The role of dendritic spines in water exchange measurements with diffusion MRI: Time-Dependent Single Diffusion Encoding MRI

Arthur Chakwizira; Kadir \c{S}im\c{s}ek; Marco Palombo; Markus Nilsson

REVIEW 3 major objections 4 minor 2 cited by

The role of dendritic spines in water exchange measurements with diffusion MRI: Time-Dependent Single Diffusion Encoding MRI

T0 review · 3 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Water in dendritic spines can mimic membrane exchange in diffusion MRI.

desk verdict A serious simulation study showing dendritic spines can mimic permeative exchange in time-dependent SDE MRI; the qualitative claim holds, but the headline 80% bias figure rests on a model the authors admit may overestimate restriction. read the letter →

arxiv 2506.18229 v2 pith:ROXZAVT4 submitted 2025-06-23 physics.med-ph physics.bio-ph

Kadir Şimşek , Arthur Chakwizira , Markus Nilsson , Marco Palombo This is my paper · ORCID

classification physics.med-phphysics.bio-ph

keywords diffusionMRIdendriticspineswaterexchangetime-dependentKärgermodelNEXISMEXnarrowescapeproblem

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that water diffusing inside impermeable spiny dendrites, moving between dendritic shafts and spines without crossing any membrane, produces time-dependent single-diffusion-encoding (SDE) MRI signals that look like the signals attributed to membrane permeability. Using Monte Carlo simulations and narrow-escape theory, the authors estimate spine to shaft exchange times of 3 to 26 ms, overlapping published cortical exchange times. They show that a modified two-compartment Kärger model fits the simulated signals but yields exchange estimates that reflect total spine volume fraction rather than spine morphology, and that unaccounted diffusion-mediated exchange can bias NEXI and SMEX exchange-time estimates by up to 80%. The paper therefore cautions that time-dependent SDE measurements in gray matter cannot be interpreted as measuring membrane permeability alone.

What carries the argument

The load-bearing machinery is the narrow-escape problem applied to spine shaft geometry, together with Kärger-style compartment exchange models. The spine-to-shaft residence time comes from an asymptotic narrow-escape formula for a spherical head connected to a cylindrical neck, while the shaft-to-spine time uses a pore-area-density formula for escape through a narrow opening in a cylinder. A modified two-compartment Kärger model treats the spine as a zero-diffusivity dot compartment exchanging with the shaft, and an extended three-compartment version adds an isotropic Gaussian extracellular compartment. Monte Carlo simulations on real and synthetic spiny dendrites supply the ground truth against which the analytical and Kärger predictions are tested.

What would settle it

Measure time-dependent SDE signals in a physical phantom of impermeable spiny tubes with known spine-volume fraction and fit NEXI or SMEX: if fitted exchange time does not decrease with spine volume fraction, the mimicry claim fails. Alternatively, simulate substrates with neck radius comparable to shaft radius across multiple neck sizes and check whether the fitted scaling factor for shaft-to-spine escape times generalizes; if it does not, the 80% bias estimate loses its quantitative support.

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Extended reading notes

Core claim

On the paper's own terms, the central claim is that the time-dependent SDE signal from impermeable spiny dendrites is indistinguishable from the signal arising from permeative exchange. The discovery is supported by matching particle-escape dynamics between real 3D-reconstructed spiny dendrites and tunable synthetic substrates, by fits of a modified two-compartment Kärger model to simulated parallel signals, and by NEXI/SMEX fits that produce exchange times of 10 to 150 ms for typical spine volume fractions, values comparable to in-vivo gray-matter estimates. The paper further proposes an extended three-compartment Kärger model of shaft, spine, and extracellular space that captures both mechanisms, but shows that it cannot uniquely separate membrane permeability from spine volume fraction. The consequence is that previously reported exchange times in gray matter may partly reflect spine density rather than membrane properties.

Load-bearing premise

The quantitative claims rest on the narrow-escape assumption that spine necks are far narrower than the dendritic shaft, which realistic spines violate; the paper patches the discrepancy with a fitted scaling factor, and the central numbers would shift if that correction is not robust.

