Theoretical neuroanatomy and the connectivity of the cerebral cortex

Dissociative symptoms and interregional EEG cross-correlations in paranoid schizophrenia

Recent findings indicate that binding and synchronization of distributed activities are crucial for the mechanism of consciousness, and there is increased evidence that disruptions in feature binding produce disintegration of consciousness in schizophrenia. These data suggest that the disrupted binding and disintegration of consciousness could be related to dissociation, which is historically linked to Bleuler's concept of splitting in schizophrenia. In the present study we aimed to investigate relations among electroencephalogram (EEG) activities of cortical sites and used psychometric measures of positive and negative schizophrenia symptoms (Positive and Negative Syndrome Scale) and the Dissociative Experiences Scale (DES) in 58 patients with paranoid schizophrenia. The results show statistically significant Spearman correlations of the DES with cross-correlation function in nine (of 16) EEG pairs. Positive symptoms display significant Spearman correlation with mean of cross-correlation function in only one EEG pair (F4-C4). Results of the Mann-Whitney test between patients with higher (DES > or = 30) and lower dissociation show statistically significant differences between the groups for cross-correlations in nine EEG pairs. The results of this study provide the first supportive evidence for a negative relationship between cross-correlation indices and symptoms of dissociation in schizophrenia.

Human midsagittal brain shape variation: patterns, allometry and integration

Midsagittal cerebral morphology provides a homologous geometrical reference for brain shape and cortical vs. subcortical spatial relationships. In this study, midsagittal brain shape variation is investigated in a sample of 102 humans, in order to describe and quantify the major patterns of correlation between morphological features, the effect of size and sex on general anatomy, and the degree of integration between different cortical and subcortical areas. The only evident pattern of covariation was associated with fronto-parietal cortical bulging. The allometric component was weak for the cortical profile, but more robust for the posterior subcortical areas. Apparent sex differences were evidenced in size but not in brain shape. Cortical and subcortical elements displayed scarcely integrated changes, suggesting a modular separation between these two areas. However, a certain correlation was found between posterior subcortical and parietal cortical variations. These results should be directly integrated with information ranging from functional craniology to wiring organization, and with hypotheses linking brain shape and the mechanical properties of neurons during morphogenesis.

Consciousness as the Emergent Property of the Interaction Between Brain, Body, and Environment

Neuromotor rehabilitation, typically seen with stroke patients, is usually mistakenly focused on the recovery of movements while disregarding the insufficient or missing awareness of the affected part of the body. Thus, the functional recovery of sensorimotor abilities is fundamentally a problem of consciousness. The paper addresses the implications of this concept in the design of optimal robot-assistance in the training of patients, according to the assumption that consciousness is the emergent property of the interaction between brain, body, and environment. Optimal assistance is formulated as a process that follows three basic guidelines: (1) limitation of the assistance level to the minimum value capable of allowing patients to initiate the movements; (2) trial-to-trial reduction of assistance in order to promote the emergence of voluntary control; (3) nonmonotonic modulation from session to session in order to promote memory consolidation.

Tractography-based priors for dynamic causal models

Functional integration in the brain rests on anatomical connectivity (the presence of axonal connections) and effective connectivity (the causal influences mediated by these connections). The deployment of anatomical connections provides important constraints on effective connectivity, but does not fully determine it, because synaptic connections can be expressed functionally in a dynamic and context-dependent fashion. Although it is generally assumed that anatomical connectivity data is important to guide the construction of neurobiologically realistic models of effective connectivity; the degree to which these models actually profit from anatomical constraints has not yet been formally investigated. Here, we use diffusion weighted imaging and probabilistic tractography to specify anatomically informed priors for dynamic causal models (DCMs) of fMRI data. We constructed 64 alternative DCMs, which embodied different mappings between the probability of an anatomical connection and the prior variance of the corresponding of effective connectivity, and fitted them to empirical fMRI data from 12 healthy subjects. Using Bayesian model selection, we show that the best model is one in which anatomical probability increases the prior variance of effective connectivity parameters in a nonlinear and monotonic (sigmoidal) fashion. This means that the higher the likelihood that a given connection exists anatomically, the larger one should set the prior variance of the corresponding coupling parameter; hence making it easier for the parameter to deviate from zero and represent a strong effective connection. To our knowledge, this study provides the first formal evidence that probabilistic knowledge of anatomical connectivity can improve models of functional integration.

