Interconnected Regions Research Articles

Brainwide Anatomical Connectivity and Prediction of Longitudinal Outcomes in Antipsychotic-Naïve First-Episode Psychosis

BackgroundDisruptions of axonal connectivity are thought to be a core pathophysiological feature of psychotic illness, but whether they are present early in the illness, prior to antipsychotic exposure, and whether they can predict clinical outcome remain unknown. MethodsWe acquired diffusion-weighted magnetic resonance images to map structural connectivity between each pair of 319 parcellated brain regions in 61 antipsychotic-naïve individuals with first-episode psychosis (15–25 years, 46% female) and a demographically matched sample of 27 control participants. Clinical follow-up data were also acquired in patients 3 and 12 months after the scan. We used connectome-wide analyses to map disruptions of inter-regional pairwise connectivity and connectome-based predictive modeling to predict longitudinal change in symptoms and functioning. ResultsIndividuals with first-episode psychosis showed disrupted connectivity in a brainwide network linking all brain regions compared with controls (familywise error–corrected p = .03). Baseline structural connectivity significantly predicted change in functioning over 12 months (r = 0.44, familywise error–corrected p = .041), such that lower connectivity within fronto-striato-thalamic systems predicted worse functional outcomes. ConclusionsBrainwide reductions of structural connectivity exist during the early stages of psychotic illness and cannot be attributed to antipsychotic medication. Moreover, baseline measures of structural connectivity can predict change in patient functional outcomes up to 1 year after engagement with treatment services.

Exploring Kainic Acid-Induced Alterations in Circular Tripartite Networks with Advanced Analysis Tools.

Brain activity implies the orchestrated functioning of interconnected brain regions. Typical in vitro models aim to mimic the brain using single human pluripotent stem cell-derived neuronal networks. However, the field is constantly evolving to model brain functions more accurately through the use of new paradigms, e.g., brain-on-a-chip models with compartmentalized structures and integrated sensors. These methods create novel data requiring more complex analysis approaches. The previously introduced circular tripartite network concept models the connectivity between spatially diverse neuronal structures. The model consists of a microfluidic device allowing axonal connectivity between separated neuronal networks with an embedded microelectrode array to record both local and global electrophysiological activity patterns in the closed circuitry. The existing tools are suboptimal for the analysis of the data produced with this model. Here, we introduce advanced tools for synchronization and functional connectivity assessment. We used our custom-designed analysis to assess the interrelations between the kainic acid (KA)-exposed proximal compartment and its nonexposed distal neighbors before and after KA. Novel multilevel circuitry bursting patterns were detected and analyzed in parallel with the inter- and intracompartmental functional connectivity. The effect of KA on the proximal compartment was captured, and the spread of this effect to the nonexposed distal compartments was revealed. KA induced divergent changes in bursting behaviors, which may be explained by distinct baseline activity and varied intra- and intercompartmental connectivity strengths. The circular tripartite network concept combined with our developed analysis advances importantly both face and construct validity in modeling human epilepsy in vitro.

Advanced Neurocircuit Mapping via Non-invasive Magnetic Resonance Imaging Techniques

The brain comprises a complex network of anatomically distinct regions (each with specialized functions) that collaborate to support various cognitive processes. Therefore, it is important to understand the brain from the perspective of a complex network. Functional magnetic resonance imaging (fMRI) is increasingly being accepted for its ability to provide useful insights into brain function. Among the fMRI techniques available in clinical practice, resting-state fMRI (rsfMRI) represents the core method for mapping brain activity in the absence of specific tasks; studies have reported the usefulness of rsfMRI in the investigation of various human diseases. Functional brain networks, which consist of interconnected regions that show correlated activities, are typically depicted as functional connectivity (FC). FC analysis using rsfMRI data provides extensive information, revealing intrinsic resting-state networks and highlights deviations in network structure among patients with psychiatric disorders. Such network insights not only deepen our understanding of the brain but also facilitate assessment of network alterations associated with psychiatric and neurodegenerative diseases.

