The role of long-range connections on the specificity of the macaque interareal cortical network

Comparison of diffusion tractography and tract-tracing measures of connectivity strength in rhesus macaque connectome

With the mapping of macroscale connectomes by means of in vivo diffusion-weighted MR Imaging (DWI) rapidly gaining in popularity, one of the necessary steps is the examination of metrics of connectivity strength derived from these reconstructions. In the field of human macroconnectomics the number of reconstructed fiber streamlines (NOS) is more and more used as a metric of cortico-cortical interareal connectivity strength, but the link between DWI NOS and in vivo animal tract-tracing measurements of anatomical connectivity strength remains poorly understood. In this technical report, we communicate on a comparison between DWI derived metrics and tract-tracing metrics of projection strength. Tract-tracing information on projection strength of interareal pathways was extracted from two commonly used macaque connectome datasets, including (1) the CoCoMac database of collated tract-tracing experiments of the macaque brain and (2) the high-resolution tract-tracing dataset of Markov and Kennedy and coworkers. NOS and density of reconstructed fiber pathways derived from DWI data acquired across 10 rhesus macaques was found to positively correlate to tract-tracing based measurements of connectivity strength across both the CoCoMac and Markov dataset (both P < 0.001), suggesting DWI NOS to form a valid method of assessment of the projection strength of white matter pathways. Our findings provide confidence of in vivo DWI connectome reconstructions to represent fairly realistic estimates of the wiring strength of white matter projections. Our cross-modal comparison supports the notion of in vivo DWI to be a valid methodology for robust description and interpretation of brain wiring.

Cortical Connections of the Caudal Portion of Posterior Parietal Cortex in Prosimian Galagos

Posterior parietal cortex (PPC) of prosimian galagos includes a rostral portion (PPCr) where electrical stimulation evokes different classes of complex movements from different subregions, and a caudal portion (PPCc) where such stimulation fails to evoke movements in anesthetized preparations ( Stepniewska, Fang et al. 2009). We placed tracer injections into PPCc to reveal patterns of its cortical connections. There were widespread connections within PPCc as well as connections with PPCr and extrastriate visual areas, including V2 and V3. Weaker connections were with dorsal premotor cortex, and the frontal eye field. The connections of different parts of PPCc with visual areas were roughly retinotopic such that injections to dorsal PPCc labeled more neurons in the dorsal portions of visual areas, representing lower visual quadrant, and injections to ventral PPCc labeled more neurons in ventral portions of these visual areas, representing the upper visual quadrant. We conclude that much of the PPCc contains a crude representation of the contralateral visual hemifield, with inputs largely, but not exclusively, from higher-order visual areas that are considered part of the dorsal visuomotor processing stream. As in galagos, the caudal half of PPC was likely visual in early primates, with the rostral PPC half mediating sensorimotor functions.

Using Pareto optimality to explore the topology and dynamics of the human connectome

Graph theory has provided a key mathematical framework to analyse the architecture of human brain networks. This architecture embodies an inherently complex relationship between connection topology, the spatial arrangement of network elements, and the resulting network cost and functional performance. An exploration of these interacting factors and driving forces may reveal salient network features that are critically important for shaping and constraining the brain's topological organization and its evolvability. Several studies have pointed to an economic balance between network cost and network efficiency with networks organized in an ‘economical’ small-world favouring high communication efficiency at a low wiring cost. In this study, we define and explore a network morphospace in order to characterize different aspects of communication efficiency in human brain networks. Using a multi-objective evolutionary approach that approximates a Pareto-optimal set within the morphospace, we investigate the capacity of anatomical brain networks to evolve towards topologies that exhibit optimal information processing features while preserving network cost. This approach allows us to investigate network topologies that emerge under specific selection pressures, thus providing some insight into the selectional forces that may have shaped the network architecture of existing human brains.

