Thalamocortical connections provide important inputs into the developing cortex during the second half of human gestation, where they play a key role in guiding cortical areal differentiation and establishing the circuitry responsible for sensory integration across the lifespan (Jones, 2007; Price et al., 2006; Schummers et al., 2005; Sharma et al., 2000; Sur and Rubenstein, 2005). Their importance is highlighted by previous work implicating disruptions to thalamocortical development during the perinatal period in the pathophysiology of neurodevelopmental disorders, such as schizophrenia (Klingner et al., 2014; Marenco et al., 2012), bipolar disorder (Anticevic et al., 2014), and autism (Nair et al., 2013). Altered thalamocortical connectivity has also been described in preterm infants, and was used to predict cognitive outcome (Ball et al., 2013; Ball et al., 2015; Toulmin et al., 2021), highlighting the specific vulnerability of these pathways during the second to third trimester. Although thalamocortical development has been studied in rodents and non-human primates (Brody et al., 1987; Kostović and Jovanov-Milosević, 2006; Molnár and Blakemore, 1995; Yakovlev et al., 1960) and post-mortem human tissue (Krsnik et al., 2017; Takahashi et al., 2012; Wilkinson et al., 2017), little is known about in vivo white matter maturation during fetal development.

White matter development in the late second and third trimesters of human gestation (between 21 and 37 weeks) is characterised by a sequence of precisely timed biological processes occurring in transient compartments of the fetal brain. These processes include the migration of neurons along the radial glial scaffold, accumulation of thalamocortical axons in the superficial subplate, innervation of the target cortical area, conversion of radial glial cells into astrocytes, and ensheathment of axonal fibres (Krsnik et al., 2017; Molliver et al., 1973; Kostović et al., 2002, Kostovic and Judas, 2006). The challenge for in vivo neuroimaging studies is to disentangle the effect of these different neurobiological processes on the diffusion magnetic resonance imaging (dMRI) signal, to improve mechanistic insight about the transformation of transient fetal compartments into segments of developing white matter (Kostovic 2012).

Recent advances in diffusion weighted imaging now allow in vivo characterisation and estimation of white matter development during the fetal period. Tractography has been used to estimate the fetal brain’s major white matter bundles and quantitatively characterise the evolution of the microstructure across the second half of gestation (Bui et al., 2006; Zanin et al., 2011; Jaimes et al., 2020; Jakab et al., 2015; Keunen et al., 2018; Khan et al., 2019; Machado-Rivas et al., 2021; Wilson et al., 2021). Advanced acquisition and analysis methods enable the relative contribution of constituent tissue and fluid compartments to the diffusion signal to be estimated (Jeurissen et al., 2014; Pietsch et al., 2019). Using this approach, previous work has identified non-linear trends in diffusion metrics over the second to third trimester (Wilson et al., 2021). Namely, we observed an initial decrease in tissue fraction within developing white matter between 22 and 29 weeks, which could be due to the radial glial scaffold disassembling (Rakic, 2003). Subsequently, we observed an increase from 30 to 36 weeks, potentially linked to more coherent fibre organisation, axonal outgrowth, and ensheathment (Wimberger et al., 1995; Back et al., 2002; Haynes et al., 2005), increasing the structural integrity of maturing white matter. Interpreting these trends is especially challenging in the rapidly developing fetal brain, because of the high sensitivity and low specificity of diffusion metrics to various co-occurring biological processes.

We hypothesise that the biological processes occurring in different fetal compartments leads to predictable changes in diffusion metrics along tracts, reflecting the appearance and resolution of these transient zones. When a mean value across the whole tract is calculated, sensitivity to the unique neurobiological properties of each transient compartment is lost. For example, in the early prenatal and mid prenatal period, the subplate is a highly water-rich compartment containing extracellular matrix, whereas the cortical plate and the deep grey matter are relatively cell dense (Kostović, 2020). We therefore predict that the tissue fraction would be higher in the deep grey matter and the cortical plate and lower in the subplate. We investigate this by characterising the entire trajectory of tissue composition changes between the thalamus and the cortex, to explore the role of transient fetal brain developmental structures on white matter maturational trajectories.

We acquired diffusion weighted imaging from 140 fetuses over a wide gestational age (GA) range (21–37 weeks) and use tractography to delineate five distinct thalamocortical pathways. To investigate whether the immature axonal bundles can be traced back to specific and distinct locations within thalamus, we parcellate the thalamus according to streamline connectivity (Behrens et al., 2003). We find consistent and distinct origins of different tracts, resembling the adult topology of thalamic nuclei (Toulmin et al., 2015; Behrens et al., 2003) as early as 23 weeks’ gestation. We then apply a multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) diffusion model (Jeurissen et al., 2014) and derive tissue and fluid fraction values, charting tract-specific maturational profiles over the second to third trimester. We overlay the tracts on an atlas of transitioning fetal compartments and correlate changes in the dMRI signal across time with critical neurodevelopmental processes, such as the dissolution of the subplate and lamination of the cortical plate. We demonstrate that along-tract sampling of diffusion metrics can capture temporal and compartmental differences in the second to third trimester, reflecting the maturing neurobiology of the fetal brain described in histological studies. With these methods, we provide a detailed, accurate reference of the unique developing microstructure in each tract that improves mechanistic insight about fibre maturation, bridging the gap between MRI and histology.

In this work, we studied in utero development of five distinct thalamocortical pathways using state-of-the-art dMRI methods and bespoke pre-processing pipeline (Christiaens et al., 2019a; Cordero-Grande et al., 2019; Hutter et al., 2018a; Pietsch et al., 2019; Wilson et al., 2021) in 140 fetuses aged 21–37 weeks’ gestation. We show that these pathways connect to distinct thalamic nuclei, which could be clearly defined at group level even at 23 weeks. To disentangle the impact of different neurobiological processes on diffusion metrics, we characterised the tissue composition profile along each of the thalamocortical tracts as they traverse the different developmental tissue layers of the fetal brain. We used MSMT-CSD to model this fetal DWI dataset because it does not mandate a specific set of b-values. The technique exploits the unique b-value dependencies of different tissue types, and so depends inherently on the characteristics of the tissue in the fetal brain. The distinct properties of different fetal compartments after the application of MSMT-CSD are highlighted by the ODFs (Appendix 1—figure 4). Readers are directed to Tournier et al., 2019, for a more comprehensive review of the optimisation process involved to select appropriate b-shell values for the acquisition.

We found that the spatiotemporal changes in the diffusion signal reflected known developmental processes that take place between the early, mid, and late prenatal period. The early period is characterised by higher tissue fractions in the middle of the tract, where there is a radial scaffold for migrating neurons. As this scaffold dissipates in the mid prenatal period, this is accompanied by a reduction in the tissue fraction in the middle of the tract, and an increase in tissue fraction towards the termination of the tracts as the neurons of the cortical plate mature. Finally in the late prenatal period, we observe the highest tissue fraction values at the start and end of the axis, as the pre-myelination phase of white matter development commences. This study demonstrates how the dMRI signal can be modelled to create in vivo spatiotemporal trajectories which relate to underlying neurobiological properties and are consistent with described trends from post-mortem histology (Kostović, 2020).

Early embryonic patterning of gene expression and cell division in the thalamus provide a template for specialised nuclei to emerge over the course of development, such that specific cells eventually occupy distinct locations within the thalamus (Clascá et al., 2012; Nakagawa, 2019, Kostovic and Goldman-Rakic, 1983). Rodent studies labelling the embryonic thalamus demonstrated that there is a characteristic topography of thalamic projections which roughly exists at their time of arrival; anterior to posterior movement along the convexity of the cortex is represented in a medial-to-lateral axis within the thalamus, while ventral-dorsal movement across the cortex is represented in an anterior-to-posterior axis within the thalamus (Molnár et al., 1998; Molnár et al., 2012). Thalamocortical tracts emerge over the same timescale as the thalamus parcellates and matures into its specialised group of nuclei (Clascá et al., 2012). Although the topography of thalamic nuclei and their cortical connectivity is acquired embryonically, no in vivo parcellation of the thalamus in the fetal brain has been published. Using tract density imaging, we observed that the cortical areas were connected to specific thalamic regions, organised in an anterior-posterior axis. This anterior-posterior representation of cortical connectivity in the thalamus was consistent across the second to third trimester and is in accordance with the topology of thalamic nuclei described in rodent studies and histology (Molnár and Blakemore, 1995; Molnár et al., 1998).

In addition, our fetal structural connectivity parcellation resembles the functionally derived thalamic parcellation in neonates, supporting the view that there is a strong association between structure and function in thalamocortical circuitry that begins early in life (Johansen-Berg et al., 2005; Toulmin et al., 2015; Alcauter et al., 2014). However, it is worth noting that this thalamic parcellation is dependent on streamline count through a voxel, and in the fetal brain streamlines are prone to spurious detection. This limitation is particularly relevant in the youngest fetuses, where we observe an extremely dense connectome (due to a fixed number of streamlines in a smaller brain) but there are very few coherent axonal bundles, so tracts might be overrepresented in the thalamic parcellation. The topography of thalamic nuclei is also not static during the embryonic and fetal period (Le Gros Clark, 1936), as pulvinar size increases and the dorsal lateral geniculate nucleus shifts its position from dorsolateral to ventromedial (Rakic, 1977; Mojsilović and Zecević, 1991). However, the image resolution of this study and the timespan in development over which this data was collected limit us from visualising these differences across age.

Recent studies characterising developing white matter pathways using human fetal MRI identified second-order polynomial maturational trends in diffusion metrics unique to this developmental period (Wilson et al., 2021; Machado-Rivas et al., 2021). Here, we replicated these methods with a different group of tracts and found the same U-shaped trends in thalamocortical white matter development. The inflection point at around 29–30 weeks was hypothesised to be the result of the dissipating radial glial scaffold followed by the pre-myelination phase of white matter development (Wilson et al., 2021; Machado-Rivas et al., 2021). The sensitivity of HARDI to radially organised structure in the fetal brain has been described by previous studies (Miyazaki et al., 2016; Takahashi et al., 2012; Xu et al., 2014) combining it with post-mortem tissue analysis to show that radially coherent diffusion signal corresponded to radial glial fibres in the early prenatal period, transitioning to cortico-cortical fibres around 30 weeks, coinciding with the appearance of astrocytes (Takahashi et al., 2012; Xu et al., 2014). However, with whole-tract average values, it is not possible to establish the precise effect of different neurodevelopmental processes on diffusion metrics across gestation.

To address this ambiguity, we characterised the entire trajectory of tissue composition changes between the thalamus and the cortex. We found that age-related changes in the tissue and fluid fraction along the tracts concurred with histological observations (Kostović and Judas, 2010). During the early prenatal period (22–25.5 GW), neuronal precursors migrate along the radial glial scaffold from proliferative zones to their destination in the cortical plate and thalamocortical axons accumulate in the superficial subplate, entering a ‘waiting phase’, forming transient synaptic connections (Ghosh et al., 1990; Kostovic and Rakic, 1984; Kostovic and Rakic, 1990). In terms of the diffusion signal, this strongly aligned microstructure of the radial glia is represented in our results by a higher tissue fraction in the transient compartments containing the most migratory cells (such as the VZ, IZ) (Kostović and Judas, 2010). Conversely, we observe the lowest tissue fraction in the early prenatal SP, which predominantly contains hydrophilic extracellular matrix, as demonstrated by rodent and non-human primate studies (Allendoerfer and Shatz, 1994; Miller et al., 2014; Molnár and Hoerder-Suabedissen, 2016).

