Network Activity Research Articles

Regulation of craving for real-time fMRI neurofeedback based on individual classification.

In previous real-time functional magnetic resonance imaging neurofeedback (rtfMRI-NF) studies on smoking craving, the focus has been on within-region activity or between-region connectivity, neglecting the potential predictive utility of broader network activity. Moreover, there is debate over the use and relative predictive power of individual-specific and group-level classifiers. This study aims to further advance rtfMRI-NF for substance use disorders by using whole-brain rtfMRI-NF to assess smoking craving-related brain patterns, evaluate the performance of group-level or individual-level classification (n = 31) and evaluate the performance of an optimized classifier across repeated NF runs. Using real-time individual-level classifiers derived from whole-brain support vector machines, we found that classification accuracy between crave and no-crave conditions and between repeated NF runs increased across repeated runs at both individual and group levels. In addition, individual-level accuracy was significantly greater than group-level accuracy, highlighting the potential increased utility of an individually trained whole-brain classifier for volitional control over brain patterns to regulate smoking craving. This study provides evidence supporting the feasibility of using whole-brain rtfMRI-NF to modulate smoking craving-related brain responses and the potential for learning individual strategies through optimization across repeated feedback runs. This article is part of the theme issue 'Neurofeedback: new territories and neurocognitive mechanisms of endogenous neuromodulation'.

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.

EEG Oscillatory Activity and Resting-State Networks Associated with Neurocognitive Function in Mild Traumatic Brain Injury.

This study aimed to investigate the characteristics of resting-state electroencephalography (EEG) activity and brain networks in patients with mild traumatic brain injury (mTBI) and their association with neurocognitive function (NCF). We analyzed 26 patients with subacute mTBI and 21 healthy controls. The subacute mTBI group (9 females, 17 males) had a mean age of 29.9 ± 9.9 years, and the healthy controls (11 females, 10 males) had a mean age of 29.7 ± 11.5 years. Current source density, lagged phase synchronization, and resting-state network activity were analyzed using exact low-resolution electromagnetic tomography (eLORETA) with 60 s resting-state EEG data. In addition, a correlation analysis was performed between these EEG parameters and NCF in patients with mTBI. We used the statistical nonparametric mapping method in eLORETA to correct for multiple comparisons. There were no significant differences in EEG parameters between the patients with mTBI and healthy controls. However, in patients with mTBI, correlation analysis revealed negative correlations between theta activity in the anterior cingulate cortex and verbal short-term memory and between activity in the memory perception network and verbal memory. Our findings suggest that resting-state EEG may be clinically useful in investigating the mechanism of NCF decline in patients with mTBI.

Language Teacher Researcher Identity Construction in Activity: Navigating Entanglement of Two English PSTs in Undergraduate Thesis Work in the Indonesian Context

ABSTRACT Addressing the issue of colonial humanism ideologies plaguing undergraduate thesis work of English pre-service teachers in the Indonesian context and perpetuating the dichotomy of teacher and researcher identities, this study explored two pre-service teachers’ entanglements in their day-to-day praxes of undergraduate thesis work as a site of identity construction by framing it in the lens of postcolonial, posthuman and transnational Cultural-Historical Activity Theory. Orienting toward movement of actions and praxes, they actively participated in entanglements of identity construction by making visible layers of contradictions and continuously maneuvering the contradictions to achieve meaningful outcomes in each praxis. The contradictions at the micro level further revealed contradictions at the meso level of actions and the macro level of networked activity systems at the institutional level, indicating the pivotal role of understanding their micro level of praxes to unveil issues hindering undergraduate thesis to serve as a democratic space to generate meaningful knowledge.

Human Astrocytes Synchronize Neural Organoid Networks.