Neural complexity and structural connectivity

Tononi [Proc. Natl. Acad. Sci. U.S.A. 91, 5033 (1994)] proposed a measure of neural complexity based on mutual information between complementary subsystems of a given neural network, which has attracted much interest in the neuroscience community and beyond. We develop an approximation of the measure for a popular Gaussian model which, applied to a continuous-time process, elucidates the relationship between the complexity of a neural system and its structural connectivity. Moreover, the approximation is accurate for weakly coupled systems and computationally cheap, scaling polynomially with system size in contrast to the full complexity measure, which scales exponentially. We also discuss connectivity normalization and resolve some issues stemming from an ambiguity in the original Gaussian model.

Hierarchical organization of human cortical networks in health and schizophrenia

The complex organization of connectivity in the human brain is incompletely understood. Recently, topological measures based on graph theory have provided a new approach to quantify large-scale cortical networks. These methods have been applied to anatomical connectivity data on nonhuman species, and cortical networks have been shown to have small-world topology, associated with high local and global efficiency of information transfer. Anatomical networks derived from cortical thickness measurements have shown the same organizational properties of the healthy human brain, consistent with similar results reported in functional networks derived from resting state functional magnetic resonance imaging (MRI) and magnetoencephalographic data. Here we show, using anatomical networks derived from analysis of inter-regional covariation of gray matter volume in MRI data on 259 healthy volunteers, that classical divisions of cortex (multimodal, unimodal, and transmodal) have some distinct topological attributes. Although all cortical divisions shared nonrandom properties of small-worldness and efficient wiring (short mean Euclidean distance between connected regions), the multimodal network had a hierarchical organization, dominated by frontal hubs with low clustering, whereas the transmodal network was assortative. Moreover, in a sample of 203 people with schizophrenia, multimodal network organization was abnormal, as indicated by reduced hierarchy, the loss of frontal and the emergence of nonfrontal hubs, and increased connection distance. We propose that the topological differences between divisions of normal cortex may represent the outcome of different growth processes for multimodal and transmodal networks and that neurodevelopmental abnormalities in schizophrenia specifically impact multimodal cortical organization.

Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain

The brain is a complex dynamic system of functionally connected regions. Graph theory has been successfully used to describe the organization of such dynamic systems. Recent resting-state fMRI studies have suggested that inter-regional functional connectivity shows a small-world topology, indicating an organization of the brain in highly clustered sub-networks, combined with a high level of global connectivity. In addition, a few studies have investigated a possible scale-free topology of the human brain, but the results of these studies have been inconclusive. These studies have mainly focused on inter-regional connectivity, representing the brain as a network of brain regions, requiring an arbitrary definition of such regions. However, using a voxel-wise approach allows for the model-free examination of both inter-regional as well as intra-regional connectivity and might reveal new information on network organization. Especially, a voxel-based study could give information about a possible scale-free organization of functional connectivity in the human brain. Resting-state 3 Tesla fMRI recordings of 28 healthy subjects were acquired and individual connectivity graphs were formed out of all cortical and sub-cortical voxels with connections reflecting inter-voxel functional connectivity. Graph characteristics from these connectivity networks were computed. The clustering-coefficient of these networks turned out to be much higher than the clustering-coefficient of comparable random graphs, together with a short average path length, indicating a small-world organization. Furthermore, the connectivity distribution of the number of inter-voxel connections followed a power-law scaling with an exponent close to 2, suggesting a scale-free network topology. Our findings suggest a combined small-world and scale-free organization of the functionally connected human brain. The results are interpreted as evidence for a highly efficient organization of the functionally connected brain, in which voxels are mostly connected with their direct neighbors forming clustered sub-networks, which are held together by a small number of highly connected hub-voxels that ensure a high level of overall connectivity.

Community structure and modularity in networks of correlated brain activity

Functional connectivity patterns derived from neuroimaging data may be represented as graphs or networks, with individual image voxels or anatomically-defined structures representing the nodes, and a measure of correlation between the responses in each pair of nodes determining the edges. This explicit network representation allows network-analysis approaches to be applied to the characterization of functional connections within the brain. Much recent research in complex networks has focused on methods to identify community structure, i.e. cohesive clusters of strongly interconnected nodes. One class of such algorithms determines a partition of a network into 'sub-networks' based on the optimization of a modularity parameter, thus also providing a measure of the degree of segregation versus integration in the full network. Here, we demonstrate that a community structure algorithm based on the maximization of modularity, applied to a functional connectivity network calculated from the responses to acute fluoxetine challenge in the rat, can identify communities whose distributions correspond to anatomically meaningful structures and include compelling functional subdivisions in the brain. We also discuss the biological interpretation of the modularity parameter in terms of segregation and integration of brain function.

