Diffusion MRS tracks distinct trajectories of neuronal development in the cerebellum and thalamus of rat neonates

Reviewer #1 (Public review):

In this work, Ligneul and coauthors implemented diffusion-weighted MRS in young rats to follow longitudinally and in vivo the microstructural changes occurring during brain development. Diffusion-weighted MRS is here instrumental in assessing microstructure in a cell-specific manner, as opposed to the claimed gold-standard (manganese-enhanced MRI) that can only probe changes in brain volume. Differential microstructure and complexification of the cerebellum and the thalamus during rat brain development were observed non-invasively. In particular, lower metabolite ADC with increasing age were measured in both brain regions, reflecting increasing cellular restriction with brain maturation. Higher sphere (representing cell bodies) fraction for neuronal metabolites (total NAA, glutamate) and total creatine and taurine in the cerebellum compared to the thalamus were estimated, reflecting the unique structure of the cerebellar granular layer with a high density of cell bodies. Decreasing sphere fraction with age was observed in the cerebellum, reflecting the development of the dendritic tree of Purkinje cells and Bergmann glia. From morphometric analyses, the authors could probe non-monotonic branching evolution in the cerebellum, matching 3D representations of Purkinje cells expansion and complexification with age. Finally, the authors highlighted taurine as a potential new marker of cerebellar development.

From a technical standpoint, this work clearly demonstrates the potential of diffusion-weighted MRS at probing microstructure changes of the developing brain non-invasively, paving the way for its application in pathological cases. Ligneul and coauthors also show that diffusion-weighted MRS acquisitions in neonates are feasible, despite the known technical challenges of such measurements, even in adult rats. They also provide all necessary resources to reproduce and build upon their work, which is highly valuable for the community.

From a biological standpoint, claims are well supported by the microstructure parameters derived from advanced biophysical modelling of the diffusion MRS data.

Specific strengths:

(1) The interpretation of dMRS data in terms of cell-specific microstructure through advanced biophysical modelling (e.g. the sphere fraction, modelling the fraction of cell bodies versus neuronal or astrocytic processes) is a strong asset of the study, going beyond the more commonly used signal representation metrics such as the apparent diffusion coefficient, which lacks specificity to biological phenomena.
(2) The fairly good data quality despite the complexity of the experimental framework should be praised: diffusion-weighted MRS was acquired in two brain regions (although not in the same animals) and longitudinally, in neonates, including data at high b-values and multiple diffusion times, which altogether constitutes a large-scale dataset of high value for the diffusion-weighted MRS community.
(3) The authors have shared publicly data and codes used for processing and fitting, which will allow one to reproduce or extend the scope of this work to disease populations, and which goes in line with the current effort of the MR(S) community for data sharing.

Specific weaknesses:

Ligneul and coauthors have convincingly addressed and included my comments in their revised manuscript.

I believe the following conceptual concerns, which are inherent to the nature of the study and do not require further adjustments of the manuscript, remain:

(1) Metabolite compartmentation in one cell type or the other has often been challenged and is currently impossible to validate in vivo. Here, Ligneul and coauthors did not use this assumption a priori and supported their claims also with non-MR literature (eg. for Taurine), but the interpretation of results in that direction should be made with care.

(2) Longitudinal MR studies of the developing brain make it difficult to extract parameters with an "absolute" meaning. Indirect assumptions used to derive such parameters may change with age and become confounding factors (brain structure, cell distribution, concentrations normalizing metabolites (here macromolecules), relaxation times...). While findings of the manuscript are convincing and supported with literature, the true underlying nature of such changes might be difficult to access.

(3) Diffusion MRI in addition to diffusion MRS would have been complementary and beneficial to validate some of the signal contributions, but was unfeasible in the time constraints of experiments on young animals.

Reviewer #2 (Public review):

This second revision has partially addressed criticisms previously raised; however, substantial inadequacies, particularly concerning rigorous validation and model justification, remain unresolved. While recognizing evident strength, novelty, and technical complexity of this work, the authors have yet to fully resolve key major concerns explicitly pointed out during revision in a satisfactory manner. As currently written, the manuscript does not yet provide sufficiently robust validation, methodological rigour, or clarity required for complete acceptance in a top-tier scientific journal.