Regional Economic Agglomeration and Trans-Sumatra Toll Road Development: A Network and Spatial Review

Road and transportation networks play a crucial role in interregional connections. One of the key development programs initiated during President Jokowi’s presidency in Indonesia is aimed at improving interregional connectivity. In Sumatra, the National Strategic Project (Program Strategis Nasional/PSN) for the construction of the Trans-Sumatra Toll Road has been underway since 2014. After a decade of development, it is essential to evaluate the impact of this infrastructure on the region's agglomeration to inform future development policies. This paper reviews the changes in network structures and economic activities as influenced by the construction of the Trans-Sumatra Toll Roads. It also seeks to predict the future development of agglomeration and economic activities. The study employs regional and city planning methodologies, including space syntax, fractal dimension analysis, and cluster analysis. The findings indicate that the construction of the Trans-Sumatra Toll Road has significantly enhanced connectivity and accessibility, increased the gravitational value of the territory, and reduced the load on existing roads. Moreover, the development of toll roads has led to the growth of new economic centers, which eventually resulted in the formation of four major agglomeration regions.

Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior.

Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors. We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance. Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low time complexity, requiring <110 ms for training and <1 ms for inference. Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

Heterogeneity of morphometric similarity networks in health and schizophrenia.

Morphometric similarity is a recently developed neuroimaging phenotype of inter-regional connectivity by quantifying the similarity of a region to other regions based on multiple MRI parameters. Altered average morphometric similarity has been reported in psychotic disorders at the group level, with considerable heterogeneity across individuals. We used normative modeling to address cross-sectional and longitudinal inter-individual heterogeneity of morphometric similarity in health and schizophrenia. Morphometric similarity for 62 cortical regions was obtained from baseline and follow-up T1-weighted scans of healthy individuals and patients with chronic schizophrenia. Cortical regions were classified into seven predefined brain functional networks. Using Bayesian Linear Regression and taking into account age, sex, image quality and scanner, we trained and validated normative models in healthy controls from eleven datasets (n = 4310). Individual deviations from the norm (z-scores) in morphometric similarity were computed for each participant for each network and region at both timepoints. A z-score ≧ than 1.96 was considered supra-normal and a z-score ≦ -1.96 infra-normal. As a longitudinal metric, we calculated the change over time of the total number of infra- or supra-normal regions per participant. At baseline, patients with schizophrenia had decreased morphometric similarity of the default mode network and increased morphometric similarity of the somatomotor network when compared with healthy controls. The percentage of patients with infra- or supra-normal values for any region at baseline and follow-up was low (<6%) and did not differ from healthy controls. Mean intra-group changes over time in the total number of infra- or supra-normal regions were small in schizophrenia and healthy control groups (<1) and there were no significant between-group differences. In a case-control setting, a decrease of morphometric similarity within the default mode network may be a robust finding implicated in schizophrenia. However, normative modeling suggests that significant reductions and changes over time of regional morphometric similarity are evident only in a minority of patients.

Connectivity based on glucose dynamics reveals exaggerated sensorimotor network coupling on subject-level in Parkinson’s disease

