Global Efficiency Research Articles

Impact of long‐COVID on the local and global efficiency of brain networks

Impact of long‐COVID on the local and global efficiency of brain networks

The topology of interpersonal neural network in weak social ties

The strategies for social interaction between strangers differ from those between acquaintances, whereas the differences in neural basis of social interaction have not been fully elucidated. In this study, we examined the geometrical properties of interpersonal neural networks in pairs of strangers and acquaintances during antiphase joint tapping. Dual electroencephalogram (EEG) of 29 channels per participant was measured from 14 strangers and 13 acquaintance pairs.Intra-brain synchronizations were calculated using the weighted phase lag index (wPLI) for intra-brain electrode combinations, and inter-brain synchronizations were calculated using the phase locking value (PLV) for inter-brain electrode combinations in the theta, alpha, and beta frequency bands. For each participant pair, electrode combinations with larger wPLI/PLV than their surrogates were defined as the edges of the neural networks. We calculated global efficiency, local efficiency, and modularity derived from graph theory for the combined intra- and inter-brain networks of each pair. In the theta band networks, stranger pairs showed larger local efficiency than acquaintance pairs, indicating that the two brains of stranger pairs were more densely connected. Hence, weak social ties require extensive social interactions and result in high efficiency of information transfer between neighbors in neural network.

Disrupted small-world white matter networks in patients with major depression and recent suicide plans or attempts.

Suicide is a major concern for health, and depression is an established proximal risk factor for suicide. This study aimed to investigate white matter features associated with suicide. We constructed white matter structural networks by deterministic tractography via diffusion tensor imaging in 51 healthy controls, 47 depressed patients without suicide plans or attempts and 56 depressed patients with suicide plans or attempts. Then, graph theory analysis was used to measure global and nodal network properties. We found that local efficiency was decreased and path length was increased in suicidal depressed patients compared to healthy controls and non-suicidal depressed patients; moreover, the clustering coefficient was decreased in depressed patients compared to healthy controls; and the global efficiency and normalized characteristic path length was increased in suicidal depressed patients compared to healthy controls. Similarly, compared with those in non-suicidal depressed patients, nodal efficiency in the thalamus, caudate, medial orbitofrontal cortex, hippocampus, olfactory cortex, supplementary motor area and Rolandic operculum was decreased. In summary, compared with those of non-suicidal depressed patients, the structural connectome of suicidal depressed patients exhibited weakened integration and segregation and decreased nodal efficiency in the fronto-limbic-basal ganglia-thalamic circuitry. These alterations in the structural networks of depressed suicidal brains provide insights into the underlying neurobiology of brain features associated with suicide.

Solar Wall Technology and Its Impact on Building Performance

Solar walls provide transformative solutions by harnessing solar energy to generate electricity, improve thermal comfort, and reduce energy consumption and emissions, contributing to zero-energy buildings and mitigating climate change. In hot and humid regions, solar walls can reduce indoor temperatures by 30% to 50%, significantly improving energy efficiency. Optimizing the performance of solar walls includes factors such as glazing, shading, solar orientation, ventilation, and catalytic techniques, allowing them to be adapted to different climates. Innovative solar wall variants that include photovoltaic panels, water storage, and phase-change materials offer multifunctionality and sustainability in building design and are in line with global energy efficiency and environmentally conscious goals. In addition, innovative solar wall variants that combine photovoltaic panels, water storage, and phase-change materials promise even more sustainability in building design. These multifunctional solar wall systems can efficiently heat, cool, and generate energy, further reducing a building’s environmental impact. Solar walls have the potential to significantly reduce heating energy consumption; align with global goals for energy-efficient, environmentally conscious, and climate-responsive building design; and offer dynamic and adaptable solutions for sustainable architecture.

Multi-omics data integration for the identification of biomarkers for bull fertility.

