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BACKGROUND: Connectome is understanding the complex organization of the human brain's structural and functional connectivity is essential for gaining insights into cognitive processes and disorders. OBJECTIVE: To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia. METHOD: By using the proposed structural connectivity-deep graph neural network (sc-DGNN) model and compared with machine learning (ML) and deep learning (DL) models.This work attempts to focus on eighty-eight subjects of diffusion magnetic resonance imaging (dMRI), three classical ML, and five DL models. RESULT: The structural connectivity-deep graph neural network (sc-DGNN) model is proposed to effectively predict dysconnectedness associated with schizophrenia and exhibits superior performance compared to traditional ML and DL (GNNs) methods in terms of accuracy, sensitivity, specificity, precision, F1-score, and Area under receiver operating characteristic (AUC). CONCLUSION: The classification task on schizophrenia using structural connectivity matrices and experimental results showed that linear discriminant analysis (LDA) performed 72% accuracy rate in ML models and sc-DGNN performed at a 93% accuracy rate in DL models to distinguish between schizophrenia and healthy patients. [ABSTRACT FROM AUTHOR]

Gradient nonlinearities in magnetic resonance imaging (MRI) cause spatially varying mismatches between the imposed and the effective gradients and can cause significant biases in rotationally invariant diffusion MRI measures derived from, for example, diffusion tensor imaging. The estimation of the orientational organization of fibrous tissue, which is nowadays frequently performed with spherical deconvolution techniques ideally using higher diffusion weightings, can likewise be biased by gradient nonlinearities. We explore the sensitivity of two established spherical deconvolution approaches to gradient nonlinearities, namely constrained spherical deconvolution (CSD) and damped Richardson‐Lucy (dRL). Additionally, we propose an extension of dRL to take into account gradient imperfections, without the need of data interpolation. Simulations show that using the effective b‐matrix can improve dRL fiber orientation estimation and reduces angular deviations, while CSD can be more robust to gradient nonlinearity depending on the implementation. Angular errors depend on a complex interplay of many factors, including the direction and magnitude of gradient deviations, underlying microstructure, SNR, anisotropy of the effective response function, and diffusion weighting. Notably, angular deviations can also be observed at lower b‐values in contrast to the perhaps common assumption that only high b‐value data are affected. In in vivo Human Connectome Project data and acquisitions from an ultrastrong gradient (300 mT/m) scanner, angular differences are observed between applying and not applying the effective gradients in dRL estimation. As even small angular differences can lead to error propagation during tractography and as such impact connectivity analyses, incorporating gradient deviations into the estimation of fiber orientations should make such analyses more reliable. [ABSTRACT FROM AUTHOR]

Tensegrity structures are three-dimensional networks of truss members loaded in tension or compression. The location of the end points of the truss members, denoted as the nodes, and the associated node-member connectivity matrices are the fundamental descriptors in the modeling and design of tensegrity structures. This paper presents systematic analytical formulas for such node locations and connectivity matrices for tensegrity plates of two different topologies. The formulas apply to plates of any thickness, diameter, and complexity. As application examples, dynamic simulations demonstrating a strategy for morphing the planar plates toward domes are studied. The presented formulas allow for efficient computations and can be employed in the numerical analysis and design of shape-controllable antennas and mirrors, architectural constructions, and other applications based on tensegrity plate and dome-like structures. [ABSTRACT FROM AUTHOR]

A growing number of studies are focusing on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, it is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing the inter- and intra-subject variability of connectivity matrices, as well as graph-theoretical measures, in a large (n = 1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected, as opposed to directed, methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra-subject variabilities in both directed and undirected connectomic measures.

The coexistence of multiple species on a smaller number of limiting resources is an enduring ecological paradox. The mechanisms that maintain such biodiversity are of great interest to ecology and of central importance to conservation. We describe and prove a unique and robust mechanism for coexistence: Species that differ only in their dispersal abilities can coexist, if habitat patches are distributed at irregular distances. This mechanism is straightforward and ecologically intuitive, but can nevertheless create complex coexistence patterns that are robust to substantial environmental stochasticity. The Great Barrier Reef (GBR) is noted for its diversity of reef fish species and its complex arrangement of reef habitat. We demonstrate that this mechanism can allow fish species with different pelagic larval durations to stably coexist in the GBR. Further, coexisting species on the GBR often dominate different subregions, defined primarily by cross-shelf position. Interspecific differences in dispersal ability generate similar coexistence patterns when dispersal is influenced by larval behavior and variable oceanographic conditions. Many marine and terrestrial ecosystems are characterized by patchy habitat distributions and contain coexisting species that have different dispersal abilities. This coexistence mechanism is therefore likely to have ecological relevance beyond reef fish. [ABSTRACT FROM AUTHOR]

