Disruption Of Network Research Articles

Improving RCCE-IM for LGBTQ+ migrants: Lessons learned from COVID-19 Pandemic

Abstract Background To foster equity during a pandemic event, public health practitioners must be mindful of social and political circumstances that inform poor health. This is especially true for populations who are exposed to several factors that structure health inequity, such as lesbian, gay, bisexual, transgender, or otherwise queer identifying (LGBTQ+) populations. We aim to report on the specific challenges of LGBTQ+ people, including those who belong to the migrant communities, during the COVID-19 pandemic and find out their needs in risk communication (RC), community engagement (CE) and infodemic management (IM). Methods We will first review findings of studies conducted in Portugal, UK, Italy, Brazil, Chile, and Sweden, between 2020-2023, in the context of “Project Global Queerantine”, then report on the additional challenges that LGBTQ+ people faced when living in migrant communities. Results LGBTQ+ individuals perceived their psychological wellbeing to have been greatly affected by the pandemic. Stressors included fear of the disease itself, and fear of spreading the virus, as well as the negative consequences of adhering to the recommendations of social distancing, which constantly interplayed with the marginalized position of being a LGBTQ+ person. Limited access to the LGBTQ+ community was a common stressor for trans individuals who faced prolonged waiting times for “gender dysphoria” assessment and hormone treatment. The intersectionality of vulnerability mechanisms experienced by LGBTQ+ migrants exacerbated all these issues, as did challenges in accessing and comprehending COVID-19-related information. Conclusions During health crises, it is critical for RCCE-IM efforts to consider the increased mental health issues and disruption of social support networks that LGBTQ+ people may face. Strategies and examples of good practices for RCCE-IM that are specifically tailored to LGBTQ+ migrants will be developed in collaboration with the workshop participants.

A novel recovery controllability method on temporal networks via temporal lost link prediction

Abstract Temporal networks are essential in representing systems where interactions between elements evolve over time. A crucial aspect of these networks is their controllability the ability to guide the network to a desired state through a set of control inputs. However, as these networks evolve, links between nodes can be lost due to various reasons, such as network failures, disruptions, or attacks. The loss of these links can severely impair the network’s controllability, making it challenging to recover desired network functions. In this paper, while investigating the destructive effects of various attacks on controllability processes in temporal networks, a new controllability recovery method is proposed, in which it prevents disruptions in this type of network processes by predicting lost links. In the proposed method, using network embedding and feature extraction, the dissimilarity of the nodes is calculated and then the missing links are predicted by designing a neural network. The results of the implementation of the proposed method on the datasets have demonstrates that the proposed method performed better than other conventional methods.

A Comprehensive Review of Remote Sensing Technologies for Improved Geological Disaster Management

ABSTRACTOver the past two decades, the frequency of natural hazard incidents has steadily risen, leading to substantial human casualties, infrastructure destruction, societal and economic disruption. The occurrence of disasters, both of natural and human origin, has exhibited an upward trend in frequency over the past few decades on a global scale, posing a significant threat to diverse populations. Over time, remote sensing technologies have proven to be effective in analysing and monitoring diverse natural disasters, including but not limited to droughts, earthquakes, tsunamis, landslides and cyclones. The significant extent of its coverage capacity and the ability to repeat observations make its application economically efficient. This paper aims to elucidate the fundamental contributions and role of remote sensing in disaster management applications. In a comprehensive analysis, this study explores recent practical applications in the context of disaster events. The utilisation of diverse methodologies and functions of remote sensing in disaster monitoring and control is further expanded to encompass the domain of disaster risk management, employing cutting‐edge sensors and satellites from emerging technological advancements. This paper also addresses challenges related to disaster monitoring, detection and management. Emergencies, particularly during typical catastrophe scenarios, often witness partial disruptions in communication networks. Consequently, the role of alternative networks becomes paramount in enabling effective disaster detection and management strategies. Emerging issues are highlighted, and potential directions for future research are outlined and also support sustainable development goal 13 (climate action).

