Network Connectivity Measures Research Articles

An improved linear systems model of hydrothermal isometric tension testing to aid in assessing bone collagen quality: Effects of ribation and type-2 diabetes.

This study sought to further develop and validate a previously proposed physics-based model that maps denaturation kinetics from differential scanning calorimetry (DSC) to the isometric tension generated during hydrothermal isometric tension (HIT) testing of collagenous tissues. The primary objectives of this study were to verify and validate two physics-based model parameters: α, which indicates the amount of instantaneous isometric tension developed per unit of collagen denaturation, and β, which captures the proportionality between temperature and the generated isometric tension post denaturation initiation. These parameters were used as measures of bone collagen quality, employing data from HIT and DSC testing of human bone collagen from two previous studies. Additionally, given the physical basis of the model, the study aimed to further validate Max.Slope, the rate of change in isometric tensile stress with change in temperature, as an independent measure of collagen network connectivity. Max.Slope has previously been positively correlated with measures of cortical bone fracture resistance. Towards this verification and validation, the hypotheses were a) that α would correlate strongly with HIT denaturation temperature, Td, and the enthalpy of melting (ΔH) from DSC, and b) that β would correlate positively and strongly with Max.Slope.The model was employed in the analysis of HIT-DSC data from the testing of demineralized bone collagen isolated from cadaveric human femurs in two prior studies. In one study, data were collected from HIT-DSC testing of cortical bone collagen from 74 donors. Among them, 38 had a history of type 2 diabetes +/− chronic kidney disease, while the remaining 36 had no history of T2D again with or without CKD. Cortical bone specimens were extracted from the lateral mid-shaft. The second study involved 15 donor femora, with four cortical bone specimens extracted from each. Of these four, two specimens underwent a 4-week incubation in 0.1 M ribose at 37 °C to induce non-enzymatic ribation and advanced glycation endproducts, while the other two served as non-ribated controls.The examination involved investigating correlations between the model parameters α and β and various measures, such as Max.Slope, Td, ΔH, age, and duration of type 2 diabetes. The results revealed positive correlations between the model parameter β and Max.Slope (r = 0.55–0.58). The parameter α was found to be associated with Td, but also sensitive to the shape of the HIT curve around Td resulting in difficulties with variability and interpretation. As a result, while both hypotheses are confirmed, Max.Slope and β are better indicators of bone collagen quality because they are measures of the connectivity or, more generally, the integrity of the bone collagen network.

The Study on Anomalies of the Geomagnetic Topology Network Associated with the 2022 Ms6.8 Luding Earthquake

On 5 September 2022, the Ms 6.8 Luding earthquake occurred at 29.59°N and 102.08°E in China. To investigate the variations in geomagnetic signals before the earthquake, this study analyzes the geomagnetic data from nine stations around the epicenter. First, we apply the Multi-channel Singular Spectrum Analysis to reconstruct the periodic components of the geomagnetic data from multiple stations. Second, we employ K-means clustering to rule out the possibility of occasional anomalies caused by a single station. Subsequently, we construct a geomagnetic topology network considering the remaining stations. Network centrality is defined as a measure of overall network connectivity, where the higher the correlation between multiple stations, the greater the network centrality. Finally, we examine the network centrality 45 days before and 15 days after the Luding earthquake. The results show that several anomalies in network centrality are extracted about one week before the earthquake. We further validate the significance of the anomalies in terms of time as well as space and verify the utility of the centrality anomalies through the SEA technique. The anomalies are found to have a statistical correlation with the earthquake event. We consider that this study provides a new way and a novel observational perspective for earthquake precursor analysis of ground-based magnetic data.

