Multi-site genetic analysis of diffusion images and voxelwise heritability analysis : a pilot project of the ENIGMA–DTI working group

The ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) Consortium was set up to analyze brain measures and genotypes from multiple sites across the world to improve the power to detect genetic variants that influence the brain. Diffusion tensor imaging (DTI) yields quantitative measures sensitive to brain development and degeneration, and some common genetic variants may be associated with white matter integrity or connectivity. DTI measures, such as the fractional anisotropy (FA) of water diffusion, may be useful for identifying genetic variants that influence brain microstructure. However, genome-wide association studies (GWAS) require large populations to obtain sufficient power to detect and replicate significant effects, motivating a multi-site consortium effort. As part of an ENIGMA–DTI working group, we analyzed high-resolution FA images from multiple imaging sites across North America, Australia, and Europe, to address the challenge of harmonizing imaging data collected at multiple sites. Four hundred images of healthy adults aged 18–85 from four sites were used to create a template and corresponding skeletonized FA image as a common reference space. Using twin and pedigree samples of different ethnicities, we used our common template to evaluate the heritability of tract-derived FA measures. We show that our template is reliable for integrating multiple datasets by combining results through meta-analysis and unifying the data through exploratory mega-analyses. Our results may help prioritize regions of the FA map that are consistently influenced by additive genetic factors for future genetic discovery studies. Protocols and templates are publicly available at (http://enigma.loni.ucla.edu/ongoing/dti-working-group/)

Genetics of brain fiber architecture and intellectual performance

The study is the first to analyze genetic and environmental factors that affect brain fiber architecture and its genetic linkage with cognitive function. We assessed white matter integrity voxelwise using diffusion tensor imaging at high magnetic field (4 Tesla), in 92 identical and fraternal twins. White matter integrity, quantified using fractional anisotropy (FA), was used to fit structural equation models (SEM) at each point in the brain, generating three-dimensional maps of heritability. We visualized the anatomical profile of correlations between white matter integrity and full-scale, verbal, and performance intelligence quotients (FIQ, VIQ, and PIQ). White matter integrity (FA) was under strong genetic control and was highly heritable in bilateral frontal (a2 = 0.55, p = 0.04, left; a2 = 0.74, p = 0.006, right), bilateral parietal (a2 = 0.85, p < 0.001, left; a2 = 0.84, p < 0.001, right), and left occipital (a2 = 0.76, p = 0.003) lobes, and was correlated with FIQ and PIQ in the cingulum, optic radiations, superior fronto-occipital fasciculus, internal capsule, callosal isthmus, and the corona radiata (p = 0.04 for FIQ and p = 0.01 for PIQ, corrected for multiple comparisons). In a cross-trait mapping approach, common genetic factors mediated the correlation between IQ and white matter integrity, suggesting a common physiological mechanism for both, and common genetic determination. These genetic brain maps reveal heritable aspects of white matter integrity and should expedite the discovery of single-nucleotide polymorphisms affecting fiber connectivity and cognition

Agreement between the white matter connectivity based on the tensor-based morphometry and the volumetric white matter parcellations based on diffusion tensor imaging

We are interested in investigating white matter connectivity using a novel computational framework that does not use diffusion tensor imaging (DTI) but only uses T1-weighted magnetic resonance imaging. The proposed method relies on correlating Jacobian determinants across different voxels based on the tensor-based morphometry (TBM) framework. In this paper, we show agreement between the TBM-based white matter connectivity and the DTI-based white matter atlas. As an application, altered white matter connectivity in a clinical population is determined

HUMAN BRAIN WHITE MATTER ANALYSIS USING TRACTOGRAPHY —AN ATLAS-BASED APPROACH

The human brain is connected via a vastly complex network of white matter fiber pathways. However, this structural connectivity information cannot be obtained from conventional MRI, in which much of white matter appears homogeneous. Diffusion tensor imaging can estimate fiber orientation by measuring the anisotropy of water diffusion. Using tractography, the brain connectivity can be studied non-invasively. Past tractography studies have shown that the cores of prominent white matter tracts can be faithfully reconstructed. Superimposing the tract coordinates on various MR images, MR metrics can be quantified in a tract-specific manner. However, tractography results are often contaminated by partial volume effect and imaging noise. Particularly, tractography often fails under white matter pathological conditions, which render tract-specific analysis impractical. In order to address these issues, we introduced an atlas-based approach. Four novel atlas-based approaches were included in this data analysis framework. First, statistical templates of major white matter tracts were created using a DTI database of normal subjects. The statistical white matter tract templates can serve two purposes. First, the statistical template can be used as a reference to detect abnormal white matter anatomy in neurodegenerative diseases. Second, the statistical template can be applied to individual patient data for automated white matter parcellation and tract-specific quantification. In the second approach, the trajectory of white matter fiber bundles was used to estimate the cortical regions associated with specific tracts of interest. Using this approach, cortical regions were reproducibly identified on the population-averaged cortical maps of brain connectivity. Third, we improved the accuracy of the population-based tract analysis by incorporating a highly elastic image transformation technique, called Large Deformation Diffeomorphic Metric Mapping (LDDMM). As a testament to the power of this algorithm, we successfully applied tract-specific analysis on Alzheimer’s patients. The last approach was to analyze the brain cortical connection networks using automatic fiber tracking. A tracking pipeline was built by combining White Matter Parcellation Map (WMPM), brute-force tractography and topology-preserving image transformation LDDMM. This novel tracking pipeline was applied on patient group with Alzheimer’s disease. The connectivity networks of Alzheimer’s patients were compared with age-matched controls using multivariate pattern classification

