Structural Connectivity Networks Research Articles

Analysis of Brain Structural Connectivity Networks and White Matter Integrity in Patients With Mild Cognitive Impairment

White matter integrity and structural connectivity may be altered in mild cognitive impairment (MCI), and these changes may closely reflect decline in specific cognitive domains. Multi-shell diffusion data in healthy control (HC, n = 31) and mild cognitive impairment (MCI, n = 19) cohorts were downloaded from the ADNI3 database. The data were analyzed using an advanced approach to assess both white matter microstructural integrity and structural connectivity. Compared with HC, lower intracellular compartment (IC) and higher isotropic (ISO) values were found in MCI. Additionally, significant correlations were found between IC and Montreal Cognitive Assessment (MoCA) scores in the MCI cohort. Network analysis detected structural connectivity differences between the two groups, with lower connectivity in MCI. Additionally, significant differences between HC and MCI were observed for global network efficiency. Our results demonstrate the potential of advanced diffusion MRI biomarkers for understanding brain changes in MCI.

Structural Brain Connectivity Correlates with Outcome in Mild Traumatic Brain Injury.

We investigated the topology of structural brain connectivity networks and its association with outcome after mild traumatic brain injury, a major cause of permanent disability. Eighty-five patients with mild traumatic brain injury underwent magnetic resonance imaging (MRI) twice, about three weeks and eight months after injury, and 30 age-matched orthopedic trauma control subjects were scanned. Outcome was assessed with Extended Glasgow Outcome Scale on average eight months after injury. We performed constrained spherical deconvolution-based probabilistic streamlines tractography on diffusion MRI data and parcellated cortical and subcortical gray matter into 84 regions based on T1-weighted data to reconstruct structural brain connectivity networks weighted by the number of streamlines. Graph theoretical methods were employed to measure network properties in both patients and controls, and correlations between these properties and outcome were calculated. We found no global differences in the network properties between patients with mild traumatic brain injury and orthopedic control subjects at either stage. We found significantly increased betweenness centrality of the right pars opercularis in the chronic stage compared with control subjects, however. Further, both global and local network properties correlated significantly with outcome. Higher normalized global efficiency, degree, and strength as well as lower small-worldness were associated with better outcome. Correlations between the outcome and the local network properties were the most prominent in the left putamen and the left postcentral gyrus. Our results indicate that both global and local network properties provide valuable information about the outcome already in the acute/subacute stage and, therefore, are promising biomarkers for prognostic purposes in mild traumatic brain injury.

Temporal Graphs and Temporal Network Characteristics for Bio-Inspired Networks during Optimization

Temporal network analysis and time evolution of network characteristics are powerful tools in describing the changing topology of dynamic networks. This paper uses such approaches to better visualize and provide analytical measures for the changes in performance that we observed in Voronoi-type spatial coverage, particularly for the example of time-evolving networks with a changing number of wireless sensors being deployed. Specifically, our analysis focuses on the role different combinations of impenetrable obstacles and environmental noise play in connectivity and overall network structure. It is shown how the use of (i) temporal network graphs, and (ii) network centrality and regularity measures illustrate the differences between various options developed for the balancing act of energy and time efficiency in network coverage. Last, we compare the outcome of these measures with the less abstract classification variables, such as percent area covered and cumulative distance traveled.

The Influence of Public Mental Health Based on Artificial Intelligence Technology on the Teaching Effect of Business Administration Major.

As an emerging technology, public mental health based on artificial intelligence engineering has broad application and development prospects in improving teaching effects, realizing high efficiency and intelligence, and improving refined education. The application of public mental health based on artificial intelligence engineering in the practice of the teaching effect of business administration has a very positive significance for accurately discovering the mental health and teaching effect of business administration students. For the sake of improving the teaching effect of business administration major, we come up with a method for the influence of public mental health on the teaching effect of business administration profession based on artificial intelligence engineering. Firstly, we collect the video and audio of the teaching of business administration, conducts data screening and cleaning, and sorts out the video and audio synchronization clips as training data. Secondly, we propose a multimodal feature extraction network and a multimodal fusion network for the extraction and fusion of video and audio clips, respectively. Then, the fully connected network structure is used to evaluate and classify the effect of business administration professional teaching, and use mental health factors for evaluation to avoid human intervention and improve the prediction effect. Finally, through intensive experimental results, we prove that the method raised by us can use artificial intelligence engineering to evaluate the teaching effect of business administration majors from the perspective of public mental health and achieve good experimental results.

