changing value detrended cross correlation coefficient over time between crude oil and crop prices abstract in this paper we analyzed changing comovement relationship between prices of crude oil and food crops food index using a detrended cross correlation analysis dcca approach proposed by podobnik and stanley 2008 in order to detect the changing nature of cross correlation coefficient over time we estimated the detrended cross correlation coefficient on a rolling window and found that cross correlations were negative before 2004 for most of the items but they increased considerably after the surge in crude oil price in 2007 that caused diversion of food grains for production of biofuels

image thresholding based on the em algorithm and the generalized gaussian distribution in this paper a novel parametric and global image histogram thresholding method is presented it is based on the estimation of the statistical parameters of object and background classes by the expectation maximization em algorithm under the assumption that these two classes follow a generalized gaussian gg distribution the adoption of such a statistical model as an alternative to the more common gaussian model is motivated by its attractive capability to approximate a broad variety of statistical behaviors with a small number of parameters since the quality of the solution provided by the iterative em algorithm is strongly affected by initial conditions which if inappropriately set may lead to unreliable estimation a robust initialization strategy based on genetic algorithms gas is proposed experimental results obtained on simulated and real images confirm the effectiveness of the proposed method

functional brain connectivity is predictable from anatomic network s laplacian eigen structure abstract how structural connectivity sc gives rise to functional connectivity fc is not fully understood here we mathematically derive a simple relationship between sc measured from diffusion tensor imaging and fc from resting state fmri we establish that sc and fc are related via structural laplacian spectra whereby fc and sc share eigenvectors and their eigenvalues are exponentially related this gives for the first time a simple and analytical relationship between the graph spectra of structural and functional networks laplacian eigenvectors are shown to be good predictors of functional eigenvectors and networks based on independent component analysis of functional time series a small number of laplacian eigenmodes are shown to be sufficient to reconstruct fc matrices serving as basis functions this approach is fast and requires no time consuming simulations it was tested on two empirical sc fc datasets and was found to significantly outperform generative model simulations of coupled neural masses

dcca cross correlation coefficient quantifying level of cross correlation abstract in this paper a new coefficient is proposed with the objective of quantifying the level of cross correlation between nonstationary time series this cross correlation coefficient is defined in terms of the dfa method and the dcca method the implementation of this cross correlation coefficient will be illustrated with selected time series

multifractal detrended cross correlation analysis for two nonstationary signals we propose a method called multifractal detrended cross correlation analysis to investigate the multifractal behaviors in the power law cross correlations between two time series or higher dimensional quantities recorded simultaneously which can be applied to diverse complex systems such as turbulence finance ecology physiology geophysics and so on the method is validated with cross correlated one and two dimensional binomial measures and multifractal random walks as an example we illustrate the method by analyzing two financial time series

combinatorial fiber tracking of the human brain this paper presents a novel fiber tracking algorithm termed combinatorial tracking which uses stochastic process modeling and global optimization algorithm for tractography combinatorial tracking is a probabilistic tracking algorithm that transforms the brain s white matter into a grid in which each voxel has 26 weighted connections with adjacent voxels we model the random walk on this graph using a markov chain model and suggest two approaches for fiber reconstruction in the first approach we find the most probable paths between two voxels with prior connectivity knowledge using a shortest path algorithm in the second approach the all pairs mean first passage time mfpt matrix m or hitting time as referred to in the spectral graph theory literature is calculated analytically we suggest that m can be interpreted as a global connectivity matrix and use it for fiber reconstruction we also introduce a simulation framework that can be used to calculate specific elements of the matrix m and show how it can be employed to select the target of a fiber in a high resolution diffusion tensor imaging dti dataset because any source and any target voxel can be connected combinatorial tracking permits true connectivity analysis overcoming the limitations of conventional tracking especially stopping criteria e g low fa we applied combinatorial tracking to a standard dti dataset and demonstrated the reconstruction of the cortico thalamic pathway the pyramidal decussation and the medial cerebellar peduncle fibers while the dti ellipsoid served as input for the algorithms any diffusion imaging based orientation density function odf can be used this framework can potentially turn diffusion imaging tractography into a true connectivity measure

