Network Connectivity Research Articles

Sub-acute stroke demonstrates altered beta oscillation and connectivity pattern in working memory

IntroductionWorking memory (WM) is suggested to play a pivotal role in relearning and neural restoration during stroke rehabilitation. Using EEG, this study investigated the oscillatory mechanisms of WM in subacute stroke.MethodsThis study included 48 first subacute stroke patients (26 good-recovery, 22 poor-recovery, based on prognosis after a 4-week period) and 24 matched health controls. We examined the oscillatory characteristics and functional connectivity of the 0-back WM paradigm and assessed their associations with prognosis.ResultsPatients of poor recovery are characterised by a loss of significant beta rebound, beta-band connectivity, as well as impaired working memory speed and performances. Meanwhile, patients with good recovery have preserved these capacities to some extent. Our data further identified beta rebound to be closely associated with working memory speed and performances.ConclusionsWe provided novel findings that beta rebound and network connectivity as mechanistic evidence of impaired working memory in subacute stroke. These oscillatory features could potentially serve as a biomarker for brain stimulation technologies in stroke recovery.

Enhanced Gelation Properties and Saltiness Perception of Low-Salt Surimi Gel with Psyllium Husk Powder.

Reducing salt intake is an effective strategy for preventing and managing hypertension and cardiovascular diseases. In this study, psyllium husk powder (PHP) was incorporated into surimi to address the challenges of diminished saltiness and texture in low-salt surimi products. PHP promoted the conversion of α-helix structures into β-sheet and strengthening intermolecular interactions, such as ionic bonds, hydrogen bonds, hydrophobic interactions, and disulfide bonds. The addition of PHP enhanced the connectivity and uniformity of the surimi gel network, resulting in an increase in gel strength from 11.35 to 13.88 N and a reduction in cooking loss from 6.33% to 2.27%. Additionally, the more compact gel network structure improved the fluidity of immobile water within the low-salt surimi gel. The surimi gel containing 1.5% PHP accelerated a 30% higher Na+ release rate compared to the low-salt surimi gel, enhancing the saltiness perception to levels comparable with high-salt surimi gel, as measured by an electronic tongue. These findings present a promising approach to improving the texture and saltiness perception of surimi gel products while reducing salt content.

Assessing and Optimizing the Connectivity of the Outdoor Green Recreation Network in Zhengzhou from the Perspective of Green Travel

With the increasing demand for outdoor recreation and fitness, this study aims to assess the connectivity of the outdoor green recreation (OGR) network from the perspective of green travel and propose optimization framework. The Point of Interest (POI) and Area of Interest (AOI) datasets of OGR spots in Zhengzhou were utilized as the primary research materials. A combination of GIS spatial analysis and Graph index calculation is employed to quantify and diagnose the connectivity of the OGR network based on multi-source data (land cover, topography, and road network). The index system for cost surface establishment was improved and proposed, shifting its focus from previous biological migration and ecological network to human green travel and improving the connectivity of the OGR network. The technical optimization process of the OGR network is explored and presented. The results show that: (1) The scale, number, and distribution of OGR spots and the connectivity of the OGR network are significantly different in urban and rural areas. Numerous small-scale OGR spots and short-distance recreational paths are distributed in urban areas, while a limited number of large-scale OGR spots and long-distance recreational paths are situated in rural areas with better natural resources. (2) Compared with driving travel, the connectivity of the OGR network is poor when walking and cycling. Graph indexes of Dg, BC, and dPC can be used to reflect the connection capability, bridging role, and contribution of each spot to overall network connectivity. (3) The current OGR network is optimized through 30 new spots based on the perspective of green travel and land suitability analysis. The network connectivity will improve by 4%, and the number of recreational paths suitable for green travel increased by 41. (4) The methodologies for quantifying and optimizing OGR network connectivity from the perspective of green travel will offer valuable references for future research in this field.

