Local Efficiency Research Articles

A Hybrid Localization Algorithm for Enhanced Accuracy and Robustness in Healthcare Systems

This paper presents a novel hybrid localization algorithm designed for healthcare systems, integrating Received Signal Strength Indicator (RSSI) and Time of Arrival (ToA) measurements with machine learning techniques. The algorithm aims to enhance the accuracy, robustness, and computational efficiency of sensor localization in dynamic healthcare environments. Experimental results demonstrate that the hybrid algorithm achieves a significantly lower localization error, averaging 0.5 meters, compared to traditional RSSI-only and ToA-only methods. The algorithm's rapid convergence and low computational time make it suitable for real-time applications. Additionally, its robustness to measurement noise, a common challenge in healthcare settings, underscores its reliability. This research underscores the potential of advanced localization technologies to improve patient monitoring, safety, and overall healthcare delivery, with future work poised to further enhance performance and adaptability.

Prediction of disease progression in individuals with subjective cognitive decline using brain network analysis.

The objective of this study is to explore potential differences in brain functional networks at baseline between individuals with progressive subjective cognitive decline (P-SCD) and stable subjective cognitive decline (S-SCD), as well as to identify potential indicators that can effectively distinguish between P-SCD and S-SCD. Alzheimer's Disease Neuroimaging Initiative (ADNI) database was utilized to enroll SCD individuals with a follow-up period of over 3 years. This study included 39 individuals with S-SCD, 15 individuals with P-SCD, and 45 cognitively normal (CN) individuals. Brain functional networks were constructed based on the AAL template, and graph theory analysis was performed to determine the topological properties. For global metric, the S-SCD group exhibited stronger small-worldness with reduced connectivity among nearby nodes and accelerated compensatory information transfer capacity. For nodal efficiency, the S-SCD group showed increased connectivity in bilateral posterior cingulate gyri (PCG). However, for nodal local efficiency, the P-SCD group exhibited significantly reduced connectivity in the right cerebellar Crus I compared with the S-SCD group. There are differences in brain functional networks at baseline between P-SCD and S-SCD groups. Furthermore, the right cerebellar Crus I region may be a potentially useful brain area to distinguish between P-SCD and S-SCD.

Can the natural resource balance sheet system promote ecological civilization? --Empirical evidence from panel data of prefecture-level cities in China

As a crucial carrier of natural resource and environmental information, the natural resources balance sheet is important in enhancing ecological governance capabilities and advancing ecological civilization construction. Utilizing panel data from Chinese prefecture cities spanning 2012 to 2018, we employ a Difference-in-Differences (DID) model to examine the policy effects of the natural resources balance sheet system. The findings indicate that compiling the natural resources balance sheet significantly promotes local ecological civilization construction, and local government environmental governance efficiency partially mediates this relationship. Further analysis indicates that the outgoing audit of natural resources and public environmental concern positively moderates the relationship between the two. Drawing on the principal-agent theory, we provide in-depth analysis and elucidation of the mechanisms through which the natural resources balance sheet promotes ecological civilization construction, offering theoretical guidance and empirical data support for strengthening the compilation and application of the natural resources balance sheet.

EEG-based driving intuition and collision anticipation using joint temporal-frequency multi-layer dynamic brain network.

Intuition plays a crucial role in human driving decision-making, and this rapid and unconscious cognitive process is essential for improving traffic safety. We used the first proposed multi-layer network analysis method, "Joint Temporal-Frequency Multi-layer Dynamic Brain Network" (JTF-MDBN), to study the EEG data from the initial and advanced phases of driving intuition training in the theta, alpha, and beta bands. Additionally, we conducted a comparative study between these two phases using multi-layer metrics as well as local and global metrics of single layers. The results show that brain region activity is more stable in the advanced phase of intuition training compared to the initial phase. Particularly in the alart state task, the JTF-MDBN demonstrated stronger connection strength. Multi-layer network analysis indicates that modularity is significantly higher for the non-alert state task than the alert state task in the alpha and beta bands. In the W4 time window (1 second before a collision), we identified significant features that can differentiate situations where a car collision is imminent from those where no collision occurs. Single-layer network analysis also revealed statistical differences in node strength and local efficiency for some EEG channels in the alpha and beta bands during the W4 and W5 time windows. Using these biomarkers to predict vehicle collision risk, the classification accuracy of a linear kernel SVM reached up to 87.5%, demonstrating the feasibility of predicting driving collisions through brain network biomarkers. These findings are important for the study of human intuition and the development of brain-computer interface-based intelligent driving hazard perception assistance systems.

