A Neural Circuit Covarying with Social Hierarchy in Macaques

Despite widespread interest in social dominance, little is known of its neural correlates in primates. We hypothesized that social status in primates might be related to individual variation in subcortical brain regions implicated in other aspects of social and emotional behavior in other mammals. To examine this possibility we used magnetic resonance imaging (MRI), which affords the taking of quantitative measurements noninvasively, both of brain structure and of brain function, across many regions simultaneously. We carried out a series of tests of structural and functional MRI (fMRI) data in 25 group-living macaques. First, a deformation-based morphometric (DBM) approach was used to show that gray matter in the amygdala, brainstem in the vicinity of the raphe nucleus, and reticular formation, hypothalamus, and septum/striatum of the left hemisphere was correlated with social status. Second, similar correlations were found in the same areas in the other hemisphere. Third, similar correlations were found in a second data set acquired several months later from a subset of the same animals. Fourth, the strength of coupling between fMRI-measured activity in the same areas was correlated with social status. The network of subcortical areas, however, had no relationship with the sizes of individuals'' social networks, suggesting the areas had a simple and direct relationship with social status. By contrast a second circuit in cortex, comprising the midsuperior temporal sulcus and anterior and dorsal prefrontal cortex, covaried with both individuals'' social statuses and the social network sizes they experienced. This cortical circuit may be linked to the social cognitive processes that are taxed by life in more complex social networks and that must also be used if an animal is to achieve a high social status.     

Composition and structure of a large online social network in The Netherlands

Limitations in data collection have long been an obstacle in research on friendship networks. Most earlier studies use either a sample of ego-networks, or complete network data on a relatively small group (e.g., a single organization). The rise of online social networking services such as Friendster and Facebook, however, provides researchers with opportunities to study friendship networks on a much larger scale. This study uses complete network data from Hyves, a popular online social networking service in The Netherlands, comprising over eight million members and over 400 million online friendship relations. In the first study of its kind for The Netherlands, I examine the structure of this network in terms of the degree distribution, characteristic path length, clustering, and degree assortativity. Results indicate that this network shares features of other large complex networks, but also deviates in other respects. In addition, a comparison with other online social networks shows that these networks show remarkable similarities.     

Communication Efficiency and Congestion of Signal Traffic in Large-Scale Brain Networks

The complex connectivity of the cerebral cortex suggests that inter-regional communication is a primary function. Using computational modeling, we show that anatomical connectivity may be a major determinant for global information flow in brain networks. A macaque brain network was implemented as a communication network in which signal units flowed between grey matter nodes along white matter paths. Compared to degree-matched surrogate networks, information flow on the macaque brain network was characterized by higher loss rates, faster transit times and lower throughput, suggesting that neural connectivity may be optimized for speed rather than fidelity. Much of global communication was mediated by a “rich club” of hub regions: a sub-graph comprised of high-degree nodes that are more densely interconnected with each other than predicted by chance. First, macaque communication patterns most closely resembled those observed for a synthetic rich club network, but were less similar to those seen in a synthetic small world network, suggesting that the former is a more fundamental feature of brain network topology. Second, rich club regions attracted the most signal traffic and likewise, connections between rich club regions carried more traffic than connections between non-rich club regions. Third, a number of rich club regions were significantly under-congested, suggesting that macaque connectivity actively shapes information flow, funneling traffic towards some nodes and away from others. Together, our results indicate a critical role of the rich club of hub nodes in dynamic aspects of global brain communication.     

时间认知的年老化及其神经机制

时间认知功能的增龄性衰退表现得较晚,主要在高龄老年人中表现出时间认知功能的衰退.随着年龄的增长,老年人内部时钟的速率变慢,变异增大.注意及记忆功能随年龄增长而衰退,这些一般认知功能的改变影响老年人的时间认知功能.正常的时间认知功能依赖于"核心-背景"时间加工脑网络结构和功能的完好,这些脑区或功能通路的障碍会导致时间认知功能的损伤.老年人在一定的年龄范围内,可以通过认知补偿策略保持相对完好的时间认知功能.     

Face Patch Resting State Networks Link Face Processing to Social Cognition

Faces transmit a wealth of social information. How this information is exchanged between face-processing centers and brain areas supporting social cognition remains largely unclear. Here we identify these routes using resting state functional magnetic resonance imaging in macaque monkeys. We find that face areas functionally connect to specific regions within frontal, temporal, and parietal cortices, as well as subcortical structures supporting emotive, mnemonic, and cognitive functions. This establishes the existence of an extended face-recognition system in the macaque. Furthermore, the face patch resting state networks and the default mode network in monkeys show a pattern of overlap akin to that between the social brain and the default mode network in humans: this overlap specifically includes the posterior superior temporal sulcus, medial parietal, and dorsomedial prefrontal cortex, areas supporting high-level social cognition in humans. Together, these results reveal the embedding of face areas into larger brain networks and suggest that the resting state networks of the face patch system offer a new, easily accessible venue into the functional organization of the social brain and into the evolution of possibly uniquely human social skills.     