Biological neural networks exhibit synchronized activity within and across interconnected regions of the central nervous system. Understanding how these coordinated networks are established and maintained may reveal therapeutic targets for neurodegeneration and neuromodulation. Here, we tested the influence of astrocytes upon synchronous network activity using human pluripotent stem cell-derived bioengineered neural organoids. This study revealed that astrocytes significantly increase activity within individual organoids and across long distances among numerous rapidly merged organoids via influencing synapses and bioenergetics. Treatment of amyloid protein inhibited synchronous activity during neurodegeneration, yet this can be rescued by propagating activity from neighboring networks. Altogether, this study identifies critical contributions of human astrocytes to biological neural networks and delivers a rapid, reproducible, and scalable model to investigate long-range functional communication of the nervous system in healthy and disease states.

A distance-based network activity correlation framework for defeating anonymization overlays

As the effectiveness of modern Internet-based anonymization infrastructures grows, law enforcement agencies are experiencing a progressive erosion of their surveillance capabilities. This can severely undermine their efforts to prevent and investigate various types of unlawful activities, potentially increasing the impunity of organized criminal networks. Balancing the legitimate privacy needs of individuals with the imperative to maintain public safety and combat criminal behavior in the digital world remains a complex tradeoff for both policymakers and technologists who need to find a systematic and reliable way to link the traffic traces associated with criminal activities to their anonymized origins. Accordingly, this paper presents a simple but very effective de-anonymization approach capable of associating traffic traces captured at the edge of the overlay infrastructures, in correspondence with the true origins, to those captured in correspondence with the destinations. The approach is based on determining the minimum-distance pairs within a complete bipartite graph in which the traffic traces are the nodes. Experiments with different distance functions, applied in varied ways, show that the resulting framework appears to be a promising solution that is scalable and easily deployable on real-life network equipment.

Inflammation biomarkers and neurobehavioral performance in rural adolescents.

Systemic inflammation has been associated with lower neurobehavioral performance in diverse populations, yet the evidence in adolescents remains lacking. Cytokines can alter neural network activity to induce neurocognitive changes. This work seeks to investigate the association between inflammation and neurobehavior in adolescents living in a rural region of Ecuador. We examined 535 adolescents in rural communities of Ecuador (ESPINA study), 508 of which had neurobehavioral assessments (NEPSY-II) and circulating plasma levels of inflammatory markers (CRP, IL-6, TNF-⍺, sICAM-1, sVCAM-1, SAA, and sCD14). Associations between inflammatory biomarker concentrations and neurobehavioral scores were examined using adjusted bivariate semi-parametric models with generalized estimating equations. A partial least square regression approach was used to create composite variables from multiple inflammation biomarkers and model their association with cognitive outcomes. Higher sCD14 and TNF-α concentrations were significantly associated with lower social perception scores, by -0.47 units (95% CI: -0.80, -0.13) and -0.42 (-0.72, -0.12) for every 50% increase in inflammatory marker concentration, respectively. Similarly, every 50% increase in the inflammation summary score was associated with a significantly lower Social Perception score by -0.11 units (-0.19, -0.03). A unit increase in inflammatory composites of seven markers were associated with lower scores in language (-0.11 units, p=0.04), visuospatial processing (-0.15, p= 0.09), and social perception (-0.22, p=0.005) domains. Higher levels of inflammation were associated with lower neurobehavioral performance in adolescents, especially with social perception. In addition, using a robust analytic method to examine an association between a composite inflammatory variable integrating seven markers led to additional findings, including the domains of language and visuospatial processing. A longitudinal follow-up of such investigations could unveil potential changes in inflammation-neurobehavior performance links through developmental stages and intervention opportunities.

The Alzheimer's disease risk gene CD2AP functions in dendritic spines by remodeling F-actin.

