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.

1. 1. Clémence L

   (2025) Zenodo

   Preprocessed dMRS data (thalamic and cerebellar development).

   https://doi.org/10.5281/zenodo.17107499

1. 1. Aerts L
   2. Van Assche FA

   (2002) Taurine and taurine-deficiency in the perinatal period

   Journal of Perinatal Medicine 30:281–286.

   https://doi.org/10.1515/JPM.2002.040

   • PubMed
   • Google Scholar
2. 1. Alexander DC
   2. Dyrby TB
   3. Nilsson M
   4. Zhang H

   (2019) Imaging brain microstructure with diffusion MRI: practicality and applications

   NMR in Biomedicine 32:1–26.

   https://doi.org/10.1002/nbm.3841

   • Google Scholar
3. 1. Antón-Bolaños N
   2. Espinosa A
   3. López-Bendito G

   (2018) Developmental interactions between thalamus and cortex: a true love reciprocal story

   Current Opinion in Neurobiology 52:33–41.

   https://doi.org/10.1016/j.conb.2018.04.018

   • PubMed
   • Google Scholar
4. 1. Araujo APB
   2. Carpi-Santos R
   3. Gomes FCA

   (2019) The Role of Astrocytes in the Development of the Cerebellum

   Cerebellum 18:1017–1035.

   https://doi.org/10.1007/s12311-019-01046-0

   • PubMed
   • Google Scholar
5. 1. Beekhof GC
   2. Osório C
   3. White JJ
   4. van Zoomeren S
   5. van der Stok H
   6. Xiong B
   7. Nettersheim IH
   8. Mak WA
   9. Runge M
   10. Fiocchi FR
   11. Boele HJ
   12. Hoebeek FE
   13. Schonewille M

   (2021) Differential spatiotemporal development of Purkinje cell populations and cerebellum-dependent sensorimotor behaviors

   eLife 10:63668.

   https://doi.org/10.7554/eLife.63668

   • PubMed
   • Google Scholar
6. 1. Bushong EA
   2. Martone ME
   3. Ellisman MH

   (2004) Maturation of astrocyte morphology and the establishment of astrocyte domains during postnatal hippocampal development

   International Journal of Developmental Neuroscience 22:73–86.

   https://doi.org/10.1016/j.ijdevneu.2003.12.008

   • PubMed
   • Google Scholar
7. 1. Cengiz P
   2. Zafer D
   3. Chandrashekhar JH
   4. Chanana V
   5. Bogost J
   6. Waldman A
   7. Novak B
   8. Kintner DB
   9. Ferrazzano PA

   (2019) Developmental differences in microglia morphology and gene expression during normal brain development and in response to hypoxia-ischemia

   Neurochemistry International 127:137–147.

   https://doi.org/10.1016/j.neuint.2018.12.016

   • PubMed
   • Google Scholar
8. 1. Chahboune H
   2. Ment LR
   3. Stewart WB
   4. Rothman DL
   5. Vaccarino FM
   6. Hyder F
   7. Schwartz ML

   (2009) Hypoxic injury during neonatal development in murine brain: correlation between in vivo DTI findings and behavioral assessment

   Cerebral Cortex 19:2891–2901.

   https://doi.org/10.1093/cercor/bhp068

   • PubMed
   • Google Scholar
9. 1. Chen XR
   2. Heck N
   3. Lohof AM
   4. Rochefort C
   5. Morel MP
   6. Wehrlé R
   7. Doulazmi M
   8. Marty S
   9. Cannaya V
   10. Avci HX
   11. Mariani J
   12. Rondi-Reig L
   13. Vodjdani G
   14. Sherrard RM
   15. Sotelo C
   16. Dusart I

   (2013) Mature Purkinje cells require the retinoic acid-related orphan receptor-α (RORα) to maintain climbing fiber mono-innervation and other adult characteristics

   The Journal of Neuroscience 33:9546–9562.

   https://doi.org/10.1523/JNEUROSCI.2977-12.2013

   • PubMed
   • Google Scholar
10. 1. Chokshi FH
    2. Poretti A
    3. Meoded A
    4. Huisman TAGM

