Functional Networks Research Articles

Functional alterations in overweight/obesity: focusing on the reward and executive control network.

Overweight (OW) and obesity (OB) have become prevalent issues in the global public health arena. Serving as a prominent risk factor for various chronic diseases, overweight/obesity not only poses serious threats to people's physical and mental health but also imposes significant medical and economic burdens on society as a whole. In recent years, there has been a growing focus on basic scientific research dedicated to seeking the neural evidence underlying overweight/obesity, aiming to elucidate its causes and effects by revealing functional alterations in brain networks. Among them, dysfunction in the reward network (RN) and executive control network (ECN) during both resting state and task conditions is considered pivotal in neuroscience research on overweight/obesity. Their aberrations contribute to explaining why persons with overweight/obesity exhibit heightened sensitivity to food rewards and eating disinhibition. This review centers on the reward and executive control network by analyzing and organizing the resting-state and task-based fMRI studies of functional brain network alterations in overweight/obesity. Building upon this foundation, the authors further summarize a reward-inhibition dual-system model, with a view to establishing a theoretical framework for future exploration in this field.

Utility of intracranial EEG networks depends on re-referencing and connectivity choice

Abstract Studies of intracranial EEG (iEEG) networks have been used to reveal seizure generators in patients with drug-resistant epilepsy. iEEG is implanted to capture the epileptic network, the collection of brain tissue that forms a substrate for seizures to start and spread. Interictal iEEG measures brain activity at baseline and networks computed during this state can reveal aberrant brain tissue without requiring seizure recordings. iEEG network analyses require choosing a reference and applying statistical measures of functional connectivity. Approaches to these technical choices vary widely across studies, and the impact of these technical choices on downstream analyses is poorly understood. Our objective was to examine the effects of different re-referencing and connectivity approaches on connectivity results and on the ability to lateralize the seizure onset zone in patients with drug-resistant epilepsy. We applied 48 pre-processing pipelines to a cohort of 125 patients with drug-resistant epilepsy recorded with interictal iEEG across two epilepsy centers to generate iEEG functional connectivity networks. 24 functional connectivity measures across time and frequency domains were applied in combination with common average re-referencing (CAR) or bipolar re-referencing (BR). We applied an unsupervised clustering algorithm to identify groups of pre-processing pipelines. We subjected each pre-processing approach to three quality tests: 1) the introduction of spurious correlations, 2) robustness to incomplete spatial sampling, and 3) the ability to lateralize the clinician-defined seizure onset zone. Three groups of similar pre-processing pipelines emerged: CAR pipelines, BR pipelines, and relative entropy-based connectivity pipelines. Relative entropy and CAR networks were more robust to incomplete electrode sampling than BR and other connectivity methods (Friedman test, Dunn-Sˇidák test p < 0.0001). BR reduced spurious correlations at non-adjacent channels better than CAR (Δ mean from machine ref = -0.36 vs -0.22), and worse in adjacent channels (Δ mean from machine ref = -0.14 vs -0.40). Relative entropy-based network measures lateralized the seizure onset hemisphere better than other measures in patients with temporal lobe epilepsy (BH-corrected p < 0.05, Cohen’s d: 0.60 - 0.76). Finally, we present an interface where users can rapidly evaluate iEEG pre-processing choices to select the optimal preprocessing methods tailored to specific research questions. The choice of preprocessing methods affects downstream network analyses. Choosing a single method among highly correlated approaches can reduce redundancy in processing. Relative entropy outperforms other connectivity methods in multiple quality tests. We present a method and interface for researchers to optimize their preprocessing methods for deriving iEEG brain networks.

Label free quantitative proteomic profiling of serum samples of intellectually disabled young patients revealed dysregulation of complement coagulation and cholesterol cascade systems.