CD2AP was identified as a genetic risk factor for late-onset Alzheimer's disease (LOAD). However, it is unclear how CD2AP contributes to LOAD synaptic dysfunction underlying AD memory deficits. We have shown that loss of CD2AP function increases β-amyloid (Aβ) endocytic production, but it is unknown whether it contributes to synapse dysfunction. As CD2AP is an actin-binding protein, it may also function in F-actin-rich dendritic spines, which are the excitatory postsynaptic compartments. Here, we demonstrate that CD2AP colocalizes with F-actin in dendritic spines of primary mouse cortical neurons of both sexes. Cell-autonomous depletion of CD2AP specifically reduces spine density and volume, resulting in a functional decrease in synapse formation and neuronal network activity. Post-synaptic reexpression of CD2AP, but not blocking Aβ-production, is sufficient to rescue spine density. CD2AP overexpression increases spine density, volume, and synapse formation, while a rare LOAD CD2AP mutation induces aberrant F-actin spine-like protrusions without functional synapses. CD2AP controls postsynaptic actin turnover, with the LOAD mutation in CD2AP decreasing F-actin dynamicity. Our data support that CD2AP risk variants could contribute to LOAD synapse dysfunction by disrupting spine formation and growth by deregulating actin dynamics.Significance statement CD2AP is a candidate genetic risk factor of late-onset Alzheimer's disease (LOAD) expressed in neurons with an unknown impact on synapse dysfunction, one of the causal LOAD mechanisms. Our research has revealed CD2AP as a new synaptic protein and established a connection between a LOAD genetic variant in CD2AP and synaptic dysfunction independent of beta-amyloid accumulation. This study suggests an explanation for the CD2AP-mediated predisposition to AD. Furthermore, we have found that controlling CD2AP's impact on spinal F-actin could be a potential target for therapeutic intervention against LOAD.

Machine learning applications of network security enhancement: review

Machine learning (ML) is being used to improve intrusion detection mechanisms and identification in cyber security. Network data volume scaling (with the help of Machine learning) — Automated analysis and pattern recognition for large amounts of network-data, thereby detection of anomalies / potentially malicious activities that escape current rule-based techniques. By training ML models on historical data, these models can learn benign network behavior as well the anomalies in them that may result from malicious activities. The purpose of this essay/report is to begin taking a look under the hood at how ML can be used for security threat detection and analysis in-networking on an ongoing basis. This paper covers the automation of ML algorithms to enhance network security. Given the current state of electronic threats and their evolution, traditional security methods are typically insufficient. ML can analyze large volumes of data, learn patterns from it and this makes it suitable to complement network defense mechanisms. It then discusses different ML applications in network cybersecurity: intrusion detection, anomaly detection, spam and malware analysis; which the paper characterizes. It analyzes the potential, benefits and constrains of major ML methods in network security like supervised learning; unsupervised learning and reinforcement-learning. Finally, this paper represents recent progress in the use and impact of ML techniques along with case studies.The paper discusses the existing difficulties in the field such as the necessity for datasets and the vulnerability of machine learning models to adversarial attacks.T he paper also highlights avenues for exploration by focusing on developing scalable security solutions based on machine learning that are resilient and flexible.The goal of this examination is to offer both researchers and industry professionals valuable perspectives into the opportunities and obstacles linked to utilizing machine learning, in the domain of network security. ML methods have potential to improve network security by addressing the challenges posed by the increasing cyber threats that traditional security measures struggle to combat effectively over time. One key strength of ML lies in its capacity to analyze datasets and identify intricate patterns efficiently. The research paper delves into applications of ML in enhancing network security. The list covers security tools like Intrusion Detection Systems (IDS) examining malware and phishing attempts as well as anomalies in network activity and user behavior analysis (UEBA). The study explores both supervised and unsupervised learning methods. How they are used for quick threat detection and response in real time scenarios.You will find case studies and recent developments that showcase the implementation and effectiveness of these strategies.In addition the article delves into the obstacles linked to using machine learning techniques in network security including the necessity, for datasets, The paper's goal is to give an enlightening summary to scholars and practitioners about how machine learning can be applied to network security in order to provide solutions that are robust, adaptive, and scalable. To this end, it touches on several relevant aspects. One is the threat posed by adversarial attacks on the sorts of models that are likely to be used in this context. Another is the imperative, deriving from both adversarial threat and model drift, that models needed in this context be available in a form usable for continuous update. Keywords: Network Security, machine learning (ML), Intrusion Detection Systems (IDS), entity behavior analytics (UEBA).

Adverse childhood experiences, brain function, and psychiatric diagnoses in a large adult clinical cohort.

