Dynamic Connectivity Research Articles

A longitudinal study of enjoyment and group-level emotion regulation in online collaborative English language learning

This study investigates the dynamic connection between two emotional constructs, enjoyment and group-level emotion regulation (co- and socially-shared regulation), during one 21-week semester of online collaborative English language learning. The study involved 268 Chinese undergraduate English-major students who completed a series of English writing tasks in 88 online groups of three or four members. During their online collaboration, participants were invited to complete the enjoyment scale and the emotion regulation scale at three separate times at seven-week intervals. When analyzing the data, a latent growth curve (LGC) modeling approach was employed to examine the intercepts and slopes of participants’ scaled responses to questions regarding enjoyment, co-regulation, and socially-shared regulation and to analyze the covariance between them. The findings reveal that participants’ levels of enjoyment and group-level emotion regulation were generally high at the initial stage of their online collaboration and continued to increase toward the end of the semester. Furthermore, an increase in the level of their enjoyment was significantly and positively associated with an increase in the levels of both their co- and socially-shared regulation, with the relationship with socially-shared regulation during online collaborative learning being stronger. These findings will enable language teachers to develop strategies for supporting language learners’ emotion regulation in online contexts.

Anatomical Connectivity Constrains Dynamic Functional Connectivity among Neural Systems: Implications for Cognition and Behavior

Abstract Leslie Ungerleider had a tremendous impact across many different areas of cognitive neuroscience. Her ideas and her approach, as well as her findings, will continue to impact the field for generations to come. One of the most impactful aspects of her approach was her focus on the ways that anatomical connections constrain functional communications among brain regions. Furthermore, she emphasized that changes in these functional communications, whether from lesions to the anatomical connections or temporary modulations of the efficacy of information transmission resulting from selective attention, have consequences for cognition and behavior. By necessity, this short review cannot cover the vast amount of research that contributed to or benefited from Leslie's work. Rather, we focus on one line of research that grew directly from some of Leslie's early work and her mentoring on these important concepts. This research and the many other lines of research that arose from these same origins has helped develop our understanding of the visual system, and cognitive systems more generally, as collections of highly organized, specialized, dynamic, and interacting subsystems.

Dynamic functional connectivity: why the controversy?

Abstract In principle, dynamic functional connectivity in fMRI is just a statistical measure. A passer-by might think it to be a specialist topic, but it continues to attract widespread attention and spark controversy. Why?

Machine Learning and Remote Sensing Applications for Assessing Land Use and Land Cover Changes for Under-monitored Basin

Urban sprawling caused by industrial and economic growth has significantly affected land use and land cover (LULC). Using satellite imagery for real-time examination in Kuantan has become exceedingly expensive due to the scarcity and obsolescence of real-time LULC data. With the advent of remote sensing and geographical information systems, LULC change assessment is feasible. A quantitative assessment of image classification schemes (supervised classification using maximum likelihood and deep learning classification using random forest) was examined using 2022 Sentinel-2 satellite imagery to measure its performance. Kappa coefficient and overall accuracy were used to determine the classification accuracy. Then, 32 years of LULC changes in Kuantan were investigated using Landsat 5 TM, Landsat 8 OLI, and Sentinel-2 based on the best classifier. Random forest classification outperformed maximum likelihood classification with an overall accuracy of 85% compared to 92.8%. The findings also revealed that urbanisation is the main factor contributing to land changes in Kuantan, with a 32% increase in the build-up region and 32% in forest degradation. Despite the subtle and extremely dynamic connection between ecosystems, resources, and settlement, these LULC changes can be depicted using satellite imagery. With the precision of using a suitable classification scheme based on comprehensive, accurate and precise LULC maps can be generated, capturing the essence of spatial dynamics, especially in under-monitored basins. This study provides an overview of the current situation of LULC changes in Kuantan, along with the driving factors that can help the authorities promote sustainable development goals.

