Triple Network Research Articles

Functional and structural abnormalities of thalamus in individuals at early stage of schizophrenia

BackgroundThalamic abnormalities in schizophrenia are recognized, alongside cognitive deficits. However, the current findings about these abnormalities during the prodromal period remain relatively few and inconsistent. This study applied multimodal methods to explore the alterations in thalamic function and structure and their relationship with cognitive function in first-episode schizophrenia (FES) patients and ultra-high-risk (UHR) individuals, aiming to affirm the thalamus's role in schizophrenia development and cognitive deficits. Methods75 FES patients, 60 UHR individuals, and 60 healthy controls (HC) were recruited. Among the three groups, gray matter volume (GMV) and functional connectivity (FC) were evaluated to reflect the structural and functional abnormalities in the thalamus. Pearson correlation was used to calculate the association between these abnormalities and cognitive impairments. ResultsNo significant difference in GMV of the thalamus was found among the abovementioned three groups. Compared with HC individuals, FES patients had decreased thalamocortical FC mostly in the thalamocortical triple network, including the default mode network (DMN), salience network (SN), and executive control network (ECN). UHR individuals had similar but milder dysconnectivity as the FES group. Furthermore, FC between the left thalamus and right putamen was significantly correlated with execution speed and attention in the FES group. ConclusionsOur findings revealed decreased thalamocortical FC associated with cognitive deficits in FES and UHR subjects. This improves our understanding of the functional alterations in thalamus in prodromal stage of schizophrenia and the related factors of the cognitive impairment of the disease. Trial registrationClinicalTrials.govNCT03965598; https://clinicaltrials.gov/ct2/show/NCT03965598.

Strain-Temperature Dual Sensor Based on Deep Learning Strategy for Human-Computer Interaction Systems.

Thermoelectric (TE) hydrogels, mimicking human skin, possessing temperature and strain sensing capabilities, are well-suited for human-machine interaction interfaces and wearable devices. In this study, a TE hydrogel with high toughness and temperature responsiveness was created using the Hofmeister effect and TE current effect, achieved through the cross-linking of PVA/PAA/carboxymethyl cellulose triple networks. The Hofmeister effect, facilitated by Na+ and SO42- ions coordination, notably increased the hydrogel's tensile strength (800 kPa). Introduction of Fe2+/Fe3+ as redox pairs conferred a high Seebeck coefficient (2.3 mV K-1), thereby enhancing temperature responsiveness. Using this dual-responsive sensor, successful demonstration of a feedback mechanism combining deep learning with a robotic hand was accomplished (with a recognition accuracy of 95.30%), alongside temperature warnings at various levels. It is expected to replace manual work through the control of the manipulator in some high-temperature and high-risk scenarios, thereby improving the safety factor, underscoring the vast potential of TE hydrogel sensors in motion monitoring and human-machine interaction applications.

Is parental overcontrol a specific form of child maltreatment? Insights from a resting state EEG connectivity study

IntroductionRecent studies suggest that parental overcontrol could be considered a specific form of childhood trauma (CT). Although previous research has shown that CT alters the functional and structural architecture of large-scale networks in the brain, the neural basis associated with parental overcontrol has not been sufficiently explored. Therefore, the main aim of the current study was to investigate the relationship between parental overcontrol and electroencephalography (EEG) triple network (TN) functional connectivity during the resting state (RS) condition in a non-clinical sample (N = 71; 39 females, mean age 23.94 ± 5.89 SD). MethodsEEG was recorded during 5 min of RS with eyes closed. All participants were asked to self-report maternal and paternal overcontrol, CT and general psychopathology. All EEG analyses were performed using the exact low-resolution electromagnetic tomography software (eLORETA). ResultsOur results showed a significant positive correlation between maternal overcontrol and theta connectivity between the salience network and the central executive network. This connectivity pattern was independently associated with maternal overcontrol even when controlling for relevant confounding variables, including the severity of CT and the general level of psychopathology. This neurophysiological pattern may reflect a predisposition to detect and respond to potentially threatening stimuli in the environment, which is typically associated with excessive overcontrol. ConclusionsOur findings support the hypothesis that parental overcontrol should be considered a form of CT in all respects independent of the forms traditionally studied in the literature (i.e., emotional abuse, physical abuse, sexual abuse, and physical and emotional neglect).

Triple network resting-state functional connectivity patterns of alcohol heavy drinking.

