FMRI ANALYSIS



Compare Region Of Interest analysis to whole brain approaches in FMRI analysis

Compare Region-Of -Interest analysis to whole brain approaches in FMRI analysis

Introduction

Most hypotheses that are addressed using functional magnetic resonance imaging (fMRI) are stated in terms of the specific functionality of brain regions of interest (ROIs). These regions are frequently defined based on their cytoarchitectonic structure (e.g., Brodmann areas) or anatomical landmarks such as sulci (Rademacher et al 1993) and (Caviness et al 1996). It is widely acknowledged (though rarely measured) that there exists a considerable degree of intersubject variability in the shape and location of these regions. We begin this article by presenting evidence that standard normalization techniques only partially accommodate intersubject variability and that after a full-brain normalization procedure there exists a considerable degree of residual variability in the shape and location of regions defined based on anatomical markers.

Since most fMRI experiments are built on multiple-subject data, standard functional analysis techniques based on voxel-level statistics attempt to compensate for this variability by spatially smoothing the functional series after normalization. Smoothing not only attempts to compensate for divergent functional anatomy but also enforces the validity of standard statistical analysis based on Gaussian field theory (Friston et al., 1996). A troubling aspect of this solution is the loss of clear regional boundaries resulting from the smoothing of the BOLD response across neighboring but possibly functionally dissimilar regions. Furthermore, the sensitivity of the resulting statistical tests is expected to decrease with the extent of anatomical variability across subjects.

In the present work we take a different strategy and present a methodology for the analysis of functional data that focuses on the activation of specific brain regions of interest. The proposed methodology is based on the definition of subject-specific ROIs and the testing of functional hypotheses directly at the level of the (multivariate) whole region activation. In this way, we avoid the need for full-brain intersubject coregistration and, most importantly, the need for spatially smoothing the functional series. By providing confirmatory analyses at the level of ROIs, the proposed methodology serves as a more direct link between the initial research hypotheses stated in terms of the functionality of discrete brain regions and the functional analyses used to test these hypotheses. This confirmatory approach to regional functional analysis is expected to ultimately increase the replicability of fMRI experiments and facilitate a knowledge buildup from functional results.

The outline of the article is as follows. The following subsection introduces the motivation for the proposed ROI analysis methodology. Intersubject Anatomical Variability describes a tool for the definition of ROIs based on anatomical markers and illustrates the extent of intersubject anatomical variability in temporal lobe cortical regions on a set of nine subjects. Functional Analysis summarizes the proposed methodology for the functional analysis of regional imaging data. Simulations comparing the expected sensitivity of the proposed methodology to one based on intersubject full-brain normalization and voxel- or cluster-level analyses are presented under simulations. Finally, conclusion presents Monte Carlo simulations validating the proposed statistical functional analyses on a range ...