What to expect from MRI in the investigation of the central nervous system?

Functional magnetic resonance imaging (fMRI) has appeared as a new tool that is very powerful for cognitive neuroscience, offering the potential to look at the dynamics of cerebral processes underlying cognition, non-invasively and on an individual basis. Work remains to be done to optimize the technique and to better understand its basic mechanisms, but one may expect to build in a foreseeable future a functional list of the main brain cortical networks implicated in sensory-motor or cognitive processes. Still, the real understanding of brain function requires direct access to the functional unit consisting of the neuron, so that one may look at the transient temporal relationships that exist between largely distributed groups of hundreds or thousands of neurons. Furthermore, communication pathways between networks, which are carried by brain white matter, must be identified to establish connectivity maps at the individual scale, taking into account individual variability resulting from genetic factors and cerebral plasticity. In this respect, MRI of molecular diffusion is very sensitive to water molecular motion and, thus, to tissue dynamic microstructure, such as cell size and geometry. Preliminary data suggest that diffusion MRI visualizes dynamic tissue changes associated with large neuronal activation and space orientation of large bundles of myelinated axons in the white matter.     

Network cohesion,group size and neocortex size in female-bonded Old World primates

Most primates are intensely social and spend a large amount of time servicing social relationships. In this study, we use social network analysis to examine the relationship between primate group size, total brain size, neocortex ratio and several social network metrics concerned with network cohesion. Using female grooming networks from a number of Old World monkey species, we found that neocortex size was a better predictor of network characteristics than endocranial volumes. We further found that when we controlled for group size, neocortex ratio was negatively correlated with network density, connectivity, relative clan size and proportional clan membership, while there was no effect of neocortex ratio on change in connectivity following the removal of the most central female in the network. Thus, in species with larger neocortex ratios, females generally live in more fragmented networks, belong to smaller grooming clans and are members of relatively fewer clans despite living in a closely bonded group. However, even though groups are more fragmented to begin with among species with larger neocortices, the removal of the most central individual does cause groups to fall apart, suggesting that social complexity may ultimately involve the management of highly fragmented social groups while at the same time maintaining overall social cohesion. These results emphasize a need for more detailed brain data on a wider sample of primate species.     

Molecular differences in Alzheimer's disease between male and female patients determined by integrative network analysis

Alzheimer's disease (AD) is a complex neurodegenerative disease and the most common cause of dementia among the elderly. There has been increasing recognition of sex differences in AD prevalence, clinical manifestation, disease course and prognosis. However, there have been few studies on the molecular mechanism underlying these differences. To address this issue, we carried out global gene expression and integrative network analyses based on expression profiles (GSE84422) across 17 cortical regions of 125 individuals with AD. There were few genes that were differentially expressed across the 17 regions between the two sexes, with only four (encoding glutamate metabotropic receptor 2, oestrogen‐related receptor beta, kinesin family member 26B, and aspartoacylase) that were differentially expressed in three regions. A pan‐cortical brain region co‐expression network analysis identified pathways and genes (eg, glycogen synthase kinase 3β) that were significantly associated with clinical characteristics of AD (such as neurofibrillary score) in males only. Similarity analyses between region‐specific networks indicated that male patients exhibited greater variability, especially in the superior parietal lobule, dorsolateral prefrontal cortex and occipital visual cortex. A network module analysis revealed an association between clinical traits and crosstalk of sex‐specific modules. An examination of temporal and spatial patterns of sex differences in AD showed that molecular networks were more conserved in females than in males in different cortical regions and at different AD stages. These findings provide insight into critical molecular pathways governing sex differences in AD pathology.     