A dynamic multidimensional Granger causality based on wavelet decomposition analysis of the tourism and economic growth in China

ABSTRACT This study employs an innovative multimodal approach, integrating Granger causality analysis with wavelet decomposition, to explore the intricate relationship between China’s tourism sector and its economic development spanning from 1995 to 2022. The primary aim is to uncover the causal dynamics between the advancement of tourism and the expansion of the economy across various temporal scopes and dimensions. Through rigorous panel bootstrap causality examinations and meticulous assessments for cross-sectional dependence and slope homogeneity, this research underscores a dynamic and symbiotic connection between China’s economic progress and the growth of its tourism industry, particularly emphasising the enduring significance of this relationship over the short-run, mid-run, and long-run.

Interconnectedness of mindfulness facets and their impact on mental health outcomes in Asian American and European American college students: A network approach.

Despite extensive research supporting the effectiveness of mindfulness-based interventions, the understanding of the dynamic connections between various mindfulness facets, particularly across diverse cultures, remained limited. This study aimed to investigate the networks among mindfulness aspects across different cultural backgrounds and their individual associations with mental health and well-being. Using the data collected from 710 undergraduate students in Hawaii (445 Asian Americans, 265 European Americans), we constructed sparse networks for each group to investigate their centrality index. To compare the overall structures of these networks, we utilized permutation-based tests. In addition, we used relative weight analysis to evaluate the distinct contributions of each mindfulness facet to positive and negative mental health outcomes. The results indicated similar trends in both racial groups, emphasizing the importance of specific mindfulness aspects like describing and acting with awareness within the mindfulness construct. However, notable variations were observed, particularly in how observing and nonjudging facets related among Asian Americans. In addition, acting with awareness and nonjudging showed substantial inverse associations with negative mental health outcomes across groups, whereas the observing aspect displayed positive connections with anxiety and stress among Asian Americans. The findings suggest which aspects are fundamental to mindfulness, aiding in a nuanced understanding of the construct. They also emphasize the importance of acknowledging cultural diversity in both mindfulness research and its clinical applications. Practical implications and directions for future research were discussed. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Examining the Influence of Renewable Energy Consumption, Technological Innovation, and Export Diversification on Economic Growth: Empirical Insights from E-7 Nations

The present research focuses on the endogenous development theory and investigates the relationships between economic growth (dependent variable) and renewable energy consumption, technological innovation, and export diversification (independent variables) in seven emerging economies known as the E-7. Previous studies have examined these factors individually but have not explored their combined impact on the E-7 economies. Therefore, this study contributes to the existing literature on the effects of renewable energy consumption, technological advancement, and export diversification on economic development. This study analyses the dynamic connections among these variables in seven selected emerging countries: Brazil, China, Indonesia, India, Mexico, Russia, and Turkey. Panel data from 1990 to 2022 are utilised, and various methodologies, including panel cointegration, the pooled mean group–autoregressive distributed lag (PMG-ARDL) estimator, and robustness tests, such as the fully modified ordinary least square and dynamic ordinary least square tests, are employed. Empirical inferences are drawn using the Dumitrescu–Hurlin panel causality (DHC) test, and the long-run relationships among the variables are validated using the Westerlund residual cointegration tests. The results from the PMG-ARDL estimator show that renewable energy consumption, technological advancement, and export diversification have a significant and positive impact on economic expansion, confirming the validity of the endogenous growth model in the E-7 countries. The control variable of the financial sector has a positive but insignificant effect on economic growth, while trade openness has a negative and significant effect. The DHC test results indicate a neutral feedback effect of renewable energy consumption on economic growth. The findings also reveal a unidirectional causal relationship between technological innovation and economic growth. Overall, these findings provide valuable insights for economic policymakers in the E-7 countries. By removing barriers to renewable energy consumption, technological innovation, and export diversification, policymakers can promote sustainable economic development.