Editorial extensions

If this is right

Time-dependent SDE exchange estimates in gray matter cannot be read as pure membrane permeability; spine volume fraction contributes and can dominate the signal.
NEXI and SMEX exchange-time estimates reflect total spine volume fraction rather than specific spine morphology, so morphological inferences from exchange times alone are degenerate.
The proposed three-compartment Kärger model can fit both diffusion-mediated and permeative exchange, but cannot separate the two, meaning a single SDE protocol is insufficient to identify either mechanism.
Regional differences in exchange times across the brain may partly reflect regional spine-density differences rather than membrane-permeability differences.
Wide gradient pulses violate the Kärger model's assumptions and yield underestimated exchange times; narrow-pulse protocols are safer for interpreting these estimates.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A direct test of the mimicry claim would be to scan a physical phantom containing impermeable spiny tubes with known spine-volume fraction and check whether fitted exchange time tracks spine density with zero permeability.
If the mimicry holds in vivo, studies of conditions that change spine density, such as autism spectrum disorder, aging, or learning paradigms, may need to re-interpret exchange-time changes as potentially microstructural rather than purely permeability-driven.
The fitted scaling factor used to correct the shaft-to-spine narrow-escape formula suggests that a more general first-passage theory relaxing the narrow-neck assumption would place the 80% bias estimate on firmer ground.
The authors point toward a companion DDE and free-waveform study; if published, combining SDE with those encodings could break the degeneracy between spine volume fraction and permeability and give a practical acquisition design.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Kadir et al. make a solid case that water diffusing inside impermeable spiny dendrites produces time-dependent SDE signals that look like permeative exchange. The Monte Carlo work is careful: they simulate real EM-reconstructed branches and synthetic mushroom-spine substrates, tune the synthetic ones to match particle escape dynamics, and validate spine-to-shaft escape times against narrow escape theory within 8% under narrow pulses. That part is genuinely new—the earlier ISMRM abstracts hypothesized the effect, but here it is quantified with real morphology and a reproducible digital substrate pipeline. The two-compartment modified Kärger model describing the parallel signal is also a useful addition.

The soft spots are where the quantitative claims outrun the validation. The shaft-to-spine theory (Eq. 3) misses simulation results by orders of magnitude—the paper says so plainly—and the correction is a fitted scaling factor from the same simulations. That is fine as an internal calibration, but the resulting numbers carry no uncertainty and are not independently predictive. More important, the up-to-80% bias in NEXI/SMEX estimates comes from fitting the extended three-compartment Kärger model, which assumes D_spine = 0 and neglects the neck. The authors acknowledge in Section 5.5 that this 'might overestimate the restriction effect.' The stress-test note makes the right point: the three-compartment model was not validated against MC the way the two-compartment model was. If spine water has finite diffusivity, the bias percentage could shrink.

There are also no error bars on the key fitted exchange times, and code/data are promised 'upon publication' but not yet linked. Those are fixable. The central qualitative claim—that spines confound exchange estimates—holds up. The specific 3-26 ms and 80% numbers should be treated as order-of-magnitude illustrations until the model is tested against MC with finite spine diffusivity and real spine populations.

Who is this for? Anyone interpreting NEXI/SMEX exchange times in gray matter, and method developers working on DDE or free-waveform encoding. It deserves a serious peer review; the flaws are in the quantitative extrapolation, not the core experiment. I would engage with it and cite it, with a caveat on the bias figure.

Referee Report

3 major / 4 minor

Summary. This manuscript investigates whether water diffusion inside impermeable spiny dendrites can mimic the time-dependent single diffusion encoding (SDE) signal that is usually attributed to permeative exchange. Using Monte Carlo simulations on real 3D-EM-derived and synthetic spiny dendrites, the authors derive spine-to-shaft and shaft-to-spine residence times from narrow escape theory (Eqs. 1-3), fit a modified two-compartment Kärger model to simulated parallel signals, and fit NEXI/SMEX to voxel-level signals with and without an extended three-compartment Kärger model. They report exchange times of 3-26 ms that overlap cortical permeative exchange estimates, show that spine density biases NEXI exchange-time estimates by up to 80%, and conclude that time-dependent SDE cannot disentangle diffusion-mediated from permeative exchange.