PurposeWhile fMRI provides information on the temporal changes in blood oxygenation, 2- [18F]fluoro-2-deoxy-D-glucose ([18F]FDG)-PET has traditionally offered a static snapshot of brain glucose consumption. As a result, studies investigating metabolic brain networks as potential biomarkers for neurodegeneration have primarily been conducted at the group level. However, recent pioneering studies introduced time-resolved [18F]FDG-PET with constant infusion, which enables metabolic connectivity studies at the individual level.MethodsIn the current study, this technique was employed to explore Parkinson’s disease (PD)-related alterations in individual metabolic connectivity, in comparison to inter-subject measures and hemodynamic connectivity. Fifteen PD patients and 14 healthy controls with comparable cognition underwent sequential resting-state dynamic PET with constant infusion and functional MRI. Intrinsic networks were identified by independent component analysis and interregional connectivity calculated for summed static PET images, PET time series and functional MRI.ResultsOur findings revealed an intrinsic sensorimotor network in PD patients that has not been previously observed to this extent. In PD, a significantly higher number of connections in cortical motor areas was observed compared to elderly control subjects, as indicated by both static PET and functional MRI (pBonferroni−Holm = 0.027), as well as constant infusion PET and functional MRI connectomes (pBonferroni−Holm = 0.012). This intensified coupling was associated with disease severity (ρ = 0.56, p = 0.036).ConclusionMetabolic connectivity, as revealed by both static and dynamic PET, provides unique information on metabolic network activity. Subject-level metabolic connectivity based on constant infusion PET may serve as a potential marker for the metabolic network signature in neurodegeneration.

A novel modified Cheetah Optimizer for designing fractional-order PID-LFC placed in multi-interconnected system with renewable generation units

Establishing robust electrical interconnections between nations is a pivotal foundation for significant investments and addressing energy shortfalls in regions grappling with generational challenges. However, developing electrical load disruptions within interconnected systems can lead to substantial variations in frequencies and energy transmission. Load Frequency Control (LFC) is a crucial mechanism to mitigate these disruptions and ensure stable operations in interconnected regions. While meta-heuristics have been employed for LFC design, some techniques face challenges like early convergence and poor accuracy due to a lack of population diversity. In this study, a novel Modified Cheetah Optimizer (mCO) is proposed to optimize the parameters of LFC system, incorporating fractional-order proportional integral derivative (FOPID) controllers within multi-interconnected system with renewable energy integration. The mCO integrates learning-from-experience and random contraction strategies to enhance convergence accuracy and overcome local optima, demonstrating superior efficiency in solving optimization problems. The proposed mCO is evaluated by solving twelve functions from the CEC2022 test suite, showcasing its effectiveness. The optimization problem involves minimizing the Integral Time Absolute Error (ITAE) of the area control error, considering changes in frequencies and exchanged power, with controller parameters λd, kd, ki, kp, and μ to be identified. Two interconnected systems, photovoltaic (PV)-thermal and thermal-wind turbine (WT)-thermal-PV, are assessed under various load disturbances. The mCO is compared with other methods, including Modified Hunger Games Search Optimizer (MHGS), Driving Training-Based Optimizer (DTBO), Grey Wolf Optimizer (GWO), Aquila Optimal Search (AOS), and Cheetah Optimizer (CO). In the case of PV-thermal linked system, the proposed mCO succeeded in mitigating the ITAE by 19.21% compared to the reported MHGS and 8.63% compared to the conventional CO. In the four interconnected systems, the suggested approach reduced the ITAE by 89.21% and 15.26% compared to the reported MHGS and conventional CO, respectively. This confirmed the efficacy of FOPID-LFC, which was designed using the proposed mCO in all examined scenarios.

Divergent functional connectivity changes associated with white matter hyperintensities

Age-related white matter hyperintensities are a common feature and are known to be negatively associated with structural integrity, functional connectivity, and cognitive performance. However, this has yet to be fully understood mechanistically. We analyzed multiple MRI modalities acquired in 465 non-demented individuals from the Swedish BioFINDER study including 334 cognitively normal and 131 participants with mild cognitive impairment. White matter hyperintensities were automatically quantified using fluid-attenuated inversion recovery MRI and parameters from diffusion tensor imaging were estimated in major white matter fibre tracts. We calculated fMRI resting state-derived functional connectivity within and between predefined cortical regions structurally linked by the white matter tracts. How change in functional connectivity is affected by white matter lesions and related to cognition (in the form of executive function and processing speed) was explored. We examined the functional changes using a measure of sample entropy. As expected hyperintensities were associated with disrupted structural white matter integrity and were linked to reduced functional interregional lobar connectivity, which was related to decreased processing speed and executive function. Simultaneously, hyperintensities were also associated with increased intraregional functional connectivity, but only within the frontal lobe. This phenomenon was also associated with reduced cognitive performance. The increased connectivity was linked to increased entropy (reduced predictability and increased complexity) of the involved voxels’ blood oxygenation level-dependent signal. Our findings expand our previous understanding of the impact of white matter hyperintensities on cognition by indicating novel mechanisms that may be important beyond this particular type of brain lesions.