Summary of Authors' Aim:

In this revised version, the authors aimed to address prior reviewer critiques harshly pinpointing the need for greater clarity in the manuscript's logical flow, rigorous external validation, clearer explanation of methodological normalization choices, and deeper elaboration of diffusion MRI method relevance and potential translation. The authors present a diffusion-weighted MRS approach paired with complex biophysical modelling to elucidate differential developmental trajectories of cellular structures in cerebellum and thalamus in rat neonates, providing a novel, non-invasive avenue for monitoring cellular microstructure.

Major Comments:

Rigorous Validation (Reviewer #1 - point R1.1, Reviewer #2 - point R2.2):

The major concern previously raised and reiterated here is the insufficient external cross-validation of the dMRS-derived interpretations about cellular changes, including the particularly speculative interpretation that taurine undergoes compartment switching between neuronal and glial compartments in the thalamus. The authors acknowledge this important shortcoming (R1.1, R2.2) but attempt to mitigate these concerns merely through additional contextual comparisons from existing literature (page 23, lines 877-878, Figure S11, Table S2). While better contextualization is welcome, the modified manuscript still falls notably short of the level of rigour necessary to validate such striking switches in compartmentalization. To justify claims of metabolites changing cellular compartments, explicit verification against independent molecular/histological data, ideally with additional immunohistochemical staining for cellular markers (e.g., glial fibrillary acidic protein, NeuN), is necessary. The mere presence of literature correlations (such as the reported visual comparisons to morphometric reconstructions, page 24, lines 883-884) does not constitute rigorous validation at the required standard for high-impact publication. The revised manuscript remains fundamentally weakened without such validation. To properly improve, the authors must consider incorporating independent ex vivo experiments or, if this is no longer feasible, extensively temper their compartment-switching claims, acknowledging explicitly and prominently the speculative nature of current interpretations.

Normalization of Metabolite Concentrations (Reviewer #1 - point R1.3):

The authors clearly responded to a reviewer wish for justification of metabolite normalisation to macromolecular concentrations (page 13, lines 493-503, Figure S2). However, the rationale provided remains only partially convincing. While the authors appropriately acknowledge the unusual nature of their methodological choice and possible confounding factors, they opt to supplement rather than substitute this approach with a more standard method (normalisation by water) in the main body of the manuscript. The additional supplementary Figure S2 is helpful, yet the conclusions derived with macromolecular normalization still remain potentially confounded by age-dependent macromolecular changes (Tkac et al., 2003). The justification given in the revised manuscript remains vague, unsatisfactory, and somewhat contradictory-authors accept macromolecules changes likely with age, yet largely overlook this effect. At least, the comparison between normalization by macromolecules and water should be explicitly discussed in the main text, and conclusions drawn from macromolecular normalization must be cautiously framed.

Choice and Justification of Biophysical Model (Reviewer #1 - point R1.4):

The reviewers questioned model assumptions, particularly ignoring macroscopic anisotropy effects due to white matter presence, myelination, and fibre orientation dispersion in the cerebellar voxel. Authors provided newly included DTI data and acknowledged this limitation explicitly (R1.4, Figure S8, page 25, lines 921-924). However, the addition of these poor-quality DTI data with limited interpretability paradoxically weakens rather than strengthens the manuscript as a whole, since the authors now present unclear supplementary results with little additional interpretative value. Recognizing poor data quality in this scenario, although intellectually honest, does not substantially increase the current robustness of their chosen model nor improve justification. To address this fully, either higher-quality data should be collected to robustly probe anisotropy or fibre dispersion effects, or the authors must much further restrict their interpretations in view of this clear limitation. Currently, the solution proposed is incomplete and insufficient to clarify the consequences of their chosen model.

Logical Flow and Clarity (Reviewer #2 - points R2.1 and R2.3):

The authors attempted to respond to reviewer comments on logical flow and accessibility (page 3, introduction restructuring). While the manuscript readability has improved, the introduction and discussion remain overly intricate, and at times, detail-oriented without clear links into central claims. In particular, the biological rationale for choosing the specific metabolite markers (especially tCho, Ins, Tau, etc.) and their known relevance must be further streamlined and simplified to increase accessibility and directness. Although some helpful restructuring was carried out, further careful paragraph-level revision for logical flow and readability remains necessary.

