Coordinate-independent mapping of structural and functional data by objective relational transformation (ORT)

A hierarchy index for networks in the brain reveals a complex entangled organizational structure

Networks involved in information processing often have their nodes arranged hierarchically, with the majority of connections occurring in adjacent levels. However, despite being an intuitively appealing concept, the hierarchical organization of large networks, such as those in the brain, is difficult to identify, especially in absence of additional information beyond that provided by the connectome. In this paper, we propose a framework to uncover the hierarchical structure of a given network, that identifies the nodes occupying each level as well as the sequential order of the levels. It involves optimizing a metric that we use to quantify the extent of hierarchy present in a network. Applying this measure to various brain networks, ranging from the nervous system of the nematode Caenorhabditis elegans to the human connectome, we unexpectedly find that they exhibit a common network architectural motif intertwining hierarchy and modularity. This suggests that brain networks may have evolved to simultaneously exploit the functional advantages of these two types of organizations, viz., relatively independent modules performing distributed processing in parallel and a hierarchical structure that allows sequential pooling of these multiple processing streams. An intriguing possibility is that this property we report may be common to information processing networks in general.

Evidence for increased parallel information transmission in human brain networks compared to macaques and male mice

Brain communication, defined as information transmission through white-matter connections, is at the foundation of the brain's computational capacities that subtend almost all aspects of behavior: from sensory perception shared across mammalian species, to complex cognitive functions in humans. How did communication strategies in macroscale brain networks adapt across evolution to accomplish increasingly complex functions? By applying a graph- and information-theory approach to assess information-related pathways in male mouse, macaque and human brains, we show a brain communication gap between selective information transmission in non-human mammals, where brain regions share information through single polysynaptic pathways, and parallel information transmission in humans, where regions share information through multiple parallel pathways. In humans, parallel transmission acts as a major connector between unimodal and transmodal systems. The layout of information-related pathways is unique to individuals across different mammalian species, pointing at the individual-level specificity of information routing architecture. Our work provides evidence that different communication patterns are tied to the evolution of mammalian brain networks.

Mesoscopic architecture enhances communication across the macaque connectome revealing structure-function correspondence in the brain

Analyzing the brain in terms of organizational structures at intermediate scales provides an approach to unravel the complexity arising from interactions between its large number of components. Focusing on a wiring diagram that spans the cortex, basal ganglia, and thalamus of the macaque brain, we identify robust modules in the network that provide a mesoscopic-level description of its topological architecture. Surprisingly, we find that the modular architecture facilitates rapid communication across the network, instead of localizing activity as is typically expected in networks having community organization. By considering processes of diffusive spreading and coordination, we demonstrate that the specific pattern of inter- and intramodular connectivity in the network allows propagation to be even faster than equivalent randomized networks with or without modular structure. This pattern of connectivity is seen at different scales and is conserved across principal cortical divisions, as well as subcortical structures. Furthermore, we find that the physical proximity between areas is insufficient to explain the modular organization, as similar mesoscopic structures can be obtained even after factoring out the effect of distance constraints on the connectivity. By supplementing the topological information about the macaque connectome with physical locations, volumes, and functions of the constituent areas and analyzing this augmented dataset, we reveal a counterintuitive role played by the modular architecture of the brain in promoting global coordination of its activity. It suggests a possible explanation for the ubiquity of modularity in brain networks.

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

Connectome verification: inter-rater and connection reliability of tract-tracing-based intrinsic hypothalamic connectivity

MOTIVATION

Structural connectomics supports understanding aspects of neuronal dynamics and brain functions. Conducting metastudies of tract-tracing publications is one option to generate connectome databases by collating neuronal connectivity data. Meanwhile, it is a common practice that the neuronal connections and their attributes of such retrospective data collations are extracted from tract-tracing publications manually by experts. As the description of tract-tracing results is often not clear-cut and the documentation of interregional connections is not standardized, the extraction of connectivity data from tract-tracing publications could be complex. This might entail that different experts interpret such non-standardized descriptions of neuronal connections from the same publication in variable ways. Hitherto, no investigation is available that determines the variability of extracted connectivity information from original tract-tracing publications. A relatively large variability of connectivity information could produce significant misconstructions of adjacency matrices with faults in network and graph analyzes. The objective of this study is to investigate the inter-rater and inter-observation variability of tract-tracing-based documentations of neuronal connections. To demonstrate the variability of neuronal connections, data of 16 publications which describe neuronal connections of subregions of the hypothalamus have been assessed by way of example.

RESULTS

A workflow is proposed that allows detecting variability of connectivity at different steps of data processing in connectome metastudies. Variability between three blinded experts was found by comparing the connection information in a sample of 16 publications that describe tract-tracing-based neuronal connections in the hypothalamus. Furthermore, observation scores, matrix visualizations of discrepant connections and weight variations in adjacency matrices are analyzed.

AVAILABILITY

The resulting data and software are available at http://neuroviisas.med.uni-rostock.de/neuroviisas.shtml.