Previous neuroimaging research in alcohol use disorder (AUD) has found altered functional connectivity in the brain's salience, default mode, and central executive (CEN) networks (i.e. the triple network model), though their specific associations with AUD severity and heavy drinking remains unclear. This study utilized resting-state fMRI to examine functional connectivity in these networks and measures of alcohol misuse. Seventy-six adult heavy drinkers completed a 7-min resting-state functional MRI scan during visual fixation. Linear regression models tested if connectivity in the three target networks was associated with past 12-month AUD symptoms and number of heavy drinking days in the past 30days. Exploratory analyses examined correlations between connectivity clusters and impulsivity and psychopathology measures. Functional connectivity within the CEN network (right and left lateral prefrontal cortex [LPFC] seeds co-activating with 13 and 15 clusters, respectively) was significantly associated with AUD symptoms (right LPFC: β = .337, p-FDR = .016; left LPFC: β = .291, p-FDR = .028) but not heavy drinking (p-FDR > .749). Post-hoc tests revealed six clusters co-activating with the CEN network were associated with AUD symptoms-right middle frontal gyrus, right inferior parietal gyrus, left middle temporal gyrus, and left and right cerebellum. Neither the default mode nor the salience network was significantly associated with alcohol variables. Connectivity in the left LPFC was correlated with monetary delay discounting (r =.25, p = .03). These findings support previous associations between connectivity within the CEN network and AUD severity, providing additional specificity to the relevance of the triple network model to AUD.

Functional connectivity associations with menstrual pain characteristics in adolescents: an investigation of the triple network model.

Menstrual pain is associated with deficits in central pain processing, yet neuroimaging studies to date have all been limited by focusing on group comparisons of adult women with vs without menstrual pain. This study aimed to investigate the role of the triple network model (TNM) of brain networks in adolescent girls with varied menstrual pain severity ratings. One hundred participants (ages 13-19 years) completed a 6-min resting state functional magnetic resonance imaging (fMRI) scan and rated menstrual pain severity, menstrual pain interference, and cumulative menstrual pain exposure. Imaging analyses included age and gynecological age (years since menarche) as covariates. Menstrual pain severity was positively associated with functional connectivity between the cingulo-opercular salience network (cSN) and the sensory processing regions, limbic regions, and insula, and was also positively associated with connectivity between the left central executive network (CEN) and posterior regions. Menstrual pain interference was positively associated with connectivity between the cSN and widespread brain areas. In addition, menstrual pain interference was positively associated with connectivity within the left CEN, whereas connectivity both within the right CEN and between the right CEN and cortical areas outside the network (including the insula) were negatively associated with menstrual pain interference. Cumulative menstrual pain exposure shared a strong negative association with connectivity between the default mode network and other widespread regions associated with large-scale brain networks. These findings support a key role for the involvement of TNM brain networks in menstrual pain characteristics and suggest that alterations in pain processing exist in adolescents with varying levels of menstrual pain.

Resilient 3D porous self-healable triple network hydrogels reinforced with graphene oxide for high-performance flexible supercapacitors

The application of gel polymer electrolyte (GPE) in flexible supercapacitor (FSC) has attracted much attention due to its excellent mechanical properties and chemical stability. However, traditional polymer substrates have high crystallinity, resulting in sluggish ion migration and low ionic conductivity. This study reports the successful fabrication of a resilient highly porous triple hydrogel network (TNH) comprising the natural polymer of sodium alginate and biocompatible components of polyvinyl alcohol and graphene oxide (GO), exhibiting remarkably high ionic conductivity and astonishing mechanical strength, self-healing, and flame retardant properties. The physical and electrochemical features of the GPEs were optimized by tuning the GO wt% within the polymeric matrix. Adding a low wt% of GO (0.8 wt%) led to remarkable improvements in ionic conductivity (83.33 mS cm−1), mechanical strength (2.21 MPa), and self-healing and flame-retardant properties of the resulting PVA/SA/GO GPE than the pristine GPE. This was attributed to the formation of a microporous three-dimensional polymeric network based on reversible non-covalent bonds. The carbon cloth-based symmetric SC prepared based on this GPE exhibited a long cycle life, high specific capacitance of 633.3 mF cm−2 at 0.5 mA cm−2, and an energy density of 84 mWh cm−2 at a high power density of 500.2 mW cm−2 (at 1 mA cm−2)), which surpassed the values reported in previous studies. The electrochemical performance of the FSC prepared based on PVA/SA/GO GPE remained constant when subjected to bending deformation, indicating the harmonious physical, chemical, and electrochemical properties of the developed GPE and its suitability for flexible energy storage devices, and wearable electronics