Bull fertility is an important economic trait, and the use of subfertile semen for artificial insemination decreases the global efficiency of the breeding sector. Although the analysis of semen functional parameters can help to identify infertile bulls, no tools are currently available to enable precise predictions and prevent the commercialization of subfertile semen. Because male fertility is a multifactorial phenotype that is dependent on genetic, epigenetic, physiological and environmental factors, we hypothesized that an integrative analysis might help to refine our knowledge and understanding of bull fertility. We combined -omics data (genotypes, sperm DNA methylation at CpGs and sperm small non-coding RNAs) and semen parameters measured on a large cohort of 98 Montbéliarde bulls with contrasting fertility levels. Multiple Factor Analysis was conducted to study the links between the datasets and fertility. Four methodologies were then considered to identify the features linked to bull fertility variation: Logistic Lasso, Random Forest, Gradient Boosting and Neural Networks. Finally, the features selected by these methods were annotated in terms of genes, to conduct functional enrichment analyses. The less relevant features in -omics data were filtered out, and MFA was run on the remaining 12,006 features, including the 11 semen parameters and a balanced proportion of each type of-omics data. The results showed that unlike the semen parameters studied the-omics datasets were related to fertility. Biomarkers related to bull fertility were selected using the four methodologies mentioned above. The most contributory CpGs, SNPs and miRNAs targeted genes were all found to be involved in development. Interestingly, fragments derived from ribosomal RNAs were overrepresented among the selected features, suggesting roles in male fertility. These markers could be used in the future to identify subfertile bulls in order to increase the global efficiency of the breeding sector.

Integrating patch stability and network connectivity to optimize ecological security pattern

ContextDesigning and optimizing ecological security pattern (ESP) is an effective solution to formulate landscape planning. The commonly used network analysis for ESP optimization abstracts ecological sources and ecological corridors as homogeneous nodes and edges, ignoring the differentiated landscape pattern and patch stability.ObjectivesIt is aimed to construct ESP considering pattern and function, to explore the relationship of patch stability and network connectivity conservation objectives, and to optimize ESP with the integration of the two objectives.MethodsWe proposed a framework for constructing ESP based on ecosystem health and human footprint, and optimizing ESP with network robustness analysis. Land use conflict analysis and node/ edge removal method were further used to assess patch stability and network connectivity, respectively.ResultsESP of Ningxia was composed of 71 ecological sources covering an area of 10970.25 km2, and 150 ecological corridors covering an area of 3950.88 km2. The ecological sources and ecological corridors along the Yellow River had high patch stability but low network connectivity, while the largest ecological source had both high values of the two indicators. With the removal of nodes and edges, connectivity robustness, global efficiency, and equivalent connectivity of the ESP decreased from 1, 0.29, and 342.80 to 0, respectively. The variation trends of the three indicators under different removal scenarios were inconsistent. There was a distinct trade-off between the conservation objectives of patch stability and network connectivity.ConclusionsThis study highlighted how to balance different conservation objectives in landscape planning. Our framework can provide guidance for conservation planners to construct and optimize ESP without losing information due to the element abstraction in network analysis.

A Novel Improved Whale Optimization Algorithm for Global Optimization and Engineering Applications

The Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm based on natural heuristics, which has gained considerable attention from researchers and engineers. However, WOA still has some limitations, including limited global search efficiency and a slow convergence rate. To address these issues, this paper presents an improved whale optimization algorithm with multiple strategies, called Dynamic Gain-Sharing Whale Optimization Algorithm (DGSWOA). Specifically, a Sine–Tent–Cosine map is first adopted to more effectively initialize the population, ensuring a more uniform distribution of individuals across the search space. Then, a gaining–sharing knowledge based algorithm is used to enhance global search capability and avoid falling into a local optimum. Finally, to increase the diversity of solutions, Dynamic Opposition-Based Learning is incorporated for population updating. The effectiveness of our approach is evaluated through comparative experiments on blackbox optimization benchmarking and two engineering application problems. The experimental results suggest that the proposed method is competitive in terms of solution quality and convergence speed in most cases.

