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

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'Muicheartaigh

    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

• 1,120

  views

• 212

  downloads

• 12

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.