By the mid prenatal period (26–31.5 weeks), we observe increased tissue fraction in the cortical plate, coinciding with the innervation of the cortical plate by thalamocortical axons, increasing soma volume and dendritic branching of CP neurons and CP synaptogenesis (Huttenlocher, 1979; Mrzljak et al., 1992; Huttenlocher and Dabholkar, 1997). We also observe increased tissue fraction in the SP zone in the mid prenatal period, consistent with histological observations of increased coherence of axonal fibres between cortical areas (Takahashi et al., 2012; Xu et al., 2014). The tissue fraction in the VZ and IZ decreases compared to the early prenatal period, corresponding to the timeframe when the radial glial scaffold dissipates (Kinney et al., 1988; Back et al., 2001; Haynes et al., 2005).

From the mid to late prenatal period, there is a marked increase in tissue fraction in last third of the axis between thalamus and cortex. By this point in development, the radial glia have converted into oligodendrocyte precursor cells which ensheath the axonal fibres to commence pre-myelination, enhancing the structural integrity of the fibre pathways (Back et al., 2001; Back et al., 2002; Haynes et al., 2005; Kinney et al., 1988; Kinney et al., 1994). A previous study in perinatal rabbits has shown that this oligodendrocyte lineage progression correlates with diffusion metrics (Drobyshevsky et al., 2005), suggesting it is likely to contribute to the increased tissue fraction we observe in the late prenatal period. The tissue fraction increase in the CP area is consistent in time with the lamination of the CP, the elaboration of thalamocortical terminals in layer IV, and a rapid growth of basal dendrites of layer III and V pyramidal neurons (Kostović and Jovanov-Milosević, 2006; Krsnik et al., 2017; Molliver et al., 1973). These high tissue fraction values at the origin and termination of the tracts suggest co-maturation between ascending and descending pathways between the thalamus and cortex to eventually form continuous, structurally mature fibre bundles. This concept was proposed in the 1990s by Blakemore and Molnar, termed the ‘handshake hypothesis’. They suggested that thalamocortical pathways ascending through the internal capsule project to their cortical targets with assistance from reciprocal descending cortical pathways (Molnár and Blakemore, 1995). We hypothesise that continuing this analysis over subsequent weeks into the neonatal period would lead to an increasing tissue fraction in the middle of the axis, as fibre bundles become more uniformly structurally mature and the subplate completely resolves (Kinney et al., 1988; Haynes et al., 2005; Kostović and Jovanov-Milosević, 2006).

We observed that tracts terminating superiorly (motor, sensory, and parietal) shared very similar trajectories in the early, mid, and late periods. However, the OR and the AT had more distinct trajectories. The microstructural change along the AT suggests increasing tissue fraction between the deep grey matter, VZ and IZ. We hypothesise that the high tissue fraction in the IZ is due to densely packed ascending and descending bundles within the anterior limb of the internal capsule (Emos et al., 2022). The precise timeline for outgrowth and intermingling of the thalamocortical and corticothalamic projections remains ambiguous, and appears to be different between rodents and non-human primates (Alzu’bi et al., 2019).

On the other hand, the OR traverses the deep parietal lobe along the border of the lateral ventricle and has smoother transitions in tissue fraction between the fetal compartments. This is likely due to the tract area running more parallel to the tissue interfaces. Another explanation for the regional differences in microstructural properties is the variation in subplate remnants. In the late prenatal trajectories, all tracts except the OR have a large dip in tissue fraction along the tract. In the primary visual cortex, the subplate disappears during the final weeks of gestation, whereas in the somatosensory cortex there are still subplate neurons present in term-born neonates (Kostovic and Rakic, 1990) and the subplate of the pre-frontal associative cortex gradually disappears over the 6 postnatal months. Therefore, the peaks of fluid fraction in the frontal and sensory trajectories might reflect the lasting presence of subplate in these areas (Kostović and Jovanov-Milosević, 2006; Kostovic and Rakic, 1990).

There are several important considerations to our work which may limit the interpretation of our findings. MRI has an inherently low signal:noise ratio as signal attenuation is deliberately introduced and EPI acquisitions are highly susceptible to motion and distortion artefacts. Furthermore, the biophysical properties of the fetal environment introduce a series of additional inter-dependent challenges for acquiring high-quality dMRI data (Christiaens et al., 2019a; Christiaens et al., 2019b, Tournier et al., 2020). In utero imaging methods must adapt and account for unpredictable, sometimes abrupt motion of fetus, in addition to the motion introduced by maternal breathing. Achieving even coverage of the magnetic coil is challenging due to varying fetal and maternal positions inside the scanner, introducing unique bias fields for every subject. The dHCP acquisition protocol and pre-processing pipelines have incorporated dynamic distortion and motion correction algorithms that were designed to specifically tackle the unique artefacts-associated imaging fetuses (Deprez et al., 2020; Cordero-Grande et al., 2018; Cordero-Grande et al., 2019; Christiaens et al., 2019a; Christiaens et al., 2019b; Hutter et al., 2018b; Price et al., 2019). However, even after the extensive correction procedures, there is still some residual motion and distortion in the data. For a more extensive and comprehensive review of challenges associated specifically with in utero diffusion imaging and constructive suggestions to address them, readers are directed to Christiaens et al., 2019b.

The conclusions that can be drawn from analysing the diffusion MR signal are limited more generally by the level of noise and lack of directionality in the signal, and therefore we are not able to distinguish between ascending and descending thalamic projections as they develop. The fibre-tracking process itself is also very sensitive to the residual motion and distortion in fetal data, producing highly variable results in terms of streamline count per tract. Therefore, the metrics generally used in this field to investigate fibre bundle morphology, such as streamline count, a proxy for ‘innervation density’, are not an accurate, quantitative measure (Calamante, 2019) and cannot be reliably used to assess fibre morphology. Consequently, we have just used tractography to delineate regions of interest and used the signal contrast in the diffusion maps to quantify microstructure.

The methods described allow the direct study of the maturational effects of the subplate and intermediate zones, which are known to represent critical substrates for early synaptogenesis and the spatial guidance of thalamocortical axons (Ghosh et al., 1990). Damage to this essential structural framework for developing cortical circuitry has been implicated in the origins of numerous developmental disorders and is suspected to underly the altered structural and functional connectivity of the thalamus in preterm infants (Back and Volpe, 1997; Volpe, 2001; Volpe, 2009; Hüppi et al., 2001; Counsell et al., 2003; Hadders-Algra et al., 2017; Mathur and Inder, 2009; Toulmin et al., 2015; Ball et al., 2012; Ball et al., 2015). It is therefore critical to use clinically relevant tools, such as in utero MRI, to relate the microstructural properties of these transient fetal compartments to neurobiological processes. This improves mechanistic insight about both healthy white matter maturation and the developmental origins of white matter pathologies.

With this study we explore the development of thalamocortical white matter by quantifying microstructure in the different layers of the fetal brain. Using diffusion metrics, we characterise the emergence of structural connectivity from the thalamus to spatially and functionally distinct cortical brain regions. We observe correlations between the transitioning tissue components and key neurobiological processes in white matter development. By providing a detailed normative reference of MR signal change during the second to third trimester, this will help future studies to identify if the tissue properties of specific compartments are affected by preterm birth or other perinatal injury. To this effect, all fetal MRI data is made available to the research community.

Developing Human Connectome project data is open-access and available for download following completion of a data-usage agreement via: http://www.developingconnectome.org/. Data is also available at: https://nda.nih.gov/edit_collection.html?id=3955.

1. 1. Uus A
   2. Kyriakopoulou V
   3. Cordero Grande L
   4. Pietsch M
   5. Price A
   6. Wilson S
   7. Patkee P
   8. Karolis V
   9. Schuch A
   10. Gartner A
   11. Williams L
   12. Hughes E
   13. Arichi T
   14. O'Muircheartaigh J
   15. Hutter J
   16. Robinson E
   17. Tournier JD
   18. Rueckert D
   19. Counsell SJ
   20. Rutherford MA
   21. Deprez M
   22. Hajnal JV
   23. Edwards AD

   (2023) G-Node Open Data

   Multi-channel spatio-temporal MRI atlas of normal fetal brain development (dHCP project).

   https://doi.org/10.12751/g-node.ysgsy1

1. 1. Gholipour A
   2. Rollins CK
   3. Velasco-Annis C
   4. Ouaalam A
   5. Akhondi-Asl A
   6. Afacan O
   7. Ortinau C
   8. Clancy S
   9. Limperopoulos C
   10. Yang E
   11. Estroff JA
   12. Warfield SK

   (2017) Harcard CRL/fetal brain atlas

   ID fetal_brain_atlas/. A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth.

   http://crl.med.harvard.edu/research/fetal_brain_atlas/

1. 1. Alcauter S
   2. Lin W
   3. Smith JK
   4. Short SJ
   5. Goldman BD
   6. Reznick JS
   7. Gilmore JH
   8. Gao W

   (2014) Development of thalamocortical connectivity during infancy and its cognitive correlations

   The Journal of Neuroscience 34:9067–9075.

   https://doi.org/10.1523/JNEUROSCI.0796-14.2014

   • PubMed
   • Google Scholar
2. 1. Allendoerfer KL
   2. Shatz CJ

   (1994) The subplate, a transient neocortical structure: Its role in the development of connections between thalamus and cortex

   Annual Review of Neuroscience 17:185–218.

   https://doi.org/10.1146/annurev.ne.17.030194.001153

   • PubMed
   • Google Scholar
3. 1. Alzu’bi A
   2. Homman-Ludiye J
   3. Bourne JA
   4. Clowry GJ

   (2019) Corrigendum: Thalamocortical afferents innervate the cortical subplate much earlier in development in primate than in rodent

   Cerebral Cortex 29:5316.

   https://doi.org/10.1093/cercor/bhz056

   • PubMed
   • Google Scholar
4. 1. Anticevic A
   2. Cole MW
   3. Repovs G
   4. Murray JD
   5. Brumbaugh MS
   6. Winkler AM
   7. Savic A
   8. Krystal JH
   9. Pearlson GD
   10. Glahn DC

   (2014) Characterizing thalamo-cortical disturbances in schizophrenia and bipolar illness

   Cerebral Cortex 24:3116–3130.

   https://doi.org/10.1093/cercor/bht165

   • PubMed
   • Google Scholar
5. 1. Avants BB
   2. Epstein CL
   3. Grossman M
   4. Gee JC

   (2008) Symmetric diffeomorphic image registration with cross-correlation: Evaluating automated labeling of elderly and neurodegenerative brain

   Medical Image Analysis 12:26–41.

   https://doi.org/10.1016/j.media.2007.06.004

   • PubMed
   • Google Scholar
6. 1. Back SA
   2. Volpe JJ

   (1997) Cellular and molecular pathogenesis of periventricular white matter injury

   Mental Retardation and Developmental Disabilities Research Reviews 3:96–107.

   https://doi.org/10.1002/(SICI)1098-2779(1997)3:1<96::AID-MRDD12>3.0.CO;2-M

   • Google Scholar
7. 1. Back SA
   2. Luo NL
   3. Borenstein NS
   4. Levine JM
   5. Volpe JJ
   6. Kinney HC

   (2001) Late oligodendrocyte progenitors coincide with the developmental window of vulnerability for human perinatal white matter injury

   The Journal of Neuroscience 21:1302–1312.

   https://doi.org/10.1523/JNEUROSCI.21-04-01302.2001

   • PubMed
   • Google Scholar
8. 1. Back SA
   2. Luo NL
   3. Borenstein NS
   4. Volpe JJ
   5. Kinney HC

   (2002) Arrested oligodendrocyte lineage progression during human cerebral white matter development: Dissociation between the timing of progenitor differentiation and myelinogenesis

   Journal of Neuropathology and Experimental Neurology 61:197–211.

   https://doi.org/10.1093/jnen/61.2.197

   • PubMed
   • Google Scholar
9. 1. Ball G
   2. Boardman JP
   3. Rueckert D
   4. Aljabar P
   5. Arichi T
   6. Merchant N
   7. Gousias IS
   8. Edwards AD
   9. Counsell SJ