Multimodal investigation of dynamic brain network alterations in autism spectrum disorder: Linking connectivity dynamics to symptoms and developmental trajectories

BackgroundAutism spectrum disorder (ASD) has been associated with disrupted brain connectivity, yet a comprehensive understanding of the dynamic neural underpinnings remains lacking. This study employed concurrent electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) techniques to investigate dynamic functional connectivity (dFC) patterns and neurovascular characteristics in children with ASD. We also explored associations between neurovascular characteristics and the developmental trajectory of adaptive behavior in individuals with ASD. MethodsResting-state EEG and fNIRS data were simultaneously recorded from 58 ASD and 63 TD children. We implemented a k-means clustering approach to extract the dFC states for each modality. In addition, a multimodal covariance network (MCN) was constructed from the EEG and fNIRS dFC features to capture the neurovascular characteristics linked to ASD. ResultsEEG analyses revealed atypical properties of dFC states in the beta and gamma bands in children with ASD compared to TD children. For fNIRS, the ASD group exhibited atypical properties of dFC states such as duration and transitions relative to the TD group. The MCN analysis revealed significantly suppressed functional covariance between right superior temporal and left Broca's areas, alongside enhanced right dorsolateral prefrontal-left Broca covariance in ASD. Notably, we found that early neurovascular characteristics can predict the developmental progress of adaptive functioning in ASD. ConclusionThe multimodal investigation revealed distinct dFC patterns and neurovascular characteristics associated with ASD, elucidating potential neural mechanisms underlying core symptoms and their developmental trajectories. Our study highlights that integrating complementary neuroimaging modalities may aid in unraveling the complex neurobiology of ASD.

Enhanced ADHD classification through deep learning and dynamic resting state fMRI analysis.

Attention Deficit Hyperactivity Disorder (ADHD) is characterized by deficits in attention, hyperactivity, and/or impulsivity. Resting-state functional connectivity analysis has emerged as a promising approach for ADHD classification using resting-state functional magnetic resonance imaging (rs-fMRI), although with limited accuracy. Recent studies have highlighted dynamic changes in functional connectivity patterns among ADHD children. In this study, we introduce Skip-Vote-Net, a novel deep learning-based network designed for classifying ADHD from typically developing children (TDC) by leveraging dynamic connectivity analysis on rs-fMRI data collected from 222 participants included in the NYU dataset within the ADHD-200 database. Initially, for each subject, functional connectivity matrices were constructed from overlapping segments using Pearson's correlation between mean time series of 116 regions of interest defined by the Automated Anatomical Labeling (AAL) 116 atlas. Skip-Vote-Net was then developed, employing a majority voting mechanism to classify ADHD/TDC children, as well as distinguishing between the two main subtypes: the inattentive subtype (ADHDI) and the predominantly combined subtype (ADHDC). The proposed method was evaluated across four classification scenarios: (1) two-class classification of ADHD from TD children using balanced data, (2) two-class classification between ADHD and TD children using unbalanced data, (3) two-class classification between ADHDI and ADHDC, and (4) three-class classification among ADHDI, ADHDC, and TD children. Using Skip-Vote-Net, we achieved mean classification accuracies of 97% ± 1.87 and 97.7% ± 2.2 for the balanced and unbalanced classification cases, respectively. Furthermore, the mean classification accuracy for discriminating between ADHDI and ADHDC reached 99.4% ± 1.21. Finally, the proposed method demonstrated an average accuracy of 98.86% ± 1.03 in classifying ADHDI, ADHDC, and TD children collectively. Our findings highlight the superior performance of Skip-Vote-Net over existing methods in the classification of ADHD, showcasing its potential as an effective diagnostic tool for identifying ADHD subtypes and distinguishing ADHD from typically developing children.

