Boris A. Gutman

Optimizing power to track brain degeneration in Alzheimer’s disease and mild cognitive impairment with tensor-based morphometry: An ADNI study of 515 subjects

Tensor-based morphometry (TBM) is a powerful method to map the 3D profile of brain degeneration in Alzheimers disease (AD) and mild cognitive impairment (MCI). We optimized a TBM-based image analysis method to determine what methodological factors, and which image-derived measures, maximize statistical power to track brain change. 3D maps, tracking rates of structural atrophy over time, were created from 1030 longitudinal brain MRI scans (1-year follow-up) of 104 AD patients (age: 75.7+/-7.2 years; MMSE: 23.3+/-1.8, at baseline), 254 amnestic MCI subjects (75.0+/-7.2 years; 27.0+/-1.8), and 157 healthy elderly subjects (75.9+/-5.1 years; 29.1+/-1.0), as part of the Alzheimers Disease Neuroimaging Initiative (ADNI). To determine which TBM designs gave greatest statistical power, we compared different linear and nonlinear registration parameters (including different regularization functions), and different numerical summary measures derived from the maps. Detection power was greatly enhanced by summarizing changes in a statistically-defined region-of-interest (ROI) derived from an independent training sample of 22 AD patients. Effect sizes were compared using cumulative distribution function (CDF) plots and false discovery rate methods. In power analyses, the best method required only 48 AD and 88 MCI subjects to give 80% power to detect a 25% reduction in the mean annual change using a two-sided test (at alpha=0.05). This is a drastic sample size reduction relative to using clinical scores as outcome measures (619 AD/6797 MCI for the ADAS-Cog, and 408 AD/796 MCI for the Clinical Dementia Rating sum-of-boxes scores). TBM offers high statistical power to track brain changes in large, multi-site neuroimaging studies and clinical trials of AD.

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 Alzheimers Disease Neuroimaging Initiative [6]. User guides, forums, instructions and downloads of the LONI Pipeline environment are available at http://pipeline.loni.ucla.edu.

When more is less: Associations between corpus callosum size and handedness lateralization

Although not consistently replicated, a substantial number of studies suggest that left-handers have larger callosal regions than right-handers. We challenge this notion and propose that callosal size is not linked to left-handedness or right-handedness per se but to the degree of handedness lateralization. To test this hypothesis, we investigated the thickness of the corpus callosum in a large data set (n=361). We analyzed the correlations between callosal thickness and the degree of handedness lateralization in 324 right-handers and 37 left-handers at 100 equidistant points across the corpus callosum. We revealed significant negative correlations within the anterior and posterior midbody suggesting that larger callosal dimensions in these regions are associated with a weaker handedness lateralization. Significant positive correlations were completely absent. In addition, we compared callosal thickness between moderately lateralized left-handers (n=37) and three equally sized groups (n=37) of right-handers (strongly, moderately, and weakly lateralized). The outcomes of these group analyses confirmed the negative association between callosal size and handedness lateralization, although callosal differences between right- and left-handers did not reach statistical significance. This suggests that callosal differences are rather small, if examined as a dichotomy between two handedness groups. Future studies will expand this line of research by increasing the number of left-handers to boost statistical power and by combining macro- and microstructural, as well as functional and behavioral measurements to identify the biological mechanisms linking callosal morphology and handedness lateralization.

Unbiased tensor-based morphometry: Improved robustness and sample size estimates for Alzheimer's disease clinical trials

Various neuroimaging measures are being evaluated for tracking Alzheimers disease (AD) progression in therapeutic trials, including measures of structural brain change based on repeated scanning of patients with magnetic resonance imaging (MRI). Methods to compute brain change must be robust to scan quality. Biases may arise if any scans are thrown out, as this can lead to the true changes being overestimated or underestimated. Here we analyzed the full MRI dataset from the first phase of Alzheimers Disease Neuroimaging Initiative (ADNI-1) from the first phase of Alzheimers Disease Neuroimaging Initiative (ADNI-1) and assessed several sources of bias that can arise when tracking brain changes with structural brain imaging methods, as part of a pipeline for tensor-based morphometry (TBM). In all healthy subjects who completed MRI scanning at screening, 6, 12, and 24months, brain atrophy was essentially linear with no detectable bias in longitudinal measures. In power analyses for clinical trials based on these change measures, only 39AD patients and 95 mild cognitive impairment (MCI) subjects were needed for a 24-month trial to detect a 25% reduction in the average rate of change using a two-sided test (α=0.05, power=80%). Further sample size reductions were achieved by stratifying the data into Apolipoprotein E (ApoE) ε4 carriers versus non-carriers. We show how selective data exclusion affects sample size estimates, motivating an objective comparison of different analysis techniques based on statistical power and robustness. TBM is an unbiased, robust, high-throughput imaging surrogate marker for large, multi-site neuroimaging studies and clinical trials of AD and MCI.