Quantitative assessment of congestion diffusion and cascading effect under rainfall-flood disasters: A case study of Nanjing, China

Urban road networks are frequently disrupted by flooding rainfall-flood disasters, which can cause severe traffic disruptions and leading to traffic congestion due to cascading effect. This paper investigates the reliability issues under rainfall-flood conditions. A coupled model, integrating a rainfall-flood model with an improved cascading failure model, is proposed to assess how rainfall intensities and flooding will influence traffic congestion and bring network instability. Utilizing an improved Nonlinear Load-Capacity model, we quantify the impact of congestion and analyze cascading processes under various rainfall-flood conditions. The case study in Nanjing, China reveal that, when congestion causes network pressure to exceed the traffic percolation threshold, traffic congestion diffusion becomes more pronounced, putting excessive strain on other passable roads. Network cascading failures due to traffic congestion diffusion can lead to excessive focus on the remaining passable roads, resulting in a sharp increase in the average importance. The significance of this work lies in its provision of an effective method for predicting potential network disruptions and cascading failures in advance, thereby enhancing post-disaster road operations.

Functional network disruptions in youth with concussion using the Adolescent Brain Cognitive Development study

ABSTRACT Objective This study aimed to compare psychosocial outcomes and functional neuroimaging among youth with concussion, youth with anxiety, and age- and sex-matched controls. Methods Using archival data from the Adolescent Brain Cognitive DevelopmentSM Study, we analyzed between-group differences in psychosocial outcomes measured by the Child Behavior Checklist’s internalizing and externalizing problem scales, and assessed brain function using resting-state fMRI network-region connectivity (specifically frontoparietal network (FPN) and default mode network (DMN) connectivity with the amygdala). Results Significant differences in psychosocial outcomes were found across all groups, with the anxiety group reporting the most internalizing problems, followed by the concussion group which significantly differed from controls. Additionally, FPN-amygdala connectivity was significantly reduced in the concussion group only; this reduced connectivity did not predict psychosocial outcomes across groups. Conclusion This study provided preliminary findings that brain connectivity is reduced exclusively in individuals with concussion. Although disruptions were observed in the concussion group, further investigation is warranted to understand how disruptions may be associated with concussion symptoms. Studies that utilize well-defined control and study groups, and comprehensive cognitive and mental health measures will offer a deeper understanding of the relationship between brain function and psychosocial outcomes.

Iowa Brain-Behavior Modeling Toolkit: An Open-Source MATLAB Tool for Inferential and Predictive Modeling of Imaging-Behavior and Lesion-Deficit Relationships.

The traditional analytical framework taken by neuroimaging studies in general, and lesion-behavior studies in particular, has been inferential in nature and has focused on identifying and interpreting statistically significant effects within the sample under study. While this framework is well-suited for hypothesis testing approaches, achieving the modern goal of precision medicine requires a different framework that is predictive in nature and that focuses on maximizing the predictive power of models and evaluating their ability to generalize beyond the data that were used to train them. However, few tools exist to support the development and evaluation of predictive models in the context of neuroimaging or lesion-behavior research, creating an obstacle to the widespread adoption of predictive modeling approaches in the field. Further, existing tools for lesion-behavior analysis are often unable to accommodate categorical outcome variables and often impose restrictions on the predictor data. Researchers therefore often must use different software packages and analytical approaches depending on whether they are addressing a classification vs. regression problem and on whether their predictor data correspond to binary lesion images, continuous lesion-network images, connectivity matrices, or other data modalities. To address these limitations, we have developed a MATLAB software toolkit that supports both inferential and predictive modeling frameworks, accommodates both classification and regression problems, and does not impose restrictions on the modality of the predictor data. The toolkit features both a graphical user interface and scripting interface, includes implementations of multiple mass-univariate, multivariate, and machine learning models, features built-in and customizable routines for hyper-parameter optimization, cross-validation, model stacking, and significance testing, and automatically generates text-based descriptions of key methodological details and modeling results to improve reproducibility and minimize errors in the reporting of methods and results. Here, we provide an overview and discussion of the toolkit features and demonstrate its functionality by applying it to the question of how expressive and receptive language impairments relate to lesion location, structural disconnection, and functional network disruption in a large sample of patients with left hemispheric brain lesions. We find that impairments in expressive vs. receptive language are most strongly associated with left lateral prefrontal and left posterior temporal/parietal damage, respectively. We also find that impairments in expressive vs. receptive language are associated with partially overlapping patterns of fronto-temporal structural disconnection, and that the associated functional networks are also similar. Importantly, we find that lesion location and lesion-derived network measures are highly predictive of both types of impairment, with predictions from models trained on these measures explaining ~30-40% of the variance on average when applied to data from patients not used to train the models. We have made the toolkit publicly available, and we have included a comprehensive set of tutorial notebooks to support new users in applying the toolkit in their studies.