Environmental performance of bamboo-based office paper production: A comparative study with eucalyptus

Bamboo has being used as alternative raw material for construction, reinforcing fibers, paper production, among other applications. Although its recognized potentials as raw material, there are doubts about its environmental performance compared to traditional wood-based products, including paper production, which hinders bamboo-based paper plants in large scales. This study aims to assess the environmental performance of producing office paper from bamboo. Emergy synthesis (with ‘m’) and global warming potential indicators are calculated and compared with the traditional eucalyptus paper-based production. Results highlights the importance in including the renewability fractions of each input resources into emergy calculations for production systems with high human-labor intensity such as the bamboo agricultural production. Office paper produced from bamboo has similar renewability (28%), moderate environmental load (3.23 vs. 2.49), and emergy unsustainability (0.34 vs. 0.60) compared to paper produced from eucalyptus, but bamboo showed lower performance for global efficiency (568 vs. 442 sej/tonpaper), emergy yield (1.09 vs. 1.49), and emergy investment (10.79 vs. 2.05). Focusing on global warming potential, office paper produced from bamboo releases 98 kgCO2 eq./tonpaper compared to 56–267 kgCO2 eq./tonpaper for eucalyptus. Notwithstanding, bamboo-based office paper demands four times more land area of agricultural production than eucalyptus, but it has a positive social aspect by requiring higher amount of direct human labor. This work shows the advantages of using eucalyptus rather than bamboo in producing office paper from an emergy and land demand perspectives, while global warming can still be considered inconclusive. Future efforts should consider a quantitative and qualitative analysis of human labor availability in both systems, as well an economic analysis to support discussions towards more sustainable office paper production from different raw materials.

Cortical activation and brain network efficiency during dual tasks: An fNIRS study

ObjectiveDual task (DT) is a commonly used paradigm indicative of executive functions. Brain activities during DT walking is usually measured by portable functional near infrared spectroscopy (fNIRS). Previous studies focused on cortical activation in prefrontal cortex and overlooked other brain regions such as sensorimotor cortices. This study is aimed at investigating the modulations of cortical activation and brain network efficiency in multiple brain regions from single to dual tasks with different complexities and their relationships with DT performance. MethodsForty-two healthy adults [12 males; mean age: 27.7 (SD=6.5) years] participated in this study. Participants performed behavioral tasks with portable fNIRS simultaneous recording. There were three parts of behavioral tasks: cognitive tasks while standing (serial subtraction of 3′s and 7′s), walking alone and DT (walk while subtraction, including serial subtraction of 3′s and 7′s). Cognitive cost, walking cost and cost sum (i.e., sum of cognitive and walking costs) were calculated for DT. Cortical activation, local and global network efficiency were calculated for each task. ResultsThe cognitive cost was greater and the walking cost was less during DT with subtraction 3′s compared with 7′s (P’s = 0.032 and 0.019, respectively). Cortical activation and network efficiency were differentially modulated among single and dual tasks (P's < 0.05). Prefrontal activation during DT was positively correlated with DT costs, while network efficiency was negatively correlated with DT costs (P's < 0.05). ConclusionsOur results revealed prefrontal over-activation and reduced network efficiency in individuals with poor DT performance. Our findings suggest that reduced network efficiency could be a possible mechanism contributing to poor DT performance, which is accompanied by compensatory prefrontal over-activation.

