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Modeling and Nonlinear Analysis in fMRI via Statistical Learning

Signal Processing and Communications, 2005

Multi-Scale Information, Network, Causality, and Dynamics: Mathematical Computation and Bayesian Inference to Cognitive Neuroscience and Aging

InTech eBooks, Jun 19, 2013

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PARTICLE FILTERING WITH SEQUENTIAL PARAMETER LEARNING FOR NONLINEAR BOLD fMRI SIGNALS

Advances and applications in statistics

Analyzing the blood oxygenation level dependent (BOLD) effect in the functional magnetic resonanc... more Analyzing the blood oxygenation level dependent (BOLD) effect in the functional magnetic resonance imaging (fMRI) is typically based on recent ground-breaking time series analysis techniques. This work represents a significant improvement over existing approaches to system identification using nonlinear hemodynamic models. It is important for three reasons. First, instead of using linearized approximations of the dynamics, we present a nonlinear filtering based on the sequential Monte Carlo method to capture the inherent nonlinearities in the physiological system. Second, we simultaneously estimate the hidden physiological states and the system parameters through particle filtering with sequential parameter learning to fully take advantage of the dynamic information of the BOLD signals. Third, during the unknown static parameter learning, we employ the low-dimensional sufficient statistics for efficiency and avoiding potential degeneration of the parameters. The performance of the p...

Content-Based Image Orientation Detection Wiht Support Vector Machines

Statistical shape and smoothness models for boundary finding with correspondence

Integrated approaches to non-rigid registration in medical images

Proceedings Fourth IEEE Workshop on Applications of Computer Vision. WACV'98 (Cat. No.98EX201)

This paper describes two new atlas-based methods of 2D single modality non-rigid registration usi... more This paper describes two new atlas-based methods of 2D single modality non-rigid registration using the combined power of physical and statistical shape models. The transformations are constrained to be consistent with the physical properties of deformable elastic solids in the first method and those of viscous fluids in the second to maintain smoothness and continuity. A Bayesian formulation, based on each physical model, on an intensity similarity measure, and on statistical shape information embedded in corresponding boundary points, is employed to derive more accurate and robust approaches to non-rigid registration. A dense set of forces arises from the intensity similarity measure to accommodate complex anatomical details. A sparse set of forces constrains consistency with statistical shape models derived from a training set. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approaches. It is shown that statistical boundary shape information significantly augments and improves physical model based non-rigid registration and the two methods we present each have advantages under different conditions.

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Boundary finding with correspondence using statistical shape models

Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231), 1998

We propose an approach for boundary finding where the correspondence of a subset of boundary poin... more We propose an approach for boundary finding where the correspondence of a subset of boundary points to a model is simultaneously determined. Global shape parameters derived from the statistical variation of object boundary points in a training set are used to model the object. A Bayesian formulation, based on this prior knowledge and the edge information of the input image, is employed to find the object boundary with its subset points in correspondence with boundaries in the training set or the mean boundary. We compared the use of a generic smoothness prior and a uniform independent prior with the training set prior in order to demonstrate the power of this statistical information. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approach, including the validation of the dependence of the method on image quality, different initialization and prior information.

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Nonlinear Estimation and Modeling of fMRI Data Using Spatio-temporal Support Vector Regression

Lecture Notes in Computer Science, 2003

This paper presents a new and general nonlinear framework for fMRI data analysis based on statist... more This paper presents a new and general nonlinear framework for fMRI data analysis based on statistical learning methodology: support vector machines. Unlike most current methods which assume a linear model for simplicity, the estimation and analysis of fMRI signal within the proposed framework is nonlinear, which matches recent findings on the dynamics underlying neural activity and hemodynamic physiology. The approach utilizes spatio-temporal support vector regression (SVR), within which the intrinsic spatio-temporal autocorrelations in fMRI data are reflected. The novel formulation of the problem allows merging model-driven with data-driven methods, and therefore unifies these two currently separate modes of fMRI analysis. In addition, multiresolution signal analysis is achieved and developed. Other advantages of the approach are: avoidance of interpolation after motion estimation, embedded removal of low-frequency noise components, and easy incorporation of multi-run, multi-subject, and multi-task studies into the framework.