Towards Differential Connectomics with NeuroVIISAS

The comparison of connectomes is an essential step to identify changes in structural and functional neuronal networks. However, the connectomes themselves as well as the comparisons of connectomes could be manifold. In most applications, comparisons of connectomes are applied to specific sets of data. In many studies collections of scripts are applied optimized for certain species (non-generic approaches) or diseases (control versus disease group connectomes). These collections of scripts have a limited functionality which do not support functional and topographic mappings of connectomes (hemispherical asymmetries, peripheral nervous system). The platform-independent and generic neuroVIISAS framework is built to circumvent limitations that come with variants of nomenclatures, connectivity lists and connectional hierarchies as well as restrictions to structural connectome analyses. A new analytical module is introduced into the framework to compare different types of connectomes and different representations of the same connectome within a unique software environment. As an example a differential analysis of the partial connectome of the laboratory rat that is based on virus tract tracing with the same regions of non-virus tract tracing has been performed. A relatively large connectional coherence between the two different techniques was found. However, some detected connections are described by virus tract-tracing only.

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

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

Predicting the Existence and Stability of Phase-Locked Mode in Neural Networks Using Generalized Phase-Resetting Curve

We used the phase-resetting method to study a biologically relevant three-neuron network in which one neuron receives multiple inputs per cycle. For this purpose, we first generalized the concept of phase resetting to accommodate multiple inputs per cycle. We explicitly showed how analytical conditions for the existence and the stability of phase-locked modes are derived. In particular, we solved newly derived recursive maps using as an example a biologically relevant driving-driven neural network with a dynamic feedback loop. We applied the generalized phase-resetting definition to predict the relative-phase and the stability of a phase-locked mode in open loop setup. We also compared the predicted phase-locked mode against numerical simulations of the fully connected network.

A generalized phase resetting method for phase-locked modes prediction

We derived analytically and checked numerically a set of novel conditions for the existence and the stability of phase-locked modes in a biologically relevant master-slave neural network with a dynamic feedback loop. Since neural oscillators even in the three-neuron network investigated here receive multiple inputs per cycle, we generalized the concept of phase resetting to accommodate multiple inputs per cycle. We proved that the phase resetting produced by two or more stimuli per cycle can be recursively computed from the traditional, single stimulus, phase resetting. We applied the newly derived generalized phase resetting definition to predicting the relative phase and the stability of a phase-locked mode that was experimentally observed in this type of master-slave network with a dynamic loop network.

Mapping complementary features of cross-species structural connectivity to construct realistic "Virtual Brains"

Modern systems neuroscience increasingly leans on large-scale multi-lab neuroinformatics initiatives to provide necessary capacity for biologically realistic modeling of primate whole-brain activity. Here, we present a framework to assemble primate brain's biologically plausible anatomical backbone for such modeling initiatives. In this framework, structural connectivity is determined by adding complementary information from invasive macaque axonal tract tracing and non-invasive human diffusion tensor imaging. Both modalities are combined by means of available interspecies registration tools and a newly developed Bayesian probabilistic modeling approach to extract common connectivity evidence. We demonstrate how this novel framework is embedded in the whole-brain simulation platform called The Virtual Brain (TVB). Hum Brain Mapp 38:2080-2093, 2017. © 2017 Wiley Periodicals, Inc.

Building the Ferretome

Databases of structural connections of the mammalian brain, such as CoCoMac (cocomac.g-node.org) or BAMS (https://bams1.org), are valuable resources for the analysis of brain connectivity and the modeling of brain dynamics in species such as the non-human primate or the rodent, and have also contributed to the computational modeling of the human brain. Another animal model that is widely used in electrophysiological or developmental studies is the ferret; however, no systematic compilation of brain connectivity is currently available for this species. Thus, we have started developing a database of anatomical connections and architectonic features of the ferret brain, the Ferret(connect)ome, www.Ferretome.org. The Ferretome database has adapted essential features of the CoCoMac methodology and legacy, such as the CoCoMac data model. This data model was simplified and extended in order to accommodate new data modalities that were not represented previously, such as the cytoarchitecture of brain areas. The Ferretome uses a semantic parcellation of brain regions as well as a logical brain map transformation algorithm (objective relational transformation, ORT). The ORT algorithm was also adopted for the transformation of architecture data. The database is being developed in MySQL and has been populated with literature reports on tract-tracing observations in the ferret brain using a custom-designed web interface that allows efficient and validated simultaneous input and proofreading by multiple curators. The database is equipped with a non-specialist web interface. This interface can be extended to produce connectivity matrices in several formats, including a graphical representation superimposed on established ferret brain maps. An important feature of the Ferretome database is the possibility to trace back entries in connectivity matrices to the original studies archived in the system. Currently, the Ferretome contains 50 reports on connections comprising 20 injection reports with more than 150 labeled source and target areas, the majority reflecting connectivity of subcortical nuclei and 15 descriptions of regional brain architecture. We hope that the Ferretome database will become a useful resource for neuroinformatics and neural modeling, and will support studies of the ferret brain as well as facilitate advances in comparative studies of mesoscopic brain connectivity.

A cross-modal, cross-species comparison of connectivity measures in the primate brain