   (2012) The effect of preterm birth on thalamic and cortical development

   Cerebral Cortex 22:1016–1024.

   https://doi.org/10.1093/cercor/bhr176

   • PubMed
   • Google Scholar
10. 1. Ball G
    2. Boardman JP
    3. Aljabar P
    4. Pandit A
    5. Arichi T
    6. Merchant N
    7. Rueckert D
    8. Edwards AD
    9. Counsell SJ

    (2013) The influence of preterm birth on the developing thalamocortical connectome

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 49:1711–1721.

    https://doi.org/10.1016/j.cortex.2012.07.006

    • PubMed
    • Google Scholar
11. 1. Ball G
    2. Pazderova L
    3. Chew A
    4. Tusor N
    5. Merchant N
    6. Arichi T
    7. Allsop JM
    8. Cowan FM
    9. Edwards AD
    10. Counsell SJ

    (2015) Thalamocortical connectivity predicts cognition in children born preterm

    Cerebral Cortex 25:4310–4318.

    https://doi.org/10.1093/cercor/bhu331

    • PubMed
    • Google Scholar
12. 1. Batalle D
    2. Hughes EJ
    3. Zhang H
    4. Tournier JD
    5. Tusor N
    6. Aljabar P
    7. Wali L
    8. Alexander DC
    9. Hajnal JV
    10. Nosarti C
    11. Edwards AD
    12. Counsell SJ

    (2017) Early development of structural networks and the impact of prematurity on brain connectivity

    NeuroImage 149:379–392.

    https://doi.org/10.1016/j.neuroimage.2017.01.065

    • PubMed
    • Google Scholar
13. 1. Behrens TEJ
    2. Johansen-Berg H
    3. Woolrich MW
    4. Smith SM
    5. Wheeler-Kingshott CAM
    6. Boulby PA
    7. Barker GJ
    8. Sillery EL
    9. Sheehan K
    10. Ciccarelli O
    11. Thompson AJ
    12. Brady JM
    13. Matthews PM

    (2003) Non-Invasive mapping of connections between human thalamus and cortex using diffusion imaging

    Nature Neuroscience 6:750–757.

    https://doi.org/10.1038/nn1075

    • PubMed
    • Google Scholar
14. 1. Brody BA
    2. Kinney HC
    3. Kloman AS
    4. Gilles FH

    (1987) Sequence of central nervous system myelination in human infancy. I. An autopsy study of myelination

    Journal of Neuropathology and Experimental Neurology 46:283–301.

    https://doi.org/10.1097/00005072-198705000-00005

    • PubMed
    • Google Scholar
15. 1. Bui T
    2. Daire JL
    3. Chalard F
    4. Zaccaria I
    5. Alberti C
    6. Elmaleh M
    7. Garel C
    8. Luton D
    9. Blanc N
    10. Sebag G

    (2006) Microstructural development of human brain assessed in utero by diffusion tensor imaging

    Pediatric Radiology 36:1133–1140.

    https://doi.org/10.1007/s00247-006-0266-3

    • PubMed
    • Google Scholar
16. 1. Calamante F
    2. Tournier JD
    3. Jackson GD
    4. Connelly A

    (2010) Track-density imaging (TDI): Super-resolution white matter imaging using whole-brain track-density mapping

    NeuroImage 53:1233–1243.

    https://doi.org/10.1016/j.neuroimage.2010.07.024

    • PubMed
    • Google Scholar
17. 1. Calamante F

    (2019) The seven deadly SINS of measuring brain structural connectivity using diffusion MRI streamlines fibre-tracking

    Diagnostics 9:115.

    https://doi.org/10.3390/diagnostics9030115

    • PubMed
    • Google Scholar
18. Conference

    1. Christiaens D
    2. Cordero-Grande L
    3. Price AN
    4. Hutter J
    5. Hughes E
    6. Counsell SJ
    7. Tournier JD
    8. Hajnal JV

    (2019a)

    Fetal diffusion MRI acquisition and analysis in the developing Human Connectome Project

    ISMRM Annual Meeting & Exhibition.

    • Google Scholar
19. 1. Christiaens D
    2. Slator PJ
    3. Cordero-Grande L
    4. Price AN
    5. Deprez M
    6. Alexander DC
    7. Rutherford M
    8. Hajnal JV
    9. Hutter J

    (2019b) In utero diffusion MRI: Challenges, advances, and applications

    Topics in Magnetic Resonance Imaging 28:255–264.

    https://doi.org/10.1097/RMR.0000000000000211

    • PubMed
    • Google Scholar
20. 1. Christiaens D
    2. Cordero-Grande L
    3. Pietsch M
    4. Hutter J
    5. Price AN
    6. Hughes EJ
    7. Vecchiato K
    8. Deprez M
    9. Edwards AD
    10. Hajnal JV
    11. Tournier JD

    (2021) Scattered slice SHARD reconstruction for motion correction in multi-shell diffusion MRI

    NeuroImage 225:117437.

    https://doi.org/10.1016/j.neuroimage.2020.117437

    • PubMed
    • Google Scholar
21. 1. Clascá F
    2. Rubio-Garrido P
    3. Jabaudon D

    (2012) Unveiling the diversity of thalamocortical neuron subtypes

    European Journal of Neuroscience 35:1524–1532.

    https://doi.org/10.1111/j.1460-9568.2012.08033.x

    • PubMed
    • Google Scholar
22. Conference

    1. Cordero-Grande L
    2. Price AN
    3. Ferrazzi G
    4. Hutter J
    5. Christiaens D
    6. Hughes E
    7. Hajnal JV

    (2018)

    Spin And Field Echo (SAFE) dynamic field correction in 3T fetal EPI

    Joint Annual Meeting ISMRM-ESMRMB.

    • Google Scholar
23. 1. Cordero-Grande L
    2. Christiaens D
    3. Hutter J
    4. Price AN
    5. Hajnal JV

    (2019) Complex diffusion-weighted image estimation via matrix recovery under general noise models

    NeuroImage 200:391–404.

    https://doi.org/10.1016/j.neuroimage.2019.06.039

    • PubMed
    • Google Scholar
24. 1. Counsell SJ
    2. Allsop JM
    3. Harrison MC
    4. Larkman DJ
    5. Kennea NL
    6. Kapellou O
    7. Cowan FM
    8. Hajnal JV
    9. Edwards AD
    10. Rutherford MA

    (2003) Diffusion-Weighted imaging of the brain in preterm infants with focal and diffuse white matter abnormality

    Pediatrics 112:1–7.

    https://doi.org/10.1542/peds.112.1.1

    • PubMed
    • Google Scholar
25. 1. Deprez M
    2. Price A
    3. Christiaens D
    4. Lockwood Estrin G
    5. Cordero-Grande L
    6. Hutter J
    7. Daducci A
    8. Tournier JD
    9. Rutherford M
    10. Counsell SJ
    11. Cuadra MB
    12. Hajnal JV

    (2020) Higher order spherical harmonics reconstruction of fetal diffusion MRI with intensity correction

    IEEE Transactions on Medical Imaging 39:1104–1113.

    https://doi.org/10.1109/TMI.2019.2943565

    • PubMed
    • Google Scholar
26. 1. Drobyshevsky A
    2. Song S-K
    3. Gamkrelidze G
    4. Wyrwicz AM
    5. Derrick M
    6. Meng F
    7. Li L
    8. Ji X
    9. Trommer B
    10. Beardsley DJ
    11. Luo NL
    12. Back SA
    13. Tan S

    (2005) Developmental changes in diffusion anisotropy coincide with immature oligodendrocyte progression and maturation of compound action potential

    The Journal of Neuroscience 25:5988–5997.

    https://doi.org/10.1523/JNEUROSCI.4983-04.2005

    • PubMed
    • Google Scholar
27. Book

    1. Emos MC
    2. Khan Suheb MZ
    3. Agarwal S

    (2022)

    Neuroanatomy, Internal Capsule

    StatPearls Publishing.

    • Google Scholar
28. 1. Ghiglia DC
    2. Romero LA

    (1994) Robust two-dimensional weighted and unweighted phase unwrapping that uses fast transforms and iterative methods

    Journal of the Optical Society of America A 11:107.

    https://doi.org/10.1364/JOSAA.11.000107

    • Google Scholar
29. 1. Gholipour A
    2. Rollins CK
    3. Velasco-Annis C
    4. Ouaalam A
    5. Akhondi-Asl A
    6. Afacan O
    7. Ortinau CM
    8. Clancy S
    9. Limperopoulos C
    10. Yang E
    11. Estroff JA
    12. Warfield SK

    (2017) A normative spatiotemporal MRI atlas of the fetal brain for automatic segmentation and analysis of early brain growth

    Scientific Reports 7:476.

    https://doi.org/10.1038/s41598-017-00525-w

    • PubMed
    • Google Scholar
30. 1. Ghosh A
    2. Antonini A
    3. McConnell SK
    4. Shatz CJ

    (1990) Requirement for subplate neurons in the formation of thalamocortical connections

    Nature 347:179–181.

    https://doi.org/10.1038/347179a0

    • PubMed
    • Google Scholar
31. 1. Hadders-Algra M
    2. Boxum AG
    3. Hielkema T
    4. Hamer EG

    (2017) Effect of early intervention in infants at very high risk of cerebral palsy: a systematic review

    Developmental Medicine and Child Neurology 59:246–258.

    https://doi.org/10.1111/dmcn.13331

    • PubMed
    • Google Scholar
32. 1. Haynes RL
    2. Borenstein NS
    3. Desilva TM
    4. Folkerth RD
    5. Liu LG
    6. Volpe JJ
    7. Kinney HC

    (2005) Axonal development in the cerebral white matter of the human fetus and infant

    The Journal of Comparative Neurology 484:156–167.

    https://doi.org/10.1002/cne.20453

    • PubMed
    • Google Scholar
33. 1. Hüppi PS
    2. Murphy B
    3. Maier SE
    4. Zientara GP
    5. Inder TE
    6. Barnes PD
    7. Kikinis R
    8. Jolesz FA
    9. Volpe JJ

    (2001) Microstructural brain development after perinatal cerebral white matter injury assessed by diffusion tensor magnetic resonance imaging

    Pediatrics 107:455–460.

    https://doi.org/10.1542/peds.107.3.455

    • PubMed
    • Google Scholar
34. 1. Huttenlocher PR

    (1979) Synaptic density in human frontal cortex-developmental changes and effects of aging

    Brain Research 163:195–205.

    https://doi.org/10.1016/0006-8993(79)90349-4

    • PubMed
    • Google Scholar
35. 1. Huttenlocher PR
    2. Dabholkar AS

    (1997) Regional differences in synaptogenesis in human cerebral cortex

    The Journal of Comparative Neurology 387:167–178.

    https://doi.org/10.1002/(sici)1096-9861(19971020)387:2<167::aid-cne1>3.0.co;2-z

    • PubMed
    • Google Scholar
36. 1. Hutter J
    2. Christiaens DJ
    3. Schneider T
    4. Cordero-Grande L
    5. Slator PJ
    6. Deprez M
    7. Price AN
    8. Tournier J-D
    9. Rutherford M
    10. Hajnal JV

    (2018a) Slice-level diffusion encoding for motion and distortion correction

    Medical Image Analysis 48:214–229.

    https://doi.org/10.1016/j.media.2018.06.008

    • PubMed
    • Google Scholar
37. 1. Hutter J
    2. Slator PJ
    3. Christiaens D
    4. Teixeira RPAG
    5. Roberts T
    6. Jackson L
    7. Price AN
    8. Malik S
    9. Hajnal JV

