Quantitative comparison and analysis of brain image registration using frequency-adaptive wavelet shrinkage

Wavelets and statistical analysis of functional magnetic resonance images of the human brain

Wavelets provide an orthonormal basis for multiresolution analysis and decorrelation or ‘whitening’ of nonstationary time series and spatial processes. Wavelets are particularly well suited to analysis of biological signals and images, such as human brain imaging data, which often have fractal or scale-invariant properties. We briefly define some key properties of the discrete wavelet transform (DWT) and review its applications to statistical analysis of functional magnetic resonance imaging (fMRI) data. We focus on time series resampling by ‘wavestrapping’ of wavelet coefficients, methods for efficient linear model estimation in the wavelet domain, and wavelet-based methods for multiple hypothesis testing, all of which are somewhat simplified by the decorrelating property of the DWT.

The perfect neuroimaging-genetics-computation storm: collision of petabytes of data, millions of hardware devices and thousands of software tools

The volume, diversity and velocity of biomedical data are exponentially increasing providing petabytes of new neuroimaging and genetics data every year. At the same time, tens-of-thousands of computational algorithms are developed and reported in the literature along with thousands of software tools and services. Users demand intuitive, quick and platform-agnostic access to data, software tools, and infrastructure from millions of hardware devices. This explosion of information, scientific techniques, computational models, and technological advances leads to enormous challenges in data analysis, evidence-based biomedical inference and reproducibility of findings. The Pipeline workflow environment provides a crowd-based distributed solution for consistent management of these heterogeneous resources. The Pipeline allows multiple (local) clients and (remote) servers to connect, exchange protocols, control the execution, monitor the states of different tools or hardware, and share complete protocols as portable XML workflows. In this paper, we demonstrate several advanced computational neuroimaging and genetics case-studies, and end-to-end pipeline solutions. These are implemented as graphical workflow protocols in the context of analyzing imaging (sMRI, fMRI, DTI), phenotypic (demographic, clinical), and genetic (SNP) data.

The effects of changing water content, relaxation times, and tissue contrast on tissue segmentation and measures of cortical anatomy in MR images

Water content is the dominant chemical compound in the brain and it is the primary determinant of tissue contrast in magnetic resonance (MR) images. Water content varies greatly between individuals, and it changes dramatically over time from birth through senescence of the human life span. We hypothesize that the effects that individual- and age-related variations in water content have on contrast of the brain in MR images also have important, systematic effects on in vivo, MRI-based measures of regional brain volumes. We also hypothesize that changes in water content and tissue contrast across time may account for age-related changes in regional volumes, and that differences in water content or tissue contrast across differing neuropsychiatric diagnoses may account for differences in regional volumes across diagnostic groups. We demonstrate in several complementary ways that subtle variations in water content across age and tissue compartments alter tissue contrast, and that changing tissue contrast in turn alters measures of the thickness and volume of the cortical mantle: (1) We derive analytic relations describing how age-related changes in tissue relaxation times produce age-related changes in tissue gray-scale intensity values and tissue contrast; (2) We vary tissue contrast in computer-generated images to assess its effects on tissue segmentation and volumes of gray matter and white matter; and (3) We use real-world imaging data from adults with either Schizophrenia or Bipolar Disorder and age- and sex-matched healthy adults to assess the ways in which variations in tissue contrast across diagnoses affects group differences in tissue segmentation and associated volumes. We conclude that in vivo MRI-based morphological measures of the brain, including regional volumes and measures of cortical thickness, are a product of, or at least are confounded by, differences in tissue contrast across individuals, ages, and diagnostic groups, and that differences in tissue contrast in turn likely derive from corresponding differences in water content of the brain across individuals, ages, and diagnostic groups.

