Spatial Correlation Research Articles

Altered correlation of concurrently recorded EEG-fMRI connectomes in temporal lobe epilepsy

Abstract Whole-brain functional connectivity networks (connectomes) have been characterized at different scales in humans using EEG and fMRI. Multimodal epileptic networks have also been investigated, but the relationship between EEG and fMRI defined networks on a whole-brain scale is unclear. A unified multimodal connectome description, mapping healthy and pathological networks would close this knowledge gap. Here, we characterize the spatial correlation between the EEG and fMRI connectomes in right and left temporal lobe epilepsy (rTLE/lTLE). From two centers, we acquired resting-state concurrent EEG-fMRI of 35 healthy controls and 34 TLE patients. EEG-fMRI data was projected into the Desikan brain atlas, and functional connectomes from both modalities were correlated. EEG and fMRI connectomes were moderately correlated. This correlation was increased in rTLE when compared to controls for EEG-delta/theta/alpha/beta. Conversely, multimodal correlation in lTLE was decreased in respect to controls for EEG-beta. While the alteration was global in rTLE, in lTLE it was locally linked to the default mode network. The increased multimodal correlation in rTLE and decreased correlation in lTLE suggests a modality-specific lateralized differential reorganization in TLE, which needs to be considered when comparing results from different modalities. Each modality provides distinct information, highlighting the benefit of multimodal assessment in epilepsy.

A sequence decomposition genetic algorithm for petrophysical inversion with spatial correlation

Predicting petrophysical properties based on seismic attributes is essential for accurate subsurface characterization. Typically, a forward model is first established to relate the petrophysical properties to geophysical measurements, and then the petrophysical properties are inverted from the actual measurements. Imposing prior constraints of the spatial relationship is also desirable to regularize the ill-posed inverse problem. However, petrophysical inversion involving spatial correlation presents a high-dimensional optimization problem with multiple local extrema. This problem can be challenging to solve, particularly when discrete variables (such as lithofacies) and continuous variables (such as porosity and fluid saturation) are considered together. Therefore, we develop a global optimization algorithm that uses the Markov chain decomposition and a genetic algorithm for a joint discrete-continuous petrophysical inversion with spatial correlation. The innovation of this algorithm is to spatially decouple the sequence optimization problem into multiple low-dimensional subproblems and overcome the initial value dependence to converge to the global optimal solution. Furthermore, an approximation operation is introduced to expedite the optimization algorithm. Numerical experiments show that after algorithmic acceleration, the spatially correlated inversion requires the same computational time as a simple spatially uncorrelated inversion and achieves significantly better accuracy than the latter. Finally, we apply the method to a case study in East China, wherein the lithofacies, clay content, porosity, and water saturation are inferred from the data of the wave velocities and density. The inversion results around the wells generally match the well-logging data, verifying the feasibility of the new method.

Spatial correlation of defect-selective etching and dark luminescence spots in Al x Ga1−x N

Defect-selective etching with molten Ba(OH)2/MgO etch drops was performed on c-plane AlGaN layers covering the entire composition range between GaN and AlN. Regardless of the aluminum content, the etchant produced shallow, hexagonal etch pits with depth-to-diameter ratios of 1/10–1/100. Two predominant types of etch pits were observed, which differed in size. In addition, the etch rate decreased from the center to the edge of the area exposed to the etch drops, providing a radially symmetric variation in etch pit size. For all AlGaN compositions, the positions of the etch pits correlate perfectly with the positions of the dark luminescence spots in cathodoluminescence measurements. Areas on the AlGaN samples that were not exposed to the etching procedure showed identical dark spots with the same size and density as those in the etched regions. Additionally, the density of etch pits and dark spots corresponded to the density of threading dislocations in the AlGaN layers. These observations demonstrate that the density of threading dislocations in c-plane AlGaN layers can be determined by destructive defect-selective etching with Ba(OH)2/MgO and etch pit counting, as well as by nondestructive counting of the dark spots in cathodoluminescence images.