Effect of Froth on the Interaction Between Coal Particles and Cake Structures in the Dewatering Process of Clean Coal

Effective coal slurry water solid–liquid separation is indispensable for the recycling and sustainable development of coal resources. The interaction between bubble and coal particles plays a critical role in the process of dewatering for clean coal. In this study, we firstly conducted a comprehensive investigation of the impact of froth on the interactions between coal particles by rheological measurement and particle aggregation behavior. Furthermore, the macroscopic dewatering performance of coal slurry in the presence of froth and its microscopic cake structure were investigated using the filtration test and X-ray microtomography (CT). It was found that the interaction between coal particles in the presence of froth was enhanced as a result of the dynamic shear value, combined with the large floc size and compact structure, which led to a higher cake moisture and higher filtration velocity. The CT results indicated that the enhanced interaction of particles in the presence of froth also led to a dense microstructure of the filter cake. The porosity of the filter cake decreased to 2.05% when the aeration time increased from 0 s to 90 s, the throat radius in the filter cake was reduced to 1.32 μm, and the number of throat passages was reduced to one third. Multiple blind pores and low coordination numbers led to a poor connectivity of the pore network and high moisture content.

Alterations of the resting-state brain network connectivity and gray matter volume in patients with fibromyalgia in comparison to ankylosing spondylitis

Fibromyalgia (FM) and ankylosing spondylitis (AS) are both rheumatic diseases characterized by significant musculoskeletal pain. In this study, we investigated the differences of the resting-state network (RSN) connectivity and gray matter volume (GMV) between FM, AS and healthy controls (HCs). We recruited 38 FM patients, 82 AS patients and 61 HCs in this study. All the participants underwent resting-state functional MRI (rs-fMRI) scans in a GE 3.0T MR system. Independent component analysis (ICA) was conducted on the rs-fMRI data, and group differences of the rsFC between different resting-state networks were calculated using dual regression. We also conducted voxel-based morphometry (VBM) analysis to investigate the differences of the GMV in FM, AS and HCs. The rsFC between the dorsal default mode network (DDMN) and the body of left caudate nucleus was significantly decreased in FM patients in comparison to AS patients (87 voxels, p = 0.025). VBM analysis showed that the GMV of the left posterior lobe of cerebellum was significantly increased in FM patients compared with AS patients (88 voxels, p = 0.036). Neither ICA nor VBM analysis revealed significant differences of RSN connectivity or GMV between FM patients and HCs. The altered rsFC between DMN and the caudate nucleus suggested an aberrant cortico-striato-thalamo-cortical circuit in FM patients, indicating aberrant reward processing, with potential association with mood, motivation and cognitive functions. The increased GMV in the left posterior lobe of cerebellum indicated the participation of cerebellum in the abnormal pain processing in FM patients.

Evolving alterations of structural organization and functional connectivity in feedforward neural networks after induced P301L tau mutation

AbstractReciprocal structure–function relationships underlie both healthy and pathological behaviours in complex neural networks. Thus, understanding neuropathology and network dysfunction requires a thorough investigation of the complex interactions between structural and functional network reconfigurations in response to perturbation. Such adaptations are often difficult to study in vivo. For example, subtle, evolving changes in synaptic connectivity, transmission and the electrophysiological shift from healthy to pathological states, for example alterations that may be associated with evolving neurodegenerative disease, such as Alzheimer's, are difficult to study in the brain. Engineered in vitro neural networks are powerful models that enable selective targeting, manipulation and monitoring of dynamic neural network behaviour at the micro‐ and mesoscale in physiological and pathological conditions. In this study, we engineered feedforward cortical neural networks using two‐nodal microfluidic devices with controllable connectivity interfaced with microelectrode arrays (mMEAs). We induced P301L mutated tau protein to the presynaptic node of these networks and monitored network dynamics over three weeks. Induced perturbation resulted in altered structural organization and extensive axonal retraction starting in the perturbed node. Perturbed networks also exhibited functional changes in intranodal activity, which manifested as an overall decline in both firing rate and bursting activity, with a progressive increase in synchrony over time and a decrease in internodal signal propagation between pre‐ and post‐synaptic nodes. These results provide insights into dynamic structural and functional reconfigurations at the micro‐ and mesoscale as a result of evolving pathology and illustrate the utility of engineered networks as models of network function and dysfunction.

An Interstitial Boron Inserted Metastable Hexagonal Rh Nanocrystal for Efficient Hydrogen Oxidation Electrocatalysis

