An Optimized Individual Target Brain in the Talairach Coordinate System

Abstract

The goal of regional spatial normalization is to remove anatomical differences between individual three-dimensional brain images by warping them to match features of a single target brain. Current target brains are either an average, suitable for low-resolution brain mapping studies, or a single brain. While a single high-resolution target brain is desirable to match anatomical detail, it can lead to bias in anatomical studies. An optimization method to reduce the individual anatomical bias of the ICBM high-resolution brain template (HRBT), a high-resolution MRI target brain image used in many laboratories, is presented. The HRBT was warped to all images in a group of 27 normal subjects. Displacement fields were averaged to calculate the "minimal deformation target" (MDT) transformation for optimization. The greatest anatomical changes in the HRBT, following optimization, were observed in the superior precentral and postcentral gyri on the right, the right inferior occipital, the right posterior temporal lobes, and the lateral ventricles. Compared with the original HRBT, the optimized HRBT showed better anatomical matching to the group of 27 brains. This was quantified by the improvements in spatial cross-correlation and between the group of brains and the optimized HRBT (P < 0.05). An intended use of this processing is to create a digital volumetric atlas that represents anatomy of a normal adult brain by optimizing the HRBT to the group consisting of 100+ normal subjects.

Effects of spatial transformation on regional brain volume estimates

“…Of course, there should be no expectation that the two approaches, which also employ different warping methods (piecewise-linear versus non-linear), should produce identical results. Templates based on averaging multiple brains (e.g., Tal50, MNI305, IA38) should incorporate fewer spatial biases than the classic, single-brain based Tal transformation (Kochunov et al, 2002). We found that while the different transformations provide similar results, there were many instances in which the templates provided somewhat divergent patterns of transformation from the native space.…”

“…Spatial transformation is also an essential component of tissue-density-based statistical techniques, such as voxelbased morphometry (Ashburner and Friston, 2000) and other tissue-based assessment tools (Mega et al, 2005). Lastly, group-level functional neuroimaging analyses rely on spatial normalization (Fox et al, 1985;Mazziota et al, 2000;Kochunov et al, 2002).…”

Gray Matter Alterations in Obsessive–Compulsive Disorder: An Anatomic Likelihood Estimation Meta-Analysis

“…A conservative threshold for statistical significance was set at po0.05 (false discovery rate corrected) with a minimum cluster size of 400 mm 3 . We used MRIcron software (http:// www.sph.sc.edu/comd/rorden/mricron/) to visualize ALE maps overlaid onto a high-resolution brain template generated by the International Consortium for Brain Mapping (Kochunov et al, 2002).…”

A comparison of label‐based review and ALE meta‐analysis in the Stroop task

“…Three different ALE maps were computed for all Stroop studies, Stroop studies that required an overt or covert verbal response, and Stroop studies that required a manual response. Whole-brain maps of the ALE values were imported into AFNI [Cox, 1996] and overlaid onto an anatomical template generated by spatially normalizing the International Consortium for Brain Mapping (ICBM) template to Talairach space [Kochunov et al, 2002].…”