Voxel-Based Morphometry

TL;DR: The Computational Anatomy Toolbox (CAT) is introduced - a powerful suite of tools for morphometric analyses with an intuitive graphical user interface, but also usable as a shell script.

TL;DR: The goal of this work was to build a toolbox comprising all the necessary functionalities for multivariate analyses of neuroimaging data, based on machine learning models, and to facilitate novel contributions from developers, aiming to improve the interaction between the neuroim imaging and machine learning communities.

Abstract: A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community.

TL;DR: An overview of the methods for measuring macro- and mesoscopic structure and for inferring microstructural properties is provided and, although methods need to improve and caution is required in interpretation, structural MRI continues to have great promise in furthering the authors' understanding of how the brain works.

TL;DR: In this paper, the authors describe the steps involved in VBM, with particular emphasis on segmenting gray matter from MR images with non-uniformity artifact and provide evaluations of the assumptions that underpin the method, including the accuracy of the segmentation and the assumptions made about the statistical distribution of the data.

TL;DR: DARTEL has been applied to intersubject registration of 471 whole brain images, and the resulting deformations were evaluated in terms of how well they encode the shape information necessary to separate male and female subjects and to predict the ages of the subjects.

TL;DR: A new method is proposed which attempts to keep the sensitivity benefits of cluster-based thresholding (and indeed the general concept of "clusters" of signal), while avoiding (or at least minimising) these problems, and is referred to as "threshold-free cluster enhancement" (TFCE).

TL;DR: In an age where the amount of data collected from brain imaging is increasing constantly, it is of critical importance to analyse those data within an accepted framework to ensure proper integration and comparison of the information collected as discussed by the authors.