Probabilistic Clustering of the Human Connectome Identifies Communities and Hubs

A fundamental assumption in neuroscience is that brain function is constrained by its structural properties. This motivates the idea that the brain can be parcellated into functionally coherent regions based on anatomical connectivity patterns that capture how different areas are interconnected. Several studies have successfully implemented this idea in humans using diffusion weighted MRI, allowing parcellation to be conducted in vivo. Two distinct approaches to connectivity-based parcellation can be identified. The first uses the connection profiles of brain regions as a feature vector, and groups brain regions with similar connection profiles together. Alternatively, one may adopt a network perspective that aims to identify clusters of brain regions that show dense within-cluster and sparse between-cluster connectivity. In this paper, we introduce a probabilistic model for connectivity-based parcellation that unifies both approaches. Using the model we are able to obtain a parcellation of the human brain whose clusters may adhere to either interpretation. We find that parts of the connectome consistently cluster as densely connected components, while other parts consistently result in clusters with similar connections. Interestingly, the densely connected components consist predominantly of major cortical areas, while the clusters with similar connection profiles consist of regions that have previously been identified as the ‘rich club’; regions known for their integrative role in connectivity. Furthermore, the probabilistic model allows quantification of the uncertainty in cluster assignments. We show that, while most clusters are clearly delineated, some regions are more difficult to assign. These results indicate that care should be taken when interpreting connectivity-based parcellations obtained using alternative deterministic procedures.     

Parameterizable consensus connectomes from the Human Connectome Project: the Budapest Reference Connectome Server v3.0

Connections of the living human brain, on a macroscopic scale, can be mapped by a diffusion MR imaging based workflow. Since the same anatomic regions can be corresponded between distinct brains, one can compare the presence or the absence of the edges, connecting the very same two anatomic regions, among multiple cortices. Previously, we have constructed the consensus braingraphs on 1015 vertices first in five, then in 96 subjects in the Budapest Reference Connectome Server v1.0 and v2.0, respectively. Here we report the construction of the version 3.0 of the server, generating the common edges of the connectomes of variously parameterizable subsets of the 1015-vertex connectomes of 477 subjects of the Human Connectome Project’s 500-subject release. The consensus connectomes are downloadable in CSV and GraphML formats, and they are also visualized on the server’s page. The consensus connectomes of the server can be considered as the “average, healthy” human connectome since all of their connections are present in at least k subjects, where the default value of \(k=209\), but it can also be modified freely at the web server. The webserver is available at http://connectome.pitgroup.org.     

Connectotyping: Model Based Fingerprinting of the Functional Connectome

A better characterization of how an individual’s brain is functionally organized will likely bring dramatic advances to many fields of study. Here we show a model-based approach toward characterizing resting state functional connectivity MRI (rs-fcMRI) that is capable of identifying a so-called “connectotype”, or functional fingerprint in individual participants. The approach rests on a simple linear model that proposes the activity of a given brain region can be described by the weighted sum of its functional neighboring regions. The resulting coefficients correspond to a personalized model-based connectivity matrix that is capable of predicting the timeseries of each subject. Importantly, the model itself is subject specific and has the ability to predict an individual at a later date using a limited number of non-sequential frames. While we show that there is a significant amount of shared variance between models across subjects, the model’s ability to discriminate an individual is driven by unique connections in higher order control regions in frontal and parietal cortices. Furthermore, we show that the connectotype is present in non-human primates as well, highlighting the translational potential of the approach.     

Semi-Metric Topology of the Human Connectome: Sensitivity and Specificity to Autism and Major Depressive Disorder

Introduction

The human functional connectome is a graphical representation, consisting of nodes connected by edges, of the inter-relationships of blood oxygenation-level dependent (BOLD) time-series measured by MRI from regions encompassing the cerebral cortices and, often, the cerebellum. Semi-metric analysis of the weighted, undirected connectome distinguishes an edge as either direct (metric), such that there is no alternative path that is accumulatively stronger, or indirect (semi-metric), where one or more alternative paths exist that have greater strength than the direct edge. The sensitivity and specificity of this method of analysis is illustrated by two case-control analyses with independent, matched groups of adolescents with autism spectrum conditions (ASC) and major depressive disorder (MDD).