Translation to Human Studies (Reviewer #2 - point R2.4):

The authors have extended contextual discussion on translational potential regarding taurine as a developmental marker in humans (pages 24-25, lines 906-917). However, mention remains vague and cursory, without presenting sufficiently solid arguments nor drawing from human developmental studies adequately. Translational potential must be assessed within the realistic limitations inherent in clinical translation of MRS studies, particularly given the technical complexities clearly identified even in preclinical studies of this paper. Discussion remains relatively superficial, and if retained, must be expanded to fully discuss realistic human translational hurdles and requirements.

Author response:

The following is the authors’ response to the original reviews

Summary of revisions:

Thanks to the careful review and comments from the reviewers, we restructured the introduction and the discussion to improve clarity and better contextualise findings. We notably discuss further the f_sphere decrease observations in the cerebellum and the Tau-specific findings (Tau being a possible marker for Purkinje cells development and Tau switching compartment in the thalamus). We added material in Supplementary Information to support these discussion points. We added a figure to show the metabolic profiles normalised by water or by macromolecules and a figure and table related to a rough approximation of f_sphere, leaning on existing literature. We report the DTI results for thoroughness.

Public Reviews:

Reviewer #1 (Public Review):

In this work, Ligneul and coauthors implemented diffusion-weighted MRS in young rats to follow longitudinally and in vivo the microstructural changes occurring during brain development. Diffusion-weighted MRS is here instrumental in assessing microstructure in a cell-specific manner, as opposed to the claimed gold-standard (manganese-enhanced MRI) that can only probe changes in brain volume. Differential microstructure and complexification of the cerebellum and the thalamus during rat brain development were observed noninvasively. In particular, lower metabolite ADC with increasing age were measured in both brain regions, reflecting increasing cellular restriction with brain maturation. Higher sphere (representing cell bodies) fraction for neuronal metabolites (total NAA, glutamate) and total creatine and taurine in the cerebellum compared to the thalamus were estimated, reflecting the unique structure of the cerebellar granular layer with a high density of cell bodies. Decreasing sphere fraction with age was observed in the cerebellum, reflecting the development of the dendritic tree of Purkinje cells and Bergmann glia. From morphometric analyses, the authors could probe non-monotonic branching evolution in the cerebellum, matching 3D representations of Purkinje cells expansion and complexification with age. Finally, the authors highlighted taurine as a potential new marker of cerebellar development.

From a technical standpoint, this work clearly demonstrates the potential of diffusion-weighted MRS at probing microstructure changes of the developing brain non-invasively, paving the way for its application in pathological cases. Ligneul and coauthors also show that diffusionweighted MRS acquisitions in neonates are feasible, despite the known technical challenges of such measurements, even in adult rats. They also provide all necessary resources to reproduce and build upon their work, which is highly valuable for the community.

From a biological standpoint, claims are well supported by the microstructure parameters derived from advanced biophysical modelling of the diffusion MRS data. The assumption of metabolite compartmentation, forming the basis of cell-specific microstructure interpretation of dMRS data, remains debated and should be considered with care (Rae, Neurochem Res, 2014, https://doi.org/10.1007/s11064-013-1199-5). External cross-validation of some of the authors' claims, in particular taurine in the thalamus switching from neurons to astrocytes during brain development, would be a highly valuable addition to this study.

R1.1: We understand the reviewer's concerns. Metabolic compartmentation is not a one-toone correspondence. Although we interpret the results in the light of metabolic compartmentation, our results are not driven by this assumption. We could not perform a direct cross-validation of the taurine switch in the thalamus, but we now clarify in the discussion why the dMRS results themselves indicate a switch, and we integrate our results better with existing literature on taurine. We now discuss this in more detail for the cerebellar results too.

Specific strengths:

(1) The interpretation of dMRS data in terms of cell-specific microstructure through advanced biophysical modelling (e.g. the sphere fraction, modelling the fraction of cell bodies versus neuronal or astrocytic processes) is a strong asset of the study, going beyond the more commonly used signal representation metrics such as the apparent diffusion coefficient, which lacks specificity to biological phenomena.

(2) The fairly good data quality despite the complexity of the experimental framework should be praised: diffusion-weighted MRS was acquired in two brain regions (although not in the same animals) and longitudinally, in neonates, including data at high b-values and multiple diffusion times, which altogether constitutes a large-scale dataset of high value for the diffusion-weighted MRS community.

(3) The authors have shared publicly data and codes used for processing and fitting, which will allow one to reproduce or extend the scope of this work to disease populations, and which goes in line with the current effort of the MR(S) community for data sharing.

