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Biological networks, such as cellular metabolic pathways or networks of corticocortical connections in the brain, are intricately organized, yet remarkably robust toward structural damage. Whereas many studies have investigated specific aspects of robustness, such as molecular mechanisms of repair, this article focuses more generally on how local structural features in networks may give rise to their global stability. In many networks the failure of single connections may be more likely than the extinction of entire nodes, yet no analysis of edge importance (edge vulnerability) has been provided so far for biological networks. We tested several measures for identifying vulnerable edges and compared their prediction performance in biological and artificial networks. Among the tested measures, edge frequency in all shortest paths of a network yielded a particularly high correlation with vulnerability and identified intercluster connections in biological but not in random and scale-free benchmark networks. We discuss different local and global network patterns and the edge vulnerability resulting from them.
OBJECTIVE: Functional connectivity maps the distributed network of brain regions fluctuating synchronously during a continuous brain state. This study sought to investigate whether patients with left temporal lobe epilepsy (TLE) differ from controls in their resting-state functional connectivity between typical language regions. METHODS: We studied 17 patients with left TLE, together with eight healthy controls, using seeded functional connectivity. Seed regions were defined using the regions of maximal activation and deactivation during a language functional magnetic resonance imaging (fMRI) task in a separate cohort of 30 controls. RESULTS: Language fMRI produced the expected activation pattern, which was not different between patients and controls. However, functional connectivity between language areas during rest was markedly different; whereas controls showed connectivity between each of the seed areas and the majority of the language areas, patients showed connectivity only with a few areas, particularly the seed area itself. This difference was significant in the direct comparison of patients and control connectivity maps. INTERPRETATION: We suggest that this reduced connectivity in left temporal lobe epilepsy may reflect a disturbance of the language network during resting state in patients and may be related to subtle language difficulties in this patient population.
BACKGROUND: Autistic spectrum disorders (ASD) are neurodevelopmental conditions that may be caused by abnormal connectivity between brain regions constituting neurocognitive networks for specific aspects of social cognition. METHODS: We used three-way multidimensional scaling of regionally parcellated functional magnetic resonance imaging (fMRI) data to explore the hypothesis of abnormal functional connectivity in people with ASD. Thirteen high-functioning individuals with Asperger's syndrome and 13 healthy volunteers were scanned during incidental processing of fearful facial expressions. RESULTS: Using permutation tests for inference, we found evidence for significant abnormality of functional integration of amygdala and parahippocampal gyrus (p < .05, false discovery rate [FDR] corrected) in people with Asperger's syndrome. There were less salient abnormalities in functional connectivity of anterior cingulate, inferior occipital, and inferior frontal cortex, but there was no significant difference between groups in whole brain functional connectivity. CONCLUSIONS: We conclude there is evidence that functional connectivity of medial temporal lobe structures specifically is abnormal in people with Asperger's syndrome during fearful face processing.
Neural networks are formed by accurate connectivity of neurons and glial cells in the brain. These networks employ a three-dimensional bio-surface that both assigns precise coordinates to cells during development and facilitates their connectivity and functionality throughout life. Using specific topographic and chemical features, we have taken steps towards the development of poly(dimethylsiloxane; PDMS) neurochips that can be used to generate and study synthetic neural networks. These neurochips have micropatterned structures that permit adequate cell positioning and support cell survival. Within days of plating, cells differentiate into neurons displaying excitability and communication, as evidenced by intracellular calcium oscillations and action potentials. The structural and functional capacities of such simple neural networks open up new opportunities to study synaptic communication and plasticity.