    (2018b) Integrated and efficient diffusion-relaxometry using zebra

    Scientific Reports 8:15138.

    https://doi.org/10.1038/s41598-018-33463-2

    • PubMed
    • Google Scholar
38. 1. Jaimes C
    2. Machado-Rivas F
    3. Afacan O
    4. Khan S
    5. Marami B
    6. Ortinau CM
    7. Rollins CK
    8. Velasco-Annis C
    9. Warfield SK
    10. Gholipour A

    (2020) In vivo characterization of emerging white matter microstructure in the fetal brain in the third trimester

    Human Brain Mapping 41:3177–3185.

    https://doi.org/10.1002/hbm.25006

    • PubMed
    • Google Scholar
39. 1. Jakab A
    2. Pogledic I
    3. Schwartz E
    4. Gruber G
    5. Mitter C
    6. Brugger PC
    7. Langs G
    8. Schöpf V
    9. Kasprian G
    10. Prayer D

    (2015) Fetal cerebral magnetic resonance imaging beyond morphology

    Seminars in Ultrasound, CT, and MR 36:465–475.

    https://doi.org/10.1053/j.sult.2015.06.003

    • PubMed
    • Google Scholar
40. 1. Jenkinson M
    2. Bannister P
    3. Brady M
    4. Smith S

    (2002) Improved optimization for the robust and accurate linear registration and motion correction of brain images

    NeuroImage 17:825–841.

    https://doi.org/10.1016/s1053-8119(02)91132-8

    • PubMed
    • Google Scholar
41. 1. Jenkinson M
    2. Beckmann CF
    3. Behrens TEJ
    4. Woolrich MW
    5. Smith SM

    (2012) FSL

    NeuroImage 62:782–790.

    https://doi.org/10.1016/j.neuroimage.2011.09.015

    • PubMed
    • Google Scholar
42. 1. Jeurissen B
    2. Tournier JD
    3. Dhollander T
    4. Connelly A
    5. Sijbers J

    (2014) Multi-tissue constrained spherical deconvolution for improved analysis of multi-shell diffusion MRI data

    NeuroImage 103:411–426.

    https://doi.org/10.1016/j.neuroimage.2014.07.061

    • PubMed
    • Google Scholar
43. 1. Johansen-Berg H
    2. Behrens TEJ
    3. Sillery E
    4. Ciccarelli O
    5. Thompson AJ
    6. Smith SM
    7. Matthews PM

    (2005) Functional-anatomical validation and individual variation of diffusion tractography-based segmentation of the human thalamus

    Cerebral Cortex 15:31–39.

    https://doi.org/10.1093/cercor/bhh105

    • PubMed
    • Google Scholar
44. Book

    1. Jones EG

    (2007)

    The thalamus (2nd ed)

    Cambridge University Press.

    • Google Scholar
45. 1. Keunen K
    2. van der Burgh HK
    3. de Reus MA
    4. Moeskops P
    5. Schmidt R
    6. Stolwijk LJ
    7. de Lange SC
    8. Išgum I
    9. de Vries LS
    10. Benders MJ
    11. van den Heuvel MP

    (2018) Early human brain development: Insights into macroscale connectome wiring

    Pediatric Research 84:829–836.

    https://doi.org/10.1038/s41390-018-0138-1

    • PubMed
    • Google Scholar
46. 1. Khan S
    2. Vasung L
    3. Marami B
    4. Rollins CK
    5. Afacan O
    6. Ortinau CM
    7. Yang E
    8. Warfield SK
    9. Gholipour A

    (2019) Fetal brain growth portrayed by a spatiotemporal diffusion tensor MRI atlas computed from in utero images

    NeuroImage 185:593–608.

    https://doi.org/10.1016/j.neuroimage.2018.08.030

    • PubMed
    • Google Scholar
47. 1. Kinney HC
    2. Brody BA
    3. Kloman AS
    4. Gilles FH

    (1988) Sequence of central nervous system myelination in human infancy. II. Patterns of myelination in autopsied infants

    Journal of Neuropathology and Experimental Neurology 47:217–234.

    https://doi.org/10.1097/00005072-198805000-00003

    • PubMed
    • Google Scholar
48. 1. Kinney HC
    2. Karthigasan J
    3. Borenshteyn NI
    4. Flax JD
    5. Kirschner DA

    (1994) Myelination in the developing human brain: Biochemical correlates

    Neurochemical Research 19:983–996.

    https://doi.org/10.1007/BF00968708

    • PubMed
    • Google Scholar
49. 1. Klingner CM
    2. Langbein K
    3. Dietzek M
    4. Smesny S
    5. Witte OW
    6. Sauer H
    7. Nenadic I

    (2014) Thalamocortical connectivity during resting state in schizophrenia

    European Archives of Psychiatry and Clinical Neuroscience 264:111–119.

    https://doi.org/10.1007/s00406-013-0417-0

    • PubMed
    • Google Scholar
50. 1. Kostovic I
    2. Goldman-Rakic PS

    (1983) Transient cholinesterase staining in the Mediodorsal nucleus of the thalamus and its connections in the developing human and monkey brain

    The Journal of Comparative Neurology 219:431–447.

    https://doi.org/10.1002/cne.902190405

    • PubMed
    • Google Scholar
51. 1. Kostovic I
    2. Rakic P

    (1984) Development of prestriate visual projections in the monkey and human fetal cerebrum revealed by transient cholinesterase staining

    The Journal of Neuroscience 4:25–42.

    https://doi.org/10.1523/JNEUROSCI.04-01-00025.1984

    • PubMed
    • Google Scholar
52. 1. Kostovic I
    2. Rakic P

    (1990) Developmental history of the transient subplate zone in the visual and somatosensory cortex of the macaque monkey and human brain

    The Journal of Comparative Neurology 297:441–470.

    https://doi.org/10.1002/cne.902970309

    • PubMed
    • Google Scholar
53. 1. Kostović I
    2. Judas M
    3. Rados M
    4. Hrabac P

    (2002) Laminar organization of the human fetal cerebrum revealed by histochemical markers and magnetic resonance imaging

    Cerebral Cortex 12:536–544.

    https://doi.org/10.1093/cercor/12.5.536

    • PubMed
    • Google Scholar
54. 1. Kostović I.
    2. Jovanov-Milosević N

    (2006) The development of cerebral connections during the first 20-45 weeks’ gestation

    Seminars in Fetal & Neonatal Medicine 11:415–422.

    https://doi.org/10.1016/j.siny.2006.07.001

    • PubMed
    • Google Scholar
55. 1. Kostovic I
    2. Judas M

    (2006) Prolonged coexistence of transient and permanent circuitry elements in the developing cerebral cortex of fetuses and preterm infants

    Developmental Medicine and Child Neurology 48:388–393.

    https://doi.org/10.1017/S0012162206000831

    • PubMed
    • Google Scholar
56. 1. Kostović I
    2. Judas M

    (2010) The development of the subplate and thalamocortical connections in the human foetal brain

    Acta Paediatrica 99:1119–1127.

    https://doi.org/10.1111/j.1651-2227.2010.01811.x

    • PubMed
    • Google Scholar
57. 1. Kostović I

    (2020) The enigmatic fetal subplate compartment forms an early tangential cortical nexus and provides the framework for construction of cortical connectivity

    Progress in Neurobiology 194:101883.

    https://doi.org/10.1016/j.pneurobio.2020.101883

    • PubMed
    • Google Scholar
58. 1. Krsnik Ž
    2. Majić V
    3. Vasung L
    4. Huang H
    5. Kostović I

    (2017) Growth of thalamocortical fibers to the somatosensory cortex in the human fetal brain

    Frontiers in Neuroscience 11:233.

    https://doi.org/10.3389/fnins.2017.00233

    • PubMed
    • Google Scholar
59. 1. Le Gros Clark WE

    (1936)

    The topography and homologies of the hypothalamic nuclei in man

    Journal of Anatomy 70:203–214.

    • Google Scholar
60. 1. Machado-Rivas F
    2. Afacan O
    3. Khan S
    4. Marami B
    5. Velasco-Annis C
    6. Lidov H
    7. Warfield SK
    8. Gholipour A
    9. Jaimes C

    (2021) Spatiotemporal changes in diffusivity and anisotropy in fetal brain tractography

    Human Brain Mapping 42:5771–5784.

    https://doi.org/10.1002/hbm.25653

    • PubMed
    • Google Scholar
61. 1. Marenco S
    2. Stein JL
    3. Savostyanova AA
    4. Sambataro F
    5. Tan HY
    6. Goldman AL
    7. Verchinski BA
    8. Barnett AS
    9. Dickinson D
    10. Apud JA
    11. Callicott JH
    12. Meyer-Lindenberg A
    13. Weinberger DR

    (2012) Investigation of anatomical thalamo-cortical connectivity and fMRI activation in schizophrenia

    Neuropsychopharmacology 37:499–507.

    https://doi.org/10.1038/npp.2011.215

    • PubMed
    • Google Scholar
62. 1. Mathur A
    2. Inder T

    (2009) Magnetic resonance imaging -- insights into brain injury and outcomes in premature infants

    Journal of Communication Disorders 42:248–255.

    https://doi.org/10.1016/j.jcomdis.2009.03.007

    • PubMed
    • Google Scholar
63. 1. Miller JA
    2. Ding S-L
    3. Sunkin SM
    4. Smith KA
    5. Ng L
    6. Szafer A
    7. Ebbert A
    8. Riley ZL
    9. Royall JJ
    10. Aiona K
    11. Arnold JM
    12. Bennet C
    13. Bertagnolli D
    14. Brouner K
    15. Butler S
    16. Caldejon S
    17. Carey A
    18. Cuhaciyan C
    19. Dalley RA
    20. Dee N
    21. Dolbeare TA
    22. Facer BAC
    23. Feng D
    24. Fliss TP
    25. Gee G
    26. Goldy J
    27. Gourley L
    28. Gregor BW
    29. Gu G
    30. Howard RE
    31. Jochim JM
    32. Kuan CL
    33. Lau C
    34. Lee C-K
    35. Lee F
    36. Lemon TA
    37. Lesnar P
    38. McMurray B
    39. Mastan N
    40. Mosqueda N
    41. Naluai-Cecchini T
    42. Ngo N-K
    43. Nyhus J
    44. Oldre A
    45. Olson E
    46. Parente J
    47. Parker PD
    48. Parry SE
    49. Stevens A
    50. Pletikos M
    51. Reding M
    52. Roll K
    53. Sandman D
    54. Sarreal M
    55. Shapouri S
    56. Shapovalova NV
    57. Shen EH
    58. Sjoquist N
    59. Slaughterbeck CR
    60. Smith M
    61. Sodt AJ
    62. Williams D
    63. Zöllei L
    64. Fischl B
    65. Gerstein MB
    66. Geschwind DH
    67. Glass IA
    68. Hawrylycz MJ
    69. Hevner RF
    70. Huang H
    71. Jones AR
    72. Knowles JA
    73. Levitt P
    74. Phillips JW
    75. Sestan N
    76. Wohnoutka P
    77. Dang C
    78. Bernard A
    79. Hohmann JG
    80. Lein ES

    (2014) Transcriptional landscape of the prenatal human brain

    Nature 508:199–206.

    https://doi.org/10.1038/nature13185

    • PubMed
    • Google Scholar
64. 1. Miyazaki Y
    2. Song JW
    3. Takahashi E

    (2016) Asymmetry of radial and symmetry of tangential neuronal migration pathways in developing human fetal brains

    Frontiers in Neuroanatomy 10:2.

    https://doi.org/10.3389/fnana.2016.00002

    • PubMed
    • Google Scholar
65. 1. Mojsilović J
    2. Zecević N

    (1991) Early development of the human thalamus: Golgi and nissl study

    Early Human Development 27:119–144.

    https://doi.org/10.1016/0378-3782(91)90033-y

    • PubMed
    • Google Scholar
66. 1. Molliver ME
    2. Kostović I
    3. van der Loos H

    (1973) The development of synapses in cerebral cortex of the human fetus

    Brain Research 50:403–407.

    https://doi.org/10.1016/0006-8993(73)90741-5

    • PubMed
    • Google Scholar
67. 1. Molnár Z
    2. Blakemore C

    (1995) How do thalamic axons find their way to the cortex?