Chemistry of Reduced Graphene Oxide: Implications for the Electrophysical Properties of Segregated Graphene-Polymer Composites.

Conductive polymer composites (CPCs) with nanocarbon fillers are at the high end of modern materials science, advancing current electronic applications. Herein, we establish the interplay between the chemistry and electrophysical properties of reduced graphene oxide (rGO), separately and as a filler for CPCs with the segregated structure conferred by the chemical composition of the initial graphene oxide (GO). A set of experimental methods, namely X-ray photoelectron spectroscopy (XPS), ultraviolet-visible spectroscopy, van der Paw and temperature-dependent sheet resistance measurements, along with dielectric spectroscopy, are employed to thoroughly examine the derived materials. The alterations in the composition of oxygen groups along with their beneficial effect on nitrogen doping upon GO reduction by hydrazine are tracked with the help of XPS. The slight defectiveness of the graphene network is found to boost the conductivity of the material due to facilitating the impact of the nitrogen lone-pair electrons in charge transport. In turn, a sharp drop in material conductivity is indicated upon further disruption of the π-conjugated network, predominantly governing the charge transport. Particularly, the transition from the Mott variable hopping transport mechanism to the Efros-Shklovsky one is signified. Finally, the impact of rGO chemistry and physics on the electrophysical properties of CPCs with the segregated structure is evaluated. Taken together, our results give a hint at how GO chemistry manifests the properties of rGO and the CPC derived from it, offering compelling opportunities for their practical applications.

Addressing Hispanic Veterans that Live in Rural Area's Needs to Improve Suicide Prevention Efforts.

Despite the alarming recent increase in suicide rates among Hispanic Veterans, suicide among this population remains relatively understudied and little is known about their needs and how to prevent suicide in this population. A mixed methods approach was utilized to conduct a needs assessment of community suicide prevention services and resources available to Hispanic Veterans living in rural areas in the Veterans Health Administration's Veterans Integrated Services Network (VISN) 8. Five themes related to the suicide prevention's needs and gaps in services were identified: (1) lack of adequate information; (2) disruptions in social support network; (3) limited or lack of access to services; (4) risky behaviors; and (5) natural disasters. Understanding the unique needs of Hispanic Veterans in rural communities and the gaps in services in these areas can help in the development of tailored suicide prevention efforts and potentially mitigate suicide disparities.

Diagnosis of the fragmentation of urban ecological network structure and its social-ecological responses.

The fragmentation of ecological network structures has become a common problem faced by cities. By establishing the urban ecological network under a specific socio-ecological system framework, we aimed to propose a quantitative index to diagnose the fragmentation of the network structure, and to construct detection model to explore the driving factors and mechanism of the network fragmentation. Using Shenzhen City as an example, we used the Floyd-Prim algorithm to generate the skeleton structure of the ecological network and construct a density discontinuity index to diagnose network fragmentation. Combined with the ecological network scenario, social-ecological system framework and a two-layer indicator system were constructed. The detection models were then established to explore the drivers of network disruption and their mode of impact. The models show that the average degree of network fragmentation in Shenzhen was 0.13, and the density of about 85% of corridor discontinuities was greater than 0.01, reflecting the serious state of structural fragmentation. Corridors with more severe structural fragmentation have poorer social-ecological coordination. The fragmentation in Shenzhen was mainly affected by the activities of actors (A) at the microlevel and the resource system (RS) at the macrolevel. The methods and the framework of socio-ecosystem analysis proposed in this paper can reveal the driving factors and influence modes of network fragmentation, providing decision-making reference for ecological restoration practice in urbanized areas.

