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

The cerebellum is a well-conserved structure across species and particularly across mammals. The cerebellar cortex is a highly folded structure organised in three layers: the molecular layer (containing the notable Purkinje cell dendritic trees), the Purkinje layer (containing the somas of Purkinje cells), and the granular layer (containing the granule cells). Projections to the deep cerebellar nuclei form the cerebellar white matter. Despite its small size (compared to the cerebrum), it contains more than half of the total brain neurons: this is mainly due to the granule cells, very small and densely packed cells, some of the smallest of the brain, presenting a very simple morphology (Haines and Dietrichs, 2012). On the other hand, the Purkinje cells are one of the largest types of neurons and most complex cells in the brain. Cerebellar development is protracted (Szulc et al., 2015), starting from birth (or the last trimester of pregnancy) to a few weeks old in rodents (or 2 y.o. in humans) (Sathyanesan et al., 2019). The cerebellum is involved in many basic and high-level functions, from motor control to social skills, and it is known to be affected in a number of neurodevelopmental disorders (Stoodley, 2016).

During early development, neurons arborise. Their morphology becomes more complex and exhibits denser branching. In rodents, Purkinje cells’ dendritic trees start growing at birth. In 30 days, they evolve from a simple soma (~10 µm diameter) to a large cell with a highly branched and planar dendritic tree (soma diameter ~20 µm, area ~10,000 µm² in the mouse; Beekhof et al., 2021). Purkinje cell morphology can be altered in autism spectrum disorders (ASD), as shown post-mortem, in humans (Fatemi et al., 2002) and multiple mouse models of ASD. Specifically, they show an increased spine density in Tsc1 mutant mice (Tsai et al., 2012), an overgrowth of the dendritic tree and abnormal branching in PTEN KO mice (Cupolillo et al., 2016), a reduction of the dendritic tree in AUTS2 mice (Yamashiro et al., 2020), and general alterations in developmental disorders (Kim et al., 2011; Robinson et al., 2012; Usui et al., 2017; Sundberg et al., 2018).

All cerebellar outputs transit via the deep cerebellar nuclei, and a couple of them project to the thalamus before spreading to the cortex. The thalamus is considered a relay station in the brain and contains about 60 nuclei on unique pathways. Some thalamic nuclei are involved in psychiatric disorders, and more specifically, the microstructure of the mediodorsal nucleus and the pulvinar nucleus is altered in schizophrenia and psychosis (Hwang et al., 2022). Some other nuclei are involved in neurodevelopmental disorders (e.g. the lateral reticular nuclei in ASD; Krol et al., 2018). Generally, these disorders are associated with an altered thalamic connectivity (Hwang et al., 2022). Thalamocortical projections are crucial for the cytoarchitectural development of the cortex (Antón-Bolaños et al., 2018; Sato et al., 2022).

Neurodevelopmental disorders, or even psychiatric disorders with a neurodevelopmental origin, such as schizophrenia (Fatemi and Folsom, 2009), generally need better neurobiological characterisation during development (Ismail and Shapiro, 2019).

Rodents reach adulthood after a couple of months and are an ideal model for mammal brain development. While brain cell developmental trajectories are key to assessing neurodevelopment, there is a lack of translational techniques allowing for a non-invasive measure of their properties. Typically, EEG and MEG signals contain brain activity cell-specific information. Still, it is hard to retrieve in practice, and the existing forward or inverse models notably use prior information on cell morphology. PET can be cell-specific and provide information about metabolism, but it is blind to cell morphology and relies on radioactive ligands, making it very expensive and potentially harmful. fNIRS and ultrasounds are too indirect to measure cell-specific information as they rely on the haemodynamic response. Last but not least, MRI is non-invasive and non-harmful. The MR signal can be sensitised to assess different tissue properties. In the following paragraphs, we will summarise the importance and limitations of some MR methods sensitive to brain structure and microstructure during development and introduce our primary method, namely diffusion-weighted MR spectroscopy (dMRS).

(Structural MRI: anatomy) Manganese-enhanced MRI (MEMRI) is particularly suited to studying whole-brain early development in rodents. It provides high structural contrast and a fast acquisition time in neonates (Szulc et al., 2015; Qiu et al., 2018). Still, the method is blind to the microstructural changes that lead to the volumetric changes reported.