In systems neuroscience, the term "connectivity" has been defined in numerous ways, according to the particular empirical modality from which it is derived. Due to large differences in the phenomena measured by these modalities, the assumptions necessary to make inferences about axonal connections, and the limitations accompanying each, brain connectivity remains an elusive concept. Despite this, only a handful of studies have directly compared connectivity as inferred from multiple modalities, and there remains much ambiguity over what the term is actually referring to as a biological construct. Here, we perform a direct comparison based on the high-resolution and high-contrast Enhanced Nathan Klein Institute (NKI) Rockland Sample neuroimaging data set, and the CoCoMac database of tract tracing studies. We compare four types of commonly-used primate connectivity analyses: tract tracing experiments, compiled in CoCoMac; group-wise correlation of cortical thickness; tractographic networks computed from diffusion-weighted MRI (DWI); and correlational networks obtained from resting-state BOLD (fMRI). We find generally poor correspondence between all four modalities, in terms of correlated edge weights, binarized comparisons of thresholded networks, and clustering patterns. fMRI and DWI had the best agreement, followed by DWI and CoCoMac, while other comparisons showed striking divergence. Networks had the best correspondence for local ipsilateral and homotopic contralateral connections, and the worst correspondence for long-range and heterotopic contralateral connections. k-Means clustering highlighted the lowest cross-modal and cross-species consensus in lateral and medial temporal lobes, anterior cingulate, and the temporoparietal junction. Comparing the NKI results to those of the lower resolution/contrast International Consortium for Brain Imaging (ICBM) dataset, we find that the relative pattern of intermodal relationships is preserved, but the correspondence between human imaging connectomes is substantially better for NKI. These findings caution against using "connectivity" as an umbrella term for results derived from single empirical modalities, and suggest that any interpretation of these results should account for (and ideally help explain) the lack of multimodal correspondence.

Unraveling the multiscale structural organization and connectivity of the human brain: the role of diffusion MRI

The structural architecture and the anatomical connectivity of the human brain show different organizational principles at distinct spatial scales. Histological staining and light microscopy techniques have been widely used in classical neuroanatomical studies to unravel brain organization. Using such techniques is a laborious task performed on 2-dimensional histological sections by skilled anatomists possibly aided by semi-automated algorithms. With the recent advent of modern magnetic resonance imaging (MRI) contrast mechanisms, cortical layers and columns can now be reliably identified and their structural properties quantified post-mortem. These developments are allowing the investigation of neuroanatomical features of the brain at a spatial resolution that could be interfaced with that of histology. Diffusion MRI and tractography techniques, in particular, have been used to probe the architecture of both white and gray matter in three dimensions. Combined with mathematical network analysis, these techniques are increasingly influential in the investigation of the macro-, meso-, and microscopic organization of brain connectivity and anatomy, both in vivo and ex vivo. Diffusion MRI-based techniques in combination with histology approaches can therefore support the endeavor of creating multimodal atlases that take into account the different spatial scales or levels on which the brain is organized. The aim of this review is to illustrate and discuss the structural architecture and the anatomical connectivity of the human brain at different spatial scales and how recently developed diffusion MRI techniques can help investigate these.

Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations

For more than a century, cerebral cartography has been driven by investigations of structural and morphological properties of the brain across spatial scales and the temporal/functional phenomena that emerge from these underlying features. The next era of brain mapping will be driven by studies that consider both of these components of brain organization simultaneously--elucidating their interactions and dependencies. Using this guiding principle, we explored the origin of slowly fluctuating patterns of synchronization within the topological core of brain regions known as the rich club, implicated in the regulation of mood and introspection. We find that a constellation of densely interconnected regions that constitute the rich club (including the anterior insula, amygdala and precuneus) play a central role in promoting a stable, dynamical core of spontaneous activity in the primate cortex. The slow timescales are well matched to the regulation of internal visceral states, corresponding to the somatic correlates of mood and anxiety. In contrast, the topology of the surrounding 'feeder' cortical regions shows unstable, rapidly fluctuating dynamics likely to be crucial for fast perceptual processes. We discuss these findings in relation to psychiatric disorders and the future of connectomics.

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

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

Comparative analysis of the macroscale structural connectivity in the macaque and human brain

The macaque brain serves as a model for the human brain, but its suitability is challenged by unique human features, including connectivity reconfigurations, which emerged during primate evolution. We perform a quantitative comparative analysis of the whole brain macroscale structural connectivity of the two species. Our findings suggest that the human and macaque brain as a whole are similarly wired. A region-wise analysis reveals many interspecies similarities of connectivity patterns, but also lack thereof, primarily involving cingulate regions. We unravel a common structural backbone in both species involving a highly overlapping set of regions. This structural backbone, important for mediating information across the brain, seems to constitute a feature of the primate brain persevering evolution. Our findings illustrate novel evolutionary aspects at the macroscale connectivity level and offer a quantitative translational bridge between macaque and human research.

What are the commonalities and differences of human brains when compared to the brains of other primates? The brain can be conceived as a complex network. Its topological properties constrain its function. Ethical and technical reasons necessitate the use of animal brains, like the macaque monkey, as models for the human brain. However, evolutionary changes, including “brain rewiring”, might result in unique human features. Hence, a detailed and quantitative comparative analysis of the connectivity of the brains of the two species is needed. Here, we undertake this task by adopting techniques analogous to those used in comparative studies in other scientific fields. Our approach reveals converging but also diverging wiring patterns. The brain of the two species as a whole is similarly wired. The majority of the brain regions appear to have evolutionary conserved connectivity patterns while for certain regions this appears not to be the case. We also uncover an evolutionary conserved “structural backbone” in the brain of the two species. Our findings highlight common and unique “wiring properties” of the brains of these two primate species and offer a quantitative basis for translating findings from macaque research to human research.