Adverse childhood experiences (ACEs) are linked to higher rates of psychiatric disorders in adults. Previous neuroimaging studies with small samples have shown associations between ACEs and alterations in brain volume, connectivity, and blood flow. However, no study has explored these associations in a large clinical population to identify brain regions that may mediate the relationship between ACEs and psychiatric diagnoses. This study aims to evaluate how patient-reported ACEs are associated with brain function in adults, across diagnoses. We analyzed 7,275 adults using HMPAO SPECT scans at rest and during a continuous performance task (CPT). We assessed the impact of ACEs on brain function across psychiatric diagnoses and performed mediation analyses where brain functional regions of interest acted as mediators between patient-reported ACEs and specific psychiatric diagnoses. We further evaluated the risk of being diagnosed with specific classes of mental illnesses as a function of increasing ACEs and identified which specific ACE questions were statistically related to each diagnosis in this cohort. Increased ACEs were associated with higher activity in cognitive control and default mode networks and decreased activity in the dorsal striatum and cerebellum. Higher ACEs increased the risk of anxiety-related disorders, substance abuse, and depression. Several brain regions were identified as potential mediators between ACEs and adult psychiatric diagnoses. This study, utilizing a large clinical cohort, provides new insights into the neurobiological mechanisms linking ACEs to adult psychiatric conditions. The findings suggest that specific brain regions mediate the effects of ACEs on the risk of developing mental health disorders, highlighting potential targets for therapeutic interventions.

Functional activity and connectivity signatures of ketamine and lamotrigine during negative emotional processing: a double-blind randomized controlled fMRI study

Ketamine is a highly effective antidepressant (AD) that targets the glutamatergic system and exerts profound effects on brain circuits during negative emotional processing. Interestingly, the effects of ketamine on brain measures are sensitive to modulation by pretreatment with lamotrigine, which inhibits glutamate release. Examining the antagonistic effects of ketamine and lamotrigine on glutamate transmission holds promise to identify effects of ketamine that are mediated through changes in the glutamatergic system. Investigating this modulation in relation to both the acute and sustained effects of ketamine on functional activity and connectivity during negative emotional processing should therefore provide novel insights. 75 healthy subjects were investigated in a double-blind, single-dose, randomized, placebo-controlled, parallel-group study with three treatment conditions (ketamine, lamotrigine pre-treatment, placebo). Participants completed an emotional face viewing task during ketamine infusion and 24 h later. Acute ketamine administration decreased hippocampal and Default Mode Network (DMN) activity and increased fronto-limbic coupling during negative emotional processing. Furthermore, while lamotrigine abolished the ketamine-induced increase in functional connectivity, it had no acute effect on activity. Sustained (24 h later) effects of ketamine were only found for functional activity, with a significant reduction in the posterior DMN. This effect was blocked by pretreatment with lamotrigine. Our results suggest that both the acute increases in fronto-limbic coupling and the delayed decrease in posterior DMN activity, but not the attenuated limbic and DMN recruitment after ketamine, are mediated by altered glutamatergic transmission.

Investigate Effects of Music Therapy on Functional Connectivity in Papez Circuit of Breast Cancer Patients Using fMRI.

The aim of this study is to investigate activity and functional connectivity (FC) of Papez circuit networks associated with music processing using functional magnetic resonance imaging (fMRI) in depressed breast cancer patients. Twenty-three breast cancer patients listened to four different Iranian/Persian music paradigms during the resting-state fMRI scanning session: negative stimulation of traditional music, negative stimulation of pop music, positive stimulation of traditional music and positive stimulation of pop music. The amplitude of low-frequency fluctuation (ALFF) was used to evaluate the local characteristics of spontaneous brain activity. FC maps were created using multivariate ROI-to-ROI connectivity (mRRC) and Papez circuit-based regions of interest (ROIs) selection. We found that music increases FC within various brain networks which are involved in memory, emotion, and cognitive function, including the limbic system, the default mode network (DMN), salience network (SN), and central executive network (CEN). Moreover, it seems that the traditional types (both positive and negative) of Iranian music may be more effective to affect brain activity in the patients with breast cancer, than the Iranian pop music. These findings demonstrate that music therapy, as an effective and easily applicable approach, supports the neuropsychological recovery and can contribute to standard treatment protocols in patients with breast cancer.