Beyond Multidimensional Vulnerability Approach: A Triple Network Notion for Urban Cohesion in At-Risk Neighborhoods of Jaen’s Historic Center

Today’s cities need more than ever methodological devices that ensure a specific urbanism, in keeping with the complexity of relationships that occur in the urban space. Networks or systems of a diverse nature must be activated to achieve a certain success of the city, even more so in its fragile central fabric. This study is focused on the historic neighborhoods of the city of Jaén (Spain), which have a high vulnerability index. The aim is to establish a strategy to revitalize their urban and social capital and improve their articulation with the rest of the urban and territorial structure. A methodological proposal for the analysis of vulnerability factors and an urban and cartographic analysis of the public space is addressed through an interweaving of a triple network: environmental, social, and urban. For this purpose, the configuration of this overlapping network of analysis and the formulation of criteria is presented, which considers the urban multiplicity and promotes new dynamics for the integrated urban regeneration of the area and for the activation of public and collective urban spaces.

Index of embedded networks in the sphere

In this paper, we will compute the Morse index and nullity of some embedded stationary geodesic networks in the sphere. The key theorem in the computation is that the index (and nullity) for the whole network is related to the index (and nullity) of small networks and the Dirichlet-to-Neumann map defined in this paper. Finally, we will show that for all stationary triple junction networks in S2, there is only one eigenvalue (without multiplicity) −1, which is less than 0, and the corresponding eigenfunctions are locally constant. Besides, the multiplicity of eigenvalues 0 is 3 for these networks, and their eigenfunctions are generated by the rotations on the sphere.

Generating high-resolution hyperspectral time series datasets based on unsupervised spatial-temporal-spectral fusion network incorporating a deep prior

Over the past decade, image fusion has emerged as an indispensable tool for surface monitoring due to its capability to reconstruct high-quality surface reflectance. While satisfactory progress has been made in the spatial-spectral and spatial-temporal fusion of remote sensing images, the fusion of spatial-temporal-spectral data remains a challenging task. This challenge arises from the presence of multiple nonlinear relationships caused by inconsistent temporal, spatial, and spectral resolutions. In this paper, we propose a novel approach to address the spatial-temporal-spectral fusion of remote sensing images using a triple discriminator Generative Adversarial Network called TDGAN. Our approach represents the first attempt at spatial-temporal-spectral fusion based on deep learning techniques. Specifically, TDGAN achieves unsupervised learning by incorporating a deep spectral transformation prior, explores the application performance of the Swin Transformer in spectral super-resolution for the first time. Additionally, three discriminators are designed based on the prior consistencies: phenological consistency for data acquired at different times and spatial-spectral consistency for data acquired at the same time. These discriminators effectively enhance the prediction accuracy of missing hyperspectral images. Extensive simulations and real experiments were conducted to evaluate the effectiveness of our proposed TDGAN. The results demonstrate its robustness and comparable performance to other state-of-the-art methods. The proposed approach provides high-quality hyperspectral datasets with high spatial and temporal resolutions, facilitating continuous land cover observations.

Ultra-stretchable, self-healing, bonding, and skin-inspired conductive triple network hydrogel for wearable strain sensors and friction nanogenerators

Hydrogel-based flexible sensors hold significant potential for applications in skin-like electronics, human motion detection, and human-machine interfaces. The development of strain-sensitive hydrogel with high stretchability, self-healing, and high conductivity is highly sought after. A hydrogel sensor with a strain-sensitive semi-interpenetrating network consisting of polyacrylamide (PAM), poly(vinyl alcohol) (PVA), and sodium alginate (SA). With environmentally benign KCl as a conductive filler, SA/PVA/PAM/KCl hydrogel has a high toughness (4.8 MJ m−3), high stretchability (1250 %), high tensile strength (510 kPa), excellent recoverability, and excellent self-adhesiveness toward various substrates. The hydrogel sensors present high sensitivity with good linearity in the majority of the detection range with a gauge factor (GF) over 6.78, a response time of 268 ms, and excellent durability. The ionic conductive hydrogel has shown excellent energy harvesting capability as a triboelectric nanogenerators (TENG). Multifunctional organic hydrogels provide a protocol for the design and preparation of multifunctional hydrogel for wearable electronics.