In this paper we outline a conception of attentional networks arising from imaging studies as connections between activated brain areas carrying out localized mental operations. We consider both the areas of functional activation (nodes) and the structural (DTI) and functional connections (DCM) between them in real time (EEG, frequency analysis) as important tools in analyzing the network. The efficiency of network function involves the time course of activation of nodes and their connectivity to other areas of the network. We outline landmarks in the development of brain networks underlying executive attention from infancy and childhood. We use individual differences in network efficiency to examine genetic alleles that are related to performance. We consider how animal studies might be used to determine the genes that influence network development. Finally we indicate how training may aid in enhancing attentional networks. Our goal is to show the wide range of methods that can be used to suggest and analyze models of network function in the study of attention.
Anatomical, electrophysiological and functional neuroimaging studies show that the human brain is a complex network, where cortico-cortical and thalamo-cortical connections are organized in a specific pattern giving rise to brain function. In our recent studies we found that chemical connectivity between brain regions might be changed in different conditions (e.g. aging, chronic pain, cognitive interference). The elucidation of properties of the human brain multi-chemical networking profile is the subject of this study. In vivo proton magnetic resonance spectroscopy was used to determine relative concentrations of multiple chemicals (N-Acetyl aspartate, choline, glutamate, glutamine, GABA, inositol, glucose, and lactate in relation to creatine/phosphocreatine complex) in 6 brain regions: thalamus, and cingulate, insula, sensorimotor, orbital frontal, and dorsolateral prefrontal cortices. The properties of the brain multi-chemical networking profile within and across the studied regions were examined using correlation analysis. Strong positive correlations were seen between chemicals within brain regions. Negative correlations were primarily seen across brain regions. The cortical connectivity for both neurotransmitters (GABA and glutamate) was stronger than for the other chemicals, and was stronger than for the same neurotransmitters in the thalamus. Factor analysis indicated that the natural clustering of regional chemical concentrations is by brain region and not by chemicals. These findings support the idea for the existence of a specific pattern of multi-chemical networking profile in the brain where the major excitatory and inhibitory neurotransmitters in neocortex perform a regulatory function.
The combined light and electron microscopic analysis of Golgi-impregnated neural tissue is a potent tool for determining the connectivity of neural networks within the brain. In the experimental paradigms commonly applied in these studies, the Golgi-impregnated neurons are typically examined as the postsynaptic neuronal components. The structural characteristics and the pattern of distribution of their synaptic connections with other groups of identified neurons are analyzed. Due to the high power of resolution of the Golgi-electron microscopic technique, the ultrastructural analysis of Golgi-impregnated neurons can be expanded to elucidate activity-dependent structural alterations in their cytoarchitecture. These structural alterations can then be correlated under different physiological conditions with changes in the functional efficacy of the subcellular neuronal components.
Brain activity in people with high-functioning autism has been shown to be atypical in a number of ways, including reduced synchronization across areas of activation measured by functional magnetic resonance imaging. This activation atypicality has been observed mostly during the performance of cognitive tasks. This study compares the resting-state network of 57 participants with autism and 57 control participants matched for age and intelligence quotient. The results indicate that both groups have a resting-state network that is very similar both in volume and in organization, but in autism this network is much more loosely connected. This functional underconnectivity was observed in the anterior-posterior connections. The results expand the theory of cortical underconnectivity in autism to the resting state of the brain.
OBJECTIVE: Glaucoma is a complex neurodegenerative ocular disorder accompanied by brain functional abnormalities that extend beyond the visual system. However, the causal association between the two remains unclear at present. This study aimed to investigate the potential causal relationships between glaucoma and brain functional networks in order to provide novel insights into the neuropathic mechanism of glaucoma. METHODS AND ANALYSIS: Based on the genome-wide association studies data of glaucoma and resting-state functional MRI (Rs-fMRI), a bidirectional Mendelian randomisation (MR) analysis was conducted between glaucoma and brain functional networks. Inverse variance weighting was applied as the primary method to estimate causality with false discovery rate correction. Additional sensitivity analyses were conducted to evaluate the robustness of the results. RESULTS: Forward MR analysis suggested that glaucoma was causally associated with two brain networks between the subcortical cerebellum and the attention or visual network (p=0.022), as well as the default mode and central executive network (p=0.008), but without significance after false discovery rate correction (q>0.1). Reverse MR analysis revealed 19 Rs-fMRI traits related to glaucoma risk, including the salience or central executive network in the frontal region (p=0.0005, q=0.08) and the motor network (p=0.0009, q=0.08) with significant causality. CONCLUSIONS: This MR study revealed potentially causal relationships between glaucoma and brain functional networks. Especially, the functional connectivity of the motor network between the postcentral or precentral areas may potentially lead to increased risk of glaucoma.
