Why voxel-based morphometric analysis should be used with great caution when characterizing group differences

By Christos Davatzikos. 2004

446 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Geometry and Topology, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “Machine learning based methods Davatzikos (2004) Cuingnet et al. (2011) De Martino et al. (2008) Fan et al. (2007) Gaonkar and Davatzikos (2012) Xu et al. (2009) Van De Ville et al. (2007) Van De Ville et al. (2004) Mingoia et al. (2012) can offer diagnostics based on neuroimaging data.”
COMPARE: Classification of Morphological Patterns Using Adaptive Regional Elements (2007, methodology): “In order to interpret the classification results, we utilize a discriminative direction method, as used in [10] and [14], to estimate the group differences between schizophrenia patients and normal controls.”
Deriving statistical significance maps for SVM based image classification and group comparisons (2012, background): “It has been argued that such univariate analysis might miss group difference patterns that span multiple voxels or regions [2].”
Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data (2006, unknown): “There has been much debate about the strengths and limitations of VBM (Bookstein, 2001; Ashburner and Friston, 2001; Davatzikos, 2004; Ashburner and Friston, 2004).”
Spatial regularization of SVM for the detection of diffusion alterations associated with stroke outcome (2011, background): “However, the sensitivity of theses approaches is limited when the differences are spatially complex and involve a combination of different voxels or brain structures (Davatzikos, 2004).”
A new ranking method for principal components analysis and its application to face image analysis (2010, unknown): “As a consequence, SVM is more robust to outliers, zooming into the subtleties of group differences [3].”
Meta-analysis of gray matter anomalies in schizophrenia: application of anatomic likelihood estimation and network analysis. (2008, unknown): “Furthermore, they may be biased toward detecting highly localized group differences and biased against detecting group differences when these differences are spatially complex (52).”
Voxel-based morphometry of the human brain: Methods and applications (2005, background): “As recently pointed out by Davatzikos (2004) [40], mass uni-variate approaches such as VBM are biased toward group differences that are localized in space.”
Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification (2013, unknown): “This fact is not generally being true of a univariate type of analysis (Davatzikos, 2004).”
The Parieto-Frontal Integration Theory (P-FIT) of intelligence: Converging neuroimaging evidence (2007, methodology): “Of note, this methodology is not without controversy (Allen et al. 2005; Ashburner & Friston 2001; Bookstein 2001; Davatzikos 2004), although several recent studies have compared VBM to traditional region-of-interest (ROI) tracing techniques in the same subjects and determined generally good corresp”
Diffusion tensor imaging with tract-based spatial statistics reveals local white matter abnormalities in preterm infants (2007, background): “(2005) have observed that VBM findings do not always replicate ROI analyses of DTI data, and given that the choice of spatial smoothing extent of the segmentation output data also remains unresolved, some investigators have concluded that VBM should not be used with imperfectly registered images (Bo”
Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection (2005, unknown): “The important distinction between a voxel-based analysis and the analysis of a spatio-temporal pattern is the same as the distinction between (mass) uni-variate and multi-variate analysis (Davatzikos, 2004).”
Geodesic Information Flows: Spatially-Variant Graphs and Their Application to Segmentation and Fusion (2015, background): “Bottom) The information ﬂows only between the neighbours of the target subject. a problem which has generated wide criticism [4], [5]; and (3) groupwise spaces confound the two sources of morphological variation by averaging natural variability (e.g. sulcal patterns, brain shape) and pathological…”
Generative Discriminative Models for Multivariate Inference and Statistical Mapping in Medical Imaging (2018, background): “However, it does not fully exploit the available data since it ignores correlations between different brain regions [5].”
Structural and functional biomarkers of prodromal Alzheimer's disease: A high-dimensional pattern classification study (2008, methodology): “Towards this goal, high-dimensional pattern classification methods have been pursued in recent years (Davatzikos, 2004; Duchesne et al., 2006; Fan et al., 2006a, 2007a, 2005, 2006b, 2007b; Golland et al., 2002; Lao et al., 2004; Liu et al., 2004; Timoner et al., 2002), and have shown great…”