Optogenetic inhibition of the orbitofrontal cortex disrupts inhibitory control during stop-change performance in male rats.

Historically, the orbitofrontal cortex (OFC) has been implicated in a variety of behaviors ranging from reversal learning and inhibitory control to more complex representations of reward value and task space. While modern interpretations of the OFC's function have focused on a role in outcome evaluation, these cognitive processes often require an organism to inhibit a maladaptive response or strategy. Single unit recordings from the OFC in rats performing a stop-change task show that the OFC responds strongly to STOP trials. To investigate the role that the OFC plays in stop-change performance we expressed halorhodopsin (eNpHR3.0) in excitatory neurons in the OFC and tested rats on the stop-change task. Previous work suggests that the OFC differentiates between STOP trials based on trial sequence (i.e., gS trials: STOP trials preceded by a GO versus sS trials: STOP trials preceded by a STOP). We found that yellow light activation of the eNpHR3.0 expressing neurons significantly decreased accuracy only on STOP trials that followed GO trials (gS trials). Further, optogenetic inhibition of OFC speeded reaction times on error trials. This suggests that the OFC plays a role in inhibitory control processes, and that this role needs to be accounted for in modern interpretations of OFC function.Significance Statement The orbitofrontal cortex (OFC) is a highly interconnected brain region thought to be essential for healthy cognitive function. Historically, research has implicated the OFC in the inhibition of inappropriate actions, however, more recent work has focused on its role in guiding value-based decision making. Using a modified version of a canonical inhibitory control task in combination with optogenetics, we show that while important for value-based decision making, disruption of excitatory neurons in the OFC impairs inhibitory control processes as well. This highlights the need for theoretical accounts of OFC function to accommodate its role in both types of cognitive processes.

Multilayer time-variant network edges synchronization method

In this work, we propose a method for measuring synchronization between the edges of a pair of networks with connections that vary over time. The product of this method is an Incidence-Fidelity network, indicating the intensity of edge synchronization during a time interval. Two distinct validations were performed: in the first, we employed time-varying multilayer graphs with random connections to validate the algorithm; and in the second, we used the coupled Rössler oscillator to validate the method. In the algorithm validation, we compared the algebraically predicted results with the results obtained by using random networks. In the method validation, we used the coupled Rössler oscillator to generate time series representing the existence of two edges, one in each layer of the time-varying multilayer graphs. We assessed the relationship between the pre-defined coupling values and the achieved synchronization indices. In the application of our method, we considered time series of 17 years of climate data for the Normalized Difference Vegetation Index (NDVI) and precipitation for all municipalities in the state of Bahia, Brazil. To do so, we first obtained the individual layers of the multilayer network using a motif synchronization method. From the individual layers, we generated the Interlayer Incidence-Fidelity network. The obtained Interlayer Incidence-Fidelity network for the NDVI and precipitation pair reflects a connection pattern that incorporates the configurations of the individual layers. This is because synchronizations are primarily distributed in the more interconnected regions of the individual layers. The Interlayer Incidence-Fidelity networks generated by our method can identify synchronization patterns in the evolution of relationships between networks over time. This method presents a promising tool for evaluating synchronization between networks, generating a weighted network that quantifies the different levels of temporal synchronization for each edge in the networks.