Identifying risks in temporal supernetworks: an IO-SuperPageRank algorithm

Complex systems pose risks characterized by factors such as uncertainty, nonlinearity, and diversity, making traditional risk measurement methods based on a probabilistic framework inadequate. Supernetworks can effectively model complex systems, and temporal supernetworks can capture the dynamic evolution of these systems. From the perspective of network stability, supernetworks can aid in risk identification for complex systems. In this paper, an IO-SuperPageRank algorithm is proposed based on the supernetwork topological structure. This algorithm reveals network instability by calculating changes in node importance, thereby helping to identify risks in complex systems. To validate the effectiveness of this algorithm, a four-layer supernetwork composed of scale-free networks is constructed. Simulated experiments are conducted to assess the impact of changes in intralayer edge numbers, intralayer node numbers, and interlayer superedge numbers on the risk indicator IO value. Linear regression and multiple tests were used to validate these relationships. The experiments show that changes in the three network topological indicators all bring about risks, with changes in intralayer node numbers having the most significant correlation with the risk indicator IO value. Compared to traditional measures of network node centrality and connectivity, this algorithm can more accurately predict the impact of node updates on network stability. Additionally, this paper collected trade data for crude oil, chemical light oil, man-made filaments and man-made staple fibers from the UN Comtrade Database. We constructed a man-made filaments and fibers supply chain temporal supernetwork, utilizing the algorithm to identify supply chain risks from December 2020 to October 2023. The study revealed that the algorithm effectively identified risks brought about by changes in international situations such as the Russia-Ukraine war, Israel–Hamas conflict, and the COVID-19 pandemic. This demonstrated the algorithm’s effectiveness in empirical analysis. In the future, we plan to further expand its application based on different scenarios, assess risks by analyzing changes in specific system elements, and implement effective risk intervention measures.

Reliable data transmission for a VANET-IoIT architecture: A DNN approach

The challenges and resilience of vehicular ad hoc network (VANET) and deep neural network (DNN) hybrid architectures in terms of reliability in smart cities have attracted much global interest stemming from the rollout of the next generation of intelligent networks. In this paper, we propose a novel distributed DNN (D-DNN) scheme with blockchain to support the Internet of Intelligent Things (IoIT) infrastructure in the VANET environment of the future. In particular, because the communication links between edge nodes are very unstable in VANETs, a new neuro-fuzzy server that serves the dual roles of finding reliable links between edge nodes and performing optimal routing path selection is proposed. Next, a blockchain layer is employed at the edge nodes, which are initially scrutinized before establishing communication links to ensure reliability during data transfer. Then, the proposed D-DNN (PD-DNN) scheme is applied to enhance the performance of the VANET-IoIT architecture by improving the data flow and convergence rate and mitigating erratic variations in output. To address reliability concerns, the coverage probability (CP) metric is investigated as a measure of network connectivity. Furthermore, we present an analysis of the PD-DNN scheme in comparison with the traditional DNN (T-DNN) scheme. Finally, simulation results for VANET-IoIT scenarios show that, subject to data protection and privacy constraints, the CP values corresponding to different communication links are improved to a greater extent under our scheme than under the traditional scheme, demonstrating the feasibility of the proposed scheme.

Longitudinal default mode sub-networks in the language and visual variants of Alzheimer's disease.