Dynamic Functional Connectivity Better Predicts Disability Than Structural and Static Functional Connectivity in People With Multiple Sclerosis.

Background: Advanced imaging techniques such as diffusion and functional MRI can be used to identify pathology-related changes to the brain's structural and functional connectivity (SC and FC) networks and mapping of these changes to disability and compensatory mechanisms in people with multiple sclerosis (pwMS). No study to date performed a comparison study to investigate which connectivity type (SC, static or dynamic FC) better distinguishes healthy controls (HC) from pwMS and/or classifies pwMS by disability status.Aims: We aim to compare the performance of SC, static FC, and dynamic FC (dFC) in classifying (a) HC vs. pwMS and (b) pwMS who have no disability vs. with disability. The secondary objective of the study is to identify which brain regions' connectome measures contribute most to the classification tasks.Materials and Methods: One hundred pwMS and 19 HC were included. Expanded Disability Status Scale (EDSS) was used to assess disability, where 67 pwMS who had EDSS<2 were considered as not having disability. Diffusion and resting-state functional MRI were used to compute the SC and FC matrices, respectively. Logistic regression with ridge regularization was performed, where the models included demographics/clinical information and either pairwise entries or regional summaries from one of the following matrices: SC, FC, and dFC. The performance of the models was assessed using the area under the receiver operating curve (AUC).Results: In classifying HC vs. pwMS, the regional SC model significantly outperformed others with a median AUC of 0.89 (p <0.05). In classifying pwMS by disability status, the regional dFC and dFC metrics models significantly outperformed others with a median AUC of 0.65 and 0.61 (p < 0.05). Regional SC in the dorsal attention, subcortical and cerebellar networks were the most important variables in the HC vs. pwMS classification task. Increased regional dFC in dorsal attention and visual networks and decreased regional dFC in frontoparietal and cerebellar networks in certain dFC states was associated with being in the group of pwMS with evidence of disability.Discussion: Damage to SCs is a hallmark of MS and, unsurprisingly, the most accurate connectomic measure in classifying patients and controls. On the other hand, dynamic FC metrics were most important for determining disability level in pwMS, and could represent functional compensation in response to white matter pathology in pwMS.

Analysis of brain structural connectivity networks and white matter integrity in patients with mild cognitive impairment

AbstractBackgroundMild cognitive impairment (MCI) is considered a prodromal phase of Alzheimer’s disease (AD) and patients with MCI convert to AD at a rate of about 10–15% per year. Because of this high conversion rate to AD, studies of MCI may yield insight into early pathological changes in AD. Here, we used a novel approach to analyze structural connectivity and white matter (WM) integrity using fractional anisotropy (FA), intra‐cellular (IC), extra‐cellular (EC), and isotropic (ISO) maps between patients with MCI and healthy controls (HC).MethodThirty‐two HC (age mean ± S.D. = 69 ± 6 years; 22 females) and 20 MCI (71 ± 9 years; 7 females) were included in this study. All subjects completed the Mini‐Mental State Exam (MMSE) and the Montreal Cognitive Assessment (MoCA). Multi‐shell DTI were download from ADNI (http://adni.loni.usc.edu/). Pre‐processing was performed by Mrtrix3, FSL, and ANTs. Tractography was performed with 5 million seeds using the iFOD2 algorithm. Connectome was generated from the Desikan‐Killiany parcellation file and was subsequently filtered using the COMMIT2 algorithm. A three‐compartment Stick‐Zeppelin‐Ball model was used to create the IC, the EC, and the ISO compartments, respectively. Statistical analyses (ANCOVA) were performed by connectomestats (Mrtrix3), randomise (FLS), and by an in‐house Matlab script for Spearman’s correlations.ResultThe two groups did differ in MMSE and MoCA (Mann–Whitney U test: Z=2.031; p=0.021; Z=1.683; p=0.046, respectively). Differences between HC and MCI were found principally in the fornix for FA and in several WM areas for IC and ISO (Figure 1, Table 1). Connectometry analysis found differences between groups in two distinct tracts: Tract#1 between the left‐lateraloccipital and the left‐insula (t=4.25, p=0.023) and Tract#2 between the left‐middletemporal and the left‐parsopercularis (t=3.34, p=0.013) (Figure 2). Figure 3 and Table 2 show the results for the correlations between DTI metrics and the neurological scores.ConclusionIn this study, by using a multi‐shell diffusion MRI acquisition and robust processing of DTI data, we detected alterations in WM integrity and structural connectivity in MCI subjects. Our results demonstrate the potential of these advanced diffusion MRI methods as biomarkers to distinguish MCI and HC.

