Multimodal abnormalities of brain structures in adolescents and young adults with major depressive disorder:An activation likelihood estimation meta-analysis

BACKGROUND Major depressive disorder(MDD)in adolescents and young adults contributes significantly to global morbidity,with inconsistent findings on brain structural changes from structural magnetic resonance imaging studies.Activation likeli-hood estimation(ALE)offers a method to synthesize these diverse findings and identify consistent brain anomalies.METHODS We performed a comprehensive literature search in PubMed,Web of Science,Embase,and Chinese National Knowledge Infrastructure databases for neuroi-maging studies on MDD among adolescents and young adults published up to November 19,2023.Two independent researchers performed the study selection,quality assessment,and data extraction.The ALE technique was employed to synthesize findings on localized brain function anomalies in MDD patients,which was supplemented by sensitivity analyses.RESULTS Twenty-two studies comprising fourteen diffusion tensor imaging(DTI)studies and eight voxel-based morphome-try(VBM)studies,and involving 451 MDD patients and 465 healthy controls(HCs)for DTI and 664 MDD patients and 946 HCs for VBM,were included.DTI-based ALE demonstrated significant reductions in fractional anisotropy(FA)values in the right caudate head,right insula,and right lentiform nucleus putamen in adolescents and young adults with MDD compared to HCs,with no regions exhibiting increased FA values.VBM-based ALE did not demonstrate significant alterations in gray matter volume.Sensitivity analyses highlighted consistent findings in the right caudate head(11 of 14 analyses),right insula(10 of 14 analyses),and right lentiform nucleus putamen(11 of 14 analyses).CONCLUSION Structural alterations in the right caudate head,right insula,and right lentiform nucleus putamen in young MDD patients may contribute to its recurrent nature,offering insights for targeted therapies.

Vulnerable brain regions in adolescent major depressive disorder:A resting-state functional magnetic resonance imaging activation likelihood estimation meta-analysis

BACKGROUND Adolescent major depressive disorder(MDD)is a significant mental health concern that often leads to recurrent depression in adulthood.Resting-state functional magnetic resonance imaging(rs-fMRI)offers unique insights into the neural mechanisms underlying this condition.However,despite previous research,the specific vulnerable brain regions affected in adolescent MDD patients have not been fully elucidated.AIM To identify consistent vulnerable brain regions in adolescent MDD patients using rs-fMRI and activation likelihood estimation(ALE)meta-analysis.METHODS We performed a comprehensive literature search through July 12,2023,for studies investigating brain functional changes in adolescent MDD patients.We utilized regional homogeneity(ReHo),amplitude of low-frequency fluctuations(ALFF)and fractional ALFF(fALFF)analyses.We compared the regions of aberrant spontaneous neural activity in adolescents with MDD vs healthy controls(HCs)using ALE.RESULTS Ten studies(369 adolescent MDD patients and 313 HCs)were included.Combining the ReHo and ALFF/fALFF data,the results revealed that the activity in the right cuneus and left precuneus was lower in the adolescent MDD patients than in the HCs(voxel size:648 mm3,P<0.05),and no brain region exhibited increased activity.Based on the ALFF data,we found decreased activity in the right cuneus and left precuneus in adolescent MDD patients(voxel size:736 mm3,P<0.05),with no regions exhibiting increased activity.CONCLUSION Through ALE meta-analysis,we consistently identified the right cuneus and left precuneus as vulnerable brain regions in adolescent MDD patients,increasing our understanding of the neuropathology of affected adolescents.

Life prediction for vacuum fluorescent display using maximum likelihood estimation

In order to obtain the life information of the vacuum fluorescent display （VFD） in a short time, a model of constant stress accelerated life tests （CSALT） is established with its filament temperature increased, and four constant stress tests are conducted. The Weibull function is applied to describe the life distribution of the VFD, and the maximum likelihood estimation （MLE） and its iterative flow chart are used to calculate the shape parameters and the scale parameters. Furthermore, the accelerated life equation is determined by the least square method, the Kolmogorov-Smirnov test is performed to verify whether the VFD life meets the Weibull distribution or not, and selfdeveloped software is employed to predict the average life and the reliable life. Statistical data analysis results demonstrate that the test plans are feasible and versatile, that the VFD life follows the Weibull distribution, and that the VFD accelerated model satisfies the linear Arrhenius equation. The proposed method and the estimated life information of the VFD can provide some significant guideline to its manufacturers and customers.