Disruption of the default mode network is a hallmark of Alzheimer's disease, which has not been extensively examined in atypical phenotypes. We investigated cross-sectional and 1-year longitudinal changes in default mode network sub-systems in the visual and language variants of Alzheimer's disease, in relation to age and tau. Sixty-one amyloid-positive Alzheimer's disease participants diagnosed with posterior cortical atrophy (n = 33) or logopenic progressive aphasia (n = 28) underwent structural MRI, resting-state functional MRI and [18F]flortaucipir PET. One-hundred and twenty-two amyloid-negative cognitively unimpaired individuals and 60 amyloid-positive individuals diagnosed with amnestic Alzheimer's disease were included as controls and as a comparison group, respectively, and had structural and resting-state functional MRI. Forty-one atypical Alzheimer's disease participants, 26 amnestic Alzheimer's disease participants and 40 cognitively unimpaired individuals had one follow-up functional MRI ∼1-2 years after the baseline scan. Default mode network connectivity was calculated using the dual regression method for posterior, ventral, anterior ventral and anterior dorsal sub-systems derived from independent component analysis. A global measure of default mode network connectivity, the network failure quotient, was also calculated. Linear mixed-effects models and voxel-based analyses were computed for each connectivity measure. Both atypical and amnestic Alzheimer's disease participants had lower cross-sectional posterior and ventral and higher anterior dorsal connectivity and network failure quotient relative to cognitively unimpaired individuals. Age had opposite effects on connectivity in Alzheimer's disease participants and cognitively unimpaired individuals. While connectivity declined with age in cognitively unimpaired individuals, younger Alzheimer's disease participants had lower connectivity than the older ones, particularly in the ventral default mode network. Greater baseline tau-PET uptake was associated with lower ventral and anterior ventral default mode network connectivity in atypical Alzheimer's disease. Connectivity in the ventral default mode network declined over time in atypical Alzheimer's disease, particularly in older participants, with lower tau burden. Voxel-based analyses validated the findings of higher anterior dorsal default mode network connectivity, lower posterior and ventral default mode network connectivity and decline in ventral default mode network connectivity over time in atypical Alzheimer's disease. Visuospatial symptoms were associated with default mode network connectivity disruption. In summary, default mode connectivity disruption was similar between atypical and amnestic Alzheimer's disease variants, and discriminated Alzheimer's disease from cognitively unimpaired individuals, with decreased posterior and increased anterior connectivity and with disruption more pronounced in younger participants. The ventral default mode network declined over time in atypical Alzheimer's disease, suggesting a shift in default mode network connectivity likely related to tau pathology.

Sleep and physical activity measures are associated with resting-state network segregation in non-demented older adults

Greater physical activity and better sleep are associated with reduced risk of cognitive decline and dementia among older adults, but little is known about their combined associations with measures of brain function and neuropathology. This study investigated potential independent and interactive cross-sectional relationships between actigraphy-estimated total volume of physical activity (TVPA) and sleep patterns [i.e., total sleep time (TST), sleep efficiency (SE)] with resting-state functional magnetic resonance imaging (rs-fMRI) measures of large scale network connectivity and positron emission tomography (PET) measures of amyloid-β. Participants were 135 non-demented older adults from the BIOCARD study (116 cognitively normal and 19 with mild cognitive impairment; mean age = 70.0 years). Using multiple linear regression analyses, we assessed the association between TVPA, TST, and SE with connectivity within the default-mode, salience, and fronto-parietal control networks, and with network modularity, a measure of network segregation. Higher TVPA and SE were independently associated with greater network modularity, although the positive relationship of SE with modularity was only present in amyloid-negative individuals. Additionally, higher TVPA was associated with greater connectivity within the default-mode network, while greater SE was related to greater connectivity within the salience network. In contrast, longer TST was associated with lower network modularity, particularly among amyloid-positive individuals, suggesting a relationship between longer sleep duration and greater network disorganization. Physical activity and sleep measures were not associated with amyloid positivity. These data suggest that greater physical activity levels and more efficient sleep may promote more segregated and potentially resilient functional networks and increase functional connectivity within specific large-scale networks and that the relationship between sleep and functional networks connectivity may depend on amyloid status.