(dMRI: microstructure) Diffusion-weighted MRI (dMRI) measures the diffusion properties of water in the brain: restricted and hindered by cell membranes, water diffusion provides an indirect measure of the tissue microstructure (Alexander et al., 2019). dMRI is versatile and applied in numerous applications, including the characterisation of the microstructural organisation during neurodevelopment in rodents and, to a lesser extent, in humans. Comparison with post-mortem microscopy shows a correlation between a high cortical fractional anisotropy (FA) at birth and the presence of radial glia. The latter progressively disappears in the first week along with an FA decrease, illustrating the potential of dMRI to characterise neurodevelopment (Hüppi, 2010; Chokshi et al., 2011). In the cerebellum, the dense folding limits diffusion imaging to the exploration of white matter or ex vivo samples (Zheng et al., 2023).

(dMRS: cell-specific microstructure) Water is ubiquitous and can only partially disentangle the contribution from different tissue compartments (e.g. intra- and extracellular space). dMRS measures the diffusion properties of metabolites. Brain metabolites are mostly intracellular, with some more specific to neurons (e.g. N-acetyl-aspartate [NAA] and glutamate [Glu]) and others more specific to glial cells (e.g. choline compounds, tCho, and myo-inositol, Ins); therefore, different cellular compartments can be resolved (Ronen and Valette, 2015). However, metabolites are 10⁴-fold less concentrated than water, requiring large measurement volumes for dMRS (typically ~50–100 mm³ in rodents), and long acquisition times. As for dMRI, some dMRS acquisition parameters, such as the diffusion weighting and diffusion time, enable the signal to be sensitised to different cellular morphological scales (e.g. cellular processes, cell bodies, cell extension). Using multiple diffusion times and a post hoc model, cellular morphological parameters can be estimated from dMRS (Ligneul et al., 2024). They correlate with neuronal and glial morphologies extracted from histological measures in the healthy, adult mouse (Palombo et al., 2016; Palombo et al., 2017). More specifically, it has been shown that myo-inositol diffusion properties reflect astrocytic morphology in an induced model of astrocytic hypertrophy (Ligneul et al., 2019) and in a mouse model of demyelination where astrocytic activation was expected (Genovese et al., 2021). Choline compound diffusion properties were associated with microglial activation in human disease (Ercan et al., 2016) or in an LPS-induced brain inflammation model (de Marco et al., 2022). However, to our knowledge, very few studies have used dMRS to probe changes in neuronal morphology, and none have attempted to probe neuronal development in early life.

Here, we propose a non-invasive method to monitor cellular brain development. We focus on two regions with different developmental timelines: the cerebellum and the thalamus. As described above, the cerebellum expands greatly after birth, when the Purkinje cells’ dendritic trees start growing. This slow maturation exposes the cerebellum longer to developmental insults. In contrast to the cerebellum, the thalamus develops relatively quickly, and its neurons expand and complexify more rapidly (Szulc et al., 2015; Takeuchi et al., 2014; Figure 1A).

Figure 1

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By contrasting these two regions, we want to assess the sensitivity of the proposed method. We scanned developing rats from postnatal day 5 (P5) to P30. We first used MEMRI to confirm the protracted cerebellar development compared to the thalamus (Figure 1B). We then acquired diffusion properties of metabolites to probe short- and long-range restriction (i.e. soma restriction and branching). To interpret our data, we applied two biophysical models (one analytical and one computational) to characterise the specific changes in microstructure (Figure 1C). We expected monotonic changes in microstructural parameters in both regions, reflecting cell growth, with thalamic properties plateauing earlier than cerebellar properties. This is mostly what we report in our observations. However, we report a U-shape trend for the cerebellar segment length (L_segment, the distance between two embranchments of a dendritic tree) that matches openly available 3D cell reconstructions. In contrast to common findings in the adult brain, we report that tNAA and Glu do not provide reliable morphological neuronal markers in neonates. We identify instead total creatine (tCr) and taurine (Tau) as more reliable markers for tracking early neuronal development. We believe that this is a promising method to track early neuronal development, particularly in the cerebellum, where microstructure changes drastically postnatally.