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Elastic model based non-rigid registration incorporating statistical shape information

Lecture Notes in Computer Science, 1998

This paper describes a new method of non-rigid registration using the combined power of elastic a... more This paper describes a new method of non-rigid registration using the combined power of elastic and statistical shape models. The transformations are constrained to be consistent with a physical model of elasticity to maintain smoothness and continuity. A Bayesian formulation, based on this model, on an intensity similarity measure, and on statistical shape information embedded in corresponding boundary points, is employed to find a more accurate and robust non-rigid registration. A dense set of forces arises from the intensity similarity measure to accommodate complex anatomical details. A sparse set of forces constrains consistency with statistical shape models derived from a training set. A number of experiments were performed on both synthetic and real medical images of the brain and heart to evaluate the approach. It is shown that statistical boundary shape information significantly augments and improves elastic model based non-rigid registration. 1

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Integrating and classifying parametric features from fMRI data for brain function characterization

SPIE Proceedings, 2006

Recent advances in functional magnetic resonance imaging (fMRI) provide an unparalleled opportuni... more Recent advances in functional magnetic resonance imaging (fMRI) provide an unparalleled opportunity for measuring and characterizing brain function in humans. However, the typically small signal change is very noisy and susceptible to various artifacts, such as those caused by scanner drift, head motion, and cardio-respiratory effects. This paper presents an integrated and exploratory approach to characterize brain function from fMRI

Efficient Moments-based Permutation Tests

Advances in neural information processing systems, 2009

In this paper, we develop an efficient moments-based permutation test approach to improve the tes... more In this paper, we develop an efficient moments-based permutation test approach to improve the test's computational efficiency by approximating the permutation distribution of the test statistic with Pearson distribution series. This approach involves the calculation of the first four moments of the permutation distribution. We propose a novel recursive method to derive these moments theoretically and analytically without any permutation. Experimental results using different test statistics are demonstrated using simulated data and real data. The proposed strategy takes advantage of nonparametric permutation tests and parametric Pearson distribution approximation to achieve both accuracy and efficiency.

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Functional Interactivity in fMRI Using Multiple Seeds’ Correlation Analyses – Novel Methods and Comparisons

Lecture Notes in Computer Science

This paper presents novel statistical methods for estimating brain networks from fMRI data. Funct... more This paper presents novel statistical methods for estimating brain networks from fMRI data. Functional interactions are detected by simultaneously examining multi-seed correlations via multiple correlation coefficients. Spatially structured noise in fMRI is also taken into account during the identification of functional interconnection networks through non-central F hypothesis tests. Furthermore, partial multiple correlations are introduced and formulated to measure any additional task-induced but not stimulus-locked relation over brain regions so that we can take the analysis of functional connectivity closer to the characterization of direct functional interactions of the brain. Evaluation for accuracy and advantages of the new approaches and comparison with the existing single-seed method were performed extensively using both simulated data and real fMRI data.

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On clustering fMRI using Potts and mixture regression models

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2009

In this paper, we propose a model based clustering method for functional magnetic resonance imagi... more In this paper, we propose a model based clustering method for functional magnetic resonance imaging (fMRI) data to detect the functional connectivity network. The Potts model, which represents spatial interactions of neighboring voxels, is introduced to integrate the temporal mixture regression modeling into one single unified model. The estimation of the parameters is achieved through a restoration maximization (RM) algorithm for computation efficiency and accuracy. Additional features of our method include: the optimal number of clusters can be automatically determined; global trends and informative paradigms of the data are extracted by a dimension reduction algorithm based on principal component analysis (PCA) and a statistical significance test. Experimental results demonstrate that our approach can lead to robust and sensitive detection of functional networks.

FMRI analysis through Bayesian variable selection with a spatial prior

2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009

ABSTRACT This paper presents a novel spatial Bayesian method for simultaneous activation detectio... more ABSTRACT This paper presents a novel spatial Bayesian method for simultaneous activation detection and hemodynamic response function (HRF) estimation of functional magnetic resonance imaging (fMRI) data. A Bayesian variable selection approach is used to induce shrinkage and sparsity, with a spatial prior on latent variables representing activated hemodynamic response components. Then, the activation map is generated from the full spectrum of posterior inference constructed through a Markov chain Monte Carlo scheme, and HRFs at different voxels are estimated non-parametrically with information pooling from neighboring voxels. By integrating functional activation detection and HRFs estimation in a unified framework, our method is more robust to noise and less sensitive to model mis-specification.