Myosin A and F-Actin play a critical role in mitochondrial dynamics and inheritance in Toxoplasma gondii.

The single mitochondrion of the obligate intracellular parasite Toxoplasma gondii is highly dynamic. Toxoplasma's mitochondrion changes morphology as the parasite moves from the intracellular to the extracellular environment and during division. Toxoplasma's mitochondrial dynamic is dependent on an outer mitochondrion membrane-associated protein LMF1 and its interaction with IMC10, a protein localized at the inner membrane complex (IMC). In the absence of either LMF1 or IMC10, parasites have defective mitochondrial morphology and inheritance defects. As little is known about mitochondrial inheritance in Toxoplasma, we have used the LMF1/IMC10 tethering complex as an entry point to dissect the machinery behind this process. Using a yeast two-hybrid screen, we previously identified Myosin A (MyoA) as a putative interactor of LMF1. Although MyoA is known to be located at the parasite's pellicle, we now show through ultrastructure expansion microscopy (U-ExM) that this protein accumulates around the mitochondrion in the late stages of parasite division. Parasites lacking MyoA show defective mitochondrial morphology and a delay in mitochondrion delivery to the daughter parasite buds during division, indicating that this protein is involved in organellar inheritance. Disruption of the parasite's actin network also affects mitochondrion morphology. We also show that parasite-extracted mitochondrion vesicles interact with actin filaments. Interestingly, mitochondrion vesicles extracted out of parasites lacking LMF1 pulled down less actin, showing that LMF1 might be important for mitochondrion and actin interaction. Accordingly, we are showing for the first time that actin and Myosin A are important for Toxoplasma mitochondrial morphology and inheritance.

Unsupervised Learning for Lateral-Movement-Based Threat Mitigation in Active Directory Attack Graphs

Cybersecurity threats, particularly those involving lateral movement within networks, pose significant risks to critical infrastructures such as Microsoft Active Directory. This study addresses the need for effective defense mechanisms that minimize network disruption while preventing attackers from reaching key assets. Modeling Active Directory networks as a graph in which the nodes represent the network components and the edges represent the logical interactions between them, we use centrality metrics to derive the impact of hardening nodes in terms of constraining the progression of attacks. We propose using Unsupervised Learning techniques, specifically density-based clustering algorithms, to identify those nodes given the information provided by their metrics. Our approach includes simulating attack paths using a snowball model, enabling us to analytically evaluate the impact of hardening on delaying Domain Administration compromise. We tested our methodology on both real and synthetic Active Directory graphs, demonstrating that it can significantly slow down the propagation of threats from reaching the Domain Administration across the studied scenarios. Additionally, we explore the potential of these techniques to enable flexible selection of the number of nodes to secure. Our findings suggest that the proposed methods significantly enhance the resilience of Active Directory environments against targeted cyber-attacks.

Melanoma-specific mutation hotspots in distal, non-coding, promoter-interacting regions implicate novel candidate driver genes.

