Large-scale Network Analysis Research Articles

축제 산업생태계 네트워크 구조 분석 : 명예 문화관광축제를 중심으로

The demand for the industrialization of festivals has become increasingly prominent both academically and in policy discussions. Approaching festivals from an industrial perspective is regarded as a key strategy for improving supply chain efficiency and expanding the festival business ecosystem. The demand for the industrialization of festivals is increasingly strong, both academically and in policy discussions. This study aims to analyze the structural characteristics (connectivity and cohesion) and positional characteristics (influence) of South Korea's festival industry through social network analysis, grounded in industry ecosystem theory. The research utilizes 595 expenditure data points from four major cultural tourism festivals in Korea. The data were preprocessed in Excel, using pivot tables to identify relationships between attributes. After data cleaning, the dataset was divided into node and edge files, which were analyzed using Gephi 0.10, an open-source Java-based software optimized for large-scale network analysis. This multidimensional approach was critical in visualizing and optimizing the network data. The findings reveal that Korea’s festival business ecosystem displays characteristics of early-stage industrialization, characterized by low network density and limited two-way interactions between actors. While cohesive clusters within the network show diversity, the influence of major networks remains low, highlighting the need for focused efforts to strengthen them. In terms of positional characteristics, festival organizations, venue construction industries, and content development companies were identified as influential actors. Academically, this study contributes to the empirical identification of the network structure within the festival business ecosystem, providing foundational data for understanding its dynamic changes. Practically, it offers policy recommendations, including fostering key actors to enhance cohesion and expanding opportunities for inter-company collaboration to further develop the ecosystem.

Development of a Large-Scale Horizontal Curve Inventory for Safety Analysis Using Open GIS Data

Abstract Horizontal curves are crucial for the safety and mobility of roadways. To enhance the understanding of these curves, transportation agencies are investigating methods to accurately determine their spatial locations, geometric attributes, and conditions within their extensive road networks. Leveraging the widespread availability of Global Positioning System (GPS) and Geographic Information System (GIS) data, significant advancements have been made in developing automated algorithms that identify horizontal curves from GPS trajectories and GIS basemaps. This study introduces a novel dataset—the Horizontal Curve Inventory—that encapsulates detailed features of horizontal curves. We employed a modified automated cycle regression algorithm designed for large-scale road network analysis to extract horizontal curve data. This method was applied nationally to process an expansive network utilizing open-source GIS basemaps, encompassing over 300,000 miles across interstate, U.S. highway, and state highway systems. The inventory includes comprehensive data on curve radius, rotation angle, start and end point coordinates, and the curve’s center points. These data are essential for supporting safety research pertaining to road alignment. Our approach demonstrated robust performance across various open GIS data sources and provides the first accurate, efficient, and comprehensive inventory of horizontal curves for all major highway systems in the United States. The dataset is available at http://aichengbo.com/research/national-horizontal-curve-inventory/.

Network dynamics-based subtyping of Alzheimer’s disease with microglial genetic risk factors

BackgroundThe potential of microglia as a target for Alzheimer’s disease (AD) treatment is promising, yet the clinical and pathological diversity within microglia, driven by genetic factors, poses a significant challenge. Subtyping AD is imperative to enable precise and effective treatment strategies. However, existing subtyping methods fail to comprehensively address the intricate complexities of AD pathogenesis, particularly concerning genetic risk factors. To address this gap, we have employed systems biology approaches for AD subtyping and identified potential therapeutic targets.MethodsWe constructed patient-specific microglial molecular regulatory network models by utilizing existing literature and single-cell RNA sequencing data. The combination of large-scale computer simulations and dynamic network analysis enabled us to subtype AD patients according to their distinct molecular regulatory mechanisms. For each identified subtype, we suggested optimal targets for effective AD treatment.ResultsTo investigate heterogeneity in AD and identify potential therapeutic targets, we constructed a microglia molecular regulatory network model. The network model incorporated 20 known risk factors and crucial signaling pathways associated with microglial functionality, such as inflammation, anti-inflammation, phagocytosis, and autophagy. Probabilistic simulations with patient-specific genomic data and subsequent dynamics analysis revealed nine distinct AD subtypes characterized by core feedback mechanisms involving SPI1, CASS4, and MEF2C. Moreover, we identified PICALM, MEF2C, and LAT2 as common therapeutic targets among several subtypes. Furthermore, we clarified the reasons for the previous contradictory experimental results that suggested both the activation and inhibition of AKT or INPP5D could activate AD through dynamic analysis. This highlights the multifaceted nature of microglial network regulation.ConclusionsThese results offer a means to classify AD patients by their genetic risk factors, clarify inconsistent experimental findings, and advance the development of treatments tailored to individual genotypes for AD.