Significance. If correct, the paper's central qualitative conclusion is important: time-dependent SDE exchange estimates in gray matter may reflect spine-shaft water exchange rather than membrane permeability alone, with implications for NEXI/SMEX interpretation across regions and pathologies. The paper's strengths include the use of independent Monte Carlo simulations against an analytical narrow-escape prediction for spine-to-shaft residence time (agreement better than 8% under narrow pulses), the use of real 3D EM reconstructions to tune synthetic substrates, and the planned release of analysis code. However, the headline 80% bias figure is produced by an extended three-compartment model that is not validated against the Monte Carlo simulator and whose spine-as-dot assumption is acknowledged as potentially overestimating restriction; the shaft-to-spine theory in Eq. (3) is shown to fail by orders of magnitude and is patched with a fitted scaling factor. These issues leave the quantitative claims materially less secure than the qualitative mimicry claim.

major comments (3)

[Section 5.2, Supplementary Figure S3, Figure 5B] Figure 5B reports that the total diffusion-mediated exchange time t_DM estimated from the modified two-compartment Kärger model matches the theoretical prediction, yet Section 5.2 and Supplementary Figure S3A state that the shaft-to-spine residence time τ_shaft→spine predicted by Eq. (3) is orders of magnitude smaller than the Monte Carlo estimates. Because t_DM is defined in Section 2.2 as 1/(k_spine→shaft + k_shaft→spine), the theoretical t_DM must be pulled far below the MC-based value if Eq. (3) is used uncorrected. The authors should state explicitly whether Figure 5B uses the uncorrected or corrected Eq. (3) values; if uncorrected, the claimed validation of t_DM is unsupported, and quantitative exchange-time ranges derived from the total exchange rate need to be revisited.
[Section 5.5, Figure 9, abstract] The abstract and conclusion claim that unaccounted diffusion-mediated exchange can introduce up to 80% bias in NEXI/SMEX exchange-time estimates. This number comes from fitting NEXI to signals generated by the extended three-compartment Kärger model (Section 2.5, Figure 9D), not from Monte Carlo simulations. That model assumes D_spine=0 (a 'dot' compartment) and neglects the spine neck; Section 5.5 explicitly acknowledges that this assumption 'might overestimate the restriction effect.' Since the three-compartment model has not been validated against the Monte Carlo simulator that is the paper's principal evidence source, the 80% figure is not established. The authors should either validate the extended model (including finite D_spine) against MC simulations, or present the 80% figure as an illustrative model prediction with appropriate caveats rather than as a headline quantitative result.
[Section 4.3, Supplementary Figure S3] The corrected shaft-to-spine residence times in Figure 7B-D are obtained by multiplying Eq. (3) by a scaling factor estimated from linear regression against Monte Carlo simulations (Supplementary Figure S3B). This scaling factor is a free parameter; the paper does not report its value, its uncertainty, or tests of whether the factor is constant across the morphological ranges used in Figure 7. If the factor is morphology-dependent, the degeneracy analysis and the corrected τ_shaft→spine values are not robust. Please report the fitted factor, its confidence interval, and a sensitivity analysis over the morphological distribution used in Figure 7.

minor comments (4)

[Throughout] The manuscript contains numerous typographical errors (e.g., 'modikied', 'kirst', 'kitting', 'kindings', 'reklect') that should be corrected by careful copyediting.
[Section 2.4, Eq. (7)] The notation for the apparent diffusivities D_1F and D_2F and the initial conditions in Eq. (7) is dense; explicitly listing the definitions of X_shaft^F and X_spine^F as separate labelled equations would improve readability.
[Section 4.4, Figure 8] The sentence 'The data points are smoothed with a one-point window' is unclear; please specify the exact smoothing procedure (e.g., boxcar width, kernel, or moving average).
[Abstract and Section 4.4] The abstract refers to both NEXI and SMEX, but the text in Section 4.4 describes fitting only the NEXI model; please state explicitly whether the same fits apply to SMEX or whether SMEX is treated as equivalent.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: central claims rest on independent Monte Carlo simulations and externally derived narrow escape theory; self-citations are preliminary and non-load-bearing.

full rationale

The paper's central hypothesis—that impermeable spiny dendrites produce time-dependent SDE signals that mimic permeative exchange—is supported by an independent chain: Monte Carlo simulations (disimpy) on EM-derived and synthetic substrates; narrow escape theory from Holcman/Schuss and Yang/Koslover (external refs 93, 96); and NEXI/SMEX models from Jelescu et al. and Olesen et al. (external refs 13, 14). The modified two-compartment Kärger model is attributed to Moutal et al. and Price et al. (refs 12, 100), not to the present authors, and the fitted exchange times are compared with analytical NET predictions rather than used to define them. The up-to-80% bias in NEXI/SMEX estimates is a sensitivity analysis of an explicitly proposed extended three-compartment Kärger model, and the paper acknowledges in Section 5.5 that the dot-compartment assumption 'might overestimate the restriction effect'; this is a stated limitation, not a circular derivation. Self-citations (refs 46–48, 122) are preliminary ISMRM abstracts and a companion preprint; they are cited for motivation and complementary methods, not as the load-bearing proof. The only circularity-adjacent element is the empirical scaling factor applied to Eq. (3) shaft-to-spine residence times (Supplementary Figure S3) before reporting 'corrected' values in Figure 7B-D; those values are explicitly labeled as corrected, are not used in the central 80% bias analysis, and do not make the derivation self-referential.

