2/3 (L2/3) neurons project almost exclusively to other DMN regions, whereas L5 neurons project in and out of the DMN. In the retrosplenial cortex, a core DMN region, we identify two L5 projection types differentiated by in- or out-DMN targets, laminar position, and gene expression. These results provide a multi-scale description of the anatomical correlates of the mouse DMN.

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Mouse resting-state default mode network anatomy described at high resolution in 3D

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Systematic axon tracing shows cortical DMN regions are preferentially interconnected

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Layer 2/3 DMN neurons project mostly in the DMN; layer 5 neurons project in and out

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Retrosplenial cortex contains distinct types of in- and out-DMN projection neurons

Structure-function subsystem models of female and male forebrain networks integrating cognition, affect, behavior, and bodily functions

The forebrain is the first of three primary vertebrate brain subdivisions. Macrolevel network analysis in a mammal (rat) revealed that the 466 gray matter regions composing the right and left sides of the forebrain are interconnected by 35,738 axonal connections forming a large set of overlapping, hierarchically arranged subsystems. This hierarchy is bilaterally symmetrical and sexually dimorphic, and it was used to create a structure-function conceptual model of intraforebrain network organization. Two mirror image top-level subsystems are presumably the most fundamental ontogenetically and phylogenetically. They essentially form the right and left forebrain halves and are relatively weakly interconnected. Each top-level subsystem in turn has two second-level subsystems. A ventromedial subsystem includes the medial forebrain bundle, functionally coordinating instinctive survival behaviors with appropriate physiological responses and affect. This subsystem has 26/24 (female/male) lowest-level subsystems, all using a combination of glutamate and GABA as neurotransmitters. In contrast, a dorsolateral subsystem includes the lateral forebrain bundle, functionally mediating voluntary behavior and cognition. This subsystem has 20 lowest-level subsystems, and all but 4 use glutamate exclusively for their macroconnections; no forebrain subsystems are exclusively GABAergic. Bottom-up subsystem analysis is a powerful engine for generating testable hypotheses about mechanistic explanations of brain function, behavior, and mind based on underlying circuit organization. Targeted computational (virtual) lesioning of specific regions of interest associated with Alzheimer's disease, clinical depression, and other disorders may begin to clarify how the effects spread through the entire forebrain network model.

Mapping the community structure of the rat cerebral cortex with weighted stochastic block modeling

The anatomical architecture of the mammalian brain can be modeled as the connectivity between functionally distinct areas of cortex and sub-cortex, which we refer to as the connectome. The community structure of the connectome describes how the network can be parsed into meaningful groups of nodes. This process, called community detection, is commonly carried out to find internally densely connected communities-a modular topology. However, other community structure patterns are possible. Here we employ the weighted stochastic block model (WSBM), which can identify a wide range of topologies, to the rat cerebral cortex connectome, to probe the network for evidence of modular, core, periphery, and disassortative organization. Despite its algorithmic flexibility, the WSBM identifies substantial modular and assortative topology throughout the rat cerebral cortex connectome, significantly aligning to the modular approach in some parts of the network. Significant deviations from modular partitions include the identification of communities that are highly enriched in core (rich club) areas. A comparison of the WSBM and modular models demonstrates that the former, when applied as a generative model, more closely captures several nodal network attributes. An analysis of variation across an ensemble of partitions reveals that certain parts of the network participate in multiple topological regimes. Overall, our findings demonstrate the potential benefits of adopting the WSBM, which can be applied to a single weighted and directed matrix such as the rat cerebral cortex connectome, to identify community structure with a broad definition that transcends the common modular approach.

The network architecture of rat intrinsic interbrain (diencephalic) macroconnections

The endbrain and interbrain form 2 great vertebrate forebrain divisions, and the interbrain is subdivided into the hypothalamus ventrally and thalamus dorsally. General organizing principles of intrainterbrain axonal circuitry were examined here at the level of gray matter regions using network analysis tools in a mammal with the most complete available dataset-before examining interbrain input-output relationships with other nervous system parts. The dataset was curated expertly from the neuroanatomical literature using experimental axonal pathway-tracing methods, and evidence from 74,242 connection reports indicates the existence of 10,836 macroconnections of the possible 49,062 macroconnections between the 222 gray matter regions forming the right and left halves of the interbrain. Two identical sets of 6 putative hubs were identified in the intrainterbrain network and form a continuous tissue mass in a part of the right and left medial hypothalamus associated functionally with physiological mechanisms controlling bodily functions. The intrainterbrain network shows only weak evidence of small-world attributes, rich club organization is absent, and multiresolution consensus cluster analysis indicates a solution with only 3 top-level subsystems or modules. In contrast, a previous analysis employing the same methodology to the significantly denser 244-node intraendbrain network revealed 2 identical sets of 13 hubs, small-world and rich club attributes, and 4 top-level subsystems. These differences in intrinsic network architecture across subdivisions suggest that intrinsic connections shape regional functional specialization to a varying extent, in part driven by differences in density and centrality, with extrinsic input-output connectivity playing a greater role in subdivisions that are sparser and less centralized.

