A fundamental question in neuroscience is about the role of experience in neurodevelopment (Arcaro and Livingstone, 2021; Barlow, 1975; Holtmaat and Svoboda, 2009; Nithianantharajah and Hannan, 2006). Vision, given its ecological universality and importance across species, has long been taken as a representative modality to investigate such question (Barlow, 1975; Crair et al., 1998; Gödecke and Bonhoeffer, 1996; Li et al., 2008; Li et al., 2006; Roy et al., 2020), and a framework consisting of two distinct phases was proposed to describe the development of visual cortex (Barlow, 1975; Li et al., 2006; White and Fitzpatrick, 2007). This framework includes an early, experience-independent phase in which the basic layout of neural map is established, and a subsequently experience-dependent phase in which the visual experience refines and shapes the initial neural map. Recent studies further revealed the importance of visual experience during the early period of cortical development, suggesting the intricate interaction between early sensory experience and the endogenous mechanisms in neurodevelopment (Li et al., 2008; Li et al., 2006; Roy et al., 2020). However, these studies were carried out on the model animals (e.g., cat and ferret) using electrophysiological techniques. Due to the lack of noninvasive methods to probe the human infant brain, the influence of early postnatal experience on the development of visual cortex in human infants remains unclear.

Neuroimaging techniques, especially magnetic resonance imaging (MRI), provided an ideal tool to noninvasively measure both the brain structure and function, which, however, remains challenging for the infants’ brain due to subject motion, limited resolution, and difficulty in patient recruitment (Cordero-Grande et al., 2018). Owing to the recent technical advances in both images acquisition and processing methods (Hughes et al., 2017), MRI studies of the perinatal brain, for example, the developing human connectome project (dHCP; http://www.developingconnectome.org/) was initiated to investigate the early structural and functional development of human cerebral cortex. Herein we collected a dataset of multimodal MRI data of 407 neonatal subjects from dHCP to address the above question. The different time intervals between gestational age (GA) at birth and the postmenstrual age (PMA) at scan indicated the difference in the postnatal experience across neonates, which reflects the individual variation of the visual experience. Thus, this time window provides us the opportunity to investigate the contributions of early visual experience versus endogenous neurodevelopment on the development of visual cortex in human newborns.

The main purpose of this study is to describe the early structural and functional development of the ventral visual cortex in human newborns and estimate the contribution of postnatal time (PT) to this process. Particularly, the cortical thickness (CT, Lyall et al., 2015; Sowell et al., 2004) and T1w/T2w-based cortical myelination (CM, Glasser and Van Essen, 2011; Soun et al., 2017) data from dHCP was used to measure the development of cortical morphology and microstructure, respectively. The resting-state functional MRI (r-fMRI, Fitzgibbon et al., 2020) data was used to evaluate the development of cortical circuits based on functional connectivity between cortical areas. We focused on the primary visual cortex (V1) and higher-level visual cortex, namely, the ventral occipital temporal cortex (VOTC, Bi et al., 2016; Grill-Spector and Weiner, 2014). The VOTC contains function-specific regions for biologically important categories such as faces (Kanwisher et al., 1998), bodies (Downing et al., 2001), and scenes (Epstein and Kanwisher, 1998), making this region a critical component in the ventral pathway of visual processing (Bi et al., 2016; Grill-Spector and Malach, 2004; Grill-Spector and Weiner, 2014; Kanwisher, 2010), and the impact of early visual experience on those regions is not fully understood.

Previous studies have described the developmental trajectory of CT and CM in human infants and found a generally increasing trend for both two measurements with PMA in whole brain (Bozek et al., 2018; Fenchel et al., 2020). But the contribution of PT to the development was not clarified because PMA reflects both prenatal and postnatal factors. Using r-fMRI, studies found that the primate newborns at a few days of age already had a proto-organization in visual system (Arcaro and Livingstone, 2017) and human infants within a few weeks of age showed a category-specific network (Kamps et al., 2020). However, the small sample size of those studies might undermine reliability of the results given the well-known instability of r-fMRI signals (Poldrack et al., 2017). Moreover, the human infants participating in a previous study (Kamps et al., 2020) were around 1 month old (mean age: 27 days; range from 6 to 57 days), who might already acquire some visual experience, and thus this study could not exclude postnatal visual experience on the innate functional connectivity.