AbstractConstructing metastable phase structure plays an important role in changing the physicochemical properties and improving the catalytic performance of nanocrystals. Unfortunately, the synthesis of metastable phase metallic nanocrystals is highly challenging, mainly due to the thermodynamically unstable ground‐state. Here, we report a synthesis of unconventional metastable hexagonal rhodium nanocrystal (Bint−Rhhcp/C) via interstitial boron insertion. The insertion of boron atoms into the interstitial sites of cubic Rh lattice not only induces the atomic arrangements from face‐centered cubic (fcc) to hexagonal close‐packed (hcp), but also stabilizes the metastable hexagonal Rh structure. Benefiting from the phase transition and interstitial boron doping, the Bint−Rhhcp/C catalyst exhibits remarkable catalytic performance toward hydrogen oxidation reaction (HOR) under alkaline media, with a mass activity of 1.413 mA μgPGM−1. Experimental measurements including in situ surface‐enhanced infrared absorption spectroscopy (SEIRAS) and density functional theory (DFT) calculations indicate that the strengthened adsorption of hydroxyl species on the electrode surface of Bint−Rhhcp/C is responsible for the reconstruction of interfacial water structure and increased water proportions in the gap region in the electric double layers. As a result, the increased water connectivity and hydrogen bond network facilitate high‐efficiency hydrogen transfer across the interface, thereby boost the alkaline HOR performance.

Dynamic brain states underlying advanced concentrative absorption meditation: A 7T fMRI intensive case study

Abstract Advanced meditation consists of states and stages of practice that unfold with mastery and time. Dynamic functional connectivity (dFC) analysis of fMRI could identify brain states underlying advanced meditation. We conducted an intensive dFC case study of a meditator who completed 27 runs of jhāna advanced absorptive concentration meditation (ACAM-J), concurrently with 7-Tesla fMRI and phenomenological reporting. We identified three brain states that marked differences between ACAM-J and non-meditative control conditions. These states were characterized as a DMN-anticorrelated brain state, a hyperconnected brain state, and a sparsely connected brain state. Our analyses indicate higher prevalence of the DMN-anticorrelated brain state during ACAM-J than control states, and the prevalence increased significantly with deeper ACAM-J states. The hyperconnected brain state was also more common during ACAM-J, and was characterized by elevated thalamo-cortical connectivity and somatomotor network connectivity. The hyperconnected brain state significantly decreased over the course of ACAM-J, associating with self-reports of wider attention and diminished physical sensations. This brain state may be related to sensory awareness. Advanced meditators have developed well-honed abilities to move in and out of different altered states of consciousness, and this study provides initial evidence that functional neuroimaging can objectively track their dynamics.

Power Spectral Density and Default Mode Network Connectivity in Generalized Epilepsy Syndromes: What to Expect from Drug-Resistant Patients

Background: Recent studies have described unique aspects of default mode network connectivity in patients with idiopathic generalized epilepsy (IGE). A complete background in this field could be gained by combining this research with spectral analysis. Objectives: An important objective of this study was to compare linear connectivity and power spectral densities across different activity bands of patients with juvenile absence epilepsy (JAE), juvenile myoclonic epilepsy (JME), generalized tonic–clonic seizures alone (EGTCSA), and drug-resistant IGE (DR-IGE) with healthy, age-matched controls. Methods: This was an observational case–control study. We performed EEG spectral analysis in MATLAB and connectivity analysis with LORETA for 39 patients with IGE and 12 drug-resistant IGE (DR-IGE) and healthy, age-matched subjects. We defined regions of interest (ROIs) from the default mode network (DMN) and performed connectivity statistics using time-varying spectra for paired samples. Using the same EEG data, we compared mean power spectral density (PSD) with epilepsy subgroups and controls across different activity bands. Results: We obtained a modified value for the mean power spectral density in the beta band for the JME group as follows. The connectivity analysis showed that, in general, there was increased linear connectivity in the DMN for the JAE, JME, and EGCTSA groups compared to the healthy controls. Reduced linear connectivity between regions of the DMN was found for DR-IGE. Conclusions: Spectral analysis of electroencephalography (EEG) for generalized epilepsy syndromes seems to be less informative than connectivity analysis for DMN. DMN connectivity analysis, especially for DR-IGE, opens up the possibility of finding biomarkers related to drug response in IGE.