Results

Significance differences in the global percentage of semi-metric edges was observed in both groups, with increases in ASC and decreases in MDD relative to controls. Furthermore, MDD was associated with regional differences in left frontal and temporal lobes, the right limbic system and cerebellum. In contrast, ASC had a broadly increased percentage of semi-metric edges with a more generalised distribution of effects and some areas of reduction. In summary, MDD was characterised by localised, large reductions in the percentage of semi-metric edges, whilst ASC is characterised by more generalised, subtle increases. These differences were corroborated in greater detail by inspection of the semi-metric backbone for each group; that is, the sub-graph of semi-metric edges present in >90% of participants, and by nodal degree differences in the semi-metric connectome.

Conclusion

These encouraging results, in what we believe is the first application of semi-metric analysis to neuroimaging data, raise confidence in the methodology as potentially capable of detection and characterisation of a range of neurodevelopmental and psychiatric disorders.     

Optimal Information Representation and Criticality in an Adaptive Sensory Recurrent Neuronal Network

Recurrent connections play an important role in cortical function, yet their exact contribution to the network computation remains unknown. The principles guiding the long-term evolution of these connections are poorly understood as well. Therefore, gaining insight into their computational role and into the mechanism shaping their pattern would be of great importance. To that end, we studied the learning dynamics and emergent recurrent connectivity in a sensory network model based on a first-principle information theoretic approach. As a test case, we applied this framework to a model of a hypercolumn in the visual cortex and found that the evolved connections between orientation columns have a "Mexican hat" profile, consistent with empirical data and previous modeling work. Furthermore, we found that optimal information representation is achieved when the network operates near a critical point in its dynamics. Neuronal networks working near such a phase transition are most sensitive to their inputs and are thus optimal in terms of information representation. Nevertheless, a mild change in the pattern of interactions may cause such networks to undergo a transition into a different regime of behavior in which the network activity is dominated by its internal recurrent dynamics and does not reflect the objective input. We discuss several mechanisms by which the pattern of interactions can be driven into this supercritical regime and relate them to various neurological and neuropsychiatric phenomena.     

A Model of Speciation Preconditions in Terms of Percolation and Self-Organized Criticality Theories

Biophysics - Several models have been considered: those that describe mutation fixation in asexually or sexually reproducing species during speciation and are based on stochastic processes and a...     

Behavior of Early Warnings near the Critical Temperature in the Two-Dimensional Ising Model

Among the properties that are common to complex systems, the presence of critical thresholds in the dynamics of the system is one of the most important. Recently, there has been interest in the universalities that occur in the behavior of systems near critical points. These universal properties make it possible to estimate how far a system is from a critical threshold. Several early-warning signals have been reported in time series representing systems near catastrophic shifts. The proper understanding of these early-warnings may allow the prediction and perhaps control of these dramatic shifts in a wide variety of systems. In this paper we analyze this universal behavior for a system that is a paradigm of phase transitions, the Ising model. We study the behavior of the early-warning signals and the way the temporal correlations of the system increase when the system is near the critical point.     