Chronic Post-Concussion Neurocognitive Deficits. I. Relationship with White Matter Integrity.

We previously identified visual tracking deficits and associated degradation of integrity in specific white matter tracts as characteristics of concussion. We re-explored these characteristics in adult patients with persistent post-concussive symptoms using independent new data acquired during 2009-2012. Thirty-two patients and 126 normal controls underwent cognitive assessments and MR-DTI. After data collection, a subset of control subjects was selected to be individually paired with patients based on gender and age. We identified patients' cognitive deficits through pairwise comparisons between patients and matched control subjects. Within the remaining 94 normal subjects, we identified white matter tracts whose integrity correlated with metrics that indicated performance degradation in patients. We then tested for reduced integrity in these white matter tracts in patients relative to matched controls. Most patients showed no abnormality in MR images unlike the previous study. Patients' visual tracking was generally normal. Patients' response times in an attention task were slowed, but could not be explained as reduced integrity of white matter tracts relating to normal response timing. In the present patient cohort, we did not observe behavioral or anatomical deficits that we previously identified as characteristic of concussion. The recent cohort likely represented those with milder injury compared to the earlier cohort. The discrepancy may be explained by a change in the patient recruitment pool circa 2007 associated with an increase in public awareness of concussion

An in vivo MRI Template Set for Morphometry, Tissue Segmentation, and fMRI Localization in Rats

Over the last decade, several papers have focused on the construction of highly detailed mouse high field magnetic resonance image (MRI) templates via non-linear registration to unbiased reference spaces, allowing for a variety of neuroimaging applications such as robust morphometric analyses. However, work in rats has only provided medium field MRI averages based on linear registration to biased spaces with the sole purpose of approximate functional MRI (fMRI) localization. This precludes any morphometric analysis in spite of the need of exploring in detail the neuroanatomical substrates of diseases in a recent advent of rat models. In this paper we present a new in vivo rat T2 MRI template set, comprising average images of both intensity and shape, obtained via non-linear registration. Also, unlike previous rat template sets, we include white and gray matter probabilistic segmentations, expanding its use to those applications demanding prior-based tissue segmentation, e.g., statistical parametric mapping (SPM) voxel-based morphometry. We also provide a preliminary digitalization of latest Paxinos and Watson atlas for anatomical and functional interpretations within the cerebral cortex. We confirmed that, like with previous templates, forepaw and hindpaw fMRI activations can be correctly localized in the expected atlas structure. To exemplify the use of our new MRI template set, were reported the volumes of brain tissues and cortical structures and probed their relationships with ontogenetic development. Other in vivo applications in the near future can be tensor-, deformation-, or voxel-based morphometry, morphological connectivity, and diffusion tensor-based anatomical connectivity. Our template set, freely available through the SPM extension website, could be an important tool for future longitudinal and/or functional extensive preclinical studies

Spatio-temporal analysis of early brain development

pre-printAnalysis of human brain development is a crucial step for improved understanding of neurodevelopmental disorders. We focus on normal brain development as is observed in the multimodal longitudinal MRI/DTI data of neonates to two years of age. We present a spatio-temporal analysis framework using Gompertz function as a population growth model with three different spatial localization strategies: voxel-based, data driven clustering and atlas driven regional analysis. Growth models from multimodal imaging channels collected at each voxel form feature vectors which are clustered using the Dirichlet Process Mixture Models (DPMM). Clustering thus combines growth information from different modalities to subdivide the image into voxel groups with similar properties. The processing generates spatial maps that highlight the dynamic progression of white matter development. These maps show progression of white matter maturation where primarily, central regions mature earlier compared to the periphery, but where more subtle regional differences in growth can be observed. Atlas based analysis allows a quantitative analysis of a specific anatomical region, whereas data driven clustering identifies regions of similar growth patterns. The combination of these two allows us to investigate growth patterns within an anatomical region. Specifically, analysis of anterior and posterior limb of internal capsule show that there are different growth trajectories within these anatomies, and that it may be useful to divide certain anatomies into subregions with distinctive growth patterns