CoCoTools: open-source software for building connectomes using the CoCoMac anatomical database

Neuroanatomical tracer studies in the nonhuman primate macaque monkey are a valuable resource for cognitive neuroscience research. These data ground theories of cognitive function in anatomy, and with the emergence of graph theoretical analyses in neuroscience, there is high demand for these data to be consolidated into large-scale connection matrices ("macroconnectomes"). Because manual review of the anatomical literature is time consuming and error prone, computational solutions are needed to accomplish this task. Here we describe the "CoCoTools" open-source Python library, which automates collection and integration of macaque connectivity data for visualization and graph theory analysis. CoCoTools both interfaces with the CoCoMac database, which houses a vast amount of annotated tracer results from 100 years (1905-2005) of neuroanatomical research, and implements coordinate-free registration algorithms, which allow studies that use different parcellations of the brain to be translated into a single graph. We show that using CoCoTools to translate all of the data stored in CoCoMac produces graphs with properties consistent with what is known about global brain organization. Moreover, in addition to describing CoCoTools' processing pipeline, we provide worked examples, tutorials, links to on-line documentation, and detailed appendices to aid scientists interested in using CoCoTools to gather and analyze CoCoMac data.

The history of CoCoMac

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

Superficially located white matter structures commonly seen in the human and the macaque brain with diffusion tensor imaging

The white matter of the brain consists of fiber tracts that connect different regions of the brain. Among these tracts, the intrahemispheric cortico-cortical connections are called association fibers. The U-fibers are short association fibers that connect adjacent gyri. These fibers were thought to work as part of the cortico-cortical networks to execute associative brain functions. However, their anatomy and functions have not been documented in detail for the human brain. In past studies, U-fibers have been characterized in the human brain with diffusion tensor imaging (DTI). However, the validity of such findings remains unclear. In this study, DTI of the macaque brain was performed, and the anatomy of U-fibers was compared with that of the human brain reported in a previous study. The macaque brain was chosen because it is the most commonly used animal model for exploring cognitive functions and the U-fibers of the macaque brain have been already identified by axonal tracing studies, which makes it an ideal system for confirming the DTI findings. Ten U-fibers found in the macaque brain were also identified in the human brain, with a similar organization and topology. The delineation of these species-conserved white matter structures may provide new options for understanding brain anatomy and function.

Non-uniformity of cell density and networks in the monkey brain

The brain is a very complex structure. Over the past several decades, many studies have aimed to understand how various non-uniform variables relate to each other. The current study compared the whole-brain network organization and global spatial distribution of cell densities in the monkey brain. Wide comparisons between 27 graph theoretical measures and cell densities revealed that only participation coefficients (PCs) significantly correlated with cell densities. Interestingly, PCs did not show a significant correlation with spatial coordinates. Furthermore, the significance of the correlation between cell densities and spatial coordinates disappeared only with the removal of the visual module, while the significance of the correlation between cell densities and PCs disappeared with the removal of any one module. Taken together, these results suggested the presence of a combinatorial effect of modular architectures in the network organization related to the non-uniformity of cell densities additional to the spatially monotonic change.

CoCoMac 2.0 and the future of tract-tracing databases

The CoCoMac database contains the results of several hundred published axonal tract-tracing studies in the macaque monkey brain. The combined results are used for constructing the macaque macro-connectome. Here we discuss the redevelopment of CoCoMac and compare it to six connectome-related projects: two online resources that provide full access to raw tracing data in rodents, a connectome viewer for advanced 3D graphics, a partial but highly detailed rat connectome, a brain data management system that generates custom connectivity matrices, and a software package that covers the complete pipeline from connectivity data to large-scale brain simulations. The second edition of CoCoMac features many enhancements over the original. For example, a search wizard is provided for full access to all tables and their nested dependencies. Connectivity matrices can be computed on demand in a user-selected nomenclature. A new data entry system is available as a preview, and is to become a generic solution for community-driven data entry in manually collated databases. We conclude with the question whether neuronal tracing will remain the gold standard to uncover the wiring of brains, thereby highlighting developments in human connectome construction, tracer substances, polarized light imaging, and serial block-face scanning electron microscopy.

Hundreds of brain maps in one atlas: registering coordinate-independent primate neuro-anatomical data to a standard brain

Non-invasive measuring methods such as EEG/MEG, fMRI and DTI are increasingly utilised to extract quantitative information on functional and anatomical connectivity in the human brain. These methods typically register their data in Euclidean space, so that one can refer to a particular activity pattern by specifying its spatial coordinates. Since each of these methods has limited resolution in either the time or spatial domain, incorporating additional data, such as those obtained from invasive animal studies, would be highly beneficial to link structure and function. Here we describe an approach to spatially register all cortical brain regions from the macaque structural connectivity database CoCoMac, which contains the combined tracing study results from 459 publications (http://cocomac.g-node.org). Brain regions from 9 different brain maps were directly mapped to a standard macaque cortex using the tool Caret (Van Essen and Dierker, 2007). The remaining regions in the CoCoMac database were semantically linked to these 9 maps using previously developed algebraic and machine-learning techniques (Bezgin et al., 2008; Stephan et al., 2000). We analysed neural connectivity using several graph-theoretical measures to capture global properties of the derived network, and found that Markov Centrality provides the most direct link between structure and function. With this registration approach, users can query the CoCoMac database by specifying spatial coordinates. Availability of deformation tools and homology evidence then allow one to directly attribute detailed anatomical animal data to human experimental results.

