Diffusion tensor imaging (DTI)-based white matter mapping in brain research: a review
Abstract
Diffusion tensor imaging (DTI) has become one of the most popular MRI techniques in brain research, as well as in clinical practice. The number of brain studies with DTI is growing steadily and, over the last decade, has produced more than 700 publications. Diffusion tensor imaging enables visualization and characterization of white matter fascicli in two and three dimensions. Since the introduction of this methodology in 1994, it has been used to study the white matter architecture and integrity of the normal and diseased brains (multiple sclerosis, stroke, aging, dementia, schizophrenia, etc.). Although it provided image contrast that was not available with routine MR techniques, unique information on white matter and 3D visualization of neuronal pathways, many questions were raised regarding the origin of the DTI signal. Diffusion tensor imaging is constantly validated, challenged, and developed in terms of acquisition scheme, image processing, analysis, and interpretation. While DTI offers a powerful tool to study and visualize white matter, it suffers from inherent artifacts and limitations. The partial volume effect and the inability of the model to cope with non-Gaussian diffusion are its two main drawbacks. Nevertheless, when combined with functional brain mapping, DTI provides an efficient tool for comprehensive, noninvasive, functional anatomy mapping of the human brain. This review summarizes the development of DTI in the last decade with respect to the specificity…
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Many factors, such as cell death, edema, gliosis, inflammation, change in myelination, increase in connectivity of crossing fibers, and increase in extracellular or intracellular water, may cause changes in FA.
“For example, many factors (e.g., cell death, edema, gliosis, inflammation, change in myelination, increase in connectivity of crossing fibers, increase in extracellular or intracellular water, etc.) may cause changes in FA (Assaf and Pasternak, 2008; Jones et al., 2013; Le Bihan and Johansen-Berg, 2012).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Assaf and Pasternak, 2008
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- Authors
- Assaf, Y., & Pasternak, O.
- Affiliations
- Tel Aviv University (IL)
- Corresponding author
- Assaf, Yaniv
- Venue
- Journal of Molecular Neuroscience
- Published
- 2007-09-27
- Volume
- 34
- Issue
- 1
- Pages
- 51-61
- ISSN
- 0895-8696 (ISSN-L)
- 1559-1166
- OpenAlex
- W1997222702
- Cited by
- 1,537
- References
- 96
- Citations by year
- 2026: 50 · 2025: 113 · 2024: 94 · 2023: 93 · 2022: 96 · 2021: 138 · 2020: 101 · 2019: 102 · 2018: 85 · 2017: 95 · 2016: 86 · 2015: 90 · 2014: 91 · 2013: 71 · 2012: 64
- FWCI
- 3.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Molecular Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Keywords
- diffusion tensor imaging, partial volume effect, non-Gaussian diffusion, functional brain mapping, white matter integrity, multiple sclerosis, stroke, dementia, schizophrenia, brain aging
- MeSH terms
- Brain, Brain Diseases, Brain Mapping, Humans, Image Processing, Computer-Assisted, Nerve Fibers, Myelinated, Neural Pathways, Predictive Value of Tests, Reproducibility of Results, Artifacts, Diffusion Magnetic Resonance Imaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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