Molecular Dynamics (MD) Simulations Provide Insights into the Activation Mechanisms of 5-HT2A Receptors

Recent breakthroughs in the determination of atomic resolution 3-D cryo-electron microscopy structures of membrane proteins present an unprecedented opportunity for drug discovery. Structure-based drug discovery utilizing in silico methods enables the study of dynamic connectivity of stable conformations induced by the drug in achieving its effect. With the ever-expanding computational power, simulations of this type reveal protein dynamics in the nano-, micro-, and even millisecond time scales. In the present study, aiming to characterize the protein dynamics of the 5HT2A receptor stimulated by ligands (agonist/antagonist), we performed 1 µs MD simulations on 5HT2A/DOI (agonist), 5HT2A/GSK215083 (antagonist), and 5HT2A (APO, no ligand) systems. The crystal structure of 5HT2A/zotepine (antagonist) (PDB: 6A94) was used to set up the simulation systems in a lipid bilayer environment. We found the monitoring of the ionic lock residue pair (R3.50-E6.30) of 5HT2A in MD simulations to be a good approximation of the effects of agonists (ionic lock breakage) or antagonists (ionic lock formation) on receptor activation. We further performed analyses of the MD trajectories, including Principal Component Analysis (PCA), hydrogen bond, salt bridge, and hydrophobic interaction network analyses, and correlation between residues to identify key elements of receptor activation. Our results suggest that in order to trigger receptor activation, DOI must interact with 5HT2A through residues V5.39, G5.42, S5.43, and S5.46 on TM5, inducing significant conformational changes in the backbone angles of G5.42 and S5.43. DOI also interacted with residues W6.48 (toggle switch) and F6.51 on TM6, causing major conformational shifts in the backbone angles of F6.44 and V6.45. These structural changes were transmitted to the intracellular ends of TM5, TM6, and ICL3, resulting in the breaking of the ionic lock and subsequent G protein activation. The studies could be helpful in future design of selective agonists/antagonists for various serotonin receptors (5HT1A, 5HT2A, 5HT2B, 5HT2C, and 5HT7) involved in detrimental disorders, such as addiction and schizophrenia.

Brain-to-brain synchrony increased during interpersonal touch in romantic lovers: an EEG-based hyperscanning study

BackgroundInterpersonal touch is an essential element of human social life. It’s unclear whether the neural patterns of interpersonal touch are specific to intimate relationships or generally apply to other social relationships. Romantic lovers are typically intimate and have a high level of interpersonal touch. Currently, researchers focused on the neurobiological basis and neural processes of romantic love.Methods110 participants finished two resting-state blocks, no-handholding and handholding conditions, with Electroencephalogram (EEG). We aimed to explore the differences in the brain-brain synchrony pattern of interpersonal touch between romantic lovers and strangers by calculating dynamic interpersonal functional connectivity (dIFC) via EEG-based hyperscanning.ResultsOur results supported that the neural processing of interpersonal touch is a dynamic process. At first half, both groups tended to adapt, and then interpersonal touch increased the dIFC between romantic lovers and decreased the dIFC between strangers. Finally, we employed Support Vector Machine (SVM) to classify EEG signals into two different relationships. SVM recognized two relationships with an accuracy of 71% and 0.77 AUC of ROC at the first half, a 73% accuracy and 0.8 AUC of ROC at the second half.ConclusionsOur study indicates that interpersonal touch may have different meanings between romantic lovers and strangers. Specifically, interpersonal touch enhances the dIFC between romantic lovers while reducing the dIFC between strangers. The research has important implications for planning touch-based interventions in social and medical care.

Unraveling the brain dynamics of Depersonalization-Derealization Disorder: a dynamic functional network connectivity analysis

