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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/)., (Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.)

Anatomical global spatial normalization (aGSN) is presented as a method to scale high-resolution brain images to control for variability in brain size without altering the mean size of other brain structures. Two types of mean preserving scaling methods were investigated, "shape preserving" and "shape standardizing". aGSN was tested by examining 56 brain structures from an adult brain atlas of 40 individuals (LPBA40) before and after normalization, with detailed analyses of cerebral hemispheres, all gyri collectively, cerebellum, brainstem, and left and right caudate, putamen, and hippocampus. Mean sizes of brain structures as measured by volume, distance, and area were preserved and variance reduced for both types of scale factors. An interesting finding was that scale factors derived from each of the ten brain structures were also mean preserving. However, variance was best reduced using whole brain hemispheres as the reference structure, and this reduction was related to its high average correlation with other brain structures. The fractional reduction in variance of structure volumes was directly related to ρ (2), the square of the reference-to-structure correlation coefficient. The average reduction in variance in volumes by aGSN with whole brain hemispheres as the reference structure was approximately 32%. An analytical method was provided to directly convert between conventional and aGSN scale factors to support adaptation of aGSN to popular spatial normalization software packages.

Shifting of echoplanar images (EPI) in the phase-encoding direction during functional magnetic resonance imaging (fMRI) experiments can be observed due to B(0) drift. These shifts can cause artifacts in functional activation maps that can be corrected using a navigator echo (NE) technique, but the NE correction requires pulse sequence modifications not available on many clinical systems. A fast, postprocessing correction method based on edge root-mean-square error reduction (ERMSR) is introduced and shown to provide an equivalent correction. J. Magn. Reson. Imaging 2000;12:956-959., (Copyright 2000 Wiley-Liss, Inc.)

An automated coordinate-based system to retrieve brain labels from the 1988 Talairach Atlas, called the Talairach Daemon (TD), was previously introduced [Lancaster et al., 1997]. In the present study, the TD system and its 3-D database of labels for the 1988 Talairach atlas were tested for labeling of functional activation foci. TD system labels were compared with author-designated labels of activation coordinates from over 250 published functional brain-mapping studies and with manual atlas-derived labels from an expert group using a subset of these activation coordinates. Automated labeling by the TD system compared well with authors' labels, with a 70% or greater label match averaged over all locations. Author-label matching improved to greater than 90% within a search range of +/-5 mm for most sites. An adaptive grey matter (GM) range-search utility was evaluated using individual activations from the M1 mouth region (30 subjects, 52 sites). It provided an 87% label match to Brodmann area labels (BA 4 & BA 6) within a search range of +/-5 mm. Using the adaptive GM range search, the TD system's overall match with authors' labels (90%) was better than that of the expert group (80%). When used in concert with authors' deeper knowledge of an experiment, the TD system provides consistent and comprehensive labels for brain activation foci. Additional suggested applications of the TD system include interactive labeling, anatomical grouping of activation foci, lesion-deficit analysis, and neuroanatomy education.

The goal of regional spatial normalization is to remove anatomical differences between individual three-dimensional (3-D) brain images by warping them to match features of a standard brain atlas. Full-resolution volumetric spatial normalization methods use a high-degree-of-freedom coordinate transform, called a deformation field, for this task. Processing to fit features at the limiting resolution of a 3-D MR image volume is computationally intensive, limiting broad use of full-resolution regional spatial normalization. A highly efficient method, designed using an octree decomposition and analysis scheme, is presented to resolve the speed problem while targeting accuracy comparable to current volumetric methods. Translation and scaling capabilities of octree spatial normalization (OSN) were tested using computer models of solid objects (cubes and spheres). Boundary mismatch between transformed and target objects was zero for cubes and less than 1% for spheres. Regional independence of warping was tested using brain models consisting of a homogenous brain volume with one internal homogenous region (lateral ventricle). Boundary mismatch improved with successively smaller octant-level processing and approached levels of less than 1% for the brain and 5% for the lateral ventricle. Five 3-D MR brain images were transformed to a target 3-D brain image to assess boundary matching. Residual boundary mismatch was approximately 4% for the brain and 8% for the lateral ventricle, not as good as with homogeneous brain models, but similar to other results. Total processing time for OSN with a 256(3) brain image (1-mm voxel spacing) was less than 10 min., (Copyright 1999 Academic Press.)

Unlabelled: Global spatial normalization transforms a brain image so that its principal global spatial features (position, orientation and dimensions) match those of a standard or atlas brain, supporting consistent analysis and referencing of brain locations. The convex hull (CH), derived from the brain's surface, was selected as the basis for automating and standardizing global spatial normalization. The accuracy and precision of CH global spatial normalization of PET and MR brain images were evaluated in normal human subjects., Methods: Software was developed to extract CHs of brain surfaces from tomographic brain images. Pelizzari's hat-to-head least-square-error surface-fitting method was modified to fit individual CHs (hats) to a template CH (head) and calculate a nine-parameter coordinate transformation to perform spatial normalization. A template CH was refined using MR images from 12 subjects to optimize global spatial feature conformance to the 1988 Talairach Atlas brain. The template was tested in 12 additional subjects. Three major performance characteristics were evaluated: (a) quality of spatial normalization with anatomical MR images, (b) optimal threshold for PET and (c) quality of spatial normalization for functional PET images., Results: As a surface model of the human brain, the CH was shown to be highly consistent across subjects and imaging modalities. In MR images (n = 24), mean errors for anterior and posterior commissures generally were <1 mm, with SDs < 1.5 mm. Mean brain-dimension errors generally were <1.3 mm, and bounding limits were within 1-2 mm of the Talairach Atlas values. The optimal threshold for defining brain boundaries in both 18F-fluorodeoxyglucose (n = 8) and 15O-water (n = 12) PET images was 40% of the brain maximum value. The accuracy of global spatial normalization of PET images was shown to be similar to that of MR images., Conclusion: The global features of CH-spatially normalized brain images (position, orientation and size) were consistently transformed to match the Talairach Atlas in both MR and PET images. The CH method supports intermodality and intersubject global spatial normalization of tomographic brain images.