Intellectual disability is a heterogeneous disorder, diagnosed using intelligence quotient (IQ) score criteria. Currently, no specific clinical test is available to diagnose the disease and its subgroups due to inadequate understanding of the pathophysiology. Therefore, current study was designed to explore the molecular mechanisms involved in disease perturbation, and to identify potential biomarkers for disease diagnosis and prognosis. A total of 250 participants were enrolled in this study, including 200 intellectually disabled (ID) subjects from the subgroups (mild, moderate, and severe) with age and gender matched healthy controls (n = 50). Initially, IQ testing score and biochemical profile of each subject was generated, followed by label-free quantitative proteomics of subgroups of IQ and healthy control group through nano-LC/MS- mass spectrometry. A total of 310 proteins were identified, among them198 proteins were common among all groups. Statistical analysis (ANOVA) of the subgroups of ID showed 142 differentially expressed proteins, in comparison to healthy control group. From these, 120 proteins were found to be common among all subgroups. The remaining 22 proteins were categorized as exclusive proteins found only in disease subgroups. Furthermore, the hierarchical cluster analysis (HCL) of common significant proteins was also performed, followed by PANTHER protein classification and GO functional enrichment analysis. Results provides that the datasets of differentially expressed proteins, belong to the categories of immune / defense proteins, transfer carrier proteins, apolipoproteins, complement proteins, protease inhibitors, hemoglobin proteins etc., they are known to involvein immune system, and complement and coagulation pathway cascade and cholesterol metabolism pathway. Exclusively expressed 22 proteins were found to be disease stage specific and strong PPI network specifically those that have significant role in platelets activation and degranulation, such as Filamin A (FLNA). Furthermore, to validate the mass spectrometric findings, four highly significant proteins (APOA4, SAP, FLNA, and SERPING) were quantified by ELISA in all the study subjects. AUROC analysis showed a significant association of APOA4 (0.830), FLNA (0.958), SAP (0.754) and SERPING (0.600) with the disease. Apolipoprotein A4 (APOA4) has a significant role in cholesterol transport, and in modulation of glucose and lipid metabolism in the CNS. Similarly, FLNA has a crucial role in the nervous system, especially in the functioning of synaptic network. Therefore, both APOA4, and FLNA proteins represent good potential for candidate biomarkers for the diagnosis and prognosis of the intellectual disability. Overall, serum proteome of ID patients provides valuable information of proteins/pathways that are altered during ID progression.

Effects and mechanisms of computerized cognitive training in Huntington's disease: protocol for a pilot study

Huntington's disease (HD) causes progressive cognitive decline, with no available treatments. Computerized cognitive training (CCT) has shown efficacy in other populations, but its effects in HD are largely unknown. This pilot study will explore the effects and neural mechanisms of CCT in HD. The intervention group participants will complete 12 weeks of multidomain CCT. Control group participants will receive lifestyle education and access to CCT after the study. The primary outcome is change in processing speed. Secondary outcomes include – change in other cognitive domains, functional brain network connectivity (derived from MRI) and psychosocial function. Feasibility outcomes include rates of recruitment, adherence and retention. This study may provide insights into the effects of CCT in HD and guide future trials. Clinical Trial Registration: ACTRN12622000908730 ( ClinicalTrials.gov )

Genetic-driven biomarkers for liver fibrosis through bioinformatic approach

BackgroundLiver fibrosis is a widespread chronic liver ailment linked to substantial mortality and limited therapeutic options. An in-depth comprehension of the genetic underpinnings of liver fibrogenesis is crucial for the development of effective management and treatment approaches.ResultsUsing bioinformatics tools and the DisGeNET database, we pinpointed 105 genes significantly linked to liver fibrosis. Subsequently, we conducted functional assessments, incorporating gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and the STRING database, to construct protein–protein interaction networks (PPI) for these 105 liver fibrosis-associated genes. These analyses were executed via the WebGestalt 2019 online platform. We employed Cytoscape plugins, MCODE, and CytoHubba, to identify potential biomarker genes from these functional networks. Noteworthy hub genes encompassed TGF-β1, MMP2, CTNNB1, FGF2, IL6, LOX, CTGF, SMAD3, ALB, and VEGFA. TGF-β1 and MMP-2 exhibited substantial promise as liver fibrosis biomarkers, as denoted by their high systemic scores determined through the MCC algorithm in the CytoHubba methodology.ConclusionsIn summary, this study presents a robust genetic biomarker strategy that may prove invaluable in the identification of potential liver fibrosis biomarkers.