Dynamic interaction between the cerebrum and the cerebellum during visual word processing

Numerous studies have investigated the relationship between the cerebellum and reading. Yet, the specific contribution of the cerebellum to reading and its interaction with the cerebrum remain elusive. To address these issues, we combined dynamic brain state analysis with large-scale network analysis to examine the imaging data gathered from the reading tasks (i.e., orthographic, phonological, and semantic tasks) and the resting period. Our analysis revealed three dynamic brain states. The first state (DFS1) exhibited a higher ratio and a longer duration in all tasks, indicating its involvement in general task-related processes. The second state (DFS2) was predominantly active during the resting stage, representing a resting-related state. The third state (DFS3) displayed a higher ratio in the reading tasks compared to the non-reading tasks, indicating its association with reading-dependent processes. In all states, hubs were predominantly distributed in the cerebrum. For DFS2, one hub was also observed in the cerebellum. Furthermore, DFS2 showed significant modularity between the cerebrum and the cerebellum. This study sheds light on the dynamic collaboration between the cerebrum and the cerebellum across different imaging modalities, offering a deeper and more comprehensive understanding of their interaction during reading and non-reading periods.

An efficient network clustering approach using graph-boosting and nonnegative matrix factorization

Network clustering is a critical task in data analysis, aimed at uncovering the underlying structure and patterns within complex networks. Traditional clustering methods often struggle with large-scale and noisy data, leading to suboptimal results. Also, the efficiency of positive samples in network clustering depends on the carefully constructed data augmentation, and the pre-training process of the model deals with large-scale data. To address these issues, in this paper, we introduce an efficient network clustering approach that leverages Graph-Boosting and Nonnegative Matrix Factorization to enhance clustering performance (GBNMF). Our algorithm addresses the limitations of traditional clustering techniques by incorporating the strengths of graph-boosting, which iteratively improves the quality of clusters, and Nonnegative Matrix Factorization (NMF), which effectively captures latent structures within the data. We validate our algorithm through extensive experiments on various benchmark network datasets, demonstrating significant improvements in clustering accuracy and robustness. The proposed algorithm not only achieves superior clustering results but also exhibits remarkable computational efficiency, making it a valuable tool for large-scale network analysis applications.

Large-scale meta-analyses and network analyses of neural substrates underlying human escalated aggression

Escalated aggression represents a frequent and severe form of violence, sometimes manifesting as antisocial behavior. Driven by the pressures of modern life, escalated aggression is of particular concern due to its rising prevalence and its destructive impact on both individual well-being and socioeconomic stability. However, a consistent neural circuitry underpinning it remains to be definitively identified. Here, we addressed this issue by comparing brain alterations between individuals with escalated aggression and those without such behavioral manifestations. We first conducted a meta-analysis to synthesize previous neuroimaging studies on functional and structural alterations of escalated aggression (325 experiments, 2997 foci, 16,529 subjects). Following-up network and functional decoding analyses were conducted to provide quantitative characterizations of the identified brain regions. Our results revealed that brain regions constantly involved in escalated aggression were localized in the subcortical network (amygdala and lateral orbitofrontal cortex) associated with emotion processing, the default mode network (dorsal medial prefrontal cortex and middle temporal gyrus) associated with mentalizing, and the salience network (anterior cingulate cortex and anterior insula) associated with cognitive control. These findings were further supported by additional meta-analyses on emotion processing, mentalizing, and cognitive control, all of which showed conjunction with the brain regions identified in the escalated aggression. Together, these findings advance the understanding of the risk biomarkers of escalated aggressive populations and refine theoretical models of human aggression.