In this study, we first characterized the general development of structural morphology (CT) and microstructure (CM) in the ventral cortex and estimate the contributions of prenatal time and PT on the structural development. Then, we used r-fMRI to characterize the innate organization of the ventral cortex directly after birth and investigated the effect of PT on functional networks of V1 and VOTC areas. Furthermore, we carried out a mediation analysis to investigate the relationship between functional and structural development in area V1. Finally, we evaluated the structural and functional differences of visual cortex between the term- and preterm-born babies as the latter group had shorter GA at birth but longer PT compared to the former group with equivalent PMA.

Experience-dependent plasticity is one of the most striking features of human brain and also the foundation of acquired cognitive ability. Utilizing the large datasets of neonatal multimodal MRI images from dHCP, we reported the early structural and functional maps in the ventral visual cortex and their development during the first month of age, focusing on the contributions of prenatal time and PT to the cortical development in this early period. Particularly, we found that PT was significantly related to the cortical thickening and specific functional circuits (e.g., homotopic functional connections between the bilateral visual cortex), while GA was significantly correlated with the CT, CM, and ipsilateral connections in the visual cortex. Such separation reflected the influence of the developmental environment (in utero and outside world) on the cortical development in the infants. Furthermore, we found that the preterm-born infants had higher CT (e.g., V1), lower CM, and lower homotopic connections (e.g., VOTC) compared to term-born infants at equivalent PMA. Taken together, those results suggested that the development of human visual cortex does not strictly follow the two-stage hypothesis (Barlow, 1975; Li et al., 2006; White and Fitzpatrick, 2007) as the cortical properties are modulated by postnatal environment even within the first month of life.

Similar to the previous findings in human infants (Bozek et al., 2018; Fenchel et al., 2020), both the CT and CM in ventral cortex increased between 37 and 44 weeks of PMA in our study. We went one step further to investigate the different mechanisms underlying these trends and found the development of CT was considerably modulated by both postnatal and prenatal times while the CM was heavily influenced by prenatal duration. CT is related to the synaptogenesis and synaptic pruning processes that are shaped by both innate and postnatal factors. It was reported that human visual cortex thickens to a maximum during the first 2 years and gradually thins thereafter (Lyall et al., 2015), mirroring the trajectory of synapses in human striate cortex (Huttenlocher and de Courten, 1987). The previous evidence that both congenitally blind and sighted subjects showed uptrend of CT in the early stage, leading to the conclusion that the visual experience might not be involved in this process (Bourgeois and Rakic, 1996; Jiang et al., 2009). Our results in the human neonates suggested the postnatal environment could accelerate the thickening of neonatal visual cortex, even if it was not necessary.

Is it the visual experience out of various postnatal stimuli that modulated the development of CT in ventral visual cortex? Although we could not provide a direct evidence from this study, there were indications that the CT in this study might reflect an experience-dependent synaptogenesis induced by early visual experience (Holtmaat and Svoboda, 2009). On the one hand, we found that across whole brain, PT-dependent increase of CT was most prominent in the V1, primary auditory area, and central sulcus (Figure 2—figure supplement 2), which directly receive visual, auditory, sensorimotor stimuli; while the increases of CM were mainly distributed in the frontal areas (Figure 2—figure supplement 2). This whole-brain pattern suggested that CT was shaped by postnatal environment universally, and it is reasonable to expect that ventral visual cortex is closely related to visual experience. On the other hand, we found a well-organized spatial pattern of cortical thickening in ventral cortex with the posterior area showing faster development than anterior regions. This might reflect the influence of visual flow along the ventral cortex, for example, the primary visual area in the posterior area processes all kinds of visual information while higher-level visual cortex responds less. Future study could combine the behavioral measurements to test the relation between these two factors or compare the sighted and congenitally blind infants to further illustrate such question.

T1w/T2w-based myelination measurement, which takes advantage of the covariation between cortical myelin content and T1w (positive covariation), as well as T2w (negative covariation) intensities, could be used as a surrogate maker of myelination (Glasser and Van Essen, 2011; Soun et al., 2017). CM is related to various cognitive performance (Glasser et al., 2014), which might reflect the increase of myelinated fibers in the cortex. Previous studies reported the coupling between CM and face processing, suggesting an experience-dependent development of visual cortical microstructure in later life (e.g., 5–12 years old; Nordt et al., 2021). However, we found a significant correlation between CM in the ventral cortex and GA but not PT in the neonatal period, suggesting that early myelination was primarily determined by the endogenic growth in the uterine environment. The posterior-to-anterior spatial pattern of CM in the ventral visual cortex agreed with a previous study on infants at around 6 months of age (Natu et al., 2021). Our results further suggested that this hierarchical pattern occurs as early as the neonatal period.