The Brain Connectivity Workshops: moving the frontiers of computational systems neuroscience

Understanding the link between neurobiology and cognition requires that neuroscience moves beyond mere structure-function correlations. An explicit systems perspective is needed in which putative mechanisms of how brain function is constrained by brain structure are mathematically formalized and made accessible for experimental investigation. Such a systems approach critically rests on a better understanding of brain connectivity in its various forms. Since 2002, frontier topics of connectivity and neural system analysis have been discussed in a multidisciplinary annual meeting, the Brain Connectivity Workshop (BCW), bringing together experimentalists and theorists from various fields. This article summarizes some of the main discussions at the two most recent workshops, 2006 at Sendai, Japan, and 2007 at Barcelona, Spain: (i) investigation of cortical micro- and macrocircuits, (ii) models of neural dynamics at multiple scales, (iii) analysis of "resting state" networks, and (iv) linking anatomical to functional connectivity. Finally, we outline some central challenges and research trajectories in computational systems neuroscience for the next years.

A dual-networks architecture of top-down control

Complex systems ensure resilience through multiple controllers acting at rapid and slower timescales. The need for efficient information flow through complex systems encourages small-world network structures. On the basis of these principles, a group of regions associated with top-down control was examined. Functional magnetic resonance imaging showed that each region had a specific combination of control signals; resting-state functional connectivity grouped the regions into distinct 'fronto-parietal' and 'cingulo-opercular' components. The fronto-parietal component seems to initiate and adjust control; the cingulo-opercular component provides stable 'set-maintenance' over entire task epochs. Graph analysis showed dense local connections within components and weaker 'long-range' connections between components, suggesting a small-world architecture. The control systems of the brain seem to embody the principles of complex systems, encouraging resilient performance.

Identification of genetically mediated cortical networks: a multivariate study of pediatric twins and siblings

Structural magnetic resonance imaging data from 308 twins, 64 singleton siblings of twins, and 228 singletons were analyzed using structural equation modeling and selected multivariate methods to identify genetically mediated intracortical associations. Principal components analyses (PCA) of the genetic correlation matrix indicated a single factor accounting for over 60% of the genetic variability in cortical thickness. When covaried for mean global cortical thickness, PCA, cluster analyses, and graph models identified genetically mediated fronto-parietal and occipital networks. Graph theoretical models suggest that the observed genetically mediated relationships follow small world architectural rules. These findings are largely concordant with other multivariate studies of brain structure and function, the twin literature, and current understanding on the role of genes in cortical neurodevelopment.

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.

Directed cortical information flow during human object recognition: analyzing induced EEG gamma-band responses in brain's source space

The increase of induced gamma-band responses (iGBRs; oscillations >30 Hz) elicited by familiar (meaningful) objects is well established in electroencephalogram (EEG) research. This frequency-specific change at distinct locations is thought to indicate the dynamic formation of local neuronal assemblies during the activation of cortical object representations. As analytically power increase is just a property of a single location, phase-synchrony was introduced to investigate the formation of large-scale networks between spatially distant brain sites. However, classical phase-synchrony reveals symmetric, pair-wise correlations and is not suited to uncover the directionality of interactions. Here, we investigated the neural mechanism of visual object processing by means of directional coupling analysis going beyond recording sites, but rather assessing the directionality of oscillatory interactions between brain areas directly. This study is the first to identify the directionality of oscillatory brain interactions in source space during human object recognition and suggests that familiar, but not unfamiliar, objects engage widespread reciprocal information flow. Directionality of cortical information-flow was calculated based upon an established Granger-Causality coupling-measure (partial-directed coherence; PDC) using autoregressive modeling. To enable comparison with previous coupling studies lacking directional information, phase-locking analysis was applied, using wavelet-based signal decompositions. Both, autoregressive modeling and wavelet analysis, revealed an augmentation of iGBRs during the presentation of familiar objects relative to unfamiliar controls, which was localized to inferior-temporal, superior-parietal and frontal brain areas by means of distributed source reconstruction. The multivariate analysis of PDC evaluated each possible direction of brain interaction and revealed widespread reciprocal information-transfer during familiar object processing. In contrast, unfamiliar objects entailed a sparse number of only unidirectional connections converging to parietal areas. Considering the directionality of brain interactions, the current results might indicate that successful activation of object representations is realized through reciprocal (feed-forward and feed-backward) information-transfer of oscillatory connections between distant, functionally specific brain areas.