Highly conductive triple network hydrogel thermoelectrochemical cells with low-grade heat harvesting

Quasi-solid thermoelectrochemical cells (TECs) are promising candidates for wearable energy harvesting devices as they enable the continuous conversion of low-grade heat into electricity. However, the TEC performance remains limited by inadequate ionic conductivity. Herein, a triple-network hydrogel consisting of polyacrylamide/poly(vinyl alcohol)/cellulose nanofiber (PAAM/PVA/CNF) is synthesized via a freeze-thaw method, and soaked in the thermogalvanic redox couple Fe(CN)63−/4– as the electrolyte. By optimizing the polymer composition and soaking time, a superior ionic conductivity of 168 mS cm−1 is achieved while maintaining a thermopower of ∼1.69 mV K−1. The high ionic conductivity is provided by the fractal microstructure of the hydrogel, as revealed by small-angle X-ray scattering (SAXS). Moreover, the as-fabricated thermoelectric generator array exhibits an exceptional power output of 28.7 μW at a temperature difference of 11.9 K. This work provides insights on the development of wearable thermoelectric materials for practical and reliable energy harvesting applications.

A hybrid machine learning model for sentiment analysis and satisfaction assessment with Turkish universities using Twitter data

Social media platforms, such as X (Twitter), contain enormous amounts of data, and collecting valuable information from that data assists in making more informed decisions. In recent years, governments and institutions have begun to explore the possibilities of utilizing social networks as a platform to supply and enhance the quality of their services. Consequently, there is an increased demand to estimate people’s satisfaction with Turkish Universities written in Turkish wherever it is challenging and requires more effort to cope with its rich morphological structure to perform a Sentiment Analysis task. This study proposes a Turkish text sentiment analysis methodology for estimating people’s satisfaction with Turkish Universities by employing nine conventional machine learning models, deep learning techniques, and BERT-based transformers upon an original manually annotated dataset consisting of 17,793 tweets in Turkish. An innovative hybrid architecture, named BERT-BiLSTM-CNN, integrates Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Long Short-Term Memory (BiLSTM), and a triple parallel Convolutional Neural Network (CNN) branch, achieving exceptional accuracy in sentiment analysis of Turkish tweets. During testing, the proposed architecture revealed an impressive accuracy rate of over 0.9101, an F1 Score of 0.8801, and a Receiver Operating Characteristic (ROC) of 0.9632 for analyzing sentiment, demonstrating that the model outperformed the state-of-the-art models, and it is capable of coping with the linguistic complexities of Turkish sentiment analysis. This work provides new insights into sentiment analysis by proposing a hybrid model that combines several computational methodologies to improve the understanding of attitudes.

TPN:Triple network algorithm for deep reinforcement learning

The target net method has been the foundation of deep reinforcement learning since Deepmind first proposed it in 2015. Almost all the current popular reinforcement learning algorithms include target net. However, while the slowly updated target network improves the stability of the algorithm, it also reduces the performance of the algorithm. In this paper, the authors design a novel triple-network algorithm(TPN). TPN combines the temporal-difference(TD) algorithm and policy gradient(PG) theorem. Using three networks to estimate the state value(v), action value (q), and policy(π). These networks have no primary or secondary distinction but are trained synchronously and influence each other. The author found that through this TPN architecture, the convergence and stability of the algorithm can be greatly improved without increasing the amount of calculation. Although it is only a basic framework at present. The calculation process of TPN is simple and easy to implement. Experiments prove that the convergence speed and stability of TPN in discrete cases are better than PPO.

3D Printing Silk Fibroin/Polyacrylamide Triple-Network Composite Hydrogels with Stretchability, Conductivity, and Strain-Sensing Ability as Bionic Electronic Skins.

Electronic skins have received increasing attention due to their great application potential in wearable electronics. Meanwhile, tremendous efforts are still needed for the fabrication of multifunctional composite hydrogels with complex structures for electronic skins via simple methods. In this work, a novel three-dimensional (3D) printing composite hydrogel with stretchability, conductivity, and strain-sensing ability is produced using a one-step photocuring method to achieve a dual-signal response of the electronic skin. The composite hydrogel exhibits a triple-network structure composed of silk microfibers (SMF), regenerated silk fibroin (RSF), and polyacrylamide (PAM). The establishment of triple networks is based on the electrostatic interaction between SMF and RSF, as well as the chemically cross-linked RSF and PAM. Thanks to its specific structure and components, the composite hydrogel possesses enhanced mechanical properties (elastic modulus of 140 kPa, compressive stress of 21 MPa, and compression modulus of 600 kPa) and 3D printability while retaining stretchability and flexibility. The interaction between negatively charged SMF and cations in phosphate-buffered saline endows the composite hydrogel with good conductivity and strain-sensing ability after immersion in a low-concentration (10 mM) salt solution. Moreover, the 3D printing composite hydrogel scaffold successfully realizes real-time monitoring. Therefore, the proposed hydrogel-based ionic sensor is promising for skin tissue engineering, real-time monitoring, soft robotics, and human-machine interfaces.

