共查询到20条相似文献,搜索用时 31 毫秒
1.
《IEEE transactions on medical imaging》2009,28(7):1023-1036
2.
Saxena S Schieber MH Thakor NV Sarma SV 《IEEE transactions on bio-medical engineering》2012,59(7):2030-2039
An extraordinary amount of electrophysiological data has been collected from various brain nuclei to help us understand how neural activity in one region influences another region. In this paper, we exploit the point process modeling (PPM) framework and describe a method for constructing aggregate input-output (IO) stochastic models that predict spiking activity of a population of neurons in the "output" region as a function of the spiking activity of a population of neurons in the "input" region. We first build PPMs of each output neuron as a function of all input neurons, and then cluster the output neurons using the model parameters. Output neurons that lie within the same cluster have the same functional dependence on the input neurons. We first applied our method to simulated data, and successfully uncovered the predetermined relationship between the two regions. We then applied our method to experimental data to understand the input-output relationship between motor cortical neurons and 1) somatosensory and 2) premotor cortical neurons during a behavioral task. Our aggregate IO models highlighted interesting physiological dependences including relative effects of inhibition/excitation from input neurons and extrinsic factors on output neurons. 相似文献
3.
《IEEE transactions on medical imaging》2009,28(9):1473-1487
4.
《IEEE transactions on image processing》2009,18(8):1760-1771
5.
This paper unifies our earlier work on detection of brain activation (Rajapakse and Piyaratna, 2001) and connectivity (Rajapakse and Zhou, 2007) in a probabilistic framework for analyzing effective connectivity among activated brain regions from functional magnetic resonance imaging (fMRI) data. Interactions among brain regions are expressed by a dynamic Bayesian network (DBN) while contextual dependencies within functional images are formulated by a Markov random field. The approach simultaneously considers both the detection of brain activation and the estimation of effective connectivity and does not require a priori model of connectivity. Experimental results show that the present approach outperforms earlier fMRI analysis techniques on synthetic functional images and robustly derives brain connectivity from real fMRI data. 相似文献
6.
Li X Coyle D Maguire L McGinnity TM Benali H 《IEEE transactions on medical imaging》2011,30(7):1365-1380
In this paper a model selection algorithm for a nonlinear system identification method is proposed to study functional magnetic resonance imaging (fMRI) effective connectivity. Unlike most other methods, this method does not need a pre-defined structure/model for effective connectivity analysis. Instead, it relies on selecting significant nonlinear or linear covariates for the differential equations to describe the mapping relationship between brain output (fMRI response) and input (experiment design). These covariates, as well as their coefficients, are estimated based on a least angle regression (LARS) method. In the implementation of the LARS method, Akaike's information criterion corrected (AICc) algorithm and the leave-one-out (LOO) cross-validation method were employed and compared for model selection. Simulation comparison between the dynamic causal model (DCM), nonlinear identification method, and model selection method for modelling the single-input-single-output (SISO) and multiple-input multiple-output (MIMO) systems were conducted. Results show that the LARS model selection method is faster than DCM and achieves a compact and economic nonlinear model simultaneously. To verify the efficacy of the proposed approach, an analysis of the dorsal and ventral visual pathway networks was carried out based on three real datasets. The results show that LARS can be used for model selection in an fMRI effective connectivity study with phase-encoded, standard block, and random block designs. It is also shown that the LOO cross-validation method for nonlinear model selection has less residual sum squares than the AICc algorithm for the study. 相似文献
7.
Zhu H Ibrahim JG Tang N Rowe DB Hao X Bansal R Peterson BS 《IEEE transactions on medical imaging》2007,26(7):954-966
Methods for the analysis of brain morphology, including voxel-based morphology and surface-based morphometries, have been used to detect associations between brain structure and covariates of interest, such as diagnosis, severity of disease, age, IQ, and genotype. The statistical analysis of morphometric measures usually involves two statistical procedures: 1) invoking a statistical model at each voxel (or point) on the surface of the brain or brain subregion, followed by mapping test statistics (e.g., t test) or their associated p values at each of those voxels; 2) correction for the multiple statistical tests conducted across all voxels on the surface of the brain region under investigation. We propose the use of new statistical methods for each of these procedures. We first use a heteroscedastic linear model to test the associations between the morphological measures at each voxel on the surface of the specified subregion (e.g., cortical or subcortical surfaces) and the covariates of interest. Moreover, we develop a robust test procedure that is based on a resampling method, called wild bootstrapping. This procedure assesses the statistical significance of the associations between a measure of given brain structure and the covariates of interest. The value of this robust test procedure lies in its computationally simplicity and in its applicability to a wide range of imaging data, including data from both anatomical and functional magnetic resonance imaging (fMRI). Simulation studies demonstrate that this robust test procedure can accurately control the family-wise error rate. We demonstrate the application of this robust test procedure to the detection of statistically significant differences in the morphology of the hippocampus over time across gender groups in a large sample of healthy subjects. 相似文献
8.