The level of decoupling between digital inclusive finance and carbon emissions and its spatial correlation network characteristics

The level of decoupling between digital inclusive finance and carbon emissions and its spatial correlation network characteristics

Spatial correlation analysis between altitude and medium air temperature in São Paulo State

A temperatura do ar é um dos fenômenos de maior importância na determinação do clima, e, como a maioria dos demais fenômenos climáticos, apresenta uma variabilidade espacial contínua, que pode ser detectada por meio de poderosos métodos geoestatísticos. Conhecida a grande influência do relevo topográfico no clima específico de cada região, é de se esperar que esta variável apresente correlações espaciais com a temperatura do ar, e que o conhecimento desta correlação possa, de alguma maneira, auxiliar na elaboração de conclusões mais precisas, que envolvam estes fenômenos. Neste trabalho, analisa-se rigorosamente a variabilidade espacial da altitude e dos índices térmicos, além da correlação existente entre estas variáveis. Concluiu-se, assim, que estas variáveis apresentam forte dependência espacial e são inversamente correlacionadas. Esta última conclusão pode ser claramente evidenciada por meio da comparação dos mapas de isolinhas da ocorrência de cada fenômeno.

Cholinergic Neuromodulation of Prefrontal Attractor Dynamics Controls Performance in Spatial Working Memory.

The behavioral and neural effects of the endogenous release of acetylcholine following stimulation of the nucleus basalis (NB) of Meynert have been recently examined in two male monkeys (Qi et al., 2021). Counterintuitively, NB stimulation enhanced behavioral performance while broadening neural tuning in the prefrontal cortex (PFC). The mechanism by which a weaker mnemonic neural code could lead to better performance remains unclear. Here, we show that increased neural excitability in a simple continuous bump attractor model can induce broader neural tuning and decrease bump diffusion, provided neural rates are saturated. Increased memory precision in the model overrides memory accuracy, improving overall task performance. Moreover, we show that bump attractor dynamics can account for the nonuniform impact of neuromodulation on distractibility, depending on distractor distance from the target. Finally, we delve into the conditions under which bump attractor tuning and diffusion balance in biologically plausible heterogeneous network models. In these discrete bump attractor networks, we show that reducing spatial correlations or enhancing excitatory transmission can improve memory precision. Altogether, we provide a mechanistic understanding of how cholinergic neuromodulation controls spatial working memory through perturbed attractor dynamics in the PFC.

Intra-plate Jal Az-Zor strike-slip faulting deciphering the enigma of local earthquakes in Kuwait

Abstract Kuwait National Seismic Network (KNSN) data show the clustering of earthquakes in northern and southern clusters. Spatial correlation between these clusters and oil fields led previous studies to declare that oil production/injection triggered earthquakes, but some suggested the possibility of tectonic causes. This study addresses the genuine objective of uncovering the origin of Kuwait earthquakes by analysing relationships between earthquake spatial and temporal patterns, oil production/injection, structural and tectonic setting, and subsurface fluid pressures. A kinematic model of the Jal Az-Zor dextral-slip fault was presented as a decipherer to the earthquake's origin. The northern and southern clusters represent leading quadrants, where increased mean stress causes earthquakes. Dibdibah Trough and Kuwait Bay represent trailing quadrants with decreased mean stress and a lack of earthquakes. The small percentage of earthquakes falling outside clusters are caused by the concentration of regional compressive stresses related to Arabian Plate motion on pre-existing faults. Triggering earthquakes by oil field operations requires 10% pressure increase above the original pressures, which never occurs in Kuwait oil fields. The results of this study emphasize the significance of understanding fault kinematics to assess earthquake hazards and the need to focus on engineering requirements for developments in the leading quadrants areas.

Motion segmentation with event camera: [formula omitted]-patches optical flow estimation and Pairwise Markov Random Fields

This paper proposes the N-patches optical flow estimation-based motion compensation for motion segmentation using event camera. The requirement for prior knowledge of the scenario to cluster events into different motion types is addressed by the N-patches optical flow estimation. The framework of motion segmentation combines motion compensation and Pairwise Markov Random Fields, which not only realize the alignment of events in space but also enhance the spatial correlation between events. The proposed approach aims to maximize the contrast of the images of warped events. It does so by minimizing a specially designed energy function that jointly estimates motion parameters and discrete label items. The problem of selecting the optimal value for N∗ when executing N-patches optical flow estimation is discussed in Section 4.2 of the experiments. Finally, the proposed approach is evaluated in several scenes involving high-speed motion, low-light conditions, occlusions, and multiple moving objects. The experimental results show that the proposed approach achieves better segmentation performance than the event-based motion segmentation by motion compensation.