Structural network topologies are associated with deep brain stimulation outcomes in Meige syndrome

Deep brain stimulation (DBS) is an effective therapy for Meige syndrome (MS). However, the DBS efficacy varies across MS patients and the factors contributing to the variable responses remain enigmatic. We aim to explain the difference in DBS efficacy from a network perspective. We collected preoperative T1-weighted MRI images of 76 MS patients who received DBS in our center. According to the symptomatic improvement rates, all MS patients were divided into two groups: the high improvement group (HIG) and the low improvement group (LIG). We constructed group-level structural covariance networks in each group and compared the graph-based topological properties and interregional connections between groups. Subsequent functional annotation and correlation analyses were also conducted. The results indicated that HIG showed a higher clustering coefficient, longer characteristic path length, lower small-world index, and lower global efficiency compared with LIG. Different nodal betweennesses and degrees between groups were mainly identified in the precuneus, sensorimotor cortex, and subcortical nuclei, among which the gray matter volume of the left precentral gyrus and left thalamus were positively correlated with the symptomatic improvement rates. Moreover, HIG had enhanced interregional connections within the somatomotor network and between the somatomotor network and default-mode network relative to LIG. We concluded that the high and low DBS responders have notable differences in large-scale network architectures. Our study sheds light on the structural network underpinnings of varying DBS responses in MS patients.

Predicting individual autistic symptoms for patients with autism spectrum disorder using interregional morphological connectivity

Intelligent predictive models for autistic symptoms based on neuroimaging datasets were beneficial for the precise intervention of patients with ASD. The goals of this study were twofold: investigating predictive models for autistic symptoms and discovering the brain connectivity patterns for ASD-related behaviors. To achieve these goals, we obtained a cohort of patients with ASD from the ABIDE project. The autistic symptoms were measured using the Autism Diagnostic Observation Schedule (ADOS). The anatomical MRI datasets were preprocessed using the Freesurfer package, resulting in regional morphological features. For each individual, the interregional morphological network was constructed using a novel feature distance-based method. The predictive models for autistic symptoms were built using the support vector regression (SVR) algorithm with feature selection method. The predicted autistic symptoms (i.e., ADOS social score, ADOS behavior) were significantly correlated to the original measures. The most predictive features for ADOS social scores were located in the bilateral fusiform. The most predictive features for ADOS behavior scores were located in the temporal pole and the lingual gyrus. In summary, the autistic symptoms could be predicted using the interregional morphological connectivity and machine learning. The interregional morphological connectivity could be a potential biomarker for autistic symptoms.

Complex activity and short-term plasticity of human cerebral organoids reciprocally connected with axons

An inter-regional cortical tract is one of the most fundamental architectural motifs that integrates neural circuits to orchestrate and generate complex functions of the human brain. To understand the mechanistic significance of inter-regional projections on development of neural circuits, we investigated an in vitro neural tissue model for inter-regional connections, in which two cerebral organoids are connected with a bundle of reciprocally extended axons. The connected organoids produced more complex and intense oscillatory activity than conventional or directly fused cerebral organoids, suggesting the inter-organoid axonal connections enhance and support the complex network activity. In addition, optogenetic stimulation of the inter-organoid axon bundles could entrain the activity of the organoids and induce robust short-term plasticity of the macroscopic circuit. These results demonstrated that the projection axons could serve as a structural hub that boosts functionality of the organoid-circuits. This model could contribute to further investigation on development and functions of macroscopic neuronal circuits in vitro.

The influence of the precuneus on the medial temporal cortex determines the subjective quality of memory during the retrieval of naturalistic episodes.