Controllability of Structural Brain Networks and the Waxing and Waning of Negative Affect in Daily Life

BackgroundThe waxing and waning of negative affect in daily life is normative, reflecting an adaptive capacity to respond flexibly to changing circumstances. However, understanding of the brain structure correlates of affective variability in naturalistic settings has been limited. Using network control theory, we examine facets of brain structure that may enable negative affect variability in daily life. MethodsWe used diffusion-weighted imaging data from 95 young adults (age [in years]: mean = 20.19, SD = 1.80; 56 women) to construct structural connectivity networks that map white matter fiber connections between 200 cortical and 14 subcortical regions. We applied network control theory to these structural networks to estimate the degree to which each brain region’s pattern of structural connectivity facilitates the spread of activity to other brain systems. We examined how the average controllability of functional brain systems relates to negative affect variability, computed by taking the standard deviation of negative affect self-reports collected via smartphone-based experience sampling twice per day over 28 days as participants went about their daily lives. ResultsWe found that high average controllability of the cingulo-insular system is associated with increased negative affect variability. We also found that greater negative affect variability is related to the presence of more depressive symptoms, yet average controllability of the cingulo-insular system was not associated with depressive symptoms. ConclusionsOur results highlight the role that brain structure plays in affective dynamics as observed in the context of daily life, suggesting that average controllability of the cingulo-insular system promotes normative negative affect variability.

Brain Structural Changes in Carpal Tunnel Syndrome Patients: From the Perspectives of Structural Connectivity and Structural Covariance Network.

Carpal tunnel syndrome (CTS) is a common peripheral entrapment neuropathy. However, CTS-related changes of brain structural covariance and structural covariance networks (SCNs) patterns have not been clearly studied. To explore CTS-related brain changes from perspectives of structural connectivity and SCNs. Brain structural magnetic resonance images were acquired from 27 CTS patients and 19 healthy controls (HCs). Structural covariance and SCNs were constructed based on gray matter volume. The global network properties including clustering coefficient (Cp), characteristic path length (Lp), small-worldness index, global efficiency (Eglob), and local efficiency (Eloc) and regional network properties including degree, betweenness centrality (BC), and Eloc of a given node were calculated with graph theoretical analysis. Compared with HCs, the strength of structural connectivity between the dorsal anterior insula and medial prefrontal thalamus decreased (P <.001) in CTS patients. There was no intergroup difference of area under the curve for Cp, Lp¸ Eglob, and Eloc (all P >.05). The real-world SCN of CTS patients showed a small-world topology ranging from 2% to 32%. CTS patients showed lower nodal degrees of the dorsal anterior insula and medial prefrontal thalamus, and higher Eloc of a given node and BC in the lateral occipital cortex (P <.001) and the dorsolateral middle temporal gyrus (P <.001) than HCs, respectively. CTS had a profound impact on brain structures from perspectives of structural connectivity and SCNs.

NIMG-31. PROPOSED ORIGINS AND PATHWAYS OF DIFFUSE GLIOMAS REVEALED BY LESION COVARIANCE NETWORKS

Abstract Diffuse gliomas have been hypothesized to originate from neural stem cells in the subventricular zone and develop along previously healthy brain networks. Here, we evaluated these hypotheses by mapping independent sources of glioma localization and determining their relationships with neurogenic niches, genetic markers, and large-scale connectivity networks. Using lesion data from a total of 410 patients with high- and low-grade glioma, we identified – and replicated in an independent sample – three lesion covariance networks (LCNs), which reflect clusters of frequent glioma localization. These three LCNs overlapped with the anterior, posterior, and inferior horns of the lateral ventricles respectively, extending into the frontal, parietal, and temporal cortices. The first LCN, which overlapped with the anterior horn, was associated with low-grade, IDH-mutated/1p19q-codeleted tumors, as well as a neural transcriptomic signature and improved overall survival. Each LCN significantly coincided with multiple structural and functional connectivity networks, with LCN1 bearing an especially strong relationship with brain connectivity, consistent with its neural transcriptomic profile. Finally, we identified subcortical, periventricular structures with functional connectivity patterns to the cortex that significantly matched each LCN. These results build upon prior reports of glioma growth along white matter pathways, as well as evidence for the coordination of glioma stem cell proliferation by neuronal activity. Cumulatively, our findings support a model wherein periventricular brain connectivity guides glioma development from the subventricular zone into distributed brain regions.