Combining collation and annotation efforts toward completion of the rat and mouse connectomes in BAMS

Many different independently published neuroanatomical parcellation schemes (brain maps, nomenclatures, or atlases) can exist for a particular species, although one scheme (a standard scheme) is typically chosen for mapping neuroanatomical data in a particular study. This is problematic for building connection matrices (connectomes) because the terms used to name structures in different parcellation schemes differ widely and interrelationships are seldom defined. Therefore, data sets cannot be compared across studies that have been mapped on different neuroanatomical atlases without a reliable translation method. Because resliceable 3D brain models for relating systematically and topographically different parcellation schemes are still in the first phases of development, it is necessary to rely on qualitative comparisons between regions and tracts that are either inserted directly by neuroanatomists or trained annotators, or are extracted or inferred by collators from the available literature. To address these challenges, we developed a publicly available neuroinformatics system, the Brain Architecture Knowledge Management System (BAMS; http://brancusi.usc.edu/bkms). The structure and functionality of BAMS is briefly reviewed here, as an exemplar for constructing interrelated connectomes at different levels of the mammalian central nervous system organization. Next, the latest version of BAMS rat macroconnectome is presented because it is significantly more populated with the number of inserted connectivity reports exceeding a benchmark value (50,000), and because it is based on a different classification scheme. Finally, we discuss a general methodology and strategy for producing global connection matrices, starting with rigorous mapping of data, then inserting and annotating it, and ending with online generation of large-scale connection matrices.

Large-Scale Analysis of Gene Expression and Connectivity in the Rodent Brain: Insights through Data Integration

Recent research in C. elegans and the rodent has identified correlations between gene expression and connectivity. Here we extend this type of approach to examine complex patterns of gene expression in the rodent brain in the context of regional brain connectivity and differences in cellular populations. Using multiple large-scale data sets obtained from public sources, we identified two novel patterns of mouse brain gene expression showing a strong degree of anti-correlation, and relate this to multiple data modalities including macroscale connectivity. We found that these signatures are associated with differences in expression of neuronal and oligodendrocyte markers, suggesting they reflect regional differences in cellular populations. We also find that the expression level of these genes is correlated with connectivity degree, with regions expressing the neuron-enriched pattern having more incoming and outgoing connections with other regions. Our results exemplify what is possible when increasingly detailed large-scale cell- and gene-level data sets are integrated with connectivity data.

The retrosplenial cortex: intrinsic connectivity and connections with the (para)hippocampal region in the rat. An interactive connectome

A connectome is an indispensable tool for brain researchers, since it quickly provides comprehensive knowledge of the brain's anatomical connections. Such knowledge lies at the basis of understanding network functions. Our first comprehensive and interactive account of brain connections comprised the rat hippocampal–parahippocampal network. We have now added all anatomical connections with the retrosplenial cortex (RSC) as well as the intrinsic connections of this region, because of the interesting functional overlap between these brain regions. The RSC is involved in a variety of cognitive tasks including memory, navigation, and prospective thinking, yet the exact role of the RSC and the functional differences between its subdivisions remain elusive. The connectome presented here may help to define this role by providing an unprecedented interactive and searchable overview of all connections within and between the rat RSC, parahippocampal region and hippocampal formation.

The dynamical balance of the brain at rest

The authors review evidence that spontaneous, that is, not stimulus or task driven, activity in the brain at the level of large-scale neural systems is not noise, but orderly and organized in a series of functional networks that maintain, at all times, a high level of coherence. These networks of spontaneous activity correlation or resting state networks (RSN) are closely related to the underlying anatomical connectivity, but their topography is also gated by the history of prior task activation. Network coherence does not depend on covert cognitive activity, but its strength and integrity relates to behavioral performance. Some RSN are functionally organized as dynamically competing systems both at rest and during tasks. Computational studies show that one of such dynamics, the anticorrelation between networks, depends on noise-driven transitions between different multistable cluster synchronization states. These multistable states emerge because of transmission delays between regions that are modeled as coupled oscillators systems. Large-scale systems dynamics are useful for keeping different functional subnetworks in a state of heightened competition, which can be stabilized and fired by even small modulations of either sensory or internal signals.

Network architecture of the long-distance pathways in the macaque brain

Understanding the network structure of white matter communication pathways is essential for unraveling the mysteries of the brain's function, organization, and evolution. To this end, we derive a unique network incorporating 410 anatomical tracing studies of the macaque brain from the Collation of Connectivity data on the Macaque brain (CoCoMac) neuroinformatic database. Our network consists of 383 hierarchically organized regions spanning cortex, thalamus, and basal ganglia; models the presence of 6,602 directed long-distance connections; is three times larger than any previously derived brain network; and contains subnetworks corresponding to classic corticocortical, corticosubcortical, and subcortico-subcortical fiber systems. We found that the empirical degree distribution of the network is consistent with the hypothesis of the maximum entropy exponential distribution and discovered two remarkable bridges between the brain's structure and function via network-theoretical analysis. First, prefrontal cortex contains a disproportionate share of topologically central regions. Second, there exists a tightly integrated core circuit, spanning parts of premotor cortex, prefrontal cortex, temporal lobe, parietal lobe, thalamus, basal ganglia, cingulate cortex, insula, and visual cortex, that includes much of the task-positive and task-negative networks and might play a special role in higher cognition and consciousness.