Assumptions & free parameters 2 free parameters · 4 assumptions · 0 invented entities

The central results rest on established narrow escape theory, Monte Carlo simulations, and Karger model fitting. No new physical entities are introduced. The main ledger items are a fitted scaling factor for the shaft-to-spine theoretical values and the tuned synthetic spine geometry. The axioms are standard asymptotic and modeling assumptions, several of which the paper itself identifies as violated in biologically realistic regimes.

free parameters (2)

shaft-to-spine exchange scaling factor = Slope from linear regression, value not reported in the text
Supplementary Figure S3: theoretical tau_shaft_to_spine from Eq. (3) is orders of magnitude smaller than Monte Carlo estimates, and a scaling factor estimated by linear regression is applied to obtain 'corrected' values used in Figure 7B-D.
synthetic spine geometry parameters = R_head = 0.4 um, L_neck = 1.5 um, R_neck = 0.125 um
Section 3.1.2: parameters were systematically optimized so particle escape dynamics in toy substrates match real 3D EM branches. Central simulation results, including the exchange time estimates and bias percentages, depend on this tuning.

assumptions (4)

domain assumption Karger model's barrier-limited exchange and negligible exchange during gradient pulses
Section 2.3: used for the modified two-compartment and three-compartment Karger fits. The paper itself notes this assumption is violated for fast spine-shaft transport, which contributes to model bias.
standard math Narrow escape formula Eq. (1) for spine-to-shaft escape applies to a spherical head connected to the neck at a right angle, with the O(1) term neglected
Section 2.2: from Holcman and Schuss, used to compute spine-to-shaft residence times, and validated against Monte Carlo simulations in Figure 5. It is an asymptotic approximation.
standard math Berg-Purcell / Yang-Koslover Eq. (3) requires R_neck much less than R_shaft and negligible radial transit time
Section 5.2: this condition is violated for realistic spine necks where R_neck is comparable to R_shaft, so the paper applies an empirical scaling factor to correct the theoretical values.
domain assumption Dendritic spines are treated as fully restricted dot compartments with D_spine = 0 in the extended Karger model
Sections 2.5 and 5.5: the paper acknowledges this assumption may overestimate the restriction effect on molecular diffusion in spines.

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Cite this review

Pith. "Pith review of The role of dendritic spines in water exchange measurements with diffusion MRI: Time-Dependent Single Diffusion Encoding MRI." pith.science (2026). https://pith.science/paper/ROXZAVT4

@misc{pith2026250618229,
  author       = {Pith},
  title        = {Pith review of: The role of dendritic spines in water exchange measurements with diffusion MRI: Time-Dependent Single Diffusion Encoding MRI},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ROXZAVT4}},
  note         = {Machine review of arXiv:2506.18229}
}

read the original abstract

Time-dependent diffusion MRI (dMRI) with single diffusion encoding (SDE) probes water dynamics in biological tissues, but signal interpretation depends on microstructure. While prior work focused on restricted/hindered diffusion and membrane permeation, diffusion-mediated exchange between dendritic shafts and spines in gray matter (GM) remains understudied. We hypothesize that impermeable spiny dendrites produce time-dependent SDE signals mimicking permeative exchange and investigate how spine density biases exchange time estimates. Using Monte Carlo simulations and narrow escape theory, we quantify spine-shaft exchange times (3-26 ms), matching cortical permeative exchange estimates. A modified two-compartment Karger model characterizes time-dependent SDE signals but yields biased exchange estimates, reflecting spine volume fraction rather than morphology. Unaccounted diffusion-mediated exchange introduces up to 80% bias in NEXI/SMEX model estimates. We propose an extended three-compartment Karger model incorporating both diffusion-mediated (spine-shaft) and permeative (intra-extracellular) exchange. However, this model cannot uniquely separate membrane permeability from spine volume effects. Our findings emphasize that dendritic spines should be considered in SDE-based exchange studies and caution against attributing exchange solely to permeability. Advanced methods are needed to disentangle these mechanisms in GM.