Optimal mechanical interactions direct multicellular network formation on elastic substrates

Cells self-organize into functional, ordered structures during tissue morphogenesis, a process that is evocative of colloidal self-assembly into engineered soft materials. Understanding how intercellular mechanical interactions may drive the formation of ordered and functional multicellular structures is important in developmental biology and tissue engineering. Here, by combining an agent-based model for contractile cells on elastic substrates with endothelial cell culture experiments, we show that substrate deformation-mediated mechanical interactions between cells can cluster and align them into branched networks. Motivated by the structure and function of vasculogenic networks, we predict how measures of network connectivity like percolation probability and fractal dimension as well as local morphological features including junctions, branches, and rings depend on cell contractility and density and on substrate elastic properties including stiffness and compressibility. We predict and confirm with experiments that cell network formation is substrate stiffness dependent, being optimal at intermediate stiffness. We also show the agreement between experimental data and predicted cell cluster types by mapping a combined phase diagram in cell density substrate stiffness. Overall, we show that long-range, mechanical interactions provide an optimal and general strategy for multicellular self-organization, leading to more robust and efficient realizations of space-spanning networks than through just local intercellular interactions.

Spatial variability in topology, connectivity and permeability of deformation band networks: Insights from the Jurassic Entrada Sandstone, Utah

Networks of deformation bands have the potential to act as baffles to fluid flow, yet little work has been devoted to addressing their network properties, such as topology and connectivity. Motivated by this, we investigate the two-dimensional spatial variability of areal intensity, node and branch topology, and network connectivity within select deformation band networks in the Jurassic Entrada Sandstone (Utah). The results highlight distinct topological signatures for deformation band networks, dominated by Y-nodes, and IC- and CC-branches. Low proportion of isolated II-branches reflects the evolution of deformation bands through bifurcation and abutment (Y-nodes) to form interconnected networks. A key observation is that network connectivity and topology exhibit great spatial variability within one and the same deformation band network. We evaluate effective permeabilities across the deformation band network, incorporating a topological measure of network connectivity into the permeability calculations. Results highlight that deformation band networks with >1.5 connections per branch can significantly reduce effective permeabilities, whereas networks with low connectivity may offer pathways for tortuous fluid flow.

LOCUS: A REGULARIZED BLIND SOURCE SEPARATION METHOD WITH LOW-RANK STRUCTURE FOR INVESTIGATING BRAIN CONNECTIVITY.

Network-oriented research has been increasingly popular in many scientific areas. In neuroscience research, imaging-based network connectivity measures have become the key for understanding brain organizations, potentially serving as individual neural fingerprints. There are major challenges in analyzing connectivity matrices, including the high dimensionality of brain networks, unknown latent sources underlying the observed connectivity, and the large number of brain connections leading to spurious findings. In this paper we propose a novel blind source separation method with low-rank structure and uniform sparsity (LOCUS) as a fully data-driven decomposition method for network measures. Compared with the existing method that vectorizes connectivity matrices ignoring brain network topology, LOCUS achieves more efficient and accurate source separation for connectivity matrices using low-rank structure. We propose a novel angle-based uniform sparsity regularization that demonstrates better performance than the existing sparsity controls for low-rank tensor methods. We propose a highly efficient iterative node-rotation algorithm that exploits the block multiconvexity of the objective function to solve the nonconvex optimization problem for learning LOCUS. We illustrate the advantage of LOCUS through extensive simulation studies. Application of LOCUS to Philadelphia Neurodevelopmental Cohort neuroimaging study reveals biologically insightful connectivity traits which are not found using the existing method.

Neurocognitive Analysis of Low-level Arsenic Exposure and Executive Function Mediated by Brain Anomalies Among Children, Adolescents, and Young Adults in India