Emphasizing symbolic capital: its superior influence on the association between family socioeconomic status and adolescent subjective well-being uncovered by a large-scale multivariate network analysis.

Family socioeconomic status (FSES) serves as a significant determinant for subjective well-being. However, extant research has provided conflicting evidence on the correlation between FSES and adolescent students' subjective well-being (SSWB). Data were collected from 12,058 adolescent students (16 years of age) by the Programme for International Student Assessment (PISA) 2018. Multivariate canonical correlation and Mantel test were utilized to investigate the specific connection between FSES and SSWB. Furthermore, a Gaussian EBICglasso graph-theoretical model was used to capture the topological properties of the FSES-SSWB network and reveal the interplay among multifarious components of FSES and SSWB. FSES was positively correlated with SSWB. In the FSES-SSWB network, parental educational attainment and occupation status demonstrated the highest centrality values, thereby contributing significantly to the relationship between FSES and SSWB. However, family wealth, along with educational and cultural resources, displayed lower centrality values, signifying their weaker roles in this relationship. Our findings suggest that symbolic capital, rather than family affluence, exerts a dominant influence on adolescent SSWB. In other words, SSWB may not be detrimentally influenced by a deficiency in monetary resources. However, it is more susceptible to being unfavorably impacted by inferior parental educational attainment and occupational standing.

Complex-valued sensitivity analysis tool aimed to power flow optimization

Abstract In this paper, the use of complex differentiation is presented as a robust and concise tool for modeling and sensitivity analysis of large-scale electrical networks. A complex power flow model was employed to determine the network’s operating point, from which the sensitivity analysis of the electrical system is conducted. The proposed methodology, utilizing complex differentiation, was applied to two systems with data extracted from the MATPOWER software. The obtained results are depicted in a network topology graph, highlighting the identified sensitivities.

Shared and unique alterations of large-scale network connectivity in drug-free adolescent-onset and adult-onset major depressive disorder

Differences in clinical manifestations and biological underpinnings between Major Depressive Disorder (MDD) onset during adolescence and adulthood have been posited in previous studies, implying an influential role of age of onset (AOO) in the clinical subtyping and therapeutic approaches to MDD. However, direct comparisons between the two cohorts and their age-matched controls have been lacking in extant investigations. In this investigation, 156 volunteers participated, comprising 46 adolescents with MDD (adolescent-onset group), 35 adults with MDD (adult-onset group), 19 healthy adolescents, and 56 healthy adults. Resting-state functional MRI scans were undergone by all participants. Large-scale network analyses were applied. Subsequently, a 2 × 2 ANOVA was employed to analyze the main effects of diagnosis, age, and their interaction effect on functional connectivity (FC). Furthermore, regression analysis was employed to scrutinize the association between anomalous FC and HAMD sub-scores. Increased FC in visual network (VN), limbic network (LN), VN-dorsal attention network (DAN), VN-LN, and LN-Default Mode (DMN) was found in both adolescent-onset and adult-onset MDD; however, the increased FC in DAN and LN were only found in adult-onset MDD and the decreased FC in DAN was only found in adolescent-onset MDD. Additionally, the relationship between HAMD factor 1 anxiety somatization and altered FC of DAN, VN, and VN-DAN was moderated by AOO. In conclusion, shared and distinctive large-scale network alterations in adolescent-onset and adult-onset MDD patients were suggested by our findings, providing valuable contributions towards refining clinical subtyping and treatment approaches for MDD.

Unraveling minnan imagery: a comprehensive analysis of traditional and modern minnan nursery rhymes through complex networks