    (2011) Normal and abnormal development of the cerebellum and brainstem as depicted by diffusion tensor imaging

    Seminars in Ultrasound, CT and MRI 32:539–554.

    https://doi.org/10.1053/j.sult.2011.06.005

    • Google Scholar
11. 1. Clavreul S
    2. Abdeladim L
    3. Hernández-Garzón E
    4. Niculescu D
    5. Durand J
    6. Ieng SH
    7. Barry R
    8. Bonvento G
    9. Beaurepaire E
    10. Livet J
    11. Loulier K

    (2019) Cortical astrocytes develop in a plastic manner at both clonal and cellular levels

    Nature Communications 10:1–14.

    https://doi.org/10.1038/s41467-019-12791-5

    • Google Scholar
12. Software

    1. clemoune

    (2025) Dmrs-neonates, version swh:1:rev:460122ab5c3612865f80f8516a1dc0d7c90d3852

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:f27994212b8f325e04d645f0f3590250c30d92a3;origin=https://github.com/clemoune/dmrs-neonates;visit=swh:1:snp:d0afcdd000b4f8c67b1a2d34adf1caa538ae2a94;anchor=swh:1:rev:460122ab5c3612865f80f8516a1dc0d7c90d3852
13. 1. Coupe P
    2. Yger P
    3. Prima S
    4. Hellier P
    5. Kervrann C
    6. Barillot C

    (2008) An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images

    IEEE Transactions on Medical Imaging 27:425–441.

    https://doi.org/10.1109/TMI.2007.906087

    • Google Scholar
14. 1. Cupolillo D
    2. Hoxha E
    3. Faralli A
    4. De Luca A
    5. Rossi F
    6. Tempia F
    7. Carulli D

    (2016) Autistic-Like traits and cerebellar dysfunction in purkinje cell PTEN knock-out mice

    Neuropsychopharmacology 41:1457–1466.

    https://doi.org/10.1038/npp.2015.339

    • PubMed
    • Google Scholar
15. 1. de Marco R
    2. Ronen I
    3. Branzoli F
    4. Amato ML
    5. Asllani I
    6. Colasanti A
    7. Harrison NA
    8. Cercignani M

    (2022) Diffusion-weighted MR spectroscopy (DW-MRS) is sensitive to LPS-induced changes in human glial morphometry: A preliminary study

    Brain, Behavior, and Immunity 99:256–265.

    https://doi.org/10.1016/j.bbi.2021.10.005

    • PubMed
    • Google Scholar
16. 1. Emir UE
    2. Auerbach EJ
    3. van de Moortele PF
    4. Marjańska M
    5. Uğurbil K
    6. Terpstra M
    7. Tkáč I
    8. Oz G

    (2012) Regional neurochemical profiles in the human brain measured by

    NMR in Biomedicine 25:152–160.

    https://doi.org/10.1002/nbm.1727

    • PubMed
    • Google Scholar
17. 1. Ercan E
    2. Magro-Checa C
    3. Valabregue R
    4. Branzoli F
    5. Wood ET
    6. Steup-Beekman GM
    7. Webb AG
    8. Huizinga TWJ
    9. van Buchem MA
    10. Ronen I

    (2016) Glial and axonal changes in systemic lupus erythematosus measured with diffusion of intracellular metabolites

    Brain 139:1447–1457.

    https://doi.org/10.1093/brain/aww031

    • PubMed
    • Google Scholar
18. 1. Fatemi SH
    2. Halt AR
    3. Realmuto G
    4. Earle J
    5. Kist DA
    6. Thuras P
    7. Merz A

    (2002) Purkinje cell size is reduced in cerebellum of patients with autism

    Cellular and Molecular Neurobiology 22:171–175.

    https://doi.org/10.1023/a:1019861721160

    • PubMed
    • Google Scholar
19. 1. Fatemi SH
    2. Folsom TD

    (2009) The neurodevelopmental hypothesis of schizophrenia, revisited

    Schizophrenia Bulletin 35:528–548.

    https://doi.org/10.1093/schbul/sbn187

    • PubMed
    • Google Scholar
20. 1. Friedel M
    2. van Eede MC
    3. Pipitone J
    4. Chakravarty MM
    5. Lerch JP