Arsenic, a contaminant of groundwater and irrigated crops, is a global public health hazard. Exposure to low levels of arsenic through food extends well beyond the areas with high arsenic content in water. To identify cognitive impairments following commonly prevalent low-level arsenic exposure and characterize their underlying brain mechanisms. This multicenter population-based cohort study analyzed cross-sectional data of the Indian Consortium on Vulnerability to Externalizing Disorders and Addictions (cVEDA) cohort, recruited between November 4, 2016, and May 4, 2019. Participants aged 6 to 23 years were characterized using deep phenotyping measures of behavior, neuropsychology, psychopathology, brain neuroimaging, and exposure to developmental adversities and environmental neurotoxins. All analyses were performed between June 1, 2020, and December 31, 2021. Arsenic levels were measured in urine as an index of exposure. Executive function measured using the cVEDA neuropsychological battery, gray matter volume (GMV) from T1-weighted magnetic resonance imaging, and functional network connectivity measures from resting state functional magnetic resonance imaging. A total of 1014 participants aged 6 to 23 years (589 male [58.1%]; mean [SD] age, 14.86 [4.79] years) were included from 5 geographic locations. Sparse-partial least squares analysis was used to describe a negative association of arsenic exposure with executive function (r = -0.12 [P = 5.4 × 10-4]), brain structure (r = -0.20 [P = 1.8 × 10-8]), and functional connectivity (within network, r = -0.12 [P = 7.5 × 10-4]; between network, r = -0.23 [P = 1.8 × 10-10]). Alterations in executive function were partially mediated by GMV (b = -0.004 [95% CI, -0.007 to -0.002]) and within-network functional connectivity (b = -0.004 [95% CI, -0.008 to -0.002]). Socioeconomic status and body mass index moderated the association between arsenic and GMV, such that the association was strongest in participants with lower socioeconomic status and body mass index. The findings of this cross-sectional study suggest that low-level arsenic exposure was associated with alterations in executive functioning and underlying brain correlates. These results indicate potential detrimental consequences of arsenic exposure that are below the currently recommended guidelines and may extend beyond endemic risk areas. Precision medicine approaches to study global mental health vulnerabilities highlight widespread but potentially modifiable risk factors and a mechanistic understanding of the impact of low-level arsenic exposure on brain development.

Evaluation of Per- and Polyfluoroalkyl Substances (PFAS) In Vitro Toxicity Testing for Developmental Neurotoxicity.

Per- and polyfluoroalkyl substances (PFAS) are a diverse set of commercial chemicals widely detected in humans and the environment. However, only a limited number of PFAS are associated with epidemiological or experimental data for hazard identification. To provide developmental neurotoxicity (DNT) hazard information, the work herein employed DNT new approach methods (NAMs) to generate in vitro screening data for a set of 160 PFAS. The DNT NAMs battery was comprised of the microelectrode array neuronal network formation assay (NFA) and high-content imaging (HCI) assays to evaluate proliferation, apoptosis, and neurite outgrowth. The majority of PFAS (118/160) were inactive or equivocal in the DNT NAMs, leaving 42 active PFAS that decreased measures of neural network connectivity and neurite length. Analytical quality control indicated 43/118 inactive PFAS samples and 10/42 active PFAS samples were degraded; as such, careful interpretation is required as some negatives may have been due to loss of the parent PFAS, and some actives may have resulted from a mixture of parent and/or degradants of PFAS. PFAS containing a perfluorinated carbon (C) chain length ≥8, a high C:fluorine ratio, or a carboxylic acid moiety were more likely to be bioactive in the DNT NAMs. Of the PFAS positives in DNT NAMs, 85% were also active in other EPA ToxCast assays, whereas 79% of PFAS inactives in the DNT NAMs were active in other assays. These data demonstrate that a subset of PFAS perturb neurodevelopmental processes in vitro and suggest focusing future studies of DNT on PFAS with certain structural feature descriptors.

Genetic architecture of the white matter connectome of the human brain.

White matter tracts form the structural basis of large-scale brain networks. We applied brain-wide tractography to diffusion images from 30,810 adults (U.K. Biobank) and found significant heritability for 90 node-level and 851 edge-level network connectivity measures. Multivariate genome-wide association analyses identified 325 genetic loci, of which 80% had not been previously associated with brain metrics. Enrichment analyses implicated neurodevelopmental processes including neurogenesis, neural differentiation, neural migration, neural projection guidance, and axon development, as well as prenatal brain expression especially in stem cells, astrocytes, microglia, and neurons. The multivariate association profiles implicated 31 loci in connectivity between core regions of the left-hemisphere language network. Polygenic scores for psychiatric, neurological, and behavioral traits also showed significant multivariate associations with structural connectivity, each implicating distinct sets of brain regions with trait-relevant functional profiles. This large-scale mapping study revealed common genetic contributions to variation in the structural connectome of the human brain.

