Elsevier

NeuroImage

Volume 19, Issue 3, July 2003, Pages 1233-1239
NeuroImage

Case report
An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets

https://doi.org/10.1016/S1053-8119(03)00169-1Get rights and content

Abstract

Analysis and interpretation of functional MRI (fMRI) data have traditionally been based on identifying areas of significance on a thresholded statistical map of the entire imaged 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. These tools must be able to generate region of interest masks for a priori hypothesis testing consistently and with minimal effort. Current tools that generate region of interest masks required for a priori hypothesis testing can be time-consuming and are often laboratory specific. In this paper we demonstrate a method of hypothesis-driven data analysis using an automated atlas-based masking technique. We provide a powerful method of probing fMRI data using automatically generated masks based on lobar anatomy, cortical and subcortical anatomy, and Brodmann areas. Hemisphere, lobar, anatomic label, tissue type, and Brodmann area atlases were generated in MNI space based on the Talairach Daemon. Additionally, we interfaced these multivolume atlases to a widely used fMRI software package, SPM99, and demonstrate the use of the atlas tool with representative fMRI data. This tool represents a necessary evolution in fMRI data analysis for testing of more spatially complex hypotheses.

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: Left Cerebrum, Parietal Lobe, Post Central Gyrus, Gray matter, Brodmann area 3. 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

References (16)

  • A.C. Evans et al.

    Anatomical mapping of functional activation in stereotactic coordinate space

    NeuroImage

    (1992)
  • F. Yasuno et al.

    Template-based method for multiple volumes of interest of human brain PET images

    NeuroImage

    (2002)
  • C. Bohm et al.

    Specification and selection of regions of interest (ROIs) in a computerized brain atlas

    J. Cereb. Blood Flow Metab.

    (1991)
  • D.L. Collins et al.

    Automatic 3-D model-based neuroanatomical segmentation

    Hum. Brain Mapp.

    (1995)
  • J. Duncan et al.

    A neural basis for general intelligence

    Science

    (2000)
  • A.C. Evans et al.

    Anatomical-functional correlation using an adjustable MRI-based region of interest atlas with positron emission tomography

    J. Cereb. Blood Flow Metab.

    (1988)
  • A.C. Evans et al.

    MRI-PET correlation in three dimensions using a volume-of-interest (VOI) atlas

    J. Cereb. Blood Flow Metab.

    (1991)
  • K. Friston et al.

    Statistical parametric maps in functional imaginga general linear approach

    Hum. Brain Mapp.

    (1995)
There are more references available in the full text version of this article.

Cited by (4435)

  • Label-based meta-analysis of functional brain dysconnectivity across mood and psychotic disorders

    2024, Progress in Neuro-Psychopharmacology and Biological Psychiatry
View all citing articles on Scopus
View full text