SPatial REgression Analysis of Diffusion tensor imaging (SPREAD) for longitudinal progression of neurodegenerative disease in individual subjects

Geostatistical modeling of positive-definite matrices: An application to diffusion tensor imaging

Geostatistical modeling for continuous point-referenced data has extensively been applied to neuroimaging because it produces efficient and valid statistical inference. However, diffusion tensor imaging (DTI), a neuroimaging technique characterizing the brain's anatomical structure, produces a positive-definite (p.d.) matrix for each voxel. Currently, only a few geostatistical models for p.d. matrices have been proposed because introducing spatial dependence among p.d. matrices properly is challenging. In this paper, we use the spatial Wishart process, a spatial stochastic process (random field), where each p.d. matrix-variate random variable marginally follows a Wishart distribution, and spatial dependence between random matrices is induced by latent Gaussian processes. This process is valid on an uncountable collection of spatial locations and is almost-surely continuous, leading to a reasonable way of modeling spatial dependence. Motivated by a DTI data set of cocaine users, we propose a spatial matrix-variate regression model based on the spatial Wishart process. A problematic issue is that the spatial Wishart process has no closed-form density function. Hence, we propose an approximation method to obtain a feasible Cholesky decomposition model, which we show to be asymptotically equivalent to the spatial Wishart process model. A local likelihood approximation method is also applied to achieve fast computation. The simulation studies and real data application demonstrate that the Cholesky decomposition process model produces reliable inference and improved performance, compared to other methods.

Spatial regression analysis of MR diffusion reveals subject-specific white matter changes associated with repetitive head impacts in contact sports

Repetitive head impacts (RHI) are a growing concern due to their possible neurocognitive effects, with research showing a season of RHI produce white matter (WM) changes seen on neuroimaging. We conducted a secondary analysis of diffusion tensor imaging (DTI) data for 28 contact athletes to compare WM changes. We collected pre-season and post-season DTI scans for each subject, approximately 3 months apart. We collected helmet data for the athletes, which we correlated with DTI data. We adapted the SPatial REgression Analysis of DTI (SPREAD) algorithm to conduct subject-specific longitudinal DTI analysis, and developed global inferential tools using functional norms and a novel robust p value combination test. At the individual level, most detected injured regions (93.3%) were associated with decreased FA values. Using meta-analysis techniques to combine injured regions across subjects, we found the combined injured region at the group level occupied the entire WM skeleton, suggesting the WM damage location is subject-specific. Several subject-specific functional summaries of SPREAD-detected WM change, e.g., the [Formula: see text] norm, significantly correlated with helmet impact measures, e.g. cumulative unweighted rotational acceleration (adjusted p = 0.0049), time between hits rotational acceleration (adjusted p value 0.0101), and time until DTI rotational acceleration (adjusted p = 0.0084), suggesting RHIs lead to WM changes.

Highly efficient hypothesis testing methods for regression-type tests with correlated observations and heterogeneous variance structure

BACKGROUND

For many practical hypothesis testing (H-T) applications, the data are correlated and/or with heterogeneous variance structure. The regression t-test for weighted linear mixed-effects regression (LMER) is a legitimate choice because it accounts for complex covariance structure; however, high computational costs and occasional convergence issues make it impractical for analyzing high-throughput data. In this paper, we propose computationally efficient parametric and semiparametric tests based on a set of specialized matrix techniques dubbed as the PB-transformation. The PB-transformation has two advantages: 1. The PB-transformed data will have a scalar variance-covariance matrix. 2. The original H-T problem will be reduced to an equivalent one-sample H-T problem. The transformed problem can then be approached by either the one-sample Student's t-test or Wilcoxon signed rank test.

RESULTS

In simulation studies, the proposed methods outperform commonly used alternative methods under both normal and double exponential distributions. In particular, the PB-transformed t-test produces notably better results than the weighted LMER test, especially in the high correlation case, using only a small fraction of computational cost (3 versus 933 s). We apply these two methods to a set of RNA-seq gene expression data collected in a breast cancer study. Pathway analyses show that the PB-transformed t-test reveals more biologically relevant findings in relation to breast cancer than the weighted LMER test.