Changes in the geometry and robustness of diffusion tensor imaging networks: Secondary analysis from a randomized controlled trial of young autistic children receiving an umbilical cord blood infusion.

Diffusion tensor imaging (DTI) has been used as an outcome measure in clinical trials for several psychiatric disorders but has rarely been explored in autism clinical trials. This is despite a large body of research suggesting altered white matter structure in autistic individuals. The current study is a secondary analysis of changes in white matter connectivity from a double-blind placebo-control trial of a single intravenous cord blood infusion in 2-7-year-old autistic children (1). Both clinical assessments and DTI were collected at baseline and 6 months after infusion. This study used two measures of white matter connectivity: change in node-to-node connectivity as measured through DTI streamlines and a novel measure of feedback network connectivity, Ollivier-Ricci curvature (ORC). ORC is a network measure which considers both local and global connectivity to assess the robustness of any given pathway. Using both the streamline and ORC analyses, we found reorganization of white matter pathways in predominantly frontal and temporal brain networks in autistic children who received umbilical cord blood treatment versus those who received a placebo. By looking at changes in network robustness, this study examined not only the direct, physical changes in connectivity, but changes with respect to the whole brain network. Together, these results suggest the use of DTI and ORC should be further explored as a potential biomarker in future autism clinical trials. These results, however, should not be interpreted as evidence for the efficacy of cord blood for improving clinical outcomes in autism. This paper presents a secondary analysis using data from a clinical trial that was prospectively registered with ClinicalTrials.gov(NCT02847182).

Objective Sleep Quality and the Underlying Functional Neural Correlates Among Older Adults with Possible Mild Cognitive Impairment.

Poor sleep quality is common among older individuals with mild cognitive impairment (MCI) and may be a consequence of functional alterations in the brain; yet few studies have investigated the underlying neural correlates of actigraphy-measured sleep quality in this cohort. The objective of this study was to examine the relationship between brain networks and sleep quality measured by actigraphy. In this cross-sectional analysis, sleep efficiency and sleep fragmentation were estimated using Motionwatch8 (MW8) over a period of 14 days in 36 community-dwelling older adults with possible MCI aged 65-85 years. All 36 participants underwent resting-state functional magnetic resonance imaging (fMRI) scanning. Independent associations between network connectivity and MW8 measures of sleep quality were determined using general linear modeling via FSL. Networks examined included the somatosensory network (SMN), frontoparietal network (FPN), and default mode network (DMN). Across the 36 participants (mean age 71.8 years; SD = 5.2 years), mean Montreal Cognitive Assessment score was 22.5 (SD = 2.7) and Mini-Mental State Examination score was 28.3 (SD = 1.5). Mean sleep efficiency and fragmentation index was 80.1% (SD = 10.0) and 31.8 (SD = 10.4) respectively. Higher sleep fragmentation was significantly correlated with increased connectivity between the SMN and insula, the SMN and posterior cingulate, as well as FPN and primary motor area (FDR-corrected, p < 0.004). Functional connectivity between brain regions involved in attentional and somatosensory processes may be associated with disrupted sleep in older adults with MCI.

Comparing Sexual Network Mean Active Degree Measurement Metrics Among Men Who Have Sex With Men.