Sekihara K Owen JP Trisno S Nagarajan SS 《IEEE transactions on bio-medical engineering》2011,58(11):3121-3129
Source-space coherence analysis has become a popular method to estimate functional connectivity based on MEG/EEG. Source-space analysis involves solving the inverse problem, estimating the time courses of specific brain regions, and then examining the coherence between activities at different brain regions. However, source-space coherence analysis can be confounded by spurious coherence caused due to the leakage properties of the inverse algorithm employed. Such spurious coherence is typically manifested as an artifactual large peak around the seed voxel, called seed blur, in the resulting coherence images. This seed blur often obscures important details of brain interactions. This paper proposes the use of the imaginary part of the coherence to remove the spurious coherence caused by the leakage of an imaging algorithm. We present a theoretical analysis that explains how the use of imaginary part can remove this spurious coherence. We then present results from both computer simulations and experiments using resting-state MEG data which demonstrate the validity of our analysis. 相似文献
9.
Neural networks approach to clustering of activity in fMRI data 总被引:3,自引:0,他引:3
Clusters of correlated activity in functional magnetic resonance imaging data can identify regions of interest and indicate interacting brain areas. Because the extraction of clusters is computationally complex, we apply an approximative method which is based on artificial neural networks. It allows one to find clusters of various degrees of connectivity ranging between the two extreme cases of cliques and connectivity components. We propose a criterion which allows to evaluate the relevance of such structures based on the robustness with respect to parameter variations. Exploiting the intracluster correlations, we can show that regions of substantial correlation with an external stimulus can be unambiguously separated from other activity. 相似文献
10.
为了充分挖掘高维特征空间中辐射源的细微特征, 提出一种基于全局潜在低秩表示(Global Latent Low Rank Representation, GLat-LRR)的通信辐射源潜在细微特征提取方法.首先, 提取通信辐射源信号的瞬时频率, 通过傅里叶变换将信号投影到高维特征空间; 挖掘特征样本之间全局的低秩结构和维度之间全局的潜在低秩关系, 将特征样本集作为整体应用到潜在低秩表示模型中, 利用维度之间低秩关系得到特征样本集的潜在部分矩阵, 每个列向量即为每个通信辐射源信号的潜在细微特征向量.在实际采集的同厂家同型号FM电台数据集上, 该方法提取的潜在细微特征能够显著提高通信辐射源个体识别的性能. 相似文献
11.
Exact Bayesian curve fitting and signal segmentation 总被引:1,自引:0,他引:1
We consider regression models where the underlying functional relationship between the response and the explanatory variable is modeled as independent linear regressions on disjoint segments. We present an algorithm for perfect simulation from the posterior distribution of such a model, even allowing for an unknown number of segments and an unknown model order for the linear regressions within each segment. The algorithm is simple, can scale well to large data sets, and avoids the problem of diagnosing convergence that is present with Monte Carlo Markov Chain (MCMC) approaches to this problem. We demonstrate our algorithm on standard denoising problems, on a piecewise constant AR model, and on a speech segmentation problem. 相似文献
12.
Baoqi Huang Changbin Yu Brian D.O. Anderson Guoqiang Mao 《Wireless Communications and Mobile Computing》2014,14(5):541-556
Distance estimation is vital for localization and many other applications in wireless sensor networks. In this paper, we develop a method that employs a maximum‐likelihood estimator to estimate distances between a pair of neighboring nodes in a static wireless sensor network using their local connectivity information, namely the numbers of their common and non‐common one‐hop neighbors. We present the distance estimation method under a generic channel model, including the unit disk (communication) model and the more realistic log‐normal (shadowing) model as special cases. Under the log‐normal model, we investigate the impact of the log‐normal model uncertainty; we numerically evaluate the bias and standard deviation associated with our method, which show that for long distances our method outperforms the method based on received signal strength; and we provide a Cramér–Rao lower bound analysis for the problem of estimating distances via connectivity and derive helpful guidelines for implementing our method. Finally, on implementing the proposed method on the basis of measurement data from a realistic environment and applying it in connectivity‐based sensor localization, the advantages of the proposed method are confirmed. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
13.
Yi-Ou Li Tom Eichele Vince D. Calhoun Tulay Adali 《Journal of Signal Processing Systems》2012,68(1):31-48
In this work, we apply a novel statistical method, multiset canonical correlation analysis (M-CCA), to study a group of functional
magnetic resonance imaging (fMRI) datasets acquired during simulated driving task. The M-CCA method jointly decomposes fMRI
datasets from different subjects/sessions into brain activation maps and their associated time courses, such that the correlation
in each group of estimated activation maps across datasets is maximized. Therefore, the functional activations across all
datasets are extracted in the order of consistency across different dataset. On the other hand, M-CCA preserves the uniqueness
of the functional maps estimated from each dataset by avoiding concatenation of different datasets in the analysis. Hence,
the cross-dataset variation of the functional activations can be used to test the hypothesis of functional-behavioral association.
