Murine lymphoid procoagulant activity induced by bacterial lipopolysaccharide and immune complexes is a monocyte prothrombinase

Murine lymphoid cells respond rapidly to bacterial lipopolysaccharide or antigen-antibody complexes to initiate or accelerate the blood coagulation pathways. The monocyte or macrophage has been identified as the cellular source, although lymphocyte collaboration is required for the rapid induction of the procoagulant response. This procoagulant activity is identified in the present study as a direct prothrombin activator, i.e., a prothrombinase. Studies with plasmas deficient in single coagulation factors demonstrate that the induced murine procoagulant activity effector molecule does not require factors XII, VIII, VII, X, or V, but does require prothrombin to transform fibrinogen to fibrin. This enzyme(s) produces limited proteolysis of prothrombin to yield thrombin or thrombinlike products that are functionally capable of converting fibrinogen to fibrin. The prothrombinase is undetectable in freshly isolated Murine lymphoid cells respond rapidly to bacterial lipopolysaccharide or antigen-antibody complexes to initiate or accelerate the blood coagulation pathways. The monocyte or macrophage has been identified as the cellular source, although lymphocyte collaboration is required for the rapid induction of the procoagulant response. This procoagulant activity is identified in the present study as a direct prothrombin activator, i.e., a prothrombinase. Studies with plasmas deficient in single coagulation factors demonstrate that the induced murine procoagulant activity effector molecule does not require factors XII, VIII, VII, X, or V, but does require prothrombin to transform fibrinogen to fibrin. This enzyme(s) produces limited proteolysis of prothrombin to yield thrombin or thrombinlike products that are functionally capable of converting fibrinogen to fibrin. The prothrombinase is undetectable in freshly isolate

Resting State Functional Connectivity in Perfusion Imaging: Correlation Maps with BOLD Connectivity and Resting State Perfusion

Functional connectivity is a property of the resting state that may provide biomarkers of brain function and individual differences. Classically, connectivity is estimated as the temporal correlation of spontaneous fluctuations of BOLD signal. We investigated differences in connectivity estimated from the BOLD and CBF signal present in volumes acquired with arterial spin labeling technique in a large sample (N = 265) of healthy individuals. Positive connectivity was observable in both BOLD and CBF signal, and was present in the CBF signal also at frequencies lower than 0.009 Hz, here investigated for the first time. Negative connectivity was more variable. The validity of positive connectivity was confirmed by the existence of correlation across individuals in its intensity estimated from the BOLD and CBF signal. In contrast, there was little or no correlation across individuals between intensity of connectivity and mean perfusion levels, suggesting that these two biomarkers correspond to distinct sources of individual differences

Functional Brain Network Modularity Captures Inter- and Intra-Individual Variation in Working Memory Capacity

Cognitive abilities, such as working memory, differ among people; however, individuals also vary in their own day-to-day cognitive performance. One potential source of cognitive variability may be fluctuations in the functional organization of neural systems. The degree to which the organization of these functional networks is optimized may relate to the effective cognitive functioning of the individual. Here we specifically examine how changes in the organization of large-scale networks measured via resting state functional connectivity MRI and graph theory track changes in working memory capacity.Twenty-two participants performed a test of working memory capacity and then underwent resting-state fMRI. Seventeen subjects repeated the protocol three weeks later. We applied graph theoretic techniques to measure network organization on 34 brain regions of interest (ROI). Network modularity, which measures the level of integration and segregation across sub-networks, and small-worldness, which measures global network connection efficiency, both predicted individual differences in memory capacity; however, only modularity predicted intra-individual variation across the two sessions. Partial correlations controlling for the component of working memory that was stable across sessions revealed that modularity was almost entirely associated with the variability of working memory at each session. Analyses of specific sub-networks and individual circuits were unable to consistently account for working memory capacity variability.The results suggest that the intrinsic functional organization of an a priori defined cognitive control network measured at rest provides substantial information about actual cognitive performance. The association of network modularity to the variability in an individual's working memory capacity suggests that the organization of this network into high connectivity within modules and sparse connections between modules may reflect effective signaling across brain regions, perhaps through the modulation of signal or the suppression of the propagation of noise

Expression of tissue factor in non-small-cell lung cancers and its relationship to metastasis