Mathematically gifted adolescents mobilize enhanced workspace configuration of theta cortical network during deductive reasoning

Previous studies have established the importance of the fronto-parietal brain network in the information processing of reasoning. At the level of cortical source analysis, this eletroencepalogram (EEG) study investigates the functional reorganization of the theta-band (4-8Hz) neurocognitive network of mathematically gifted adolescents during deductive reasoning. Depending on the dense increase of long-range phase synchronizations in the reasoning process, math-gifted adolescents show more significant adaptive reorganization and enhanced "workspace" configuration in the theta network as compared with average-ability control subjects. The salient areas are mainly located in the anterior cortical vertices of the fronto-parietal network. Further correlation analyses have shown that the enhanced workspace configuration with respect to the global topological metrics of the theta network in math-gifted subjects is correlated with the intensive frontal midline theta (fm theta) response that is related to strong neural effort for cognitive events. These results suggest that by investing more cognitive resources math-gifted adolescents temporally mobilize an enhanced task-related global neuronal workspace, which is manifested as a highly integrated fronto-parietal information processing network during the reasoning process.

Individual diversity of functional brain network economy

On average, brain network economy represents a trade-off between communication efficiency, robustness, and connection cost, although an analogous understanding on an individual level is largely missing. Evaluating resting-state networks of 42 healthy participants with seven Tesla functional magnetic resonance imaging and graph theory revealed that not even half of all possible connections were common across subjects. The strongest similarities among individuals were observed for interhemispheric and/or short-range connections, which may relate to the essential feature of the human brain to develop specialized systems within each hemisphere. Despite this marked variability in individual network architecture, all subjects exhibited equal small-world properties. Furthermore, interdependency between four major network economy metrics was observed across healthy individuals. The characteristic path length was associated with the clustering coefficient (peak correlation r=0.93), the response to network attacks (r=-0.97), and the physical connection cost in three-dimensional space (r=-0.62). On the other hand, clustering was negatively related to attack response (r=-0.75) and connection cost (r=-0.59). Finally, increased connection cost was associated with better response to attacks (r=0.65). This indicates that functional brain networks with high global information transfer also exhibit strong network resilience. However, it seems that these advantages come at the cost of decreased local communication efficiency and increased physical connection cost. Except for wiring length, the results were replicated on a subsample at three Tesla (n=20). These findings highlight the finely tuned interrelationships between different parameters of brain network economy. Moreover, the understanding of the individual diversity of functional brain network economy may provide further insights in the vulnerability to mental and neurological disorders.

The contribution of TMS-EEG coregistration in the exploration of the human cortical connectome

Recent developments in neuroscience have emphasised the importance of integrated distributed networks of brain areas for successful cognitive functioning. Our current understanding is that the brain has a modular organisation in which segregated networks supporting specialised processing are linked through a few long-range connections, ensuring processing integration. Although such architecture is structurally stable, it appears to be flexible in its functioning, enabling long-range connections to regulate the information flow and facilitate communication among the relevant modules, depending on the contingent cognitive demands. Here we show how insights brought by the coregistration of transcranial magnetic stimulation and electroencephalography (TMS-EEG) integrate and support recent models of functional brain architecture. Moreover, we will highlight the types of data that can be obtained through TMS-EEG, such as the timing of signal propagation, the excitatory/inhibitory nature of connections and causality. Last, we will discuss recent emerging applications of TMS-EEG in the study of brain disorders.

Spatial embedding of structural similarity in the cerebral cortex

Recent anatomical tracing studies have yielded substantial amounts of data on the areal connectivity underlying distributed processing in cortex, yet the fundamental principles that govern the large-scale organization of cortex remain unknown. Here we show that functional similarity between areas as defined by the pattern of shared inputs or outputs is a key to understanding the areal network of cortex. In particular, we report a systematic relation in the monkey, human, and mouse cortex between the occurrence of connections from one area to another and their similarity distance. This characteristic relation is rooted in the wiring distance dependence of connections in the brain. We introduce a weighted, spatially embedded random network model that robustly gives rise to this structure, as well as many other spatial and topological properties observed in cortex. These include features that were not accounted for in any previous model, such as the wide range of interareal connection weights. Connections in the model emerge from an underlying distribution of spatially embedded axons, thereby integrating the two scales of cortical connectivity--individual axons and interareal pathways--into a common geometric framework. These results provide insights into the origin of large-scale connectivity in cortex and have important implications for theories of cortical organization.