Search for schizophrenia and bipolar biotypes using functional network properties.

IntroductionRecent studies support the identification of valid subtypes within schizophrenia and bipolar disorder using cluster analysis. Our aim was to identify meaningful biotypes of psychosis based on network properties of the electroencephalogram. We hypothesized that these parameters would be more altered in a subgroup of patients also characterized by more severe deficits in other clinical, cognitive, and biological measurements.MethodsA clustering analysis was performed using the electroencephalogram‐based network parameters derived from graph‐theory obtained during a P300 task of 137 schizophrenia (of them, 35 first episodes) and 46 bipolar patients. Both prestimulus and modulation of the electroencephalogram were included in the analysis. Demographic, clinical, cognitive, structural cerebral data, and the modulation of the spectral entropy of the electroencephalogram were compared between clusters. Data from 158 healthy controls were included for further comparisons.ResultsWe identified two clusters of patients. One cluster presented higher prestimulus connectivity strength, clustering coefficient, path‐length, and lower small‐world index compared to controls. The modulation of clustering coefficient and path‐length parameters was smaller in the former cluster, which also showed an altered structural connectivity network and a widespread cortical thinning. The other cluster of patients did not show significant differences with controls in the functional network properties. No significant differences were found between patients´ clusters in first episodes and bipolar proportions, symptoms scores, cognitive performance, or spectral entropy modulation.ConclusionThese data support the existence of a subgroup within psychosis with altered global properties of functional and structural connectivity.

A computational model to guide the functional decoupling of two brain regions with transcranial alternating current stimulation

Abstract Recent research has shown that cortical hyper-synchronization between frontoparietal areas in resting state could be an early biomarker of Alzheimer’s disease (AD). It is expected that reducing this synchronization could slow down the development of AD typical symptoms. In this sense, non-invasive neurostimulation techniques are candidate tools to desynchronize distant regions of the brain. In specific, transcranial Alternating Current Stimulation (tACS) can influence the oscillatory dynamics of neuronal groups in the brain specially modifying signals’ frequency and amplitude. Here we explore the possibility of using tACS to desynchronize frontoparietal brain regions: would this be possible? What frequencies would be the most suitable to minimize synchronization? Thus, we built ten personalized brain models in The Virtual Brain from magnetic resonance imaging and magnetoencephalography recordings. They are built of a structural connectivity network linking brain regions (from AAL2 atlas) and neural mass models per region to simulate electrophysiological dynamics. We applied tACS to stimulate those virtual brains areas on the parietal cortex (P3, P4 positions), using ROAST to calculate a current propagation model that determines how much electric field reaches each part of the brain. Our results show that it is possible to reduce cortical synchronization between two regions with tACS applying frequencies around the natural (i.e. in resting state) oscillation frequency of the stimulated region. These results could lead the experimental search of brain desynchronization with tACS on real subjects. Additionally, they strengthen the possibility of using that technique as an early intervention in Alzheimer’s disease. Keywords: Simulation, The virtual brain, Magnetoencephalography, tACS

Lesion covariance networks reveal proposed origins and pathways of diffuse gliomas.