A conceptual framework for a neurophysiological basis of art therapy for PTSD.

Post-traumatic stress disorder (PTSD) is a heterogeneous condition that affects many civilians and military service members. Lack of engagement, high dropout rate, and variable response to psychotherapy necessitates more compelling and accessible treatment options that are based on sound neuroscientific evidence-informed decision-making. Art therapy incorporates elements proven to be effective in psychotherapy, such as exposure, making it a potentially valuable treatment option. This conceptual paper aims to inform the neurophysiological rationale for the use of art therapy as a therapeutic approach for individuals with PTSD. A narrative synthesis was conducted using literature review of empirical research on the neurophysiological effects of art therapy, with supporting literature on neuroaesthetics and psychotherapies to identify art therapy factors most pertinent for PTSD. Findings were synthesized through a proposed framework based on the triple network model considering the network-based dysfunctions due to PTSD. Art therapy's active components, such as concretization and metaphor, active art engagement, emotion processing and regulation, perspective taking and reframing, and therapeutic alliance, may improve symptoms of PTSD and prompt adaptive brain functioning. Given the scarcity of rigorous studies on art therapy's effectiveness and mechanisms of alleviating PTSD symptoms, the suggested framework offers a neurophysiological rationale and a future research agenda to investigate the impact of art therapy as a therapeutic approach for individuals with PTSD.

Aberrant Brain Triple-Network Effective Connectivity Patterns in Type2 Diabetes Mellitus.

Aberrant brain functional connectivity network is thought to be related to cognitive impairment in patients with type2 diabetes mellitus (T2DM). This study aims to investigate the triple-network effective connectivity patterns in patients with T2DM within and between the default mode network (DMN), salience network (SN), and executive control network (ECN) and their associations with cognitive declines. In total, 92 patients with T2DM and 98 matched healthy controls (HCs) were recruited and underwent resting-state functional magnetic resonance imaging (rs-fMRI). Spectral dynamic causal modeling (spDCM) was used for effective connectivity analysis within the triple network. The posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), lateral prefrontal cortex (LPFC), supramarginal gyrus (SMG), and anterior insula (AINS) were selected as the regions of interest. Group comparisons were performed for effective connectivity calculated using the fully connected model, and the relationships between effective connectivity alterations and cognitive impairment as well as clinical parameters were detected. Compared to HCs, patients with T2DM exhibited increased or decreased effective connectivity patterns within the triple network. Furthermore, diabetes duration was significantly negatively correlated with increased effective connectivity from the r-LPFC to the mPFC, while body mass index (BMI) was significantly positively correlated with increased effective connectivity from the l-LPFC to the l-AINS (r = - 0.353, p = 0.001; r = 0.377, p = 0.004). These results indicate abnormal effective connectivity patterns within the triple network model in patients with T2DM and provide new insight into the neurological mechanisms of T2DM and related cognitive dysfunction.

Design, characterization, and performance evaluation of novel PVA/CS/CNF/MOP TN ionic conductive hydrogels for flexible sensors

AbstractFlexible hydrogel polymers with excellent stretchability, high conductivity, and biocompatibility are widely used to prepare flexible sensing devices. However, it remains difficult to simultaneously combine excellent tensile capacity and high ion conducting ability into a simple hydrogel material. While PVA‐based hydrogels have been developed before, this work focuses on a unique preparation method. It solves the engineering problem of making hydrogels that have both high elongation at break and high ionic conductivity. In the study, we introduced cellulose nanofibers (CNFs) into polyvinyl alcohol (PVA)/chitosan (CS) cross‐linked polymer networks to form triple network (TN) composite hydrogel. The labyrinthine three‐dimensional (3D) honeycomb through‐hole network nanostructure generated by the addition of CNF provides the hydrogel with excellent mechanical properties. Then, the simple salting‐out strategy of muriate of potash (MOP) solution gives the hydrogel superior mechanical properties and high ionic conductivity. The prepared PVA/CS/CNF/MOP TN ionic conductive hydrogel not only exhibits excellent mechanical properties (toughness up to 2.39 MJ m−3, fracture strength up to 1.78 MPa, fracture strain up to 350%) but also has impressive ionic conductivity (highest to 8.4 S m−1). The flexible hydrogel strain sensor assembled from the PVA/CS/CNF/MOP TN ionic conductive hydrogel showed remarkable sensitivity (gauge factor for 6.9) and the ability to detect human motions reliably. Besides, the hydrogel also has maintained long‐term antimicrobial and antioxidant properties. The study provided a new perspective on designing multifunctional materials featuring strong mechanical properties and high ionic conductivity, contributing to the advancement of flexible sensor technologies.