Emergence of Blind Areas in Information Spreading

Recently, contagion-based (disease, information, etc.) spreading on social networks has been extensively studied. In this paper, other than traditional full interaction, we propose a partial interaction based spreading model, considering that the informed individuals would transmit information to only a certain fraction of their neighbors due to the transmission ability in real-world social networks. Simulation results on three representative networks (BA, ER, WS) indicate that the spreading efficiency is highly correlated with the network heterogeneity. In addition, a special phenomenon, namely Information Blind Areas where the network is separated by several information-unreachable clusters, will emerge from the spreading process. Furthermore, we also find that the size distribution of such information blind areas obeys power-law-like distribution, which has very similar exponent with that of site percolation. Detailed analyses show that the critical value is decreasing along with the network heterogeneity for the spreading process, which is complete the contrary to that of random selection. Moreover, the critical value in the latter process is also larger than that of the former for the same network. Those findings might shed some lights in in-depth understanding the effect of network properties on information spreading.     

Selection of preconfigured cell assemblies for representation of novel spatial experiences

Internal representations about the external world can be driven by the external stimuli or can be internally generated in their absence. It has been a matter of debate whether novel stimuli from the external world are instructive over the brain network to create de novo representations or, alternatively, are selecting from existing pre-representations hosted in preconfigured brain networks. The hippocampus is a brain area necessary for normal internally generated spatial–temporal representations and its dysfunctions have resulted in anterograde amnesia, impaired imagining of new experiences, and hallucinations. The compressed temporal sequence of place cell activity in the rodent hippocampus serves as an animal model of internal representation of the external space. Based on our recent results on the phenomenon of novel place cell sequence preplay, we submit that the place cell sequence of a novel spatial experience is determined, in part, by a selection of a set of cellular firing sequences from a repertoire of existing temporal firing sequences in the hippocampal network. Conceptually, this indicates that novel stimuli from the external world select from their pre-representations rather than create de novo our internal representations of the world.     

Associations between age and gray matter volume in anatomical brain networks in middle‐aged to older adults

Aging is associated with cognitive decline, diminished brain function, regional brain atrophy, and disrupted structural and functional brain connectivity. Understanding brain networks in aging is essential, as brain function depends on large‐scale distributed networks. Little is known of structural covariance networks to study inter‐regional gray matter anatomical associations in aging. Here, we investigate anatomical brain networks based on structural covariance of gray matter volume among 370 middle‐aged to older adults of 45–85 years. For each of 370 subjects, we acquired a T1‐weighted anatomical MRI scan. After segmentation of structural MRI scans, nine anatomical networks were defined based on structural covariance of gray matter volume among subjects. We analyzed associations between age and gray matter volume in anatomical networks using linear regression analyses. Age was negatively associated with gray matter volume in four anatomical networks (P < 0.001, corrected): a subcortical network, sensorimotor network, posterior cingulate network, and an anterior cingulate network. Age was not significantly associated with gray matter volume in five networks: temporal network, auditory network, and three cerebellar networks. These results were independent of gender and white matter hyperintensities. Gray matter volume decreases with age in networks containing subcortical structures, sensorimotor structures, posterior, and anterior cingulate cortices. Gray matter volume in temporal, auditory, and cerebellar networks remains relatively unaffected with advancing age.     

The overlapping community structure of structural brain network in young healthy individuals

Community structure is a universal and significant feature of many complex networks in biology, society, and economics. Community structure has also been revealed in human brain structural and functional networks in previous studies. However, communities overlap and share many edges and nodes. Uncovering the overlapping community structure of complex networks remains largely unknown in human brain networks. Here, using regional gray matter volume, we investigated the structural brain network among 90 brain regions (according to a predefined anatomical atlas) in 462 young, healthy individuals. Overlapped nodes between communities were defined by assuming that nodes (brain regions) can belong to more than one community. We demonstrated that 90 brain regions were organized into 5 overlapping communities associated with several well-known brain systems, such as the auditory/language, visuospatial, emotion, decision-making, social, control of action, memory/learning, and visual systems. The overlapped nodes were mostly involved in an inferior-posterior pattern and were primarily related to auditory and visual perception. The overlapped nodes were mainly attributed to brain regions with higher node degrees and nodal efficiency and played a pivotal role in the flow of information through the structural brain network. Our results revealed fuzzy boundaries between communities by identifying overlapped nodes and provided new insights into the understanding of the relationship between the structure and function of the human brain. This study provides the first report of the overlapping community structure of the structural network of the human brain.     