Specific weaknesses:

(1) This work lacks an introduction and a discussion about diffusion MRI, which is already a validated technique to assess brain development non-invasively. Although water lacks cellspecificity compared to metabolites, several studies have reported a decrease in water ADC and increased fractional anisotropy with brain maturation, associated with the myelination process and decreased water content (overview in Hüppi, Chapt. 30 of "Diffusion MRI: Theory, Methods, and Applications", Oxford University Press, 2010). Interestingly, the same observations are found in this work (decreased ADC with age for most metabolites in both brain regions), which should have been commented on. Moreover, the authors could have reported water diffusion properties in addition to metabolites', as I believe the water signal, used for coil combination and/or Eddy currents corrections, is usually naturally acquired during diffusion-weighted MRS scans.

R1.2: Thank you for these helpful suggestions. We have now improved our introduction of the various modalities, and we contextualise the study in light of previous DTI findings in the as suggested by the reviewer. We agree with the reviewer that the comparison with previous human DTI is relevant, and we now mention it at the beginning of the discussion. However, the very different nature of the dMRS signal compared to dMRI (intracellular and absence of exchange for metabolites) prevents us from drawing any strong conclusions.

(2) It is unclear why the authors have normalized metabolite concentrations (measured from low b-values diffusion-weighted MRS spectra) to the macromolecule concentrations. First, it is not specified whether in vivo macromolecules were acquired at each age or just at one time point. Second, such ratios are not standard practice in the MRS community so this choice should have been explained. Third, the macromolecule content was reported to change with age (Tkac et al., Magn Reson Med, 2003), therefore a change in metabolite to macromolecule ratio with age cannot be interpreted unequivocally.

R1.3: We agree with the reviewer that this needed further explanations. We now clarify in the Results section “Metabolic profile changes with age” the reasoning behind choosing macromolecules for normalisation. We also added in the Supplementary Information the metabolite concentrations change with age when normalising by water, and a direct comparison with MM normalisation (Figure S2).

(3) Some discussion is missing about the choice of the analytical biophysical model (although a few are compared in Supplementary Materials), in particular: is a model of macroscopic anisotropy relevant in cerebellum, made of a large fraction of oriented white matter tracks, and does the model remain valid at different ages given white matter maturation and the ongoing myelination process?

R1.4: We agree with the reviewer that this is a valid concern. We actually acquired some standard DTI at the end of the acquisition sessions (where possible) having in mind the fibre dispersion estimation. However, data could not be acquired in all animals, and the data quality was poor (see Figure S8, the experimental conditions would have required further optimisation). We now add a couple of sentences at the beginning and in the end of discussion to address this limitation, and we include the DTI data in Supplementary Information.

Reviewer #2 (Public Review):

Summary:

The authors set out to non-invasively track neuronal development in rat neonates, which they achieved with notable success. However, the direct relationship between the results and broader conclusions regarding developmental biology and potential human implications is somewhat overstretched without further validation.

Strengths:

If adequately revised and validated, this work could have a significant impact on the field, providing a non-invasive tool for longitudinal studies of brain development and neurodevelopmental disorders in preclinical settings.

Weaknesses:

(1) Consistency and Logical Flow:

The manuscript suffers from a lack of strategic flow in some sections. Specifically, transitions between major findings and methodological discussions need refinement to ensure a logical progression of ideas. For example, the jump from the introduction of developmental trajectories and the technicalities of MRS (Magnetic Resonance Spectroscopy) processing on page 3 could benefit from a bridging paragraph that explicitly states the study's hypotheses based on existing literature gaps.

R2.1: Thank you for this general feedback (along with your point (3)) that helped us restructure the introduction and the discussion to improve the clarity and flow.

(2) Scientific Rigour:

While the novel application of diffusion-weighted MRS is commendable, there's a notable gap in the rigorous validation of this approach against gold-standard histological or molecular techniques. Particularly, the assertions regarding the sphere fraction and morphological changes inferred from biophysical modelling mandates direct validation to solidify the claims made. A study comparing these in vivo findings with ex vivo confirmation in at least a subset of samples would significantly enhance the reliability of these conclusions.

R2.2: We agree with the reviewer that this would have been a great addition to the manuscript. Although we could not run new experiments to address these flaws, we now discuss the results more quantitatively, leaning on existing literature (addition of Figure S11 and Table S2). This helps us understand the results around Tau in both regions better, and illustrate the R_sphere trend.