To develop targeted treatments, it is crucial to identify the full spectrum of genetic drivers in melanoma, including those in non-coding regions. However, recent efforts to explore non-coding regions have primarily focused on gene-adjacent elements such as promoters and non-coding RNAs, leaving intergenic distal regulatory elements largely unexplored. We used Hi-C chromatin contact data from melanoma cells to map distal, non-coding, promoter-interacting regulatory elements genome-wide in melanoma. Using this "promoter-interaction network", alongside whole-genome sequence and gene expression data from the Pan Cancer Analysis of Whole Genomes, we developed multivariate linear regression models to identify distal somatic mutation hotspots that affect promoter activity. We identified eight recurrently mutated hotspots that are novel, melanoma-specific, located in promoter-interacting distal regulatory elements, alter transcription factor binding motifs, and affect the expression of genes (e.g., HSPB7, CLDN1, ADCY9 and FDXR) previously implicated as tumour suppressors/oncogenes in various cancers. Our study suggests additional non-coding drivers beyond the well-characterised TERT promoter in melanoma, offering new insights into the disruption of complex regulatory networks by non-coding mutations that may contribute to melanoma development. Furthermore, our study provides a framework for integrating multiple levels of biological data to uncover cancer-specific non-coding drivers.

Distinct Longitudinal Changes in EEG Measures Reflecting Functional Network Disruption in ALS Cognitive Phenotypes

Amyotrophic lateral sclerosis (ALS) is characterised primarily by motor system degeneration, with clinical evidence of cognitive and behavioural change in up to 50% of cases. We have shown previously that resting-state EEG captures dysfunction in motor and cognitive networks in ALS. However, the longitudinal development of these dysfunctional patterns, especially in networks linked with cognitive-behavioural functions, remains unclear. Longitudinal studies on non-motor changes in ALS are essential to further develop our understanding of disease progression, improve care and enhance the evaluation of new treatments. To address this gap, we examined 124 ALS individuals with 128-channel resting-state EEG recordings, categorised by cognitive impairment (ALSci, n = 25), behavioural impairment (ALSbi, n = 58), or non-impaired (ALSncbi, n = 53), with 12 participants meeting the criteria for both ALSci and ALSbi. Using linear mixed-effects models, we characterised the general and phenotype-specific longitudinal changes in brain network, and their association with cognitive performance, behaviour changes, fine motor symptoms, and survival. Our findings revealed a significant decline in \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:\ heta\\:$$\\end{document}-band spectral power over time in the temporal region along with increased \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:{\\gamma\\:}_{l}$$\\end{document}-band power in the fronto-temporal region in the ALS group. ALSncbi participants showed widespread β-band synchrony decrease, while ALSci participants exhibited increased co-modulation correlated with verbal fluency decline. Longitudinal network-level changes were specific of ALS subgroups and correlated with motor, cognitive, and behavioural decline, as well as with survival. Spectral EEG measures can longitudinally track abnormal network patterns, serving as a candidate stratification tool for clinical trials and personalised treatments in ALS.

Cryoprotective effect of wheat starch granular surface proteins on frozen HMW and LMW glutenins: Structure, property and functionality across length scales

Although frozen dough technology has demonstrated significant benefits, the mechanisms underlying dough deterioration during freezing remain unclear. To overcome this obstacle, the effect of freezing-induced deteriorations of wheat starch granular surface proteins (SGSPs)-high/low molecular weight (HMW/LMW) glutenins complexes were analyzed from the molecular to macroscopic scales. After 7 cycles of freezing/thawing treatment, SGSPs-LMW complex showed a higher antifreeze stability than SGSPs-HMW complex. The freezable water content of SGSPs-HMW increased from 32 % to 39 %, indicating a marked migration and recrystallization of ice. In this situation, the interactions of SGSPs-HMW complex were affected and destabilized, leading to partially denatured and depolymerized molecular structures. Furthermore, the bulk protein aggregation network was also dissociated under the ice tearing and splitting, which irreversibly collapsed to small molecular protein particles. In comparison, the resistance of SGSPs-LMW complex on continued network disruption appear to be the key to maintain the quality of frozen dough.