studying the human brain anatomical network via diffusion weighted mri and graph theory our goal is to study the human brain anatomical network for this the anatomical connection probabilities acp between 90 cortical and subcortical brain gray matter areas are estimated from diffusion weighted magnetic resonance imaging dw mri techniques the acp between any two areas gives the probability that those areas are connected at least by a single nervous fiber then the brain is modeled as a non directed weighted graph with continuous arc weights given by the acp matrix based on this approach complex networks properties such as small world attributes efficiency degree distribution vulnerability betweenness centrality and motifs composition are studied the analysis was carried out for 20 right handed healthy subjects mean age 31 10 s d 7 43 according to the results all networks have small world and broad scale characteristics additionally human brain anatomical networks present bigger local efficiency and smaller global efficiency than the corresponding random networks in a vulnerability and betweenness centrality analysis the most indispensable and critical anatomical areas were identified putamens precuneus insulas superior parietals and superior frontals interestingly some areas have a negative vulnerability e g superior temporal poles pallidums supramarginals and hechls which suggest that even at the cost of losing in global anatomical efficiency these structures were maintained through the evolutionary processes due to their important functions finally symmetrical characteristic building blocks motifs of size 3 and 4 were calculated obtaining that motifs of size 4 are the expanded version of motif of size 3 these results are in agreement with previous anatomical studies in the cat and macaque cerebral cortex

deconvolution in diffusion spectrum imaging diffusion spectrum magnetic resonance imaging dsi allows the estimation of the displacement probability density function pdf of water molecules which contain valuable information about the microgeometry of the medium where the diffusion process occurs it provides a more general approach to disentangle complex fiber structures in biological tissues because it does not assume any particular model of diffusion even so it has a number of limitations that remain unstudied for instance the theoretical model used to compute the displacement pdf is based on a fourier transformation defined in the whole measurement space however in practice it is computed using discrete signals with a finite support as a consequence the displacement pdf obtained from the experiments is the convolution between the true pdf and a point spread function psf that completely depends on the experimental sampling scheme in this work a general framework to rectify and decontaminate the displacement pdf reconstructed from dsi is introduced this framework is based on model free deconvolution techniques that allow obtaining clearer and sharper dsi estimates the method was tested in synthetic data as well as in real data measured from a healthy human volunteer the results demonstrated that the angular resolution of dsi can be increased potentially revealing new real fiber components and reducing both the artefactual peaks and the uncertainty of the local diffusion orientational distribution furthermore the deconvolution process provides scalar maps of quantities derived from the propagator such as the zero displacement probability with higher tissue contrast

transfer entropy coefficient quantifying level of information flow between financial time series abstract in this paper a new coefficient is proposed with the objective of quantifying the level of information flow between financial time series this transfer entropy coefficient which provides an assessment on the multiscale information flow between measurements is defined in terms of the transfer entropy method and the multiscale method the implementation of this transfer entropy coefficient is illustrated with simulated time series and financial time series examples taken from simulated and financial data demonstrate that the dynamic mechanism of a complex system cannot be detected solely on the basis of transfer entropy of single scale

chaos based nonlinear analysis to study cardiovascular responses to changes in posture abstract cardiovascular responses to changes in posture have been the focus of numerous investigations in the past analysis of ecg and arterial blood pressure abp data for different postures obtained from physionet was done from a new perspective of analyzing nonlinear time series having multifractal features multifractal detrended fluctuation analysis mfdfa and also the cross correlation between ecg and bp with another technique multifractal detrended cross correlation analysis mfdxa reveals ecg and abp time series of different subjects to be multifractal in nature with different degree of complexity different degree of auto and cross correlation for both ecg and abp signals is also observed for all subjects further significant information obtained from the cross correlation parameters obtained from mfdxa analysis is very important and relevant particularly for understanding the dynamics of orthostatic stress

autocorrelation and cross correlation in time series of homicide and attempted homicide abstract we propose in this paper to establish the relationship between homicides and attempted homicides by a non stationary time series analysis this analysis will be carried out by detrended fluctuation analysis dfa detrended cross correlation analysis dcca and dcca cross correlation coefficient d c c a n through this analysis we can identify a positive cross correlation between homicides and attempted homicides at the same time looked at from the point of view of autocorrelation dfa this analysis can be more informative depending on time scale for short scale days we cannot identify auto correlations on the scale of weeks dfa presents anti persistent behavior and for long time scales n 90 days dfa presents a persistent behavior finally the application of this new type of statistical analysis proved to be efficient and in this sense this paper can contribute to a more accurate descriptive statistics of crime