    Trends in Neurosciences 18:389–397.

    https://doi.org/10.1016/0166-2236(95)93935-q

    • PubMed
    • Google Scholar
68. 1. Molnár Z
    2. Adams R
    3. Blakemore C

    (1998) Mechanisms underlying the early establishment of thalamocortical connections in the rat

    The Journal of Neuroscience 18:5723–5745.

    https://doi.org/10.1523/JNEUROSCI.18-15-05723.1998

    • PubMed
    • Google Scholar
69. 1. Molnár Z
    2. Garel S
    3. López-Bendito G
    4. Maness P
    5. Price DJ

    (2012) Mechanisms controlling the guidance of thalamocortical axons through the embryonic forebrain

    The European Journal of Neuroscience 35:1573–1585.

    https://doi.org/10.1111/j.1460-9568.2012.08119.x

    • PubMed
    • Google Scholar
70. 1. Molnár Z
    2. Hoerder-Suabedissen A

    (2016) Regional scattering of primate subplate

    PNAS 113:9676–9678.

    https://doi.org/10.1073/pnas.1611194113

    • PubMed
    • Google Scholar
71. 1. Morel A
    2. Magnin M
    3. Jeanmonod D

    (1997) Multiarchitectonic and stereotactic atlas of the human thalamus

    The Journal of Comparative Neurology 387:588–630.

    https://doi.org/10.1002/(sici)1096-9861(19971103)387:4<588::aid-cne8>3.0.co;2-z

    • PubMed
    • Google Scholar
72. 1. Mrzljak L
    2. Uylings HB
    3. Kostovic I
    4. van Eden CG

    (1992) Prenatal development of neurons in the human prefrontal cortex. II. A quantitative Golgi study

    The Journal of Comparative Neurology 316:485–496.

    https://doi.org/10.1002/cne.903160408

    • PubMed
    • Google Scholar
73. 1. Nair A
    2. Treiber JM
    3. Shukla DK
    4. Shih P
    5. Müller RA

    (2013) Impaired thalamocortical connectivity in autism spectrum disorder: A study of functional and anatomical connectivity

    Brain 136:1942–1955.

    https://doi.org/10.1093/brain/awt079

    • PubMed
    • Google Scholar
74. 1. Najdenovska E
    2. Alemán-Gómez Y
    3. Battistella G
    4. Descoteaux M
    5. Hagmann P
    6. Jacquemont S
    7. Maeder P
    8. Thiran JP
    9. Fornari E
    10. Bach Cuadra M

    (2018) In-vivo probabilistic atlas of human thalamic nuclei based on diffusion- weighted magnetic resonance imaging

    Scientific Data 5:180270.

    https://doi.org/10.1038/sdata.2018.270

    • PubMed
    • Google Scholar
75. 1. Nakagawa Y

    (2019) Development of the thalamus: From early patterning to regulation of cortical functions

    Wiley Interdisciplinary Reviews. Developmental Biology 8:e345.

    https://doi.org/10.1002/wdev.345

    • PubMed
    • Google Scholar
76. 1. Niemann K
    2. Mennicken VR
    3. Jeanmonod D
    4. Morel A

    (2000) The morel stereotactic atlas of the human thalamus: Atlas-to-MR registration of internally consistent canonical model

    NeuroImage 12:601–616.

    https://doi.org/10.1006/nimg.2000.0650

    • PubMed
    • Google Scholar
77. 1. Nosarti C
    2. Nam KW
    3. Walshe M
    4. Murray RM
    5. Cuddy M
    6. Rifkin L
    7. Allin MPG

    (2014) Preterm birth and structural brain alterations in early adulthood

    NeuroImage. Clinical 6:180–191.

    https://doi.org/10.1016/j.nicl.2014.08.005

    • PubMed
    • Google Scholar
78. Book

    1. Pietsch M

    (2018)

    Advanced Diffusion MRI Analysis Methods for Neonatal Imaging

    King’s College.

    • Google Scholar
79. 1. Pietsch M
    2. Christiaens D
    3. Hutter J
    4. Cordero-Grande L
    5. Price AN
    6. Hughes E
    7. Edwards AD
    8. Hajnal JV
    9. Counsell SJ
    10. Tournier JD

    (2019) A framework for multi-component analysis of diffusion MRI data over the neonatal period

    NeuroImage 186:321–337.

    https://doi.org/10.1016/j.neuroimage.2018.10.060

    • PubMed
    • Google Scholar
80. 1. Price DJ
    2. Kennedy H
    3. Dehay C
    4. Zhou L
    5. Mercier M
    6. Jossin Y
    7. Goffinet AM
    8. Tissir F
    9. Blakey D
    10. Molnár Z

    (2006) The development of cortical connections

    The European Journal of Neuroscience 23:910–920.

    https://doi.org/10.1111/j.1460-9568.2006.04620.x

    • PubMed
    • Google Scholar
81. Conference

    1. Price AN
    2. Cordero-Grande L
    3. Hughes E
    4. Hiscocks S
    5. Green E
    6. McCabe L
    7. Hutter J
    8. Ferrazzi G
    9. Deprez M
    10. Roberts T
    11. Christiaens D
    12. Duff E
    13. Karolis V
    14. Malik S
    15. Rutherford M
    16. Edwards AD
    17. Hajnal JV

    (2019)

    The developing Human Connectome Project (dHCP): fetal acquisition protocol

    ISMRM Annual Meeting & Exhibition.

    • Google Scholar
82. 1. Raffelt D
    2. Tournier JD
    3. Fripp J
    4. Crozier S
    5. Connelly A
    6. Salvado O

    (2011) Symmetric diffeomorphic registration of fibre orientation distributions

    NeuroImage 56:1171–1180.

    https://doi.org/10.1016/j.neuroimage.2011.02.014

    • PubMed
    • Google Scholar
83. 1. Rakic P

    (1977) Prenatal development of the visual system in rhesus monkey

    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 278:245–260.

    https://doi.org/10.1098/rstb.1977.0040

    • PubMed
    • Google Scholar
84. 1. Rakic P

    (2003) Elusive radial glial cells: historical and evolutionary perspective

    Glia 43:19–32.

    https://doi.org/10.1002/glia.10244

    • PubMed
    • Google Scholar
85. 1. Raybaud C
    2. Ahmad T
    3. Rastegar N
    4. Shroff M
    5. Al Nassar M

    (2013) The premature brain: developmental and lesional anatomy

    Neuroradiology 55 Suppl 2:23–40.

    https://doi.org/10.1007/s00234-013-1231-0

    • PubMed
    • Google Scholar
86. 1. Schummers J
    2. Sharma J
    3. Sur M

    (2005) Bottom-up and top-down dynamics in visual cortex

    Progress in Brain Research 149:65–81.

    https://doi.org/10.1016/S0079-6123(05)49006-8

    • PubMed
    • Google Scholar
87. 1. Sharma J
    2. Angelucci A
    3. Sur M

    (2000) Induction of visual orientation modules in auditory cortex

    Nature 404:841–847.

    https://doi.org/10.1038/35009043

    • PubMed
    • Google Scholar
88. 1. Smith RE
    2. Tournier JD
    3. Calamante F
    4. Connelly A

    (2012) Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information

    NeuroImage 62:1924–1938.

    https://doi.org/10.1016/j.neuroimage.2012.06.005

    • PubMed
    • Google Scholar
89. 1. Smith RE
    2. Tournier JD
    3. Calamante F
    4. Connelly A

    (2013) SIFT: spherical-deconvolution informed filtering of tractograms

    NeuroImage 67:298–312.

    https://doi.org/10.1016/j.neuroimage.2012.11.049

    • PubMed
    • Google Scholar
90. 1. Sur M
    2. Rubenstein JLR

    (2005) Patterning and plasticity of the cerebral cortex

    Science 310:805–810.

    https://doi.org/10.1126/science.1112070

    • PubMed
    • Google Scholar
91. 1. Takahashi E
    2. Folkerth RD
    3. Galaburda AM
    4. Grant PE

    (2012) Emerging cerebral connectivity in the human fetal brain: an mr tractography study

    Cerebral Cortex 22:455–464.

    https://doi.org/10.1093/cercor/bhr126

    • PubMed
    • Google Scholar
92. 1. Toulmin H
    2. Beckmann CF
    3. O’Muircheartaigh J
    4. Ball G
    5. Nongena P
    6. Makropoulos A
    7. Ederies A
    8. Counsell SJ
    9. Kennea N
    10. Arichi T
    11. Tusor N
    12. Rutherford MA
    13. Azzopardi D
    14. Gonzalez-Cinca N
    15. Hajnal JV
    16. Edwards AD

    (2015) Specialization and integration of functional thalamocortical connectivity in the human infant

    PNAS 112:6485–6490.

    https://doi.org/10.1073/pnas.1422638112

    • PubMed
    • Google Scholar
93. 1. Toulmin H
    2. O’Muircheartaigh J
    3. Counsell SJ
    4. Falconer S
    5. Chew A
    6. Beckmann CF
    7. Edwards AD

    (2021) Functional thalamocortical connectivity at term equivalent age and outcome at 2 years in infants born preterm

    Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 135:17–29.

    https://doi.org/10.1016/j.cortex.2020.09.022

    • PubMed
    • Google Scholar
94. 1. Tournier JD
    2. Calamante F
    3. Connelly A

    (2013) Determination of the appropriate B value and number of gradient directions for high-angular-resolution diffusion-weighted imaging

    NMR in Biomedicine 26:1775–1786.

    https://doi.org/10.1002/nbm.3017

    • PubMed
    • Google Scholar
95. 1. Tournier JD
    2. Smith R
    3. Raffelt D
    4. Tabbara R
    5. Dhollander T
    6. Pietsch M
    7. Christiaens D
    8. Jeurissen B
    9. Yeh CH
    10. Connelly A

    (2019) MRtrix3: a fast, flexible and open software framework for medical image processing and visualisation

    NeuroImage 202:116137.

    https://doi.org/10.1016/j.neuroimage.2019.116137

    • PubMed
    • Google Scholar
96. 1. Tournier J-D
    2. Christiaens D
    3. Hutter J
    4. Price AN
    5. Cordero-Grande L
    6. Hughes E
    7. Bastiani M
    8. Sotiropoulos SN
    9. Smith SM
    10. Rueckert D
    11. Counsell SJ
    12. Edwards AD
    13. Hajnal JV

    (2020) A data-driven approach to optimising the encoding for multi-shell diffusion MRI with application to neonatal imaging

    NMR in Biomedicine 33:e4348.

    https://doi.org/10.1002/nbm.4348

    • PubMed
    • Google Scholar
97. 1. Volpe JJ

    (2001) Neurobiology of periventricular leukomalacia in the premature infant

    Pediatric Research 50:553–562.

    https://doi.org/10.1203/00006450-200111000-00003

    • PubMed
    • Google Scholar
98. 1. Volpe JJ

    (2009) The encephalopathy of prematurity -- brain injury and impaired brain development inextricably intertwined

    Seminars in Pediatric Neurology 16:167–178.

    https://doi.org/10.1016/j.spen.2009.09.005

    • PubMed
    • Google Scholar
99. 1. Wilkinson M
    2. Kane T
    3. Wang R
    4. Takahashi E

    (2017) Migration pathways of thalamic neurons and development of thalamocortical connections in humans revealed by diffusion mr tractography