A scale-free systems theory of motivation and addiction

Scale-free organizations, characterized by uneven distributions of linkages between nodal elements, describe the structure and function of many life-based complex systems developing under evolutionary pressures. We explore motivated behavior as a scale-free map toward a comprehensive translational theory of addiction. Motivational and behavioral repertoires are reframed as link and nodal element sets, respectively, comprising a scale-free structure. These sets are generated by semi-independent information-processing streams within cortical-striatal circuits that cooperatively provide decision-making and sequential processing functions necessary for traversing maps of motivational links connecting behavioral nodes. Dopamine modulation of cortical-striatal plasticity serves a central-hierarchical mechanism for survival-adaptive sculpting and development of motivational-behavioral repertoires by guiding a scale-free design. Drug-induced dopamine activity promotes drug taking as a highly connected behavioral hub at the expense of natural-adaptive motivational links and behavioral nodes. Conceptualizing addiction as pathological alteration of scale-free motivational-behavioral repertoires unifies neurobiological, neurocomputational and behavioral research while addressing addiction vulnerability in adolescence and psychiatric illness. This model may inform integrative research in defining more effective prevention and treatment strategies for addiction.

The topographic brain: from neural connectivity to cognition

A hallmark feature of vertebrate brain organization is ordered topography, wherein sets of neuronal connections preserve the relative organization of cells between two regions. Although topography is often found in projections from peripheral sense organs to the brain, it also seems to participate in the anatomical and functional organization of higher brain centers, for reasons that are poorly understood. We propose that a key function of topography might be to provide computational underpinnings for precise one-to-one correspondences between abstract cognitive representations. This perspective offers a novel conceptualization of how the brain approaches difficult problems, such as reasoning and analogy making, and suggests that a broader understanding of topographic maps could be pivotal in fostering strong links between genetics, neurophysiology and cognition.

Small-world brain networks

Many complex networks have a small-world topology characterized by dense local clustering or cliquishness of connections between neighboring nodes yet a short path length between any (distant) pair of nodes due to the existence of relatively few long-range connections. This is an attractive model for the organization of brain anatomical and functional networks because a small-world topology can support both segregated/specialized and distributed/integrated information processing. Moreover, small-world networks are economical, tending to minimize wiring costs while supporting high dynamical complexity. The authors introduce some of the key mathematical concepts in graph theory required for small-world analysis and review how these methods have been applied to quantification of cortical connectivity matrices derived from anatomical tract-tracing studies in the macaque monkey and the cat. The evolution of small-world networks is discussed in terms of a selection pressure to deliver cost-effective information-processing systems. The authors illustrate how these techniques and concepts are increasingly being applied to the analysis of human brain functional networks derived from electroencephalography/magnetoencephalography and fMRI experiments. Finally, the authors consider the relevance of small-world models for understanding the emergence of complex behaviors and the resilience of brain systems to pathological attack by disease or aberrant development. They conclude that small-world models provide a powerful and versatile approach to understanding the structure and function of human brain systems.

The brainstem reticular formation is a small-world, not scale-free, network

Recently, it has been demonstrated that several complex systems may have simple graph-theoretic characterizations as so-called 'small-world' and 'scale-free' networks. These networks have also been applied to the gross neural connectivity between primate cortical areas and the nervous system of Caenorhabditis elegans. Here, we extend this work to a specific neural circuit of the vertebrate brain--the medial reticular formation (RF) of the brainstem--and, in doing so, we have made three key contributions. First, this work constitutes the first model (and quantitative review) of this important brain structure for over three decades. Second, we have developed the first graph-theoretic analysis of vertebrate brain connectivity at the neural network level. Third, we propose simple metrics to quantitatively assess the extent to which the networks studied are small-world or scale-free. We conclude that the medial RF is configured to create small-world (implying coherent rapid-processing capabilities), but not scale-free, type networks under assumptions which are amenable to quantitative measurement.