A stepwise fast leakage localization method applying the strategy of dynamic area narrowing down for large-scale water distribution network

ABSTRACT Urban water distribution networks (WDNs) are facing serious leakage problems. As cities expand, the leakage localization burden on large WDNs gradually increases. Although methods have been widely researched, there is a lack of studies and successful applications for large-scale WDNs. To deal with this, a stepwise fast leakage localization Method, SFLLM, utilizing the `dynamic area narrowing down (DAND)' strategy and coupled leakage features (CLF) is proposed. The SFLLM includes a fast-and-dynamic stage using the DAND strategy to reduce the potential leakage area and an accurate localization stage. Only partial representative candidate locations are required to simulate leakages by DAND, and meantime CLF is used to analyze the leakage similarities so that the localization accuracy and efficiency can be improved. SFLLM was tested on a benchmark WDN, saving more than 88% of simulation by DAND strategy and achieving localization in 11 seconds. The results also proved the enhanced performance of CLF in ensuring the stability of the accuracy against various types of uncertainties that may occur in real WDNs. Moreover, three real burst leaks in an actual large-scale WDN were localized within 205 m in about 22 minutes by SFLLM, showing the method's reliable applicability in guiding field leak exploration.

Experimental investigation on a microfluidic U-turn channel for heat transfer applications

In the present work, an experimental set-up designed to allow investigations of the carrier fluid local behaviours in terms of velocity and temperature fields is presented. The study is carried out on a U-turn microfluidic channel, to bear a complete view of the thermo-fluidic system. The experiments have been performed by Infrared Thermal Imaging, which allows deriving the thermal field on the microchannel external walls, and inferring the internal thermal profiles through specific transfer functions in laminar and transitional regimes. Maps of thermal transients allow deriving cooling performances, which are used to identify the local thermal efficiency of the microchannel and relate it to the global efficiency. Velocimetry measurements have been conducted with a Micro Particle Image Velocimetry (μPIV) setup at different flow rates. This analysis has been coupled with thermal results to obtain a description of the effects of local fluid flow phenomena in transitional turbulent regime on the global heat transfer efficiency of a U shaped microchannel. The interactions among thermal and flow fields are specifically related to secondary recirculating flows, close to the turns, where high cooling rates and high magnitude of local velocity fluctuations are measured.

Analysis of contaminant dispersion in constructed wetland flows with absorptive boundaries

Pollution in wetland flows poses a significant challenge to ecosystem preservation, underscoring the importance of wetland restoration. Constructed wetlands have emerged as a widely embraced solution for contaminant mitigation. In this study, we focus on the contaminant removal process within the wetland flows to contribute to wetland restoration. A constructed wetland with ecological floating and bottom absorptive beds has been employed to enhance the capacity of contaminant removal within wetland flows. However, the environmental dispersion in constructed wetland flows is further complicated by the presence of irreversible first-order absorptive boundaries at the upper and bottom beds. Therefore, our work focuses on comprehending the expansion processes of contaminant clouds under absorptive boundaries through a multiscale analysis to demonstrate the removal effects of constructed wetlands. The findings highlight significant deviations in the spatial distribution of contaminant clouds, particularly in the vertical dimension, attributed to absorptive effects. To analyze the impact of removal mechanisms on the dispersion patterns of contaminant clouds, variations in the vertical concentration peak are examined. A predictive model is developed to estimate the position of the concentration crest in the vertical direction based on changes in absorptive ratios between the upper and bottom boundaries. Furthermore, in order to explore the key factor on removal capacity and efficiency within constructed wetland flows, we look into the process of contaminant depletion by examining variations in removed mass and local absorptive and removal efficiency along the streamwise direction in response to deviations in absorptive strengths at the upper and bottom beds. The ratios of the two boundary removal intensities play a significant role in the absorptive dynamics within wetland flows, particularly during the early stages of cloud expansion. By adjusting the absorption ratio between two absorptive beds to 1:1 with a certain total depletion strength in wetland flows, the enhancement ratio of depletion efficiency can increase by nearly 30%. It has been proven that employing identical absorption strengths at two absorptive beds is an economical and efficient means to amplify the absorption efforts within designed constructed wetland flows. This study provides guidelines for the design of constructed wetlands aimed at enhancing their capacity for sewage treatment.