Functional role of circRNA CHRC through miR-431-5p/KLF15 signaling axis in the progression of heart failure

Pathological myocardial hypertrophy is a common early clinical manifestation of heart failure, with noncoding RNAs exerting regulatory influence. However, the molecular function of circular RNAs (circRNAs) in the progression from cardiac hypertrophy to heart failure remains unclear. To uncover functional circRNAs and identify the core circRNA signaling pathway in heart failure, we construct a global triple network (microRNA, circRNA, and mRNA) based on the competitive endogenous RNA (ceRNA) theory. We observe that cardiac hypertrophy-related circRNA (circRNA CHRC), within the ceRNA network, is down-regulated in both transverse aortic constriction mice and Ang-II--treated primary mouse cardiomyocytes. Silencing circRNA CHRC increases cross-sectional cell area, atrial natriuretic peptide, and β-myosin heavy chain levels in primary mouse cardiomyocytes. Further screening shows that circRNA CHRC targets the miR-431-5p/KLF15 axis implicated in heart failure progression in vivo and in vitro. Immunoprecipitation with anti-Ago2-RNA confirms the interaction between circRNA CHRC and miR-431-5p, while miR-431-5p mimics reverse Klf15 activation caused by circRNA CHRC overexpression. In summary, circRNA CHRC attenuates cardiac hypertrophy via sponging miR-431-5p to maintain the normal level of Klf15 expression.

A Stepwise Multivariate Granger Causality Method for Constructing Hierarchical Directed Brain Functional Network.

The directed brain functional network construction gives us the new insights into the relationships between brain regions from the causality point of view. The Granger causality analysis is one of the powerful methods to model the directed network. The complex brain network is also hierarchically constructed, which is particularly suited to facilitate segregated functions and the global integration of the segregated functions. Therefore, it is of great interest to explore new approach to model the hierarchical architecture of the directed network. In the present study, we proposed a new approach, namely, stepwise multivariate Granger causality (SMGC), considering both the directed and hierarchical features of brain functional network to explore the stepwise causal relationship in the network. The simulation study demonstrated that the diverse and complex hierarchical organization could be embedded in the apparently simple directed network. The proposed SMGC method could capture the multiple hierarchy of the directed network. When applying to the real functional magnetic resonance imaging (fMRI) datasets, the core triple resting-state networks in human brain showed within-network directed connections in the first-level directed network and rich and diverse between-network pathways in the second-level hierarchical network. The default mode network (DMN) had a prominent role in the resting-state acting as both the causal source and the important relay station. Further exploratory research on the adaption of directed hierarchical network in athletes suggested the enhanced bidirectional communication between the DMN and the central executive network (CEN) and the enhanced directed connections from the salience network (SN) to the CEN in the athlete group. The SMGC approach is capable of capturing the hierarchical architecture of the brain directed functional network, which refreshes the new stepwise causal relationship in the directed network. This might shed light on the potential application for exploring the altered hierarchical organization of brain directed network in neuropsychiatric disorders.

Neurobiological correlates of religious coping among older adults with and without mood disorders: An exploratory study

In this study, 32 older adults with and without mood disorders completed resting-state functional Magnetic Resonance Imaging and measures of demographics, spirituality/religion, positive and negative religious coping, and depression. Group Independent Component Analysis identified and selected three a priori resting state networks [cingulo-opercular salience (cSN), central executive (CEN) and Default Mode Networks (DMN)] within the Triple Network Mode. We investigated associations of religious coping with within- and between-network connectivity, controlling for age. Insular connectivity within the cSN was associated with negative religious coping. Religious coping was associated with anti-correlation between the DMN and CEN even when controlling for depression.