The neurocognitive bases of human multimodal food perception: sensory integration

This review addresses a fundamental neuroscientific question in food perception: how multimodal features of food are integrated. Much research and conceptualization has emerged related to multisensory integration in vision, audition and somatosensation, while it remains poorly understood and researched within the chemical and mouth feel senses. This review aims to bridge this gap. We discuss the main concepts in the fields of auditory, visual and somatosensory multisensory integration and relate them to oral-sensory (gustatory and somatosensory) and olfactory (orolfactory) interactions. We systematically review the psychophysical literature pertaining to intra- and intermodal interactions related to food perception, while making explicit distinctions between peripheral and central interactions. As the neural bases of crossmodal orolfaction currently are poorly understood, we introduce several plausible neuroscientific models, which provide a framework for further neuroscientific exploration in this area. We are guided by a new meta-analysis of the odor-taste neuroimaging literature, as well as by single-unit, anatomical and psychophysical studies. Finally, we propose strong involvement of recurrent neural networks in multisensory integration and make suggestions for future research.

Neurophysiological Architecture of Functional Magnetic Resonance Images of Human Brain

We investigated large-scale systems organization of the whole human brain using functional magnetic resonance imaging (fMRI) data acquired from healthy volunteers in a no-task or 'resting' state. Images were parcellated using a prior anatomical template, yielding regional mean time series for each of 90 regions (major cortical gyri and subcortical nuclei) in each subject. Significant pairwise functional connections, defined by the group mean inter-regional partial correlation matrix, were mostly either local and intrahemispheric or symmetrically interhemispheric. Low-frequency components in the time series subtended stronger inter-regional correlations than high-frequency components. Intrahemispheric connectivity was generally related to anatomical distance by an inverse square law; many symmetrical interhemispheric connections were stronger than predicted by the anatomical distance between bilaterally homologous regions. Strong interhemispheric connectivity was notably absent in data acquired from a single patient, minimally conscious following a brainstem lesion. Multivariate analysis by hierarchical clustering and multidimensional scaling consistently defined six major systems in healthy volunteers-- corresponding approximately to four neocortical lobes, medial temporal lobe and subcortical nuclei- - that could be further decomposed into anatomically and functionally plausible subsystems, e.g. dorsal and ventral divisions of occipital cortex. An undirected graph derived by thresholding the healthy group mean partial correlation matrix demonstrated local clustering or cliquishness of connectivity and short mean path length compatible with prior data on small world characteristics of non-human cortical anatomy. Functional MRI demonstrates a neurophysiological architecture of the normal human brain that is anatomically sensible, strongly symmetrical, disrupted by acute brain injury, subtended predominantly by low frequencies and consistent with a small world network topology.

A two-phase growth strategy in cultured neuronal networks as reflected by the distribution of neurite branching angles

Neurite outgrowth and branching patterns are instrumental in dictating the wiring diagram of developing neuronal networks. We study the self-organization of single cultured neurons into complex networks focusing on factors governing the branching of a neurite into its daughter branches. Neurite branching angles of insect ganglion neurons in vitro were comparatively measured in two neuronal categories: neurons in dense cultures that bifurcated under the presence of extrinsic (cellular environment) cues versus neurons in practical isolation that developed their neurites following predominantly intrinsic cues. Our experimental results were complemented by theoretical modeling and computer simulations. A preferred regime of branching angles was found in isolated neurons. A model based on biophysical constraints predicted a preferred bifurcation angle that was consistent with this range shown by our real neurons. In order to examine the origin of the preferred regime of angles we constructed simulations of neurite outgrowth in a developing network and compared the simulated developing neurons with our experimental results. We tested cost functions for neuronal growth that would be optimized at a specific regime of angles. Our results suggest two phases in the process of neuronal development. In the first, reflected by our isolated neurons, neurons are tuned to make first contact with a target cell as soon as possible, to minimize the time of growth. After contact is made, that is, after neuronal interconnections are formed, a second branching strategy is adopted, favoring higher efficiency in neurite length and volume. The two-phase development theory is discussed in relation to previous results.