    Cerebral Cortex 27:5683–5695.

    https://doi.org/10.1093/cercor/bhw339

    • PubMed
    • Google Scholar
100. 1. Wilson S
     2. Pietsch M
     3. Cordero-Grande L
     4. Price AN
     5. Hutter J
     6. Xiao J
     7. McCabe L
     8. Rutherford MA
     9. Hughes EJ
     10. Counsell SJ
     11. Tournier J-D
     12. Arichi T
     13. Hajnal JV
     14. Edwards AD
     15. Christiaens D
     16. O’Muircheartaigh J

     (2021) Development of human white matter pathways in utero over the second and third trimester

     PNAS 118:e2023598118.

     https://doi.org/10.1073/pnas.2023598118

     • PubMed
     • Google Scholar
101. 1. Wimberger DM
     2. Roberts TP
     3. Barkovich AJ
     4. Prayer LM
     5. Moseley ME
     6. Kucharczyk J

     (1995) Identification of “premyelination” by diffusion-weighted MRI

     Journal of Computer Assisted Tomography 19:28–33.

     https://doi.org/10.1097/00004728-199501000-00005

     • PubMed
     • Google Scholar
102. 1. Xu G
     2. Takahashi E
     3. Folkerth RD
     4. Haynes RL
     5. Volpe JJ
     6. Grant PE
     7. Kinney HC

     (2014) Radial coherence of diffusion tractography in the cerebral white matter of the human fetus: neuroanatomic insights

     Cerebral Cortex 24:579–592.

     https://doi.org/10.1093/cercor/bhs330

     • PubMed
     • Google Scholar
103. 1. Yakovlev PI
     2. Locke S
     3. Koskoff DY
     4. Patton RA

     (1960) Limbic nuclei of thalamus and connections of limbic cortex

     Archives of Neurology 3:620–641.

     https://doi.org/10.1001/archneur.1960.00450060008002

     • PubMed
     • Google Scholar
104. 1. Zanin E
     2. Ranjeva J-P
     3. Confort-Gouny S
     4. Guye M
     5. Denis D
     6. Cozzone PJ
     7. Girard N

     (2011) White matter maturation of normal human fetal brain. An in vivo diffusion tensor tractography study

     Brain and Behavior 1:95–108.

     https://doi.org/10.1002/brb3.17

     • PubMed
     • Google Scholar

Decision letter after peer review:

Thank you for submitting your article "Spatiotemporal tissue maturation of thalamocortical pathways in the human fetal brain" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, including involved in the review of your submission have agreed to reveal their identity: Finnegan as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Jonathan Roiser as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: Cassandra Hendrix (Reviewer #2); Zoltan Molnar (Reviewer #3).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

The reviewers were in agreement that the paper represents an impressive accomplishment that will be impactful to the field, and which generates multiple novel insights. Several points were raised for aspects of the manuscript that could be strengthened.

1) Justify and clarify aspects of the methodological approach, including the population characteristics, several aspects of the statistical approaches employed, and potential limitations of the acquisition being applied to this population.

2) Demonstrate and discuss the robustness of the sensitivity of the results presented.

3) Consider further implications of these findings in the context of known anatomy of the thalamus and its development in prior literature.

For further details please see the "Public reviews" and "Recommendations for the authors" below.

Reviewer #1 (Recommendations for the authors):

This is an impressive study that overcomes a number of obstacles in performing fetal imaging analysis.

Regarding the application of MSMT-CSD modeling, the b-values used in the current study (400, 1000) seem comparatively low to previous studies employing MSMT modeling which I am aware of. Some clarification regarding the validity of these approaches given the sequences used (e.g., in consideration of Tournier, et al., 2013), e.g., including citing prior studies demonstrating validity for a similar range of b-values, would lend support to the application of these analyses to the current data set.

Assessment of the location-specific developmental effects is accomplished by normalizing each tract on a 0-100 scale between the thalamus and cortex. Does this normalization, in the context of an expanding brain, imply that a given point may be comparatively closer to the terminal zone at different ages? Could this account for (all or some of the) age-related changes in microstructure identified (i.e., if certain features are age-invariant with respect to absolute, rather than relative, position along the tract)? Demonstrating that group differences persist when selecting (e.g.) constant length segments at the ends and middle of each tract would help rule out brain size-induced topographic change as driving these effects (though other statistical approaches could likely also accomplish this).

I'm unsure what statistics support conclusions regarding position-specific differences in tissue fraction. E.g., the discussion summarizes that "the early period is characterized by higher tissue fractions in the middle of the [thalamocortical] tract". While this appears likely based on visual inspection of the tracts in Figure 5, it is not clear what specific statistical analyses support this inference. Further, given that differences were assessed both as a function of GA (though perhaps exclusively by group) and as a function of distance, some degree of multiple comparison correction is probably warranted, but not currently employed (or at least, not described).

Reviewer #2 (Recommendations for the authors):

First, I would like to commend the authors on a thoughtful, creative combination of histological maps and dMRI data. One of the major challenges of fetal neuroimaging work is understanding how co-occurring and dynamic neurobiological processes contribute to metrics derived from in vivo imaging. The present study presents an exciting model for contextualizing dMRI findings in relation to what we know about brain development from histology studies. The following recommendations are offered with the hope that they can strengthen an already exceptional paper:

1. Please provide a table with sociodemographic information about the sample. This information is necessary for determining the representativeness of the sample, inferring the generalizability of the present findings, and working towards appropriate diversification of developmental neuroscience as a field. Given that age-related change is the primary question of interest, it would also strengthen the study to show that fetuses of different ages do not systematically differ from one another in terms of other factors that may alter developmental trajectories.

2. The manuscript would benefit from more detail regarding motion correction. The authors describe using slice-to-volume motion correction (SHARD) and implementing quality control based on slice dropouts and gradient of motion parameters. However, additional information is required to enhance transparency and aid future replication efforts. What cut-offs were used to determine whether to include vs. exclude a subject? How many fetuses were excluded based on the selected quality control thresholds? Can the authors please provide more information about the visual assessment that was conducted? Were post-processing motion parameters correlated with tissue or fluid fraction values or with fetal age? It would also be helpful to include visual examples of residual or uncorrected artefacts that resulted in subject exclusion, even if only in the supplemental materials. Finally, Christiaens et al., 2021 is cited several times in the text (including in reference to preprocessing techniques used for correcting head motion) but is not included in the reference list.

3. What size were the along-tract analysis segments and how was this segment size selected? Showing the results replicate (or don't) with different segment sizes would show the robustness of results and could inform an ideal resolution at which to examine tissue maturation in the fetal brain.

4. Additional analyses that show the primary findings do not replicate with noise data but do replicate across different preprocessing choices would enhance rigor, especially since the field of fetal neuroimaging lacks a widely agreed-upon set of best practices. For instance, if segments from the along-tract analysis are scrambled, does the outward-to-inward maturational trajectory disappear? The disappearance of associations with scrambled data would enhance confidence that this finding is not simply a byproduct of the way the data were preprocessed. In addition, showing the results replicate following different preprocessing choices would aid in showing effect robustness.

5. Several studies have identified sex differences in fetal brain maturation. Given the even split in the present sample, examining whether tissue maturation trajectories differ by fetal sex would be informative. I wonder if the authors would consider adding this analysis.

6. The limitations of fetal dMRI should be clearly described in the discussion to aid reader interpretation.

7. It would improve manuscript readability to clarify why the authors believe it is important to report both tissue and fluid fractions. They appear to be reciprocals of one another so the added value of reporting both is unclear.

8. I would avoid subjective phrases like "thalamocortical connections represent the most important inputs into the developing cortex" and instead use more neutral language like "thalamocortical connections play a key role in guiding…"

Reviewer #3 (Recommendations for the authors):

Overall, this is a very valuable study with excellent datasets. The material provides a unique and detailed description of the changes in microstructure in the thalamus and cortex. I am very supportive of publication, but I feel that more could have been done to bridge the gap between MRI and histology and to link the results to the basic principles of thalamocortical development.

Criticisms:

1. Using tract density imaging, the authors observed that the cortical areas were connected to specific thalamic regions, organised in an anterior-posterior axis. This anterior-posterior representation of cortical connectivity in the thalamus was consistent with previous descriptions in rodent thalamocortical connectivity in rodent. However, the relationship between the cortical surface and the volume of the developing thalamus is not explained clearly. In their studies, Molnar and Blakemore emphasized that at the time of arrival, the thalamic projections are already arranged according to rough topography. Anterior to posterior movement along the convexity of the cortex represented a medial-to-lateral movement of labelling in the embryonic rodent thalamus. Ventral to dorsal movement in the cortex resulted in an anterior-to-posterior movement within the thalamus https://doi.org/10.1523/JNEUROSCI.18-15-05723.1998; https://doi.org/10.1111/j.1460-9568.2012.08119.x. It would be good to improve Figure 3 and represent the overall pattern. The very general pattern should be described. Could the thalamic volumes be color coded and the same fainter shade used on the convexity of the cortex of the 3D outline of the brain?

2. The study does not attempt to link the thalamic fibre topography to the histological structure of the thalamus. Thalamic nuclei undergo considerable changes at these early stages, e.g. pulvinar increases enormously and the dorsal lateral geniculate nucleus is changing position from dorsolateral to ventromedial. Such changes were already emphasized in the publications of Le Gros Clark and subsequently Rakic. The authors note that the topography of thalamic nuclei and their cortical connectivity is acquired embryonically, but they do not emphasize the considerable changes in this topography at early stages. The in vivo parcellation of the thalamus is only described as part of the fibre topography, no references are made to the changes in relative positions of the thalamic nuclei, and only the adult similarity is emphasized.

3. The density of the thalamocortical innervation is not equal across the developing or adult cortex. E.g. retrogradely tracing thalamic projections from various parts of the cortex using carbocyanine dyes, it was observed that the same sized carbocyanine dye can label 3-5 times more backlabelled thalamic neurons from the putative somatosensory cortex than from the visual cortex (see references in Z. Molnár, Development of Thalamocortical Connections (Springer, 1998)). The methodologies and the data would have enabled the authors to compare the parameters of the various thalamocortical pathways that they resolved. It would have been good to include some results and comments on these area-specific comparisons.

4. The authors found the spatiotemporal changes in the diffusion signal reflected known developmental processes that take place between the early, mid, and late prenatal period. "The early period is characterized by higher tissue fractions in the middle of the tract, where there is a radial scaffold for migrating neurons. As this scaffold dissipates in the mid-prenatal period, this is accompanied by a reduction in the tissue fraction in the middle of the tract, and an increase in towards the termination of the tracts as the neurons of the cortical plate mature." The fact that these changes can be now resolved with in vivo imaging is excellent, but the data itself is under-analysed.

The authors correctly describe the possible anatomical explanations for the changes of the signals: "In terms of the diffusion signal, this strongly aligned microstructure of the radial glia is represented in our results by a higher tissue fraction in the transient compartments containing the most migratory cells (such as the VZ, IZ). Conversely, we observe the lowest tissue fraction in the early prenatal SP, as this compartment predominantly contains hydrophilic extracellular matrix." These are key processes of cortical development and the authors have the material to draw conclusions on the various gradients on the cortical plate and subplate changes. It would have been good to get a feel for these dynamic changes and include some representations of the area-specificity of these changes. Surely the increase of the cortical plate has areal variation and also the increase and resolution of the subplate are also area specific. Measuring and representing these differences would increase the impact of the study. Moreover, it would also open avenues to study left-right and gender differences.

