Mathematical/computational challenges in creating deformable and probabilistic atlases of the human brain

By Paul M. Thompson, R. Woods, M. Mega, Arthur W. Toga. Human Brain Mapping, 2000

368 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/0ff6991be894588d49dd6ec3d2064bc213091039

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In Cognitive Neuroscience

In Geometry and Topology

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Mapping Continued Brain Growth and Gray Matter Density Reduction in Dorsal Frontal Cortex: Inverse Relationships during Postadolescent Brain Maturation (2001, methodology): “…on the cortical surfaces surrounding and between the sulcal contours drawn on each individual’s brain surface were calculated using the averaged sulcal contours as anchors to drive three-dimensional cortical surface mesh models from each subject into correspondence (Thompson et al., 2000, 2001).”
Symmetric Atlasing and Model Based Segmentation: An Application to the Hippocampus in Older Adults (2006, methodology): “The utility of a method whereby features can be automatically identified and ex-tracted from cortical data is far reaching but can be problematic [16].”
A comprehensive study of gray matter loss in patients with Alzheimer's disease using optimized voxel-based morphometry (2003, methodology): “niques have been developed (Freeborough and Fox, 1997, 1998; Frisoni et al., 1996; Smith et al., 2001; Thompson et al., 2000), the goal of which is to automatically analyze whole-brain structural MRI scans, avoiding a priori selection of regions and eliminating observer variability.”
Automatic parcellation of human cortical gyri and sulci using standard anatomical nomenclature (2010, background): “Due to the sheetlike topology of the cortex, surface based coordinate systems (Fischl et al., 1999a,b; Thompson et al., 2000; Van Essen et al., 1998) may be more appropriate for the anatomy of the brain than classical volume based coordinate systems (Talairach and Tournoux, 1988): they provide bette”
Mapping cortical change across the human life span (2003, methodology): “Points on the cortical surfaces surrounding and between the sulcal contours drawn on each individual's brain surface were calculated using the averaged sulcal contours as anchors to drive 3D cortical surface mesh models from each subject into correspondenc”
Brain templates and atlases (2012, neutral): “…al., 2005; Christensen et al., 1997; Chung et al., 2001; Grenander and Miller, 2007; Guo et al., 2005; Lian and Davatzikos, 2011; Miller, 2004; Miller et al., 1993, 2002, 2006; Thompson and Toga, 1996, 1999; Thompson et al., 2000a,b; Wang et al., 2005; Yeo et al., 2010a; Yushkevich et al., 2008).”
A Population-Average, Landmark- and Surface-based (PALS) atlas of human cerebral cortex (2005, neutral): “... MRI data include ROI methods that compare manually identified regions of interest (e.g., Loftus et al. 1993; Ochiai et al., 2004); voxel-based morphometry (VBM) methods that compare the distribution of voxels categorized as gray matter, white matter, and ventricular (e.g., Good et al., 2001; Wat”
Cortical change in Alzheimer's disease detected with a disease-specific population-based brain atlas. (2001, methodology): “To allow point-to-point cortical averaging, each subject's cortical model is converted to a 'f lat map' as previously described (Thompson et al., 2000a).”
A Review of Geometric Transformations for Nonrigid Body Registration (2008, background): “Atlases can be either probabilistic [33], [34], [35], intensity based [36], label based [37], [38] or deformation based [39].”
The problem of functional localization in the human brain (2002, background): “The algorithms need to be constrained so that prominent invariant sulci drive the matching more strongly than variable feature”
Non-rigid image registration: theory and practice. (2004, methodology): “Surfaces are also used to drive volumetric registration in Thompson et al [52] to analyse normal and Alzheimer brains with respect to an anatomical image database.”
Automatic segmentation of brain MRIs of 2-year-olds into 83 regions of interest (2008, background): “The growth in the use of imaging to assess brain morphology has been accompanied by an increase in the number of available investigations focussing on the organization of the brain (Evans et al., 1988; Zilles et al., 1995; Thompson et al., 2000; Mazziotta et al., 2001a; Hammers et al., 2002; Behrens”
Dynamics of Gray Matter Loss in Alzheimer's Disease (2003, methodology): “However, these tests are highly correlated, and the spatial covariance of their residuals is incorporated into the null distribution; this controls for the number of independent tests (Thompson et al., 2000a,b).”
Automatically parcellating the human cerebral cortex. (2004, background): “The advantage of using an atlas space is that coordinates in the atlas have more anatomical meaning than the native coordinate system of the image (Bajcsy et al., 1983; Bookstein, 1989; Miller et al., 1993; Gee et al., 1994; Vannier et al., 1994; Christensen et al., 1996; Collins et al., 1996; Ashbu”
Tensor-Based Morphometry as a Neuroimaging Biomarker for Alzheimer’s Disease: An MRI Study of 676 AD, MCI, and Normal Subjects (2008, neutral): “…were used to illustrate regions of volume expansion, i.e. those with ( ) 1 det > r J , or contraction, i.e., ( ) 1 det < r J ( Freeborough and Fox, 1998; Toga, 1999; Thompson et al., 2000a; Chung et al., 2001; Ashburner and Friston, 2003; Riddle et al., 2004) relative to the normal group template.”