Diffuse gliomas have been hypothesized to originate from neural stem cells in the subventricular zone and develop along previously healthy brain networks. Here, we evaluated these hypotheses by mapping independent sources of glioma localization and determining their relationships with neurogenic niches, genetic markers and large-scale connectivity networks. By applying independent component analysis to lesion data from 242 adult patients with high- and low-grade glioma, we identified three lesion covariance networks, which reflect clusters of frequent glioma localization. Replicability of the lesion covariance networks was assessed in an independent sample of 168 glioma patients. We related the lesion covariance networks to important clinical variables, including tumour grade and patient survival, as well as genomic information such as molecular genetic subtype and bulk transcriptomic profiles. Finally, we systematically cross-correlated the lesion covariance networks with structural and functional connectivity networks derived from neuroimaging data of over 4000 healthy UK BioBank participants to uncover intrinsic brain networks that may that underlie tumour development. The three lesion covariance networks overlapped with the anterior, posterior and inferior horns of the lateral ventricles respectively, extending into the frontal, parietal and temporal cortices. These locations were independently replicated. The first lesion covariance network, which overlapped with the anterior horn, was associated with low-grade, isocitrate dehydrogenase -mutated/1p19q-codeleted tumours, as well as a neural transcriptomic signature and improved overall survival. Each lesion covariance network significantly coincided with multiple structural and functional connectivity networks, with the first bearing an especially strong relationship with brain connectivity, consistent with its neural transcriptomic profile. Finally, we identified subcortical, periventricular structures with functional connectivity patterns to the cortex that significantly matched each lesion covariance network. In conclusion, we demonstrated replicable patterns of glioma localization with clinical relevance and spatial correspondence with large-scale functional and structural connectivity networks. These results are consistent with prior reports of glioma growth along white matter pathways, as well as evidence for the coordination of glioma stem cell proliferation by neuronal activity. Our findings describe how the locations of gliomas relate to their proposed subventricular origins, suggesting a model wherein periventricular brain connectivity guides tumour development.

Decoupling of Structural and Functional Connectivity in Hubs and Cognitive Impairment After Mild Traumatic Brain Injury.

Introduction: Mild traumatic brain injury (mild TBI) exhibited abnormal brain network topologies associated with cognitive dysfunction. However, it was still unclear which aspects of network organization were critical underlying the key pathology of mild TBI. Here, a multi-imaging strategy was applied to capture dynamic topological features of both structural and functional connectivity networks (SCNs and FCNs), to provide more sensitive detection of altered FCN from its anatomical backbone and identify novel biomarkers of mild TBI outcomes. Methods: A total of 62 mild TBI patients (30 subjects as an original sample with 3-12 months of follow-up, 32 subjects as independent replicated sample) and 37 healthy controls were recruited. Both diffusion tensor imaging and resting-state functional magnetic resonance imaging were used to create global connectivity matrices in the same individuals. Global and regional network analyses were applied to identify group differences and correlations with clinical assessments. Results: Most global network properties were conserved in both SCNs and FCNs in subacute mild TBI, whereas SCNs presented decreased global efficiency and characteristic path length at follow-up. Specifically, some hubs in healthy brain networks typically became nonhubs in patients and vice versa, such as the medial prefrontal cortex, superior temporal gyrus, and middle frontal gyrus. The relationship between structural and functional connectivity (SC and FC) in patients also showed salient decoupling as a function of time, primarily located in the hubs. Conclusions: These results suggested mild TBI influences the relationship between SCN and FCN, and the SC-FC coupling strength may be used as a potential biomarker to predict long-term outcomes after injury. Impact statement The current study was the first study to explore the coupling of structural-functional connectivity (SC-FC) from large-scale whole-brain networks in patients with mild traumatic brain injury. The results showed that the global properties homeostatically conserved in functional connectivity networks while altered in structural connectivity networks. The SC-FC coupling strength showed salient decoupling as a function of time and primarily occurred in hub regions.

Alterations of structural connectivity and structural co-variance network in focal cortical dysplasia

BackgroundThe aim of this study was to investigate alterations in structural connectivity and structural co-variance network in patients with focal cortical dysplasia (FCD).MethodsWe enrolled 37 patients with FCD and 35 healthy controls. All subjects underwent brain MRI with the same scanner and with the same protocol, which included diffusion tensor imaging (DTI) and T1-weighted imaging. We analyzed the structural connectivity based on DTI, and structural co-variance network based on the structural volume with T1-weighted imaging. We created a connectivity matrix and obtained network measures from the matrix using the graph theory. We tested the difference in network measure between patients with FCD and healthy controls.ResultsIn the structural connectivity analysis, we found that the local efficiency in patients with FCD was significantly lower than in healthy controls (2.390 vs. 2.578, p = 0.031). Structural co-variance network analysis revealed that the mean clustering coefficient, global efficiency, local efficiency, and transitivity were significantly decreased in patients with FCD compared to those in healthy controls (0.527 vs. 0.635, p = 0.036; 0.545 vs. 0.648, p = 0.026; 2.699 vs. 3.801, p = 0.019; 0.791 vs. 0.954, p = 0.026, respectively).ConclusionsWe demonstrate that there are significant alterations in structural connectivity, based on DTI, and structural co-variance network, based on the structural volume, in patients with FCD compared to healthy controls. These findings suggest that focal lesions with FCD could affect the whole-brain network and that FCD is a network disease.