CONCLUSIONS

As fast and numerically stable replacements for the weighted LMER test, the PB-transformed tests are especially suitable for "messy" high-throughput data that include both independent and matched/repeated samples. By using our method, the practitioners no longer have to choose between using partial data (applying paired tests to only the matched samples) or ignoring the correlation in the data (applying two sample tests to data with some correlated samples). Our method is implemented as an R package 'PBtest' and is available at https://github.com/yunzhang813/PBtest-R-Package .

Improved spatial regression analysis of diffusion tensor imaging for lesion detection during longitudinal progression of multiple sclerosis in individual subjects

Subject-specific longitudinal DTI study is vital for investigation of pathological changes of lesions and disease evolution. Spatial Regression Analysis of Diffusion tensor imaging (SPREAD) is a non-parametric permutation-based statistical framework that combines spatial regression and resampling techniques to achieve effective detection of localized longitudinal diffusion changes within the whole brain at individual level without a priori hypotheses. However, boundary blurring and dislocation limit its sensitivity, especially towards detecting lesions of irregular shapes. In the present study, we propose an improved SPREAD (dubbed improved SPREAD, or iSPREAD) method by incorporating a three-dimensional (3D) nonlinear anisotropic diffusion filtering method, which provides edge-preserving image smoothing through a nonlinear scale space approach. The statistical inference based on iSPREAD was evaluated and compared with the original SPREAD method using both simulated and in vivo human brain data. Results demonstrated that the sensitivity and accuracy of the SPREAD method has been improved substantially by adapting nonlinear anisotropic filtering. iSPREAD identifies subject-specific longitudinal changes in the brain with improved sensitivity, accuracy, and enhanced statistical power, especially when the spatial correlation is heterogeneous among neighboring image pixels in DTI.

Spatial regression analysis of serial DTI for subject-specific longitudinal changes of neurodegenerative disease

Quantitative measurement of localized longitudinal changes in brain abnormalities at an individual level may offer critical information for disease diagnosis and treatment. The voxel-wise permutation-based method SPREAD/iSPREAD, which combines resampling and spatial regression of neighboring voxels, provides an effective and robust method for detecting subject-specific longitudinal changes within the whole brain, especially for longitudinal studies with a limited number of scans. As an extension of SPREAD/iSPREAD, we present a general method that facilitates analysis of serial Diffusion Tensor Imaging (DTI) measurements (with more than two time points) for testing localized changes in longitudinal studies. Two types of voxel-level test statistics (model-free test statistics, which measure intra-subject variability across time, and test statistics based on general linear model that incorporate specific lesion evolution models) were estimated and tested against the null hypothesis among groups of DTI data across time. The implementation and utility of the proposed statistical method were demonstrated by both Monte Carlo simulations and applications on clinical DTI data from human brain in vivo. By a design of test statistics based on the disease progression model, it was possible to apportion the true significant voxels attributed to the disease progression and those caused by underlying anatomical differences that cannot be explained by the model, which led to improvement in false positive (FP) control in the results. Extension of the proposed method to include other diseases or drug effect models, as well as the feasibility of global statistics, was discussed. The proposed statistical method can be extended to a broad spectrum of longitudinal studies with carefully designed test statistics, which helps to detect localized changes at the individual level.