As in Szulc et al., 2015, we used MEMRI to validate the protracted development of the cerebellum in rat neonates (Sathyanesan et al., 2019), unfolding and tripling volume between P5 and P30. Early developmental cell-specific cerebellar and thalamic microstructural features were assessed with dMRS.

We specifically report diffusion properties for neuronal markers tNAA and Glu, glial markers tCho and Ins, and the non-specific markers tCr and Tau. Metabolites exhibit significantly different diffusion properties between regions, demonstrating the relevance of dMRS to probing early cell structure development. f_sphere is notably higher in the cerebellum for metabolites compartmentalised fully (tNAA, Glu) or partially (tCr, Tau) in neurons, suggesting the influence of the cell-body dense granular layer of the cerebellum. f_sphere possibly reflects the relative volume of sphere-like structures (e.g. cell bodies) compared to the relative volume of fibrous structures (e.g. cell processes, fibres).

A study of human WM using diffusion MRI reported an increase in FA and a decrease in MD with age in early life (Hüppi, 2010). In mice, the same relationship was found in both WM and GM (Chahboune et al., 2009). We report similar dMRI trends, in both cerebellum and thalamus (Figure 4—figure supplement 7).

We also report dMRS changes with age in both regions. The general trend observed for lower signal attenuation and ADC decrease (for some metabolites) with increasing age aligns with these reported dMRI observations and could reflect overall microstructural complexification. However, the different nature of the signal (metabolites: mostly intracellular and low compartmental exchange vs. water: ubiquitous and fast exchange) prevents close co-interpretation.

f_sphere(tCr, Tau) decreases with age in the cerebellum, meaning that tCr and Tau are located in compartments where the relative contribution of sphere-like structures (e.g. cell-bodies) decreases. In other words, it reflects cerebellar cell growth and complexification (Figure 4A, Figure 5A). In the cerebellum, while soma morphological maturation mostly happens in the first week (McKay and Turner, 2005), Purkinje cell dendritic expansion is rapid before P15 but then continues to mature up to the end of the 4th week (McKay and Turner, 2005). Tau diffusion properties might reflect the development of Purkinje cells: (i) in cats, which cannot synthesise Tau and must source it from their diet, Tau dietary deficiency delays cortical and cerebellar maturation (Aerts and Van Assche, 2002), meaning that Tau is also crucial in early development, and (ii) in the cerebellum, immunohistochemistry studies locate Tau in adult Purkinje cells (Madsen et al., 1985; Magnusson et al., 1988; Hussy et al., 2000). These observations align well too with the high Tau levels we still detect in the cerebellum at P30 (Figure 3B).

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The diffusion properties of glial metabolites, f_sphere(Ins) and f_sphere(tCho), also tend to decrease with age. This could be explained by another cerebellar peculiarity: Bergmann glia develop long processes along Purkinje cells, and this specific growth could contribute to the f_sphere(tCr,Tau) decrease.

Validation would require an atlas of all types of cerebellar cellular processes and bodies with estimates of their relative contribution. Such a resource would be immensely valuable but does not exist. Using literature data, we use the relative thicknesses of the EGL, IGL, PL (‘cell body-like’ layers) and ML (‘fibre-like’ layer) to derive f_sphere,LT, a proxy of f_sphere, as illustrated in Figure 5A and reported in Supplementary file 2 and Figure 5—figure supplement 1. f_sphere,LT represents the contribution of sphere-rich layers compared to fibre-rich layers. The evolution of f_sphere,LT trend with age is comparable to f_sphere(tCr). Although f_sphere(tCr) and f_sphere(Tau) have similar trends, f_sphere(Tau) values are overall lower and below f_sphere,LT values (Figure 5—figure supplement 1). This lends weight to the idea that Tau is preferentially located in cerebellar neurons during cerebellar development (and potentially less so in granule cells). In comparison, tCr, which appears more evenly spread between all cerebellar cell types, tracks the evolution of f_sphere,LT more closely. Note that Tau has a higher D_intra (Ligneul and Valette, 2017) compared to the other metabolites, potentially affecting the parameter estimation given for Tau. As described in Methods, the fit for Tau was substantially improved for D_intra >0.6 µm²/ms. The results for D_intra = 0.7 µm²/ms are similar for all the other metabolites (Figure 4—figure supplement 8), and generally improved the estimated sphere radius standard deviation. Additionally, the evolution with age of (R,f_sphere) is then very similar for tNAA and Tau, reinforcing the idea that Tau is neuronal in the cerebellum.