Towards personalized precision functional mapping in infancy

Abstract The precise network topology of functional brain systems is highly specific to individuals and undergoes dramatic changes during critical periods of development. Large amounts of high-quality resting state data are required to investigate these individual differences, but are difficult to obtain in early infancy. Using the template matching method, we generated a set of infant network templates to use as priors for individualized functional resting-state network mapping in two independent neonatal datasets with extended acquisition of resting-state functional MRI (fMRI) data. We show that template matching detects all major adult resting-state networks in individual infants and that the topology of these resting-state network maps is individual-specific. Interestingly, there was no plateau in within-subject network map similarity with up to 25 minutes of resting-state data, suggesting that the amount and/or quality of infant data required to achieve stable or high-precision network maps is higher than adults. These findings are a critical step towards personalized precision functional brain mapping in infants, which opens new avenues for clinical applicability of resting-state fMRI and potential for robust prediction of how early functional connectivity patterns relate to subsequent behavioral phenotypes and health outcomes.

Artificial Molecular Systems for Complex Functions Based on DNA Nanotechnology and Cell‐Sized Lipid Vesicles

Cells are highly functional and complex molecular systems. Artificially creating such systems remains a challenge, which has been extensively studied in various research fields, including synthetic biology and molecular robotics. DNA nanotechnology is a powerful tool for bottom‐up engineering for constructing functional nanostructures or chemical reaction networks which can be utilized as components for artificial molecular systems. Encapsulation of these components into a giant unilamellar vesicle (GUV) composed of a lipid bilayer, the base structure of the cellular membrane, results in a functional cell‐sized structure that partially mimics some cellular functions. This review discusses the studies contributing to the construction of GUV‐based artificial molecular systems based on DNA nanotechnology. Molecular transport and signal transduction through lipid membranes are essential to uptake molecules from the environment and respond to stimuli. Membrane shaping relates to various functions, including motility and signaling. A chemical reaction network is required to autonomously regulate the system's functions. This review describes the functions realized using DNA nanostructures and DNA reaction networks. Given the designability and programmability of DNA nanotechnology, it may be possible that the functionality of artificial molecular systems could be comparable to or even surpass that of natural molecular systems.

Mendelian randomization analyses reveal causal relationships between brain functional networks and risk of psychiatric disorders

Mendelian randomization analyses reveal causal relationships between brain functional networks and risk of psychiatric disorders

Ventromedial frontoinsular connectivity is associated with long-term smoking behavior change in aging

Abstract A central question in the field of cognitive aging and behavioral neuroscience is what enables some individuals to successfully change their behavior more than others? Smoking is a significant risk factor for cognitive decline, particularly in vulnerable populations, including those who are at an elevated risk for Alzheimer’s disease (AD). Developing effective smoking reduction strategies is therefore a public health priority. The goal of the current study is to better understand the brain mechanisms underlying long-term smoking behavior change in cognitively normal, but at-risk, older adults. Neuroimaging and human lesion studies have implicated the insula and its functional network in subjective interoceptive awareness of cigarette craving and smoking-cue reactivity. We sought to characterize the extent to which anterior insular resting-state functional connectivity (RSFC) MRI predicted long-term smoking reduction (mean: 2.7 years, range 8 months–4 years) using a seed-to-voxel approach. Twenty-three (18 women; 26% APOE4 carriers; 61.5 years, SD = 3.7) cognitively unimpaired older individuals who smoked cigarettes at their baseline visit and have a first-degree family history of AD (at least one parent or multiple siblings affected) were included from a prospective longitudinal cohort, Pre-symptomatic Evaluation of Experimental or Novel Treatments for Alzheimer Disease (PREVENT-AD) in the current study. We found that reduced long-term smoking behavior was associated with enhanced antagonistic RSFC between bilateral anterior insula (aINS) and ventromedial prefrontal cortex (vmPFC). In a second pre-registered replication study within a larger, independent sample of 118 cognitively normal older adults who smoked cigarettes at baseline from the UK Biobank (73 women; 27.9% APOE4 carriers; 60.3 years, SD = 2.7), we found that baseline enhanced antagonistic RSFC between anterior insula and vmPFC predicted long-term smoking reduction (mean 5.2 years; ranging from 3 years to 7 years). To our knowledge, this is the largest study to examine the neural substrates of long-term smoking cessation in human aging. Our results suggest that antagonistic RSFC between aINS and vmPFC is a brain marker of future smoking reduction and disease prevention in older adults at risk for AD.