5. The authors have interesting discussions on the corticofugal and thalamocortical connectivity through the internal capsule. "The microstructural change along the anterior thalamic radiation suggests increasing tissue fraction between the deep grey matter, VZ, and IZ. We hypothesise that the high tissue fraction in the IZ is due to densely packed ascending and descending bundles within the anterior limb of the internal capsule." The exact timing of the outgrowth, intermingling, and arrival of the thalamocortical and corticothalamic projections are not known, but there are some suggestions that these are relatively earlier than in rodents (https://doi.org/10.1093/cercor/bhz056; https://doi.org/10.1016/B978-0-444-53860-4.00003-9).

6. A wide range of references from various species are used. This is excellent, but it can be also somewhat confusing. I recommend stating the species explicitly when non-human studies are referenced.

7. The formatting of the references will need attention: e.g. in line 69: (I. Kostović and Judaš 2015; Ivica Kostović and Judaš 2010); In line 257: Hutter, Christiaens, et al. 2018; In lines 275 and 277: Clascá, Rubio-Garrido, and Jabaudon 2012; In line 299: Miyazaki, Song, and Takahashi 2016; in line 361: (Ivica Kostovic and Rakic 1990b); in line 364: (Ivica Kostović and Judaš 2006; Ivica Kostovic and Rakic 1990a). In line 374: "Toulmin cerebral cortex" is mentioned, but no specific reference is cited.

Overall, I am very enthusiastic about this very significant and high-quality human developmental imaging data. I understand that the authors are making all this data available for future studies in the field. Nevertheless, I feel that they are in the best position to present some fundamental initial descriptions firsthand and present them in a format with suitable illustrations that will make it into textbooks on human development.

Reviewer #1 (Recommendations for the authors):

This is an impressive study that overcomes a number of obstacles in performing fetal imaging analysis.

Regarding the application of MSMT-CSD modeling, the b-values used in the current study (400, 1000) seem comparatively low to previous studies employing MSMT modeling which I am aware of. Some clarification regarding the validity of these approaches given the sequences used (e.g., in consideration of Tournier, et al., 2013), e.g., including citing prior studies demonstrating validity for a similar range of b-values, would lend support to the application of these analyses to the current data set.

We agree that providing insight about the choice of acquisition parameters would benefit readers, as these were specifically optimised for the unique biophysical properties of the fetal environment. We have now signposted the publications that provide detail on the optimisation process used to define the b-shell values used (Tournier et al., 2019 and Christiaens et al., 2019) more clearly in the text (line 451). In essence, these papers demonstrate that there is a marked loss of signal at higher b-shells (> 1000 s/mm3) in the neonatal brain compared to adults, due to the tissue being inherently much less restricted. There is also increased noise at higher b-shells. Both issues are amplified in the fetal brain, there are long relaxation times in the water-rich, relatively unrestricted fetal brain tissues and the amount of noise is higher.

In terms of the validity of MSMT-CSD for this dataset, an important consideration is that the MSMT-CSD approach doesn't mandate a specific set of b-values which are required for the modelling process, only that there are multiple shells in addition to b = 0. The technique depends on the signals related to different tissue types having sufficiently distinct behaviours across the b-values sampled, and so depends inherently on the characteristics of the tissue. To demonstrate the distinct properties of the different compartments in the fetal brain, we have now included a figure in the Appendix (Appendix 1-Figure 4) showing the signal amplitude per tissue type as a function of b-value, and the ODFs after MSMT-CSD has been applied. We have also provided further detail on the motivation for this choice in the discussion (line 265-271).

There is a general scarcity of literature in the fetal diffusion imaging field, but to support that the MSMT-CSD model is as valid as traditional approaches (DTI), in our previous work (Wilson et al., 2021), we demonstrated a high linear correlation between diffusion tensor metrics and the MSMT-CSD components when whole-tract averages are calculated.

Assessment of the location-specific developmental effects is accomplished by normalizing each tract on a 0-100 scale between the thalamus and cortex. Does this normalization, in the context of an expanding brain, imply that a given point may be comparatively closer to the terminal zone at different ages? Could this account for (all or some of the) age-related changes in microstructure identified (i.e., if certain features are age-invariant with respect to absolute, rather than relative, position along the tract)? Demonstrating that group differences persist when selecting (e.g.) constant length segments at the ends and middle of each tract would help rule out brain size-induced topographic change as driving these effects (though other statistical approaches could likely also accomplish this).

We agree that it is important to consider the effect of increasing fetal brain size when studying maturational changes across gestational age. We have operated under the assumption that the morphology of the tracts is consistent with age and thus that the tracts consistently project from the same origin to termination point in all fetuses (for example, from the thalamus to the primary motor cortex). Therefore, any change in the length of tract across gestational age will remain proportional to the change in overall brain size (or distance between the start and end of the tract) and thus each sampling point will represent the same relative distance from the origin and termination across different fetal ages.

We also used this normalisation approach together with an atlas, to cross-reference our trajectories of diffusion values. By overlaying the tracts on the atlas, and corroborating each sampling point with an atlas region, we characterised the diffusion properties of the different fetal compartments independent of their change in size and maturity across gestation. We have included more detail on this choice in the Results section to clarify it for readers (line 218-220).

I'm unsure what statistics support conclusions regarding position-specific differences in tissue fraction. E.g., the discussion summarizes that "the early period is characterized by higher tissue fractions in the middle of the [thalamocortical] tract". While this appears likely based on visual inspection of the tracts in Figure 5, it is not clear what specific statistical analyses support this inference. Further, given that differences were assessed both as a function of GA (though perhaps exclusively by group) and as a function of distance, some degree of multiple comparison correction is probably warranted, but not currently employed (or at least, not described).

Thank you for this suggestion, we agree that a more formal statistical comparison supports interpretation and strengthens the conclusion about microstructural differences between compartments. We have therefore correlated the tissue fraction at each sampling point with gestational age in the thalamic motor tract to demonstrate that there are significant associations with age. As there are 100 sampling points, we also used strict Bonferroni correction for multiple comparisons. This additional analysis has been mentioned in the Results section (line 233-237) and is shown in Author response image 1.

Author response image 1

Download asset Open asset

Reviewer #2 (Recommendations for the authors):

First, I would like to commend the authors on a thoughtful, creative combination of histological maps and dMRI data. One of the major challenges of fetal neuroimaging work is understanding how co-occurring and dynamic neurobiological processes contribute to metrics derived from in vivo imaging. The present study presents an exciting model for contextualizing dMRI findings in relation to what we know about brain development from histology studies. The following recommendations are offered with the hope that they can strengthen an already exceptional paper:

1. Please provide a table with sociodemographic information about the sample. This information is necessary for determining the representativeness of the sample, inferring the generalizability of the present findings, and working towards appropriate diversification of developmental neuroscience as a field. Given that age-related change is the primary question of interest, it would also strengthen the study to show that fetuses of different ages do not systematically differ from one another in terms of other factors that may alter developmental trajectories.

We thank the reviewer for their enthusiasm about our work, and for highlighting the absence of this important information. We have included a sentence in the methods section (line 440) about the recruitment process, where this data was acquired and sociodemographic information about the cohort in the Appendix 1-Figure 1 (including histograms to demonstrate that IMD and ethnicity distributions are well matched to the diverse population in London). We have also included Author response image 2 to demonstrate that there is no significant relationship between IMD and gestational age, suggesting that this is not influencing the microstructural trajectories we observe. When the dataset becomes publicly available later this year, a supporting publication will also be released containing all this information.

Author response image 2

Download asset Open asset

2. The manuscript would benefit from more detail regarding motion correction. The authors describe using slice-to-volume motion correction (SHARD) and implementing quality control based on slice dropouts and gradient of motion parameters. However, additional information is required to enhance transparency and aid future replication efforts. What cut-offs were used to determine whether to include vs. exclude a subject? How many fetuses were excluded based on the selected quality control thresholds? Can the authors please provide more information about the visual assessment that was conducted? Were post-processing motion parameters correlated with tissue or fluid fraction values or with fetal age? It would also be helpful to include visual examples of residual or uncorrected artefacts that resulted in subject exclusion, even if only in the supplemental materials. Finally, Christiaens et al., 2021 is cited several times in the text (including in reference to preprocessing techniques used for correcting head motion) but is not included in the reference list.

We appreciate that for reproducibility, more detail about precise exclusion criteria would benefit the reader. The methods section now contains additional information about this process (line 464-474), and the appendix includes examples of subjects that were excluded due to motion and residual artefacts (Appendix 1 – Figure 2).

We have also included correlations between motion-related parameters extracted from the SHARD pipeline, (translation, rotation and SNR) (Christiaens et al., 2021) and gestational age, demonstrating no relationship with age. This is included in Appendix 1-Figure 3. Thank you for pointing out the referencing error, we have now corrected it. We would also like to highlight that when this dataset becomes publicly available later this year, QC scores and motion parameters will also be released that will enhance reproducibility for future studies.

3. What size were the along-tract analysis segments and how was this segment size selected? Showing the results replicate (or don't) with different segment sizes would show the robustness of results and could inform an ideal resolution at which to examine tissue maturation in the fetal brain.

The normalisation approach we used was to standardise the sampling interval along all the tracts to 100 points between the origin and termination. This was to ensure consistency across the cohort, account for changing brain size and image resolution. We have assumed that the morphology of the tracts remains consistent across all ages, as we were able to delineate tracts even in the youngest fetuses which projected from the same origin to termination points (for example, from the thalamus to the primary motor cortex). As the start and end points of the tracts are the same regardless of gestational age, any maturational change in length is proportional to a corresponding change in brain size. The smooth transition between each segment, as demonstrated by the consistency of the curves and their gradual shift with age, suggests that this sampling interval is appropriate for the aims of this study. The error bars shown on Figure 5 that reflect the standard error of the mean also support this, as they demonstrate minimal variation between subjects and a lack of overlap between sections in each of the time windows.

4. Additional analyses that show the primary findings do not replicate with noise data but do replicate across different preprocessing choices would enhance rigor, especially since the field of fetal neuroimaging lacks a widely agreed-upon set of best practices. For instance, if segments from the along-tract analysis are scrambled, does the outward-to-inward maturational trajectory disappear? The disappearance of associations with scrambled data would enhance confidence that this finding is not simply a byproduct of the way the data were preprocessed. In addition, showing the results replicate following different preprocessing choices would aid in showing effect robustness.

We agree with the reviewer that an important caveat in the field of fetal imaging is the lack of a standardised set of procedures with which to conduct analysis of MR data. However, we would like to emphasise that the pre-processing pipeline for the dHCP data has been optimised through an extensive and rigorous procedure, such that the methods used for the reconstruction, motion correction and distortion correction produce the highest quality data (more detail on this procedure is available in the following publications cited in the article: Christiaens et al., 2019, 2021, Cordero-Grande 2019, Pietsch et al., 2019). Please see Appendix 1-Figure 3, where we use parameters derived from the SHARD reconstruction pipeline to demonstrate there are no correlations between motion and gestational age. This figure also highlights that for fetuses of the same age, there is a broad distribution of SNR/translation/slice weight values. We feel that this enhances confidence in our results, because the differences between SNR within a gestational week are not reflected in the tissue and fluid fraction values, which in contrast are consistent for subjects of the same age.

In reference to the suggestion to scramble the segments, we feel that shuffling the values presented in figure 5 would lead to disappearance of the trajectory because of the non-overlapping error bars between compartments at each age. To strengthen our analysis, we have taken on board the initial lack of statistical testing and computed correlations between each sampling point tissue fraction and gestational age and corrected for multiple comparisons (see above, response to reviewer one).

5. Several studies have identified sex differences in fetal brain maturation. Given the even split in the present sample, examining whether tissue maturation trajectories differ by fetal sex would be informative. I wonder if the authors would consider adding this analysis.

We agree this would be beneficial to include and have tested for statistical significance between male and female in the average values across the whole tract. We found there was no significant difference between the average diffusion values in any of the tracts. Please see a statement about this finding in the Results section (line 189).