Pervasive alterations of intra-axonal volume and network organization in young children with a 16p11.2 deletion

Reciprocal Copy Number Variants (CNVs) at the 16p11.2 locus confer high risk for autism spectrum disorder (ASD) and other neurodevelopmental disorders (NDDs). Morphometric MRI studies have revealed large and pervasive volumetric alterations in carriers of a 16p11.2 deletion. However, the specific neuroanatomical mechanisms underlying such alterations, as well as their developmental trajectory, are still poorly understood. Here we explored differences in microstructural brain connectivity between 24 children carrying a 16p11.2 deletion and 66 typically developing (TD) children between 2 and 8 years of age. We found a large pervasive increase of intra-axonal volume widespread over a high number of white matter tracts. Such microstructural alterations in 16p11.2 deletion children were already present at an early age, and led to significant changes in the global efficiency and integration of brain networks mainly associated to language, motricity and socio-emotional behavior, although the widespread pattern made it unlikely to represent direct functional correlates. Our results shed light on the neuroanatomical basis of the previously reported increase of white matter volume, and align well with analogous evidence of altered axonal diameter and synaptic function in 16p11.2 mice models. We provide evidence of a prevalent mechanistic deviation from typical maturation of brain structural connectivity associated with a specific biological risk to develop ASD. Future work is warranted to determine how this deviation contributes to the emergence of symptoms observed in young children diagnosed with ASD and other NDDs.

A novel outlier calendrical heterogeneity reconstruction deep learning model for electricity demand forecasting

The development of an accurate electricity demand forecasting model is of paramount importance for promoting global energy efficiency and sustainability. Nonetheless, the presence of outliers and inappropriate model training can result in suboptimal performance. To tackle these challenges, this study explores the potential of Convolutional Neural Network (CNN) and active learning theory as forecasting solutions, offering high efficiency and advantages for long time series. In this study, a hybrid model that combines Isolation Forest (IF), Outlier Reconstruction (OR), CNN and Random Forest (RF) is conducted to mitigate computational complexity and enhance the accuracy of electricity demand forecasting in the presence of outliers. IF is employed to detect outliers in electricity demand time series, while OR is used to reconstruct subsequences based on calendrical heterogeneity for training. CNN is applied for both training and forecasting, and the final output is combined using RF. The effectiveness of the proposed IF-OR-CNN-RF model is validated using electricity data collected from recent sources in Australia at different sampling frequency. The experimental results demonstrate that, in comparison with other popular CNN-based electricity demand forecasting models, IF-OR-CNN-RF model outperforms with significantly improved performance metrics. Specifically, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and R-squared values are 77.92, 179.18 and 0.9769 in 5-minute frequency; 162.67, 353.96 and 0.9775 in 10-minute frequency; 841.27, 1374.79 and 0.9622 in 30-minute frequency; 2746.01, 3824.00 and 0.9262 in 60-minute frequency; 9106.08, 12269.04 and 0.8044 in 120-minute frequency. IF-OR-CNN-RF model represents a valuable framework for future electricity demand forecasting, particularly in scenarios involving outliers.

Development of Patented Autonomous Quantum Gravitational Electric Energy Generator Prototypes

After quantum physics modelling of Gravitational interaction from hypotheses, we performed experimental confirmation experiments of predicted increased Faraday&amp;rsquo;s induction. Brief and low energy electric discharges were made, at room temperature, into partially superconducting Graphite based devices, patented and named &amp;ldquo;emitters&amp;rdquo;, inserted in series with the primary low inductance of a transformer. The high voltage secondary inductance of that transformer was connected to a capacitor, so the secondary current oscillated during several milliseconds.&#x0D; &#x0D; We Measured the global energy efficiency of Faraday&amp;rsquo;s induction, during each electric discharge into the emitter of the primary circuit. We also measured evolution of the peak primary discharge current, and of its derivative, versus the initial charge voltage. We observed systematically a much larger than 100% energy efficiency. That energy efficiency increases with the primary discharge energy. The peak discharge current is observed to be much larger than predicted by Ohm&amp;rsquo;s Law, and the discharge current derivative is also observed to be much larger than classically predicted.&#x0D; &#x0D; Same experiments, performed with &amp;ldquo;normal conductive devices (control)&amp;rdquo;, gave energy efficiencies much lower than 100 %, independent of the stored energy. And their peak discharge currents and derivatives followed Ohm&amp;rsquo;s Law.&#x0D; &#x0D; From these confirmations of predicted results, we proposed and tested successfully concepts of Autonomous electric Generators prototypes, extracting their energy from the cosmological Gravitational quantum field. Their industrial use into electric vehicles should preserve Earth fossil energy resources, as well as reduce the detrimental climate effects of greenhouse gases emissions. Hypotheses are suggested to explain the observed experimental facts.