BackgroundDepersonalization-Derealization Disorder (DPD), a prevalent psychiatric disorder, fundamentally disrupts self-consciousness and could significantly impact the quality of life of those affected. While existing research has provided foundational insights for this disorder, the limited exploration of brain dynamics in DPD hinders a deeper understanding of its mechanisms. It restricts the advancement of diagnosis and treatment strategies. To address this, our study aimed to explore the brain dynamics of DPD.MethodsIn our study, we recruited 84 right-handed DPD patients and 67 healthy controls (HCs), assessing them using the Cambridge Depersonalization Scale and a subliminal self-face recognition task. We also conducted a Transcranial Direct Current Stimulation (tDCS) intervention to understand its effect on brain dynamics, evidenced by Functional Magnetic Resonance Imaging (fMRI) scans. Our data preprocessing and analysis employed techniques such as Independent Component Analysis (ICA) and Dynamic Functional Network Connectivity (dFNC) to establish a comprehensive disease atlas for DPD. We compared the brain's dynamic states between DPDs and HCs using ANACOVA tests, assessed correlations with patient experiences and symptomatology through Spearman correlation analysis, and examined the tDCS effect via paired t-tests.ResultsWe identified distinct brain networks corresponding to the Frontoparietal Network (FPN), the Sensorimotor Network (SMN), and the Default Mode Network (DMN) in DPD using group Independent Component Analysis (ICA). Additionally, we discovered four distinct dFNC states, with State-1 displaying significant differences between DPD and HC groups (F = 4.10, P = 0.045). Correlation analysis revealed negative associations between the dwell time of State-2 and various clinical assessment factors. Post-tDCS analysis showed a significant change in the mean dwell time for State-2 in responders (t-statistic = 4.506, P = 0.046), consistent with previous clinical assessments.ConclusionsOur study suggests the brain dynamics of DPD could be a potential biomarker for diagnosis and symptom analysis, which potentially leads to more personalized and effective treatment strategies for DPD patients.Trial registrationsThe trial was registered at the Chinese Clinical Trial Registry on 03/01/2021 (Registration number: ChiCTR2100041741, https://www.chictr.org.cn/showproj.html?proj=66731) before the trial.

EEG spatial inter-channel connectivity analysis: A GCN-based dual stream approach to distinguish mental fatigue status

Mental fatigue is defined as a decline in the ability and efficiency of mental activities. A lot of research suggests that the transition from alertness to fatigue is accompanied by alterations in correlation patterns among various brain regions. However, conventional methods for detecting mental fatigue seldom emphases inter-channel connectivity in the spatial domain. To fill this gap, this paper explores the spatial inter-channel connectivity in alertness and fatigue, employing spectral graph convolutional networks (GCN) for mental fatigue detection. We utilized Pearson correlation coefficients (PCC) to establish temporal connections and magnitude-squared coherence (MSC) for spectral connections. Topological features of the brain network were then analysed. To enhance the learning of spatial inter-channel connectivity, a dual-graph strategy transforms edge features into node features, serving as inputs to the spectral GCN. By simultaneously learning PCC and MSC features, the model results indicate significant differences in some brain network characteristics between alert and fatigue states. It confirms that the synchronicity of brain operations differs in the alert state compared to mental fatigue, and indicates that fatigue states can influence correlation patterns among different brain regions. Our approach is evaluated on a self-designed experimental dataset containing 7 subjects, demonstrating a classification accuracy of 89.59 % in group-level experiments and 95.24 % at the subject level. Additionally, on the public dataset SEED-VIG containing 23 subjects, our method achieves an accuracy of 86.58 %. In summary, this paper proposes a neural network approach based on a dynamic functional connectivity network. The network integrates both temporal and spectral connections with the goal of simultaneously learning spatial inter-channel connectivity in time and frequency domains. This effectively accomplishes fatigue state detection, highlighting that fatigue significantly influences correlations among different brain regions.

Unveiling the Nexus: Carbon finance and climate technology advancements

This study examines the dynamic connections between carbon finance mechanisms such as sustainable bonds and emissions trading systems and their role in advancing climate technology. It also explores direct and indirect contributions of carbon finance mechanisms to the progress of climate technology. It employs a causal mediation analysis and high-dimensional fixed-effects panel regression models to derive complex relationships governing carbon finance and climate technology. The findings highlight not only the direct influence of sustainable finance on climate technology but also the indirect pathways through which carbon finance contributes to technological advancements. The analysis demonstrates that sustainable bonds have a positive and direct influence on the advancement of climate technology, whereas a voluntary carbon market indirectly promotes climate-technology development by influencing the implementation of sustainable bonds.