Automatic clustering of white matter fibers in brain diffusion mri with an application to genetics

To understand factors that affect brain connectivity and integrity, it is beneficial to automatically cluster white matter (WM) fibers into anatomically recognizable tracts. Whole brain tractography, based on diffusion-weighted MRI, generates vast sets of fibers throughout the brain; clustering them into consistent and recognizable bundles can be difficult as there are wide individual variations in the trajectory and shape of WM pathways. Here we introduce a novel automated tract clustering algorithm based on label fusion--a concept from traditional intensity-based segmentation. Streamline tractography generates many incorrect fibers, so our top-down approach extracts tracts consistent with known anatomy, by mapping multiple hand-labeled atlases into a new dataset. We fuse clustering results from different atlases, using a mean distance fusion scheme. We reliably extracted the major tracts from 105-gradient high angular resolution diffusion images (HARDI) of 198 young normal twins. To compute population statistics, we use a pointwise correspondence method to match, compare, and average WM tracts across subjects. We illustrate our method in a genetic study of white matter tract heritability in twins.

Irritable Bowel Syndrome in female patients is associated with alterations in structural brain networks

Summary Compared to healthy females, females with chronic visceral pain show alterations in regional gray matter volume and network properties of large‐scale structural brain networks. ABSTRACT Alterations in gray matter (GM) density/volume and cortical thickness (CT) have been demonstrated in small and heterogeneous samples of subjects with differing chronic pain syndromes, including irritable bowel syndrome (IBS). Aggregating across 7 structural neuroimaging studies conducted at University of California, Los Angeles, Los Angeles, CA, USA, between August 2006 and April 2011, we examined group differences in regional GM volume in 201 predominantly premenopausal female subjects (82 IBS, mean age: 32 ± 10 SD, 119 healthy controls [HCs], 30 ± 10 SD). Applying graph theoretical methods and controlling for total brain volume, global and regional properties of large‐scale structural brain networks were compared between the group with IBS and the HC group. Relative to HCs, the IBS group had lower volumes in the bilateral superior frontal gyrus, bilateral insula, bilateral amygdala, bilateral hippocampus, bilateral middle orbital frontal gyrus, left cingulate, left gyrus rectus, brainstem, and left putamen. Higher volume was found in the left postcentral gyrus. Group differences were no longer significant for most regions when controlling for the Early Trauma Inventory global score, with the exception of the right amygdala and the left postcentral gyrus. No group differences were found for measures of global and local network organization. Compared to HCs, in patients with IBS, the right cingulate gyrus and right thalamus were identified as being significantly more critical for information flow. Regions involved in endogenous pain modulation and central sensory amplification were identified as network hubs in IBS. Overall, evidence for central alterations in patients with IBS was found in the form of regional GM volume differences and altered global and regional properties of brain volumetric networks.

Accurate Measurement of Brain Changes in Longitudinal MRI Scans using Tensor-Based Morphometry

This paper responds to Thompson and Holland (2011), who challenged our tensor-based morphometry (TBM) method for estimating rates of brain changes in serial MRI from 431 subjects scanned every 6 months, for 2 years. Thompson and Holland noted an unexplained jump in our atrophy rate estimates: an offset between 0 and 6 months that may bias clinical trial power calculations. We identified why this jump occurs and propose a solution. By enforcing inverse-consistency in our TBM method, the offset dropped from 1.4% to 0.28%, giving plausible anatomical trajectories. Transitivity error accounted for the minimal remaining offset. Drug trial sample size estimates with the revised TBM-derived metrics are highly competitive with other methods, though higher than previously reported sample size estimates by a factor of 1.6 to 2.4. Importantly, estimates are far below those given in the critique. To demonstrate a 25% slowing of atrophic rates with 80% power, 62 AD and 129 MCI subjects would be required for a 2-year trial, and 91 AD and 192 MCI subjects for a 1-year trial.