A quantum otto heat engine driven by three quantum dots

A quantum heat engine composed of three coupled quantum dots as a working substance is proposed. Since quantum dots naturally obey the Fermi Hubbard Hamiltonian, the strong coupling interaction regime allows the working substance to be evaluated under an effective Heisenberg Hamiltonian. Indeed, the influence of the strength coupling, between the three dots, on quantum machine efficiency and work in the presence of a homogeneous magnetic field is also examined. Furthermore, the influence of entanglement on the efficiency & work of the quantum dot Otto heat engine is well analyzed. As a tripartite working substance, we are interested in analyzing the local work and efficiency associated with each single and pair of quantum dots. The results show that the local efficiency associated with a pair of quantum dots achieves a maximum value, unlike the global efficiency. Indeed, the entanglement impact on Global/local work is studied.

The Role of Corporate Social Responsibility in Enhancing Resilience and Sustainability of Manufacturing SMEs: A European Perspective

This study investigates the implementation of Corporate Social Responsibility (CSR) practices in the manufacturing sector, with a focus on comparing the experiences and outcomes of small and large enterprises in Europe. Utilizing a mixed-methods approach, the research examines how these enterprises adopt CSR practices and the resultant impacts on financial and environmental performance. The findings indicate that both small and large enterprises benefit significantly from CSR, evidenced by improvements in key financial metrics such as Return on Assets (ROA) and Return on Equity (ROE), as well as reductions in carbon emissions and enhancements in energy efficiency. Small enterprises, despite facing challenges such as limited financial resources and lack of expertise, achieve notable local community engagement and operational efficiencies through targeted CSR initiatives. Large enterprises leverage their extensive resources to implement comprehensive CSR strategies, resulting in substantial environmental and financial gains. The study highlights the importance of stakeholder engagement, government support, and a robust organizational culture in facilitating effective CSR adoption. These insights underscore the need for supportive measures to help SMEs overcome barriers and enhance their contributions to sustainable development.

Real-time estimation of underwater sound speed profiles with a data fusion convolutional neural network model

Sound speed profiles (SSPs) characterize the spatial distribution of sound velocity, significantly impacting the trajectory of acoustic signals. Consequently, they play a crucial role in determining the efficiency and precision of communication, localization, and other practical applications. Recently, SSP inversion methods significantly improve real-time performance compared to the direct measurement method, but it is highly dependent on sonar observation data so that the application scope is limited. To obtain real-time sound speed distribution in larger scale areas, we proposes a data-fusion-driven multi-input multi-output convolutional regression neural network (DF-MIMO-RCNN) , which combines satellite-based real-time remote sensing sea surface temperature (SST), historical SSP feature vector, historical mean temperature below the sea surface with corresponding spatial coordinate information. The model gets rid of the dependence on sonar observation data and can be applied to a wider spatial region. Then two sets of experiments were conducted based on the Argo database and marine experimental data respectively. Experimental results based on Argo data show that the proposed method is not only suitable for deep-sea environments at the same depth, but even for shallow water of different depths. At the same time, compared with other algorithms, the root mean square error (RMSE) and RMSE distributions are smaller and more robust. Finally, we conducted deep-sea experiments in the South China Sea. The results show that even in non-strictly gridded sea areas, our proposed model still has lower RMSE and higher robustness, which demonstrates the superiority of our proposed algorithm.

Characteristics of the White Matter Structural Network in Individuals with Subjective Cognitive Decline with and without APOEε4 Based on Graph Theory Study.