Thermorheological properties and structural characteristics of soy and pumpkin seed protein blends for high-moisture meat analogs

This study explored the effects of soy protein isolate (SPI) and pumpkin seed protein concentrate (PSC) blends on the thermorheological properties and microstructure of high-moisture meat analogs. Using a Discovery Hybrid Rheometer, we observed that increasing PSC content weakened SPI network formation, resulting in decreased gel stability and a more porous, less aligned structure. Microstructural analyses (FE-SEM and FT-IR) revealed a correlation between protein network disruption and increased PSC content. Additionally, higher PSC levels reduced overall bonding (ionic, hydrogen, hydrophobic, disulfide), modifying the texture and softening the gel while retaining solid-like characteristics essential for replicating meat textures. Although full high-moisture extrusion conditions were not replicated, the observed trends offer valuable insights into texture development for meat substitutes. Future research using a closed-system rheometer is required to validate these findings and improve precision.

Neuropsychiatric Symptoms Cluster and Fluctuate Over Time in Behavioral Variant Frontotemporal Dementia.

Cognitive and behavioral phenomena define behavioral variant frontotemporal dementia (bvFTD), but neuropsychiatric symptoms (NPS) outside the core criteria are common throughout the illness. Identifying how NPS cluster in bvFTD may clarify the underlying neurobiology of bvFTD-related NPS and guide development of therapies. Participants (N=354) with sporadic and genetic bvFTD were enrolled in the ARTFL LEFFTDS Longitudinal Frontotemporal Lobar Degeneration Consortium. Dementia stage was defined as early (CDR® plus NACC FTLD ≤ 1) or advanced (CDR® plus NACC FTLD ≥ 1). Baseline and annual follow-up visit data were analyzed to compare NPS across stages of bvFTD. Psychiatric states were captured using the Neuropsychiatric Inventory-Questionnaire and Clinician Judgement of Symptoms. Polychoric cluster analysis was used to describe NPS clusters. NPS were highly prevalent (≥ 90%) in early and late bvFTD. Four NPS clusters were identified based on magnitude of factor loadings: affective, disinhibited, compulsive, and psychosis. Neuropsychiatric symptoms fluctuated across visits. In the affective cluster, depression and anxiety showed the least visit-to-visit stability. In the disinhibited cluster, elation showed the least stability. Symptoms in the psychosis and compulsive clusters (hallucinations, delusions, obsessions/compulsions, and hyperorality) were largely stable, persisting from visit-to-visit in more than 50% of cases. NPS in bvFTD are frequent and cluster into four discrete groups in bvFTD. These clusters may result from specific neural network disruptions that could serve as targets for future interventions. The fluctuating nature of NPS in bvFTD suggests that they are not reliable markers of disease progression or stage.

α-costic acid, the main sesquiterpenoid isolated from Dittrichia viscosa (L) Greuter, induces oxidative stress and autophagy in tomato.

Dittrichia viscosa (L.) Greuter, a perennial plant in the Asteraceae, has strong allelopathic activity due to the high content of various secondary metabolites. The bicyclic sesquiterpenoid α-costic acid is the most abundant secondary metabolite of D. viscosa. Its remarkable insecticidal, antiparasitic, and phytotoxic activities point to its potential use as a natural herbicide, but information on its mode of action is lacking. To shed light on the mechanism of action of α-costic acid in plant cells, we investigated the phytotoxicity of α-costic acid in tomato plants (Solanum lycopersicum L.) through in vivo assays, the underlying cellular effects using biochemical assays, and the effect on subcellular organelles using confocal microscopy on tomato protoplasts incubated with organelle-specific fluorescent probes. In vivo tests showed that α-costic acid inhibited the growth of tomato seedlings and induced chlorosis and spot lesions in leaves. Biochemical assays demonstrated that α-costic acid caused ion leakage, chlorophyll depletion, H2O2 overproduction, callose deposition, and membrane lipid peroxidation. Confocal microscopy demonstrated that α-costic acid determined ROS overproduction and network disruption in mitochondria, singlet oxygen overproduction in chloroplasts, vacuole disintegration, and autophagosome formation. Overall, our data are consistent with a model according to which α-costic acid phytotoxicity is related to oxidative stress in mitochondria and chloroplasts that induces extensive membrane damage, ultimately resulting in cell death associated with autophagy.