In the thalamus, the decrease in Tau content (Figure 3B) and the change in Tau diffusion properties (Figure 4B) point towards a change of compartmentation with age. Contrary to the trend in the cerebellum, f_sphere(Tau) consistently increases with age in the thalamus (Figure 4—figure supplement 8). This means that Tau diffuses in a space with fewer and fewer processes during development, which is incompatible with the hypothesis that Tau stays in the same compartment. Neurons, as well as microglia (Cengiz et al., 2019) and astrocytes (Bushong et al., 2004; Somaiya et al., 2022; Clavreul et al., 2019), develop with increasing ramification. Neurons are already branched at P10 in the thalamus (Takeuchi et al., 2014) (shown by a low f_sphere(tNAA, Glu) at P10). f_sphere(Tau) at P10 nears f_sphere(tNAA, Glu), indicating that Tau might be located in neuronal compartments in the neonatal thalamus. Although this hypothesis is not verifiable in existing literature, some in vitro studies show that Tau contributes to neuronal proliferation (Hernández-Benítez et al., 2010) and neuronal growth (Mersman et al., 2020). At P30, f_sphere(Tau) is closer to f_sphere(tCho, Ins), indicating that Tau might be located in glial compartments in adulthood. Indeed, immunohistochemistry studies locate Tau in adult thalamic glia (Hussy et al., 2000).

We suggest that Tau should be explored further as a cerebellar marker of early neuronal development, possibly more specifically of Purkinje cells. We confirm in this study the observation from Tkác et al., 2003, that both tCr and Tau have high spectroscopic signals in neonates. They are the two metabolites most reliably quantified up to P15 in both regions (continuing for Tau in the cerebellum up to P30), and the estimates of their diffusion properties are the most confident, contrary to the usual neuronal markers, tNAA and Glu. tCr stands as a good marker, too. However, there is no indication of a preferential neuronal compartmentation for this metabolite during early development; hence, it is less specific to neurons and probably even less to Purkinje cells. Because Tau seems to switch compartments in the thalamus between birth and adulthood, we do not think its diffusion properties reliably inform thalamic neuronal morphological development. In both human (Hüppi et al., 1991; Pouwels et al., 1999) and rhesus monkey (Sturman et al., 1980) brains, Tau content increases at birth and then decreases with age and is therefore a marker of the immature brain in both species. Although Tau concentration is low in the adult human brain, it is more concentrated in the cerebellum in adulthood (Pouwels et al., 1999; Emir et al., 2012). A similar finding was made in the adult rhesus monkey brain (Sturman et al., 1980), indicating a conserved pattern across species of Tau properties in the cerebellum. These observations form a good basis for translation. Although dMRS in infants has substantial challenges, they are worth trying to overcome for a putative cerebellar marker of early neuronal development which could help understand neurodevelopmental disorders (Sathyanesan et al., 2019).

We also observed a protracted increase in tNAA signal in the cerebellum compared to the thalamus. tNAA is usually described as a neuronal osmolyte, meaning it could reflect neuronal growth and maturation and be a marker of neuronal development. However, at the voxel level, its signal reflects the neuronal volumetric contribution, i.e., differences in neuronal densities might mask the effect of development. In contrast, we want to reiterate that dMRS is a normalised measure and dMRS outcomes are not affected by the neuronal density: they represent the average morphological properties of the cell types the metabolite belongs to.