Unimodal primary sensory cortices are directly connected by long-range horizontal projections in the rat sensory cortex

Research based on functional imaging and neuronal recordings in the barrel cortex subdivision of primary somatosensory cortex (SI) of the adult rat has revealed novel aspects of structure-function relationships in this cortex. Specifically, it has demonstrated that single whisker stimulation evokes subthreshold neuronal activity that spreads symmetrically within gray matter from the appropriate barrel area, crosses cytoarchitectural borders of SI and reaches deeply into other unimodal primary cortices such as primary auditory (AI) and primary visual (VI). It was further demonstrated that this spread is supported by a spatially matching underlying diffuse network of border-crossing, long-range projections that could also reach deeply into AI and VI. Here we seek to determine whether such a network of border-crossing, long-range projections is unique to barrel cortex or characterizes also other primary, unimodal sensory cortices and therefore could directly connect them. Using anterograde (BDA) and retrograde (CTb) tract-tracing techniques, we demonstrate that such diffuse horizontal networks directly and mutually connect VI, AI and SI. These findings suggest that diffuse, border-crossing axonal projections connecting directly primary cortices are an important organizational motif common to all major primary sensory cortices in the rat. Potential implications of these findings for topics including cortical structure-function relationships, multisensory integration, functional imaging, and cortical parcellation are discussed.

Patterns of cortical input to the primary motor area in the marmoset monkey

In primates the primary motor cortex (M1) forms a topographic map of the body, whereby neurons in the medial part of this area control movements involving trunk and hindlimb muscles, those in the intermediate part control movements involving forelimb muscles, and those in the lateral part control movements of facial and other head muscles. This topography is accompanied by changes in cytoarchitectural characteristics, raising the question of whether the anatomical connections also vary between different parts of M1. To address this issue, we compared the patterns of cortical afferents revealed by retrograde tracer injections in different locations within M1 of marmoset monkeys. We found that the entire extent of this area is unified by projections from the dorsocaudal and medial subdivisions of premotor cortex (areas 6DC and 6M), from somatosensory areas 3a, 3b, 1/2, and S2, and from posterior parietal area PE. While cingulate areas projected to all subdivisions, they preferentially targeted the medial part of M1. Conversely, the ventral premotor areas were preferentially connected with the lateral part of M1. Smaller but consistent inputs originated in frontal area 6DR, ventral posterior parietal cortex, the retroinsular cortex, and area TPt. Connections with intraparietal, prefrontal, and temporal areas were very sparse, and variable. Our results demonstrate that M1 is unified by a consistent pattern of major connections, but also shows regional variations in terms of minor inputs. These differences likely reflect requirements for control of voluntary movement involving different body parts.

Anatomy of hierarchy: feedforward and feedback pathways in macaque visual cortex

The laminar location of the cell bodies and terminals of interareal connections determines the hierarchical structural organization of the cortex and has been intensively studied. However, we still have only a rudimentary understanding of the connectional principles of feedforward (FF) and feedback (FB) pathways. Quantitative analysis of retrograde tracers was used to extend the notion that the laminar distribution of neurons interconnecting visual areas provides an index of hierarchical distance (percentage of supragranular labeled neurons [SLN]). We show that: 1) SLN values constrain models of cortical hierarchy, revealing previously unsuspected areal relations; 2) SLN reflects the operation of a combinatorial distance rule acting differentially on sets of connections between areas; 3) Supragranular layers contain highly segregated bottom-up and top-down streams, both of which exhibit point-to-point connectivity. This contrasts with the infragranular layers, which contain diffuse bottom-up and top-down streams; 4) Cell filling of the parent neurons of FF and FB pathways provides further evidence of compartmentalization; 5) FF pathways have higher weights, cross fewer hierarchical levels, and are less numerous than FB pathways. Taken together, the present results suggest that cortical hierarchies are built from supra- and infragranular counterstreams. This compartmentalized dual counterstream organization allows point-to-point connectivity in both bottom-up and top-down directions.

Network hubs in the human brain

Virtually all domains of cognitive function require the integration of distributed neural activity. Network analysis of human brain connectivity has consistently identified sets of regions that are critically important for enabling efficient neuronal signaling and communication. The central embedding of these candidate 'brain hubs' in anatomical networks supports their diverse functional roles across a broad range of cognitive tasks and widespread dynamic coupling within and across functional networks. The high level of centrality of brain hubs also renders them points of vulnerability that are susceptible to disconnection and dysfunction in brain disorders. Combining data from numerous empirical and computational studies, network approaches strongly suggest that brain hubs play important roles in information integration underpinning numerous aspects of complex cognitive function.

