A framework for a streamline‐based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements

By G. Parker, Hamied A. Haroon, Claudia A. M. Gandini Wheeler-Kingshott. Journal of Magnetic Resonance Imaging, 2003

582 citations recorded by the source corpus.

Subfields: Materials Chemistry, Mechanics of Materials, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/f1c1f098f1d8465c82a94fe4f625f8b4eac8c18d

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Citations in the corpus

Diffusion MRI at 25: Exploring brain tissue structure and function (2012, methodology): “An alternative group of techniques was developed a few years later that use probabilistic, rather than deterministic, models of diffusion (Behrens et al., 2003b; Hagmann et al., 2003; Lazar and Alexander, 2005; Parker et al., 2003).”
Characterizing brain anatomical connections using diffusion weighted MRI and graph theory (2007, methodology): “…although probability of connection between seed and target voxels has been previously used (Tuch, 2002; Parker et al., 2002; Koch et al., 2002; Parker et al., 2003; Behrens et al., 2003; Staempfli et al., 2006), the generalization of this concept to characterize anatomical connections between…”
Diffusion MRI fiber tractography of the brain (2019, background): “Brain regions that contain higher densities of the resulting trajectories are then deemed to have a higher probability of “connection” with the seed point.(66,67) Probabilistic streamline results are, therefore, often quantified by generating visitation count maps of the number of trajectories that ”
Probabilistic fiber tracking using the residual bootstrap with constrained spherical deconvolution (2011, background): “Random vector generation, for example [Lazar and Alexander, 2002; Parker et al., 2003], relates the probability of a tract to the number of times it is reconstructed in a Monte Carlo random walk, where the characteristics of the random walk are determined by the properties of the underlying diffusio”
Global fiber reconstruction becomes practical (2011, background): “The reconstruction of long neuronal pathways is performed in small successive steps, either deterministically (Mori et al., 1999; Basser et al., 2000) or probabilistically (Hagmann et al., 2003; Parker et al., 2003) by following the local, voxelwise defined distribution of fiber directions.”
MRtrix: Diffusion tractography in crossing fiber regions (2012, background): “…tracking results (Calamante et al., 2010, 2011, 2012); to compute ‘‘visitation’’ maps from the tracks generated (as in Behrens et al., 2003; Parker et al., 2003; Newton et al., 2006); and to sample scalar values (e.g., FA) along the tracks generated, as in Jones et al. (2005) and Zhu et…”
Assessment of the early organization and maturation of infants' cerebral white matter fiber bundles: A feasibility study using quantitative diffusion tensor imaging and tractography (2006, methodology): “Non-linear tractography algorithms would be interesting to consider, such as methods based on probabilistic approaches (Hagmann et al., 2003; Parker et al., 2003; Lazar and Alexander, 2005), or on the minimization of the fibers global energy, with regularization of their trajectory (Poupon et al.”
A modified damped Richardson-Lucy algorithm to reduce isotropic background effects in spherical deconvolution (2010, background): “, 1994) and related tractography techniques (Basser et al., 2000; Behrens et al., 2003; Conturo et al., 1999; Mori et al., 1999; Parker et al., 2003) are the best known of these techniques.”
Anatomically-constrained tractography: Improved diffusion MRI streamlines tractography through effective use of anatomical information (2012, background): “Conversely, probabilistic streamlines tractography (e.g. Parker et al., 2003) considers the uncertainty in the calculation of the fibre orientations given the image data available, and statistically samples from these distributions as the streamlines are constructed.”
Diffusion Tensor Imaging and Beyond (2011, unknown): “Probabilistic streamlines results are, therefore, often quantified by generating maps of the number of tracks that traverse each voxel, which can then be analyzed and compared more readily (174,175).”
Characterization of Cerebral White Matter Properties Using Quantitative MRI Stains (2011, methodology): “…attempt to characterize the uncertainty in the tract reconstruction , by performing a Monte Carlo tractography experiment by repeating the streamline reconstruction multiple times and perturbing the propagation vector field for every iteration (Lazar and Alexander, 2005; Parker et al., 2003).”
Ventral and dorsal pathways for language (2008, methodology): “Second, a Monte Carlo simulation of random walks (MCRW) similar to the probabilistic index of connectivity (PICo) method (41) was used to calculate probabilistic maps separately for each seed region.”
Hemispheric asymmetries in language-related pathways: A combined functional MRI and tractography study (2006, methodology): “Newer probabilistic tractography algorithms adapt the commonly used streamline approach by incorporating the uncertainty in the orientation of the principal direction of diffusion defined for each voxel to generate maps of probability of connection to chosen start points (Behrens et al., 2003a,b; La”
Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral tissue (2005, methodology): “…methods for determining the connectivity between brain regions using information obtained from diffusion-weighted MRI (DWI) have recently been introduced (Tuch et al. 2001; Koch et al. 2002; Lazar & Alexander 2002; Parker et al. 2002a–c, 2003; Behrens et al. 2003; Parker & Alexander 2003a,b).”