Decentralized Payment Framework for Low-Connectivity Areas Using Ethereum Blockchains

This paper presents a pioneering analytical framework for a secure payment system leveraging blockchain technology tailored to regions with suboptimal network connectivity. Contemporary payment mechanisms utilizing Ethereum are predominantly optimized for areas with robust network infrastructure, neglecting regions with less connectivity. To address this gap, the proposed model integrates novel security attributes and employs an analytical method to design a decentralized payment system. The framework facilitates communication between low-connectivity zones and Internet service providers through auxiliary nodes, creating a local blockchain network for residents, merchants, and auditors. A mathematical model quantifies operational costs, transaction processing, and synchronization of auxiliary nodes, ensuring a resilient and secure payment architecture. A unique aspect of the proposed approach is its robustness against auditor outages and network variability, coupled with an empirical analysis of incentive structures for auditors' block validation activities. Moreover, it delineates the minimum requirements for secure transaction completion. Empirical findings showed a significant improvement in system efficiency, including a 79% reduction in block time, a 28% increase in transaction throughput, a 30% decrease in energy consumption, a 68% shorter confirmation time, a 63% reduction in execution time, a 46% increase in block production rate, and 82% reduced network variability. This study's significant contribution lies in introducing a sustainable, cost-effective, and secure payment system for regions with inadequate network services.

Assessing restorative effects of soundscapes in VR through EEG and HRV

This study aimed to extend the application of soundscape analysis by utilizing electroencephalogram (EEG) analysis as a physiological evaluation tool to determine the actual restorative impact on individual environmental perceptions and psycho-physiological responses stemming from various soundscape experiences. Initially, we constructed three distinct virtual reality (VR) environments: waterfront, urban, and green areas, each accompanied by three content variations. A total of 60 subjects participated in the study. Data were gathered through a survey assessing individual characteristics, psychological restorative responses, and soundscape perceptions. Additionally, quantitative physiological responses such as heart rate variability (HRV) and EEG, were measured. Subsequently, subjects were categorized into restoration and non-restoration groups based on HRV responses using cluster analysis. The analysis revealed positive HRV changes indicative of reduced stress levels. In EEG analysis, differences were observed in network connectivity rather than power spectral density. As a result of connectivity analysis, global efficiency increased overall in the restoration group, and differences in nodal efficiency occurred in a total of eight brain regions, enabling soundscape experience to efficiently process cognitive functions in the cerebrum, thereby having a positive effect on neural communication. This study is significant as it represents the first examination of soundscape restorative responses in terms of brain wave connectivity, underscoring the feasibility of employing physiological evaluations, including brain wave analysis, in the study of soundscapes.

Evaluating the connectivity of the road network in Ramadi city using sustainable transport indicators

Evaluating the connectivity of the road network in Ramadi city using sustainable transport indicators

Fault-tolerant topology construction and down-link rate maximization in air-ground integrated networks

Due to the advantages in flexible deployment, fast communication recovery and high quality of service (QoS), unmanned aerial vehicles (UAVs) have been widely used in wireless communications and networking. However, existing works have not sufficiently solved the problem of reliable and survivable connectivity of UAV networks. In this paper, we investigate the deployment of multiple UAVs, cooperating with a ground mobile station (GMS), to provide wireless coverage for ground users. Under the constraints of satisfying different QoS of users and the resource limitation of UAVs, our aim is to minimize the number of deployed UAVs and maximize the sum down-link rate of all users. In order to ensure the survivability of the deployed UAV networks, we also constraint that each UAV has at least two disjoint paths to GMS to form a fault-tolerant topology. Unfortunately, the formulate problem is intractable and cannot be solved directly. To solve this problem, we decouple the original problem into two subproblems: (1) minimized fault-tolerant topology construction concerning user demands; (2) maximized down-link rate with access policy optimization. Then, we propose a heuristic link-cost minimization method and a potential-game-based user rate maximization method to solve the two subproblems, respectively. The effectiveness of the solution is validated through simulations. Meanwhile, ours also outperforms some baselines in minimized UAVs, down-link transmission rate as well as running time.

Views and experiences of young people on using mHealth platforms for sexual and reproductive health services in rural low-and middle-income countries: A qualitative systematic review.