Surface-based TBM boosts power to detect disease effects on the brain: An N = 804 ADNI study

Computational anatomy methods are now widely used in clinical neuroimaging to map the profile of disease effects on the brain and its clinical correlates. In Alzheimers disease (AD), many research groups have modeled localized changes in hippocampal and lateral ventricular surfaces, to provide candidate biomarkers of disease progression for drug trials. We combined the power of parametric surface modeling and tensor-based morphometry to study hippocampal differences associated with AD and mild cognitive impairment (MCI) in 490 subjects (97 AD, 245 MCI, 148 controls) and ventricular differences in 804 subjects scanned as part of the Alzheimers Disease Neuroimaging Initiative (ADNI; 184 AD, 391 MCI, 229 controls). We aimed to show that a new multivariate surface statistic based on multivariate tensor-based morphometry (mTBM) and radial distance provides a more powerful way to detect localized anatomical differences than conventional surface-based analysis. In our experiments, we studied correlations between hippocampal atrophy and ventricular enlargement and clinical measures and cerebrospinal fluid biomarkers. The new multivariate statistics gave better effect sizes for detecting morphometric differences, relative to other statistics including radial distance, analysis of the surface tensor and the Jacobian determinant. In empirical tests using false discovery rate curves, smaller sample sizes were needed to detect associations with diagnosis. The analysis pipeline is generic and automated. It may be applied to analyze other brain subcortical structures including the caudate nucleus and putamen. This publically available software may boost power for morphometric studies of subcortical structures in the brain.

Multivariate tensor-based morphometry on surfaces: application to mapping ventricular abnormalities in HIV/AIDS.

Here we developed a new method, called multivariate tensor-based surface morphometry (TBM), and applied it to study lateral ventricular surface differences associated with HIV/AIDS. Using concepts from differential geometry and the theory of differential forms, we created mathematical structures known as holomorphic one-forms, to obtain an efficient and accurate conformal parameterization of the lateral ventricular surfaces in the brain. The new meshing approach also provides a natural way to register anatomical surfaces across subjects, and improves on prior methods as it handles surfaces that branch and join at complex 3D junctions. To analyze anatomical differences, we computed new statistics from the Riemannian surface metrics-these retain multivariate information on local surface geometry. We applied this framework to analyze lateral ventricular surface morphometry in 3D MRI data from 11 subjects with HIV/AIDS and 8 healthy controls. Our method detected a 3D profile of surface abnormalities even in this small sample. Multivariate statistics on the local tensors gave better effect sizes for detecting group differences, relative to other TBM-based methods including analysis of the Jacobian determinant, the largest and smallest eigenvalues of the surface metric, and the pair of eigenvalues of the Jacobian matrix. The resulting analysis pipeline may improve the power of surface-based morphometry studies of the brain.

Global and Regional Alterations of Hippocampal Anatomy in Long-Term Meditation Practitioners

Studies linking meditation and brain structure are still relatively sparse, but the hippocampus is consistently implicated as one of the structures altered in meditation practitioners. To explore hippocampal features in the framework of meditation, we analyzed high‐resolution structural magnetic resonance imaging data from 30 long‐term meditators and 30 controls, closely matched for sex, age, and handedness. Hippocampal formations were manually traced following established protocols. In addition to calculating left and right hippocampal volumes (global measures), regional variations in surface morphology were determined by measuring radial distances from the hippocampal core to spatially matched surface points (local measures). Left and right hippocampal volumes were larger in meditators than in controls, significantly so for the left hippocampus. The presence and direction of this global effect was confirmed locally by mapping the exact spatial locations of the group differences. Altogether, radial distances were larger in meditators compared to controls, with up to 15% difference. These local effects were observed in several hippocampal regions in the left and right hemisphere though achieved significance primarily in the left hippocampal head. Larger hippocampal dimensions in long‐term meditators may constitute part of the underlying neurological substrate for cognitive skills, mental capacities, and/or personal traits associated with the practice of meditation. Alternatively, given that meditation positively affects autonomic regulation and immune activity, altered hippocampal dimensions may be one result of meditation‐induced stress reduction. However, given the cross‐sectional design, the lack of individual stress measures, and the limited resolution of brain data, the exact underlying neuronal mechanisms remain to be established. Hum Brain Mapp 34:3369–3375, 2013.