An Analytical Approach for Naturalistic Cooperative and Competitive EEG-Hyperscanning Data: A Proof-of-Concept Study

Investigating the neural mechanisms underlying both cooperative and competitive joint actions may have a wide impact in many social contexts of human daily life. An effective pipeline of analysis for hyperscanning data recorded in a naturalistic context with a cooperative and competitive motor task has been missing. We propose an analytical pipeline for this type of joint action data, which was validated on electroencephalographic (EEG) signals recorded in a proof-of-concept study on two dyads playing cooperative and competitive table tennis. Functional connectivity maps were reconstructed using the corrected imaginary part of the phase locking value (ciPLV), an algorithm suitable in case of EEG signals recorded during turn-based competitive joint actions. Hyperbrain, within-, and between-brain functional connectivity maps were calculated in three frequency bands (i.e., theta, alpha, and beta) relevant during complex motor task execution and were characterized with graph theoretical measures and a clustering approach. The results of the proof-of-concept study are in line with recent findings on the main features of the functional networks sustaining cooperation and competition, hence demonstrating that the proposed pipeline is promising tool for the analysis of joint action EEG data recorded during cooperation and competition using a turn-based motor task.

A new wide input voltage DC-DC converter for solar PV systems with hybrid MPPT controller

The present working conventional power generation systems utilization is reducing day by day because of their demerits are more functioning cost, high carbon dioxide emission, more complexity in handling, and required high installation area. So, the current power generation company focuses on Renewable Energy Sources (RES) which are wind, tidal, and solar. Here, the solar power network is utilized for supplying electricity to the electrical vehicle battery charging system. The Solar photovoltaic (PV) modules supply nonlinear power which is not useful for automotive systems. To maximize the supply power of the solar PV system, an Adaptive Step Genetic Algorithm Optimized (ASGAO) Radial Basis Functional Network (RBFN) is utilized for tracking the working point of the solar PV module thereby enhancing the operating efficiency of the overall system. The features of this proposed hybrid Maximum Power Point Tracking (MPPT) controller are quick system dynamic response, easy operation, quick convergence speed, more robustness, and high operating efficiency when equalized with the basic MPPT controllers. The major issue of solar PV modules is low supply voltage which is increased by introducing the wide input voltage DC-DC converter. The merits of this introduced converter are low-level voltage stress on diodes, good quality supply power, high voltage gain, plus low implementation cost. Here, the introduced converter along with the AGAO-RBFN controller is analyzed by selecting the MATLAB/Simulink environment. Also, the proposed converter is tested with the help of a programable DC source.

Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit

Artificial intelligence (AI) refers to computer algorithms that replicate the cognitive function of humans. Machine learning is widely applicable using structured and unstructured data, while deep learning is derived from the neural networks of the human brain that process and interpret information. During the last decades, AI has been introduced in several aspects of healthcare. In this review, we aim to present the current application of AI in the neonatal intensive care unit. AI-based models have been applied to neurocritical care, including automated seizure detection algorithms and electroencephalogram-based hypoxic-ischemic encephalopathy severity grading systems. Moreover, AI models evaluating magnetic resonance imaging contributed to the progress of the evaluation of the neonatal developing brain and the understanding of how prenatal events affect both structural and functional network topologies. Furthermore, AI algorithms have been applied to predict the development of bronchopulmonary dysplasia and assess the extubation readiness of preterm neonates. Automated models have been also used for the detection of retinopathy of prematurity and the need for treatment. Among others, AI algorithms have been utilized for the detection of sepsis, the need for patent ductus arteriosus treatment, the evaluation of jaundice, and the detection of gastrointestinal morbidities. Finally, AI prediction models have been constructed for the evaluation of the neurodevelopmental outcome and the overall mortality of neonates. Although the application of AI in neonatology is encouraging, further research in AI models is warranted in the future including retraining clinical trials, validating the outcomes, and addressing serious ethics issues.