Longitudinal multi-omics reveals pathogenic TSC2 variants disrupt developmental trajectories of human cortical organoids derived from Tuberous Sclerosis Complex.

Tuberous Sclerosis Complex (TSC), an autosomal dominant condition, is caused by heterozygous mutations in either the TSC1 or TSC2 genes, manifesting in systemic growth of benign tumors. In addition to brain lesions, neurologic sequelae represent the greatest morbidity in TSC patients. Investigations utilizing TSC1/2 -knockout animal or human stem cell models suggest that TSC deficiency-causing hyper-activation of mTOR signaling might precipitate anomalous neurodevelopmental processes. However, how the pathogenic variants of TSC1/2 genes affect the longitudinal trajectory of human brain development remains largely unexplored. Here, we employed 3-dimensional cortical organoids derived from induced pluripotent stem cells (iPSCs) from TSC patients harboring TSC2 variants, alongside organoids from age- and sex-matched healthy individuals as controls. Through comprehensively longitudinal molecular and cellular analyses of TSC organoids, we found that TSC2 pathogenic variants dysregulate neurogenesis, synaptogenesis, and gliogenesis, particularly for reactive astrogliosis. The altered developmental trajectory of TSC organoids significantly resembles the molecular signatures of neuropsychiatric disorders, including autism spectrum disorders, epilepsy, and intellectual disability. Intriguingly, single cell transcriptomic analyses on TSC organoids revealed that TSC2 pathogenic variants disrupt the neuron/reactive astrocyte crosstalk within the NLGN-NRXN signaling network. Furthermore, cellular and electrophysiological assessments of TSC cortical organoids, along with proteomic analyses of synaptosomes, demonstrated that the TSC2 variants precipitate perturbations in synaptic transmission, neuronal network activity, mitochondrial translational integrity, and neurofilament formation. Notably, similar perturbations were observed in surgically resected cortical specimens from TSC patients. Collectively, our study illustrates that disease-associated TSC2 variants disrupt the neurodevelopmental trajectories through perturbations of gene regulatory networks during early cortical development, leading to mitochondrial dysfunction, aberrant neurofilament formation, impaired synaptic formation and neuronal network activity.

Comparison of extracellular Giant depolarizing potentials in vitro and early sharp waves in vivo in the CA1 hippocampus of neonatal rats

Giant Depolarizing Potentials (GDPs) and Early Sharp Waves (eSPWs) are the major patterns of neuronal network activity in the developing hippocampus of neonatal rodents in vitro and in vivo, respectively. Because of certain similarities in their electrographic traits, GDPs and eSPWs were originally considered as homologous patterns. Here, we compared electrographic features and current density profiles of field GDPs (fGDPs) and eSPWs using extracellular multisite silicon probe recordings from neonatal rat CA1 hippocampus. We found that fGDPs in hippocampal slices were much less in amplitude than eSPWs, and were characterized by electronegativity and current sinks in CA1 pyramidal cell layer and stratum radiatum, and positive waves/sources in stratum lacunosum-moleculare. eSPWs in vivo showed a remarkably different depth profile, with positivity and current source in the CA1 pyramidal cell layer, and negativity/sinks in stratum radiatum and stratum lacunosum-moleculare. Current sinks of CA3-evoked responses corresponded to sinks of fGDPs and eSPWs in the stratum radiatum. However, current sinks of entorhinal inputs - evoked responses in stratum lacunosum-moleculare, which were characteristic of eSPWs, were absent in fGDPs. In addition, fGDPs more strongly modulated neuronal firing in CA1 compared to eSPWs. Thus, we show important differences in the electrographic properties of GDPs and eSPWs that challenge the homology of these two activity patterns.