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.

The strength of weak connections in the macaque cortico-cortical network

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

Patterns of afferent input to the caudal and rostral areas of the dorsal premotor cortex (6DC and 6DR) in the marmoset monkey

Corticocortical projections to the caudal and rostral areas of dorsal premotor cortex (6DC and 6DR, also known as F2 and F7) were studied in the marmoset monkey. Both areas received their main thalamic inputs from the ventral anterior and ventral lateral complexes, and received dense projections from the medial premotor cortex. However, there were marked differences in their connections with other cortical areas. While 6DR received consistent inputs from prefrontal cortex, area 6DC received few such connections. Conversely, 6DC, but not 6DR, received major projections from the primary motor and somatosensory areas. Projections from the anterior cingulate cortex preferentially targeted 6DC, while the posterior cingulate and adjacent medial wall areas preferentially targeted 6DR. Projections from the medial parietal area PE to 6DC were particularly dense, while intraparietal areas (especially the putative homolog of LIP) were more strongly labeled after 6DR injections. Finally, 6DC and 6DR were distinct in terms of inputs from the ventral parietal cortex: projections to 6DR originated preferentially from caudal areas (PG and OPt), while 6DC received input primarily from rostral areas (PF and PFG). Differences in connections suggest that area 6DR includes rostral and caudal subdivisions, with the former also involved in oculomotor control. These results suggest that area 6DC is more directly involved in the preparation and execution of motor acts, while area 6DR integrates sensory and internally driven inputs for the planning of goal-directed actions. They also provide strong evidence of a homologous organization of the dorsal premotor cortex in New and Old World monkeys.

Rich-club organization of the newborn human brain

Combining diffusion magnetic resonance imaging and network analysis in the adult human brain has identified a set of highly connected cortical hubs that form a "rich club"--a high-cost, high-capacity backbone thought to enable efficient network communication. Rich-club architecture appears to be a persistent feature of the mature mammalian brain, but it is not known when this structure emerges during human development. In this longitudinal study we chart the emergence of structural organization in mid to late gestation. We demonstrate that a rich club of interconnected cortical hubs is already present by 30 wk gestation. Subsequently, until the time of normal birth, the principal development is a proliferation of connections between core hubs and the rest of the brain. We also consider the impact of environmental factors on early network development, and compare term-born neonates to preterm infants at term-equivalent age. Though rich-club organization remains intact following premature birth, we reveal significant disruptions in both in cortical-subcortical connectivity and short-distance corticocortical connections. Rich club organization is present well before the normal time of birth and may provide the fundamental structural architecture for the subsequent emergence of complex neurological functions. Premature exposure to the extrauterine environment is associated with altered network architecture and reduced network capacity, which may in part account for the high prevalence of cognitive problems in preterm infants.

Contributions and challenges for network models in cognitive neuroscience

The confluence of new approaches in recording patterns of brain connectivity and quantitative analytic tools from network science has opened new avenues toward understanding the organization and function of brain networks. Descriptive network models of brain structural and functional connectivity have made several important contributions; for example, in the mapping of putative network hubs and network communities. Building on the importance of anatomical and functional interactions, network models have provided insight into the basic structures and mechanisms that enable integrative neural processes. Network models have also been instrumental in understanding the role of structural brain networks in generating spatially and temporally organized brain activity. Despite these contributions, network models are subject to limitations in methodology and interpretation, and they face many challenges as brain connectivity data sets continue to increase in detail and complexity.

Cortical high-density counterstream architectures

Small-world networks provide an appealing description of cortical architecture owing to their capacity for integration and segregation combined with an economy of connectivity. Previous reports of low-density interareal graphs and apparent small-world properties are challenged by data that reveal high-density cortical graphs in which economy of connections is achieved by weight heterogeneity and distance-weight correlations. These properties define a model that predicts many binary and weighted features of the cortical network including a core-periphery, a typical feature of self-organizing information processing systems. Feedback and feedforward pathways between areas exhibit a dual counterstream organization, and their integration into local circuits constrains cortical computation. Here, we propose a bow-tie representation of interareal architecture derived from the hierarchical laminar weights of pathways between the high-efficiency dense core and periphery.