Linear theory of the spatial signatures of critical slowing down

Critical slowing down is considered one of the main signs that a dynamical system is close to a tipping point. For this reason, early-warning indicators have been developed to identify it, mainly based on the temporal evolution of the system under analysis. Yet, it has been shown that critical slowing down also displays spatial signatures, such as an increase in the spatial correlation. However, these signatures are based only on heuristic observations and the analysis of numerical simulations and do not have a sound theoretical foundation. In this work I will derive analytical expressions for typical spatial early-warning indicators of critical transitions in spatially extended systems, such as spatial correlation, spatial variance, and spatial permutation entropy, in the linearized limit. As a result, I will show that the common belief that the spatial correlation increases when a bifurcation is imminent is false. Other indicators, instead, might represent more robust alternatives, such as the spatial permutation entropy or the spatial variance. Published by the American Physical Society 2024

DMOT-SLAM: visual SLAM in dynamic environments with moving object tracking

Visual simultaneous localization and mapping (SLAM) in dynamic environments has received significant attention in recent years, and accurate segmentation of real dynamic objects is the key to enhancing the accuracy of pose estimation in such environments. In this study, we propose a visual SLAM approach based on ORB-SLAM3, namely dynamic multiple object tracking SLAM (DMOT-SLAM), which can accurately estimate the camera’s pose in dynamic environments while tracking the trajectories of moving objects. We introduce a spatial point correlation constraint and combine it with instance segmentation and epipolar constraint to identify dynamic objects. We integrate the proposed motion check method into DeepSort, an object tracking algorithm, to facilitate inter-frame tracking of dynamic objects. This integration not only enhances the stability of dynamic features detection but also enables the estimation of global motion trajectories for dynamic objects and the construction of object-level semi-dense semantic maps. We evaluate our approach on the public TUM, Bonn, and KITTI dataset, and the results show that our approach has a significant improvement over ORB-SLAM3 in dynamic scenes and performs better compared to other state-of-the-art SLAM approaches. Moreover, experiments in real-world scenarios further substantiate the effectiveness of our approach.

STC-PSSA: A New Model of Traffic Flow Forecasting Based on Spatiotemporal Convolution and Probabilistic Sparse Self-Attention

Traffic flow forecasting is the foundation of the dynamic control and application of intelligent transportation systems (ITS). It is also of significant practical value in alleviating road congestion. Given the periodic and dynamic changes in traffic flow and the spatiotemporal coupling interaction of complex road networks, traffic flow forecasting is challenging and rarely yields satisfactory prediction results. To capture the dynamic spatiotemporal characteristics of traffic flow, a new model of traffic flow forecasting based on spatiotemporal convolution and probabilistic sparse self-attention (STC-PSSA) is proposed. It consists of a spatiotemporal graph convolution network (ST-GCN) module, a spatiotemporal convolution module (ST-Conv), and a probabilistic sparse attention module (PSSA). ST-GCN consists of the gated temporal convolutional network (G-TCN) and the graph convolution network (GCN), which are used to capture the temporal dependence and spatial correlation of the traffic flow, respectively. Multiple ST-GCNs are stacked to handle spatial features at various time levels. The ST-Conv captures intricate temporal dependence at the same location and dynamic spatial features at neighboring locations simultaneously. The PSSA combines dynamic spatiotemporal features and performs long-term forecasting efficiently. The experimental results demonstrate that the STC-PSSA model can accurately extract the dynamic spatiotemporal characteristics of traffic flow and outperforms the popular baseline methods in forecasting accuracy.