Miniaturization in plethodontid salamanders (Gaudata: Plethodontidae) and its consequences for the brain and visual system

In seven species of plethodontid salamanders (Desmognathus ochrophaeus, Eurycea bislineata, Plethodon cinereus, Batrachoseps attenuatus, Hydromantes italicus, Thorius narisovalis and Bolitoglossa subpalmata), absolute and relative volumes of the eye, the brain, major regions of the brain, and regions containing the major visual and visuomotor centres (i.e. thalamus, praetectum, tectum and tegmentum mesencephali), and the density and number of neurons in these regions were determined. The seven species range from moderately large to extremely small in body size and from the smallest to the largest genome sizes found in terrestrial salamanders. The following processes were observed in miniaturized salamanders with intermediate to large genome and cell sizes (Batrachoseps, Thorius) as compared to small and medium-sized salamanders with small genome and cell sizes: (1) increase in the relative size of the brain, from 3.9 to 12.4% of head volume; (2) reduction in relative size of the ventricles from 10.9 to 5.8% of brain volume; (3) increase in relative volume of those brain regions containing the major visual and visuomotor centres from 29.2 to 37% of brain volume; (4) increase in volume of grey matter relative to white matter, from 33.2 to 44.4% of midbrain volume; (5) increase in volume of tectal relative to tegmental grey matter, from 54.8 to 76.8% of total midbrain volume; (6) increase in neuron packing density in the regions containing the visual centres, from 16 to 31.5%. Because of these compensatory processes, Thorius, the smallest species with a head 1/27 and a brain 1/9 the size of that of the largest one, Hydromantes, has 1/3 as many central visual neurons (58 000 vs. 187 000). Some of these processes found in miniaturized salamanders, such as increase in tectal cell density, also occur in large salamanders with very large genome and cell sizes, viz. in Bolitoglossa (25%) and Hydromantes (29%). Thus, increase in genome size and cell size seem to pose functional problems similar to miniaturization; both cases involve an increase in cell size relative to overall organismal structure.     

Large Scale Brain Functional Networks Support Sentence Comprehension: Evidence from Both Explicit and Implicit Language Tasks

Previous studies have indicated that sentences are comprehended via widespread brain regions in the fronto-temporo-parietal network in explicit language tasks (e.g., semantic congruency judgment tasks), and through restricted temporal or frontal regions in implicit language tasks (e.g., font size judgment tasks). This discrepancy has raised questions regarding a common network for sentence comprehension that acts regardless of task effect and whether different tasks modulate network properties. To this end, we constructed brain functional networks based on 27 subjects’ fMRI data that was collected while performing explicit and implicit language tasks. We found that network properties and network hubs corresponding to the implicit language task were similar to those associated with the explicit language task. We also found common hubs in occipital, temporal and frontal regions in both tasks. Compared with the implicit language task, the explicit language task resulted in greater global efficiency and increased integrated betweenness centrality of the left inferior frontal gyrus, which is a key region related to sentence comprehension. These results suggest that brain functional networks support both explicit and implicit sentence comprehension; in addition, these two types of language tasks may modulate the properties of brain functional networks.     

The Self-Liking Brain: A VBM Study on the Structural Substrate of Self-Esteem

Abundant evidence suggests that self-esteem is an important personality resource for emotion regulation in response to stressful experiences. It was thus hypothesized that the relative grey matter volume of brain regions involved in responding to and coping with stress is related to individual differences in trait self-esteem. Using structural magnetic resonance imaging of 48 healthy adults in conjunction with voxel-based morphometry and diffeomorphic anatomical registration using exponentiated lie algebra (VBM-DARTEL), positive associations between self-esteem and regional grey matter volume were indeed found in the anterior cingulate cortex (ACC), right lateral prefrontal cortex (LPFC), right hippocampus, and left hypothalamus. In addition, self-esteem positively covaried with grey matter volume in the right temporo-parietal junction (TPJ), which has been implicated in pride and theory of mind. The results suggest that persons with low self-esteem have reduced grey matter volume in brain regions that contribute to emotion/stress regulation, pride, and theory of mind. The findings provide novel neuroanatomical evidence for the view that self-esteem constitutes a vital coping resource.     

Inferring general relations between network characteristics from specific network ensembles

Different network models have been suggested for the topology underlying complex interactions in natural systems. These models are aimed at replicating specific statistical features encountered in real-world networks. However, it is rarely considered to which degree the results obtained for one particular network class can be extrapolated to real-world networks. We address this issue by comparing different classical and more recently developed network models with respect to their ability to generate networks with large structural variability. In particular, we consider the statistical constraints which the respective construction scheme imposes on the generated networks. After having identified the most variable networks, we address the issue of which constraints are common to all network classes and are thus suitable candidates for being generic statistical laws of complex networks. In fact, we find that generic, not model-related dependencies between different network characteristics do exist. This makes it possible to infer global features from local ones using regression models trained on networks with high generalization power. Our results confirm and extend previous findings regarding the synchronization properties of neural networks. Our method seems especially relevant for large networks, which are difficult to map completely, like the neural networks in the brain. The structure of such large networks cannot be fully sampled with the present technology. Our approach provides a method to estimate global properties of under-sampled networks in good approximation. Finally, we demonstrate on three different data sets (C. elegans neuronal network, R. prowazekii metabolic network, and a network of synonyms extracted from Roget's Thesaurus) that real-world networks have statistical relations compatible with those obtained using regression models.     