Structural brain atlases: design, rationale, and applications in normal and pathological cohorts

Structural magnetic resonance imaging (MRI) provides anatomical information about the brain in healthy as well as in diseased conditions. On the other hand, functional MRI (fMRI) provides information on the brain activity during performance of a specific task. Analysis of fMRI data requires the registration of the data to a reference brain template in order to identify the activated brain regions. Brain templates also find application in other neuroimaging modalities, such as diffusion tensor imaging and multi-voxel spectroscopy. Further, there are certain differences (e.g., brain shape and size) in the brains of populations of different origin and during diseased conditions like in Alzheimer's disease (AD), population and disease-specific brain templates may be considered crucial for accurate registration and subsequent analysis of fMRI as well as other neuroimaging data. This manuscript provides a comprehensive review of the history, construction and application of brain atlases. A chronological outline of the development of brain template design, starting from the Talairach and Tournoux atlas to the Chinese brain template (to date), along with their respective detailed construction protocols provides the backdrop to this manuscript. The manuscript also provides the automated workflow-based protocol for designing a population-specific brain atlas from structural MRI data using LONI Pipeline graphical workflow environment. We conclude by discussing the scope of brain templates as a research tool and their application in various neuroimaging modalities.

Multi-scale classification of disease using structural MRI and wavelet transform

Recently, multivariate analysis algorithms have become a popular tool to diagnose neurological diseases based on neuroimaging data. Most studies, however, are biased for one specific scale, namely the scale given by the spatial resolution (i.e. dimension) of the data. In the present study, we propose to use the dual-tree complex wavelet transform to extract information on different spatial scales from structural MRI data and show its relevance for disease classification. Based on the magnitude representation of the complex wavelet coefficients calculated from the MR images, we identified a new class of features taking scale, directionality and potentially local information into account simultaneously. By using a linear support vector machine, these features were shown to discriminate significantly between spatially normalized MR images of 41 patients suffering from multiple sclerosis and 26 healthy controls. Interestingly, the decoding accuracies varied strongly among the different scales and it turned out that scales containing low frequency information were partly superior to scales containing high frequency information. Usually, this type of information is neglected since most decoding studies use only the original scale of the data. In conclusion, our proposed method has not only a high potential to assist in the diagnostic process of multiple sclerosis, but can be applied to other diseases or general decoding problems in structural or functional MRI.

Retrospective evaluation of PET-MRI registration algorithms

The purpose of this study is to evaluate the accuracy of registration positron emission tomography (PET) head images to the MRI-based brain atlas. The [(18)F]fluoro-2-deoxyglucose PET images were normalized to the MRI-based brain atlas using nine registration algorithms including objective functions of ratio image uniformity (RIU), normalized mutual information (NMI), and normalized cross correlation (CC) and transformation models of rigid-body, linear, affine, and nonlinear transformations. The accuracy of normalization was evaluated by visual inspection and quantified by the gray matter (GM) concordance between normalized PET images and the brain atlas. The linear and affine registration based on the RIU provided the best GM concordance (average similarity index of 0.71 for both). We also observed that the GM concordances of linear and affine registration were higher than those of the rigid and nonlinear registration among the methods evaluated.

Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline

Modern computational neuroscience employs diverse software tools and multidisciplinary expertise to analyze heterogeneous brain data. The classical problems of gathering meaningful data, fitting specific models, and discovering appropriate analysis and visualization tools give way to a new class of computational challenges—management of large and incongruous data, integration and interoperability of computational resources, and data provenance. We designed, implemented and validated a new paradigm for addressing these challenges in the neuroimaging field. Our solution is based on the LONI Pipeline environment [3], [4], a graphical workflow environment for constructing and executing complex data processing protocols. We developed study-design, database and visual language programming functionalities within the LONI Pipeline that enable the construction of complete, elaborate and robust graphical workflows for analyzing neuroimaging and other data. These workflows facilitate open sharing and communication of data and metadata, concrete processing protocols, result validation, and study replication among different investigators and research groups. The LONI Pipeline features include distributed grid-enabled infrastructure, virtualized execution environment, efficient integration, data provenance, validation and distribution of new computational tools, automated data format conversion, and an intuitive graphical user interface. We demonstrate the new LONI Pipeline features using large scale neuroimaging studies based on data from the International Consortium for Brain Mapping [5] and the Alzheimer's Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu.