Topological approach to neural complexity

Considerable effort in modern statistical physics is devoted to the study of networked systems. One of the most important example of them is the brain, which creates and continuously develops complex networks of correlated dynamics. An important quantity which captures fundamental aspects of brain network organization is the neural complexity C(X) introduced by Tononi et al. [Proc. Natl. Acad. Sci. USA 91, 5033 (1994)]. This work addresses the dependence of this measure on the topological features of a network in the case of a Gaussian stationary process. Both analytical and numerical results show that the degree of complexity has a clear and simple meaning from a topological point of view. Moreover, the analytical result offers a straightforward and faster algorithm to compute the complexity of a graph than the standard one.

Neural networks involved in mathematical thinking: evidence from linear and non-linear analysis of electroencephalographic activity

Using linear and non-linear methods, electroencephalographic (EEG) signals were measured at various brain regions to provide information regarding patterns of local and coordinated activity during performance of three arithmetic tasks (number comparison, single-digit multiplication, and two-digit multiplication) and two control tasks that did not require arithmetic operations. It was hypothesized that these measures would reveal the engagement of local and increasingly complex cortical networks as a function of task specificity and complexity. Results indicated regionally increased neuronal signalling as a function of task complexity at frontal, temporal and parietal brain regions, although more robust task-related changes in EEG-indices of activation were derived over the left hemisphere. Both linear and non-linear indices of synchronization among EEG signals recorded from over different brain regions were consistent with the notion of more "local" processing for the number comparison task. Conversely, multiplication tasks were associated with a widespread pattern of distant signal synchronizations, which could potentially indicate increased demands for neural networks cooperation during performance of tasks that involve a greater number of cognitive operations.

An information integration theory of consciousness

BACKGROUND

Consciousness poses two main problems. The first is understanding the conditions that determine to what extent a system has conscious experience. For instance, why is our consciousness generated by certain parts of our brain, such as the thalamocortical system, and not by other parts, such as the cerebellum? And why are we conscious during wakefulness and much less so during dreamless sleep? The second problem is understanding the conditions that determine what kind of consciousness a system has. For example, why do specific parts of the brain contribute specific qualities to our conscious experience, such as vision and audition?

PRESENTATION OF THE HYPOTHESIS

This paper presents a theory about what consciousness is and how it can be measured. According to the theory, consciousness corresponds to the capacity of a system to integrate information. This claim is motivated by two key phenomenological properties of consciousness: differentiation - the availability of a very large number of conscious experiences; and integration - the unity of each such experience. The theory states that the quantity of consciousness available to a system can be measured as the Phi value of a complex of elements. Phi is the amount of causally effective information that can be integrated across the informational weakest link of a subset of elements. A complex is a subset of elements with Phi>0 that is not part of a subset of higher Phi. The theory also claims that the quality of consciousness is determined by the informational relationships among the elements of a complex, which are specified by the values of effective information among them. Finally, each particular conscious experience is specified by the value, at any given time, of the variables mediating informational interactions among the elements of a complex.

TESTING THE HYPOTHESIS

The information integration theory accounts, in a principled manner, for several neurobiological observations concerning consciousness. As shown here, these include the association of consciousness with certain neural systems rather than with others; the fact that neural processes underlying consciousness can influence or be influenced by neural processes that remain unconscious; the reduction of consciousness during dreamless sleep and generalized seizures; and the time requirements on neural interactions that support consciousness.

IMPLICATIONS OF THE HYPOTHESIS

The theory entails that consciousness is a fundamental quantity, that it is graded, that it is present in infants and animals, and that it should be possible to build conscious artifacts.

Integration and segregation of inputs to higher-order neuropils of the crayfish brain

Information about the input and output pathways of higher-order brain neuropils is essential for gaining an understanding of their functions. The present study examines the connectivity of two higher-order neuropils in the central olfactory pathway of the crayfish: the accessory lobe and its target neuropil, the hemiellipsoid body. It is known that the two subregions of the accessory lobe, the cortex and medulla, receive different inputs; the medulla receives visual and tactile inputs, whereas the cortex receives neither (Sandeman et al. [1995] J Comp Neurol 352:263-279). By using dye injections into the olfactory lobe, we demonstrate that the accessory lobe cortex and medulla also have differing connections with the olfactory lobe. These injections show that local interneurons joining the olfactory and accessory lobes branch primarily within the cortex with only limited branching within the medulla. Injections of different dyes into the two subregions of the hemiellipsoid body, HBI and HBII, show that the accessory lobe cortex and medulla also have separate output pathways. HBI is innervated by the output pathway from the cortex while HBII is innervated by the output pathway from the medulla. These injections also show that HBI and HBII are innervated by separate populations of local interneurons with differing connections to higher-order neuropils in the olfactory and visual pathways. These results suggest a segregation of olfactory and multimodal (including olfactory) inputs within both the accessory lobe and the hemiellipsoid body and provide evidence of important functional subdivisions within both neuropils.