Utero-tubal Embryo Transfer and Vasectomy in the Mouse Model

The transfer of preimplantation embryos to a surrogate female is a required step for the production of genetically modified mice or to study the effects of epigenetic alterations originated during preimplantation development on subsequent fetal development and adult health. The use of an effective and consistent embryo transfer technique is crucial to enhance the generation of genetically modified animals and to determine the effect of different treatments on implantation rates and survival to term. Embryos at the blastocyst stage are usually transferred by uterine transfer, performing a puncture in the uterine wall to introduce the embryo manipulation pipette. The orifice performed in the uterus does not close after the pipette has been withdrawn, and the embryos can outflow to the abdominal cavity due to the positive pressure of the uterus. The puncture can also produce a hemorrhage that impairs implantation, blocks the transfer pipette and may affect embryo development, especially when embryos without zona are transferred. Consequently, this technique often results in very variable and overall low embryo survival rates. Avoiding these negative effects, utero-tubal embryo transfer take advantage of the utero-tubal junction as a natural barrier that impedes embryo outflow and avoid the puncture of the uterine wall. Vasectomized males are required for obtaining pseudopregnant recipients. A technique to perform vasectomy is described as a complement to the utero-tubal embryo transfer.     

Optimal Information Transfer in the Cortex through Synchronization

In recent experimental work it has been shown that neuronal interactions are modulated by neuronal synchronization and that this modulation depends on phase shifts in neuronal oscillations. This result suggests that connections in a network can be shaped through synchronization. Here, we test and expand this hypothesis using a model network. We use transfer entropy, an information theoretical measure, to quantify the exchanged information. We show that transferred information depends on the phase relation of the signal, that the amount of exchanged information increases as a function of oscillations in the signal and that the speed of the information transfer increases as a function of synchronization. This implies that synchronization makes information transport more efficient. In summary, our results reinforce the hypothesis that synchronization modulates neuronal interactions and provide further evidence that gamma band synchronization has behavioral relevance.     

Directional Information Transfer in Protein Helices

IN the course of studies on the relation between conformation and primary sequence in globular proteins, it has become clear that the choice a residue makes between a right handed α-helical conformation (H?) and any other conformation (H) is determined mainly by that residue and its near neighbours in the primary sequence^1–9. When looking for physical mechanisms to explain these findings it is important to know whether the influence of one residue on the conformational state of a neighbouring residue has any directional characteristics. The possibility of certain residues exerting helix-forming influence in either the COOH-terminal or NH₂-terminal direction preferentially is suggested by the non-random distribution of amino-acid residues between the two ends of helical regions in globular proteins^4,10. Ptitsyn's analysis⁴ suggested that a group of residues containing alanine and leucine tends to occur within helical regions, while positively and negatively charged side chain residues are distributed preferentially at the carboxyl-and amino-terminal ends, respectively, of helices.     

人类线粒体转移方法初报

将人肝脏匀浆、差速离心、等密度梯度离心纯化得到线粒体,通过显微注射法将其转移到小鼠的受精卵细胞质内,有61 % 的卵注射后存活,而存活的卵中53 % 能顺利完成第一次卵裂。提示将人类线粒体向实验动物小鼠转移具有可行性     

Stochastic Blockmodeling of the Modules and Core of the Caenorhabditis elegans Connectome

Recently, there has been much interest in the community structure or mesoscale organization of complex networks. This structure is characterised either as a set of sparsely inter-connected modules or as a highly connected core with a sparsely connected periphery. However, it is often difficult to disambiguate these two types of mesoscale structure or, indeed, to summarise the full network in terms of the relationships between its mesoscale constituents. Here, we estimate a community structure with a stochastic blockmodel approach, the Erdős-Rényi Mixture Model, and compare it to the much more widely used deterministic methods, such as the Louvain and Spectral algorithms. We used the Caenorhabditis elegans (C. elegans) nervous system (connectome) as a model system in which biological knowledge about each node or neuron can be used to validate the functional relevance of the communities obtained. The deterministic algorithms derived communities with 4–5 modules, defined by sparse inter-connectivity between all modules. In contrast, the stochastic Erdős-Rényi Mixture Model estimated a community with 9 blocks or groups which comprised a similar set of modules but also included a clearly defined core, made of 2 small groups. We show that the “core-in-modules” decomposition of the worm brain network, estimated by the Erdős-Rényi Mixture Model, is more compatible with prior biological knowledge about the C. elegans nervous system than the purely modular decomposition defined deterministically. We also show that the blockmodel can be used both to generate stochastic realisations (simulations) of the biological connectome, and to compress network into a small number of super-nodes and their connectivity. We expect that the Erdős-Rényi Mixture Model may be useful for investigating the complex community structures in other (nervous) systems.     