Research on the Functional Microbe Activation System in a Post-Polymer Flooded Reservoir

Further exploitation of the residual oil underground in post-polymer flooded reservoirs is attractive and challenging. Microbial-enhanced oil recovery (MEOR) is a promising strategy to enhance the recovery of residual oil in post-polymer flooded reservoirs. Identifying and selectively activating indigenous microorganisms with oil displacement capabilities is an urgent requirement in the current design of efficient microbial-enhanced oil recovery technologies. This study combines high-throughput sequencing with functional network analysis to identify the core functional microbes within the reservoirs. Concurrently, it devises targeted activation strategies tailored to oligotrophic conditions through an analysis of environmental factor influences. The feasibility of these strategies is then validated through physical simulation experiments. With nutrient stimulation, the overall diversity of microorganisms decreases while the abundance of functional microorganisms increases. The core displacement results showed that the oil recovery factor increased by 3.82% on the basis of polymer flooding. In summary, this research has established a system for the efficient activation of functional microorganisms under oligotrophic conditions by utilizing bioinformatics, network analysis, and indoor simulation systems. This achievement will undoubtedly lay a solid foundation for the practical implementation of microbial enhancement techniques in the field.

Abnormal dynamic functional connectivity in young nondisabling intracerebral hemorrhage patients.

Previous resting-state functional magnetic resonance imaging studies on intracerebral hemorrhage patients have focused more on the static characteristics of brain activity, while the time-varying effects during scanning have received less attention. Therefore, the current study aimed to explore the dynamic functional network connectivity changes of intracerebral hemorrhage patients. Using independent component analysis, the sliding window approach, and the k-means clustering analysis method, different dynamic functional network connectivity states were detected from resting-state functional magnetic resonance imaging data of 37 intracerebral hemorrhage patients and 44 healthy controls. The inter-group differences in dynamic functional network connectivity patterns and temporal properties were investigated, followed by correlation analyses between clinical scales and abnormal functional indexes. Ten resting-state networks were identified, and the dynamic functional network connectivity matrices were clustered into four different states. The transition numbers were decreased in the intracerebral hemorrhage patients compared with healthy controls, which was associated with trail making test scores in patients. The cerebellar network and executive control network connectivity in State 1 was reduced in patients, and this abnormal dynamic functional connectivity was positively correlated with the animal fluency test scores of patients. The current study demonstrated the characteristics of dynamic functional network connectivity in intracerebral hemorrhage patients and revealed that abnormal temporal properties and functional connectivity may be related to the performance of different cognitive domains after ictus. These results may provide new insights into exploring the neurocognitive mechanisms of intracerebral hemorrhage.

The Insular Cortex: An Interface Between Sensation, Emotion and Cognition.

The insula is a complex brain region central to the orchestration of taste perception, interoception, emotion, and decision-making. Recent research has shed light on the intricate connections between the insula and other brain regions, revealing the crucial role of this area in integrating sensory, emotional, and cognitive information. The unique anatomical position and extensive connectivity allow the insula to serve as a critical hub in the functional network of the brain. We summarize its role in interoceptive and exteroceptive sensory processing, illustrating insular function as a bridge connecting internal and external experiences. Drawing on recent research, we delineate the insular involvement in emotional processes, highlighting its implications in psychiatric conditions, such as anxiety, depression, and addiction. We further discuss the insular contributions to cognition, focusing on its significant roles in time perception and decision-making. Collectively, the evidence underscores the insular function as a dynamic interface that synthesizes diverse inputs into coherent subjective experiences and decision-making processes. Through this review, we hope to highlight the importance of the insula as an interface between sensation, emotion, and cognition, and to inspire further research into this fascinating brain region.