brain tractography using q ball imaging and graph theory improved connectivities through fibre crossings via a model based approach brain tractography techniques utilize a set of diffusion weighted magnetic resonance images to reconstruct white matter tracts non invasively and in vivo streamline tracking techniques propagate curves from a seed to imply connectivity to distal voxels alternative approaches have been developed that attempt to quantify connection strength between all voxels and the seed tractography based on graph theory is amongst them despite its potential graph based tracking through complex fibre configurations has not been extensively studied and existing methods have inherent limitations anatomically unlikely connections may be identified in fibre crossing regions by assigning relatively high connection strengths to all crossing populations in this study we discuss these limitations and present a new approach for robustly propagating through fibre crossings as described by the orientation distribution functions odfs derived from q ball imaging each image voxel is treated as a graph node and graph arcs connect neighbouring voxels weights representative of both structural and diffusivity features are assigned to each arc to account for the existence of crossing fibre populations within a voxel complex odfs are decomposed into components representative of single fibre populations and an image multigraph is created the multigraph is searched exhaustively but efficiently to find the strongest paths and assign connectivity strengths between a seed and all the other image voxels a comparison with the existing graph based tractography as well as q ball driven front evolution tractography is performed on simulated data and on human q ball imaging data acquired from five healthy volunteers the new approach improves the connection strengths through fibre crossing regions reducing the strengths of paths that are less anatomically plausible

a hopf differential for constant mean curvature surfaces in s2 r and h2 r a basic tool in the theory of constant mean curvature cmc surfaces in space forms is the holomorphic quadratic differential discovered by h hopf in this paper we generalize this differential to immersed cmc surfaces in the product spaces s r and h r and prove a corresponding result about the geometry of immersed cmc spheres s in these target spaces

bootgraph probabilistic fiber tractography using bootstrap algorithms and graph theory bootstrap methods have recently been introduced to diffusion weighted magnetic resonance imaging to estimate the measurement uncertainty of ensuing diffusion parameters directly from the acquired data without the necessity to assume a noise model these methods have been previously combined with deterministic streamline tractography algorithms to allow for the assessment of connection probabilities in the human brain thereby the local noise induced disturbance in the diffusion data is accumulated additively due to the incremental progression of streamline tractography algorithms graph based approaches have been proposed to overcome this drawback of streamline techniques for this reason the bootstrap method is in the present work incorporated into a graph setup to derive a new probabilistic fiber tractography method called bootgraph the acquired data set is thereby converted into a weighted undirected graph by defining a vertex in each voxel and edges between adjacent vertices by means of the cone of uncertainty which is derived using the wild bootstrap a weight is thereafter assigned to each edge two path finding algorithms are subsequently applied to derive connection probabilities while the first algorithm is based on the shortest path approach the second algorithm takes all existing paths between two vertices into consideration tracking results are compared to an established algorithm based on the bootstrap method in combination with streamline fiber tractography and to another graph based algorithm the bootgraph shows a very good performance in crossing situations with respect to false negatives and permits incorporating additional constraints such as a curvature threshold by inheriting the advantages of the bootstrap method and graph theory the bootgraph method provides a computationally efficient and flexible probabilistic tractography setup to compute connection probability maps and virtual fiber pathways without the drawbacks of streamline tractography algorithms or the assumption of a noise distribution moreover the bootgraph can be applied to common dti data sets without further modifications and shows a high repeatability thus it is very well suited for longitudinal studies and meta studies based on dti

cross correlations between crude oil and agricultural commodity markets abstract in this paper we investigate cross correlations between crude oil and agricultural commodity markets based on a popular statistical test proposed by podobnik et al 2009 we find that the linear return cross correlations are significant at larger lag lengths and the volatility cross correlations are highly significant at all of the lag lengths under consideration using a detrended cross correlation analysis dcca we find that the return cross correlations are persistent for corn and soybean and anti persistent for oat and soybean the volatility cross correlations are strongly persistent using a nonlinear cross correlation measure our results show that cross correlations are relatively weak but they are significant for smaller time scales for larger time scales the cross correlations are not significant the reason may be that information transmission from crude oil market to agriculture markets can complete within a certain period of time finally based on multifractal extension of dcca we find that the cross correlations are multifractal and high oil prices partly contribute to food crisis during the period of 2006 mid 2008

