Detection and analysis of spatiotemporal patterns in brain activity.

Robust and fast stochastic 4D Flow Vector-field signature technique for quantifying composite flow dynamics from 4D Flow MRI: Application to left atrial flow in atrial fibrillation

The introduction of autonomous delivery systems is expected to improve productivity in numerous application fields. Since such systems comprise both unmanned and manned units, it is important to control the whole system using appropriate behavioral models of manned vehicles or humans. This work builds on the results of a previous study, where a velocity vector field was used to model human movement, and extends the use of velocity vector fields to the simulation of vehicle behavior by modeling variance in given trajectories. This extension enables the velocity vector field to regenerate an average trajectory with a band and consequently allows one to calculate the vehicle's predicted region in arbitrary future time steps and anomaly detection. These applications are expected to facilitate collision avoidance and should help one to control other applications of unmanned vehicles.

We address the problem of tracking points in dense vector fields. Such vector fields may come from computational fluid dynamics simulations, environmental monitoring sensors, or dense point tracking of video data. To track points in vector fields, we capture the distribution of higher-order properties (e.g., properties derived from the gradient of the velocity vector field) in a novel local descriptor called a vector spin-image. Our distribution-based approach has a number of advantages over methods that use topology analysis to track points in vector fields. The local distributions are robust to noise, adaptable to changes in the feature, and can be used to extrapolate the location of features after they have disappeared. We describe the vector spin-image data structure, the higher-order properties we record to track vector field points, and show results of tracking points in the simulated flow through a diesel engine cylinder.

Wave is formed from the movement of air caused by pressure variations that make airflow move from high pressure toward places of low pressure. Understanding the wave patterns is challenging since it is highly changeable in space as they travel in variety of directions and heights. Wave are also changing over time especially during the monsoon seasons. Hence, to extract significant information from this highly changeable behaviour of wave this study has utilized a multi-dimensional clustering technique called co-clustering. This technique is able to cluster spatio-temporal data with similar behavior into spatial and temporal components simultaneously. To reveal the spatial and temporal patterns, an algorithm called Bregman Block Average co-clustering with I-divergence (BBAC_I) has been implemented for extracting wave patterns. In order to discover the wave behaviour, the extracted wave patterns were visualized in the form of heatmap that contain information of co-clusters; spatial clusters and temporal clusters dimensions. Then, both spatial and temporal clusters from the heatmap were transformed into geographical maps to depict the variation of wave patterns based on their individual dimension. From these maps, we could observe the distribution of 8 different group of clusters that representing the spatial wave patterns. Furthermore, 5 individual maps have been produced to depict the temporal wave patterns across the study area. Finally, the obtained maps were interpreted in the form of wave height which were found to be within 0.4 to 1.4 m heights. The wave height information can be used for identifying their potential for ocean energy harvesting along the coastal area. In generally, the generated spatio-temporal wave patterns from this study could aid Malaysian marine agencies to provide strategic planning for proposing future ocean energy in Malaysian coastal area.

Inducing Gamma Oscillations and Precise Spike Synchrony by Operant Conditioning via Brain-Machine Interface

THINGS-data reflects three large-scale neuroimaging and behavioral datasets of object processing in humans, comprising densely sampled functional MRI and magnetoencephalographic recordings, as well as 4.70 million similarity judgments in response to thousands of photographic images for up to 1854 objects.

Near-wall coherent structures in the turbulent channel flow of a dilute polymer solution

An analysis of spatiotemporal patterns in Chinese agricultural productivity between 2004 and 2014

Landuse and land cover change is regarded as a good indicator that represents the impact of human activities on earth’s environment. When the large collection of multi-temporal satellite images has become available, it is possible to study a long-term historical process of land cover change. This study aims to investigate the spatio-temporal pattern and driving force of land cover change in the Pearl River Delta region in southern China, where the rapid development has been witnessed since 1980s. The fast economic growth has been associated with an accelerated expansion of urban landuse, which has been recorded by historical remote sensing images. This paper reports the method and outcome of the research that attempts to model spatio-temporal pattern of land cover change using multi-temporal satellite images. The classified satellite images were compared to detect the change from various landuse types to built-up areas. The trajectories of land cover change have then been established based on the time-series of the classified land cover classes. The correlation between the expansion of built-up areas and selected economic data has also been analysed for better understanding on the driving force of the rapid urbanisation process. The result shows that, since early 1990s, the dominant trend of land cover change has been from farmland to urban landuse. The relationship between economic growth indicator (measured by GDP) and built-up area can well fit into a linear regression model with correlation coefficients greater than 0.9. It is quite clear that cities or towns have been sprawling in general, demonstrating two growth models that were closely related to the economic development stages.