Comparison of CMIP6 model performance in estimating human thermal load in Europe in the winter season

AbstractIn this work, historical simulations of CMIP6 GCMs are evaluated with respect to the ERA5 reanalysis dataset, in order to examine their ability to assess human thermal load in Europe in the winter season. The period of 1981–2010 is chosen for the analysis, and thermal load is expressed via the clothing resistance index (rcl index; expressed in clo). It is found that the GCMs are able to reproduce the areal differences of thermal load satisfactorily, the spatial correlation with the reanalysis is greater than 0.95 in all cases. The effects of the main geographical constraints (latitude, continentality and elevation) are shown by all GCM simulations, as rcl index values are greater at higher latitudes, away from the ocean and in mountainous areas, although GCMs only capture major mountains (the Caucasus, the Armenian Highlands, the Scandinavian Mountains, the Alps). The root‐mean‐square error (RMSE) is around 0.2 clo in all cases, GCMs generally perform better in homogenous lowland areas, while results are less accurate in highlands and mountains owing to the coarse horizontal resolution of GCMs (~1°). The smallest errors occur over central and western Europe and the Mediterranean region, while results tend to be less accurate over the northeastern part of Europe. Biases in the estimation of heat deficit can mainly be attributed to biases in temperature, but biases in wind speed and atmospheric downward radiation seem to be important factors as well.

A convolution neural network integrating climate variables and spatial-temporal properties to predict influenza trends

The spread of influenza is contingent upon a multitude of outbreak-related factors, including viral mutation, climate conditions, acquisition of immunity, crowded environments, vaccine efficacy, social gatherings, and the health and age profiles of individuals in contact with infected individuals. An epidemic in the region impacted by spatial transmission risk from adjacent regions. A few influenzas epidemic models start highlighting the spatial correlations between influenza patients and geographically adjacent regions. The proposed model is based on the concept of climatic, immunization, and spatial correlations which are represented by a convolution neural network (CNN) for influenza epidemic forecasting. This study presents an integration of three determinants for predicting influenza outbreaks, multivariate climate data, spatial data on influenza vaccination, and spatial-temporal data of historical influenza patients. The performance of three comparison models, CNN, recurrent neural network (RNN), and long short-term memory (LSTM) was compared by the root mean squared error metric (RMSE). The findings revealed that the CNN model represents human interaction at intervals of 12, 16, 20, 24, and 28 weeks resulting in the best effectiveness of the lowest RMSE=0.00376 with learning rate=0.0001.

Threshold-Free Symbol Decision Based on Spatial 4PPM Correlation for Uplink Screen Camera Communication Using Low-Luminance SDM Screen

Threshold-Free Symbol Decision Based on Spatial 4PPM Correlation for Uplink Screen Camera Communication Using Low-Luminance SDM Screen

Research on spatial-temporal synergistic sensor fault diagnosis method for top-blowing furnace

Top-blowing furnace systems, characterized by a large number of sensors and harsh working environments, are prone to sensor failures due to factors like component aging and external interference. These failures can significantly impact the system's safe and reliable operation. However, traditional sensor fault diagnosis methods often neglect the exploration of spatial-temporal characteristics and focus solely on learning temporal relationships between sensors, failing to effectively consider their spatial relationships. In this study, we propose a spatial correlation model based on the maximal information-based graph convolutional network (MI-GCN) by constructing a sensor network knowledge graph using maximal mutual information. The MI-GCN leverages the graph convolution mechanism to extract multi-scale spatial features and capture the spatial relationships between sensors. Additionally, we develop a spatial-temporal graph-level prediction model, known as the spatial-temporal graph transformer (STGT), to extract temporal features. By combining the spatial features extracted by the MI-GCN with the temporal features captured by the STGT, accurate predictions can be achieved. Sensor fault diagnosis is conducted by analysing the normalized residuals between the predicted values and the ground truth. Finally, the feasibility and effectiveness of the proposed method are validated using test data from a top-blowing furnace system in the nickel smelting process.