    (2014) Pydpiper: a flexible toolkit for constructing novel registration pipelines

    Frontiers in Neuroinformatics 8:1–21.

    https://doi.org/10.3389/fninf.2014.00067

    • Google Scholar
21. 1. Fukumitsu K
    2. Hatsukano T
    3. Yoshimura A
    4. Heuser J
    5. Fujishima K
    6. Kengaku M

    (2016) Mitochondrial fission protein Drp1 regulates mitochondrial transport and dendritic arborization in cerebellar Purkinje cells

    Molecular and Cellular Neuroscience 71:56–65.

    https://doi.org/10.1016/j.mcn.2015.12.006

    • Google Scholar
22. 1. Genovese G
    2. Palombo M
    3. Santin MD
    4. Valette J
    5. Ligneul C
    6. Aigrot M
    7. Abdoulkader N
    8. Langui D
    9. Millecamps A
    10. Baron‐Van Evercooren A
    11. Stankoff B
    12. Lehericy S
    13. Petiet A
    14. Branzoli F

    (2021) Inflammation‐driven glial alterations in the cuprizone mouse model probed with diffusion‐weighted magnetic resonance spectroscopy at 11.7 T

    NMR in Biomedicine 34:1–13.

    https://doi.org/10.1002/nbm.4480

    • Google Scholar
23. 1. Goerzen D
    2. Fowler C
    3. Devenyi GA
    4. Germann J
    5. Madularu D
    6. Chakravarty MM
    7. Near J

    (2020) An MRI-Derived neuroanatomical atlas of the fischer 344 rat brain

    Scientific Reports 10:1–13.

    https://doi.org/10.1038/s41598-020-63965-x

    • Google Scholar
24. Book

    1. Haines DE
    2. Dietrichs E

    (2012) The Cerebellum – Structure and Connections

    Elsevier B.V.

    https://doi.org/10.1016/B978-0-444-51892-7.00001-2

    • Google Scholar
25. 1. Hernández-Benítez R
    2. Pasantes-Morales H
    3. Saldaña IT
    4. Ramos-Mandujano G

    (2010) Taurine stimulates proliferation of mice embryonic cultured neural progenitor cells

    Journal of Neuroscience Research 88:1673–1681.

    https://doi.org/10.1002/jnr.22328

    • PubMed
    • Google Scholar
26. 1. Hetsch F
    2. Wang D
    3. Chen X
    4. Zhang J
    5. Aslam M
    6. Kegel M
    7. Tonner H
    8. Grus F
    9. von Engelhardt J

    (2022) CKAMP44 controls synaptic function and strength of relay neurons during early development of the dorsal lateral geniculate nucleus

    The Journal of Physiology 600:3549–3565.

    https://doi.org/10.1113/JP283172

    • PubMed
    • Google Scholar
27. 1. Hüppi PS
    2. Posse S
    3. Lazeyras F
    4. Burri R
    5. Bossi E
    6. Herschkowitz N

    (1991) Magnetic resonance in preterm and term newborns: 1H-spectroscopy in developing human brain

    Pediatric Research 30:574–578.

    https://doi.org/10.1203/00006450-199112000-00017

    • Google Scholar
28. Book

    1. Hüppi P

    (2010) Diffusion tensor imaging in brain development

    In: Jones DK, editors. Diffusion MRI: Theory, Methods, and Applications. Oxford Academic. pp. 500–517.

    https://doi.org/10.1093/med/9780195369779.003.0030

    • Google Scholar
29. 1. Hussy N
    2. Deleuze C
    3. Desarménien MG
    4. Moos FC

    (2000) Osmotic regulation of neuronal activity: A new role for taurine and glial cells in a hypothalamic neuroendocrine structure

    Progress in Neurobiology 62:113–134.

    https://doi.org/10.1016/S0301-0082(99)00071-4

    • Google Scholar
30. 1. Hwang WJ
    2. Kwak YB
    3. Cho KIK
    4. Lee TY
    5. Oh H
    6. Ha M
    7. Kim M
    8. Kwon JS

    (2022) Thalamic connectivity system across psychiatric disorders: current status and clinical implications

    Biological Psychiatry Global Open Science 2:332–340.

    https://doi.org/10.1016/j.bpsgos.2021.09.008

    • Google Scholar
31. 1. Ismail FY
    2. Shapiro BK

    (2019) What are neurodevelopmental disorders?

