Why Voxel-Based Morphometry Should Be Used

By John Ashburner, Karl J. Friston. 2001

886 citations recorded by the source corpus.

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In Geometry and Topology

Citations in the corpus

Visual Feature Attribution Using Wasserstein GANs (2017, methodology): “The most common approach is to use regression analysis or machine learning tools to generate population average maps, which highlights features that are salient across the population [3, 32, 61, 16, 39, 45, 14].”
Aging, training, and the brain: A review and future directions (2009, neutral): “…use of voxel-based morphometry, which has been criticized for potentially distorting estimates of atrophy, errors in classification of white and grey matter, and image registration problems (Ashburner and Friston 2001; Bookstein 2001; Karas et al. 2003; Kennedy et al. 2008; Tisserand et al. 2002).”
A comprehensive study of gray matter loss in patients with Alzheimer's disease using optimized voxel-based morphometry (2003, background): “However, the initial implementation of VBM has led to debate about the limitations of affine and low-order registration algorithms (Ashburner and Friston, 2001; Bookstein, 2001).”
Voxel-Based Morphometric Analysis of Gray Matter in First Episode Schizophrenia (2002, neutral): “…to some systematical biases, which could arise from not only the errors caused by the misregistration of anatomical structures, but also movement or gray and white matter intensity patterns that differ between groups (see Bookstein, 2001, and Ashburner and Friston, 2001, discussion in NeuroImage).”
Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease (2004, positive): “(Ashburner and Friston, 2001; Bookstein, 2001) and one of the assumptions for a correct VBM is reduced global anatomical variability (Ashburner and Friston, 2000), we enhanced the averaging process at the template-creation step by projecting the affine transformationmatrices, whichmap all scans to a”
SubCMap: Subject and Condition Specific Effect Maps (2017, methodology): “Group analysis methods aim to detect statistical differences between groups either in a univariate fashion at each measurement independently [10] or in a multivariate fashion using multiple measurements simultaneously [13, 14].”
The effect of filter size on VBM analyses of DT-MRI data (2005, positive): “The interested reader is referred elsewhere for such discussions Ashburner and Friston, 2001; Bookstein, 2001; Crum et al., 2003; Davatzikos, 2004; Salmond et al., 2002).”
Reconstructing subject‐specific effect maps (2017, neutral): “Either a group analysis technique (Ashburner and Friston, 2001; Krishnanet al., 2011; Worsleyet al., 1997) or a machine learning based predictive model (Arbabshirani et al.”
Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases (2016, methodology): “Methods that can process high-dimensional image-based measurements allow constructing detailed volumetric [1] and surface maps [2,3] that highlight disease-aﬀected areas in the brain.”
The neuroanatomy of general intelligence: sex matters (2005, methodology): “VBM and ROI methods have their respective strengths and weaknesses (Ashburner and Friston, 2001; Bookstein, 2001; Davatzikos, 2004; Friston and Ashburner, 2004).”
Non-rigid image registration: theory and practice. (2004, background): “At the time of writing, it is only in brain applications that non-rigid registration is used on a routine basis even though there remain some questions about its veracity [27, 96–98].”
Voxel-based morphometry of the human brain: Methods and applications (2005, background): “These are some of several potential systematic differences that can arise between patients and controls that may be detected by VBM [38].”