Versatile micro-electrode array to monitor human iPSC derived 3D neural tissues at air-liquid interface

Engineered 3D neural tissues made of neurons and glial cells derived from human induced pluripotent stem cells (hiPSC) are among the most promising tools in drug discovery and neurotoxicology. They represent a cheaper, faster, and more ethical alternative to in vivo animal testing that will likely close the gap between in vitro animal models and human clinical trials. Micro-Electrode Array (MEA) technology is known to provide an assessment of compound effects on neural 2D cell cultures and acute tissue preparations by real-time, non-invasive, and long-lasting electrophysiological monitoring of spontaneous and evoked neuronal activity. Nevertheless, the use of engineered 3D neural tissues in combination with MEA biochips still involves series of constraints, such as drastically limited diffusion of oxygen and nutrients within tissues mainly due to the lack of vascularization. Therefore, 3D neural tissues are extremely sensitive to experimental conditions and require an adequately designed interface that provides optimal tissue survival conditions. A well-suited technique to overcome this issue is the combination of the Air-Liquid Interface (ALI) tissue culture method with the MEA technology. We have developed a full 3D neural tissue culture process and a data acquisition system composed of high-end electronics and novel MEA biochips based on porous, flexible, thin-film membranes integrating recording electrodes, named as “Strip-MEA,” to allow the maintenance of an ALI around the 3D neural tissues. The main motivation of the porous MEA biochips development was the possibility to monitor and to study the electrical activity of 3D neural tissues under different recording configurations, (i) the Strip-MEA can be placed below a tissue, (ii) or by taking advantage of the ALI, be directly placed on top of the tissue, or finally, (iii) it can be embedded into a larger neural tissue generated by the fusion of two (or more) tissues placed on both sides of the Strip-MEA allowing the recording from its inner part. This paper presents the recording and analyses of spontaneous activity from the three positioning configurations of the Strip-MEAs. Obtained results are discussed with the perspective of developing in vitro models of brain diseases and/or impairment of neural network functioning.

Electroencephalogram Functional Connectivity Analysis and Classification of Mental Arithmetic Working Memory Task

Analyzing brain activity during mental arithmetic tasks provides insight into psychological disorders such as ADHD, dyscalculia, and autism. While most research is conducted on the static functional connectivity of the brain while performing a cognitive task, the dynamic changes of the brain, which provide meaningful information for diagnosing individual differences in cognitive tasks, are often ignored. This paper aims to classify electroencephalogram (EEG) signals for rest vs. mental arithmetic task performance, using Bayesian functional connectivity features in the sensor space as inputs into a graph convolutional network. The subject-specific (intrasubject) classification performed on 36 subjects for rest vs. mental arithmetic task performance achieved the highest subject-specific classification accuracy of 98% and an average accuracy of 91% in the beta frequency band, outperforming state-of-the-art methods. In addition, statistical analysis confirms the consistency of Bayesian functional connectivity features compared to traditional functional connectivity features. Furthermore, the graph-theoretical analysis of functional connectivity networks reveals that good-performance subjects had higher global efficiency, betweenness centrality, and closeness centrality than bad-performance subjects. The ablation study on the classification of three cognitive states (subtraction, music, and memory) achieved a classification accuracy of 97%, and visual working memory (n-back task) achieved a classification accuracy of 94%, confirming the consistency and reliability of the proposed methodology.

Reduced lateralization of multiple functional brain networks in autistic males

BackgroundAutism spectrum disorder has been linked to a variety of organizational and developmental deviations in the brain. One such organizational difference involves hemispheric lateralization, which may be localized to language-relevant regions of the brain or distributed more broadly.Methods In the present study, we estimated brain hemispheric lateralization in autism based on each participant’s unique functional neuroanatomy rather than relying on group-averaged data. Additionally, we explored potential relationships between the lateralization of the language network and behavioral phenotypes including verbal ability, language delay, and autism symptom severity. We hypothesized that differences in hemispheric asymmetries in autism would be limited to the language network, with the alternative hypothesis of pervasive differences in lateralization. We tested this and other hypotheses by employing a cross-sectional dataset of 118 individuals (48 autistic, 70 neurotypical). Using resting-state fMRI, we generated individual network parcellations and estimated network asymmetries using a surface area-based approach. A series of multiple regressions were then used to compare network asymmetries for eight significantly lateralized networks between groups.ResultsWe found significant group differences in lateralization for the left-lateralized Language (d = -0.89), right-lateralized Salience/Ventral Attention-A (d = 0.55), and right-lateralized Control-B (d = 0.51) networks, with the direction of these group differences indicating less asymmetry in autistic males. These differences were robust across different datasets from the same participants. Furthermore, we found that language delay stratified language lateralization, with the greatest group differences in language lateralization occurring between autistic males with language delay and neurotypical individuals.ConclusionsThese findings evidence a complex pattern of functional lateralization differences in autism, extending beyond the Language network to the Salience/Ventral Attention-A and Control-B networks, yet not encompassing all networks, indicating a selective divergence rather than a pervasive one. Moreover, we observed an association between Language network lateralization and language delay in autistic males.