    Current Opinion in Neurology 32:611–616.

    https://doi.org/10.1097/WCO.0000000000000710

    • Google Scholar
32. 1. Jayabal S
    2. Ljungberg L
    3. Watt AJ

    (2017) Transient cerebellar alterations during development prior to obvious motor phenotype in a mouse model of spinocerebellar ataxia type 6

    J Physiol 3:949–966.

    https://doi.org/10.1113/JP273184

    • Google Scholar
33. 1. Kim J
    2. Kwon N
    3. Chang S
    4. Kim KT
    5. Lee D
    6. Kim S
    7. Yun SJ
    8. Hwang D
    9. Kim JW
    10. Hwu Y
    11. Margaritondo G
    12. Je JH
    13. Rhyu IJ

    (2011) Altered branching patterns of Purkinje cells in mouse model for cortical development disorder

    Scientific Reports 1:1–7.

    https://doi.org/10.1038/srep00122

    • Google Scholar
34. 1. Kim BJ
    2. Scott DA

    (2014) Mouse model reveals the role of RERE in cerebellar foliation and the migration and maturation of Purkinje cells

    PLOS ONE 9:e87518.

    https://doi.org/10.1371/journal.pone.0087518

    • PubMed
    • Google Scholar
35. 1. Krol A
    2. Wimmer RD
    3. Halassa MM
    4. Feng G

    (2018) Thalamic reticular dysfunction as a circuit endophenotype in neurodevelopmental disorders

    Neuron 98:282–295.

    https://doi.org/10.1016/j.neuron.2018.03.021

    • Google Scholar
36. 1. Kumar R
    2. Delshad S
    3. Macey PM
    4. Woo MA
    5. Harper RM

    (2011) Development of T2-relaxation values in regional brain sites during adolescence

    Magnetic Resonance Imaging 29:185–193.

    https://doi.org/10.1016/j.mri.2010.08.006

    • Google Scholar
37. 1. Kuznetsova A
    2. Brockhoff PB
    3. Christensen RHB

    (2017) lmerTest package: tests in linear mixed effects models

    Journal of Statistical Software 82:13.

    https://doi.org/10.18637/jss.v082.i13

    • Google Scholar
38. 1. Ligneul C
    2. Palombo M
    3. Valette J

    (2017) Metabolite diffusion up to very high b in the mouse brain in vivo: Revisiting the potential correlation between relaxation and diffusion properties

    Magnetic Resonance in Medicine 77:1390–1398.

    https://doi.org/10.1002/mrm.26217

    • Google Scholar
39. 1. Ligneul C
    2. Valette J

    (2017) Probing metabolite diffusion at ultra-short time scales in the mouse brain using optimized oscillating gradients and “short”-echo-time diffusion-weighted MRS

    NMR in Biomedicine 30:1–10.

    https://doi.org/10.1002/nbm.3671

    • Google Scholar
40. 1. Ligneul C
    2. Palombo M
    3. Hernández-Garzón E
    4. Carrillo-de Sauvage MA
    5. Flament J
    6. Hantraye P
    7. Brouillet E
    8. Bonvento G
    9. Escartin C
    10. Valette J

    (2019) Diffusion-weighted magnetic resonance spectroscopy enables cell-specific monitoring of astrocyte reactivity in vivo

    NeuroImage 191:457–469.

    https://doi.org/10.1016/j.neuroimage.2019.02.046

    • PubMed
    • Google Scholar
41. Conference

    1. Ligneul C
    2. Andersson J
    3. Jbabdi S
    4. Lerch J
    5. Clarke WT

    (2023) Rejecting or retaining motion corrupted transients in diffusion- weighted MRS: high b-values and multiple directions

    Proceedings of the International Society for Magnetic Resonance in Medicine.