Mean active degree is an important proxy measure of cross-sectional network connectivity commonly used in HIV/sexually transmitted infection epidemiology research. No current studies have compared measurement methods of mean degree using a cross-sectional study design for men who have sex with men (MSM) in the United States. We compared mean degree estimates based on reported ongoing main and casual sexual partnerships (current method) against dates of first and last sex (retrospective method). We used data from ARTnet, a cross-sectional survey of MSM in the United States (2017-2019). ARTnet collected data on the number and types of sexual partners in the past year, limited to the 5 most recent partners (data truncation). We quantified partnerships for months 0 to 12 before the survey date (retrospective method) and compared that with ongoing partnerships on the day of survey (current method). We used linear regression to understand the impact of truncated partnership data on mean degree estimation. The retrospective method yielded similar degree estimates to the current for months proximate to the day of survey. The retrospective method mean degree systematically decreased as the month increased from 0 to 12 months before survey date. This was driven by data truncation: among participants with >5 partners in the past year compared with those with ≤5, the average change in main partnership degree between 12 and 0 months before survey date was -0.05 (95% confidence interval, -0.08 to -0.03) after adjusting for race/ethnicity, age, and education. The adjusted average change in casual partnership degree was -0.40 (95% confidence interval, -0.45 to -0.35). The retrospective method underestimates mean degree for MSM in surveys with truncated partnership data, especially for casual partnerships. The current method is less prone to bias from partner truncation when the target population has high rate of partners per year.

P01.10.B The relation between executive functioning and MEG multilayer network centrality in glioma patients

Abstract Background Many patients with glioma suffer from deficits in executive functioning (EF), regardless of tumor location and size. While some patients experience improving EF after tumor resection, others deteriorate over time. This variability in cognitive trajectories is still poorly understood, but neuroscientific approaches that view the brain as a network may bear correlative and predictive value in this respect. Particularly integrative network connectivity of the fronto-parietal network are crucial for EF, but studies so far have only investigated isolated neural frequency bands of interest. Here, we synergize fronto-parietal network connectivity across frequency bands and test its explanatory power for EF at diagnosis and 1 year after tumor resection. Material and Methods Patients with diffuse glioma (n=37) underwent neuropsychological assessments, including three tests for EF, and resting-state magnetoencephalography (MEG) at both time points. Patients’ EF performance was standardized to z-scores using validated norm scores, adjusting for age, sex and education. MEG was source-reconstructed using patients’ MRI in combination with a beamformer approach and time series were filtered into six classical frequency bands. Band-specific functional connectivity between 78 cortical regions was estimated, after which an interconnected multilayer, multi-frequency network was created per patient. Multilayer centrality was then calculated as a measure of integrative network connectivity per region, and averaged over all fronto-parietal network regions to yield a single value reflecting integrative, multi-frequency network connectivity per patient at each time point. Results At diagnosis, eight patients had z-scores <-1.5, indicating clinically relevant cognitive deficits. Preoperatively, poorer performance on the Concept Shifting Task was associated with lower multilayer centrality (p=.017), while improving performance on this task from diagnosis to 1 year after resection was associated with an increase in multilayer centrality (p=.022). Multilayer centrality at diagnosis did not significantly predict EF at follow-up. Conclusion Our results establish a significant relationship between poorer and decreasing EF with lower and decreasing multilayer, multi-frequency integrative connectivity of the fronto-parietal network in glioma patients. Studies with larger and more homogeneous samples may further explore the relevance of this multilayer approach in understanding and particularly predicting postoperative decline in EF in these patients, as our sample was characterized by large heterogeneity in cognitively relevant patient and tumor characteristics.

Measuring Connectivity of Pedestrian Street Networks in the Built Environment for Walking: A Space-Syntax Approach