SOCR Analyses: Implementation and Demonstration of a New Graphical Statistics Educational Toolkit

The web-based, Java-written SOCR (Statistical Online Computational Resource) tools have been utilized in many undergraduate and graduate level statistics courses for seven years now (Dinov 2006; Dinov et al. 2008b). It has been proven that these resources can successfully improve students' learning (Dinov et al. 2008b). Being first published online in 2005, SOCR Analyses is a somewhat new component and it concentrate on data modeling for both parametric and non-parametric data analyses with graphical model diagnostics. One of the main purposes of SOCR Analyses is to facilitate statistical learning for high school and undergraduate students. As we have already implemented SOCR Distributions and Experiments, SOCR Analyses and Charts fulfill the rest of a standard statistics curricula. Currently, there are four core components of SOCR Analyses. Linear models included in SOCR Analyses are simple linear regression, multiple linear regression, one-way and two-way ANOVA. Tests for sample comparisons include t-test in the parametric category. Some examples of SOCR Analyses' in the non-parametric category are Wilcoxon rank sum test, Kruskal-Wallis test, Friedman's test, Kolmogorov-Smirnoff test and Fligner-Killeen test. Hypothesis testing models include contingency table, Friedman's test and Fisher's exact test. The last component of Analyses is a utility for computing sample sizes for normal distribution. In this article, we present the design framework, computational implementation and the utilization of SOCR Analyses.

Brain functional localization: a survey of image registration techniques

Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem.

SOCR: Statistics Online Computational Resource

The need for hands-on computer laboratory experience in undergraduate and graduate statistics education has been firmly established in the past decade. As a result a number of attempts have been undertaken to develop novel approaches for problem-driven statistical thinking, data analysis and result interpretation. In this paper we describe an integrated educational web-based framework for: interactive distribution modeling, virtual online probability experimentation, statistical data analysis, visualization and integration. Following years of experience in statistical teaching at all college levels using established licensed statistical software packages, like STATA, S-PLUS, R, SPSS, SAS, Systat, etc., we have attempted to engineer a new statistics education environment, the Statistics Online Computational Resource (SOCR). This resource performs many of the standard types of statistical analysis, much like other classical tools. In addition, it is designed in a plug-in object-oriented architecture and is completely platform independent, web-based, interactive, extensible and secure. Over the past 4 years we have tested, fine-tuned and reanalyzed the SOCR framework in many of our undergraduate and graduate probability and statistics courses and have evidence that SOCR resources build student's intuition and enhance their learning.

A wavelet-based statistical analysis of FMRI data: I. motivation and data distribution modeling

We propose a new method for statistical analysis of functional magnetic resonance imaging (fMRI) data. The discrete wavelet transformation is employed as a tool for efficient and robust signal representation. We use structural magnetic resonance imaging (MRI) and fMRI to empirically estimate the distribution of the wavelet coefficients of the data both across individuals and spatial locations. An anatomical subvolume probabilistic atlas is used to tessellate the structural and functional signals into smaller regions each of which is processed separately. A frequency-adaptive wavelet shrinkage scheme is employed to obtain essentially optimal estimations of the signals in the wavelet space. The empirical distributions of the signals on all the regions are computed in a compressed wavelet space. These are modeled by heavy-tail distributions because their histograms exhibit slower tail decay than the Gaussian. We discovered that the Cauchy, Bessel K Forms, and Pareto distributions provide the most accurate asymptotic models for the distribution of the wavelet coefficients of the data. Finally, we propose a new model for statistical analysis of functional MRI data using this atlas-based wavelet space representation. In the second part of our investigation, we will apply this technique to analyze a large fMRI dataset involving repeated presentation of sensory-motor response stimuli in young, elderly, and demented subjects.