    https://doi.org/10.58530/2023/3934

    • Google Scholar
42. 1. Ligneul C
    2. Najac C
    3. Döring A
    4. Beaulieu C
    5. Branzoli F
    6. Clarke WT
    7. Cudalbu C
    8. Genovese G
    9. Jbabdi S
    10. Jelescu I
    11. Karampinos D
    12. Kreis R
    13. Lundell H
    14. Marjańska M
    15. Möller HE
    16. Mosso J
    17. Mougel E
    18. Posse S
    19. Ruschke S
    20. Simsek K
    21. Szczepankiewicz F
    22. Tal A
    23. Tax C
    24. Oeltzschner G
    25. Palombo M
    26. Ronen I
    27. Valette J

    (2024) Diffusion-weighted MR spectroscopy: Consensus, recommendations, and resources from acquisition to modeling

    Magnetic Resonance in Medicine 91:860–885.

    https://doi.org/10.1002/mrm.29877

    • PubMed
    • Google Scholar
43. 1. Madsen S
    2. Ottersen O
    3. Storm-mathisen J

    (1985) Immunocytochemical visualization of taurine: neuronal localization in the rat cerebellum

    Neuroscience Letters 60:255–260.

    https://doi.org/10.1016/0304-3940(85)90586-5

    • Google Scholar
44. 1. Magnusson KR
    2. Madl JE
    3. Clements JR
    4. Wu JY
    5. Larson AA
    6. Beitz AJ

    (1988) Colocalization of taurine- and cysteine sulfinic acid decarboxylase- like immunoreactivity in the cerebellum of the rat with monoclonal antibodies against taurine

    The Journal of Neuroscience 8:4551–4564.

    https://doi.org/10.1523/JNEUROSCI.08-12-04551.1988

    • Google Scholar
45. 1. Marchadour C
    2. Brouillet E
    3. Hantraye P
    4. Lebon V
    5. Valette J

    (2012) Anomalous diffusion of brain metabolites evidenced by diffusion-weighted magnetic resonance spectroscopy in vivo

    Journal of Cerebral Blood Flow and Metabolism 32:2153–2160.

    https://doi.org/10.1038/jcbfm.2012.119

    • PubMed
    • Google Scholar
46. 1. McKay BE
    2. Turner RW

    (2005) Physiological and morphological development of the rat cerebellar Purkinje cell

    J Physiol 567:829–850.

    https://doi.org/10.1113/jphysiol.2005.089383

    • Google Scholar
47. 1. Mersman B
    2. Zaidi W
    3. Syed NI
    4. Xu F

    (2020) Taurine promotes neurite outgrowth and synapse development of both vertebrate and invertebrate central neurons

    Frontiers in Synaptic Neuroscience 12:1–18.

    https://doi.org/10.3389/fnsyn.2020.00029

    • Google Scholar
48. 1. Miterko LN
    2. White JJ
    3. Lin T
    4. Brown AM
    5. O’Donovan KJ
    6. Sillitoe RV

    (2019) Persistent motor dysfunction despite homeostatic rescue of cerebellar morphogenesis in the Car8 waddles mutant mouse

    Neural Development 14:1–22.

    https://doi.org/10.1186/s13064-019-0130-4

    • Google Scholar
49. 1. Nakayama H
    2. Abe M
    3. Morimoto C
    4. Iida T
    5. Okabe S
    6. Sakimura K
    7. Hashimoto K

    (2018) Microglia permit climbing fiber elimination by promoting GABAergic inhibition in the developing cerebellum

    Nature Communications 9:2830.

    https://doi.org/10.1038/s41467-018-05100-z

    • PubMed
    • Google Scholar
50. 1. Palombo M
    2. Ligneul C
    3. Najac C
    4. Le Douce J
    5. Flament J
    6. Escartin C
    7. Hantraye P
    8. Brouillet E
    9. Bonvento G
    10. Valette J

    (2016) New paradigm to assess brain cell morphology by diffusion-weighted MR spectroscopy in vivo

    PNAS 113:6671–6676.

    https://doi.org/10.1073/pnas.1504327113

    • PubMed
    • Google Scholar
51. 1. Palombo M
    2. Ligneul C
    3. Valette J

    (2017) Modeling diffusion of intracellular metabolites in the mouse brain up to very high diffusion-weighting: Diffusion in long fibers (almost) accounts for non-monoexponential attenuation