Charting heterogeneity and spatial landscape of consensus molecular subtypes in colorectal cancer.

e15647 Background: High inter-patient heterogeneity and spatial diversity within tumors represent significant challenges to improving outcomes in colorectal cancer (CRC). Despite elucidation of consensus molecular subtypes (CMS), little progress has been made in translating this knowledge into clinical gain. Intratumoral CMS heterogeneity remains a relatively unexplored area and understanding of the interplay between subtypes holds promise for improving prognostication. We leverage spatial transcriptomics (ST) to enhance understanding of CMS heterogeneity and spatial distribution, with the goal of advancing personalized therapy through high resolution molecular sub-typing. Methods: ST using the 10x Genomics Visium platform was performed on paraffin-embedded CRC specimens for which comprehensive clinicopathological and long-term clinical outcome data were available. ST data were deconvoluted with respect to CMS single-cell RNA sequencing (scRNA-seq) data, yielding maps of CMS landscapes at a resolution of 55 microns. A pre-trained deep learning model was used to identify distinct CRC tissue classes (tumor epithelium, stroma, etc.) within H&E images, enabling spatial correlation of histomorphology with CMS. Cellular composition within these CMS maps was quantified through deconvolution of six major cell types using reference scRNA-seq data. Pseudo-bulk samples were generated from the ST data to evaluate CMS behaviors and compute pathway activities. Results: 40 CRC patients were assayed using ST and H&E profiling (13, 8, 14, and 5 from stages 1-4, respectively). Median follow-up was 112 months (Range 1–192). Intratumoral CMS heterogeneity was evident in all patients, with diverse CMS compositions. Using Simpson diversity index to quantify this heterogeneity, we observed significant correlation of heterogeneity with disease specific survival (DSS) (p = .03). CMS landscapes displayed spatial patterns that were distinct among tissue categories (average F1-score: testing = .92, validation = .89. Tissue categories based on deep learning from H&E images). Tumors with higher levels of CMS1 or 4 were associated with worse outcome. Stratification by the composite CMS biomarker (CMS1+CMS4)/(CMS2+CMS3) yielded significant separation of the Kaplan-Meier curve for DSS (p = .018). Conclusions: This study demonstrates that CRC tumors contain significant intratumoral CMS heterogeneity that is not detectable using bulk classification methods. Spatial patterns in CMS transcriptional topographies correlate with H&E-derived morphology, suggesting the potential for estimating CMS composition from histopathological features. Importantly, we show that spatially composite CMS-derived biomarkers possess strong prognostic potential. The capacity to decode CMS topographic patterns and associate these with clinical outcome offers great potential to advance personalized therapy for CRC.

Physics-inspired spatiotemporal-graph AI ensemble for the detection of higher order wave mode signals of spinning binary black hole mergers

We present a new class of AI models for the detection of quasi-circular, spinning, non-precessing binary black hole mergers whose waveforms include the higher order gravitational wave modes (ℓ,|m|)={(2,2),(2,1),(3,3),(3,2),(4,4)} , and mode mixing effects in the ℓ=3,|m|=2 harmonics. These AI models combine hybrid dilated convolution neural networks to accurately model both short- and long-range temporal sequential information of gravitational waves; and graph neural networks to capture spatial correlations among gravitational wave observatories to consistently describe and identify the presence of a signal in a three detector network encompassing the Advanced LIGO and Virgo detectors. We first trained these spatiotemporal-graph AI models using synthetic noise, using 1.2 million modeled waveforms to densely sample this signal manifold, within 1.7 h using 256 NVIDIA A100 GPUs in the Polaris supercomputer at the Argonne Leadership Computing Facility. This distributed training approach exhibited optimal classification performance, and strong scaling up to 512 NVIDIA A100 GPUs. With these AI ensembles we processed data from a three detector network, and found that an ensemble of 4 AI models achieves state-of-the-art performance for signal detection, and reports two misclassifications for every decade of searched data. We distributed AI inference over 128 GPUs in the Polaris supercomputer and 128 nodes in the Theta supercomputer, and completed the processing of a decade of gravitational wave data from a three detector network within 3.5 h. Finally, we fine-tuned these AI ensembles to process the entire month of February 2020, which is part of the O3b LIGO/Virgo observation run, and found 6 gravitational waves, concurrently identified in Advanced LIGO and Advanced Virgo data, and zero false positives. This analysis was completed in one hour using one NVIDIA A100 GPU.