Tissue factor (TF) is an initiator of the extrinsic cascade of blood coagulation. Although recent studies have revealed a relationship between metastatic properties and TF expression in some neoplastic cells, the significance of TF in lung cancer, especially in non-small-cell lung cancer (NSCLC), is still unclear. In this study, TF was detected in NSCLC cell lines by functional study, Western blot analysis and immunocytochemical staining. TF levels in eight NSCLC cell lines were also quantitated by enzyme-linked immunosorbent assay (ELISA), and TF expression was evaluated in 55 specimens of surgically resected NSCLCs. NSCLC cell lines derived from metastatic lesions produced high levels of TF (48.3 ± 23.5 ng 10−6 cells, mean ± s.e.m.), whereas those derived from primary lesions produced low levels of TF (0.2 ± 0.1 ng 10−6 cells). Immunohistochemical studies disclosed significantly stronger staining for TF in cells from NSCLC patients with metastasis than in those without metastasis. Among the 28 patients with metastasis, ten were strongly positive, 16 were moderately positive and two were negative for TF. In contrast, among the 27 patients without metastasis, only two were strongly positive, 18 were moderately positive and seven were negative for TF. Therefore, malignant cells from patients with lung cancer produce various levels of TF, and TF may play an important role in the metastatic process. © 1999 Cancer Research Campaig

Development Trends of White Matter Connectivity in the First Years of Life

The human brain is organized into a collection of interacting networks with specialized functions to support various cognitive functions. Recent research has reached a consensus that the brain manifests small-world topology, which implicates both global and local efficiency at minimal wiring costs, and also modular organization, which indicates functional segregation and specialization. However, the important questions of how and when the small-world topology and modular organization come into existence remain largely unanswered. Taking a graph theoretic approach, we attempt to shed light on this matter by an in vivo study, using diffusion tensor imaging based fiber tractography, on 39 healthy pediatric subjects with longitudinal data collected at average ages of 2 weeks, 1 year, and 2 years. Our results indicate that the small-world architecture exists at birth with efficiency that increases in later stages of development. In addition, we found that the networks are broad scale in nature, signifying the existence of pivotal connection hubs and resilience of the brain network to random and targeted attacks. We also observed, with development, that the brain network seems to evolve progressively from a local, predominantly proximity based, connectivity pattern to a more distributed, predominantly functional based, connectivity pattern. These observations suggest that the brain in the early years of life has relatively efficient systems that may solve similar information processing problems, but in divergent ways

Local Signal Time-Series during Rest Used for Areal Boundary Mapping in Individual Human Brains

It is widely thought that resting state functional connectivity likely reflects functional interaction among brain areas and that different functional areas interact with different sets of brain areas. A method for mapping areal boundaries has been formulated based on the large-scale spatial characteristics of regional interaction revealed by resting state functional connectivity. In the present study, we present a novel analysis for areal boundary mapping that requires only the signal timecourses within a region of interest, without reference to the information from outside the region. The areal boundaries were generated by the novel analysis and were compared with those generated by the previously-established standard analysis. The boundaries were robust and reproducible across the two analyses, in two regions of interest tested. These results suggest that the information for areal boundaries is readily available inside the region of interest

Quantifying the Link between Anatomical Connectivity, Gray Matter Volume and Regional Cerebral Blood Flow: An Integrative MRI Study

Background In the graph theoretical analysis of anatomical brain connectivity, the white matter connections between regions of the brain are identified and serve as basis for the assessment of regional connectivity profiles, for example, to locate the hubs of the brain. But regions of the brain can be characterised further with respect to their gray matter volume or resting state perfusion. Local anatomical connectivity, gray matter volume and perfusion are traits of each brain region that are likely to be interdependent, however, particular patterns of systematic covariation have not yet been identified. Methodology/Principal Findings We quantified the covariation of these traits by conducting an integrative MRI study on 23 subjects, utilising a combination of Diffusion Tensor Imaging, Arterial Spin Labeling and anatomical imaging. Based on our hypothesis that local connectivity, gray matter volume and perfusion are linked, we correlated these measures and particularly isolated the covariation of connectivity and perfusion by statistically controlling for gray matter volume. We found significant levels of covariation on the group- and regionwise level, particularly in regions of the Default Brain Mode Network. Conclusions/Significance Connectivity and perfusion are systematically linked throughout a number of brain regions, thus we discuss these results as a starting point for further research on the role of homology in the formation of functional connectivity networks and on how structure/function relationships can manifest in the form of such trait interdependency

