Deducing logical relationships between spatially registered cortical parcellations under conditions of uncertainty

Architecture of the inferior parietal cortex in capuchin monkey

We studied the organization of the inferior parietal cortex (IPC) in five capuchin monkey (6 hemispheres) using cytoarchitectonic (Nissl), myeloarchitectonic (Gallyas), and immune-architectonic (SMI-32 monoclonal antibody) techniques. We partitioned the IPC into five distinct areas: PFG, PG, Opt, PFop, and PGop. Since we used parasagittal sections, we were not able to study area PF due to its far lateral position, which yielded slices that were tangential to the pial surface. Areas PFG, PG, and Opt were in the convexity close to the lateral sulcus, while PFop and PGop were positioned more posteriorly, in the opercular region of IPC. Of all the five regions, area Opt was the one most similar to its analogue in the macaque, especially as revealed with SMI-32 staining. Namely, in both primate species area Opt showed a low density of large pyramidal neurons. Additionally, the apical dendrites of these neurons were sparse and vertically orientated, resembling columns. We also found area PG to be similar: both species exhibited cell body layers with a radial arrangement. On the other hand, Nissl staining revealed area PFG to be architectonically different between New and Old-World monkeys: PFG in the capuchin showed a comparatively higher cell density than in macaques, especially in layers II and IV. These results suggest that evolution may have enabled the functional specialization of these brain regions based on behavioral demands of upper limb use. The small differences in the IPC of the two primates may be linked to interspecies variability.

A macaque connectome for large-scale network simulations in TheVirtualBrain

Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.

Exploring the limits of network topology estimation using diffusion-based tractography and tracer studies in the macaque cortex

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

Quantitative Anatomical Evidence for a Dorsoventral and Rostrocaudal Segregation within the Nonhuman Primate Frontal Cortex

The intrinsic white matter connections of the frontal cortex are highly complex, and the organization of these connections is not fully understood. Quantitative graph-theoretical methods, which are not solely reliant on human observation and interpretation, can be powerful tools for describing the organizing network principles of frontal cortex. Here, we examined the network structure of frontal cortical subregions by applying graph-theoretical community detection analyses to a graph of frontal cortex compiled from over 400+ macaque white-matter tracing studies. We find evidence that the lateral frontal cortex can be partitioned into distinct modules roughly organized along the dorsoventral and rostrocaudal axis.

A Putative Multiple-Demand System in the Macaque Brain

In humans, cognitively demanding tasks of many types recruit common frontoparietal brain areas. Pervasive activation of this "multiple-demand" (MD) network suggests a core function in supporting goal-oriented behavior. A similar network might therefore be predicted in nonhuman primates that readily perform similar tasks after training. However, an MD network in nonhuman primates has not been described. Single-cell recordings from macaque frontal and parietal cortex show some similar properties to human MD fMRI responses (e.g., adaptive coding of task-relevant information). Invasive recordings, however, come from limited prespecified locations, so they do not delineate a macaque homolog of the MD system and their positioning could benefit from knowledge of where MD foci lie. Challenges of scanning behaving animals mean that few macaque fMRI studies specifically contrast levels of cognitive demand, so we sought to identify a macaque counterpart to the human MD system using fMRI connectivity in 35 rhesus macaques. Putative macaque MD regions, mapped from frontoparietal MD regions defined in humans, were found to be functionally connected under anesthesia. To further refine these regions, an iterative process was used to maximize their connectivity cross-validated across animals. Finally, whole-brain connectivity analyses identified voxels that were robustly connected to MD regions, revealing seven clusters across frontoparietal and insular cortex comparable to human MD regions and one unexpected cluster in the lateral fissure. The proposed macaque MD regions can be used to guide future electrophysiological investigation of MD neural coding and in task-based fMRI to test predictions of similar functional properties to human MD cortex.

SIGNIFICANCE STATEMENT

In humans, a frontoparietal "multiple-demand" (MD) brain network is recruited during a wide range of cognitively demanding tasks. Because this suggests a fundamental function, one might expect a similar network to exist in nonhuman primates, but this remains controversial. Here, we sought to identify a macaque counterpart to the human MD system using fMRI connectivity. Putative macaque MD regions were functionally connected under anesthesia and were further refined by iterative optimization. The result is a network including lateral frontal, dorsomedial frontal, and insular and inferior parietal regions closely similar to the human counterpart. The proposed macaque MD regions can be useful in guiding electrophysiological recordings or in task-based fMRI to test predictions of similar functional properties to human MD cortex.

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.

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.

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.