We chose to interpret the dMRS data with two biophysical models: an analytical spheres and ‘astrosticks’ model and a computational morphometric model. The analytical model was applied simultaneously to the signal attenuation at high b-values and the ADCs at long diffusion times, and on individual animals. In addition to the valuable biophysical biomarkers, it provides a good signal representation to evaluate the significance of the differences between diffusion properties of individual datasets. The experimental design was not suitable for a randomly oriented infinite cylinders (‘astrocylinders’) model as in previous studies (Palombo et al., 2017; Ligneul et al., 2019). The mixing time used for the acquisition of signal decay at high b-values (TM = 100 ms) is too long to enable sensitivity to fibre radius (Ligneul et al., 2024), hence the choice of ‘astrosticks’ model. The sphere compartment was added to account for the high sphere fraction in the cerebellum, and for the nature of the neonatal brain (under-developed neurons). The root mean square errors (RMSEs) from the different models tested (Figure 4—figure supplement 2) tend to be more similar at P30, suggesting that it is less important to add a sphere compartment for modelling metabolites diffusion in the adult brain.

The computational morphometric model was applied to attempt to extract cell extension-related parameters, but the study design and data quality are suboptimal, and we could only reliably extract L_segment and D_intra (Figure 4—figure supplement 6). D_intra is mostly stable with age in both regions. However, it sometimes drops or peaks, likely affecting the other parameters’ estimations. The longest mixing time (TM = 1000 ms) is short compared to previous studies using a similar modelling approach (Palombo et al., 2016; Ligneul et al., 2019), meaning that the diffusion path is shorter (i.e. about 30 µm in this study compared to about 45 µm in previous studies, assuming D_intra = 0.5 µm²/ms). Note that the higher D_intra of Tau is therefore an asset for probing early neuronal cerebellar growth. In our study, the number of embranchments (N_branch) was very poorly evaluated because of the high level of noise in the data. However, even with a better dataset quality and a more optimal study design, it is unlikely that the N_branch estimation could reflect the Purkinje cell morphometry because of their size and complexity in adulthood. If we had robust DTI in all animals (Figure 4—figure supplement 7), we would have included the fibre dispersion in the modelling (notably for the cerebellum), as in Ligneul et al., 2019. However, since we acquired enough directions for the dMRS data to be powder-averaged, we expect that the WM contribution in the cerebellum is mostly reflected by a slight f_sphere drop, as exemplified by the f_sphere,LT drop when including WM (Supplementary file 2).

Finally, we expected that L_segment estimated from the morphometric model in the cerebellum would increase regularly with age, matching the dendritic tree development. For neuronal, or partly neuronal metabolites, we report an L_segment decrease first. Except for tNAA, which consistently decreases in the cerebellum with age, L_segment decreases until P15 for tCr, Tau, and Glu and then increases, forming a U-shape curve. To better understand these results, we extracted the morphometries of developing Purkinje cells and developing thalamic interneurons (both in the mouse) from NeuroMorph.org. Interestingly, from these 3D reconstructed cells, we observe a decrease in L_{segment, 3D-recon} up to P13 for Purkinje cells, whilst being quite stable in the thalamus (Figure 6), matching our non-invasive measurement trend. This could reflect the complexification of the dendritic tree following its expansion.

Figure 6

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The code related to preprocessing and modelling is shared on GitHub: https://github.com/clemoune/dmrs-neonates/ (copy archived at clemoune, 2025). Each GitHub subdirectory contains the processed data necessary for the subsequent analysis, except for the preprocessed dMRS data hosted on Zenodo. The preprocessed dMRS data are shared on Zenodo: https://doi.org/10.5281/zenodo.17107499.

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Author details

1. Clémence Ligneul

   Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   clemence.ligneul@gmail.com

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5673-3009

2. Lily Qiu

   Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   Investigation

   Competing interests

   No competing interests declared

3. William T Clarke

   Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7159-7025

4. Saad Jbabdi

   Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   Software, Methodology, Writing – review and editing

   Competing interests

   Reviewing editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0003-3234-5639

5. Marco Palombo

   1. Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
   2. School of Computer Science and Informatics, Cardiff University, Cardiff, United Kingdom

   Contribution

   Conceptualization, Software, Methodology, Writing – review and editing

   Contributed equally with

   Jason P Lerch

   Competing interests

   No competing interests declared

6. Jason P Lerch

   1. Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
   2. Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Canada
   3. Department of Medical Biophysics, University of Toronto, Toronto, Canada

   Contribution

   Conceptualization, Resources, Writing – review and editing

   Contributed equally with

   Marco Palombo

   Competing interests

   Reviewing editor, eLife

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