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Forward citations

Cited by 2 Pith papers

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Reference graph

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [11]

Investigating exchange, structural disorder, and restriction in gray matter via water and metabolites diffusivity and kurtosis time-dependence

Mougel E, Valette J, Palombo M. Investigating exchange, structural disorder, and restriction in gray matter via water and metabolites diffusivity and kurtosis time-dependence. Imaging Neuroscience. 2024;2:1-14. doi:10.1162/IMAG_A_00123/120032/INVESTIGATING-EXCHANGE-STRUCTURAL-DISORDER-AND 12. Moutal N, Nilsson M, Topgaard D, Grebenkov D. The Kärger vs bi-...

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[2] [21]

Optimal experimental design for kilter exchange imaging: Apparent exchange rate measurements in the healthy brain and in intracranial tumors

Lampinen B, Szczepankiewicz F, van Westen D, et al. Optimal experimental design for kilter exchange imaging: Apparent exchange rate measurements in the healthy brain and in intracranial tumors. Magn Reson Med. 2017;77(3):1104-1114. doi:10.1002/MRM.26195 22. Lasič S, Chakwizira A, Lundell H, Westin CF, Nilsson M. Tuned exchange imaging: Can the kilter exch...

arXiv 2017

[3] [30]

Real-time measurement of diffusion exchange rate in biological tissue

Williamson NH, Ravin R, Cai TX, et al. Real-time measurement of diffusion exchange rate in biological tissue. Journal of Magnetic Resonance. 2020;317:106782. doi:10.1016/J.JMR.2020.106782 31. Bai R, Springer CS, Plenz D, Basser PJ. Fast, Na+ /K+ pump driven, steady-state transcytolemmal water exchange in neuronal tissue: A study of rat brain cortical cult...

work page arXiv 2020

[4] [40]

micro-connectivity

Shemesh N, Jespersen SN, Alexander DC, et al. Conventions and nomenclature for double diffusion encoding NMR and MRI. Magn Reson Med. 2016;75(1):82-87. doi:10.1002/MRM.25901;PAGE:STRING:ARTICLE/CHAPTER 41. Shemesh N, Özarslan E, Basser PJ, Cohen Y. Measuring small compartmental dimensions with low-q angular double-PGSE NMR: The effect of experimental para...

work page arXiv 2016

[5] [49]

An analytical model of restricted diffusion in bovine optic nerve

Stanisz GJ, Szafer A, Wright GA, Henkelman RM. An analytical model of restricted diffusion in bovine optic nerve. Magn Reson Med. 1997;37(1):103-111. doi:10.1002/MRM.1910370115 50. Cajal S. Estructura de los centros nerviosos de las aves. Rev Trim Histol Norm Patol. 1888;1:1-10. 51. Kashiwagi Y, Higashi T, Obashi K, et al. Computational geometry analysis ...

arXiv 1997

[6] [60]

Dendritic spine pathology in neuropsychiatric disorders

Penzes P, Cahill ME, Jones KA, Vanleeuwen JE, Woolfrey KM. Dendritic spine pathology in neuropsychiatric disorders. Nature Neuroscience 2011 14:3. 2011;14(3):285-293. doi:10.1038/nn.2741 61. Harris KM, Kater SB. Dendritic spines: Cellular specializations imparting both stability and klexibility to synaptic function. Annu Rev Neurosci. 1994;17(Volume 17, 1...

work page arXiv 2011

[7] [69]

Analysis of dendritic spines in rat CA1 pyramidal cells intracellularly killed with a kluorescent dye

Trommald M, Jensen V, Andersen P. Analysis of dendritic spines in rat CA1 pyramidal cells intracellularly killed with a kluorescent dye. Journal of Comparative Neurology. 1995;353(2):260-274. doi:10.1002/CNE.903530208 70. Megías M, Emri Z, Freund TF, Gulyás AI. Total number and distribution of inhibitory and excitatory synapses on hippocampal CA1 pyramida...

work page arXiv 1995

[8] [78]

Dendritic spines of rat cerebellar Purkinje cells: serial electron microscopy with reference to their biophysical characteristics

Harris KM, Stevens JK. Dendritic spines of rat cerebellar Purkinje cells: serial electron microscopy with reference to their biophysical characteristics. The Journal of Neuroscience. 1988;8(12):4455. doi:10.1523/JNEUROSCI.08-12-04455.1988 79. Braitenberg V, Schüz A. Anatomy of the Cortex. 1991;18. doi:10.1007/978-3-662-02728-8 80. Roth G, Dicke U. Evoluti...

work page arXiv 1988