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A nonparametric method for detecting subject-specific localized changes in longitudinal DTI

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Obtain sufficient statistical power even with limited scans available

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Various voxel-level test statistics for hypothesis tests among groups across time

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Excellent at controlling false positive ratio with the model-based test statistics

Correction for Eddy Current-Induced Echo-Shifting Effect in Partial-Fourier Diffusion Tensor Imaging

In most diffusion tensor imaging (DTI) studies, images are acquired with either a partial-Fourier or a parallel partial-Fourier echo-planar imaging (EPI) sequence, in order to shorten the echo time and increase the signal-to-noise ratio (SNR). However, eddy currents induced by the diffusion-sensitizing gradients can often lead to a shift of the echo in k-space, resulting in three distinct types of artifacts in partial-Fourier DTI. Here, we present an improved DTI acquisition and reconstruction scheme, capable of generating high-quality and high-SNR DTI data without eddy current-induced artifacts. This new scheme consists of three components, respectively, addressing the three distinct types of artifacts. First, a k-space energy-anchored DTI sequence is designed to recover eddy current-induced signal loss (i.e., Type 1 artifact). Second, a multischeme partial-Fourier reconstruction is used to eliminate artificial signal elevation (i.e., Type 2 artifact) associated with the conventional partial-Fourier reconstruction. Third, a signal intensity correction is applied to remove artificial signal modulations due to eddy current-induced erroneous T₂^∗-weighting (i.e., Type 3 artifact). These systematic improvements will greatly increase the consistency and accuracy of DTI measurements, expanding the utility of DTI in translational applications where quantitative robustness is much needed.

Diffusion tensor imaging of spinocerebellar ataxia type 12

Background

Spinocerebellar ataxias (SCAs) are autosomal-dominant neurodegenerative diseases that are clinically and genetically heterogeneous. SCAs are characterized by a range of neurological symptoms. SCA12 is an autosomal-dominant (AD) ataxia caused by a CAG repeat expansion mutation in a presumed promoter region of the gene PPP2R2B in a non-coding region on chromosome 5q32. This study sought to determine changes in different positions in a single Uyghur SCA12 pedigree by measuring the apparent diffusion coefficient (ADC) and fractional anisotropy (FA).

Material/Methods

A single Uyghur pedigree was collected and was confirmed to possess SCA12 by genetic diagnosis, among which 13 cases were patients and 54 cases were “healthy” individuals. Five patients were presymptomatic and 15 individuals selected as a control group were examination in the same time. DTI was performed on a 1.5T scanner, with b=1000 s/mm² and 15 directions. ADC and FA were measured by regions of interest positioned in the corticospinal tract at the level of the pons (pons), superior peduncle (SCP), middle cerebellar peduncle (MCP), cerebellar cortex (CeC), cerebral cortex (CC), and cerebellar vermis (CV) white matter.

Results

Compared with the controls, the ADC was significantly elevated in the CeC, SCP, CC, and CV regions in SCA12 patients. The FA significantly decreased in the CC region in SCA12 patients and the CC and CV regions in SCA12 presymptomatic patients. The course of the disease, SARA score, and ADC values in CV showed highly positive correlations.

Conclusions

SCA12 pedigree patients exhibited microstructural damage in the brain white matter. The damage in white matter fiber may first occur in the CC and CV regions in SCA12 presymptomatic patients. The ADC values in the CV region could reflect disease severity in SCA12 patients.

UNC-Utah NA-MIC framework for DTI fiber tract analysis

Diffusion tensor imaging has become an important modality in the field of neuroimaging to capture changes in micro-organization and to assess white matter integrity or development. While there exists a number of tractography toolsets, these usually lack tools for preprocessing or to analyze diffusion properties along the fiber tracts. Currently, the field is in critical need of a coherent end-to-end toolset for performing an along-fiber tract analysis, accessible to non-technical neuroimaging researchers. The UNC-Utah NA-MIC DTI framework represents a coherent, open source, end-to-end toolset for atlas fiber tract based DTI analysis encompassing DICOM data conversion, quality control, atlas building, fiber tractography, fiber parameterization, and statistical analysis of diffusion properties. Most steps utilize graphical user interfaces (GUI) to simplify interaction and provide an extensive DTI analysis framework for non-technical researchers/investigators. We illustrate the use of our framework on a small sample, cross sectional neuroimaging study of eight healthy 1-year-old children from the Infant Brain Imaging Study (IBIS) Network. In this limited test study, we illustrate the power of our method by quantifying the diffusion properties at 1 year of age on the genu and splenium fiber tracts.