Resting-state brain network connectivity is an independent predictor of responsiveness to language therapy in chronic post-stroke aphasia

Post-stroke aphasia recovery, especially in the chronic phase, is challenging to predict. Functional integrity of the brain and brain network topology have been suggested as biomarkers of language recovery. This study sought to investigate functional connectivity in four predefined brain networks (i.e., language, default mode, dorsal attention, and salience networks), in relation to aphasia severity and response to language therapy. Thirty patients with chronic post-stroke aphasia were recruited and received a treatment targeting word finding. Structural and functional brain scans were acquired at baseline and resting state functional connectivity for each network was calculated. Additionally, graph measures quantifying network properties were calculated for each network. These included global efficiency for all networks and average strength and clustering coefficient for the language network. Linear mixed effects models showed that mean functional connectivity in the default mode, dorsal attention, and salience networks as well as graph measures of all four networks are independent predictors of response to therapy. While greater mean functional connectivity and global efficiency of the dorsal attention and salience networks predicted greater treatment response, greater mean functional connectivity and global efficiency in the default mode network predicted poorer treatment response. Results for the language network were more nuanced with more efficient network configurations (as reflected in graph measures), but not mean functional connectivity, predicting greater treatment response. These findings highlight the prognostic value of resting-state functional connectivity in chronic treatment-induced aphasia recovery.

Exploring timescale-specific functional brain networks and their associations with aging and cognitive performance in a healthy cohort without dementia

IntroductionFunctional brain networks (FBNs) coordinate brain functions and are studied in fMRI using blood-oxygen-level-dependent (BOLD) signal correlations. Previous research links FBN changes to aging and cognitive decline, but various physiological factors influnce BOLD signals. Few studies have investigated the intrinsic components of the BOLD signal in different timescales using signal decomposition. This study aimed to explore differences between intrinsic FBNs and traditional BOLD-FBN, examining their associations with age and cognitive performance in a healthy cohort without dementia. Materials and methodsA total of 396 healthy participants without dementia (men = 157; women = 239; age range = 20–85 years) were enrolled in this study. The BOLD signal was decomposed into several intrinsic signals with different timescales using ensemble empirical mode decomposition, and FBNs were constructed based on both the BOLD and intrinsic signals. Subsequently, network features—global efficiency and local efficiency values—were estimated to determine their relationship with age and cognitive performance. ResultsThe findings revealed that the global efficiency of traditional BOLD-FBN correlated significantly with age, with specific intrinsic FBNs contributing to these correlations. Moreover, local efficiency analysis demonstrated that intrinsic FBNs were more meaningful than traditional BOLD-FBN in identifying brain regions related to age and cognitive performance. ConclusionsThese results underscore the importance of exploring timescales of BOLD signals when constructing FBN and highlight the relevance of specific intrinsic FBNs to aging and cognitive performance. Consequently, this decomposition-based FBN-building approach may offer valuable insights for future fMRI studies.

The ALMaQUEST Survey XIV: do radial molecular gas flows affect the star-forming ability of barred galaxies?