Abstract: Understanding the transport and distribution of marine larvae by ocean currents is one of the key goals of population ecology. Here we investigate circulation in the East Australian Current (EAC) and its impact on the transport of larvae and coastal connectivity. A series of Lagrangian particle trajectory experiments are conducted in summer and winter from 1992–2006 which enables us to investigate seasonal and inter-annual variability. We also estimate a mean connectivity state from the average of each of the individual realisations. Connectivity patterns are related to the movement of five individual larval species (two tropical, two temperate and one invasive species) and are found to be in qualitative agreement with historical distribution patterns found along the coast of SE Australia. We use a configuration of the Princeton Ocean Model to investigate physical processes in the ocean along the coast of SE Australia where the circulation is dominated by the EAC, a vigorous western boundary current. We assimilate hydrographic fields from a global analysis into a resolution continental shelf model to create a high-resolution hindcast of ocean state for each summer and winter from 1992–2006. Particles are released along the coast of SE Australia, and at various isobaths across the shelf (25–1000m) over timescales ranging from 10–90 days. Upstream of the EAC separation point across-shelf release location dominates the particle trajectory length scales, whereas seasonality dominates in the southern half of the domain, downstream of the separation point. Lagrangian probability density functions show dispersion pathways vary with release latitude, distance offshore and the timescale of dispersion. Northern (southern) release sites are typified by maximum (minimum) dispersal pathways. Offshore release distance also plays a role having the greatest impact at the mid-latitude release sites. Maximum alongshore dispersion occurs at the mid-latitude release sites such as Sydney. Seasonal variability is also greatest at mid-latitudes, associated with variations in the separation point of the EAC. Climatic variations such as El Niño and La Niña are also shown to play a role in dictating the connectivity patterns. La Niña periods have a tendency to increase summer time connectivity (particularly with offshore release sites) while El Niño periods are shown to increase winter connectivity. The EAC acts as a barrier to the onshore movement of particles offshore, which impacts on the connectivity of offshore release sites. Consequentially particles released inshore of the EAC jet exhibit a greater coastal connectivity than those released offshore of the EAC front. The separation point of the EAC also dictates connectivity with more sites being connected (with lower concentration) downstream of the separation point of the EAC. These results can provide a useful guide to the potential connectivity of marine populations, or the spread of invasive pests (via ballast water or release of propagules from established populations). [Copyright &y& Elsevier]

Ocean color images acquired from the Seaviewing Wide Field-of-view Sensor (SeaWiFS) from 1998 to 2006 were used to examine the patterns of physical connectivity between land and reefs, and among reefs in the Mesoamerican Barrier Reef System (MBRS) in the northwestern Caribbean Sea. Connectivity was inferred by tracking surface water features in weekly climatologies and a time series of weekly mean chlorophyll-a concentrations derived from satellite imagery. Frequency of spatial connections between 17 pre-defined, geomorphological domains that include the major reefs in the MBRS and river deltas in Honduras and Nicaragua were recorded and tabulated as percentage of connections. The 9-year time series of 466 weekly mean images portrays clearly the seasonal patterns of connectivity, including river plumes and transitions in the aftermath of perturbations such as hurricanes. River plumes extended offshore from the Honduras coast to the Bay Islands (Utila, Cayo Cochinos, Guanaja, and Roatán) in 70% of the weekly mean images. Belizean reefs, especially those in the southern section of the barrier reef and Glovers Atoll, were also affected by riverine discharges in every one of the 9 years. Glovers Atoll was exposed to river plumes originating in Honduras 104/466 times (22%) during this period. Plumes from eastern Honduras went as far as Banco Chinchorro and Cozumel in Mexico. Chinchorro appeared to be more frequently connected to Turneffe Atoll and Honduran rivers than with Glovers and Lighthouse Atolls, despite their geographic proximity. This new satellite data analysis provides long-term, quantitative assessments of the main pathways of connectivity in the region. The percentage of connections can be used to validate predictions made using other approaches such as numerical modeling, and provides valuable information to ecosystem-based management in coral reef provinces. [ABSTRACT FROM AUTHOR]

A growing number of studies are focusing on methods to estimate and analyze the functional connectome of the human brain. Graph theoretical measures are commonly employed to interpret and synthesize complex network-related information. While resting state functional MRI (rsfMRI) is often employed in this context, it is known to exhibit poor reproducibility, a key factor which is commonly neglected in typical cohort studies using connectomics-related measures as biomarkers. We aimed to fill this gap by analyzing and comparing the inter- and intra-subject variability of connectivity matrices, as well as graph-theoretical measures, in a large (n = 1003) database of young healthy subjects which underwent four consecutive rsfMRI sessions. We analyzed both directed (Granger Causality and Transfer Entropy) and undirected (Pearson Correlation and Partial Correlation) time-series association measures and related global and local graph-theoretical measures. While matrix weights exhibit a higher reproducibility in undirected, as opposed to directed, methods, this difference disappears when looking at global graph metrics and, in turn, exhibits strong regional dependence in local graphs metrics. Our results warrant caution in the interpretation of connectivity studies, and serve as a benchmark for future investigations by providing quantitative estimates for the inter- and intra-subject variabilities in both directed and undirected connectomic measures. [ABSTRACT FROM AUTHOR]