Maximum Likelihood Estimation of the Identification Parameters and Its Correction

By taking the subsequence out of the input-output sequence of a system polluted by white noise, an independent observation sequence and its probability density are obtained and then a maximum likelihood estimation of the identification parameters is given. In order to decrease the asymptotic error, a corrector of maximum likelihood (CML) estimation with its recursive algorithm is given. It has been proved that the corrector has smaller asymptotic error than the least square methods. A simulation example shows that the corrector of maximum likelihood estimation is of higher approximating precision to the true parameters than the least square methods.

Brain pathways of pain empathy activated by pained facial expressions: a meta-analysis of fMRI using the activation likelihood estimation method

OBJECTIVE: The objective of this study is to summarize and analyze the brain signal patterns of empathy for pain caused by facial expressions of pain utilizing activation likelihood estimation, a meta-analysis method. DATA SOURCES: Studies concerning the brain mechanism were searched from the Science Citation Index, Science Direct, PubMed, DeepDyve, Cochrane Library, SinoMed, Wanfang, VIP, China National Knowledge Infrastructure, and other databases, such as SpringerLink, AMA, Science Online, Wiley Online, were collected. A time limitation of up to 13 December 2016 was applied to this study. DATA SELECTION: Studies presenting with all of the following criteria were considered for study inclusion: Use of functional magnetic resonance imaging, neutral and pained facial expression stimuli, involvement of adult healthy human participants over 18 years of age, whose empathy ability showed no difference from the healthy adult, a painless basic state, results presented in Talairach or Montreal Neurological Institute coordinates, multiple studies by the same team as long as they used different raw data. OUTCOME MEASURES: Activation likelihood estimation was used to calculate the combined main activated brain regions under the stimulation of pained facial expression. RESULTS: Eight studies were included, containing 178 subjects. Meta-analysis results suggested that the anterior cingulate cortex(BA32), anterior central gyrus(BA44), fusiform gyrus, and insula(BA13) were activated positively as major brain areas under the stimulation of pained facial expression. CONCLUSION: Our study shows that pained facial expression alone, without viewing of painful stimuli, activated brain regions related to pain empathy, further contributing to revealing the brain's mechanisms of pain empathy.

MAXIMUM LIKELIHOOD ESTIMATION FOR INSAR IMAGE CO-REGISTRATION

This paper presents a closed-form robust phase correlation based algorithm for performing image registration to subpixel accuracy.The subpixel translational shift information is directly obtained from the phase of the normalized cross power spectrum by using Maximum Likelihood Estimation(MLE).The proposed algorithm also has slighter time complexity.Experimental results show that the proposed algorithm yields superior registration precision on the Cramér-Rao Bound(CRB) in the presence of aliasing and noise.

Convergence Diagnostics for Gibbs Sampler via Maximum Likelihood Estimation

A diagnostic procedure based on maximum likelihood estimation, to study the convergence of the Markov chain produced by Gibbs sampler, is presented. The unbiasedness, consistent and asymptotic normality are considered for the estimation of the parameters produced by the procedure. An example is provided to illustrate the procedure, and the numerical result is consistent with the theoretical one.

Immune Clone Maximum Likelihood Estimation of Improved Non-homogeneous Poisson Process Model Parameters

Aiming at the solving problem of improved nonhomogeneous Poisson process( NHPP) model in engineering application,the immune clone maximum likelihood estimation( MLE)method for solving model parameters was proposed. The minimum negative log-likelihood function was used as the objective function to optimize instead of using iterative method to solve complex system of equations,and the problem of parameter estimation of improved NHPP model was solved by immune clone algorithm. And the interval estimation of reliability indices was given by using fisher information matrix method and delta method. An example of failure truncated data from multiple numerical control( NC) machine tools was taken to prove the method. and the results show that the algorithm has a higher convergence rate and computational accuracy, which demonstrates the feasibility of the method.

Weighted Likelihood Estimation Method Based on the Multidimensional Nominal Response Model

The Monte Carlo study evaluates the relative accuracy of Warm＇s （1989） weighted likelihood estimate （WLE） compared to the maximum likelihood estimate （MLE） using the nominal response model. And the results indicate that WLE was more accurate than MLE.

Maximum Likelihood Estimation of Clock Synchronization Error in OFDM System

The performance degradation of an orthogonal frequency division multiplexing (OFDM) systems due to clock synchronization error is analyzed and a pilot-aided maximum likelihood (ML) estimating method is proposed to correct it. The proposed algorithm enables clock synchronization error estimation from a pilot whose duration is only two symbol periods. The study shows that this method is simple and exact. The clock synchronization error can be corrected almost entirely.