Network connectivity is an important aspect of the built environment. However, it rarely captures the attention of decision-makers while implementing pedestrian projects. In recent years, Space-Syntax (SS) analysis is gaining popularity among researchers due to its ability to clearly demonstrate the topological configuration of a physical network. This research carries out a connectivity analysis of 297 links across three pedestrian networks in Varanasi, India, thereby exploring the relationship between pedestrian volume, SS indices in the Indian context, and other built environment features, such as presence of sidewalk, right-of-way (ROW) and landuse area. Data collection includes developing a GIS database of the pedestrian network, 30-min pedestrian volume counts at various locations, and calculating SS connectivity measures from the configuration of the pedestrian networks. Resulting SS indices were analyzed with existing pedestrian volumes through correlation, stepwise multiple linear regression (MLR), and path models. Results of the analysis showed that the SS index ‘Normalized Angular Integration (NAIN)’ to be highly correlated with pedestrian volume (r = 0.57). Subsequently, the MLR and path models show a statistically significant relationship of pedestrian volume with NAIN, ROW, presence of sidewalk, and commercial area. ROW affects the pedestrian volume both directly and indirectly in a commercial area, thereby indicating a requirement for improving/providing walking facilities in such areas. The study concludes with practical implications for local authorities that highlight the importance of providing walking infrastructures at commercial streets having high connectivity. The study also shows the significance of NAIN as an effective measure of pedestrian street network connectivity.

Transient Intervals of Significantly Different Whole Brain Connectivity Predict Recovery vs. Progression from Mild Cognitive Impairment: New Insights from Interpretable LSTM Classifiers.

The high dimensionality and complexity of time-varying measures of functional brain connectivity have created an environment in which a very rich transformation of the data remains difficult to map into disease states without some form of reduction (averaging, clustering, statistical blindness to the multivariate interactions between features that modulate their contributions). In this work, employing a recently developed architecture for long short-term memory classifiers that supports use of gradient-based model interpretability techniques, we predict progression or recovery from mild cognitive impairment (MCI) from an instantaneous (windowless) wavelet-based measure of dynamic functional network connectivity. This time-attention LSTM (TA-LSTM) model achieves 0.79 AUC on the task of predicting which MCI patients who will recover (RMCI) vs. those who will progress (PMCI) to AZD within a three-year timeframe. Using a common gradient-based model interpretation technique, saliency analysis, on this TA-LSTM points to potentially important predictive dynamic biomarkers, including the duration of the highly salient time intervals and the average connectivity patterns within these highly salient intervals.

How to Measure the Network Vulnerability of Cities to Wildfires: Cases in California, U.S.A.

This paper proposes a framework to evaluate the network vulnerability of cities to wildfires. Three cities are selected from the California Public Utilities Commission (CPUC), U.S., fire-threat regions: Orinda, Paradise, and Atascadero. For each city, four different network connectivity measures are calculated, and agent-based evacuation simulations are performed by the Monte Carlo method. In the simulations, the number of isolated vehicles and evacuation time estimates are measured for the following scenarios: (i) no wildfire case with original network; and (ii) wildfire cases with randomly damaged networks that are reduced by 1%, 3%, 5%, 7%, and 10% from the original network. A city-to-city comparison is conducted in relation to network connectivity measures and evacuation simulation results. It is shown that Paradise has the worst network connectivity, and the simulation results reveal that Paradise also has the most sensitive network in relation to random roadway closures caused by wildfire propagation. Thus, among the three cities, Paradise has the most vulnerable network to wildfires as determined through the two analysis results concerning the worst network measures and the simulation results. It is expected that the proposed analysis framework can be generally applied to any city located in a fire-threat region.

Network-adaptive robust penalized estimation of time-varying coefficient models with longitudinal data

Longitudinal data are commonly encountered in many fields. Many statistical models have been developed, among which the time-varying coefficient model has been shown to be effective for many practical problems. Long-tailed/contaminated distributions are not uncommon and cannot be accommodated using non-robust likelihood-based estimation. Another common limitation shared by many of the existing methods is the insufficient account for the interconnections among covariates. In this study, we adopt a least absolute deviation loss function to achieve robustness. For the selection of relevant covariates, a penalization approach is adopted. Significantly advancing from the existing literature, we describe the interconnections among covariates using a network structure and develop novel penalties to accommodate the network connectivity and connection measures. Consistency properties are rigorously established. Numerical studies, including both simulations and data analysis, demonstrate the competitive practical performance of the proposed method.