Individual cognitive traits can be predicted from task-based dynamic functional connectivity with a deep convolutional-recurrent model.

There has been increased interest in understanding the neural substrates of intelligence and several human traits from neuroimaging data. Deep learning can be used to predict different cognitive measures, such as general and fluid intelligence, from different functional magnetic resonance imaging experiments providing information about the main brain areas involved in these predictions. Using neuroimaging and behavioral data from 874 subjects provided by the Human Connectome Project, we predicted various cognitive scores using dynamic functional connectivity derived from language and working memory functional magnetic resonance imaging task states, using a 360-region multimodal atlas. The deep model joins multiscale convolutional and long short-term memory layers and was trained under a 10-fold stratified cross-validation. We removed the confounding effects of gender, age, total brain volume, motion and the multiband reconstruction algorithm using multiple linear regression. We can explain 17.1% and 16% of general intelligence variance for working memory and language tasks, respectively. We showed that task-based dynamic functional connectivity has more predictive power than resting-state dynamic functional connectivity when compared to the literature and that removing confounders significantly reduces the prediction performance. No specific cortical network showed significant relevance in the prediction of general and fluid intelligence, suggesting a spatial homogeneous distribution of the intelligence construct in the brain.

Research on coastline extraction and dynamic change from remote sensing images based on deep learning

Accurate coastline extraction is crucial for the scientific management and protection of coastal zones. Due to the diversity of ground object details and the complexity of terrain in remote sensing images, the segmentation of sea and land faces challenges such as unclear segmentation boundaries and discontinuous coastline contours. To address these issues, this study improve the accuracy and efficiency of coastline extraction by improving the DeepLabv3+ model. Specifically, this study constructs a sea-land segmentation network, DeepSA-Net, based on strip pooling and coordinate attention mechanisms. By introducing dynamic feature connections and strip pooling, the connection between different branches is enhanced, capturing a broader context. The introduction of coordinate attention allows the model to integrate coordinate information during feature extraction, thereby allowing the model to capture longer-distance spatial dependencies. Experimental results has shown that the model can achieves a land-sea segmentation mean intersection over union (mIoU) ration and Recall of over 99% on all datasets. Visual assessment results show more complete edge details of sea-land segmentation, confirming the model’s effectiveness in complex coastal environments. Finally, using remote sensing data from a coastal area in China as an application instance, coastline extraction and dynamic change analysis were implemented, providing new methods for the scientific management and protection of coastal zones.

Aberrant individual large-scale functional network connectivity and topology in chronic insomnia disorder with and without depression

Insomnia is increasingly prevalent with significant associations with depression. Delineating specific neural circuits for chronic insomnia disorder (CID) with and without depressive symptoms is fundamental to develop precision diagnosis and treatment. In this study, we examine static, dynamic and network topology changes of individual large-scale functional network for CID with (CID-D) and without depression to reveal their specific neural underpinnings. Seventeen individual-specific functional brain networks are obtained using a regularized nonnegative matrix factorization technique. Disorders-shared and -specific differences in static and dynamic large-scale functional network connectivities within or between the cognitive control network, dorsal attention network, visual network, limbic network, and default mode network are found for CID and CID-D. Additionally, CID and CID-D groups showed compromised network topological architecture including reduced small-world properties, clustering coefficients and modularity indicating decreased network efficiency and impaired functional segregation. Moreover, the altered neuroimaging indices show significant associations with clinical manifestations and could serve as effective neuromarkers to distinguish among healthy controls, CID and CID-D. Taken together, these findings provide novel insights into the neural basis of CID and CID-D, which may facilitate developing new diagnostic and therapeutic approaches.

Aberrant dynamic functional and effective connectivity changes of the primary visual cortex in patients with retinal detachment via machine learning.