Using r-fMRI data, previous studies evaluated the functional organization of the visual system in neonatal macaques (Arcaro and Livingstone, 2017) and category-selective network in human infants (6–57 days) (Kamps et al., 2020). Both studies revealed the proto-organization prior to the emergence of functional domain such as face and place areas. Herein we validated those findings in a large sample with infants as early as 1 day of postnatal age, fully controlling the influence of postnatal visual experience. Particularly, we found two distinct visual clusters along the medial–lateral axis, which captured the central–peripheral organization of ventral visual system (Grill-Spector and Weiner, 2014; Hasson et al., 2002; Levy et al., 2001; Wiesel, 1982). Apart from the border of two medial–lateral clusters, the lateral area becomes more face-selective while the medial area is specialized in processing scene and buildings later in life (Nordt et al., 2021), and thereby our observation suggests the innate scaffold is already established at birth for subsequent experience-dependent modification in ventral visual cortex (Arcaro et al., 2017; Arcaro and Livingstone, 2021; Arcaro and Livingstone, 2017). However, the present homotopic connections in the human neonates were lower than those in neonate macaca mulattas (Arcaro and Livingstone, 2017). This difference might relate to the higher motion in human infants, less r-fMRI data in this study, coarser parcellation in the visual cortex used in this work, and the developmental difference between primates and humans in the neonatal period. Furthermore, the connections among the ventral visual cortex have developed during this early stage. Specifically, the homotopic connections between bilateral V1 and between bilateral VOTC both increased with PMA, indicating increased degree of functional distinction (Arcaro and Livingstone, 2017; Vincent et al., 2007). In contrast, the connection between V1 and VOTC decreased between 37 and 44 weeks of PMA, which might also reflect the development of functional differentiation between adjacent regions in the same hemisphere. More importantly, the connection between bilateral V1 and VOTC was significantly modified by PT, supporting that the early postnatal experience not only influences the local structural features of cortical cortex but also the functional circuits. It is worth noting that the increased homotopic connection can be direct or indirect, for example, the effect might be driven by external regions with enhanced connection to both of the areas (e.g., thalamus).

The preterm-born babies have longer PT but shorter GA compared to full-term infants at the same PMA. The findings that CT in the ventral cortex was generally lower in the term-born than preterm-born infants and that CM showed an opposite trend between the two groups supported the above analysis that CT was preferably influenced by PT while CM was largely dependent on GA during the neonatal period. In terms of functional development, we found the homotopic connections were lower in preterm than in term-born infants, in contrast to the above finding that the homotopic connection increased with PT in both V1 and VOTC. These results might be due to the mediation effect of CM on the connection.

In brief, our results suggested that early cortical development is a mixed outcome of endogenous and experience-dependent development in both cortical structure and function, and postnatal experience could selectively modify the endogenous development of visual cortex during early infancy.

The data used in this work are available online on the dHCP page (http://www.developingconnectome.org/data-release/third-data-release/).

1. 1. Arcaro MJ
   2. Livingstone MS

   (2017) A hierarchical, retinotopic proto-organization of the primate visual system at birth

   eLife 6:26196.

   https://doi.org/10.7554/eLife.26196

   • PubMed
   • Google Scholar
2. 1. Arcaro MJ
   2. Schade PF
   3. Vincent JL
   4. Ponce CR
   5. Livingstone MS
   6. Livingstone M

   (2017) Seeing faces is necessary for face-domain formation

   Nature Neuroscience 20:1404–1412.

   https://doi.org/10.1038/nn.4635

   • PubMed
   • Google Scholar
3. 1. Arcaro MJ
   2. Livingstone MS

   (2021) On the relationship between maps and domains in inferotemporal cortex

   Nature Reviews. Neuroscience 22:573–583.

   https://doi.org/10.1038/s41583-021-00490-4

   • PubMed
   • Google Scholar
4. 1. Barlow HB

   (1975) Visual experience and cortical development

   Nature 258:199–204.