Region-Specific Slowing of Alpha Oscillations is Associated with Visual-Perceptual Abilities in Children Born Very Preterm

Children born very preterm (≤32 weeks gestational age) without major intellectual or neurological impairments often express selective deficits in visual-perceptual abilities. The alterations in neurophysiological development underlying these problems, however, remain poorly understood. Recent research has indicated that spontaneous alpha oscillations are slowed in children born very preterm, and that atypical alpha-mediated functional network connectivity may underlie selective developmental difficulties in visual-perceptual ability in this group. The present study provides the first source-resolved analysis of slowing of spontaneous alpha oscillations in very preterm children, indicating alterations in a distributed set of brain regions concentrated in areas of posterior parietal and inferior temporal regions associated with visual perception, as well as prefrontal cortical regions and thalamus. We also uniquely demonstrate that slowing of alpha oscillations is associated with selective difficulties in visual-perceptual ability in very preterm children. These results indicate that region-specific slowing of alpha oscillations contribute to selective developmental difficulties prevalent in this population.

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.

Information processing architecture of functionally defined clusters in the macaque cortex

Computational and empirical neuroimaging studies have suggested that the anatomical connections between brain regions primarily constrain their functional interactions. Given that the large-scale organization of functional networks is determined by the temporal relationships between brain regions, the structural limitations may extend to the global characteristics of functional networks. Here, we explored the extent to which the functional network community structure is determined by the underlying anatomical architecture. We directly compared macaque (Macaca fascicularis) functional connectivity (FC) assessed using spontaneous blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI) to directed anatomical connectivity derived from macaque axonal tract tracing studies. Consistent with previous reports, FC increased with increasing strength of anatomical connection, and FC was also present between regions that had no direct anatomical connection. We observed moderate similarity between the FC of each region and its anatomical connectivity. Notably, anatomical connectivity patterns, as described by structural motifs, were different within and across functional modules: partitioning of the functional network was supported by dense bidirectional anatomical connections within clusters and unidirectional connections between clusters. Together, our data directly demonstrate that the FC patterns observed in resting-state BOLD-fMRI are dictated by the underlying neuroanatomical architecture. Importantly, we show how this architecture contributes to the global organizational principles of both functional specialization and integration.

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.

The co-occurrence of multisensory facilitation and cross-modal conflict in the human brain

Perceptual objects often comprise a visual and auditory signature that arrives simultaneously through distinct sensory channels, and cross-modal features are linked by virtue of being attributed to a specific object. Continued exposure to cross-modal events sets up expectations about what a given object most likely "sounds" like, and vice versa, thereby facilitating object detection and recognition. The binding of familiar auditory and visual signatures is referred to as semantic, multisensory integration. Whereas integration of semantically related cross-modal features is behaviorally advantageous, situations of sensory dominance of one modality at the expense of another impair performance. In the present study, magnetoencephalography recordings of semantically related cross-modal and unimodal stimuli captured the spatiotemporal patterns underlying multisensory processing at multiple stages. At early stages, 100 ms after stimulus onset, posterior parietal brain regions responded preferentially to cross-modal stimuli irrespective of task instructions or the degree of semantic relatedness between the auditory and visual components. As participants were required to classify cross-modal stimuli into semantic categories, activity in superior temporal and posterior cingulate cortices increased between 200 and 400 ms. As task instructions changed to incorporate cross-modal conflict, a process whereby auditory and visual components of cross-modal stimuli were compared to estimate their degree of congruence, multisensory processes were captured in parahippocampal, dorsomedial, and orbitofrontal cortices 100 and 400 ms after stimulus onset. Our results suggest that multisensory facilitation is associated with posterior parietal activity as early as 100 ms after stimulus onset. However, as participants are required to evaluate cross-modal stimuli based on their semantic category or their degree of congruence, multisensory processes extend in cingulate, temporal, and prefrontal cortices.

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.

Matching spatial with ontological brain regions using Java tools for visualization, database access, and integrated data analysis

Brain atlases are widely used in experimental neuroscience as tools for locating and targeting specific brain structures. Delineated structures in a given atlas, however, are often difficult to interpret and to interface with database systems that supply additional information using hierarchically organized vocabularies (ontologies). Here we discuss the concept of volume-to-ontology mapping in the context of macroscopical brain structures. We present Java tools with which we have implemented this concept for retrieval of mapping and connectivity data on the macaque brain from the CoCoMac database in connection with an electronic version of "The Rhesus Monkey Brain in Stereotaxic Coordinates" authored by George Paxinos and colleagues. The software, including our manually drawn monkey brain template, can be downloaded freely under the GNU General Public License. It adds value to the printed atlas and has a wider (neuro-)informatics application since it can read appropriately annotated data from delineated sections of other species and organs, and turn them into 3D registered stacks. The tools provide additional features, including visualization and analysis of connectivity data, volume and centre-of-mass estimates, and graphical manipulation of entire structures, which are potentially useful for a range of research and teaching applications.