To determine whether individuals with subjective cognitive decline (SCD) have changes in whole-brain network characteristics and intracerebral node characteristics in the structural network, and whether there is a difference between SCD with and without Apolipoprotein E4 (APOEε4). This cross-sectional study included 36 individuals without SCD without APOEε4 (healthy control, HC group), 21 individuals with SCD with APOEε4 (APOEε4+ group), and 33 individuals with SCD without APOEε4 (APOEε4- group). The white matter structural network was constructed using the fractional anisotropy (FA) based deterministic fiber tracking method. Graph theory was used to analyze the whole-brain network characteristics and intracerebral node characteristics of the three groups. Regarding the whole-brain network characteristics, all three groups exhibited small-worldness in their structural networks. The clustering coefficient (Cp) and local efficiency (Eloc) in the APOEε4+ and APOEε4- groups were significantly lower than in the HC group (p < 0.05), but no significant difference in Cp or Eloc was observed between the APOEε4+ and APOEε4- groups. Regarding intracerebral node characteristics, there were significant differences in some brain regions, mainly the default mode network (DMN), the occipital lobe, the temporal lobe, and subcortical regions. The change in intracerebral node characteristics was different between the APOEε4+ group and the APOEε4- group. Individuals with SCD demonstrate changes in whole-brain network characteristics and intracerebral node characteristics in the structural network. Moreover, differences exist between APOEε4+ and APOEε4- individuals.

Fatigue in Parkinson's Disease Is Due to Decreased Efficiency of the Frontal Network: Quantitative EEG Analysis.

Fatigue is a common, debilitating nonmotor symptom of Parkinson's disease (PD), but its mechanism is poorly understood. We aimed to determine whether electroencephalography (EEG) could objectively measure fatigue and to explore the pathophysiology of fatigue in PD. We studied 32 de novo PD patients who underwent EEG. We compared brain activity between 19 PD patients without fatigue and 13 PD patients with fatigue via EEG power spectra and graphs, including the global efficiency, characteristic path length, clustering coefficient, small-worldness, local efficiency, degree centrality, closeness centrality, and betweenness centrality. No significant differences in absolute or relative power were detected between PD patients without or with fatigue (all p > 0.02, Bonferroni-corrected). According to our network analysis, brain network efficiency differed by frequency band. Generally, the brain network in the frontal area for theta and delta bands showed greater efficiency, and in the temporal area, the alpha1 band was less efficient in PD patients without fatigue (p < 0.0001, p = 0.0011, and p = 0.0007, respectively, Bonferroni-corrected). Our study suggests that PD patients with fatigue have less efficient networks in the frontal area than PD patients without fatigue. These findings may explain why fatigue is common in PD, a frontostriatal disorder. Increased efficiency in the temporal area in PD patients with fatigue is assumed to be compensatory. Brain network analysis using graph theory is more valuable than power spectrum analysis in revealing the brain mechanism related to fatigue.

Development of the Fetal Brain Corticocortical Structural Network during the Second-to-Third Trimester Based on Diffusion MRI.

During the second-to-third trimester, the neuronal pathways of the fetal brain experience rapid development, resulting in the complex architecture of the interwired network at birth. While diffusion MRI-based tractography has been employed to study the prenatal development of structural connectivity network (SCN) in preterm neonatal and postmortem fetal brains, the in utero development of SCN in the normal fetal brain remains largely unknown. In this study, we utilized in utero dMRI data from human fetuses of both sexes between 26 and 38 gestational weeks to investigate the developmental trajectories of the fetal brain SCN, focusing on intrahemispheric connections. Our analysis revealed significant increases in global efficiency, mean local efficiency, and clustering coefficient, along with significant decrease in shortest path length, while small-worldness persisted during the studied period, revealing balanced network integration and segregation. Widespread short-ranged connectivity strengthened significantly. The nodal strength developed in a posterior-to-anterior and medial-to-lateral order, reflecting a spatiotemporal gradient in cortical network connectivity development. Moreover, we observed distinct lateralization patterns in the fetal brain SCN. Globally, there was a leftward lateralization in network efficiency, clustering coefficient, and small-worldness. The regional lateralization patterns in most language, motor, and visual-related areas were consistent with prior knowledge, except for Wernicke's area, indicating lateralized brain wiring is an innate property of the human brain starting from the fetal period. Our findings provided a comprehensive view of the development of the fetal brain SCN and its lateralization, as a normative template that may be used to characterize atypical development.