Differences in structural and functional default mode network connectivity in amyloid positive mild cognitive impairment: a longitudinal study.

Default mode network (DMN) has emerged as a potential biomarker of Alzheimer's disease (AD); however, it is not clear whether it can differentiate amnestic mild cognitive impairment with altered amyloid (aMCI-Aβ +) who will evolve to AD. We evaluated if structural and functional connectivity (FC), hippocampal volumes (HV), and cerebrospinal fluid biomarkers (CSF-Aβ42, p-Tau, and t-Tau) can differentiate aMCI-Aβ + converters from non-converters. Forty-eight individuals (18 normal controls and 30 aMCI subjects in the AD continuum - with altered Aβ42 in the CSF) were followed up for an average of 13months. We used MultiAtlas, UF2C, and Freesurfer software to evaluate diffusion tensor imaging, FC, and HV, respectively, INNOTEST® kits to measure CSF proteins, and neuropsychological tests. Besides, we performed different MANOVAs with further univariate analyses to differentiate groups. During follow-up, 8/30 aMCI-Aβ + converted (26.6%) to AD dementia. There were no differences in multivariate analysis between groups in CSF biomarkers (p = 0.092) or at DMN functional connectivity (p = 0.814). aMCI-Aβ + converters had smaller right HV than controls (p = 0.013), and greater right cingulum parahippocampal bundle radial diffusivity than controls (p < 0.001) and non-converters (p = 0.036). In this exploratory study, structural, but not functional, DMN connectivity alterations may differentiate aMCI-Aβ + subjects who converted to AD dementia.

Graph theory approach for the structural-functional brain connectome of depression

To decipher the organizational styles of neural underpinning in major depressive disorder (MDD), the current article reviewed recent neuroimaging studies (published during 2015–2020) that applied graph theory approach to the diffusion tensor imaging data or functional brain activation data acquired during task-free resting state. The global network organization of resting-state functional connectivity network in MDD were diverse according to the onset age and medication status. Intra-modular functional connections were weaker in MDD compared to healthy controls (HC) for default mode and limbic networks. Weaker local graph metrics of default mode, frontoparietal, and salience network components in MDD compared to HC were also found. On the contrary, brain regions comprising the limbic, sensorimotor, and subcortical networks showed higher local graph metrics in MDD compared to HC. For the brain white matter-based structural connectivity network, the global network organization was comparable to HC in adult MDD but was attenuated in late-life depression. Local graph metrics of limbic, salience, default-mode, subcortical, insular, and frontoparietal network components in structural connectome were affected from the severity of depressive symptoms, burden of perceived stress, and treatment effects. Collectively, the current review illustrated changed global network organization of structural and functional brain connectomes in MDD compared to HC and were varied according to the onset age and medication status. Intra-modular functional connectivity within the default mode and limbic networks were weaker in MDD compared to HC. Local graph metrics of structural connectome for MDD reflected severity of depressive symptom and perceived stress, and were also changed after treatments. Further studies that explore the graph metrics-based neural correlates of clinical features, cognitive styles, treatment response and prognosis in MDD are required.

Combination of information processing algorithms and the information system structure as a tool for building an information system of a network structure

Formulation of the problem. The development of information systems for collecting, processing and exchanging radar information occurs, on the one hand, in the direction of improving the technical characteristics of information processing facilities and data transmission facilities, on the other hand, in the direction of improving information processing algorithms and the structure of the information system. This article summarizes the possibilities for the development of information systems in the second direction.Purpose. Development of a variant of building an information system with a fully connected network structure and intended for the collection, processing and exchange of radar information.Results. The development, as a tool for building an information system of a network structure, is based on an integrated approach that provides for the use of an algorithm without branching solutions for information processing, a network semi-connected structure itself and network algorithms, a higher level than the level of collection and processing, for organizing functioning of information exchange in the network. The short processing time of information when entering it into the system makes it possible to create a dynamic array of homogeneous radar data, updated when radar information arrives from sources.Practical significance. Information systems, wholly or partly built on the basis of such an integrated approach, are applicable in areas such as air traffic control systems; multi-beam and multi-range radars (ornithological, meteo, etc.), radars for security complexes, incoherent spatially-separated radar information sources, combined into a system (for example, for studying the ionosphere).

Interactions Between Aging and Alzheimer's Disease on Structural Brain Networks.

