A quantitative comparison was performed on the relative ability of the Brett transform (i.e., “mni2tal”) (Brett et al., 2002
) and the Lancaster transform (i.e., “icbm2tal”) (Lancaster et al., 2007
) to correct for the disparity that exists between Talairach and MNI coordinates. The fMRI data of paired associates encoding and recall demonstrate that a reduction in disparity between Talairach and MNI coordinates is possible using the Lancaster transform, and that the Brett transform actually results in a poorer fit than if no conversion algorithm is applied to the MNI coordinates. As a second comparison of the Brett and Lancaster transforms, coordinate-based meta-analyses of the published paired associates literature were performed using activation likelihood estimation (ALE) (Turkeltaub et al., 2002
) to determine if the choice of transform has a substantial effect on the observed concordance patterns. Analysis revealed that the Lancaster transform results in tighter, more coherent nodes of concordance. Taken together, these results indicate that the choice of transform (e.g., Lancaster or Brett) does have an impact on the reporting of functional neuroimaging results and should therefore not be overlooked during quantitative comparison across studies. Our results were not intended to address accurate alignment with the anatomical labels delineated by the 1988 Talairach
atlas, but rather to test different methods for comparing and meta-analyzing coordinates that have already been normalized to Talairach space using accepted anatomical landmarks and transformation techniques. While linear transformations are not able to match brain shape in the same way possible with nonlinear transformations, there exist a large number of coordinates in the literature derived from affine transformations to Talairach space (). It is important that the best methods be made available to ensure that these published Talairach coordinates are comparable to published MNI coordinates, and the results of the present study suggest that the Lancaster transform provides improved outcome over the Brett transform.
Community software packages currently provide both linear and piecewise linear Talairach transformation methods, the latter of which involves dividing Talairach space into a proportional grid of 12 sub-volumes (based on axes defined from anatomical landmarks) and scaling each region independently. In 1994, our group adopted the global scaling approach since the piecewise linear technique was originally intended as a strategy for improving localization within a specific sub-volume in neurosurgical applications. In contrast, the global scaling method provides a whole-brain fit with minimal distortion (Lancaster et al., 1995
). This method was used to develop the Lancaster transform, and the present comparison of coordinate disparity does not include an analysis of how our results may differ for piecewise linear mappings. To our knowledge, no study exists that quantifies the effects of linear vs. piecewise linear normalization to Talairach space. However, Chau and McIntosh (2005)
compared coordinates extracted from images normalized to the ICBM-152 template in SPM99 to coordinates derived from piecewise linear normalization to Talairach space. They observed that the disparity between coordinates converted using the Brett transform ranged from 3.0-9.5 mm (we note that the average distance of 8.451 mm obtained here for Brett transform disparity is within this range). In addition, Chau and McIntosh reported similar effects of the Brett transform, notably that it produces the largest discrepancies in inferior, superior frontal, and occipital regions. Using data from of Chau and McIntosh, we calculated that the corresponding average discrepancy for their set of coordinates corrected using the Lancaster transform is 5.76 mm, which is smaller than what was computed for the Brett transform (6.27 mm). Thus, although the Lancaster transform was developed using the global scaling method, there is evidence to suggest that it provides improved fit over the Brett transform even for cases in which images were normalized to Talairach space using the piecewise linear scaling method.
The present study highlights a need for better publishing standards when reporting the reference space to which coordinates refer. This is an issue that has been previously raised (Poldrack et al., 2008
; Van Essen and Dierker, 2007
); however, this is a critical point that needs to be reiterated as it has important implications for both coordinate-based meta-analyses and neuroinformatics initiatives such as the BrainMap database (Fox and Lancaster, 2002
; Laird et al, 2005a
). Frequently, authors can be misleading or vague when citing the brain template used during spatial normalization. Authors should be encouraged to make a clearer distinction between the basic coordinate system as defined by Talairach and Tournoux (1998) and the reference template corresponding to a standard brain that was used during spatial normalization. Confusion between these two components of the analysis has led to frequent ambiguity in the literature. A working group has been established to provide specific guidelines on this and other issues, which should aid authors in identifying and following the appropriate standards when preparing manuscripts (http://www.fmrimethods.org
Talairach Space vs. MNI Space
In a series of commentaries between researchers (Devlin and Poldrack, 2007
; Toga and Thompson, 2007
; Tzourio-Mazoyer et al., 2007
; Van Essen and Dierker, 2007
; and others), many agreed it would be beneficial to the neuroimaging community to reach a consensus for methods of localizing neuroanatomical regions with precision and accuracy. Devlin and Poldrack (2007)
argued that the neuroimaging community should abandon the Talairach and Tournoux atlas (1988)
, and, with one exception, based their reasoning solely on the nature of the anatomical labels published in the 1988 atlas: (1) the single-subject anatomy is not representative of the general population, (2) almost all major software packages use MNI templates, (3) the atlas is based on only a single hemisphere, and (4) the precision of the labeled Brodmann areas is highly misleading. However, while these are valid criticisms as to why Talairach labels
are not optimal, they do not directly pertain to a recommendation to abandon Talairach space
. That is, being “Talairach compliant” is not the same as being limited to using the specific anatomical labels delineated in the 1988 Talairach and Tournoux
atlas. Furthermore, being Talairach compliant does not prevent the creation of probabilistic, automated naming tools such as the Talairach Daemon or the cytoarchitectonic labels of the SPM Anatomy Toolbox (Eickhoff et al., 2005
Talairach space is defined as the standard brain space with the same dimensions as the published 1988 atlas (x = 136mm, y = 172mm, z = 118mm), in which the y-axis corresponds to the anterior commissure-posterior commissure (AC-PC) line, and the origin is the AC. Any brain and any template can be made to fit this definition, including the MNI templates. For example, in the SPM Anatomy Toolbox, probabilistic cytoarchitectonic maps are corrected by a linear shift such that the origin is the anterior commissure in order to move images from “original MNI space” to “anatomical MNI space” (Eickhoff et al., 2005
). MNI templates do not conform to Talairach-compliant criteria; brains normalized to MNI templates are consistently larger than brains normalized to Talairach space, and are even consistently larger than non-normalized individual subject brains by approximately 24% (Lancaster et al., 2007
). The frequent assertion that MNI brains are more representative of the general population seems contradicted by their inflated scalar dimensions.
The popularity of the MNI templates results from the fact that they are continuously sampled MRI data sets, which allow use of automated spatial normalization algorithms that cannot be driven by the dimensions or contours derived from the 1988 Talairach
atlas. Having a procedure that is intrinsically suitable for automated spatial normalization methods is important. The Talairach atlas is not amenable to this type of analysis, since no standard group template was distributed with the 1988 publication. It is therefore undeniable that MNI templates are highly desirable, given their utility in automated spatial normalization, as well as the well-developed and validated labels that are representative of the general population (Eickhoff et al., 2005
). A significant drawback to the use of MNI template is that an anatomical atlas was not concurrently released, which is the sine qua non
for defining an anatomical space. Only through post hoc
community efforts has the MNI305 template gradually evolved toward a space with defined structures and probabilistic structural variability.
There is a vast volume of published data in the literature that is Talairach compliant. To abandon this standard undermines the advantages in neuroanatomical standards that we have achieved as a field. In any field, a researcher should have the ability to compare new experimental results to any study that preceded it, such as in quantitative, coordinate-based meta-analyses. Introducing any additional non-Talairach compliant (and non-MNI compliant) templates in the future would further compound this mistake. We therefore recommend that any future templates be distributed as Talairach compliant, or be published and released to the community only with an accompanying space-defining atlas with a validated transform to Talairach compliance.