Antibody-Dependent Transplacental Transfer of Malaria Blood-Stage Antigen Using a Human Ex Vivo Placental Perfusion Model

Prenatal exposure to allergens or antigens released by infections during pregnancy can stimulate an immune response or induce immunoregulatory networks in the fetus affecting susceptibility to infection and disease later in life. How antigen crosses from the maternal to fetal environment is poorly understood. One hypothesis is that transplacental antigen transfer occurs as immune complexes, via receptor-mediated transport across the syncytiotrophoblastic membrane and endothelium of vessels in fetal villi. This hypothesis has never been directly tested. Here we studied Plasmodium falciparum merozoite surface protein 1 (MSP1) that is released upon erythrocyte invasion. We found MSP1 in cord blood from a third of newborns of malaria-infected women and in >90% following treatment with acid dissociation demonstrating MSP1 immune complexes. Using an ex vivo human placental model that dually perfuses a placental cotyledon with independent maternal and fetal circuits, immune-complexed MSP1 transferred from maternal to fetal circulation. MSP1 alone or with non-immune plasma did not transfer; pre-incubation with human plasma containing anti-MSP1 was required. MSP1 bound to IgG was detected in the fetal perfusate. Laser scanning confocal microscopy demonstrated MSP1 in the fetal villous stroma, predominantly in fetal endothelial cells. MSP1 co-localized with IgG in endothelial cells, but not with placental macrophages. Thus we show, for the first time, antibody-dependent transplacental transfer of an antigen in the form of immune complexes. These studies imply frequent exposure of the fetus to certain antigens with implications for management of maternal infections during pregnancy and novel approaches to deliver vaccines or drugs to the fetus.     

Physiologically Realistic and Validated Mathematical Liver Model Revels Hepatobiliary Transfer Rates for Gd-EOB-DTPA Using Human DCE-MRI Data

Objectives

Diffuse liver disease (DLD), such as non-alcoholic fatty liver disease (NASH) and cirrhosis, is a rapidly growing problem throughout the Westernized world. Magnetic resonance imaging (MRI), based on uptake of the hepatocyte-specific contrast agent (CA) Gd-EOB-DTPA, is a promising non-invasive approach for diagnosing DLD. However, to fully utilize the potential of such dynamic measurements for clinical or research purposes, more advanced methods for data analysis are required.

Methods

A mathematical model that can be used for such data-analysis was developed. Data was obtained from healthy human subjects using a clinical protocol with high spatial resolution. The model is based on ordinary differential equations and goes beyond local diffusion modeling, taking into account the complete system accessible to the CA.

Results

The presented model can describe the data accurately, which was confirmed using chi-square statistics. Furthermore, the model is minimal and identifiable, meaning that all parameters were determined with small degree of uncertainty. The model was also validated using independent data.

Conclusions

We have developed a novel approach for determining previously undescribed physiological hepatic parameters in humans, associated with CA transport across the liver. The method has a potential for assessing regional liver function in clinical examinations of patients that are suffering of DLD and compromised hepatic function.     

A Kinetic Model for the Energy Transfer in Phycobilisomes

A kinetic model for the energy transfer in phycobilisome (PBS) rods of Synechococcus 6301 is presented, based on a set of experimental parameters from picosecond studies. It is shown that the enormous complexity of the kinetic system formed by 400-500 chromophores can be greatly simplified by using symmetry arguments. According to the model the transfer along the phycocyanin rods has to be taken into account in both directions, i.e., back and forth along the rods. The corresponding forward rate constants for single step energy transfer between trimeric disks are predicted to be 100-300 ns^-1. The model that best fits the experimental data is an asymmetric random walk along the rods with overall exciton kinetics that is essentially trap-limited. The transfer process from the sensitizing to the fluorescing C-PC phycocyanin chromophores (τ ≈ 10 ps) is localized in the hexamers. The transfer from the innermost phycocyanin trimer to the core is calculated to be in the range 36-44 ns^-1. These parameters lead to calculated overall rod-core transfer times of 102 and 124 ps for rods containing three and four hexamers, respectively. The model calculations confirm the previously suggested hypothesis that the energy transfer from the rods to the core is essentially described by one dominant exponential function. Extension of the model to heterogeneous PBS rods, i.e., PBS containing also phycoerythrin, is straightforward.     