In low- and middle-income countries (LMICs), reproductive health programs use mobile health (mHealth) platforms to deliver a broad range of SRH information and services to young people in rural areas. However, young people's experiences of using mobile phone platforms for SRH services in the rural contexts of LMICs remains unexplored. This review qualitatively explored the experiences and perceptions of young people's use of mobile phone platforms for SRH information and services. This qualitative evidence synthesis was conducted through a systematic search of online databases: Medline, Embase, CINAHL, PsycInfo and Scopus. We included peer reviewed articles that were conducted between 2000 to 2023 and used qualitative methods. The methodological quality of papers was assessed by two authors using Grading of Recommendations, Assessment, Development and Evaluation (GRADE) and Confidence in Evidence from Reviews of Qualitative research (CERQual) approach with the identified papers synthesized using a narrative thematic analysis approach. The 26 studies included in the review were conducted in a wide range of LMIC rural settings. The studies used seven different types of mHealth platforms in providing access to SRH information and services on contraception, family planning, sexually transmitted infections (STIs) and human immunodeficiency virus (HIV) education. Participant preferences for use of SRH service platforms centred on convenience, privacy and confidentiality, as well as ease and affordability. High confidence was found in the studies preferencing text messaging, voice messaging, and interactive voice response services while moderate confidence was found in studies focused on phone calls. The overall constraint for platforms services included poor and limited network and electricity connectivity (high confidence in the study findings), limited access to mobile phones and mobile credit due to cost, influence from socio-cultural norms and beliefs and community members (moderate confidence in the study findings), language and literacy skills constraints (high confidence in the study findings). The findings provide valuable information on the preferences of mHealth platforms for accessing SRH services among young people in rural settings in LMICs and the quality of available evidence on the topic. As such, the findings have important implications for health policy makers and implementers and mHealth technology platform developers on improving services for sustainable adoption and integration in LMIC rural health system.

FMRI neurofeedback for the modulation of the neural networks associated with depression

ObjectivesFunctional magnetic resonance imaging (fMRI) neurofeedback has emerged as a potential treatment modality for depression, but little is known about its mechanism of action. This study aims to investigate the efficacy of fMRI neurofeedback in modulating neural networks in depression. MethodsFollowing PRISMA guidelines, a systematic review was conducted focusing on fMRI neurofeedback interventions in depression. A comprehensive search across multiple databases yielded 16 eligible studies for review. ResultsThe review demonstrated that fMRI neurofeedback can modulate BOLD activity even in strategy-free protocols and within a single session, with a significant learning effect evident over sessions. Neurofeedback targeting specific regions led to changes in connectivity across broad neural networks, including the default-mode and executive control networks, with effects being region-specific. However, methodological diversity and the absence of standardized protocols in the reviewed studies highlighted the need for more uniform research approaches. ConclusionsfMRI neurofeedback shows promise as a modulatory technique for depression, with the potential to induce significant changes in neural activity and connectivity of networks implicated in depression. SignificanceThe review underscores the necessity for standardized, reproducible neurofeedback protocols with control groups to enhance research comparability and generalizability.

Seasonal dynamics shaping bacterial community structure and networks in a Multi Geomorphological Unit System (MGUS) within the eastern Ulansuhai Lake Basin, North China

Different geomorphological units such as mountains, forests, fields, grasslands, sands and water, despite their distinct morphologies, are interconnected through micro-ecosystems that collectively maintain the balance of regional ecology. However, there is a lack of research concerning the spatial correlation among different landscape micro-ecosystems within a catchment area, hindering a comprehensive understanding of watershed systematic evolution across various landscape micro-ecosystems. To address this gap, seasonal variations in bacterial community structure and ecological network connectivity were investigated across different geomorphological units in the eastern Ulansuhai Lake Basin, North China. Water and soil samples were collected in May (beginning of the growing season, 23 samples) and November (end of the growing season, 20 samples) 2021. The diversity of bacterial communities within the Multi Geomorphological Unit System (MGUS) comprising “mountain, forest, field, grassland, sand and water” increased from May to November due to precipitation and human cultivation influences. Environmental characteristics, such as water and soil variations, exert a greater influence on bacterial community structure than differences in landscape units. The stability of the macro-ecological network structure within the MGUS was relatively high, with “water” playing a pivotal role in connectivity (the average betweenness centrality value of “water” was more than twice that of the other landscape samples). This led to the gradual emergence of deterministic processes in the construction of the bacterial community, shifting from stochastic processes (90.8 % drift effect in May) to biotic interactions (29.2 % heterogeneous selection in November). Furthermore, the bacterial community structure exhibited “macroscopic stability, with ‘water’ assuming a central role, while also showing microscopic variations, reflecting fluctuations in the contribution of dominant bacterial genera to the network structure over time”. Our study offers a core information analysis aimed at characterizing the temporal and spatial evolution of microbial community structure within an MGUS, thereby addressing a gap in microbial macroecology.

A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data.