image distortion correction in fmri a quantitative evaluation a well recognized problem with the echo planar imaging epi technique most commonly used for functional magnetic resonance imaging fmri studies is geometric distortion caused by magnetic field inhomogeneity this makes it difficult to achieve an accurate registration between a functional activation map calculated from an epi time series and an undistorted high resolution anatomical image a correction method based on mapping the spatial distribution of field inhomogeneities can be used to reduce these distortions this approach is attractive in its simplicity but requires postprocessing to improve the robustness of the acquired field map and reduce any secondary artifacts furthermore the distribution of the internal magnetic field throughout the head is position dependent resulting in an interaction between distortion and head motion therefore a single field map may not be sufficient to correct for the distortions throughout a whole fmri time series in this paper we present a quantitative evaluation of image distortion correction for fmri at 2t we assess i methods for the acquisition and calculation of field maps ii the effect of image distortion correction on the coregistration between anatomical and functional images and iii the interaction between distortion and head motion assessing the feasibility of using field maps to reduce this effect we propose that field maps with acceptable noise levels can be generated easily using a dual echo time epi sequence and demonstrate the importance of distortion correction for anatomical coregistration even for small distortions using a dual echo time series to generate a unique field map at each time point we characterize the interaction between head motion and geometric distortion however we suggest that the variance between successively measured field maps introduces additional unwanted variance in the voxel time series and is therefore not adequate to correct for time varying distortions

modeling long range cross correlations in two component arfima and fiarch processes we investigate how simultaneously recorded long range power law correlated multivariate signals cross correlate to this end we introduce a two component arfima stochastic process and a two component fiarch process to generate coupled fractal signals with long range power law correlations which are at the same time long range cross correlated we study how the degree of cross correlations between these signals depends on the scaling exponents characterizing the fractal correlations in each signal and on the coupling between the signals our findings have relevance when studying parallel outputs of multiple component of physical physiological and social systems

universal and nonuniversal properties of cross correlations in financial time series we use methods of random matrix theory to analyze the cross correlation matrix c of stock price changes of the largest 1000 u s companies for the 2 year period 1994 1995 we find that the statistics of most of the eigenvalues in the spectrum of c agree with the predictions of random matrix theory but there are deviations for a few of the largest eigenvalues we find that c has the universal properties of the gaussian orthogonal ensemble of random matrices furthermore we analyze the eigenvectors of c through their inverse participation ratio and find eigenvectors with large ratios at both edges of the eigenvalue spectrum a situation reminiscent of localization theory results

economic fluctuations and statistical physics quantifying extremely rare and less rare events in finance one challenge of economics is that the systems treated by these sciences have no perfect metronome in time and no perfect spatial architecture crystalline or otherwise nonetheless as if by magic out of nothing but randomness one finds remarkably fine tuned processes in time we present an overview of recent research joining practitioners of economic theory and statistical physics to try to better understand puzzles regarding economic fluctuations one of these puzzles is how to describe outliers phenomena that lie outside of patterns of statistical regularity we review evidence consistent with the possibility that such outliers may not exist this possibility is supported by recent analysis of databases containing information about each trade of every stock r 2007 elsevier b v all rights reserved

multifractal detrended cross correlation analysis in the mena area abstract in this paper we investigated multifractal cross correlations qualitatively and quantitatively using a cross correlation test and the multifractal detrended cross correlation analysis method mf dcca for markets in the mena area we used cross correlation coefficients to measure the level of this correlation the analysis concerns four stock market indices of morocco tunisia egypt and jordan the countries chosen are signatory of the agadir agreement concerning the establishment of a free trade area comprising arab mediterranean countries we computed the bivariate generalized hurst exponent renyi exponent and spectrum of singularity for each pair of indices to measure quantitatively the cross correlations by analyzing the results we found the existence of multifractal cross correlations between all of these markets we compared the spectrum width of these indices we also found which pair of indices has a strong multifractal cross correlation