   https://doi.org/10.1038/258199a0

   • PubMed
   • Google Scholar
5. 1. Bi Y
   2. Wang X
   3. Caramazza A

   (2016) Object domain and modality in the ventral visual pathway

   Trends in Cognitive Sciences 20:282–290.

   https://doi.org/10.1016/j.tics.2016.02.002

   • PubMed
   • Google Scholar
6. 1. Bourgeois JP
   2. Rakic P

   (1996) Synaptogenesis in the occipital cortex of macaque monkey devoid of retinal input from early embryonic stages

   The European Journal of Neuroscience 8:942–950.

   https://doi.org/10.1111/j.1460-9568.1996.tb01581.x

   • PubMed
   • Google Scholar
7. 1. Bozek J
   2. Makropoulos A
   3. Schuh A
   4. Fitzgibbon S
   5. Wright R
   6. Glasser MF
   7. Coalson TS
   8. O’Muircheartaigh J
   9. Hutter J
   10. Price AN
   11. Cordero-Grande L
   12. Teixeira RPAG
   13. Hughes E
   14. Tusor N
   15. Baruteau KP
   16. Rutherford MA
   17. Edwards AD
   18. Hajnal JV
   19. Smith SM
   20. Rueckert D
   21. Jenkinson M
   22. Robinson EC

   (2018) Construction of a neonatal cortical surface atlas using multimodal surface matching in the developing human connectome project

   NeuroImage 179:11–29.

   https://doi.org/10.1016/j.neuroimage.2018.06.018

   • PubMed
   • Google Scholar
8. 1. Cordero-Grande L
   2. Hughes EJ
   3. Hutter J
   4. Price AN
   5. Hajnal JV

   (2018) Three-dimensional motion corrected sensitivity encoding reconstruction for multi-shot multi-slice MRI: application to neonatal brain imaging

   Magnetic Resonance in Medicine 79:1365–1376.

   https://doi.org/10.1002/mrm.26796

   • PubMed
   • Google Scholar
9. 1. Crair MC
   2. Gillespie DC
   3. Stryker MP

   (1998) The role of visual experience in the development of columns in cat visual cortex

   Science 279:566–570.

   https://doi.org/10.1126/science.279.5350.566

   • PubMed
   • Google Scholar
10. 1. Downing PE
    2. Jiang Y
    3. Shuman M
    4. Kanwisher N

    (2001) A cortical area selective for visual processing of the human body

    Science 293:2470–2473.

    https://doi.org/10.1126/science.1063414

    • PubMed
    • Google Scholar
11. 1. Epstein R
    2. Kanwisher N

    (1998) A cortical representation of the local visual environment

    Nature 392:598–601.

    https://doi.org/10.1038/33402

    • Google Scholar
12. 1. Eyre M
    2. Fitzgibbon SP
    3. Ciarrusta J
    4. Cordero-Grande L
    5. Price AN
    6. Poppe T
    7. Schuh A
    8. Hughes E
    9. O’Keeffe C
    10. Brandon J
    11. Cromb D
    12. Vecchiato K
    13. Andersson J
    14. Duff EP
    15. Counsell SJ
    16. Smith SM
    17. Rueckert D
    18. Hajnal JV
    19. Arichi T
    20. O’Muircheartaigh J
    21. Batalle D
    22. Edwards AD

    (2021) The developing human connectome project: typical and disrupted perinatal functional connectivity

    Brain 144:2199–2213.

    https://doi.org/10.1093/brain/awab118

    • PubMed
    • Google Scholar
13. 1. Fenchel D
    2. Dimitrova R
    3. Seidlitz J
    4. Robinson EC
    5. Batalle D
    6. Hutter J
    7. Christiaens D
    8. Pietsch M
    9. Brandon J
    10. Hughes EJ
    11. Allsop J
    12. O’Keeffe C
    13. Price AN
    14. Cordero-Grande L
    15. Schuh A
    16. Makropoulos A
    17. Passerat-Palmbach J
    18. Bozek J
    19. Rueckert D
    20. Hajnal JV
    21. Raznahan A
    22. McAlonan G
    23. Edwards AD
    24. O’Muircheartaigh J

    (2020) Development of microstructural and morphological cortical profiles in the neonatal brain

    Cerebral Cortex 30:5767–5779.