Previous neuroimaging studies have identified significant alterations in brain functional activity in retinal detachment (RD) patients, these investigations predominantly concentrated on local functional activity changes. The potential directional alterations in functional connectivity within the primary visual cortex (V1) in RD patients remain to be elucidated. In this study, we employed seed-based functional connectivity analysis along with Granger causality analysis to examine the directional alterations in dynamic functional connectivity (dFC) within the V1 region of patients diagnosed with RD. Finally, a support vector machine algorithm was utilized to classify patients with RD and healthy controls (HCs). RD patients exhibited heightened dynamic functional connectivity (dFC) and dynamic effective connectivity (dEC) between the Visual Network (VN) and default mode network (DMN), as well as within the VN, compared to HCs. Conversely, dFC between VN and auditory network (AN) decreased, and dEC between VN and sensorimotor network (SMN) significantly reduced. In state 4, RD patients had higher frequency. Notably, variations in dFC originating from the left V1 region proved diagnostically effective, achieving an AUC of 0.786. This study reveals significant alterations in the connectivity between the VN and the default mode network in patients with RD. These changes may disrupt visual information processing and higher cognitive integration in RD patients. Additionally, alterations in the left V1 region and whole-brain dFC show promising potential in aiding the diagnosis of RD. These findings offer valuable insights into the neural mechanisms underlying visual and cognitive impairments associated with RD.

Intersubject Dynamic Conditional Correlation: A Novel Method to Track the Framewise Network Implication during Naturalistic Stimuli.

Background: Naturalistic stimuli have become increasingly popular in modern cognitive neuroscience. These stimuli have high ecological validity due to their rich and multilayered features. However, their complexity also presents methodological challenges for uncovering neural network reconfiguration. Dynamic functional connectivity using the sliding-window technique is commonly used but has several limitations. In this study, we introduce a new method called intersubject dynamic conditional correlation (ISDCC). Method: ISDCC uses intersubject analysis to remove intrinsic and non-neuronal signals, retaining only intersubject-consistent stimuli-induced signals. It then applies dynamic conditional correlation (DCC) based on the generalized autoregressive conditional heteroskedasticity to calculate the framewise functional connectivity. To validate ISDCC, we analyzed simulation data with known network reconfiguration patterns and two publicly available narrative functional Magnetic Resonance Imaging (fMRI) datasets. Results: (1) ISDCC accurately unveiled the underlying network reconfiguration patterns in simulation data, demonstrating greater sensitivity than DCC; (2) ISDCC identified synchronized network reconfiguration patterns across listeners; (3) ISDCC effectively differentiated between stimulus types with varying temporal coherence; and (4) network reconfigurations unveiled by ISDCC were significantly correlated with listener engagement during narrative comprehension. Conclusion: ISDCC is a precise and dynamic method for tracking network implications in response to naturalistic stimuli.

Alternations voxel-wise interhemispheric and intrahemipheric functional connectivity dynamics in internet gaming disorder

BackgroundCurrently, numerous studies have indicated that individuals with internet gaming disorder (IGDs) have aberrant functional connection patterns between multiple brain regions and networks. However, temporal variability in the intra- and interhemispheric dynamic functional connectivity in IGDs remains unknown. MethodsThis study investigated resting-state functional magnetic resonance data from 55 IGDs and 50 demographically matched healthy controls (HCs). Functional connectivity density (FCD) combined with sliding window analysis is employed to calculate the temporal variability of global functional connectivity. The temporal variability of dynamic functional connectivity further quantified utilizing the standard deviations of global, intra-, and interhemispheric FCD. Finally, correlation analyses were performed between dynamic FCD varience (dFCD) in differential brain regions and clinical behaviors. ResultIGDs showed decreased intra- and interhemispheric dFCD variance in the visual attention network (precuneus and calcarine) and also demonstrated hemispheric-level dFCD variance abnormalities in the posterior cingulate cortex (PCC) compared to HCs. Moreover, abnormal global dFCD variability of the calcarine and ipsilateral dFCD variability of the PCC were negatively correlated with the severity of IGDs in the IGD group. ConclusionOur results demonstrate abberant intra- and interhemispheric dynamic functional connectivity in the visual attention network, which emphasizes the neurobiological basis for impaired concentration in IGDs.