Application of neuroanatomical ontologies for neuroimaging data annotation

The annotation of functional neuroimaging results for data sharing and re-use is particularly challenging, due to the diversity of terminologies of neuroanatomical structures and cortical parcellation schemes. To address this challenge, we extended the Foundational Model of Anatomy Ontology (FMA) to include cytoarchitectural, Brodmann area labels, and a morphological cortical labeling scheme (e.g., the part of Brodmann area 6 in the left precentral gyrus). This representation was also used to augment the neuroanatomical axis of RadLex, the ontology for clinical imaging. The resulting neuroanatomical ontology contains explicit relationships indicating which brain regions are "part of" which other regions, across cytoarchitectural and morphological labeling schemas. We annotated a large functional neuroimaging dataset with terms from the ontology and applied a reasoning engine to analyze this dataset in conjunction with the ontology, and achieved successful inferences from the most specific level (e.g., how many subjects showed activation in a subpart of the middle frontal gyrus) to more general (how many activations were found in areas connected via a known white matter tract?). In summary, we have produced a neuroanatomical ontology that harmonizes several different terminologies of neuroanatomical structures and cortical parcellation schemes. This neuroanatomical ontology is publicly available as a view of FMA at the Bioportal website. The ontological encoding of anatomic knowledge can be exploited by computer reasoning engines to make inferences about neuroanatomical relationships described in imaging datasets using different terminologies. This approach could ultimately enable knowledge discovery from large, distributed fMRI studies or medical record mining.

Model-driven parameterization of the cortical surface for localization and inter-subject matching

In this paper we present a generic and organized model of cortical folding, and a way to implement this model on any given cortical surface. This results in a model-driven parameterization, providing an anatomically meaningful coordinate system for cortical localization, and implicitly defining inter-subject surface matching without any deformation of surfaces. We present our cortical folding model and show how it naturally defines a parameterization of the cortex. The mapping of the model to any given cortical surface is detailed, leading to an anatomically invariant coordinate system. The process is evaluated on real data in terms of both anatomical and functional localization, and shows improved performance compared to a traditional volume-based normalization. It is fully automatic and available with the BrainVISA software platform.

The brain atlas concordance problem: quantitative comparison of anatomical parcellations

Many neuroscientific reports reference discrete macro-anatomical regions of the brain which were delineated according to a brain atlas or parcellation protocol. Currently, however, no widely accepted standards exist for partitioning the cortex and subcortical structures, or for assigning labels to the resulting regions, and many procedures are being actively used. Previous attempts to reconcile neuroanatomical nomenclatures have been largely qualitative, focusing on the development of thesauri or simple semantic mappings between terms. Here we take a fundamentally different approach, discounting the names of regions and instead comparing their definitions as spatial entities in an effort to provide more precise quantitative mappings between anatomical entities as defined by different atlases. We develop an analytical framework for studying this brain atlas concordance problem, and apply these methods in a comparison of eight diverse labeling methods used by the neuroimaging community. These analyses result in conditional probabilities that enable mapping between regions across atlases, which also form the input to graph-based methods for extracting higher-order relationships between sets of regions and to procedures for assessing the global similarity between different parcellations of the same brain. At a global scale, the overall results demonstrate a considerable lack of concordance between available parcellation schemes, falling within chance levels for some atlas pairs. At a finer level, this study reveals spatial relationships between sets of defined regions that are not obviously apparent; these are of high potential interest to researchers faced with the challenge of comparing results that were based on these different anatomical models, particularly when coordinate-based data are not available. The complexity of the spatial overlap patterns revealed points to problems for attempts to reconcile anatomical parcellations and nomenclatures using strictly qualitative and/or categorical methods. Detailed results from this study are made available via an interactive web site at http://obart.info.

Key role of coupling, delay, and noise in resting brain fluctuations

A growing body of neuroimaging research has documented that, in the absence of an explicit task, the brain shows temporally coherent activity. This so-called "resting state" activity or, more explicitly, the default-mode network, has been associated with daydreaming, free association, stream of consciousness, or inner rehearsal in humans, but similar patterns have also been found under anesthesia and in monkeys. Spatiotemporal activity patterns in the default-mode network are both complex and consistent, which raises the question whether they are the expression of an interesting cognitive architecture or the consequence of intrinsic network constraints. In numerical simulation, we studied the dynamics of a simplified cortical network using 38 noise-driven (Wilson-Cowan) oscillators, which in isolation remain just below their oscillatory threshold. Time delay coupling based on lengths and strengths of primate corticocortical pathways leads to the emergence of 2 sets of 40-Hz oscillators. The sets showed synchronization that was anticorrelated at <0.1 Hz across the sets in line with a wide range of recent experimental observations. Systematic variation of conduction velocity, coupling strength, and noise level indicate a high sensitivity of emerging synchrony as well as simulated blood flow blood oxygen level-dependent (BOLD) on the underlying parameter values. Optimal sensitivity was observed around conduction velocities of 1-2 m/s, with very weak coupling between oscillators. An additional finding was that the optimal noise level had a characteristic scale, indicating the presence of stochastic resonance, which allows the network dynamics to respond with high sensitivity to changes in diffuse feedback activity.

A proposal for a coordinated effort for the determination of brainwide neuroanatomical connectivity in model organisms at a mesoscopic scale

In this era of complete genomes, our knowledge of neuroanatomical circuitry remains surprisingly sparse. Such knowledge is critical, however, for both basic and clinical research into brain function. Here we advocate for a concerted effort to fill this gap, through systematic, experimental mapping of neural circuits at a mesoscopic scale of resolution suitable for comprehensive, brainwide coverage, using injections of tracers or viral vectors. We detail the scientific and medical rationale and briefly review existing knowledge and experimental techniques. We define a set of desiderata, including brainwide coverage; validated and extensible experimental techniques suitable for standardization and automation; centralized, open-access data repository; compatibility with existing resources; and tractability with current informatics technology. We discuss a hypothetical but tractable plan for mouse, additional efforts for the macaque, and technique development for human. We estimate that the mouse connectivity project could be completed within five years with a comparatively modest budget.