Electrophysiological dynamics of salience, default mode, and frontoparietal networks during episodic memory formation and recall: A multi-experiment iEEG replication.

Dynamic interactions between large-scale brain networks underpin human cognitive processes, but their electrophysiological mechanisms remain elusive. The triple network model, encompassing the salience (SN), default mode (DMN), and frontoparietal (FPN) networks, provides a framework for understanding these interactions. We analyzed intracranial EEG recordings from 177 participants across four diverse episodic memory experiments, each involving encoding as well as recall phases. Phase transfer entropy analysis revealed consistently higher directed information flow from the anterior insula (AI), a key SN node, to both DMN and FPN nodes. This directed influence was significantly stronger during memory tasks compared to resting-state, highlighting the AI's task-specific role in coordinating large-scale network interactions. This pattern persisted across externally-driven memory encoding and internally-governed free recall. Control analyses using the inferior frontal gyrus (IFG) showed an inverse pattern, with DMN and FPN exerting higher influence on IFG, underscoring the AI's unique role. We observed task-specific suppression of high-gamma power in the posterior cingulate cortex/precuneus node of the DMN during memory encoding, but not recall. Crucially, these results were replicated across all four experiments spanning verbal and spatial memory domains with high Bayes replication factors. Our findings advance understanding of how coordinated neural network interactions support memory processes, highlighting the AI's critical role in orchestrating large-scale brain network dynamics during both memory encoding and retrieval. By elucidating the electrophysiological basis of triple network interactions in episodic memory, our study provides insights into neural circuit dynamics underlying memory function and offer a framework for investigating network disruptions in memory-related disorders.

A longitudinal MRI analysis reveals altered brain connectivity and microstructural changes in a transgenic mouse model of Alzheimer's disease

Alzheimer's disease (AD) is characterized by progressive cognitive decline and neuropathological changes, yet the underlying neurobiological mechanisms remain elusive. Here, we employed a multimodal longitudinal neuroimaging approach, using anatomical and functional sequences on a high field magnetic resonance imaging (MRI) preclinical scanner, to investigate alterations in brain connectivity and white matter microstructure in a transgenic mouse model of AD (J20) when compared to wild-type (WT) littermates. Functional connectivity analysis revealed distinct network disruptions in J20 mice, primarily involving connections between posterior and anterior brain regions; importantly, a significant interaction between group and age highlighted an exacerbation of these connectivity changes with advancing age in J20 mice. In addition, significant reductions in fractional anisotropy (FA) were observed in the corpus callosum of J20 mice compared to WT, indicative of microstructural alterations consistent with white matter pathology. The observed alterations in brain connectivity and microstructure provide valuable insights into the spatiotemporal processes underlying AD-related decline and underscore the utility of multimodal neuroimaging in elucidating the neurobiological substrates of AD pathology in animal models.

LiverMap pipeline for 3D imaging of human liver reveals volumetric spatial dysregulation of cirrhotic vasculobiliary architecture.

The liver contains an intricate microstructure that is critical for liver function. Architectural disruption of this spatial structure is pathologic. Unfortunately, 2D histopathology - the gold standard for pathological understanding of many liver diseases - can misrepresent or leave gaps in our understanding of complex 3D structural features. Here, we utilized immunostaining, tissue clearing, microscopy, and computational software to create 3D multilobular reconstructions of both non-fibrotic and cirrhotic human liver tissue. We found that spatial architecture in human cirrhotic liver samples with varying etiologies had sinusoid zonation dysregulation, reduction in glutamine synthetase-expressing pericentral hepatocytes, regression of central vein networks, disruption of hepatic arterial networks, and fragmentation of biliary networks, which together suggest a pro-portalization/decentralization phenotype in cirrhotic tissue. Further implementation of 3D pathological analyses may provide a deeper understanding of cirrhotic pathobiology and inspire novel treatments for liver disease.