White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI
By Derek K. Jones, Thomas R. Knösche, Robert Turner. NeuroImage, 2013
2,324 citations recorded by the source corpus.
Subfields: Orthopedics and Sports Medicine, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/18c1f13bf2af5a9efb0126b7bd4683fa1b786532
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Orthopedics and Sports Medicine
- Paulo Marques
- José Miguel Soares
- Alexander Leemans
- Yong He
- Victor Alves
- Olaf Sporns
- Jan Sijbers
- Maxime Descoteaux
In Radiology, Nuclear Medicine and Imaging
- Olaf Sporns
- D. Bassett
- Yong He
- E. Bullmore
- Martijn P. van den Heuvel
- Richard F. Betzel
- Alan C. Evans
- Bratislav Misic
Citations in the corpus
- Spurious group differences due to head motion in a diffusion MRI study (2013, background): “Anisotropy and diffusivity measures derived from DW-MRI are sensitive to several confounding factors, including head motion, partial volume, and fiber crossing effects (Jones and Cercignani, 2010;MetzlerBaddeley et al., 2012; Jones et al., 2013).”
- Weighted linear least squares estimation of diffusion MRI parameters: Strengths, limitations, and pitfalls (2013, background): “Recently, it has been suggested to use non-linear tensor estimators rather than its linear alternatives (Jones et al., 2012).”
- Structural connectomics in brain diseases (2013, neutral): “Alterations of such connectivity indicators should be interpreted with caution, as their biological origins are manifold and cannot be reliably distinguished with diffusion MRI (Jones et al., 2012).”
- Network neuroscience (2017, background): “Although subject to many experimental and statistical limitations 23 and interpretational pitfalls 24 , comparative studies have suggested that representations of long-distance connectivity derived from invasive histological methods and noninvasive imaging of neuroanatomical structure are…”
- Dipy, a library for the analysis of diffusion MRI data (2014, background): “…of the myelin wrapping the axons in the voxel may affect both MD and FA, changes in other factors, such as the distribution of directions of crossing of fiber populations through the voxel may also affect these measures (Basser and Pierpaoli, 1996; Jones et al., 2013; Wandell and Yeatman, 2013).”
- A hitchhiker's guide to diffusion tensor imaging (2012, background): “As a final take-home message, WM integrity assumptions must always be made with extreme caution (Beaulieu, 2002; Jones et al., 2012).”
- Gradients of structure–function tethering across neocortex (2019, neutral): “To mitigate concerns about inconsistencies in reconstruction of individual participant connectomes (89, 90), as well as the sensitive dependence of network measures on false positives and false negatives (81), we adopted a group-consensus approach (25, 63, 80).”
- PANDA: a pipeline toolbox for analyzing brain diffusion images (2013, methodology): “These issues have been well discussed by a few recent articles (Jones and Cercignani, 2010; Jones et al., 2012).”
- Effects of aging on functional and structural brain connectivity☆ (2017, background): “These diffusion imaging measures have limitations, as pointed out by (Jones et al., 2013).”
- The human connectome: Origins and challenges (2013, background): “This point is particularly important in diffusion imaging and tractography since the appropriate characterization of white matter connectivity and microstructure requires careful application of methods and interpretation (Jones et al., 2013).”