Parameters Estimation of a Bivariate Generalized Poisson Distribution with Applications to Metabolic Syndrome Data

Background: Bivariate count data are commonly encountered in medicine, biology, engineering, epidemiology and many other applications. The Poisson distribution has been the model of choice to analyze such data. In most cases mutual independence among the variables is assumed, however this fails to take into accounts the correlation between the outcomes of interests. A special bivariate form of the multivariate Lagrange family of distribution, names Generalized Bivariate Poisson Distribution, is considered in this paper. Objectives: We estimate the model parameters using the method of maximum likelihood and show that the model fits the count variables representing components of metabolic syndrome in spousal pairs. We use the likelihood local score to test the significance of the correlation between the counts. We also construct confidence interval on the ratio of the two correlated Poisson means. Methods: Based on a random sample of pairs of count data, we show that the score test of independence is locally most powerful. We also provide a formula for sample size estimation for given level of significance and given power. The confidence intervals on the ratio of correlated Poisson means are constructed using the delta method, the Fieller’s theorem, and the nonparametric bootstrap. We illustrate the methodologies on metabolic syndrome data collected from 4000 spousal pairs. Results: The bivariate Poisson model fitted the metabolic syndrome data quite satisfactorily. Moreover, the three methods of confidence interval estimation were almost identical, meaning that they have the same interval width.

Joint polarization and DOA estimation based on improved maximum likelihood estimator and performance analysis for conformal array

The conformal array can make full use of the aperture,save space,meet the requirements of aerodynamics,and is sensitive to polarization information.It has broad application prospects in military,aerospace,and communication fields.The joint polarization and direction-of-arrival(DOA)estimation based on the conformal array and the theoretical analysis of its parameter estimation performance are the key factors to promote the engineering application of the conformal array.To solve these problems,this paper establishes the wave field signal model of the conformal array.Then,for the case of a single target,the cost function of the maximum likelihood(ML)estimator is rewritten with Rayleigh quotient from a problem of maximizing the ratio of quadratic forms into those of minimizing quadratic forms.On this basis,rapid parameter estimation is achieved with the idea of manifold separation technology(MST).Compared with the modified variable projection(MVP)algorithm,it reduces the computational complexity and improves the parameter estimation performance.Meanwhile,the MST is used to solve the partial derivative of the steering vector.Then,the theoretical performance of ML,the multiple signal classification(MUSIC)estimator and Cramer-Rao bound(CRB)based on the conformal array are derived respectively,which provides theoretical foundation for the engineering application of the conformal array.Finally,the simulation experiment verifies the effectiveness of the proposed method.

Correlation Between Brain Activation Changes and Cognitive Improvement Following Cognitive Remediation Therapy in Schizophrenia： An Activation Likelihood Estimation Meta-analysis

Background：Several studies using functional magnetic resonance imaging （fMRI） and positron emission tomography （PET） have indicated that cognitive remediation therapy （CRT） might improve cognitive function by changing brain activations in patients with schizophrenia.However,the results were not consistent in these changed brain areas in different studies.The present activation likelihood estimation （ALE） meta-analysis was conducted to investigate whether cognitive function change was accompanied by the brain activation changes,and where the main areas most related to these changes were in schizophrenia patients after CRT.Analyses of whole-brain studies and whole-brain ＋ region of interest （ROI） studies were compared to explore the effect of the different methodologies on the results.Methods：A computerized systematic search was conducted to collect fMRI and PET studies on brain activation changes in schizophrenia patients from pre-to post-CRT.Nine studies using fMRI techniques were included in the meta-analysis.Ginger ALE 2.3.1 was used to perform meta-analysis across these imaging studies.Results：The main areas with increased brain activation were in frontal and parietal lobe,including left medial frontal gyrus,left inferior frontal gyrus,right middle frontal gyrus,right postcentral gyrus,and inferior parietal lobule in patients after CRT,yet no decreased brain activation was found.Although similar increased activation brain areas were identified in ALE with or without ROI studies,analysis including ROI studies had a higher ALE value.Conclusions：The current findings suggest that CRT might improve the cognition of schizophrenia patients by increasing activations of the frontal and parietal lobe.In addition,it might provide more evidence to confirm results by including ROI studies in ALE meta-analysis.

Fast method for spreading sequence estimation of DSSS signal based on maximum likelihood function

To estimate the spreading sequence of the direct sequence spread spectrum (DSSS) signal, a fast algorithm based on maximum likelihood function is proposed, and the theoretical derivation of the algorithm is provided. By simplifying the objective function of maximum likelihood estimation, the algorithm can realize sequence synchronization and sequence estimation via adaptive iteration and sliding window. Since it avoids the correlation matrix computation, the algorithm significantly reduces the storage requirement and the computation complexity. Simulations show that it is a fast convergent algorithm, and can perform well in low signal to noise ratio (SNR).

