Diffusion time dependence, power‐law scaling, and exchange in gray matter
Open accessAbstract
Characterizing neural tissue microstructure is a critical goal for future neuroimaging. Diffusion MRI (dMRI) provides contrasts that reflect diffusing spins’ interactions with myriad microstructural features of biological systems. However, the specificity of dMRI remains limited due to the ambiguity of its signals vis-à-vis the underlying microstructure. To improve specificity, biophysical models of white matter (WM) typically express dMRI signals according to the Standard Model (SM) and have more recently in gray matter (GM) taken spherical compartments into account (the SANDI model) in attempts to represent cell soma. The validity of the assumptions underlying these models, however, remains largely undetermined, especially in GM. To validate these assumptions experimentally, observing their unique, functional properties, such as the b−1/2 power-law associated with one-dimensional diffusion, has emerged as a fruitful strategy. The absence of this signature in GM, in turn, has been explained by neurite water exchange, non-linear morphology, and/or by obscuring soma signal contributions. Here, we present diffusion simulations in realistic neurons demonstrating that curvature and branching does not destroy the stick power-law behavior in impermeable neurites, but also that their signal is drowned by the soma signal under typical experimental conditions. Nevertheless, by studying the GM dMRI signal's behavior as a function of diffusion weighting as well as time, we identify an attainable experimental regime in which the neurite signal dominates. Furthermore, we find that exchange-driven time dependence produces a signal behavior opposite to that which would be expected from restricted diffusion, thereby providing a functional signature that disambiguates the two effects. We present data from dMRI experiments in ex vivo rat brain at ultrahigh field of 16.4T and observe a time dependence that is consistent with substantial exchange but also with a GM stick power-law. The first finding suggests significant water exchange between neurites and the extracellular space while the second suggests a small sub-population of impermeable neurites. To quantify these observations, we harness the Kärger exchange model and incorporate the corresponding signal time dependence in the SM and SANDI models.
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Exchange-sensitive NEXI and SMEX/SANDIX models suggest that inter-compartment water exchange and diffusion-time dependence substantially affect gray-matter diffusion signals but are not explicitly modeled by conventional SANDI.
“Although they correlate with the Allen Brain Atlas, 93 exchange‐sensitive NEXI 55 and SMEX/SANDIX 54 models suggest that inter‐compartment water exchange and diffusion‐time dependence substantially affect gray‐matter diffusion signals but are not explicitly modeled by conventional SANDI.”
From Diffusion MRI reveals amyloid plaque–associated cellular signatures through spatial transcriptomic integration (2026) · cites this paper as [54]
The Methods (Models) describe SMEX/NEXI and SANDIX as exchange models, and the Results (Power-law scaling and time dependence in ex vivo brain) say SANDI is incompatible with the observed time dependence while SMEX describes it well. But the Methods (Diffusion time dependence as an exchange signature) explicitly describe time dependence in conventional SANDI. The claim is therefore supported for exchange and for SANDI’s failure to fit the observed time dependence, not for the assertion that SANDI does not explicitly model time dependence. Page numbers were not provided in our copy. (LLM, conf=0.86)
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Jelescu, I. O., de Skowronski, A., Geffroy, F., Palombo, M., & Novikov, D. S. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119277 · Cited by 177 · see citing works
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Ianuş, A., Carvalho, J., Fernandes, F. F., Cruz, R., Chavarrias, C., Palombo, M., & Shemesh, N. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119135 · Cited by 40 · see citing works
Self-citationHan, X., Hu, R., Ping, X., Chen, J., Liu, J., Liu, Z., Jin, X., Gao, X., Lin, M., Zhao, Y., et al. (2026) · Alzheimer's & Dementia · doi:10.1002/alz.71908
40 Years of diffusion MRI in the brain: From history to emerging frontiers
Le Bihan, D. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1365
“NEXI/SMEX address gray matter more directly by including inter-compartment exchange in a minimal model tailored to such tissue ( Jelescu et al., 2022; Olesen et al., 2022).”
- Authors
- Olesen, J. L., Østergaard, L., Shemesh, N., & Jespersen, S. N.
- Affiliations
- Aarhus University (DK)
- Corresponding author
- Jespersen, Sune N.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2022-05
- Volume
- 251
- Pages
- 118976
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC8961002
- OpenAlex
- W3195383010
- Cited by
- 125
- References
- 94
- Citations by year
- 2026: 21 · 2025: 37 · 2024: 31 · 2023: 23 · 2022: 13
- FWCI
- 14.79
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- NeuroImage (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Funders
- European Regional Development Fund
- Velux Fonden
- Arcadia Fund (00015963)
- European Research Council (679058)
- Danmarks Grundforskningsfond
- 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
- Physical Sciences › Physics and Astronomy › Nuclear and High Energy Physics › NMR spectroscopy and applications
- Keywords
- gray matter, diffusion MRI, diffusion time dependence, restricted diffusion, Standard Model, extracellular space, ultra-high field MRI, rat brain
- MeSH terms
- Brain, Cerebral Cortex, Humans, Diffusion Magnetic Resonance Imaging, Neuroimaging, White Matter, Gray Matter
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