Exploring the Immunomodulatory Potential of Pancreatic Cancer-Derived Extracellular Vesicles through Proteomic and Functional Analyses

Pancreatic cancer (PC) has a poor prognosis and displays resistance to immunotherapy. A better understanding of tumor-derived extracellular vesicle (EV) effects on immune responses might contribute to improved immunotherapy. EVs derived from Capan-2 and BxPC-3 PC cells isolated by ultracentrifugation were characterized by atomic force microscopy, Western blot (WB), nanoparticle tracking analysis, and label-free proteomics. Fresh PBMCs from healthy donors were treated with PC- or control-derived heterologous EVs, followed by flow cytometry analysis of CD8+ and CD4+ lymphocytes. The proteomics of lymphocytes sorted from EV-treated or untreated PBMCs was performed, and the IFN-γ concentration was measured by ELISA. Notably, most of the proteins identified in Capan-2 and BxPC-3 EVs by the proteomic analysis were connected in a single functional network (p = 1 × 10−16) and were involved in the “Immune System” (FDR: 1.10 × 10−24 and 3.69 × 10−19, respectively). Interestingly, the treatment of healthy donor-derived PBMCs with Capan-2 EVs but not with BxPC-3 EVs or heterologous control EVs induced early activation of CD8+ and CD4+ lymphocytes. The proteomics of lymphocytes sorted from EV-treated PBMCs was consistent with their activation by Capan-2 EVs, indicating IFN-γ among the major upstream regulators, as confirmed by ELISA. The proteomic and functional analyses indicate that PC-EVs have pleiotropic effects, and some may activate early immune responses, which might be relevant for the development of highly needed immunotherapeutic strategies in this immune-cold tumor.

Brain functional network changes in patients with juvenile myoclonic epilepsy: a study based on graph theory and Granger causality analysis

Many resting-state functional magnetic resonance imaging (rs-fMRI) studies have shown that the brain networks are disrupted in adolescent patients with juvenile myoclonic epilepsy (JME). However, previous studies have mainly focused on investigating brain connectivity disruptions from the perspective of static functional connections, overlooking the dynamic causal characteristics between brain network connections. In our study involving 37 JME patients and 35 Healthy Controls (HC), we utilized rs-fMRI to construct whole-brain functional connectivity network. By applying graph theory, we delved into the altered topological structures of the brain functional connectivity network in JME patients and identified abnormal regions as key regions of interest (ROIs). A novel aspect of our research was the application of a combined approach using the sliding window technique and Granger causality analysis (GCA). This method allowed us to delve into the dynamic causal relationships between these ROIs and uncover the intricate patterns of dynamic effective connectivity (DEC) that pervade various brain functional networks. Graph theory analysis revealed significant deviations in JME patients, characterized by abnormal increases or decreases in metrics such as nodal betweenness centrality, degree centrality, and efficiency. These findings underscore the presence of widespread disruptions in the topological features of the brain. Further, clustering analysis of the time series data from abnormal brain regions distinguished two distinct states indicative of DEC patterns: a state of strong connectivity at a lower frequency (State 1) and a state of weak connectivity at a higher frequency (State 2). Notably, both states were associated with connectivity abnormalities across different ROIs, suggesting the disruption of local properties within the brain functional connectivity network and the existence of widespread multi-functional brain functional networks damage in JME patients. Our findings elucidate significant disruptions in the local properties of whole-brain functional connectivity network in patients with JME, revealing causal impairments across multiple functional networks. These findings collectively suggest that JME is a generalized epilepsy with localized abnormalities. Such insights highlight the intricate network dysfunctions characteristic of JME, thereby enriching our understanding of its pathophysiological features.