Measuring information integration

BACKGROUND

To understand the functioning of distributed networks such as the brain, it is important to characterize their ability to integrate information. The paper considers a measure based on effective information, a quantity capturing all causal interactions that can occur between two parts of a system.

RESULTS

The capacity to integrate information, or Phi, is given by the minimum amount of effective information that can be exchanged between two complementary parts of a subset. It is shown that this measure can be used to identify the subsets of a system that can integrate information, or complexes. The analysis is applied to idealized neural systems that differ in the organization of their connections. The results indicate that Phi is maximized by having each element develop a different connection pattern with the rest of the complex (functional specialization) while ensuring that a large amount of information can be exchanged across any bipartition of the network (functional integration).

CONCLUSION

Based on this analysis, the connectional organization of certain neural architectures, such as the thalamocortical system, are well suited to information integration, while that of others, such as the cerebellum, are not, with significant functional consequences. The proposed analysis of information integration should be applicable to other systems and networks.

Information processing, dimensionality reduction and reinforcement learning in the basal ganglia

Modeling of the basal ganglia has played a major role in our understanding of this elusive group of nuclei. Models of the basal ganglia have undergone evolutionary and revolutionary changes over the last 20 years, as new research in the fields of anatomy, physiology and biochemistry of these nuclei has yielded new information. Early models dealt with a single pathway through the nuclei and focused on the nature of the processing performed within it, convergence of information versus parallel processing of information. Later, the Albin-DeLong "box-and-arrow" model characterized the inter-nuclei interaction as multiple pathways while maintaining a simplistic scalar representation of the nuclei themselves. This model made a breakthrough by providing key insights into the behavior of these nuclei in hypo- and hyper-kinetic movement disorders. The next generation of models elaborated the intra-nuclei interactions and focused on the role of the basal ganglia in action selection and sequence generation which form the most current consensus regarding basal ganglia function in both normal and pathological conditions. However, new findings challenge these models and point to a different neural network approach to information processing in the basal ganglia. Here, we take an in-depth look at the reinforcement driven dimensionality reduction (RDDR) model which postulates that the basal ganglia compress cortical information according to a reinforcement signal using optimal extraction methods. The model provides new insights and experimental predictions on the computational capacity of the basal ganglia and their role in health and disease.

Brain expansion and comparative prenatal ontogeny of the non-hominoid primate cranial base

The basicranium is the keystone of the primate skull, and understanding its morphological interdependence on surrounding soft-tissue structures, such as the brain, can reveal important mechanisms of skull development and evolution. In particular, several extensive investigations have shown that, across extant adult primates, the degree of basicranial flexion and petrous orientation are closely linked to increases in brain size relative to cranial base length. The aim of this study was to determine if an equivalent link exists during prenatal life. Specific hypotheses tested included the idea that increases in relative endocranial size (IRE5), relative infratentorial size (RIE), and differential encephalization (IDE) determine the degree of basicranial flexion and coronal petrous reorientation during non-hominoid primate fetal development. Cross-sectional fetal samples of Alouatta caraya (n=17) and Macaca nemestrina (n=24) were imaged using high-resolution magnetic resonance imaging (hrMRI). Cranial base angles (CBA), petrous orientations (IPA), base lengths, and endocranial volumes were measured from the images. Findings for both samples showed retroflexion, or flattening, of the cranial base and coronal petrous reorientation as well as considerable increases in absolute and relative brain sizes. Although significant correlations of both IRE5 and RIE were observed against CBA and IPA, the correlation with CBA was in the opposite direction to that predicted by the hypotheses. Variations of IDE were not significantly correlated with either angle. Correlations of IPA with IRE5 and RIE appeared to support the hypotheses. However, partial coefficients computed for all significant correlations indicated that changes to the fetal non-hominoid primate cranial base were more closely related to increases in body size than the hypothesized influence of relative brain enlargement. These findings were discussed together with those from a previous study of modern human fetuses.