Combining structural connectivity and response latencies to model the structure of the visual system

Several approaches exist to ascertain the connectivity of the brain, and these approaches lead to markedly different topologies, often incompatible with each other. Specifically, recent single-cell recording results seem incompatible with current structural connectivity models. We present a novel method that combines anatomical and temporal constraints to generate biologically plausible connectivity patterns of the visual system of the macaque monkey. Our method takes structural connectivity data from the CoCoMac database and recent single-cell recording data as input and employs an optimization technique to arrive at a new connectivity pattern of the visual system that is in agreement with both types of experimental data. The new connectivity pattern yields a revised model that has fewer levels than current models. In addition, it introduces subcortical–cortical connections. We show that these connections are essential for explaining latency data, are consistent with our current knowledge of the structural connectivity of the visual system, and might explain recent functional imaging results in humans. Furthermore we show that the revised model is not underconstrained like previous models and can be extended to include newer data and other kinds of data. We conclude that the revised model of the connectivity of the visual system reflects current knowledge on the structure and function of the visual system and addresses some of the limitations of previous models.

Visual perception is very important to us, something we can easily come to realize if we imagine ourselves blind. The visual system consists of numerous interconnected brain areas. If we are to understand the functioning of the visual system, then we will need to understand the connectivity between these areas. Current models of the visual system have a number of limitations. One of these is that the time it takes for the neural signal to reach a certain area often seems inconsistent with the place of that area in the overall structure of the system; e.g., the signal might arrive relatively quickly at an area generally located “higher” in the visual system and slowly at an area located in the “lower” part. We combine data about the known connectivity in the monkey brain with timing data to find a network structure that is consistent with both kinds of data. The results show that the timing data can be explained when the network contains direct routes from subcortical areas to “higher” cortical areas. We show that our model has fewer limitations than previous models and might explain unresolved issues in the study of connectivity in the human brain.

Combining structural and functional neuroimaging data for studying brain connectivity: a review

Different brain areas are thought to be integrated into large-scale networks to support cognitive function. Recent approaches for investigating structural organization and functional coordination within these networks involve measures of connectivity among brain areas. We review studies combining in vivo structural and functional brain connectivity data, where (a) structural connectivity analysis, mostly based on diffusion tensor imaging is paired with voxel-wise analysis of functional neuroimaging data or (b) the measurement of functional connectivity based on covariance analysis is guided/aided by structural connectivity data. These studies provide insights into the relationships between brain structure and function. Promising trends involve (a) studies where both functional and anatomical connectivity data are collected using high-resolution neuroimaging methods and (b) the development of advanced quantitative models of integration.

Network structure of cerebral cortex shapes functional connectivity on multiple time scales

Neuronal dynamics unfolding within the cerebral cortex exhibit complex spatial and temporal patterns even in the absence of external input. Here we use a computational approach in an attempt to relate these features of spontaneous cortical dynamics to the underlying anatomical connectivity. Simulating nonlinear neuronal dynamics on a network that captures the large-scale interregional connections of macaque neocortex, and applying information theoretic measures to identify functional networks, we find structure-function relations at multiple temporal scales. Functional networks recovered from long windows of neural activity (minutes) largely overlap with the underlying structural network. As a result, hubs in these long-run functional networks correspond to structural hubs. In contrast, significant fluctuations in functional topology are observed across the sequence of networks recovered from consecutive shorter (seconds) time windows. The functional centrality of individual nodes varies across time as interregional couplings shift. Furthermore, the transient couplings between brain regions are coordinated in a manner that reveals the existence of two anticorrelated clusters. These clusters are linked by prefrontal and parietal regions that are hub nodes in the underlying structural network. At an even faster time scale (hundreds of milliseconds) we detect individual episodes of interregional phase-locking and find that slow variations in the statistics of these transient episodes, contingent on the underlying anatomical structure, produce the transfer entropy functional connectivity and simulated blood oxygenation level-dependent correlation patterns observed on slower time scales.

Operational principles of neurocognitive networks

Large-scale neural networks are thought to be an essential substrate for the implementation of cognitive function by the brain. If so, then a thorough understanding of cognition is not possible without knowledge of how the large-scale neural networks of cognition (neurocognitive networks) operate. Of necessity, such understanding requires insight into structural, functional, and dynamical aspects of network operation, the intimate interweaving of which may be responsible for the intricacies of cognition. Knowledge of anatomical structure is basic to understanding how neurocognitive networks operate. Phylogenetically and ontogenetically determined patterns of synaptic connectivity form a structural network of brain areas, allowing communication between widely distributed collections of areas. The function of neurocognitive networks depends on selective activation of anatomically linked cortical and subcortical areas in a wide variety of configurations. Large-scale functional networks provide the cooperative processing which gives expression to cognitive function. The dynamics of neurocognitive network function relates to the evolving patterns of interacting brain areas that express cognitive function in real time. This article considers the proposition that a basic similarity of the structural, functional, and dynamical features of all neurocognitive networks in the brain causes them to function according to common operational principles. The formation of neural context through the coordinated mutual constraint of multiple interacting cortical areas, is considered as a guiding principle underlying all cognitive functions. Increasing knowledge of the operational principles of neurocognitive networks is likely to promote the advancement of cognitive theories, and to seed strategies for the enhancement of cognitive abilities.