Nursery rhymes provide insights into the traditions, beliefs, and values of a culture, thereby making it an integral part of a community’s heritage. As representative linguistic resources within the intangible cultural heritage of the Hoklo people, Minnan nursery rhymes (MNRs) play a crucial role in connecting the Chinese mainland, Taiwan Strait, and overseas Chinese communities. This study delves into features of 617 traditional and 289 modern pieces through text mining techniques, including text segmentation, the TF-IDF (term frequency-inverse document frequency) method, and the complex network analysis. We examine the frequency and emotional purity of lyrics at a larger scale than previous studies using a small set of manually annotated samples. Furthermore, we analyze the patterns of MNRs by assessing the overall, individual, core-periphery structures of the constructed MNR networks, considering key terms as nodes and co-occurrence relationships between nodes as links. Our investigation reveals the heterogeneous nature of terms in both traditional and modern MNR networks. Moreover, through the community detection method, we identify five primary imagery features presented in MNRs. Traditional MNRs place emphasis on family relationships, folk culture, and food culture, reflecting enduring aspects of Minnan cultural heritage. In contrast, modern MNRs pivot towards themes of children’s emotions and natural scenery, indicative of evolving societal values. This study represents the first large-scale complex network analysis of MNRs, providing valuable insights into the embedded Minnan culture and serving as a foundation for further research into the societal dynamics reflected in these cherished MNRs resources.

Network structures of internet gaming disorder and gaming disorder: Symptom operationalization causes variation.

From 2022, the International Classification of Diseases, eleventh edition (ICD-11) includes the first mental disorder based on digital technology, "gaming disorder," which was previously suggested as a condition for further examination in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5). In this cross-sectional study, we provide the first large-scale network analysis of various symptom structures for these constructs to understand the complex interconnections between their proposed symptoms. Culturally diverse samples of 2,846 digital game players (M = 25.3 years) and 746 esports players (M = 23.5 years) were recruited. A network approach was applied to explore a multiverse of gaming disorder symptom structures, effects of item operationalization, and possible external moderators. Gaming disorder was measured using the Internet Gaming Disorder Scale 9-Short Form (IGDS9-SF), Gaming Disorder Test, and several items borrowed from Chinese Internet Gaming Disorder Scale, Personal Internet Gaming Disorder Evaluation-9, and Clinical Video game Addiction Test 2.0 scales. Two symptoms (loss of control and continued use despite problems) present in both, the DSM-5 and ICD-11, were systematically central to most of the analyzed networks. Alternative operationalizations of single items systematically caused significant network differences. Networks were invariant across groups of play style, age, gender, gaming time, and most of the psychosocial characteristics. Our results caution practitioners and researchers when studying and interpreting gaming disorder symptoms. The data indicate that even minor operational changes in symptoms can lead to significant network-level changes, thus highlighting the need for careful wording. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Robust Parallel Pursuit for Large-Scale Association Network Learning

Sparse reduced-rank regression is an important tool to uncover the large-scale response-predictor association network, as exemplified by modern applications such as the diffusion networks, and recommendation systems. However, the association networks recovered by existing methods are either sensitive to outliers or not scalable under the big data setup. In this paper, we propose a new statistical learning method called robust parallel pursuit (ROP) for joint estimation and outlier detection in large-scale response-predictor association network analysis. The proposed method is scalable in that it transforms the original large-scale network learning problem into a set of sparse unit-rank estimations via factor analysis, thus facilitating an effective parallel pursuit algorithm. Furthermore, we provide comprehensive theoretical guarantees including consistency in parameter estimation, rank selection, and outlier detection, and we conduct an inference procedure to quantify the uncertainty of existence of outliers. Extensive simulation studies and two real-data analyses demonstrate the effectiveness and the scalability of the suggested approach. History: Accepted by Ram Ramesh, Area Editor/Data Science & Machine Learning. Funding: This work was supported by the National Key R&D Program of China [Grant 2022YFA1008000], Natural Science Foundation of China [Grants 72071187, 72091212, 71731010, and 71921001], China Postdoctoral Science Foundation [Grant 2023M733402], and Fundamental Research Funds for the Central Universities [Grants WK3470000017, WK2040000027, and WK2040000079]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0181 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0181 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