Long-read subcellular fractionation and sequencing reveals the translational fate of full-length mRNA isoforms during neuronal differentiation.

Alternative splicing (AS) alters the cis-regulatory landscape of mRNA isoforms, leading to transcripts with distinct localization, stability, and translational efficiency. To rigorously investigate mRNA isoform-specific ribosome association, we generated subcellular fractionation and sequencing (Frac-seq) libraries using both conventional short reads and long reads from human embryonic stem cells (ESCs) and neural progenitor cells (NPCs) derived from the same ESCs. We performed de novo transcriptome assembly from high-confidence long reads from cytosolic, monosomal, light, and heavy polyribosomal fractions and quantified their abundance using short reads from their respective subcellular fractions. Thousands of transcripts in each cell type exhibited association with particular subcellular fractions relative to the cytosol. Of the multi-isoform genes, 27% and 19% exhibited significant differential isoform sedimentation in ESCs and NPCs, respectively. Alternative promoter usage and internal exon skipping accounted for the majority of differences between isoforms from the same gene. Random forest classifiers implicated coding sequence (CDS) and untranslated region (UTR) lengths as important determinants of isoform-specific sedimentation profiles, and motif analyses reveal potential cell type-specific and subcellular fraction-associated RNA-binding protein signatures. Taken together, our data demonstrate that alternative mRNA processing within the CDS and UTRs impacts the translational control of mRNA isoforms during stem cell differentiation, and highlight the utility of using a novel long-read sequencing-based method to study translational control.

Brain network topology and its cognitive impact in adult glioma survivors

Structural brain network topology can be altered in case of a brain tumor, due to both the tumor itself and its treatment. In this study, we explored the role of structural whole-brain and nodal network metrics and their association with cognitive functioning. Fifty WHO grade 2–3 adult glioma survivors (> 1-year post-therapy) and 50 matched healthy controls underwent a cognitive assessment, covering six cognitive domains. Raw cognitive assessment scores were transformed into w-scores, corrected for age and education. Furthermore, based on multi-shell diffusion-weighted MRI, whole-brain tractography was performed to create weighted graphs and to estimate whole-brain and nodal graph metrics. Hubs were defined based on nodal strength, betweenness centrality, clustering coefficient and shortest path length in healthy controls. Significant differences in these metrics between patients and controls were tested for the hub nodes (i.e. n = 12) and non-hub nodes (i.e. n = 30) in two mixed-design ANOVAs. Group differences in whole-brain graph measures were explored using Mann–Whitney U tests. Graph metrics that significantly differed were ultimately correlated with the cognitive domain-specific w-scores. Bonferroni correction was applied to correct for multiple testing. In survivors, the bilateral putamen were significantly less frequently observed as a hub (pbonf < 0.001). These nodes’ assortativity values were positively correlated with attention (r(90) > 0.573, pbonf < 0.001), and proxy IQ (r(90) > 0.794, pbonf < 0.001). Attention and proxy IQ were significantly more often correlated with assortativity of hubs compared to non-hubs (pbonf < 0.001). Finally, the whole-brain graph measures of clustering coefficient (r = 0.685), global (r = 0.570) and local efficiency (r = 0.500) only correlated with proxy IQ (pbonf < 0.001). This study demonstrated potential reorganization of hubs in glioma survivors. Assortativity of these hubs was specifically associated with cognitive functioning, which could be important to consider in future modeling of cognitive outcomes and risk classification in glioma survivors.