At least twenty-two architectonic areas can be distinguished within the orbital and medial prefrontal cortex (OMPFC). Although each of these areas has a distinct structure and connections, they can be grouped into two "networks," defined by cortico-cortical connections that primarily interconnect areas within each network. The networks also have different connections to the striatum, medial thalamus, and other brain regions. The orbital network consists of most of the areas in the orbital cortex. It receives several sensory inputs (olfactory, gustatory, visceral afferent, somatic sensory, and visual) that appear to be related to feeding. It also receives many limbic inputs from the amygdala, entorhinal and perirhinal cortex, and subiculum, including a specific projection from the ventrolateral part of the basal amygdaloid nucleus. The orbital network may therefore serve as a substrate to integrate viscerosensory information with affective signals. The medial network consists of areas on the medial frontal surface together with a few select areas in the orbital cortex. These areas have few direct sensory inputs, and their limbic inputs are somewhat different than those to the orbital network (e.g., from the ventromedial part of the basal amygdaloid nucleus). However, they provide the major output from the OMPFC to the hypothalamus and brain stem (especially the periaqueductal gray). The medial network may therefore serve as a visceromotor system to provide frontal cortical influence over autonomic and endocrine function. Connections between the networks presumably allow information flow from viscerosensory to visceromotor systems. In addition to a probable role in eating behavior, this system appears to be involved in guiding behavior and regulation of mood. Lesions of the ventromedial prefrontal cortex result in sociopathic behavior and difficulty in making appropriate choices, whereas functional imaging studies indicate that subjects with unipolar and bipolar depression have abnormal activity in medial and orbital prefrontal areas. Many of these areas also show volume changes and decreased glial number and density in mood-disordered subjects.
This paper reviews architectonic subdivisions and connections of the orbital and medial prefrontal cortex (OMPFC) in rats, monkeys and humans. Cortico-cortical connections provide the basis for recognition of 'medial' and 'orbital' networks within the OMPFC. These networks also have distinct connections with structures in other parts of the brain. The orbital network receives sensory inputs from several modalities, including olfaction, taste, visceral afferents, somatic sensation and vision, which appear to be especially related to food or eating. In contrast, the medial network provides the major cortical output to visceromotor structures in the hypothalamus and brainstem. The two networks have distinct connections with areas of the striatum and mediodorsal thalamus. In particular, projections to the nucleus accumbens and the adjacent ventromedial caudate and putamen arise predominantly from the medial network. Both networks also have extensive connections with limbic structures. Based on these and other observations, the OMPFC appears to function as a sensory-visceromotor link, especially for eating. This linkage appears to be critical for the guidance of reward-related behavior and for setting of mood. Imaging and histological observations on human brains indicate that clinical depressive disorders are associated with specific functional and cellular changes in the OMPFC, including activity and volume changes, and specific changes in the number of glial cells.