    https://doi.org/10.1093/cercor/bhaa150

    • PubMed
    • Google Scholar
14. 1. Fitzgibbon SP
    2. Harrison SJ
    3. Jenkinson M
    4. Baxter L
    5. Robinson EC
    6. Bastiani M
    7. Bozek J
    8. Karolis V
    9. Cordero Grande L
    10. Price AN
    11. Hughes E
    12. Makropoulos A
    13. Passerat-Palmbach J
    14. Schuh A
    15. Gao J
    16. Farahibozorg SR
    17. O’Muircheartaigh J
    18. Ciarrusta J
    19. O’Keeffe C
    20. Brandon J
    21. Arichi T
    22. Rueckert D
    23. Hajnal JV
    24. Edwards AD
    25. Smith SM
    26. Duff E
    27. Andersson J

    (2020) The developing human connectome project (dhcp) automated resting-state functional processing framework for newborn infants

    NeuroImage 223:117303.

    https://doi.org/10.1016/j.neuroimage.2020.117303

    • PubMed
    • Google Scholar
15. 1. Glasser MF
    2. Van Essen DC

    (2011) Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI

    The Journal of Neuroscience 31:11597–11616.

    https://doi.org/10.1523/JNEUROSCI.2180-11.2011

    • PubMed
    • Google Scholar
16. 1. Glasser MF
    2. Sotiropoulos SN
    3. Wilson JA
    4. Coalson TS
    5. Fischl B
    6. Andersson JL
    7. Xu J
    8. Jbabdi S
    9. Webster M
    10. Polimeni JR
    11. Van Essen DC
    12. Jenkinson M
    13. WU-Minn HCP Consortium

    (2013) The minimal preprocessing pipelines for the human connectome project

    NeuroImage 80:105–124.

    https://doi.org/10.1016/j.neuroimage.2013.04.127

    • PubMed
    • Google Scholar
17. 1. Glasser MF
    2. Goyal MS
    3. Preuss TM
    4. Raichle ME
    5. Van Essen DC

    (2014) Trends and properties of human cerebral cortex: correlations with cortical myelin content

    NeuroImage 93:165–175.

    https://doi.org/10.1016/j.neuroimage.2013.03.060

    • PubMed
    • Google Scholar
18. 1. Glasser MF
    2. Coalson TS
    3. Robinson EC
    4. Hacker CD
    5. Harwell J
    6. Yacoub E
    7. Ugurbil K
    8. Andersson J
    9. Beckmann CF
    10. Jenkinson M
    11. Smith SM
    12. Van Essen DC

    (2016) A multi-modal parcellation of human cerebral cortex

    Nature 536:171–178.

    https://doi.org/10.1038/nature18933

    • PubMed
    • Google Scholar
19. 1. Gödecke I
    2. Bonhoeffer T

    (1996) Development of identical orientation maps for two eyes without common visual experience

    Nature 379:251–254.

    https://doi.org/10.1038/379251a0

    • PubMed
    • Google Scholar
20. 1. Grill-Spector K
    2. Malach R

    (2004) The human visual cortex

    Annual Review of Neuroscience 27:649–677.

    https://doi.org/10.1146/annurev.neuro.27.070203.144220

    • PubMed
    • Google Scholar
21. 1. Grill-Spector K
    2. Weiner KS

    (2014) The functional architecture of the ventral temporal cortex and its role in categorization

    Nature Reviews. Neuroscience 15:536–548.

    https://doi.org/10.1038/nrn3747

    • PubMed
    • Google Scholar
22. Software

    1. Harwell J

    (2022) Workbench, version swh:1:rev:42ce80a56f82edb1bea7b34595d4f2b92250e0fc

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:5e6518a819abb4b627ec7847380e1281cb6b3b74;origin=https://github.com/Washington-University/workbench;visit=swh:1:snp:384714f4e5a143b7bbfdf3acea0293453b120546;anchor=swh:1:rev:42ce80a56f82edb1bea7b34595d4f2b92250e0fc
23. 1. Hasson U
    2. Levy I
    3. Behrmann M
    4. Hendler T
    5. Malach R

    (2002) Eccentricity bias as an organizing principle for human high-order object areas

    Neuron 34:479–490.