Adoption of deep learning-based magnetic resonance image information diagnosis in brain function network analysis of Parkinson’s disease patients with end-of-dose wearing-off

Objectivethis study was to analyze the brain functional network of end-of-dose wearing-off (EODWO) in patients with Parkinson’s disease (PD) using a convolutional neural network (CNN)-based functional magnetic resonance imaging (fMRI) data classification model. Methodsone hundred PD patients were recruited and assigned to control (Ctrl) group (39 cases without EODWO) and experimental (Exp) group (61 cases with EODWO). The data classification model based on a CNN was employed to assist the analysis of the changes in brain functional network structure in the two groups. The CNN-based fMRI data classification model was primarily based on a CNN architecture, with improvements made to the initialization of convolutional kernel parameters. Firstly, a structure based on restricted Boltzmann machine (RBM) was constructed, followed by the initialization of convolutional kernel parameters. Subsequently, the model underwent training. Utilizing the data analysis module within the GRETNA toolbox, extracted feature sets were analyzed, including local measures such as betweenness centrality (BC) and degree centrality (DC), as well as global measures such as global efficiency (Eg) and local efficiency (Eloc). Resultsas sparsity increased, there was a gradual upward trend observed in Eg; however, the values of Eg in both brain functional networks remained relatively stable within the range of 0.2–0.5. The Eg value of the Ctrl group’s whole-brain functional network was 0.17 ± 0.02, while that of the Exp group’s whole-brain functional network was 0.17 ± 0.03, with no significant difference between them (P>0.05). The functional DC value of the superior frontal gyrus in the Exp group (8.71 ± 2.56) was significantly lower than that of the Ctrl group (13.32 ± 3.22), whereas the functional DC value of the anterior cingulate gyrus in the Exp group (19.33 ± 4.78) was significantly higher than that of the Ctrl group (15.21 ± 4.02) (P<0.05). There was no significant correlation observed between the functional DC value and levodopa or dopamine agonist therapy (DDT) in the Exp group, whereas the Ctrl group exhibited a significant positive correlation. Conclusionanalysis conducted via a CNN-based fMRI data classification model revealed a correlation between the occurrence of EODWO in PD patients and functional impairments in the left precuneus. Additionally, the occurrence of EODWO may potentially diminish the plasticity of the central prefrontal dopamine.

Alterations of brain structural and functional connectivity networks topology and decoupling in pediatric myelin oligodendrocyte glycoprotein antibody-associated disease

ObjectiveTo investigate the alteration in structural and functional connectivity networks (SCN and FCN) as well as their coupling in pediatric myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), and determine if these properties could serve as potential biomarkers for the disease. Materials and methodsIn total of 32 children with MOGAD and 30 age- and sex-matched healthy controls (HC) were employed to construct the SCN and FCN, respectively. The graph-theoretical analyses of the global properties, node properties of the 90 brain nodes, and the structural-functional connectivity (SC-FC) coupling of the two networks were performed. The graph-theoretical properties that exhibited significant differences were analyzed using partial correlation analysis in conjunction with the clinical scales, including the expanded disability status scale (EDSS), modified Rankin scale (mRS), and pediatric cerebral performance category (PCPC) of the MOGAD group. Subsequently, a machine learning model was developed to discriminate between MOGAD and the HC group, aiming to explore the potential of these properties as biomarkers. ResultsThe SCN of the MOGAD group exhibited aberrant global properties, including an increased characteristic path length (Lp) and a decreased global efficiency (Eg), along with reduced nodal properties such as degree centrality (Dc), nodal efficiency (Ne), and local efficiency in multiple nodes. The FCN of the MOGAD group only exhibited decreased Dc, Ne, and betweenness centrality in two nodes of nodal properties. Besides, MOGAD showed a significant decrease in SC-FC coupling compared to the HC group. The analysis of partial correlation revealed significant correlations between several properties and the scales of EDSS and mRS in the MOGAD group. The machine learning method was used to extract six features and establish the model, achieving a classification accuracy of 82.3% for MOGAD. ConclusionsPediatric MOGAD showed a more pronounced impairment in the SCN along with decoupling of SC-FC. Both partial correlation analysis and discriminant modeling suggest that alterations in brain network properties have the potential as biomarkers for assessing brain damage in MOGAD.