    Magnetic Resonance in Medicine 77:343–350.

    https://doi.org/10.1002/mrm.26548

    • PubMed
    • Google Scholar
52. 1. Pouwels PJ
    2. Brockmann K
    3. Kruse B
    4. Wilken B
    5. Wick M
    6. Hanefeld F
    7. Frahm J

    (1999) Regional age dependence of human brain metabolites from infancy to adulthood as detected by quantitative localized proton MRS

    Pediatric Research 46:474–485.

    https://doi.org/10.1203/00006450-199910000-00019

    • PubMed
    • Google Scholar
53. 1. Qiu LR
    2. Fernandes DJ
    3. Szulc-Lerch KU
    4. Dazai J
    5. Nieman BJ
    6. Turnbull DH
    7. Foster JA
    8. Palmert MR
    9. Lerch JP

    (2018) Mouse MRI shows brain areas relatively larger in males emerge before those larger in females

    Nature Communications 9:2615.

    https://doi.org/10.1038/s41467-018-04921-2

    • PubMed
    • Google Scholar
54. 1. Robinson L
    2. Guy J
    3. McKay L
    4. Brockett E
    5. Spike RC
    6. Selfridge J
    7. De Sousa D
    8. Merusi C
    9. Riedel G
    10. Bird A
    11. Cobb SR

    (2012) Morphological and functional reversal of phenotypes in a mouse model of Rett syndrome

    Brain 135:2699–2710.

    https://doi.org/10.1093/brain/aws096

    • Google Scholar
55. 1. Ronen I
    2. Valette J

    (2015) Diffusion-Weighted Magnetic Resonance Spectroscopy

    eMagRes 4:733–750.

    https://doi.org/10.1002/9780470034590.emrstm1471

    • Google Scholar
56. 1. Sathyanesan A
    2. Zhou J
    3. Scafidi J
    4. Heck DH
    5. Sillitoe RV
    6. Gallo V

    (2019) Emerging connections between cerebellar development, behaviour and complex brain disorders

    Nature Reviews. Neuroscience 20:298–313.

    https://doi.org/10.1038/s41583-019-0152-2

    • PubMed
    • Google Scholar
57. 1. Sato H
    2. Hatakeyama J
    3. Iwasato T
    4. Araki K
    5. Yamamoto N
    6. Shimamura K

    (2022) Thalamocortical axons control the cytoarchitecture of neocortical layers by area-specific supply of VGF

    eLife 11:67549.

    https://doi.org/10.7554/eLife.67549

    • PubMed
    • Google Scholar
58. 1. Somaiya RD
    2. Huebschman NA
    3. Chaunsali L
    4. Sabbagh U
    5. Carrillo GL
    6. Tewari BP
    7. Fox MA

    (2022) Development of astrocyte morphology and function in mouse visual thalamus

    The Journal of Comparative Neurology 530:945–962.

    https://doi.org/10.1002/cne.25261

    • PubMed
    • Google Scholar
59. 1. Stoodley CJ

    (2016) The cerebellum and neurodevelopmental disorders

    Cerebellum 15:34–37.

    https://doi.org/10.1007/s12311-015-0715-3

    • PubMed
    • Google Scholar
60. 1. Sturman JA
    2. Rassin DK
    3. Gaull GE
    4. Cote LJ

    (1980) Taurine in developing rhesus monkey brain

    Journal of Neurochemistry 35:304–310.

    https://doi.org/10.1111/j.1471-4159.1980.tb06265.x

    • PubMed
    • Google Scholar
61. 1. Sundberg M
    2. Tochitsky I
    3. Buchholz DE
    4. Winden K
    5. Kujala V
    6. Kapur K
    7. Cataltepe D
    8. Turner D
    9. Han MJ
    10. Woolf CJ
    11. Hatten ME
    12. Sahin M

    (2018) Purkinje cells derived from TSC patients display hypoexcitability and synaptic deficits associated with reduced FMRP levels and reversed by rapamycin

    Molecular Psychiatry 23:2167–2183.