Direct Transfer and Expression of Human GM-CSF in Tobacco Suspension Cell using Agrobacterium-Mediated Transfer System

A protocol for rapid and efficient plant regeneration from protoplasts of red cabbage was developed by a novel nurse culture method. When the protoplasts of red cabbage were cultured in modified MS medium containing various combinations of BA, NAA and 2,4-D, they did not continue dividing due to browning. However, they successfully divided and formed micro-calli at a high efficiency when they were mixed and co-cultured with those of tuber mustard at a 1:1 ratio. The presence of tuber mustard protoplasts used as nurse cells was essential for sustainable divisions and colony formation of red cabbage protoplasts. Red cabbage-like plantlets were regenerated from these protoplast-derived calli at a frequency ranging from 33 to 56% in all the experiments where three cultivars of red cabbage were tested. Over 120 protoplast-derived cabbage plants were transferred to the greenhouse, and they showed no noticeable abnormalities in morphological features. Chromosome observation revealed that all of the plants examined had the normal chromosome number of cabbage (2n = 18), suggesting that no spontaneous fusion between the two species had occurred during protoplast culture.     

DNA数进制与酶催化的信息传递

通过对DNA中四种碱基进行排列组合的计算,发现DNA是一个多种的数进制系统;20进制信息约占全部数进制信息的4.515%;每种数进制信息由它的产生方式而表现出不同的调控功能;相同的DNA片段用不同的读取方式,可以得到不同的数进制信息.从能量守恒定理和小分子RNA具有酶催化功能推断,酶的催化机制是一个信息传递的过程.     

Brain-Computer Interface Controlled Cyborg: Establishing a Functional Information Transfer Pathway from Human Brain to Cockroach Brain

An all-chain-wireless brain-to-brain system (BTBS), which enabled motion control of a cyborg cockroach via human brain, was developed in this work. Steady-state visual evoked potential (SSVEP) based brain-computer interface (BCI) was used in this system for recognizing human motion intention and an optimization algorithm was proposed in SSVEP to improve online performance of the BCI. The cyborg cockroach was developed by surgically integrating a portable microstimulator that could generate invasive electrical nerve stimulation. Through Bluetooth communication, specific electrical pulse trains could be triggered from the microstimulator by BCI commands and were sent through the antenna nerve to stimulate the brain of cockroach. Serial experiments were designed and conducted to test overall performance of the BTBS with six human subjects and three cockroaches. The experimental results showed that the online classification accuracy of three-mode BCI increased from 72.86% to 78.56% by 5.70% using the optimization algorithm and the mean response accuracy of the cyborgs using this system reached 89.5%. Moreover, the results also showed that the cyborg could be navigated by the human brain to complete walking along an S-shape track with the success rate of about 20%, suggesting the proposed BTBS established a feasible functional information transfer pathway from the human brain to the cockroach brain.     

Clustering of Human Chromosome Fragments on the Mouse Genome by Chromosome-Mediated Gene Transfer

Pieces of metaphase chromosomes prepared from mouse cells containingneo-tagged human chromosome 7 were transferred to mouse cellswith calcium phosphate to isolate G418-resistant clones. FISHanalysis revealed that the majority of them contained humanDNA at a single site on their genome. These transformants containedSTS markers mapped to various regions of chromosome 7. It isthus suggested that pieces of human chromosomes tend to assembleand integrate on the mouse genome.