ABSTRACT We investigate whether barred galaxies are statistically more likely to harbour radial molecular gas flows and what effect those flows have on their global properties. Using 46 galaxies from the ALMA-MaNGA QUEnching and STar formation (ALMaQUEST) survey, we identify galaxies hosting optical bars using a combination of the morphological classifications in Galaxy Zoo 2 and HyperLEDA. In order to detect radial molecular gas flows, we employ full 3D kinematic modelling of the ALMaQUEST 12CO(1–0) data cubes. By combining our bar classifications with our radial bar-driven flow detections, we find that galaxies classed as barred are statistically more likely to host large-scale radial gas motions compared to their un-barred and edge-on galaxy counterparts. Moreover, the majority of barred galaxies require multicomponent surface brightness profiles in their best-fitting models, indicative of the presence of resonance systems. We find that galaxies classed as barred with radial bar-driven flows (‘barred + radial flow’ subset) have significantly suppressed global star-formation efficiencies compared to barred galaxies without radial bar-driven flows and galaxies in the other morphological sub-samples. Our ‘barred + radial flow’ subset galaxies also possess consistently centrally concentrated molecular gas distributions, with no indication of depleted gas mass fractions, suggesting that gas exhaustion is not the cause of their suppressed star formation. Furthermore, these objects have higher median gas mass surface densities in their central 1 kpc, implying that central gas enhancements do not fuel central starbursts in these objects. We propose that dynamical effects, such as shear caused by large-scale inflows of gas, act to gravitationally stabilize the inner gas reservoirs.

Parameter Optimization of Friction Pendulum Bearings Based on the Adaptive Genetic Algorithm Considering the Overall Evolutionary Status

Improper design of friction pendulum bearings can lead to poor seismic reduction performance and may result in the failure of local vulnerable components. And the parameter design of friction pendulum bearings mainly relies on experience and verification calculations at present. This paper proposes an adaptive genetic algorithm considering the overall evolution state of the population, adjusting crossover and mutation probabilities adaptively based on individual fitness and population diversity. Compared to traditional algorithms, it exhibits better global search capabilities and convergence efficiency. Combining the improved genetic algorithm with finite element models, a parameter optimization method is proposed. The parameters of friction pendulum bearings are optimized. In response to the situation in this paper, the optimal friction coefficient of the friction pendulum bearing is determined to be 0.01 and the optimal equivalent radius is 3.3 m. This can provide a reference for the design of seismic isolation devices.

Plan quality assessment of modern radiosurgery technologies in the treatment of multiple brain metastases

Stereotactic radiosurgery (SRS) of multiple brain metastases has evolved over the last 40 years allowing centres to treat an increasing number of brain metastases in a single treatment fraction. HyperArcTM planning optimisation technique is one such development that streamlines the treatment of multiple metastases with a single isocentre. Several studies have investigated the plan quality of HyperArc compared to CyberKnife or Gamma Knife, however there are limited number of studies that include all three modalities. It is the aim of this study to provide an assessment of plan quality between the three SRS platforms across ten patients with multiple brain metastases ranging from three to eight metastases per patient. Strict planning workflows were established to avoid bias towards any particular treatment platform. Plan quality was assessed through dose to organs at risk, Paddick conformity index (PCI), gradient index (GI), global efficiency index (Gη) and dose to normal brain tissue. Results from this study found mean PCI observed across Gamma Knife plans was significantly lower than HyperArc and CyberKnife. HyperArc plans observed significantly shorter beam-on times which were 10 to 20 times faster than CyberKnife and Gamma Knife plans. Gamma Knife and CyberKnife were found to produce plans with significantly superior GI, global efficiency index and the volume of healthy brain receiving greater than 12 Gy (V12Gy) when compared to HyperArc plans. Lesion volume was seen to influence the relative difference in dose metrics between systems. The study revealed that all three treatment modalities produced high quality plans for the SRS treatment of multiple brain metastases, each with respective benefits and limitations.

Myocardial work across different etiologies of right ventricular dysfunction and healthy controls.