dt mri fiber tracking a shortest paths approach we derive a new fiber tracking algorithm for dt mri that parts with the locally ldquogreedyrdquo paradigm intrinsic to conventional tracking algorithms we demonstrate the ability to precisely reconstruct a diverse range of fiber trajectories in authentic and computer generated dt mri data for which well known conventional tracking algorithms are shown to fail our approach is to pose fiber tracking as a problem in computing shortest paths in a weighted digraph voxels serve as vertices and edges are included between neighboring voxels we assign probabilities weights to edges using a bayesian framework higher probabilities are assigned to edges that are aligned with fiber trajectories in their close proximity we compute optimal paths of maximum probability using computationally scalable shortest path algorithms the salient features of our approach are global optimality unlike conventional tracking algorithms local errors do not accumulate and one ldquowrong turnrdquo does not spell disaster a target point is specified a priori precise reconstruction is demonstrated for extremely low signal to noise ratio impartiality to which of two endpoints is used as a seed and faster computation times than conventional all paths tracking we can use our new tracking algorithm in either a single path tracking mode deterministic tracking or an all paths tracking mode probabilistic tracking

a dti derived measure of cortico cortical connectivity we arm researchers with a simple method to chart a macroscopic cortico cortical connectivity network in living human subjects the researcher provides a diffusion magnetic resonance imaging mri data set and n cortical regions of interest in return we provide an n times n structural adjacency matrix sam quantifying the relative connectivity between all cortical region pairs we also return a connectivity map for each pair to enable visualization of interconnecting fiber bundles the measure of connectivity we devise is 1 free of length bias 2 proportional to fiber bundle cross sectional area and 3 invariant to an exchange of seed and target we construct a 3 d lattice scaffolding graph for white matter by drawing a link between each pair of voxels in a 26 voxel neighborhood for which their two respective principal eigenvectors form a sufficiently small angle the connectivity between a cortical region pair is then measured as the maximum number of link disjoint paths that can be established between them in the white matter graph we devise an efficient edmonds karp like algorithm to compute a conservative bound on the maximum number of link disjoint paths using both simulated and authentic diffusion tensor imaging data we demonstrate that the number of link disjoint paths as a measure of connectivity satisfies properties 1 3 unlike the fraction of intersecting streamlines the measure intrinsic to most existing probabilistic tracking algorithms finally we present connectivity maps of some notoriously difficult to track longitudinal and contralateral fasciculi

filtered multitensor tractography we describe a technique that uses tractography to drive the local fiber model estimation existing techniques use independent estimation at each voxel so there is no running knowledge of confidence in the estimated model fit we formulate fiber tracking as recursive estimation at each step of tracing the fiber the current estimate is guided by those previous to do this we perform tractography within a filter framework and use a discrete mixture of gaussian tensors to model the signal starting from a seed point each fiber is traced to its termination using an unscented kalman filter to simultaneously fit the local model to the signal and propagate in the most consistent direction despite the presence of noise and uncertainty this provides a causal estimate of the local structure at each point along the fiber using two and three fiber models we demonstrate in synthetic experiments that this approach significantly improves the angular resolution at crossings and branchings in vivo experiments confirm the ability to trace through regions known to contain such crossing and branching while providing inherent path regularization

connecting and merging fibres pathway extraction by combining probability maps probability mapping of connectivity is a powerful tool to determine the fibre structure of white matter in the brain probability maps are related to the degree of connectivity to a chosen seed area in many applications however it is necessary to isolate a fibre bundle that connects two areas a frequently suggested solution is to select curves which pass only through two or more areas this is very inefficient especially for long distance pathways and small areas in this paper a novel probability based method is presented that is capable of extracting neuronal pathways defined by two seed points a monte carlo simulation based tracking method similar to the probabilistic index of connectivity pico approach was extended to preserve the directional information of the main fibre bundles passing a voxel by combining two of these extended visiting maps arising from different seed points two independent parameters are determined for each voxel the first quantifies the uncertainty that a voxel is connected to both seed points the second represents the directional information and estimates the proportion of fibres running in the direction of the other seed point connecting fibre or face a third area merging fibre both parameters are used to calculate the probability that a voxel is part of the bundle connecting both seed points the performance and limitations of this dti based method are demonstrated using simulations as well as in vivo measurements