Multimodal neuroimaging is an emerging field that leverages multiple sources of information to diagnose specific brain disorders, especially when deep learning-based AI algorithms are applied. The successful combination of different brain imaging modalities using deep learning remains a challenging yet crucial research topic. The integration of structural and functional modalities is particularly important for the diagnosis of various brain disorders, where structural information plays a crucial role in diseases such as Alzheimer's, while functional imaging is more critical for disorders such as schizophrenia. However, the combination of functional and structural imaging modalities can provide a more comprehensive diagnosis. In this work, we present MultiViT, a novel diagnostic deep learning model that utilizes vision transformers and cross-attention mechanisms to effectively fuse information from 3D gray matter maps derived from structural MRI with functional network connectivity matrices obtained from functional MRI using the ICA algorithm. MultiViT achieves an AUC of 0.833, outperforming both our unimodal and multimodal baselines, enabling more accurate classification and diagnosis of schizophrenia. In addition, using vision transformer's unique attentional maps in combination with cross-attentional mechanisms and brain function information, we identify critical brain regions in 3D gray matter space associated with the characteristics of schizophrenia. Our research not only significantly improves the accuracy of AI-based automated imaging diagnostics for schizophrenia, but also pioneers a rational and advanced data fusion approach by replacing complex, high-dimensional fMRI information with functional network connectivity, integrating it with representative structural data from 3D gray matter images, and further providing interpretative biomarker localization in a 3D structural space.

Leveraging Fog Computing for Security‐Aware Resource Allocation in Narrowband Internet of Things

ABSTRACTNarrowband Internet of Things (NB‐IoT) is LPWAN operating using narrowband spectrum in IoTs, requiring low data rates and long battery life. Since NB‐IoT does not support handover, the requirement to sustain network connectivity during mobility may result in fake base station connections, hence applications are limited to stationary use‐cases. Researchers propose extending NB‐IoT in mobile applications, owing to higher signal quality and battery life, despite DoS attacks due to low bandwidth. As NB‐IoT is resource‐constrained, resources must be allocated based on application with data processing offloaded to optimise performance. Cloud servers, being centralised and memory‐intensive, may result in increased computational delay, lower throughput, and DoS attacks in NB‐IoT. Hence, in this paper, we implement fog computing, a decentralised technology, providing scalability, reduced bandwidth, and enhanced privacy, to provide distributed processing. Additionally, we develop secure handover protocols for private and service‐provider‐controlled fog networks under normal and cell‐splitting conditions to minimise fake base station attacks and provide seamless handover. We introduce reputation‐based mechanisms to determine device integrity and differentiate faulty behaviour and attacks. Further, we implement real‐time application‐aware resource allocation and QoS‐based load‐balancing using deep learning to distribute data processing between devices, fog and cloud servers. We simulate and prototype protocols on iFogSim2 and Raspberry Pi 4. Security of the fog computing framework is validated against various attacks and formally verified using Scyther. Evaluation shows that our approach consumes 12% and 43.75% lower power and communication overhead and approximately 6 and 16 times lower execution time and memory compared with existing solutions, thus making our approach lightweight.

Associations between parental psychopathology and youth functional emotion regulation brain networks

Parental mental health is associated with children’s emotion regulation (ER) and risk for psychopathology. The relationship between parental psychopathology and children’s functional ER networks and whether connectivity patterns mediate the relationship between parent and youth psychopathology remains unexplored. Using resting-state functional magnetic resonance imaging data from the Adolescent Brain Cognitive Development Study (N = 4202, mean age = 10.0) and a multilevel approach, we analyzed the relationship between self-reported parental psychopathology and their offsprings’ connectivity of four ER networks, as well as associations with self-reported youth psychopathology at a 3-year follow-up. Parental internalizing and total problems were associated with 1) higher connectivity between a subcortical-cortical integrative and ventrolateral prefrontal cortical (PFC) network, 2) lower connectivity between dorsolateral and ventrolateral PFC networks involved in cognitive aspects of ER, and 3) lower connectivity within a subcortical ER network (β = -0.05-0.04). Parental externalizing and total problems were associated with lower connectivity within the integrative network (βext = -0.05; βtot = -0.04). Mediation analyses yielded direct effects of parental to youth psychopathology, but no mediation effect of ER network connectivity. Overall, our results show that ER network connectivity in youth is related to parental psychopathology, yet do not explain intergenerational transmission of psychopathology.

Investigation of Urban–Canal–Rural Integration Characteristics Based on Multidimensional Connectivity Network Analysis: A Case Study of the Canal in Jiangsu Province, China

Investigation of Urban–Canal–Rural Integration Characteristics Based on Multidimensional Connectivity Network Analysis: A Case Study of the Canal in Jiangsu Province, China