The role of diffusion MRI in neuroscience
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Abstract
Diffusion‐weighted imaging has pushed the boundaries of neuroscience by allowing us to examine the white matter microstructure of the living human brain. By doing so, it has provided answers to fundamental neuroscientific questions, launching a new field of research that had been largely inaccessible. We briefly summarize key questions that have historically been raised in neuroscience concerning the brain's white matter. We then expand on the benefits of diffusion‐weighted imaging and its contribution to the fields of brain anatomy, functional models and plasticity. In doing so, this review highlights the invaluable contribution of diffusion‐weighted imaging in neuroscience, presents its limitations and proposes new challenges for future generations who may wish to exploit this powerful technology to gain novel insights.
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Free-water correction, multi-shell acquisition, and joint structural-functional modeling are already making diffusion MRI less reliant on a few oversimplified scalar summaries.
“Free-water correction, multi-shell acquisition, and joint structural–functional modeling are already making diffusion MRI less reliant on a few oversimplified scalar summaries (Assaf et al., 2019; Fieremans et al., 2016).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Assaf et al., 2019
The 'Local measures of white matter microstructure' section says that common DTI indices such as mean diffusivity and FA are nonspecific, and describes compartment models that yield more detailed tissue parameters. The 'Part 2/ Functional models' section relates structural connectivity to functional organization. Our copy does not establish the specific combination of free-water correction, multi-shell acquisition, and joint structural–functional modeling claimed here (searched those terms and close variants). (LLM, conf=0.86)
Experimental — accuracy not yet measuredBasic diffusion metrics are only inferences based on local diffusion properties and are not direct measures of tissue properties.
“basic diffusion metrics are only inferences based on local diffusion properties, which are not direct measures of tissue properties (Assaf et al., 2019).”
From Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: A review (2022) · cites this paper as Assaf et al., 2019
DTI suggested that macroscopic structural connectivity could be charted in vivo, even if only approximately.
“DTI suggested that macroscopic structural connectivity could be charted in vivo, even if only approximately (Assaf et al., 2019; Hagmann et al., 2008).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Assaf et al., 2019
Diffusion MRI became a foundational tool for the Human Connectome era because it offered a scalable route to individual structural connectivity.
“Diffusion MRI became a foundational tool for the Human Connectome era because it offered a scalable route to individual structural connectivity (Assaf et al., 2019; Hagmann et al., 2003).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Assaf et al., 2019
Structural connectome analyses are most convincing when integrated with neuroanatomical knowledge, functional data, and careful control of tractography biases.
“Structural connectome analyses are most convincing when integrated with neuroanatomical knowledge, functional data, and careful control of tractography biases (Assaf et al., 2019; Daducci et al., 2016).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Assaf et al., 2019
Tractography is one of the defining achievements of the brain-centered diffusion MRI story.
“In that sense, tractography is one of the defining achievements of the brain-centered diffusion MRI story (Assaf et al., 2019; Gong, et al., 2009).”
From 40 Years of diffusion MRI in the brain: From history to emerging frontiers (2026) · cites this paper as Assaf et al., 2019
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- Authors
- Assaf, Y., Johansen‐Berg, H., & Thiebaut de Schotten, M.
- Affiliations
- Tel Aviv University (IL)
- John Radcliffe Hospital (GB)
- Brain Connectivity and Behaviour Group Frontlab, Brain and Spine Institute Paris France
- Venue
- NMR in Biomedicine
- Publisher
- Wiley
- Published
- 2017-07-11
- Volume
- 32
- Issue
- 4
- Pages
- e3762-e3762
- ISSN
- 0952-3480 (ISSN-L)
- 1099-1492
- OpenAlex
- W2953000607
- Cited by
- 158
- References
- 209
- Citations by year
- 2026: 12 · 2025: 11 · 2024: 26 · 2023: 21 · 2022: 25 · 2021: 15 · 2020: 16 · 2019: 16 · 2018: 10 · 2017: 6
- FWCI
- 1.99
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Locations
- NMR in Biomedicine (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion, cc-by)
- HAL (submittedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- Agence Nationale de la Recherche (ANR‐10‐IAIHU‐06, ANR‐13‐JSV4‐0001‐01)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Keywords
- diffusion-weighted imaging, white matter microstructure, human brain, neuroanatomy, functional model, neuroplasticity, neuroimaging limitations
- MeSH terms
- Brain, Humans, Models, Neurological, Nerve Net, Neuronal Plasticity, Neurosciences, Diffusion Magnetic Resonance Imaging
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