Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not present an anatomically specific localization of functional activity, various image registration techniques are introduced in the literature for the purpose of mapping functional activity into an anatomical image or a brain atlas. The problems addressed by these techniques differ depending on the application and the type of analysis, i.e., single-subject versus group analysis. Functional to anatomical brain image registration is the core part of functional localization in most applications and is accompanied by intersubject and subject-to-atlas registration for group analysis studies. Cortical surface registration and automatic brain labeling are some of the other tools towards establishing a fully automatic functional localization procedure. While several previous survey papers have reviewed and classified general-purpose medical image registration techniques, this paper provides an overview of brain functional localization along with a survey and classification of the image registration techniques related to this problem

Brain Functional Localization: A Survey of Image Registration Techniques

This article reviews the evaluation and optimization of the preprocessing steps for blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). This technique indirectly measures changes in local neuronal firing rates by measuring associated changes in deoxy-hemoglobin concentrations in nearby blood vessels. Based on the existing literature, it is impossible to make conclusive statements about the optimal algorithm and software implementations for any single preprocessing step, let alone entire pipelines. The author believes that the present focus on the technological testing of preprocessing steps should be balanced by approaches that test the pipeline. This should include all interactions measured using metrics that are closely linked to research and diagnostic questions addressed at the end of the processing pipeline. The goal is to avoid single expedient or default pipelines by developing a framework capable of potentially testing thousands of possible pipeline implementations per dataset. To achieve this goal, researchers depend on recent developments in software tools for managing neuroimaging workflows.

Evaluating fMRI preprocessing pipelines

http://www.mouseimaging.ca/publications/assets/archive/2006%20NeuroImage%2029%20Chen.pdf

Neuroanatomical differences between mouse strains as shown by high-resolution 3D MRI.

Automated image registration

IEEE Engineering in Medicine and Biology Magazine focuses on modern methods for the analysis of data from functional magnetic resonance imaging (fMRI) studies Accordingly, the guest editors have seen fit to begin with a brief article on the history, mechanisms and methods behind fMRI This is followed by the presentation of recent significant progress in paradigm design for fMRI as well as development of other methods for assessing the functional anatomy of the human brain, such as diffusion tensor imaging, for mapping white matter fiber tracts Thus, the future appears to promise a more integrative approach to functional brain imaging, in which data from multiple modalities are entered into comprehensive analyses of brain function and connectivity

A brief introduction to functional MRI

This paper presents a novel method for creating an unbiased and geometrically centered average from a group of images. The morphological variability of the group is modeled as a set of deformation fields which encode differences between the group average and individual members. We demonstrate the algorithm on a group of 27 MR images of mouse brains. The average image is highly resolved as a result of excellent groupwise registration. Local and global groupwise variability estimates are discussed.

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Natasa Kovacevic

Papers

Deformation Based Representation of Groupwise Average and Variability