Cartography and connectomes

The past 25 years have seen great progress in parcellating the cerebral cortex into a mosaic of many distinct areas in mice, monkeys, and humans. Quantitative studies of interareal connectivity have revealed unexpectedly many pathways and a wide range of connection strengths in mouse and macaque cortex. In humans, advances in analyzing "structural" and "functional" connectivity using powerful but indirect noninvasive neuroimaging methods are yielding intriguing insights about brain circuits, their variability across individuals, and their relationship to behavior.

Aberrant topology of striatum's connectivity is associated with the number of episodes in depression

In major depressive disorder, depressive episodes reoccur in ∼60% of cases; however, neural mechanisms of depressive relapse are poorly understood. Depressive episodes are characterized by aberrant topology of the brain's intrinsic functional connectivity network, and the number of episodes is one of the most important predictors for depressive relapse. In this study we hypothesized that specific changes of the topology of intrinsic connectivity interact with the course of episodes in recurrent depressive disorder. To address this hypothesis, we investigated which changes of connectivity topology are associated with the number of episodes in patients, independently of current symptoms and disease duration. Fifty subjects were recruited including 25 depressive patients (two to 10 episodes) and 25 gender- and age-matched control subjects. Resting-state functional magnetic resonance imaging, Harvard-Oxford brain atlas, wavelet-transformation of atlas-shaped regional time-series, and their pairwise Pearson's correlation were used to define individual connectivity matrices. Matrices were analysed by graph-based methods, resulting in outcome measures that were used as surrogates of intrinsic network topology. Topological scores were subsequently compared across groups, and, for patients only, related with the number of depressive episodes and current symptoms by partial correlation analysis. Concerning the whole brain connectivity network of patients, small-world topology was preserved but global efficiency was reduced and global betweenness-centrality increased. Aberrant nodal efficiency and centrality of regional connectivity was found in the dorsal striatum, inferior frontal and orbitofrontal cortex as well as in the occipital and somatosensory cortex. Inferior frontal changes were associated with current symptoms, whereas aberrant right putamen network topology was associated with the number of episodes. Results were controlled for effects of total grey matter volume, medication, and total disease duration. This finding provides first evidence that in major depressive disorder aberrant topology of the right putamen's intrinsic connectivity pattern is associated with the course of depressive episodes, independently of current symptoms, medication status and disease duration. Data suggest that the reorganization of striatal connectivity may interact with the course of episodes in depression thereby contributing to depressive relapse risk.

Cortical dynamics revisited

Recent discoveries on the organisation of the cortical connectome together with novel data on the dynamics of neuronal interactions require an extension of classical concepts on information processing in the cerebral cortex. These new insights justify considering the brain as a complex, self-organised system with nonlinear dynamics in which principles of distributed, parallel processing coexist with serial operations within highly interconnected networks. The observed dynamics suggest that cortical networks are capable of providing an extremely high-dimensional state space in which a large amount of evolutionary and ontogenetically acquired information can coexist and be accessible to rapid parallel search.

A predictive network model of cerebral cortical connectivity based on a distance rule

Recent advances in neuroscience have engendered interest in large-scale brain networks. Using a consistent database of cortico-cortical connectivity, generated from hemisphere-wide, retrograde tracing experiments in the macaque, we analyzed interareal weights and distances to reveal an important organizational principle of brain connectivity. Using appropriate graph theoretical measures, we show that although very dense (66%), the interareal network has strong structural specificity. Connection weights exhibit a heavy-tailed lognormal distribution spanning five orders of magnitude and conform to a distance rule reflecting exponential decay with interareal separation. A single-parameter random graph model based on this rule predicts numerous features of the cortical network: (1) the existence of a network core and the distribution of cliques, (2) global and local binary properties, (3) global and local weight-based communication efficiencies modeled as network conductance, and (4) overall wire-length minimization. These findings underscore the importance of distance and weight-based heterogeneity in cortical architecture and processing.