3-D deformable image registration: a topology preservation scheme based on hierarchical deformation models and interval analysis optimization

This paper deals with topology preservation in three-dimensional (3-D) deformable image registration. This work is a nontrivial extension of, which addresses the case of two-dimensional (2-D) topology preserving mappings. In both cases, the deformation map is modeled as a hierarchical displacement field, decomposed on a multiresolution B-spline basis. Topology preservation is enforced by controlling the Jacobian of the transformation. Finding the optimal displacement parameters amounts to solving a constrained optimization problem: The residual energy between the target image and the deformed source image is minimized under constraints on the Jacobian. Unlike the 2-D case, in which simple linear constraints are derived, the 3-D B-spline-based deformable mapping yields a difficult (until now, unsolved) optimization problem. In this paper, we tackle the problem by resorting to interval analysis optimization techniques. Care is taken to keep the computational burden as low as possible. Results on multipatient 3-D MRI registration illustrate the ability of the method to preserve topology on the continuous image domain.

Wavelets and functional magnetic resonance imaging of the human brain

The discrete wavelet transform (DWT) is widely used for multiresolution analysis and decorrelation or "whitening" of nonstationary time series and spatial processes. Wavelets are naturally appropriate for analysis of biological data, such as functional magnetic resonance images of the human brain, which often demonstrate scale invariant or fractal properties. We provide a brief formal introduction to key properties of the DWT and review the growing literature on its application to fMRI. We focus on three applications in particular: (i) wavelet coefficient resampling or "wavestrapping" of 1-D time series, 2- to 3-D spatial maps and 4-D spatiotemporal processes; (ii) wavelet-based estimators for signal and noise parameters of time series regression models assuming the errors are fractional Gaussian noise (fGn); and (iii) wavelet shrinkage in frequentist and Bayesian frameworks to support multiresolution hypothesis testing on spatially extended statistic maps. We conclude that the wavelet domain is a rich source of new concepts and techniques to enhance the power of statistical analysis of human fMRI data.

Mindboggle: a scatterbrained approach to automate brain labeling

Mindboggle (http://www.binarybottle.com/mindboggle.html) is a fully automated, feature matching approach to label cortical structures and activity anatomically in human brain MRI data. This approach does not assume that the existence of component structures and their relative spatial relationship is preserved from brain to brain, but instead disassembles a labeled atlas and reassembles its pieces to match corresponding pieces in an unlabeled subject brain before labeling. Mindboggle: (1) converts linearly coregistered subject and atlas MRI data into sulcus pieces, (2) matches each atlas piece with a combination of subject pieces by minimizing a cost function, (3) transforms atlas label boundaries to the matching subject pieces, (4) warps atlas labels to their transformed boundaries, and (5) propagates labels to fill remaining gaps in a mask derived from the subject brain. We compared Mindboggle with four registration methods: linear registration, and nonlinear registration using SPM2, AIR, and ANIMAL. Automated labeling by all of the nonlinear methods was found to be at least comparable with linear registration. Mindboggle outperformed every other method, as measured by the agreement between overlapping atlas labels and manually assigned subject labels, with respect to the union or the intersection of voxels. After applying the same procedure that Mindboggle uses to fill a subject's segmented gray matter mask with labels (step 5), the results of the other methods improved. However, after performing a one-way ANOVA (and Tukey's honestly significant difference criterion) in a multiple comparison between the results obtained by the different methods, Mindboggle was still found to be the only nonlinear method whose labeling performance was significantly better than that of linear registration or SPM2. Further advantages to Mindboggle include a high degree of robustness against image artifacts, poor image quality, and incomplete brain data. We tested the latter hypothesis by conducting all of the tests again, this time registering the atlas to an artificially lesioned version of itself, and found that Mindboggle was the only method whose performance did not degrade significantly as the lesion size increased.