Hierarchical organization of cortical and thalamic connectivity

The mammalian cortex is a laminar structure composed of many areas and cell types densely interconnected in complex ways, for which generalizable principles of organization remain mostly unknown. Here, we present a significant expansion of the Allen Mouse Brain Connectivity Atlas resource¹, with ~1,000 new tracer experiments in cortex and its major satellite structure, the thalamus, using Cre driver lines to comprehensively and selectively label brain-wide connections by layer and projection neuron class. We derived a set of generalized anatomical rules describing corticocortical, thalamocortical and corticothalamic projections through observations of axon termination patterns. We built a model to assign connection patterns between areas as either feedforward or feedback, and generated testable predictions of hierarchical positions for individual cortical and thalamic areas and for cortical network modules. Our results reveal cell class-specific connections are organized in a shallow hierarchy within the mouse cortical thalamic network.

The network organization of rat intrathalamic macroconnections and a comparison with other forebrain divisions

The thalamus is 1 of 4 major divisions of the forebrain and is usually subdivided into epithalamus, dorsal thalamus, and ventral thalamus. The 39 gray matter regions comprising the large dorsal thalamus project topographically to the cerebral cortex, whereas the much smaller epithalamus (2 regions) and ventral thalamus (5 regions) characteristically project subcortically. Before analyzing extrinsic inputs and outputs of the thalamus, here, the intrinsic connections among all 46 gray matter regions of the rat thalamus on each side of the brain were expertly collated and subjected to network analysis. Experimental axonal pathway-tracing evidence was found in the neuroanatomical literature for the presence or absence of 99% of 2,070 possible ipsilateral connections and 97% of 2,116 possible contralateral connections; the connection density of ipsilateral connections was 17%, and that of contralateral connections 5%. One hub, the reticular thalamic nucleus (of the ventral thalamus), was found in this network, whereas no high-degree rich club or clear small-world features were detected. The reticular thalamic nucleus was found to be primarily responsible for conferring the property of complete connectedness to the intrathalamic network in the sense that there is, at least, one path of finite length between any 2 regions or nodes in the network. Direct comparison with previous investigations using the same methodology shows that each division of the forebrain (cerebral cortex, cerebral nuclei, thalamus, hypothalamus) has distinct intrinsic network topological organization. A future goal is to analyze the network organization of connections within and among these 4 divisions of the forebrain.

Macroscale intrinsic network architecture of the hypothalamus

Control of multiple life-critical physiological and behavioral functions requires the hypothalamus. Here, we provide a comprehensive description and rigorous analysis of mammalian intrahypothalamic network architecture. To achieve this at the gray matter region (macroscale) level, macroscale connection (macroconnection) data for the rat hypothalamus were extracted from the primary literature. The dataset indicated the existence of 7,982 (of 16,770 possible) intrahypothalamic macroconnections. Network analysis revealed that the intrahypothalamic macroconnection network (its macroscale subconnectome) is divided into two identical top-level subsystems (or subnetworks), each composed of two nested second-level subsystems. At the top-level, this suggests a deeply integrated network; however, regional grouping of the two second-level subsystems suggested a partial separation between control of physiological functions and behavioral functions. Furthermore, inclusion of four candidate hubs (dominant network nodes) in the second-level subsystem that is associated prominently with physiological control suggests network primacy with respect to this function. In addition, comparison of network analysis with expression of gene markers associated with inhibitory (GAD65) and excitatory (VGLUT2) neurotransmission revealed a significant positive correlation between measures of network centrality (dominance) and the inhibitory marker. We discuss these results in relation to previous understandings of hypothalamic organization and provide, and selectively interrogate, an updated hypothalamus structure-function network model to encourage future hypothesis-driven investigations of identified hypothalamic subsystems.

A Network Explanation of Alzheimer's Regional Vulnerability

Studies in patients and mouse models have pinpointed a precise zone in the cerebral cortex selectively vulnerable to the earliest stages of Alzheimer's disease (AD): the borderzone covering the entorhinal and perirhinal cortical areas. An independent series of studies has revealed that this entorhinal-perirhinal borderzone is a central cortical hub, with a distinct connectivity pattern across the cerebral hemispheres. Here we develop a hypothesis that explains how this distinct network feature interacts with established pathogenic drivers of AD in explaining the disease's regional vulnerability and suggests how it acts as an anatomical source of disease spread.