Analysis of diffusion tensor imaging for subjects with Down Syndrome

pre-printAuthors: Neda Sadeghi1, Clement Vachet1, Marcel Prastawa1, Julie Korenberg1, Guido Gerig1 Institutions: 1University of Utah, Salt Lake City, UT Introduction: Down syndrome (DS) is the most common chromosome abnormality in humans. It is typically associated with delayed cognitive development and physical growth. DS is also associated with Alzheimer-like dementia [1]. In this study we analyze the white matter integrity of individuals with DS compared to control as is reflected in the diffusion parameters derived from Diffusion Tensor Imaging. DTI provides relevant information about the underlying tissue, which correlates with cognitive function [2]. We present a cross-sectional analysis of white matter tracts of subjects with DS compared to control. Methods: We study a population of 25 adults composed of 12 healthy controls (age 21 +/- 3.97) and 13 subjects diagnosed with Down syndrome (age 26.2 +/- 5.12). Each subject has diffusion tensor imaging (DTI) scans at different ages that capture different stages of development. We construct an unbiased atlas of adult brains as a population template using the method described by Joshi et al. [3]. Diffusion tensor images of the subjects were mapped to the reference space defined by this template. White matter label maps developed and disseminated by Mori et al. [4] were also registered to this template [5][6]. The labeling of regions in the atlas space allows for automatic partitioning of each subject's scans. Fractional anisotropy (FA) were extracted from each region and compared between subjects with Down syndrome and control. False discovery rate was used to adjust for multiple comparisons. Results: We performed white matter analysis on the following brain regions: anterior limb of internal capsule (ALIC, right and left), posterior limb of internal capsule (PLIC, right and left), body of corpus callosum (BCC), genu, splenium, external capsule (ExCap, right and left), and posterior thalamic radiation (PTR), which includes the optic radiation. Cross-sectional FA trajectories differ significantly in controls and DS subjects in the intercept term for ALIC R, Genu, ExCap L, PTR R and PTR L. Opposite patterns of development between controls and DS were observed in almost all the regions except for the left ALIC (ALIC L). FA tends to increase with age for the control group, whereas FA for subjects with Down syndrome tends to decrease with age with the exception of ALIC L which showed increasing trend for both populations. However, we didn't observe any significant differences in the slopes between these two groups which could be due to the small sample size and high variability between subjects. We observed only group-by-age differences for left ExCap (ExCap L). Estimated parameters for linear regression along with p-values are shown in Table 1 and 2

A framework for creating population specific multimodal brain atlas using clinical T1 and diffusion tensor images

International audienceSpatial normalization is one of the most important steps in population based statistical analysis of brain images. This involves normalizing all the brain images to a pre-defined template or a population specific template. With multiple emerging imaging modalities, it is quintessential to develop a method for building a joint template that is a statistical representation of the given population across different modalities. It is possible to create different population specific templates in different modalities using existing methods. However, they do not give an opportunity for voxelwise comparison of different modalities. A multimodal brain template with probabilistic region of interest (ROI) definitions will give opportunity for multivariate statistical frameworks for better understanding of brain diseases. In this paper, we propose a methodology for developing such a multimodal brain atlas using the anatomical T1 images and the diffusion tensor images (DTI), along with an automated workflow to probabilistically define the different white matter regions on the population specific multimodal template. The method will be useful to carry out ROI based statistics across different modalities even in the absence of expert segmentation. We show the effectiveness of such a template using voxelwise multivariate statistical analysis on population based group studies on HIV/AIDS patients. 1 The need for a probabilistic multimodal atlas The growth in brain imaging data across different modalities gives an opportunity to understand the disease progression and make correlations across them. Statistical analysis across different modalities and across population require spatial normalization. All the brain images are often normalized to a pre-defined template, for example the ICBM-152 or MNI template. However in [1] and [2], the authors have shown that choosing a generic template biases the statistical presently at Imaging Genetics Center, University of Southern California presently at MORPHENE team, INRIA Sophia-Antipoli

Diffusion tensor imaging in elderly patients with idiopathic normal pressure hydrocephalus or Parkinson’s disease: diagnosis of gait abnormalities

BACKGROUND: Gait abnormalities in the elderly, characterized by short steps and frozen gait, can be caused by several diseases, including idiopathic normal pressure hydrocephalus (INPH), and Parkinson’s disease (PD). We analyzed the relationship between these two conditions and their association with gait abnormalities using laboratory test data and findings from diffusion tensor imaging (DTI). METHODS: The study involved 10 patients with INPH, 18 with PD, and 10 healthy individuals (control group). Fractional anisotropy (FA) of five brain areas was measured and compared among the three groups. In addition, the association of INPH and PD with gait capability, frontal lobe function, and FA of each brain area was evaluated. RESULTS: The INPH group had significantly lower FA for anterior thalamic radiation (ATR) and forceps minor (Fmin) as compared to the PD group. The gait capability correlated with ATR FA in the INPH and PD groups. We found that adding DTI to the diagnosis assisted the differential diagnosis of INPH from PD, beyond what could be inferred from ventricular size alone. CONCLUSIONS: We expect that DTI will provide a useful tool to support the differential diagnosis of INPH and PD and their respective severities