6. The limitations of fetal dMRI should be clearly described in the discussion to aid reader interpretation.

We agree this is a useful suggestion to help to contextualise the study and the methods used. We have now included a specific limitations section in the discussion (line 388-425) and refer reviewers to a previous publication that comprehensively describes these challenges (Christiaens et al., 2019).

7. It would improve manuscript readability to clarify why the authors believe it is important to report both tissue and fluid fractions. They appear to be reciprocals of one another so the added value of reporting both is unclear.

Thank you for highlighting this point. When fitting this model, the two components are reciprocals of one another if the modelling method has fit the data well and the normalization procedure has worked, so we include this to aid reader interpretation of the results and confirm that data validity. To make our rationale clearer to readers, we have included a sentence in the Results section (line 203-205).

8. I would avoid subjective phrases like "thalamocortical connections represent the most important inputs into the developing cortex" and instead use more neutral language like "thalamocortical connections play a key role in guiding…"

We thank the reviewer for this suggestion and agree more neutral language is appropriate. This suggestion has now been included in the revised article (line 50).

Reviewer #3 (Recommendations for the authors):

Overall, this is a very valuable study with excellent datasets. The material provides a unique and detailed description of the changes in microstructure in the thalamus and cortex. I am very supportive of publication, but I feel that more could have been done to bridge the gap between MRI and histology and to link the results to the basic principles of thalamocortical development.

Criticisms:

1. Using tract density imaging, the authors observed that the cortical areas were connected to specific thalamic regions, organised in an anterior-posterior axis. This anterior-posterior representation of cortical connectivity in the thalamus was consistent with previous descriptions in rodent thalamocortical connectivity in rodent. However, the relationship between the cortical surface and the volume of the developing thalamus is not explained clearly. In their studies, Molnar and Blakemore emphasized that at the time of arrival, the thalamic projections are already arranged according to rough topography. Anterior to posterior movement along the convexity of the cortex represented a medial-to-lateral movement of labelling in the embryonic rodent thalamus. Ventral to dorsal movement in the cortex resulted in an anterior-to-posterior movement within the thalamus https://doi.org/10.1523/JNEUROSCI.18-15-05723.1998; https://doi.org/10.1111/j.1460-9568.2012.08119.x. It would be good to improve Figure 3 and represent the overall pattern. The very general pattern should be described. Could the thalamic volumes be color coded and the same fainter shade used on the convexity of the cortex of the 3D outline of the brain?

We are grateful that this missing but extremely important element of our discussion has been highlighted and have now modified the manuscript accordingly, please see discussion (line 286). We have also adjusted Figure 3 to highlight the anterior-posterior connectivity gradient across the cortex and shaded the different nuclei accordingly. In addition, we have changed the colours of the tracts in Figure 2 according to their anterior-posterior connectivity to clearly demonstrate this pattern.

2. The study does not attempt to link the thalamic fibre topography to the histological structure of the thalamus. Thalamic nuclei undergo considerable changes at these early stages, e.g. pulvinar increases enormously and the dorsal lateral geniculate nucleus is changing position from dorsolateral to ventromedial. Such changes were already emphasized in the publications of Le Gros Clark and subsequently Rakic. The authors note that the topography of thalamic nuclei and their cortical connectivity is acquired embryonically, but they do not emphasize the considerable changes in this topography at early stages. The in vivo parcellation of the thalamus is only described as part of the fibre topography, no references are made to the changes in relative positions of the thalamic nuclei, and only the adult similarity is emphasized.

Thank you for pointing out that this key feature of thalamic development was missing from our discussion. With the resolution (8mm³) and image contrast of our images, smaller changes in the position or size of individual nuclei are unfortunately difficult to evaluate though we agree that being able to resolve these changes concurrently would further reinforce our findings. We have now added more on the histological structure of the thalamus to our discussion (line 305-310).

3. The density of the thalamocortical innervation is not equal across the developing or adult cortex. E.g. retrogradely tracing thalamic projections from various parts of the cortex using carbocyanine dyes, it was observed that the same sized carbocyanine dye can label 3-5 times more backlabelled thalamic neurons from the putative somatosensory cortex than from the visual cortex (see references in Z. Molnár, Development of Thalamocortical Connections (Springer, 1998)). The methodologies and the data would have enabled the authors to compare the parameters of the various thalamocortical pathways that they resolved. It would have been good to include some results and comments on these area-specific comparisons.

Thank you for this suggestion. While this would be an interesting question to address, unfortunately we feel that tractography itself (particularly in fetal data) cannot be reliably used to assess fibre morphology. The diffusion MR signal has no directionality, and therefore we are unfortunately not able to distinguish between ascending and descending thalamic projections as they develop. We also find that metrics generally used in this field to investigate fibre bundle morphology, such as streamline count, a proxy for ‘innervation density’, are not an accurate, quantitative measure. These are outlined well in this review article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6787694/. Consequently, we have avoided using the data in this way, and just used tractography to delineate regions of interest. We have now added a limitations section to the article and mention this constraint of diffusion methodology specifically (line 388).

4. The authors found the spatiotemporal changes in the diffusion signal reflected known developmental processes that take place between the early, mid, and late prenatal period. "The early period is characterized by higher tissue fractions in the middle of the tract, where there is a radial scaffold for migrating neurons. As this scaffold dissipates in the mid-prenatal period, this is accompanied by a reduction in the tissue fraction in the middle of the tract, and an increase in towards the termination of the tracts as the neurons of the cortical plate mature." The fact that these changes can be now resolved with in vivo imaging is excellent, but the data itself is under-analysed.

The authors correctly describe the possible anatomical explanations for the changes of the signals: "In terms of the diffusion signal, this strongly aligned microstructure of the radial glia is represented in our results by a higher tissue fraction in the transient compartments containing the most migratory cells (such as the VZ, IZ). Conversely, we observe the lowest tissue fraction in the early prenatal SP, as this compartment predominantly contains hydrophilic extracellular matrix." These are key processes of cortical development and the authors have the material to draw conclusions on the various gradients on the cortical plate and subplate changes. It would have been good to get a feel for these dynamic changes and include some representations of the area-specificity of these changes. Surely the increase of the cortical plate has areal variation and also the increase and resolution of the subplate are also area specific. Measuring and representing these differences would increase the impact of the study. Moreover, it would also open avenues to study left-right and gender differences.

We appreciate that this is a very rich dataset and there are a lot of questions that we have not addressed or analysed within this paper. Incorporating measures of areal variation in cortical plate development will require surface projection to calculate measures such as cortical thickness and sulcal depth reliably across subjects. Unfortunately, the required processing is non-trivial for fetal MRI data and requires careful optimisation, and thus we felt was beyond the scope of this work. Work is thus currently underway to explore regional differences across the brain for a separate publication that will use a different approach with broader coverage across the whole brain, allowing us to examine areal trends more comprehensively. We will also plan to eventually release surface projected data with the dHCP data release. We imagine this subsequent study will support the findings presented in this paper however, we did not want to overcomplicate the results as we aimed to investigate thalamocortical development more specifically.

5. The authors have interesting discussions on the corticofugal and thalamocortical connectivity through the internal capsule. "The microstructural change along the anterior thalamic radiation suggests increasing tissue fraction between the deep grey matter, VZ, and IZ. We hypothesise that the high tissue fraction in the IZ is due to densely packed ascending and descending bundles within the anterior limb of the internal capsule." The exact timing of the outgrowth, intermingling, and arrival of the thalamocortical and corticothalamic projections are not known, but there are some suggestions that these are relatively earlier than in rodents (https://doi.org/10.1093/cercor/bhz056; https://doi.org/10.1016/B978-0-444-53860-4.00003-9).

We thank the reviewer for this suggestion and are grateful for them highlighting this incredibly relevant study for our discussion. Please see discussion (line 373).

6. A wide range of references from various species are used. This is excellent, but it can be also somewhat confusing. I recommend stating the species explicitly when non-human studies are referenced.

We have prefaced references to non-human studies with a description of the specific species in the revised manuscript to make it clearer for readers.

7. The formatting of the references will need attention: e.g. in line 69: (I. Kostović and Judaš 2015; Ivica Kostović and Judaš 2010); In line 257: Hutter, Christiaens, et al. 2018; In lines 275 and 277: Clascá, Rubio-Garrido, and Jabaudon 2012; In line 299: Miyazaki, Song, and Takahashi 2016; in line 361: (Ivica Kostovic and Rakic 1990b); in line 364: (Ivica Kostović and Judaš 2006; Ivica Kostovic and Rakic 1990a). In line 374: "Toulmin cerebral cortex" is mentioned, but no specific reference is cited.

Thank you for pointing out these inconsistences, we have corrected them and corrected the bibliography in the revised manuscript.

Overall, I am very enthusiastic about this very significant and high-quality human developmental imaging data. I understand that the authors are making all this data available for future studies in the field. Nevertheless, I feel that they are in the best position to present some fundamental initial descriptions firsthand and present them in a format with suitable illustrations that will make it into textbooks on human development.

Author details

1. Siân Wilson

   1. Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
   2. Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom

   Contribution

   Conceptualization, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4617-3583

2. Maximilian Pietsch

   Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

   Contribution

   Data curation, Software, Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Lucilio Cordero-Grande

   1. Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
   2. Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
   3. Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain

   Contribution

   Data curation, Software, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Daan Christiaens

   1. Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
   2. Department of Electrical Engineering (ESAT/PSI), Katholieke Universiteit Leuven, Leuven, Belgium

   Contribution

   Data curation, Formal analysis, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Alena Uus

   Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas' Hospital, London, United Kingdom

   Contribution

   Data curation, Methodology

   Competing interests

   No competing interests declared

6. Vyacheslav R Karolis

   Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

   Contribution

   Formal analysis, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Vanessa Kyriakopoulou

   Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

   Contribution

   Data curation

   Competing interests

   No competing interests declared

8. Kathleen Colford

   Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

   Contribution

   Data curation

   Competing interests

   No competing interests declared

9. Anthony N Price

   Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

   Contribution

   Data curation

   Competing interests

   No competing interests declared

10. Jana Hutter

    Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

    Contribution

    Data curation, Writing – review and editing

    Competing interests

    No competing interests declared

11. Mary A Rutherford

    Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

    Contribution

    Data curation, Funding acquisition

    Competing interests

    No competing interests declared

12. Emer J Hughes

    Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

    Contribution

    Data curation

    Competing interests

    No competing interests declared

13. Serena J Counsell

    Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

    Contribution

    Data curation, Investigation, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-8033-5673

14. Jacques-Donald Tournier

    Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

    Contribution

    Data curation, Software, Formal analysis

    Competing interests

    No competing interests declared

15. Joseph V Hajnal

    Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom

    Contribution

    Resources, Data curation, Funding acquisition, Methodology, Project administration

    Competing interests

    No competing interests declared

16. A David Edwards

    1. Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
    2. Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom

    Contribution

    Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0003-4801-7066

17. Jonathan O’Muircheartaigh

    1. Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
    2. Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
    3. Department of Forensic and Neurodevelopmental Sciences, King’s College London, London, United Kingdom
    4. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom

    Contribution

    Conceptualization, Resources, Data curation, Supervision, Funding acquisition, Validation, Investigation, Methodology, Project administration, Writing – review and editing

    For correspondence

    jonathanom@kcl.ac.uk

    Competing interests

    No competing interests declared

18. Tomoki Arichi

    1. Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
    2. Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
    3. Children’s Neurosciences, Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
    4. Department of Bioengineering, Imperial College London, London, United Kingdom

    Contribution

    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Validation, Investigation, Methodology, Project administration, Writing – review and editing

    For correspondence

    tomoki.arichi@kcl.ac.uk

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3550-1644

• 403

  Page views

• 100

  Downloads

• 1

  Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.