Efficient Blockwise Permutation Tests Preserving Exchangeability

International journal of statistics in medical research, Jan 30, 2014

In this paper, we present a new blockwise permutation test approach based on the moments of the t... more In this paper, we present a new blockwise permutation test approach based on the moments of the test statistic. The method is of importance to neuroimaging studies. In order to preserve the exchangeability condition required in permutation tests, we divide the entire set of data into certain exchangeability blocks. In addition, computationally efficient moments-based permutation tests are performed by approximating the permutation distribution of the test statistic with the Pearson distribution series. This involves the calculation of the first four moments of the permutation distribution within each block and then over the entire set of data. The accuracy and efficiency of the proposed method are demonstrated through simulated experiment on the magnetic resonance imaging (MRI) brain data, specifically the multi-site voxel-based morphometry analysis from structural MRI (sMRI).

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Multivariate voronoi outlier detection for time series

2014 IEEE Healthcare Innovation Conference (HIC), 2014

Outlier detection is a primary step in many data mining and analysis applications, including heal... more Outlier detection is a primary step in many data mining and analysis applications, including healthcare and medical research. This paper presents a general method to identify outliers in multivariate time series based on a Voronoi diagram, which we call Multivariate Voronoi Outlier Detection (MVOD). The approach copes with outliers in a multivariate framework, via designing and extracting effective attributes or features from the data that can take parametric or nonparametric forms. Voronoi diagrams allow for automatic configuration of the neighborhood relationship of the data points, which facilitates the differentiation of outliers and non-outliers. Experimental evaluation demonstrates that our MVOD is an accurate, sensitive, and robust method for detecting outliers in multivariate time series data.

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Methods for Nonrigid Image Registration

Handbook of Geometric Computing, 2005

Page 1. 17 Methods for Nonrigid Image Registration Lawrence H. Staib1 and YongmeiMichelle Wang2 .... more

Statistical Analysis for Recovery of Structure and Function from Brain Images

Biomedical Engineering, Trends, Research and Technologies, 2011

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An Efficient Permutation Approach for Classical and Bioequivalence Hypothesis Testing of Biomedical Shape Study

2008 International Conference on BioMedical Engineering and Informatics, 2008

A new statistical permutation analysis method is presented in this paper to efficiently and accur... more A new statistical permutation analysis method is presented in this paper to efficiently and accurately localize regionally specific shape differences between groups of 3D biomedical images. It can improve the system's efficiency by approximating the permutation distribution of the test statistic with Pearson distribution series. This procedure involves the calculation of the first four moments of the permutation distribution, which are derived theoretically and analytically without any permutation. Furthermore, bioequivalence testing aims for practical significances between the two groups that are statistically significant with the shape differences larger than a desired threshold. Experimental results based on both classical and bioequivalence hypothesis tests using simulated data and real biomedical images are presented to demonstrate the advantages of the proposed approach.

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3D image interpolation based on directional coherence

Proceedings IEEE Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA 2001), 2001

Image interpolation is of great importance in biomedical visualization and analysis. In this pape... more Image interpolation is of great importance in biomedical visualization and analysis. In this paper, we present a novel gray-level interpolation method called Directional Coherence Interpolation (DCI). The principal advantage of the proposed approach is that it leads to significantly higher visual quality in 3D rendering when compared with traditional image interpolation methods. The basis of DCI is a form of directional image-space coherence. DCI interpolates the missing image data along the maximum coherence directions (MCD), which are estimated from the local image intensity yet constrained by a generic smoothness term. Since the edges of the image and the contents of the objects are well preserved along the MCDs, DCI can incorporate image shape and structure information without the prior requirement of explicit representation of object boundary / surface. A number of experiments were performed on both synthetic and real medical images to evaluate the proposed approach. The experimental results show that in addition to the substantial improvement of visual effects (qualitative evaluation), the quantitative error measures of DCI are also better than the conventional gray level linear interpolation. Comparing with the shape-based interpolation scheme applied on gray-level images, DCI has much lower computation cost.

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