By finding broader temporal and spatial patterns of brain activity, dictionary learning and sparse coding techniques circumvent the constraints of typical voxel-level studies of task-based functional magnetic resonance imaging (fMRI) data. Prior applications of these algorithms to task-related fMRI data, on the other hand, have not been tuned to simultaneously uncover temporal patterns of activity that change in tandem with changes in task circumstances and spatial patterns that exploit existing neuroscience knowledge. This work, presents a sparse dictionary learning method that decomposes fMRI data into temporal patterns of signals that loosely differ between task conditions and sparse spatial patterns that are at least partially similar to known functional network hubs, based on prior knowledge of the temporal pattern of task conditions and the locations of brain regions hypothesized to be involved in the task. To over come the a major drawback of the prior work in terms being constrained to provide an equal number of spatial and temporal patterns, even when the data was more accurately represented by a larger number spatial than temporal patterns (or vice versa), this work also propose a constrained decoupled dictionary learning method that uses prior knowledge related to task paradigms and the spatial locations of task-related brain regions to estimate decoupled spatial and temporal patterns that represent the fMRI data. The utility of sparse dictionary learning methods applied to task fMRI data is increased by simultaneously exploiting the task's known temporal structure and biasing solutions towards putative network hubs. The method identifies a high percentage of the spatial and temporal patterns programmed into simulated fMRI data. The temporal and spatial patterns are identified using an effective on-line optimization methodology. The technique detects geographical and temporal patterns in synthetic task fMRI data. When applied to two real-world fMRI data sets, the method: 1. automatically identifies temporal and spatial patterns that were known by neuroscientists to be task-related a priori but were not provided as inputs to the method; 2. provides measurements that differ significantly between differing tasks. When applied to real fMRI data from 100 participants two different studies, the suggested method also identifies geographic regions known a priori to be engaged by the Attention Network Task (ANT), AX-Continuous Performance Task (AX-CPT) and Stroop Task more thoroughly than competing methods. Using task information to partially constrain dictionary learning allowed this method to identify task related networks in real and synthetic fMRI data.

Computational analyses of in-vivo cerebral activity show that gamma oscillations in the olfactory cortex originate from local feedback inhibition following each sniff and serve to segregate competing odor representations through a winner-take-all process, providing an optimal window to decode odor.

It has been identified that autapse can modulate dynamics of single neurons and spatial patterns of neuronal networks. In the present paper, based on the results that autapse can induce type II excitability changed to type I excitability, spatial pattern transitions are simulated in a two-dimensional neuronal network composed of excitatory coupled neurons with autapse which can induce excitability transition. Different spatial patterns including random-like pattern, irregular wave, regular wave, and nearly synchronous behavior are simulated with increasing the percentage (σ) of neurons with type I excitability. When noise is introduced, spiral waves are induced. By calculating signal-to-noise ratio from the spatial structure function and the mean firing probability of neurons, regular waves and spiral waves exhibit optimal spatial correlation, implying the occurrence of spatial coherence resonance phenomenon. The changes of mean firing probability of neurons show that different firing frequency between type I excitability and type II excitability may be an important factor to modulate the spatial patterns. The results are helpful to understand the spatial patterns including spiral waves observed in the biological experiment on the rat cortex perfused with drugs which can induce single neurons changed from type II excitability to type I excitability and block the inhibitory couplings between neurons. The excitability transition, absence of inhibitory coupling, noise as well as the autapse are important factors to modulate the spatial patterns including spiral waves.

Electrical waves in the heart form rotating spiral or scroll waves during life-threatening arrhythmias, such as atrial or ventricular fibrillation. The wave dynamics are typically modeled using coupled partial differential equations, which describe reaction-diffusion dynamics in excitable media. More recently, data-driven generative modeling has emerged as an alternative to generate spatio-temporal patterns in physical and biological systems. Here, we explore denoising diffusion probabilistic models for the generative modeling of electrical wave patterns in cardiac tissue. We trained diffusion models with simulated electrical wave patterns to be able to generate such wave patterns in unconditional and conditional generation tasks. For instance, we explored the diffusion-based (i) parameter-specific generation, (ii) evolution, and (iii) inpainting of spiral wave dynamics, including reconstructing three-dimensional scroll wave dynamics from superficial two-dimensional measurements. Furthermore, we generated arbitrarily shaped bi-ventricular geometries and simultaneously initiated scroll wave patterns inside these geometries using diffusion. We characterized and compared the diffusion-generated solutions to solutions obtained with corresponding biophysical models and found that diffusion models learn to replicate spiral and scroll wave dynamics so well that they could be used for data-driven modeling of excitation waves in cardiac tissue. For instance, an ensemble of diffusion-generated spiral wave dynamics exhibits similar self-termination statistics as the corresponding ensemble simulated with a biophysical model. However, we also found that diffusion models produce artifacts if training data are lacking, e.g., during self-termination, and "hallucinate" wave patterns when insufficiently constrained.