(3) Clarity and Novelty:

- The manuscript often delves deeply into technical specifics at the expense of accessibility to readers not deeply familiar with MRS technology. The introduction and discussions would benefit from a clearer elucidation of why these specific metabolite markers were chosen and their known relevance to neuronal and glial cells, placing this in the context of what is novel compared to existing literature.

- The novelty aspect could be reinforced by a more structured discussion on how this method could change the current understanding or practices within neurodevelopmental research, compared to the current state of the art.

R2.3: See answer to (1). By restructuring the introduction and the discussion, we hope to have addressed this point. We now discuss how these findings compare to the state of the art (notably added comparison with dMRI research). Along with the next comment, we better discuss potential implications of these findings for neurodevelopmental research.

(4) Completeness:

- The Discussion section requires expansion to offer a more comprehensive interpretation of how these findings impact the broader field of neurodevelopment and psychiatric disorders. Specifically, the implications for human studies or clinical translation are touched upon but not fully explored.

• Further, while supplementary material provides necessary detail on methodology, key findings from these analyses should be summarized and discussed in the main text to ensure the manuscript stands complete on its own.

R2.4: Thank you for these helpful suggestions. We now integrate the findings better into the existing literature. We notably discuss how the results might translate to humans.

(5) Grammar, Style, Orthography:

There are sporadic grammatical and typographical errors throughout the text which, while minor, detract from the overall readability. For example, inconsistencies in metabolite abbreviations (e.g., tCr vs Cr+PCr) should be standardized.

R2.5: Thank you for the careful review. This has been corrected.

(6) References and Additional Context:

The current reference list is extensive but lacks integration into the narrative. Direct comparisons with existing studies, especially those with conflicting or supportive findings, are scant. More dedicated effort to contextualize this work within the existing body of knowledge would be beneficial.

R2.6: Because the nature of this work is novel, it is difficult to find directly conflicting/similar works. However, we now integrate the findings into the broader literature.

Recommendations for the authors:

Reviewer #1 (Recommendations For The Authors):

Minor comments:

Thank you for the careful review, we have addressed most of the minor comments, except for the last one, which we discuss below.

- Some figures appear blurred in the printed PDF- Introduction: "constrained and hindered by cell membranes," - maybe use "restricted" instead of "constrained", like everywhere else in the text

- Introduction: "(typically ~8cm3 vs ~8mm3 in dMRI in humans)" - here I suggest to put the rat brain sizes instead to help the reader understand how small the voxel was at P5 in this study, thus explaining the challenges

- Fig 1 - numbers 1 and 2 on panel A,B should be clarified and they do not match 1 and 2 on panel C, which is confusing- Fig 2 - I am guessing the large dots are the mean and small are individual data points? Please clarify

- Please specify "Relative CRLB" rather than just "CRLB", in supp. mat as well

- Fig 3 - title of panel B, I would change "signal" into "concentration"

- Fig 3 - end of caption: "and levelled to get Signal(tCr,P30)/Signal(MM,P30)=8", I think "in the thalamus" is missing

- The results section "Biophysical modelling underlines different developmental trajectories of cell microstructure between the cerebellum and the thalamus" is sometimes unprecise, e.g.: "Cerebellum: The sphere fraction and the radius estimated from tNAA diffusion properties vary with age." but the tNAA sphere fraction seems to vary more with age in the thalamus according to table 1 "Cerebellum: fsphere decreases from 0.63 (P10) to 0.41 (P30), but R is stable" this is for tCr I presume

- Table 1 - "pvalues" please add "before multiple comparison correction"

- Figure 5 - Panel B, the L-segment subpanel is unclear -which metabolites is it referring to? Why does Tau have a * in panel A?

- Update Ref 37 to the journal version

- Methods: "A STELASER (Ligneul et al., MRM 2017) sequence", add numbered reference instead

- Please specify that the DIVE toolbox uses Gaussian phase distribution approximation, it is important for the dMRS reader given that your diffusion gradient length is long and cannot be neglected, and that the SGP approximation does not apply.

The Gaussian phase distribution approximation and the SGP approximation are two different concepts. The gradient duration ∂ (7 ms) is short compared to the gradient separation ∆ (100 ms), but it could still be considered too long for the SGP approximation to hold. However, the gradient duration is accounted for in DIVE in any case.