Large-scale neural networks are thought to be an essential substrate for the implementation of cognitive function by the brain. If so, then a thorough understanding of cognition is not possible without knowledge of how the large-scale neural networks of cognition (neurocognitive networks) operate. Of necessity, such understanding requires insight into structural, functional, and dynamical aspects of network operation, the intimate interweaving of which may be responsible for the intricacies of cognition. Knowledge of anatomical structure is basic to understanding how neurocognitive networks operate. Phylogenetically and ontogenetically determined patterns of synaptic connectivity form a structural network of brain areas, allowing communication between widely distributed collections of areas. The function of neurocognitive networks depends on selective activation of anatomically linked cortical and subcortical areas in a wide variety of configurations. Large-scale functional networks provide the cooperative processing which gives expression to cognitive function. The dynamics of neurocognitive network function relates to the evolving patterns of interacting brain areas that express cognitive function in real time. This article considers the proposition that a basic similarity of the structural, functional, and dynamical features of all neurocognitive networks in the brain causes them to function according to common operational principles. The formation of neural context through the coordinated mutual constraint of multiple interacting cortical areas, is considered as a guiding principle underlying all cognitive functions. Increasing knowledge of the operational principles of neurocognitive networks is likely to promote the advancement of cognitive theories, and to seed strategies for the enhancement of cognitive abilities.
Recent neuroscience research is beginning to discover the brain regions involved in decision-making under uncertainty, but little is known about whether or how these regions functionally interact with each other. Here, we used event-related functional magnetic resonance imaging to examine both changes in overall activity and changes in functional connectivity during risk-taking. Results showed that choosing high-risk over low-risk decisions was associated with increased activity in both anterior cingulate and orbitofrontal cortices. Connectivity analyses revealed that largely distinct, but somewhat overlapping, cortical and subcortical regions exhibited significant functional connectivity with anterior cingulate and orbitofrontal cortices. Additionally, connectivity with the anterior cingulate in some regions, including the orbitofrontal cortex and nucleus accumbens, was modulated by the decision participants chose. These findings (1) elucidate large networks of brain regions that are functionally connected with both anterior cingulate and orbitofrontal cortices during decision-making and (2) demonstrate that the roles of orbitofrontal and anterior cingulate cortices can be functionally differentiated by examining patterns of connectivity.
The connection matrix of the human brain (the human "connectome") represents an indispensable foundation for basic and applied neurobiological research. However, the network of anatomical connections linking the neuronal elements of the human brain is still largely unknown. While some databases or collations of large-scale anatomical connection patterns exist for other mammalian species, there is currently no connection matrix of the human brain, nor is there a coordinated research effort to collect, archive, and disseminate this important information. We propose a research strategy to achieve this goal, and discuss its potential impact.
To address the extent to which functional connectivity measures an absolute brain state, we observed the effect of prior performance of a language task on resting-state networks in regions associated with language. Six subjects were imaged during rest before and after a block-design language task. Connectivity maps were generated for each of four language regions (identified from analysis of the language activation portion of the study) in each subject for both rest periods. Conjunction analysis demonstrated distinct networks of voxels for each seed region, indicating separate functional subnetworks associated with the different regions. In a comparison of rest before and after the activation task widespread and significant changes were observed in all individuals, suggesting that the measured resting state network reflects a dynamic image of the current brain state. At the group level, an extended network was observed that was largely persistent over time. Even at the group level an increase in connectivity was observed between left and right middle frontal gyri, and between posterior cingulate cortex and medial frontal cortex in the rest after the language task. These results suggest that functional connectivity may be a powerful measure of cognitive state, sensitive to differences between controls and patients together with the particular cognitive processing occurring during the rest state.
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.
We have identified 52 novel human cadherin-like genes organized into three closely linked clusters. Comparison of the genomic DNA sequences with those of representative cDNAs reveals a striking genomic organization similar to that of immunoglobulin and T cell receptor gene clusters. The N-terminal extracellular and transmembrane domains of each cadherin protein are encoded by a distinct and unusually large exon. These exons are organized in a tandem array. By contrast, the C-terminal cytoplasmic domain of each protein is identical and is encoded by three small exons located downstream from the cluster of N-terminal exons. This unusual organization has interesting implications regarding the molecular code required to establish complex networks of neuronal connections in the brain and the mechanisms of cell-specific cadherin-like gene expression.