Large-Scale Cortical Functional Organization and Speech Perception across the Lifespan

Aging is accompanied by substantial changes in brain function, including functional reorganization of large-scale brain networks. Such differences in network architecture have been reported both at rest and during cognitive task performance, but an open question is whether these age-related differences show task-dependent effects or represent only task-independent changes attributable to a common factor (i.e., underlying physiological decline). To address this question, we used graph theoretic analysis to construct weighted cortical functional networks from hemodynamic (functional MRI) responses in 12 younger and 12 older adults during a speech perception task performed in both quiet and noisy listening conditions. Functional networks were constructed for each subject and listening condition based on inter-regional correlations of the fMRI signal among 66 cortical regions, and network measures of global and local efficiency were computed. Across listening conditions, older adult networks showed significantly decreased global (but not local) efficiency relative to younger adults after normalizing measures to surrogate random networks. Although listening condition produced no main effects on whole-cortex network organization, a significant age group x listening condition interaction was observed. Additionally, an exploratory analysis of regional effects uncovered age-related declines in both global and local efficiency concentrated exclusively in auditory areas (bilateral superior and middle temporal cortex), further suggestive of specificity to the speech perception tasks. Global efficiency also correlated positively with mean cortical thickness across all subjects, establishing gross cortical atrophy as a task-independent contributor to age-related differences in functional organization. Together, our findings provide evidence of age-related disruptions in cortical functional network organization during speech perception tasks, and suggest that although task-independent effects such as cortical atrophy clearly underlie age-related changes in cortical functional organization, age-related differences also demonstrate sensitivity to task domains

Landscape Movements of Migratory Birds and Bats Reveal an Expanded Scale of Stopover

Many species of birds and bats undertake seasonal migrations between breeding and over-wintering sites. En-route, migrants alternate periods of flight with time spent at stopover – the time and space where individuals rest and refuel for subsequent flights. We assessed the spatial scale of movements made by migrants during stopover by using an array of automated telemetry receivers with multiple antennae to track the daily location of individuals over a geographic area ∼20×40 km. We tracked the movements of 322 individuals of seven migratory vertebrate species (5 passerines, 1 owl and 1 bat) during spring and fall migratory stopover on and adjacent to a large lake peninsula. Our results show that many individuals leaving their capture site relocate within the same landscape at some point during stopover, moving as much as 30 km distant from their site of initial capture. We show that many apparent nocturnal departures from stopover sites are not a resumption of migration in the strictest sense, but are instead relocations that represent continued stopover at a broader spatial scale

Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data

Graph-based computational network analysis has proven a powerful tool to quantitatively characterize functional architectures of the brain. However, the test-retest (TRT) reliability of graph metrics of functional networks has not been systematically examined. Here, we investigated TRT reliability of topological metrics of functional brain networks derived from resting-state functional magnetic resonance imaging data. Specifically, we evaluated both short-term (<1 hour apart) and long-term (>5 months apart) TRT reliability for 12 global and 6 local nodal network metrics. We found that reliability of global network metrics was overall low, threshold-sensitive and dependent on several factors of scanning time interval (TI, long-term>short-term), network membership (NM, networks excluding negative correlations>networks including negative correlations) and network type (NT, binarized networks>weighted networks). The dependence was modulated by another factor of node definition (ND) strategy. The local nodal reliability exhibited large variability across nodal metrics and a spatially heterogeneous distribution. Nodal degree was the most reliable metric and varied the least across the factors above. Hub regions in association and limbic/paralimbic cortices showed moderate TRT reliability. Importantly, nodal reliability was robust to above-mentioned four factors. Simulation analysis revealed that global network metrics were extremely sensitive (but varying degrees) to noise in functional connectivity and weighted networks generated numerically more reliable results in compared with binarized networks. For nodal network metrics, they showed high resistance to noise in functional connectivity and no NT related differences were found in the resistance. These findings provide important implications on how to choose reliable analytical schemes and network metrics of interest