High-expanding cortical regions in human development and evolution are related to higher intellectual abilities

Cortical surface area has tremendously expanded during human evolution, and similar patterns of cortical expansion have been observed during childhood development. An intriguing hypothesis is that the high-expanding cortical regions also show the strongest correlations with intellectual function in humans. However, we do not know how the regional distribution of correlations between intellectual function and cortical area maps onto expansion in development and evolution. Here, in a sample of 1048 participants, we show that regions in which cortical area correlates with visuospatial reasoning abilities are generally high expanding in both development and evolution. Several regions in the frontal cortex, especially the anterior cingulate, showed high expansion in both development and evolution. The area of these regions was related to intellectual functions in humans. Low-expanding areas were not related to cognitive scores. These findings suggest that cortical regions involved in higher intellectual functions have expanded the most during development and evolution. The radial unit hypothesis provides a common framework for interpretation of the findings in the context of evolution and prenatal development, while additional cellular mechanisms, such as synaptogenesis, gliogenesis, dendritic arborization, and intracortical myelination, likely impact area expansion in later childhood.

Top-down beta rhythms support selective attention via interlaminar interaction: a model

Cortical rhythms have been thought to play crucial roles in our cognitive abilities. Rhythmic activity in the beta frequency band, around 20 Hz, has been reported in recent studies that focused on neural correlates of attention, indicating that top-down beta rhythms, generated in higher cognitive areas and delivered to earlier sensory areas, can support attentional gain modulation. To elucidate functional roles of beta rhythms and underlying mechanisms, we built a computational model of sensory cortical areas. Our simulation results show that top-down beta rhythms can activate ascending synaptic projections from L5 to L4 and L2/3, responsible for biased competition in superficial layers. In the simulation, slow-inhibitory interneurons are shown to resonate to the 20 Hz input and modulate the activity in superficial layers in an attention-related manner. The predicted critical roles of these cells in attentional gain provide a potential mechanism by which cholinergic drive can support selective attention.

Top-down signals originate from higher cognitive areas such as parietal and prefrontal cortex and propagate to earlier stages of the brain. They have been thought to be associated with selective attention, and recent physiological studies suggest that top-down signals in the beta frequency band can support selective attention. In this study, we employ a computational model to investigate potential mechanisms by which top-down beta rhythms can influence neural responses induced by presentation of stimuli. The model includes several cell types, reportedly crucial for generating cortical rhythmic activity in the gamma and beta frequency bands, and the simulation results show that top-down beta rhythms are capable of reproducing experimentally observed attentional effects on neural responses to visual stimuli. These modulatory effects of top-down beta rhythms are mainly induced via activation of ascending inhibition originating from deep layer slow inhibitory interneurons. Since the excitability of slow interneurons can be increased by cholinergic neuromodulators, these interneurons may mediate the effects of cholinergic tone on attention.

Why data coherence and quality is critical for understanding interareal cortical networks

Numerous studies have investigated inter-areal cortical networks using either diffusion MRI or axonal tract-tracing. While both techniques have been used in non-human primates only diffusion MRI can be used in human. The advantage of axonal tract-tracing is that unlike diffusion MRI it has a high single-cell resolution, and most importantly gives the laminar origins and terminations of inter-areal pathways. It, therefore, can be used to obtain the weighted and directed cortical graph. Axonal tract tracing has traditionally been collated from multiple experiments in order to determine the large-scale inter-areal network. Collated data of this kind present numerous problems due to lack of coherence across studies and incomplete exploitation. We have therefore developed a consistent data base which uses standardized experimental and parcellation procedures across brains. Here we review our recent publications analyzing the consistent database obtained from retrograde tracer injections in 29 cortical areas in a parcellation of 91 areas of the macaque cortex. Compared to collated data, our results show that the cortical graph is dense. Density is a graph theoretic measure, and refers to the number of observed connections in a square matrix expressed as a percentage of the possible connections. In our database 66% of the connections that can exist do exist which is considerably higher than the graph densities reported in studies using collated data (7-32%). The consistent data base reports 37% more pathways than previously reported, many of which are unidirectional. This latter and unexpected property has not been reported in earlier studies. Given the high density, the resulting cortical graph shows other unexpected properties. Firstly, the binary specificity is considerably higher than expected. As we show, this property is a consequence of the inter-areal connection probability declining with distance. Secondly, small groups of areas are found to receive high numbers of inputs. This is termed a high domination and is analyzed by a graph theoretic procedure known as a minimum dominating set analysis. We discuss these findings with respect to the long-distance connections, over half of which were previously not reported. These so called new found projections display high specificities and play an important integration role across large regions. It is to be expected that the future examination of the 62 remaining areas will disclose further levels of complexity and enable construction of a weighted directed graph revealing the hierarchical complexity of the cortex.