The electric vehicle routing problem of a new mobile charging service

Abstract A novel mobile charging service that utilizes vehicle-to-vehicle (V2V) charging technology has recently been proposed as a supplement to fixed charging infrastructure (CI), enabling electric vehicles (EVs) to exchange electricity. This study formulates a vehicle routing problem (VRP) of vehicle-to-vehicle (V2V) charging, optimizing the routing of DVs to service RVs while taking into account their willingness to join the V2V charging platform. A mixed integer linear programming (MILP) model is established to optimize the VRP-V2V (i.e. the VRP of V2V charging), which is known to be NP-hard. To solve large-scale instances for real-world applications, we propose an adaptive large neighborhood search (ALNS) algorithm, which, when combined with the structure of the VRP-V2V problem, utilizes four local search procedures to enhance solution quality following destroy and repair operators. Results indicate that the proposed ALNS algorithm outperforms the optimization solver CPLEX in small-scale instances, and can solve large-scale instances that are infeasible using CPLEX solver. In a numerical analysis of Changsha's large-scale network, we demonstrate that the V2V platform can save an average of 33.1% on the charging cost of recharging vehicles, hence raising customer satisfaction with charging services and reducing range anxiety. The platform's profitability is also increased by using V2V charging in areas lacking fixed charging infrastructure.

Uncovering the symptom relationship between anxiety, depression, and internet addiction among left-behind children: A large-scale purposive sampling network analysis

Facing long-term separation from their parents, left-behind children are at risk of the co-occurrence of internalizing and externalizing problems. Although previous research has gained substantial information examining the relationship between anxiety, depression, and internet addiction at the aggregate level of variables, little is known about the heterogeneity and interactions between these components at the symptom level with a large-scale purposive sample. Adopting the network approach, two network pathways, depression and anxiety, and associations between these variables and internet addiction were constructed. Our sample included 5367 left-behind children (Mage = 13.57; SDage = 1.37; 50.07% females). Relevant bridging, central symptoms, and network stability were identified. Two relatively stable networks were obtained. For the network of anxiety and depression, sleep problems and tachycardia were vital bridging symptoms. Central symptoms, including tachycardia, restlessness, fatigue, and emptiness, were symptoms of depression. For the network of symptoms of anxiety, depression, and internet addiction, the bridging symptoms remained the same, and the central symptoms included tachycardia, restlessness, loss of control, and emptiness. By identifying relevant bridging and central symptoms, those with higher levels of these symptoms could be regarded as intervention targets, providing a reference for the current issue of valuing diagnosis over prevention in left-behind children.

Reorganization of intrinsic functional connectivity in early-stage Parkinson’s disease patients with probable REM sleep behavior disorder

REM sleep behavior disorder (RBD) symptoms in Parkinson’s disease (PD) suggest both a clinically and pathologically malignant subtype. However, whether RBD symptoms are associated with alterations in the organization of whole-brain intrinsic functional networks in PD, especially at early disease stages, remains unclear. Here we use resting-state functional MRI, coupled with graph-theoretical approaches and network-based statistics analyses, and validated with large-scale network analyses, to characterize functional brain networks and their relationship with clinical measures in early PD patients with probable RBD (PD+pRBD), early PD patients without probable RBD (PD-pRBD) and healthy controls. Thirty-six PD+pRBD, 57 PD-pRBD and 71 healthy controls were included in the final analyses. The PD+pRBD group demonstrated decreased global efficiency (t = -2.036, P = 0.0432) compared to PD-pRBD, and decreased network efficiency, as well as comprehensively disrupted nodal efficiency and whole-brain networks (all eight networks, but especially in the sensorimotor, default mode and visual networks) compared to healthy controls. The PD-pRBD group showed decreased nodal degree in right ventral frontal cortex and more affected edges in the frontoparietal and ventral attention networks compared to healthy controls. Furthermore, the assortativity coefficient was negatively correlated with Montreal cognitive assessment scores in the PD+pRBD group (r = -0.365, P = 0.026, d = 0.154). The observation of altered whole-brain functional networks and its correlation with cognitive function in PD+pRBD suggest reorganization of the intrinsic functional connectivity to maintain the brain function in the early stage of the disease. Future longitudinal studies following these alterations along disease progression are warranted.

Large-Scale Cortical Network Analysis and Classification of MI-BCI Tasks Based on Bayesian Nonnegative Matrix Factorization.