Evaluating right ventricular (RV) function remains a challenge. Recently, novel echocardiographic assessment of RV myocardial work (RVMW) by non-invasive pressure-strain loops was proposed. This enables evaluation of right ventriculoarterial coupling and quantifies RV dyssynchrony and post-systolic shortening. We aimed to assess RVMW in patients with different etiologies of RV dysfunction and healthy controls. We investigated healthy controls (n=17), patients with severe functional tricuspid regurgitation (FTR; n=22), and patients with precapillary pulmonary hypertension (PCPH; n=20). Echocardiography and right heart catheterization were performed to assess 1) RV global constructive work (RVGCW; work needed for systolic myocardial shortening and isovolumic relaxation), 2) RV global wasted work (RVGWW; myocardial shortening following pulmonic valve closure), and 3) RV global work efficiency (RVGWE; describes the relation between RV constructive and wasted work). RVGCW correlated with invasive RV stroke work index (r=0.66, P<0.001) and increased in tandem with higher afterload, i.e., was low in healthy controls (454±73 mmHg%), moderate in patients with FTR (687±203 mmHg%), and highest among patients with PCPH (881±255 mmHg%). RVGWE was lower and RVGWW was higher in patients with FTR (86±8% and 91 mmHg% [53-140]) or PCPH (86±10% and 110 mmHg% [66-159]) as compared with healthy controls (96±3% and 10 mmHg%). RVMW by echocardiography provides a promising index of RV function to discriminate between patients with RV volume or pressure overload. The prognostic value of this measure needs to be settled in future studies.

State‐dependent indirect hp‐pseudospectral method for rigid spacecraft attitude optimal control problem

AbstractThe state‐dependent indirect ‐pseudospectral (SDIHP) method for rigid spacecraft attitude control is presented in this paper. The optimal controller is obtained by the SDIHP method through classifying the attitude optimal control problem as a nonlinear quadratic optimal control problem. In the proposed control scheme, successive state‐dependent coefficient (SDC) parameterization is initially applied to the spacecraft dynamics to derive an available linear system to decrease the calculation cost and speed. Then, the indirect ‐adaptive spectral discretization is developed to convert the resultant optimal control problem into a sequence of linear equations, which can improve the global computational efficiency and guarantee the solving precision. Hence, the optimal control can be efficiently solved using basic matrix operations under the necessary collocation points based on the ‐adaptive features. Considering the inertia uncertainties and external disturbances, a state feedback controller is generated by the receding horizon control (RHC) framework to improve the robustness. Simulation results are included to show the effectiveness and performance of the suggested optimal method.

Abstract TMP27: Structural Network Dysfunction and Aberrant Structural-Functional Connectivity Coupling Correlate With Central Post-Stroke Pain of Thalamic Origin

Introduction and Hypothesis: As the most frequent subtype of central post-stroke pain (CPSP), CPSP of thalamic origin can often be refractory to treatments and affect long-term prognosis of thalamic-stroke patients, while the mechanism remains unclear. We assumed that post-thalamic stroke CPSP might be a brain network disorder and aimed to investigate brain network alterations in post-thalamic stroke CPSP patients and explore whether these changes underlie clinical symptoms. Methods: A total of 92 participants were finally enrolled, of whom 62 were chronic thalamic stroke patients (26 had CPSP) and 30 were healthy controls. Using graph theory approaches, we compared the structural and functional brain network topological characteristics and structural-functional connectivity (SC-FC) couplings at multi-modular levels among the groups, and further explored their potential relationships with pain severity. Results: Distinct SC topological changes were found in CPSP patients, characterized as a higher clustering coefficient (Cp), global efficiency (Eg), local efficiency (Eloc), and lower characteristic path length (Lp) and normalized characteristic path length (Lambda) values than that in non-CPSP group. No significant differences were found in the FC network among the three groups. CPSP patients had significantly lower SC-FC couplings at the global and the structural Hub-modular levels. Further, we found significant and various correlations between pain severity and structural Lp, Eloc, global and Hub-modular SC-FC couplings. Results: These results indicated that thalamic stroke-induced distinct structural network dysfunction and SC-FC decoupling may be a novel neural mechanism for CPSP. It may provide new insights for future targeted neuromodulation therapies and individualized rehabilitative strategies in CPSP patients.