EEG synchronization patterns during a Go/No-Go task in individuals with aphasia in subacute and chronic phases of stroke

Stroke and subsequent neuroregenerative processes cause changes in neural organization of attentional functions. In this study, we attempted to identify differences in neural synchronization patterns during a visual Go/No-Go task in people with post-stroke aphasia in both subacute and chronic stroke phases. To identify neuronal underpinnings of the behavioral differences we investigated pairwise connectivity patterns using corrected imaginary phase locking value and graph-theoretic measures (efficiency, modularity and clustering coefficient) at global and local level in subacute (n = 13) and chronic stroke phases (n = 14) during a Go/No-Go task. We observed significantly lower phase synchronization in the Subacute Group in the alpha band in the connections spanning frontal and central areas of both hemispheres alongside lower local efficiency and clustering coefficient in the left frontal region. Additionally, we observed higher modularity in the beta band in the unaffected right parietal region in the Subacute Group which may denote inhibition of motor and attention functions. Those mechanisms could serve to align cognitive abilities between the damaged and healthy hemispheres, harmonizing the activity of the neuronal networks of both hemispheres disrupted by the effects of the stroke. Our findings have potential implications for rehabilitation therapies, which should take into account the pattern of connectivity changes during different phases of reovery.

Transient destabilization of interhemispheric functional connectivity induced by spreading depolarization

ABSTRACT Cortical spreading depolarization (CSD), a slowly propagating wave of transient cellular depolarization, is a reliable cortical response to various brain insults (stroke, trauma, seizures) and underlying mechanism of migraine aura. Little is known about CSD effects on brain network activity. Using undirected (mutual information, MI) and directed (transfer entropy, TE) measures, we studied the dynamics of cross-hemispheric connectivity associated with the development of unilateral CSD in freely behaving rats and the involvement of inhibitory transmission in mechanisms of the coupling changes. We show that the development of CSD in the cortex of one hemisphere is followed by the transient loss of undirected functional connectivity (MI) between ipsilateral and contralateral cortical regions. The post-CSD functional disconnection of the hemispheres was accompanied by an increase in driving force from an unaffected contralateral cortex to an affected one (TE). Mild cortical disinhibition produced by pretreatment with an inhibitory receptor blocking agent (penthylenetetrazole) did not affect CSD but attenuated (MI) or eliminated (TE) the CSD-induced connectivity changes. The effects of CSD on functional connectivity in awake rodents were similar at the individual and group levels, suggesting that the described connectivity response may be a promising network biomarker of CSD occurrence in patients.

Influences of short-term and long-term plasticity of memristive synapse on firing activity of neuronal network

Abstract Synaptic plasticity can greatly affect the firing behavior of neural networks, and it specifically refers to changes in the strength, morphology, and function of synaptic connections. In this paper, a novel memristor model, which can be configured as a volatile and nonvolatile memristor by adjusting its internal parameter, is proposed to mimic the short-term and long-term synaptic plasticity. Then, a bi-neuron network model, with the proposed memristor serving as a coupling synapse and the external electromagnetic radiation being emulated by the flux-controlled memristors, is established to elucidate the effects of short-term and long-term synaptic plasticity on firing activity of the neuron network. The resultant 7D neuron network has no equilibrium point and its hidden dynamical behavior is revealed by phase diagram, time series, bifurcation diagram, Lyapunov exponent spectrum and two-dimensional dynamic map. Our results show the short-term and long-term plasticity can induce different bifurcation scenarios when the coupling strength increases. In addition, memristor synaptic plasticity has a great influence on the distribution of firing patterns in the parameter space. More interestingly, when exploring the synchronous firing behavior of two neurons, the two neurons can gradually achieve phase synchronization as the coupling strength increases along the opposite directions under two different memory attributes. Finally, a microcontroller-based hardware system is implemented to verify the numerical simulation results.