surface area and cortical thickness descriptors reveal different attributes of the structural human brain networks recently a related morphometry based connection concept has been introduced using local mean cortical thickness and volume to study the underlying complex architecture of the brain networks in this article the surface area is employed as a morphometric descriptor to study the concurrent changes between brain structures and to build binarized connectivity graphs the statistical similarity in surface area between pair of regions was measured by computing the partial correlation coefficient across 186 normal subjects of the cuban human brain mapping project we demonstrated that connectivity matrices obtained follow a small world behavior for two different parcellations of the brain gray matter the properties of the connectivity matrices were compared to the matrices obtained using the mean cortical thickness for the same cortical parcellations the topology of the cortical thickness and surface area networks were statistically different demonstrating that both capture distinct properties of the interaction or different aspects of the same interaction mechanical anatomical chemical etc between brain structures this finding could be explained by the fact that each descriptor is driven by distinct cellular mechanisms as result of a distinct genetic origin to our knowledge this is the first time that surface area is used to study the morphological connectivity of brain networks

cross correlations between volume change and price change in finance one usually deals not with prices but with growth rates r defined as the difference in logarithm between two consecutive prices here we consider not the trading volume but rather the volume growth rate r the difference in logarithm between two consecutive values of trading volume to this end we use several methods to analyze the properties of volume changes r and their relationship to price changes r we analyze 14 981 daily recordings of the standard and poor s s p 500 index over the 59 year period 1950 2009 and find power law cross correlations between r and r by using detrended cross correlation analysis dcca we introduce a joint stochastic process that models these cross correlations motivated by the relationship between r and r we estimate the tail exponent of the probability density function p r r 1 for both the s p 500 index as well as the collection of 1819 constituents of the new york stock exchange composite index on 17 july 2009 as a new method to estimate we calculate the time intervals q between events where r q we demonstrate that q the average of q obeys q q we find 3 furthermore by aggregating all q values of 28 global financial indices we also observe an approximate inverse cubic law

surfaces with parallel mean curvature vector in complex space forms we consider a quadratic form defined on the surfaces with parallel mean curvature vector of an any dimensional complex space form and prove that its 2 0 part is holomorphic when the complex dimension of the ambient space is equal to 2 we define a second quadratic form with the same property and then determine those surfaces with parallel mean curvature vector on which the 2 0 parts of both of them vanish we also provide a reduction of codimension theorem and prove a non existence result for 2 spheres with parallel mean curvature vector

effect of trends on detrended fluctuation analysis scaling behavior we find that crossovers result from the competition between the scaling of the noise and the apparent scaling of the trend we study how the characteristics of these crossovers depend on i the slope of the linear trend ii the amplitude and period of the periodic trend iii the amplitude and power of the power law trend and iv the length as well as the correlation properties of the noise surprisingly we find that the crossovers in the scaling of noisy signals with trends also follow scaling laws i e long range power law dependence of the position of the crossover on the parameters of the trends we show that the dfa result of noise with a trend can be exactly determined by the superposition of the separate results of the dfa on the noise and on the trend assuming that the noise and the trend are not correlated if this superposition rule is not followed this is an indication that the noise and the superposed trend are not independent so that removing the trend could lead to changes in the correlation properties of the noise in addition we show how to use dfa appropriately to minimize the effects of trends how to recognize if a crossover indicates indeed a transition from one type to a different type of underlying correlation or if the crossover is due to a trend without any transition in the dynamical properties of the noise

the classification of the surfaces with parallel mean curvature vector in two dimensional complex space forms in this paper we find new surfaces with parallel mean curvature vectors in two dimensional complex space forms that are not of constant kahler angles and then classify all of them the surface can be isometrically deformed preserving both the length of the mean curvature vector and the kahler angle from this classification the deformations appear only when the curvature of the ambiant space is nonpositive

cross correlations between west texas intermediate crude oil and the stock markets of the bric abstract in this paper we investigate the cross correlation properties between west texas intermediate crude oil and the stock markets of the bric we use not only the qualitative analysis of the cross correlation test but also take the quantitative analysis of the mf dxa confirming the cross correlation relationship between west texas intermediate crude oil and the stock markets of the bric brazil russia india and china respectively which have strongly multifractal features and the cross correlations are more strongly multifractal in the short term than in the long term furthermore based on the multifractal spectrum we also find the multifractality strength between the crude oil wti and chinese stock market is stronger than the multifractality strength of other pairs based on the iraq war mar 20 2003 and the financial crisis in 2008 we divide sample period into four segments to research the degree of the multifractal h and the market efficiency and the risk finally we employ the technique of the rolling window to calculate the time varying ei efficiency index and dependent on the ei we can easily observe the change of stock markets furthermore we explore the relationship between bivariate cross correlation exponents h x y q and the generalized hurst exponents