Memory retrieval entails dynamic interactions between the medial temporal lobe and areas in the parietal and frontal cortices. Here, we tested the hypothesis that effective connectivity between the precuneus, in the medial parietal cortex, and the medial temporal cortex contributes to the subjective quality of remembering objects together with information about their rich spatio-temporal encoding context. During a 45min encoding session, the participants were presented with pictures of objects while they actively explored a virtual town. The following day, under fMRI, participants were presented with images of objects and had to report whether: they recognized the object and could remember the place/time of encoding, the object was familiar only, or the object was new. The hippocampus/parahippocampus, the precuneus and the ventro-medial prefrontal cortex activated when the participants successfully recognized objects they had seen in the virtual town and reported that they could remember the place/time of these events. Analyses of effective connectivity showed that the influence exerted by the precuneus on the medial temporal cortex mediates this effect of episodic recollection. Our findings demonstrate the role of the inter-regional connectivity in mediating the subjective experience of remembering and underline the relevance of studying memory in contextually-rich conditions.

Neural response during prefrontal theta burst stimulation: Interleaved TMS-fMRI of full iTBS protocols

BackgroundLeft prefrontal intermittent theta-burst stimulation (iTBS) has emerged as a safe and effective transcranial magnetic stimulation (TMS) treatment protocol in depression. Though network effects after iTBS have been widely studied, the deeper mechanistic understanding of target engagement is still at its beginning. Here, we investigate the feasibility of a novel integrated TMS-fMRI setup and accelerated echo planar imaging protocol to directly observe the immediate effects of full iTBS treatment sessions. Objective/hypothesisIn our effort to explore interleaved iTBS-fMRI feasibility, we hypothesize that TMS will induce acute BOLD signal changes in both the stimulated area and interconnected neural regions. MethodsConcurrent TMS-fMRI with full sessions of neuronavigated iTBS (i.e. 600 pulses) of the left dorsolateral prefrontal cortex (DLPFC) was investigated in 18 healthy participants. In addition, we conducted four TMS-fMRI sessions in a single patient on long-term maintenance iTBS for bipolar depression to test the transfer to clinical cases. ResultsConcurrent TMS-fMRI was feasible for iTBS sequences with 600 pulses. During interleaved iTBS-fMRI, an increase of the BOLD signal was observed in a network including bilateral DLPFC regions. In the clinical case, a reduced BOLD response was found in the left DLPFC and the subgenual anterior cingulate cortex, with high variability across individual sessions. ConclusionsFull iTBS sessions as applied for the treatment of depressive disorders can be established in the interleaved iTBS-fMRI paradigm. In the future, this experimental approach could be valuable in clinical samples, for demonstrating target engagement by iTBS protocols and investigating their mechanisms of therapeutic action.

SPECIFICS IN THE NATIONAL DANCES OF THE TURKIC PEOPLES

To date, the modern picture of the cultural space is a very diverse, multi-level style palette. In this sense, the XXI century is full of colors of several epochs at once, unfolding consistently and intensively. The relevance of the topic under consideration is also due to the fact that in the period of globalization and the "capture" of the minds of modern youth by the delights of Western European culture, turning to the dances of the Turkic world is another opportunity to preserve the desired folk traditions, which, like language, dance is a reflection of the mentality, character of the nation and even its history. Striving into the roots of Turkic culture, studying and analyzing the dance art of Turkic-speaking, fraternal peoples, its history and its actual modern interpretation is a kind of challenge of the time. The article draws attention to the characteristic features of the common and special in the Turkic circular dances. The author, referring to existing sources, noted about the worship of ancient people, came to the conclusion that the Turkic peoples have a common root, have a common culture and values. The paper analyzes the historical interrelationships and conditionality of changes between the modern period and the stage of cultural construction in the national dances of the Turkic peoples. Also, on the basis of the studied materials, the comparative analysis examines the dance culture of representatives of modern Turkic peoples (Kazakhs, Bashkirs, Uzbeks, Uighurs, Azerbaijanis). The similarities that unite them are not only the common language, historical and geographical space, similar toponyms, customs and traditions, folk crafts, oral folk art, musical culture, but also circular dances. In addition, the article examines the specifics of the in the music of the Turkic-speaking peoples. The article emphasizes that the identification of interregional connections in the musical system of the Turkic-speaking peoples occurs at several levels of research, allowing to optimally show the processes of connections, to identify their origins. The article examines the peculiarities of the development of national choreographic culture in historical retrospect; analyzes the characteristic features of the formation of ethnic dance, the degree of influence of natural factors, color symbols, everyday life on the development of national dance culture as a whole.