Brain architecture management system

The nervous system can be viewed as a biological computer whose genetically determined macrocircuitry has two basic classes of parts: gray matter regions interconnected by fiber pathways. We describe here the basic features of an online knowledge management system for storing and inferring relationships between data about the structural organization of nervous system circuitry. It is called the Brain architecture management system (BAMS; http://brancusi.usc.edu/bkms) and it stores and analyzes data specifically concerned with nomenclature and its hierarchical taxonomy, with axonal connections between regions, and with the neuronal cell types that form regions and fiber pathways.

Mapping brains without coordinates

Brain mapping has evolved considerably over the last century. While most emphasis has been placed on coordinate-based spatial atlases, coordinate-independent parcellation-based mapping is an important technique for accessing the multitude of structural and functional data that have been reported from invasive experiments, and provides for flexible and efficient representations of information. Here. we provide an introduction to motivations, concepts, techniques and implications of coordinate-independent mapping of microstructurally or functionally defined brain structures. In particular, we explain the problems of constructing mapping paths and finding adequate heuristics for their evaluation. We then introduce the three auxiliary concepts of acronym-based mapping (AM), of a generalized hierarchy (GM ontology), and of a topographically oriented regional map (RM) with adequate granularity for mapping between individual brains with different cortical folding and between humans and non-human primates. Examples from the CoCoMac database of primate brain connectivity demonstrate how these concepts enhance coordinate-independent mapping based on published relational statements. Finally, we discuss the strengths and weaknesses of spatial coordinate-based versus coordinate-independent microstructural brain mapping and show perspectives for a wider application of parcellation-based approaches in the integration of multi-model structural, functional, and clinical data.

Online retrieval, processing, and visualization of primate connectivity data from the CoCoMac database

Connectivity is the key to understanding distributed and cooperative brain functions. Detailed and comprehensive data on large-scale connectivity between primate brain areas have been collated systematically from published reports of experimental tracing studies. Although the majority of the data have been made easily available for online retrieval, the multiplicity of brain maps and the precise requirements of anatomical naming limit the intuitive access to the data. The quality of data retrieval can be improved by observing a small set of conventions in data representation. Standardized interfaces open up further opportunities for automated search and retrieval, for flexible visualization of data, and for interoperability with other databases. This article provides a discussion and examples in text and image of the capabilities of the online interface to the CoCoMac database of primate connectivity. These serve to point out sources of potential confusion and failure, and to demonstrate the automated interfacing with other neuroinformatics resources that facilitate selection and processing of connectivity data, for example, for computational modelling and interpretation of functional imaging studies.

Integrating databases and expert systems for the analysis of brain structures: connections, similarities, and homologies

The NeuroHomology Database system (NHDB) combines databases related to brain structures from different species with different knowledge management systems (KMSs) for systematization, evaluation and processing neurobiological data. Special attention is assessment of similarity of data from different species as a basis for exploring neural homologies. NHDB includes modules that handle brain structure and connectivity data, as well as inference engines for evaluation of the stored neurobiological information. The spatial inference engine evaluates the possible topological relations between cortical structures in different neuroanatomical atlases. The connectivity inference engine evaluates the reliability of information pertaining to fiber tracts as those are reflected in the literature. The inference engine for translation of neuroanatomical connections in different atlases evaluates the probability of existence of connections of interest in different parcellation schemes. Finally, the similarity inference engine calculates the overall degree of similarity of pairs of brain structures from different species by taking into account a set of eight criteria. We present examples of search for information in NHDB system, inferences of relations between cortical structures from equivalent neuroanatomical atlases, reconstruction of functional networks of brain structures from data collated from the literature, translation of connectivity matrices in equivalent parcellation schemes, and evaluations of similarities of brain structures from humans, macaques and rats.

Object-based morphometry of the cerebral cortex

Most of the approaches dedicated to automatic morphometry rely on a point-by-point strategy based on warping each brain toward a reference coordinate system. In this paper, we describe an alternative object-based strategy dedicated to the cortex. This strategy relies on an artificial neuroanatomist performing automatic recognition of the main cortical sulci and parcellation of the cortical surface into gyral patches. A set of shape descriptors, which can be compared across subjects, is then attached to the sulcus and gyrus related objects segmented by this process. The framework is used to perform a study of 142 brains of the International Consortium for Brain Mapping (ICBM) database. This study reveals some correlates of handedness on the size of the sulci located in motor areas, which was not detected previously using standard voxel based morphometry.

Coordinate-based versus structural approaches to brain image analysis

A basic issue in neurosciences is to look for possible relationships between brain architecture and cognitive models. The lack of architectural information in magnetic resonance images, however, has led the neuroimaging community to develop brain mapping strategies based on various coordinate systems without accurate architectural content. Therefore, the relationships between architectural and functional brain organizations are difficult to study when analyzing neuroimaging experiments. This paper advocates that the design of new brain image analysis methods inspired by the structural strategies often used in computer vision may provide better ways to address these relationships. The key point underlying this new framework is the conversion of the raw images into structural representations before analysis. These representations are made up of data-driven elementary features like activated clusters, cortical folds or fiber bundles. Two classes of methods are introduced. Inference of structural models via matching across a set of individuals is described first. This inference problem is illustrated by the group analysis of functional statistical parametric maps (SPMs). Then, the matching of new individual data with a priori known structural models is described, using the recognition of the cortical sulci as a prototypical example.