In this work, we study 120 simulated driving fMRI datasets and identify parietal-occipital regions and frontal lobe as the
most consistently engaged areas across all the subjects and sessions during simulated driving. The functional-behavioral association
study indicates that all the estimated brain activations are significantly correlated with the steering operation during the
driving task. M-CCA thus provides a new approach to investigate the complex relationship between the brain functions and multiple
behavioral variables, especially in naturalistic tasks as demonstrated by the simulated driving study. 相似文献
14.
Distributed source coding for satellite communications 总被引:2,自引:0,他引:2
Yeung R.W. Zhen Zhang 《IEEE transactions on information theory / Professional Technical Group on Information Theory》1999,45(4):1111-1120
Inspired by mobile satellite communications systems, we consider a source coding system which consists of multiple sources, multiple encoders, and multiple decoders. Each encoder has access to a certain subset of the sources, each decoder has access to certain subset of the encoders, and each decoder reconstructs a certain subset of the sources almost perfectly. The connectivity between the sources and the encoders, the connectivity between the encoders and the decoders, and the reconstruction requirements for the decoders are all arbitrary. Our goal is to characterize the admissible coding rate region. Despite the generality of the problem, we have developed an approach which enables us to study all cases on the same footing. We obtain inner and outer bounds of the admissible coding rate region in terms of ΓN * and Γ¯N*, respectively, which are fundamental regions in the entropy space defined by Yeung (1991). So far, there has not been a full characterization of ΓN*, so these bounds cannot be evaluated explicitly except for some special cases. Nevertheless, we obtain an alternative outer bound which can be evaluated explicitly. We show that this bound is tight for all the special cases for which the admissible coding rate region is known. The model we study in this paper is more general than all previously reported models on multilevel diversity coding, and the tools we use are new in multiuser information theory 相似文献
15.
Yi-Ou Li Adali T. Wei Wang Calhoun V.D. 《Signal Processing, IEEE Transactions on》2009,57(10):3918-3929
In this paper, we introduce a simple and effective scheme to achieve joint blind source separation (BSS) of multiple datasets using multiset canonical correlation analysis (M-CCA) [J. R. Kettenring, "Canonical analysis of several sets of variables", Biometrika, vol. 58, pp. 433-451, 1971]. We first propose a generative model of joint BSS based on the correlation of latent sources within and between datasets. We specify source separability conditions, and show that, when the conditions are satisfied, the group of corresponding sources from each dataset can be jointly extracted by M-CCA through maximization of correlation among the extracted sources. We compare source separation performance of the M-CCA scheme with other joint BSS methods and demonstrate the superior performance of the M-CCA scheme in achieving joint BSS for a large number of datasets, group of corresponding sources with heterogeneous correlation values, and complex-valued sources with circular and non-circular distributions. We apply M-CCA to analysis of functional magnetic resonance imaging (fMRI) data from multiple subjects and show its utility in estimating meaningful brain activations from a visuomotor task. 相似文献
16.
The basal ganglia have been implicated in a modulation role in idiopathic generalized epilepsy (IGE) by an invasive electrophysioigic means.This paper investigates the basal ganglia functional connectivity by using the region-wise functional connection analysis in resting-state functional magnetic resonance imaging (fMRi) in IGE.The increased functional connectivity within basal ganglia,and between the basal ganglia and the thalamus,and decreased functional connectivity between basal ganglia and motor cortex are found in IGE compared with the controls. These findings not only implicate dysfunctional integration in the motor loop in IGE and the enhanced interaction in the modulated loop,but also suggest that the basal ganglia modulate the generalized epileptic discharges with the influence over thalamus in the corticothalamus network. 相似文献
17.
18.
Analytical Model for Connectivity in Vehicular Ad Hoc Networks 总被引:2,自引:0,他引:2
《Vehicular Technology, IEEE Transactions on》2008,57(6):3341-3356
19.
This paper presents an original method for three-dimensional elastic registration of multimodal images. We propose to make use of a scheme that iterates between correcting for intensity differences between images and performing standard monomodal registration. The core of our contribution resides in providing a method that finds the transformation that maps the intensities of one image to those of another. It makes the assumption that there are at most two functional dependencies between the intensities of structures present in the images to register, and relies on robust estimation techniques to evaluate these functions. We provide results showing successful registration between several imaging modalities involving segmentations, T1 magnetic resonance (MR), T2 MR, proton density (PD) MR and computed tomography (CT). We also argue that our intensity modeling may be more appropriate than mutual information (MI) in the context of evaluating high-dimensional deformations, as it puts more constraints on the parameters to be estimated and, thus, permits a better search of the parameter space. 相似文献
20.
《IEEE transactions on medical imaging》2009,28(11):1825-1835