Intelligence and the developing human brain

Determining the brain properties that make people 'brainier' has moved well beyond early demonstrations that increasing intelligence correlates with increasing grey and white matter volumes. Both structural and functional in vivo neuroimaging techniques delineate a distributed network of brain regions, perhaps with a focus in the lateral prefrontal cortex, which varies in extent and connectivity with individual differences in intelligence. Longitudinal studies further show that the neuroanatomic correlates of intelligence are dynamic, changing most rapidly in early childhood. Several promising candidate genes affecting neuronal development and neurotransmission have been proposed that might begin to explain the marked genetic overlap between cortical morphology and intelligence. A major future challenge is to determine the cellular events that underpin the neuroanatomic differences correlated with intelligence.     

EEG-Based Functional Brain Networks: Does the Network Size Matter?

Functional connectivity in human brain can be represented as a network using electroencephalography (EEG) signals. These networks--whose nodes can vary from tens to hundreds--are characterized by neurobiologically meaningful graph theory metrics. This study investigates the degree to which various graph metrics depend upon the network size. To this end, EEGs from 32 normal subjects were recorded and functional networks of three different sizes were extracted. A state-space based method was used to calculate cross-correlation matrices between different brain regions. These correlation matrices were used to construct binary adjacency connectomes, which were assessed with regards to a number of graph metrics such as clustering coefficient, modularity, efficiency, economic efficiency, and assortativity. We showed that the estimates of these metrics significantly differ depending on the network size. Larger networks had higher efficiency, higher assortativity and lower modularity compared to those with smaller size and the same density. These findings indicate that the network size should be considered in any comparison of networks across studies.     

Baboons (Papio anubis) living in larger social groups have bigger brains

The evolutionary origin of Primates' exceptionally large brains is still highly debated. Two competing explanations have received much support: the ecological hypothesis and the social brain hypothesis (SBH). We tested the SBH in (n = 82) baboons (Papio anubis) belonging to the same research centre but housed in groups with size ranging from 2 to 63 individuals. We found that baboons living in larger social groups had larger brains. This effect was driven mainly by white matter volume and to a lesser extent by grey matter volume but not by the cerebrospinal fluid. In comparison, the size of the enclosure, an ecological variable, had no such effect. In contrast to the current re-emphasis on potential ecological drivers of primate brain evolution, the present study provides renewed support for the social brain hypothesis and suggests that the social brain plastically responds to group size. Many factors may well influence brain size, yet accumulating evidence suggests that the complexity of social life might be an important determinant of brain size in primates.     

Cerebral blood flow during rest associates with general intelligence and creativity

Recently, much scientific attention has been focused on resting brain activity and its investigation through such methods as the analysis of functional connectivity during rest (the temporal correlation of brain activities in different regions). However, investigation of the magnitude of brain activity during rest has focused on the relative decrease of brain activity during a task, rather than on the absolute resting brain activity. It is thus necessary to investigate the association between cognitive factors and measures of absolute resting brain activity, such as cerebral blood flow (CBF), during rest (rest-CBF). In this study, we examined this association using multiple regression analyses. Rest-CBF was the dependent variable and the independent variables included two essential components of cognitive functions, psychometric general intelligence and creativity. CBF was measured using arterial spin labeling and there were three analyses for rest-CBF; namely mean gray matter rest-CBF, mean white matter rest-CBF, and regional rest-CBF. The results showed that mean gray and white matter rest-CBF were significantly and positively correlated with individual psychometric intelligence. Furthermore, mean white matter rest-CBF was significantly and positively correlated with creativity. After correcting the effect of mean gray matter rest-CBF the significant and positive correlation between regional rest-CBF in the perisylvian anatomical cluster that includes the left superior temporal gyrus and insula and individual psychometric intelligence was found. Also, regional rest-CBF in the precuneus was significantly and negatively correlated with individual creativity. Significance of these results of regional rest-CBF did not change when the effect of regional gray matter density was corrected. The findings showed mean and regional rest-CBF in healthy young subjects to be correlated with cognitive functions. The findings also suggest that, even in young cognitively intact subjects, resting brain activity (possibly underlain by default cognitive activity or metabolic demand from developed brain structures) is associated with cognitive functions.