Case reportAn automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets
Introduction
As the number of functional MRI (fMRI) experiments increases, fMRI data analysis needs to become more sophisticated in order to extract meaningful information from the data. Analysis and interpretation of fMRI data are frequently based on identifying areas of significance on a thresholded statistical map of the entire brain volume. This form of analysis can be likened to a “fishing expedition.” As we become more knowledgeable about the structure–function relationships of different brain regions, tools for a priori hypothesis testing are needed. Selecting a region of interest for a priori hypothesis testing reduces the number of multiple statistical comparisons, thereby increasing sensitivity to activation. Thus, tools that support hypothesis-driven statistical analysis are more sensitive than their fishing expedition counterparts. Performance of such an analysis requires the generation of an appropriate image volume mask for defining the region to be probed. This can be a time-consuming process, fraught with error. This is especially problematic when one considers that hypothesis-driven analysis may be based on increasingly finer anatomic scales ranging from lobar analysis and anatomic subregion analyses to selective Brodmann area, Talairach, or Montreal Neurological Institute (MNI) coordinate-based analyses. The use of region of interest (ROI) analysis with anatomically defined features is not new. This has been well-described for the PET literature, and more recently for MRI Bohm et al 1991, Collins et al 1995, Evans et al 1988, Evans et al 1991, Evans et al 1992, Greitz et al 1991, Hammers et al 2002, Yasuno et al 2002. This type of analysis offers increased sensitivity to subtle activations. However, the inherent reality of ROI analyses is that activations outside the ROI will not be detected.
In this paper we describe an automated method of hypothesis-driven data analysis using an automated atlas-based masking technique. We provide a powerful method of probing data using automatically generated masks based on lobar anatomy, cortical and subcortical anatomy, and Brodmann areas. We have interfaced these multivolume atlases to a widely used fMRI software package, SPM99 (from the Wellcome Department of Cognitive Neurology, London, UK) Friston et al 1995a, Friston et al 1995b, Holmes and Friston 1998.
Section snippets
Atlas lookup tables
The Talairach Daemon Lancaster et al 1997, Lancaster et al 2000 is a web-based application that returns anatomic and Brodmann area information based on Talairach (Talairach and Tournoux, 1988) coordinates. This is a widely used application for determining Brodmann areas based on surviving areas of activation. For example, the Talairach coordinates −40, −25, −54 will return the following responses: In order to
Results
Representative images of our MNI Brodmann atlas overlaid on a T1-weighted MRI of an individual subject normalized to the SPM MNI template are demonstrated in Fig. 3. In addition to the BAs, the atlas includes subcortical structures that are commonly of interest in brain imaging studies. Further examples of anatomic regions contained in the atlas volumes are depicted in Fig. 4. This figure includes various Brodmann areas and cortical gyri displayed on a surface-rendered brain from another
Discussion
Functional MRI has revolutionized the field of neuroscience with the number of studies growing exponentially over the last decade. As sophistication in the forms of analyses grows, there is an increasing trend toward trying to define results using standard frames of reference. For many investigators this means using Talairach space and attempting a post hoc correlation of surviving clusters with Brodmann areas. In this paper, we demonstrate a powerful tool for performing hypothesis-driven
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