Brain Responses to Real and Imagined Interpretation of Tonal Versus Atonal Music

Professional musicians have been teaching/learning/interpreting Western classical tonal music for longer than atonal music. This may be reflected in their brain plasticity and playing efficiency. To test this idea, EEG connectivity networks (EEG-CNs) of expert cellists at rest and during real and imagined musical interpretation of tonal and atonal excerpts were analyzed. Graphs and connectomes were constructed as models of EEG-CNs, using functional connectivity measurements of EEG phase synchronization in different frequency bands. Tonal and atonal interpretation resulted in a global desynchronization/dysconnectivity versus resting—irrespective of frequency bands—particularly during imagined-interpretation. During the latter, the normalized local information-transfer efficiency (NLE) of graph-EEG-CN’s small-world structure at rest increased significantly during both tonal and atonal interpretation, and more significantly during atonal-interpretation. Regional results from the graphs/connectomes supported previous findings, but only certain EEG frequency bands. During imagined-interpretation, the number of disconnected regions and subnetworks, as well as regions with higher NLE, were greater in atonal-interpretation than in tonal-interpretation for delta/theta/gamma-EEG-CNs. The opposite was true during real-interpretation, specifically limited to alpha-EEG-CN. Our EEG-CN experimental paradigm revealed perceptual differences in musicians’ brains during tonal and atonal interpretations, particularly during imagined-interpretation, potentially due to differences in cognitive roots and brain plasticity for tonal and atonal music, which may affect the musicians’ interpretation.

Constructing fine-grained spatiotemporal neonatal functional atlases with spectral functional network learning.

The early stages of human development are increasingly acknowledged as pivotal in laying the groundwork for subsequent behavioral and cognitive development. Spatiotemporal (4D) brain functional atlases are important in elucidating the development of human brain functions. However, the scarcity of such atlases for early life stages stems from two primary challenges: (1) the significant noise in functional magnetic resonance imaging (fMRI) that complicates the generation of high-quality atlases for each age group, and (2) the rapid and complex changes in the early human brain that hinder the maintenance of temporal consistency in 4D atlases. This study tackles these challenges by integrating low-rank tensor learning with spectral embedding, thereby proposing a novel, data-driven 4D functional atlas generation framework based on spectral functional network learning (SFNL). This method utilizes low-rank tensor learning to capture common functional connectivity (FC) patterns across different ages, thus optimizing FCs for each age group to improve the temporal consistency of functional networks. Incorporating spectral embedding aids in mitigating potential noise in FC networks derived from fMRI data by reconstructing networks in the spectral space. Utilizing SFNL-generated functional networks enables the creation of consistent and highly qualified spatiotemporal functional atlases. The framework was applied to the developing Human Connectome Project (dHCP) dataset, generating the first neonatal 4D functional atlases with fine-grained temporal and spatial resolutions. Experimental evaluations focusing on functional homogeneity, reliability, and temporal consistency demonstrated the superiority of our framework compared to existing methods for constructing 4D atlases. Additionally, network analysis experiments, including individual identification, functional systems development, and local efficiency assessments, further corroborate the efficacy and robustness of the generated atlases. The 4D atlases and related codes will be made publicly accessible (https://github.com/zhaoyunxi/neonate-atlases).

Epilepsy alters brain networks in patients with insular glioma.

We intend to elucidate the alterations of cerebral networks in patients with insular glioma-related epilepsy (GRE) based on resting-state functional magnetic resonance images. We collected 62 insular glioma patients, who were subsequently categorized into glioma-related epilepsy (GRE) and glioma with no epilepsy (GnE) groups, and recruited 16 healthy individuals matched to the patient's age and gender to form the healthy control (HC) group. Graph theoretical analysis was applied to reveal differences in sensorimotor, default mode, visual, and executive networks among different subgroups. No significant alterations in functional connectivity were found in either hemisphere insular glioma. Using graph theoretical analysis, differences were found in visual, sensorimotor, and default mode networks (p < 0.05). When the glioma located in the left hemisphere, the degree centrality was reduced in the GE group compared to the GnE group. When the glioma located in the right insula, the degree centrality, nodal efficiency, nodal local efficiency, and nodal clustering coefficient of the GE group were lower than those of the GnE group. The impact of insular glioma itself and GRE on the brain network is widespread. The networks altered by insular GRE differ depending on the hemisphere location. GRE reduces the nodal properties of brain networks than that in insular glioma.