resolving fiber crossing using advanced fast marching tractography based on diffusion tensor imaging magnetic resonance diffusion tensor tractography is a powerful tool for the non invasive depiction of the white matter architecture in the human brain however due to limitations in the underlying tensor model the technique is often unable to reconstruct correct trajectories in heterogeneous fiber arrangements such as axonal crossings a novel tractography method based on fast marching fm is proposed which is capable of resolving fiber crossings and also permits trajectories to branch it detects heterogeneous fiber arrangements by incorporating information from the entire diffusion tensor the fm speed function is adapted to the local tensor characteristics allowing in particular to maintain the front evolution direction in crossing situations in addition the fm s discretization error is reduced by increasing the number of considered possible front evolution directions the performance of the technique is demonstrated in artificial data and in the healthy human brain comparisons with standard fm tractography and conventional line propagation algorithms show that in the presence of interfering structures the proposed method is more accurate in reconstructing trajectories the in vivo results illustrate that the elucidated major white matter pathways are consistent with known anatomy and that multiple crossings and tract branching are handled correctly

effect of nonstationarities on detrended fluctuation analysis detrended fluctuation analysis dfa is a scaling analysis method used to quantify long range power law correlations in signals many physical and biological signals are noisy heterogeneous and exhibit different types of nonstationarities which can affect the correlation properties of these signals we systematically study the effects of three types of nonstationarities often encountered in real data specifically we consider nonstationary sequences formed in three ways i stitching together segments of data obtained from discontinuous experimental recordings or removing some noisy and unreliable parts from continuous recordings and stitching together the remaining parts a cutting procedure commonly used in preparing data prior to signal analysis ii adding to a signal with known correlations a tunable concentration of random outliers or spikes with different amplitudes and iii generating a signal comprised of segments with different properties e g different standard deviations or different correlation exponents we compare the difference between the scaling results obtained for stationary correlated signals and correlated signals with these three types of nonstationarities we find that introducing nonstationarities to stationary correlated signals leads to the appearance of crossovers in the scaling behavior and we study how the characteristics of these crossovers depend on a the fraction and size of the parts cut out from the signal b the concentration of spikes and their amplitudes c the proportion between segments with different standard deviations or different correlations and d the correlation properties of the stationary signal we show how to develop strategies for preprocessing raw data prior to analysis which will minimize the effects of nonstationarities on the scaling properties of the data and how to interpret the results of dfa for complex signals with different local characteristics

characterizing brain anatomical connections using diffusion weighted mri and graph theory abstract a new methodology based on diffusion weighted magnetic resonance imaging dw mri and graph theory is presented for characterizing the anatomical connections between brain gray matter areas in a first step brain voxels are modeled as nodes of a non directed graph in which the weight of an arc linking two neighbor nodes is assumed to be proportional to the probability of being connected by nervous fibers this probability is estimated by means of probabilistic tissue segmentation and intravoxel white matter orientational distribution function obtained from anatomical mri and dw mri respectively a new tractography algorithm for finding white matter routes is also introduced this algorithm solves the most probable path problem between any two nodes leading to the assessment of probabilistic brain anatomical connection maps in a second step for assessing anatomical connectivity between k gray matter structures the previous graph is redefined as a k 1 partite graph by partitioning the initial nodes set in k non overlapped gray matter subsets and one subset clustering the remaining nodes three different measures are proposed for quantifying anatomical connections between any pair of gray matter subsets anatomical connection strength acs anatomical connection density acd and anatomical connection probability acp this methodology was applied to both artificial and actual human data results show that nervous fiber pathways between some regions of interest were reconstructed correctly additionally mean connectivity maps of acs acd and acp between 71 gray matter structures for five healthy subjects are presented

time dependent cross correlations between different stock returns a directed network of influence we study the time dependent cross correlations of stock returns i e we measure the correlation as the function of the time shift between pairs of stock return time series using tick by tick data we find a weak but significant effect showing that in many cases the maximum correlation appears at nonzero time shift indicating directions of influence between the companies due to the weakness of this effect and the shortness of the characteristic time of the order of a few minutes our findings are compatible with market efficiency the interaction of companies defines a directed network of influence