Motor imagery (MI) is a high-level cognitive process that has been widely applied to clinical rehabilitation and brain-computer interfaces (BCIs). However, the decoding of MI tasks still faces challenges, and the neural mechanisms underlying its application are unclear, which seriously hinders the development of MI-based clinical applications and BCIs. Here, we combined EEG source reconstruction and Bayesian nonnegative matrix factorization (NMF) methods to construct large-scale cortical networks of left-hand and right-hand MI tasks. Compared to right-hand MI, the results showed that the significantly increased functional network connectivities (FNCs) mainly located among the visual network (VN), sensorimotor network (SMN), right temporal network, right central executive network, and right parietal network in the left-hand MI at the β (13-30Hz) and all (8-30Hz) frequency bands. For the network properties analysis, we found that the clustering coefficient, global efficiency, and local efficiency were significantly increased and characteristic path length was significantly decreased in left-hand MI compared to right-hand MI at the β and all frequency bands. These network pattern differences indicated that the left-hand MI may need more modulation of multiple large-scale networks (i.e., VN and SMN) mainly located in the right hemisphere. Finally, based on the spatial pattern network of FNC and network properties, we propose a classification model. The proposed model achieves a top classification accuracy of 78.2% in cross-subject two-class MI-BCI tasks. Overall, our findings provide new insights into the neural mechanisms of MI and a potential network biomarker to identify MI-BCI tasks.

Altered EEG Theta and Alpha Band Functional Connectivity in Mild Cognitive Impairment During Working Memory Coding.

Individuals with mild cognitive impairment (MCI), the preclinical stage of Alzheimer disease (AD), suffer decline in their visual working memory (WM) functions. Using large-scale network analysis of electroencephalography (EEG), the current study intended to investigate if there are differences in functional connectivity properties extracted during visual WM coding stages between MCI patients and normal controls (NC). A total of 21 MCI patients and 20 NC performed visual memory tasks of load four, while 32-channel EEG recordings were acquired. The functional connectivity properties were extracted from the acquired EEGs by the directed transform function (DTF) via spectral Granger causal analysis. Brain network analyses revealed distinctive brain network patterns between the two groups during the WM coding stage. Compared with the NC, MCI patients exhibited a reduced visual network connectivity of the frontal-temporal in θ (4-7Hz) band. A likely compensation mechanism was observed in MCI patients, with a strong brain functional connectivity of the frontal-occipital and parietal-occipital in both θ and α (8-13Hz) band. Further analyses of the network core node properties based on the differential brain network showed that, in θ band, there was a significant difference in the out-degree of the frontal lobe and parietal lobe between the two groups, while in α band, such difference was located only in the parietal lobe. The current study found that, in MCI patients, dysconnectivity is found from the prefrontal lobe to bilateral temporal lobes, leading to increased recruitment of functional connectivity in the frontal-occipital and parietal-occipital direction. The dysconnectivity pattern of MCI is more complex and primarily driven by core nodes Pz and Fz. These results significantly expanded previous knowledge of MCI patients' EEG dynamics during WM tasks and provide new insights into the underpinning neural mechanism MCI. It further provided a potential therapeutic target for clinical interventions of the condition.

Shared and Distinct Neurobiological Bases of Bipolar Disorder and Attention-Deficit/Hyperactivity Disorder in Children and Adolescents: A Comparative Meta-Analysis of Structural Abnormalities