Typology and impact of follower responses to social media influencer “promotional crisis”

AbstractThis study introduces a typology of follower responses to seemingly deceptive promotional content by a social media influencer (SMI) and the effects of those responses on observers. Through a large‐scale, mixed‐methods analysis of an influencer's “promotional crisis” in early 2023, popularly known as “Mascaragate” on TikTok, we identified follower engagement types that potentially harm both SMI and the promoted brand. The crisis sparked an unusually high level of network activity, providing valuable insights into the ripple effects of follower reactions. Drawing on parasocial relationship theory and social capital theory, we proposed that interactions between influencers and their followers, as well as among followers, evolve systematically through linguistic and social cues. These interactions can shift the original meaning of the influencer's message, leading to unintended consequences for both SMI and the brand. Through netnography and k‐means cluster analysis, we identified three distinct clusters of followers with unique attitudes toward the post and SMI. One group (i.e., “Type 1”) was particularly adept at reshaping the meaning of the SMI post through rational engagement with other followers. A subsequent experiment assessed the impact of each group's comments on the observers within a broader community network, focusing on credibility, skepticism, and the impact of social media dependency. The findings offer practical tools for brands and SMIs to analyze follower comment patterns and suggest strategies for addressing crises stemming from SMI‐linked promotional content, ultimately helping to preserve brand reputation and trust.

Data Mining Techniques used for Evaluation an Efficient DDoS Attack Detection System: A Deep-Learning Model

Developed recently to solve the shortcomings of traditional networks, software-defined networking is a new paradigm (SDN). Decoupling the control plane from the data plane, which is the SDN's core property, makes network management simpler and promotes effective programmability. The new design, on the other hand, is vulnerable to a number of attacks that could exhaust the system's resources and prohibit the SDN controller from offering services to authorized users. One of these threats, the Distributed Denial of Service (DDoS) assault is one that is gaining popularity. A DDoS assault has a severe negative effect on emphasize servers have no ability to access their network facilities as a result. accommodate legitimate users. We introduce DDoS Net, a DDoS attacks system for intrusion detection for SDN systems. Our approach is based on data mining software (Rapdiminer) and neural networks working along with auto encoders. As a result, we have a lot of faith in protecting these networks thanks to our methods. We assess the CICIDS 2019 dataset, which includes network behavior associated with DDoS threat and benign network activity, is publicly available, and satisfies certain requirements. It evaluates a variety of data mining algorithms' efficacy as well as internet usage characteristics to determine the best attributes for spotting the most typical assault kinds.

Social media and entrepreneurial competence: the impact of Linkedin on boosting entrepreneurial engagement of Gen-Z

Research Objective: This study examines the impact of LinkedIn usage on entrepreneurial competence and engagement of Gen-Z aged between 18-27. Motivation: This study is motivated by the increased dependence young entrepreneurs have on social media as a channel for professional development and networking. The study builds on works of Banerji & Reimer, (2019) on start-up founders and their LinkedIn connections, Franc, Stampfl, & Geyer, (2024) worked-related learning on LinkedIn, and Vitelar (2019) on Gen-Z and the use of social media. Idea: This research tests the direct effect of the usage of LinkedIn on Gen-Z individuals' entrepreneurial competence and engagement. It assumes that high engagement in learning and networking activities on LinkedIn will be followed by an equivalent improvement in their entrepreneurial outcomes. In this research, the independent variables are the patterns of usage on LinkedIn, while the dependent variables are entrepreneurial competence and engagement. Data: The quantitative research methodology was adopted, and the survey was conducted through Google Forms among 350 Gen-Z users on the professional networking site LinkedIn. Tools: Descriptive statistics, Pearson's correlation, and multiple regression analyses were used for the collected data. Findings: The results indicated that the different usage patterns—such as the frequency of login and engagement on LinkedIn Learning—had a significantly positive correlation with both entrepreneurial competence and engagement. Regression analysis pointed out entrepreneurial competence as one of the chief predictors for entrepreneurial engagement. Therefore, it is concluded that LinkedIn is quite instrumental in enhancing the entrepreneurial capacity of Gen-Z; it therefore gives relevant insights to entrepreneurs and the developers of this particular platform. Contribution: The paper thereby provides empirical evidence on the specific impact of LinkedIn on Gen-Z entrepreneurs, which enriches the literature of social media and entrepreneurship. Keywords: LinkedIn, Gen-Z, Entrepreneurial Competence, Social Media, Professional Networking.