Normative aging and Alzheimer’s disease (AD) propagation alter anatomical connections among brain parcels. However, the interaction between the trajectories of age- and AD-linked alterations in the topology of the structural brain network is not well understood. In this study, diffusion-weighted magnetic resonance imaging (MRI) datasets of 139 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were used to document their structural brain networks. The 139 participants consist of 45 normal controls (NCs), 37 with early mild cognitive impairment (EMCI), 27 with late mild cognitive impairment (LMCI), and 30 AD patients. All subjects were further divided into three subgroups based on their age (56–65, 66–75, and 71–85 years). After the structural connectivity networks were built using anatomically-constrained deterministic tractography, their global and nodal topological properties were estimated, including network efficiency, characteristic path length, transitivity, modularity coefficient, clustering coefficient, and betweenness. Statistical analyses were then performed on these metrics using linear regression, and one- and two-way ANOVA testing to examine group differences and interactions between aging and AD propagation. No significant interactions were found between aging and AD propagation in the global topological metrics (network efficiency, characteristic path length, transitivity, and modularity coefficient). However, nodal metrics (clustering coefficient and betweenness centrality) of some cortical parcels exhibited significant interactions between aging and AD propagation, with affected parcels including left superior temporal, right pars triangularis, and right precentral. The results collectively confirm the age-related deterioration of structural networks in MCI and AD patients, providing novel insight into the cross effects of aging and AD disorder on brain structural networks. Some early symptoms of AD may also be due to age-associated anatomic vulnerability interacting with early anatomic changes associated with AD.

Disrupted Network Topology Contributed to Spatial Navigation Impairment in Patients With Mild Cognitive Impairment.

Impairment in spatial navigation (SN) and structural network topology is not limited to patients with Alzheimer’s disease (AD) dementia and can be detected earlier in patients with mild cognitive impairment (MCI). We recruited 32 MCI patients (65.91 ± 11.33 years old) and 28 normal cognition patients (NC; 69.68 ± 10.79 years old), all of whom underwent a computer-based battery of SN tests evaluating egocentric, allocentric, and mixed SN strategies and diffusion-weighted and T1-weighted Magnetic Resonance Imaging (MRI). To evaluate the topological features of the structural connectivity network, we calculated its measures such as the global efficiency, local efficiency, clustering coefficient, and shortest path length with GRETNA. We determined the correlation between SN accuracy and network topological properties. Compared to NC, MCI subjects demonstrated a lower egocentric navigation accuracy. Compared with NC, MCI subjects showed significantly decreased clustering coefficients in the left middle frontal gyrus, right rectus, right superior parietal gyrus, and right inferior parietal gyrus and decreased shortest path length in the left paracentral lobule. We observed significant positive correlations of the shortest path length in the left paracentral lobule with both the mixed allocentric–egocentric and the allocentric accuracy measured by the average total errors. A decreased clustering coefficient in the right inferior parietal gyrus was associated with a larger allocentric navigation error. White matter hyperintensities (WMH) did not affect the correlation between network properties and SN accuracy. This study demonstrated that structural connectivity network abnormalities, especially in the frontal and parietal gyri, are associated with a lower SN accuracy, independently of WMH, providing a new insight into the brain mechanisms associated with SN impairment in MCI.

IDARE2-Simultaneous Visualisation of Multiomics Data in Cytoscape.

Visual integration of experimental data in metabolic networks is an important step to understanding their meaning. As genome-scale metabolic networks reach several thousand reactions, the task becomes more difficult and less revealing. While databases like KEGG and BioCyc provide curated pathways that allow a navigation of the metabolic landscape of an organism, it is rather laborious to map data directly onto those pathways. There are programs available using these kind of databases as a source for visualization; however, these programs are then restricted to the pathways available in the database. Here, we present IDARE2 a cytoscape plugin that allows the visualization of multiomics data in cytoscape in a user-friendly way. It further provides tools to disentangle highly connected network structures based on common properties of nodes and retains structural links between the generated subnetworks, offering a straightforward way to traverse the splitted network. The tool is extensible, allowing the implementation of specialised representations and data format parsers. We present the automated reproduction of the original IDARE nodes using our tool and show examples of other data being mapped on a network of E. coli. The extensibility is demonstrated with two plugins that are available on github. IDARE2 provides an intuitive way to visualise data from multiple sources and allows one to disentangle the often complex network structure in large networks using predefined properties of the network nodes.