Spatial representation of multidimensional information in emotional faces revealed by fMRI

Face perception is a complex process that involves highly specialized procedures and mechanisms. Investigating into face perception can help us better understand how the brain processes fine-grained, multidimensional information. This research aimed to delve deeply into how different dimensions of facial information are represented in specific brain regions or through inter-regional connections via an implicit face recognition task. To capture the representation of various facial information in the brain, we employed support vector machine decoding, functional connectivity, and model-based representational similarity analysis on fMRI data, resulting in the identification of three crucial findings. Firstly, despite the implicit nature of the task, emotions were still represented in the brain, contrasting with all other facial information. Secondly, the connection between the medial amygdala and the parahippocampal gyrus was found to be essential for the representation of facial emotion in implicit tasks. Thirdly, in implicit tasks, arousal representation occurred in the parahippocampal gyrus, while valence depended on the connection between the primary visual cortex and the parahippocampal gyrus. In conclusion, these findings dissociate the neural mechanisms of emotional valence and arousal, revealing the precise spatial patterns of multidimensional information processing in faces.

Spatial-temporal evolution and trend prediction of ecological civilization construction efficiency from the perspective of audit

The performance evaluation of ecological civilization construction is an important content of ecological civilization audit. The super-efficiency slacks-based measure model measured inter-provincial ecological civilization construction efficiency based on the panel data of Chinese provinces and cities during 2000–2020. Based on the time series and spatial correlation analyses, the traditional and spatial Markov probability transfer matrixes were constructed to explore the spatial and temporal dynamic evolution characteristics of China’s ecological civilization construction efficiency and predict its long-term trend. The study revealed that China’s ecological civilization construction efficiency has a “bimodal” distribution with a stable increase in fluctuation. The gap between the peak heights is small; however, the overall ecological civilization construction efficiency is low. We also revealed that from the long-term evolution trend forecast, most provinces and cities gradually shift to higher levels of ecological civilization construction efficiency and gradually evolve into having a gradual increase pattern in the geographical context of low ecological civilization construction efficiency, resulting in the long-term evolution of the stable state performance. In the geographic context of low ecological civilization construction efficiency, the long-term evolution of the stable state is a partial “single-peaked” distribution. Finally, we analyzed the directions for improvement in this study and suggested that inter-regional ecological civilization construction policy connections and the fortification of inter-regional ecological civilization construction cooperation and exchange can effectively improve China’s ecological civilization construction efficiency, narrow the gap between provinces and cities, and promote the construction of ecological civilization.

The unique failure behavior of Ni–Mo–Cr-Gd superalloy during hot compression at ultrahigh temperatures

The unique failure behavior of the Ni–Mo–Cr-Gd superalloy deformed at 0.01 s−1 and 0.5 strain with ultra-high temperatures has been investigated. The samples compressed at 0.01 s−1 and 0.5 strain are severely cracked at 1230 °C, and almost completely crushed at 1250 °C. The microstructure observations show that the divorced γ/Ni5Gd eutectic phase transforms into the lamellar γ/Ni5Gd eutectic phase, and the Ni5Gd phase disappears in the center of samples, especially at 1250 °C. Here, the eutectic transformation is attributed to the fact that the Ni5Gd phase melts and solidifies due to the temperature rise of local interface friction between the Ni5Gd phase and the γ matrix. Furthermore, the severe failure of samples and the unexpected disappearance of the Ni5Gd phases were ascribed to the interconnected localized melting regions of the Ni5Gd phase.