    https://doi.org/10.1038/s41380-018-0018-4

    • PubMed
    • Google Scholar
62. 1. Szulc KU
    2. Lerch JP
    3. Nieman BJ
    4. Bartelle BB
    5. Friedel M
    6. Suero-Abreu GA
    7. Watson C
    8. Joyner AL
    9. Turnbull DH

    (2015) 4D MEMRI atlas of neonatal FVB/N mouse brain development

    NeuroImage 118:49–62.

    https://doi.org/10.1016/j.neuroimage.2015.05.029

    • PubMed
    • Google Scholar
63. 1. Takeuchi Y
    2. Asano H
    3. Katayama Y
    4. Muragaki Y
    5. Imoto K
    6. Miyata M

    (2014) Large-scale somatotopic refinement via functional synapse elimination in the sensory thalamus of developing mice

    The Journal of Neuroscience 34:1258–1270.

    https://doi.org/10.1523/JNEUROSCI.3865-13.2014

    • Google Scholar
64. 1. Tkác I
    2. Rao R
    3. Georgieff MK
    4. Gruetter R

    (2003) Developmental and regional changes in the neurochemical profile of the rat brain determined by in vivo 1H NMR spectroscopy

    Magnetic Resonance in Medicine 50:24–32.

    https://doi.org/10.1002/mrm.10497

    • PubMed
    • Google Scholar
65. 1. Tkáč I
    2. gilles HP
    3. Andersen P
    4. Keene CD
    5. Low WC
    6. Gruetter R

    (2004) Highly resolved in vivo 1 H NMR spectroscopy of the mouse brain at 9. 4 T

    Magnetic Resonance in Medicine 484:478–484.

    https://doi.org/10.1002/mrm.20184

    • Google Scholar
66. 1. Tsai PT
    2. Hull C
    3. Chu Y
    4. Greene-Colozzi E
    5. Sadowski AR
    6. Leech JM
    7. Steinberg J
    8. Crawley JN
    9. Regehr WG
    10. Sahin M

    (2012) Autistic-like behaviour and cerebellar dysfunction in Purkinje cell Tsc1 mutant mice

    Nature 488:647–651.

    https://doi.org/10.1038/nature11310

    • PubMed
    • Google Scholar
67. 1. Usui N
    2. Co M
    3. Harper M
    4. Rieger MA
    5. Dougherty JD
    6. Konopka G

    (2017) Sumoylation of FOXP2 regulates motor function and vocal communication through purkinje cell development

    Biological Psychiatry 81:220–230.

    https://doi.org/10.1016/j.biopsych.2016.02.008

    • PubMed
    • Google Scholar
68. 1. van der Heijden ME
    2. Sillitoe RV

    (2021) Interactions between purkinje cells and granule cells coordinate the development of functional cerebellar circuits

    Neuroscience 462:4–21.

    https://doi.org/10.1016/j.neuroscience.2020.06.010

    • PubMed
    • Google Scholar
69. 1. Yamanaka H
    2. Yanagawa Y
    3. Obata K

    (2004) Development of stellate and basket cells and their apoptosis in mouse cerebellar cortex

    Neuroscience Research 50:13–22.

    https://doi.org/10.1016/j.neures.2004.06.008

    • PubMed
    • Google Scholar
70. 1. Yamashiro K
    2. Hori K
    3. Lai ESK
    4. Aoki R
    5. Shimaoka K
    6. Egusa SF
    7. Sakamoto A
    8. Abe M
    9. Sakimura K
    10. Watanabe T
    11. Uesaka N
    12. Kano M
    13. Hoshino M

    (2020) AUTS2 governs cerebellar development, purkinje cell maturation, motor function and social communication

    iScience 23:917989.

    https://doi.org/10.1016/j.isci.2020.101820

    • Google Scholar
71. 1. Zheng J
    2. Yang Q
    3. Makris N
    4. Huang K
    5. Liang J
    6. Ye C
    7. Yu X
    8. Tian M
    9. Ma T
    10. Mou T
    11. Guo W
    12. Kikinis R
    13. Gao Y

    (2023) Three-dimensional digital reconstruction of the cerebellar cortex: lobule thickness, surface area measurements, and layer architecture

    The Cerebellum 22:249–260.

    https://doi.org/10.1007/s12311-022-01390-8

    • Google Scholar

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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