Collaborative target tracking in WSNs using the combination of maximum likelihood estimation and Kalman filtering

Target tracking using wireless sensor networks requires efficient collaboration among sensors to tradeoff between energy consumption and tracking accuracy. This paper presents a collaborative target tracking approach in wire- less sensor networks using the combination of maximum likelihood estimation and the Kalman filter. The cluster leader converts the received nonlinear distance measurements into linear observation model and approximates the covariance of the converted measurement noise using maximum likelihood estimation, then applies Kalman filter to recursively update the target state estimate using the converted measurements. Finally, a measure based on the Fisher information matrix of maximum likelihood estimation is used by the leader to select the most informative sensors as a new tracking cluster for further tracking. The advantages of the proposed collaborative tracking approach are demonstrated via simulation results.

Frame synchronization for underwater acoustic communication based on maximum likelihood estimation

Traditional frame synchronization methods for underwater acoustic communication(UWAC) merely depend on correlation coefficient when synchronization signal detection is concerned and,hence,false triggering and missed synchronization can hardly be avoided in complex UWAC channels.In order to solve this problem,firstly,we analyze the effects of interference from noise,multipath and Doppler on frame synchronization;then we propose a new frame synchronization scheme based on parameter estimation.By exploiting the parameter estimation technique,we detect the synchronization signal according to the estimated parameters,thus the false triggering rate and missed synchronization rate can be reduced.We also simplify the maximum likelihood estimation to reduce computational cost.Simulation results indicate that this new scheme outperforms the traditional method in terms of delay resolution and correlation coefficient.Both static and mobile communication experimental results show that the correlation coefficient of the new scheme is higher than that of the traditional one.Moreover,the detection ability of the receiver is improved,which helps to avoid false triggering and missed synchronization.

A novel joint navigation state error discriminator based on iterative maximum likelihood estimation

To break through the limitations of traditional discriminators used in vector tracking loops, this paper presents an iterative maximum likelihood estimation(IMLE) method for extracting navigation state errors from multi-satellite signals. The IMLE method takes into account both computational cost and estimation accuracy. The associated gradient vector and Hessian matrix of the MLE cost function are derived. The characteristics of the proposed joint discriminator are analyzed based on the properties of the MLE cost function,gradient vector, and Hessian matrix. The effectiveness of IMLE is verified by Monte Carlo simulation.

Scenario Generations for Renewable Energy Sources and Loads Based on Implicit Maximum Likelihood Estimations

Scenario generations for renewable energy sources and loads play an important role in the stable operation and risk assessment of integrated energy systems.This paper proposes a deep generative network based method to model time-series curves,e.g.,power generation curves and load curves,of renewable energy sources and loads based on implicit maximum likelihood estimations(IMLEs),which can generate realistic scenarios with similar patterns as real ones.After training the model,any number of new scenarios can be obtained by simply inputting Gaussian noises into the data generator of IMLEs.The proposed approach does not require any model assumptions or prior knowledge of the form in the likelihood function being made during the training process,which leads to stronger applicability than explicit density model based methods.The extensive experiments show that the IMLEs accurately capture the complex shapes,frequency-domain characteristics,probability distributions,and correlations of renewable energy sources and loads.Moreover,the proposed approach can be easily generalized to scenario generation tasks of various renewable energy sources and loads by fine-tuning parameters and structures.

Maximum Likelihood Estimation via Duality for Cell Probabilities Subject to Convex and Log-convex Constraints

This article proposes a method for fitting models subject to a convex and log-convex constraint on the probability vector of a product multinomial （binomial） distribution. We present an iterative algorithm for finding the restricted maximum likelihood estimates （MLEs） of the probability vector and show that the algorithm converges to the true solution. Some examples are discussed to illustrate the method.

Adaptive speckle reduction of ultrasound images based on maximum likelihood estimation

A method has been developed in this paper to gain effective speckle reduction in medical ultrasound images. To exploit full knowledge of the speckle distribution, here maximum likelihood was used to estimate speckle parameters corresponding to its statistical mode. Then the results were incorporated into the nonlinear anisotropic diffusion to achieve adaptive speckle reduction. Verified with simulated and ultrasound images, we show that this algorithm is capable of enhancing features of clinical interest and reduces speckle noise more efficiently than just applying classical filters. To avoid edge contribution, changes of contrast-to-noise ratio of different regions are also compared to investigate the performance of this approach.