Evaluation and Spatial–Temporal Pattern Evolution of Synergy Degree of Emergency Management for Urban Flood Disasters from the Perspective of Sustainable Development—The Case of Henan, China

The management of urban flood disasters is a systematic engineering project that requires a great amount of manpower, material resources, and financial resources, and the interaction and coordination degrees of various elements in the system deeply affect the efficiency of the final governance. According to the theories of synergy, composite systems, and sustainable development, this research first established an evaluation index system to determine the synergy degree of urban flood disaster emergency management from the four dimensions of prevention and preparation, monitoring and early warning, response and rescue, and recovery and reconstruction. Then, the synergy degree was explored by using the developed composite system synergy degree model on the basis of the panel data of 18 prefecture-level cities in Henan Province from 2013 to 2021, and synergy level change characteristics were analyzed from the perspectives of time and space. Finally, the obstacle degree model was applied to explore the obstacle factors affecting synergy degree development. The results showed that the overall level of the urban flood disaster emergency management coordination degree in Henan Province was relatively low, and there were significant differences in synergy among cities. Among them, 12 cities presented mild synergy, and 6 cities showed mild nonsynergy. The spatial correlation of the synergy degree was not stable, which revealed a lack of mature coordination mechanisms for flood disaster emergency management among cities. The analysis of obstacle factors showed that recovery and reconstruction subsystems were the main obstacle systems that affected the synergy degree.

Activity recognition based on spatio-temporal features with transfer learning

<span lang="EN-US">Human action recognition (HAR) has gathered significant attention recently due to its high demand in various application domains. We explore different CNN architectures for extracting Spatio-Temporal features. In addition to spatial correlation in 2D images, video actions also own the temporal domain attributes. Many research attempts addressed simple activity recognition, but complex activity recognition is still challenging. In the proposed work, the concept of transfer learning consists of two pre-trained networks, ResNet50 and VGG16, which are used to extract Spatiotemporal features and are later fused using the averaging method. Pre-processed frames are considered for the pre-trained network, the pre-processing consists of frame extraction, resizing, and contrast enhancement. The fused features are trained using a multiclass non-linear SVM classifier that achieved an accuracy of 89.71% on the UCF-101 database.</span>

Quantitative susceptibility mapping of multiple system atrophy and Parkinson's disease correlates with neurotransmitter reference maps

BackgroundMultiple system atrophy (MSA) and Parkinson's disease (PD) are neurodegenerative disorders characterized by α-synuclein pathology, disrupted iron homeostasis and impaired neurochemical transmission. Considering the critical role of iron in neurotransmitter synthesis and transport, our study aims to identify distinct patterns of whole-brain iron accumulation in MSA and PD, and to elucidate the corresponding neurochemical substrates. MethodsA total of 122 PD patients, 58 MSA patients and 78 age-, sex-matched health controls underwent multi-echo gradient echo sequences and neurological evaluations. We conducted voxel-wise and regional analyses using quantitative susceptibility mapping to explore MSA or PD-specific alterations in cortical and subcortical iron concentrations. Spatial correlation approaches were employed to examine the topographical alignment of cortical iron accumulation patterns with normative atlases of neurotransmitter receptor and transporter densities. Furthermore, we assessed the associations between the colocalization strength of neurochemical systems and disease severity. ResultsMSA patients exhibited increased susceptibility in the striatal, midbrain, cerebellar nuclei, as well as the frontal, temporal, occipital lobes, and anterior cingulate gyrus. In contrast, PD patients displayed elevated iron levels in the left inferior occipital gyrus, precentral gyrus, and substantia nigra. The excessive iron accumulation in MSA or PD correlated with the spatial distribution of cholinergic, noradrenaline, glutamate, serotonin, cannabinoids, and opioid neurotransmitters, and the degree of this alignment was related to motor deficits. ConclusionsOur findings provide evidence of the interaction between iron accumulation and non-dopamine neurotransmitters in the pathogenesis of MSA and PD, which inspires research on potential targets for pharmacotherapy.