In this paper the development and a partially verification of an anisotropic modification of linear and nonlineare models of turbulence is presented. This modification departs from the observation that there are some regions of fluid domains in which the Reynolds stresses do not depend on the magnitude of the mean flow gradients. The model is based on the hypothesis that actions of fluctuating motion on mean motion are almost exclusively due to large turbulent eddies, and that the latter contain the same kinetic energy of the small eddies of mean flow. The contribution of large turbulent eddies to the Reynolds stresses is then modelled by a linear combination of an isotropic term and an anisotropic term. The latter is obtained by multiplying a quote of the turbulent kinetic energy by a normalised redistribution tensor. The performances of the model are tested calculating the Reynolds stresses in a fully developed turbulent channel flow, and simulating the separated turbulent flow over a backward-facing step, and a fully developed turbulent flow in a squared duct. The results show that the modified models improve the prediction of the normal Reynolds stresses in a bidimensional channel and of the reattachment point in a backward-facing step. On the other hand, these models do not supply any improvement of the secondary flow in a squared duct. INTRODUCTION Algebraic two-equation models of turbulence for Reynolds averaged Navier-Stokes equations are still, today, among the most widely used in the field of engineering design. The two major alternative to such models are the second-order closure models and large-eddy simulations (LES). The weak points of the first approach are high computational cost, due to the five more equations to be solved, and wall-bounded turbulent flows. On the other hand, LES requires much more computational time than second-order closure models, and improved models for the smallscale and the wall region are needed. For these reasons these two approaches have not become yet a useful engineering tool. The aim of this work is to develop an anisotropic modification for linear and nonlinear turbulence model which improves the prediction of normal Reynolds stresses, without increasing the number of differential equations to be solved. More precisely, the objective is to simulate the contribution to the anisotropy of the turbulent stresses observable in areas characterised by low mean velocity gradients. This phenomenon, which can be noted, for example, in experimental data of the turbulent channel flow in figure 5, is not actually described by standard, explicit algebraic models. The starting point of this model consists in the substitution of turbulent kinetic energy by an appropriate vectorial magnitude which provides information about the anistropy of the turbulent stresses. The quantity is identified by the field of velocity of the small eddies of mean motion. The kinetic energy of these small eddies is obtained by assuming that these t Dr, Center of Studies and Activities for Space (CISAS). Dr, Department of Mechanical Engineering. ' Professor, Department of Mechanical Engineering. Copyright © 2001 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. 1 American Institute of Aeronautics and Astronautics (c)2001 American Institute of Aeronautics & Astronautics or Published with Permission of Author(s) and/or Author(s)' Sponsoring Organization. have the same kinetic energy content of the large turbulent eddies: the two fields of motion are said to be similar. It has been demonstrated that the field of motion of large turbulent eddies, or coherent structures^ can be obtained directly from measurements of instantaneous velocity once the quota of turbulent kinetic energy has been defined. The models obtained provides improvements in the predictions of normal Reynolds stresses present in the turbulent flow of a bidimensional channel; as for the separated turbulent flow in a backward-facing step appreciable improved results of mean flow and of normal Reynolds stresses have been attained compared to standard algebraic models. On the other side, as it will be shown in the following paragraphs, the model of the turbulent kinetic energy redistribution tensor does not supply any improvement in the case of the fully developed turbulent flow in a squared duct. ANISOTROPIC MODIFICATION OF THE REYNOLDS STRESSES Let us consider a field of turbulent motion of a viscous, homogenous and incompressible fluid; the instantaneous velocity v and the instantaneous pressure p can be set out as follows in a mean and fluctuating parts: v = v -f v' p-p + p (1) The evolution of the velocities and mean pressures can be described completely by the well-known Reynolds equations:

Application of complex network method to spatiotemporal patterns in a neuronal network