Pediatric bipolar disorder (PBD) and attention-deficit/hyperactivity disorder (ADHD) frequently co-occur and share dysfunctions in affective and cognitive domains. As the neural substrates underlying their overlapping and dissociable symptomatology have not been well delineated, a meta-analysis of whole-brain voxel-based morphometry studies in PBD and ADHD was conducted. A systematic literature search was performed in PubMed, Web of Science, and Embase. The seed-based d mapping toolbox was used to identify altered clusters of PBD or ADHD and obtain their conjunctive and comparative abnormalities. Suprathreshold patterns were subjected to large-scale network analysis to identify affected brain networks. The search revealed 10 PBD studies (268 patients) and 32 ADHD studies (1,333 patients). Decreased gray matter volumes in the right insula and anterior cingulate cortex relative to typically developing individuals were conjunctive in PBD and ADHD. Reduced volumes in the right inferior frontal gyrus, left orbitofrontal cortex, and hippocampus were more substantial in PBD, while decreased volumes in the left precentral gyrus, left inferior frontal gyrus, and right superior frontal gyrus were more pronounced in ADHD. Neurodevelopmental effects modulated patterns of the left hippocampus in PBD and those of the left inferior frontal gyrus in ADHD. These findings suggest that PBD and ADHD are characterized by both common and distinct patterns of gray matter volume alterations. Their overlapping abnormalities may represent a transdiagnostic problem of attention and emotion regulation shared by PBD and ADHD, whereas the disorder-differentiating substrates may contribute to the relative differences in cognitive and affective features that define the 2 disorders. Pediatric bipolar disorder (BD) and attention-deficit/hyperactivity disorder (ADHD) frequently co-occur, with overlapping changes in emotional and cognitive functioning. This meta-analysis summarizes findings from 10 articles on BD and 32 articles on ADHD to identify similarities and differences in brain structure between youth with BD and youth with ADHD. The authors found that both disorders share decreased gray matter volumes in the right insula and anterior cingulate cortex, which play important roles in emotion processing and attention, respectively. Youth with BD had decreased gray matter volume in the right inferior frontal gyrus, left orbitofrontal gyrus, and left hippocampus, while youth with ADHD had decreased volumes in the left precentral gyrus, left inferior frontal gyrus, and right superior frontal gyrus. Structural Brain Abnormalities of Attention-Deficit/Hyperactivity Disorder and Bipolar Disorder in Children/Adolescents: An Overlapping Meta-analysis; https://osf.io; trg4m.

Stability analysis of large-scale Boolean networks via compositional method

This paper formulates a compositional method to alleviate the high computational cost of studying large-scale Boolean networks (BNs). The algebraic expression of conjunctive composition is firstly presented through the semi-tensor product method. Accordingly, the relationship of state transitions between component BNs and conjunctive compositional BN is revealed. To describe the merging process during the conjunctive composition of state transitions, the single-node compositional equation is constructed, based on which a new criterion is built to analyze the stability of conjunctive compositional BNs. The idea of compositional equation is used to further explore the multi-node composition. Finally, the application to the BN model of Yeast Apoptosis demonstrates the effectiveness of the compositional method.

Altered intrinsic functional brain architecture in patients with functional constipation: a surface-based network study.

Functional constipation (FCon) is a common functional gastrointestinal disorder (FGID). Studies have indicated a higher likelihood of psychiatric disorders, such as anxiety, depression, sleep disturbances, and impaired concentration, among patients with FCon. However, the underlying pathophysiological mechanisms responsible for these symptoms in FCon patients remain to be fully elucidated. The human brain is a complex network architecture with several fundamental organizational properties. Neurological interactions between gut symptoms and psychiatric issues may be closely associated with these complex networks. In the present study, a total of 35 patients with FCon and 40 healthy controls (HC) were recruited for a series of clinical examinations and resting-state functional magnetic imaging (RS-fMRI). We employed the surface-based analysis (SBA) approach, utilizing the Schaefer cortical parcellation template and Tikhonov regularization. Graph theoretical analysis (GTA) and functional connectivity (FC) analysis of RS-fMRI were conducted to investigate the aberrant network alterations between the two groups. Additionally, correlation analyses were performed between the network indices and clinical variables in patients with FCon. At the global level, we found altered topological properties and networks in patients with FCon, mainly including the significantly increased clustering coefficient (CP), local efficiency (Eloc), and shortest path length (LP), whereas the decreased global efficiency (Eglob) compared to HC. At the regional level, patients with FCon exhibited increased nodal efficiency in the frontoparietal network (FPN). Furthermore, FC analysis demonstrated several functional alterations within and between the Yeo 7 networks, particularly including visual network (VN), limbic network (LN), default mode network (DMN), and somatosensory-motor network (SMN) in sub-network and large-scale network analysis. Correlation analysis revealed that there were no significant associations between the network metrics and clinical variables in the present study. These results highlight the altered topological architecture of functional brain networks associated with visual perception abilities, emotion regulation, sensorimotor processing, and attentional control, which may contribute to effectively targeted treatment modalities for patients with FCon.