    https://doi.org/10.1016/s0896-6273(02)00662-1

    • PubMed
    • Google Scholar
24. 1. Holtmaat A
    2. Svoboda K

    (2009) Experience-dependent structural synaptic plasticity in the mammalian brain

    Nature Reviews. Neuroscience 10:647–658.

    https://doi.org/10.1038/nrn2699

    • PubMed
    • Google Scholar
25. 1. Hughes EJ
    2. Winchman T
    3. Padormo F
    4. Teixeira R
    5. Wurie J
    6. Sharma M
    7. Fox M
    8. Hutter J
    9. Cordero-Grande L
    10. Price AN
    11. Allsop J
    12. Bueno-Conde J
    13. Tusor N
    14. Arichi T
    15. Edwards AD
    16. Rutherford MA
    17. Counsell SJ
    18. Hajnal JV

    (2017) A dedicated neonatal brain imaging system

    Magnetic Resonance in Medicine 78:794–804.

    https://doi.org/10.1002/mrm.26462

    • PubMed
    • Google Scholar
26. 1. Huttenlocher PR
    2. de Courten C

    (1987)

    The development of synapses in striate cortex of man

    Human Neurobiology 6:1–9.

    • PubMed
    • Google Scholar
27. 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
28. 1. Jiang J
    2. Zhu W
    3. Shi F
    4. Liu Y
    5. Li J
    6. Qin W
    7. Li K
    8. Yu C
    9. Jiang T

    (2009) Thick visual cortex in the early blind

    The Journal of Neuroscience 29:2205–2211.

    https://doi.org/10.1523/JNEUROSCI.5451-08.2009

    • PubMed
    • Google Scholar
29. 1. Kamps FS
    2. Hendrix CL
    3. Brennan PA
    4. Dilks DD

    (2020) Connectivity at the origins of domain specificity in the cortical face and place networks

    PNAS 117:6163–6169.

    https://doi.org/10.1073/pnas.1911359117

    • PubMed
    • Google Scholar
30. 1. Kanwisher N
    2. McDermott J
    3. Chun MM

    (1998) The fusiform face area: A module in human extrastriate cortex specialized for face perception

    The Journal of Neuroscience 17:4302–4311.

    https://doi.org/10.1109/CDC.2005.1583375

    • Google Scholar
31. 1. Kanwisher N

    (2010) Functional specificity in the human brain: a window into the functional architecture of the mind

    PNAS 107:11163–11170.

    https://doi.org/10.1073/pnas.1005062107

    • PubMed
    • Google Scholar
32. 1. Kuklisova-Murgasova M
    2. Quaghebeur G
    3. Rutherford MA
    4. Hajnal JV
    5. Schnabel JA

    (2012) Reconstruction of fetal brain MRI with intensity matching and complete outlier removal

    Medical Image Analysis 16:1550–1564.

    https://doi.org/10.1016/j.media.2012.07.004

    • PubMed
    • Google Scholar
33. 1. Levy I
    2. Hasson U
    3. Avidan G
    4. Hendler T
    5. Malach R

    (2001) Center-periphery organization of human object areas

    Nature Neuroscience 4:533–539.

    https://doi.org/10.1038/87490

    • PubMed
    • Google Scholar
34. 1. Li Y
    2. Fitzpatrick D
    3. White LE

    (2006) The development of direction selectivity in ferret visual cortex requires early visual experience

    Nature Neuroscience 9:676–681.

    https://doi.org/10.1038/nn1684

    • PubMed
    • Google Scholar
35. 1. Li Y
    2. Van Hooser SD
    3. Mazurek M
    4. White LE
    5. Fitzpatrick D

    (2008) Experience with moving visual stimuli drives the early development of cortical direction selectivity

    Nature 456:952–956.

    https://doi.org/10.1038/nature07417

    • PubMed
    • Google Scholar
36. Software

    1. Li M

    (2022) Matlab codes for the main process in the paper "Development of visual cortex in human neonates are selectively modified by postnatal experience, version swh:1:rev:d9e50dc27b6ae577e644b6da4abc2ec6d68b7900

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:72580b4758aeaf586d88e786ecefe4653d1015c9;origin=https://github.com/MingyangLeee/Neonatal-Visual-Cortex;visit=swh:1:snp:2b27d8be902076ea5c35fbfc1f6b387cfb0a6ce3;anchor=swh:1:rev:d9e50dc27b6ae577e644b6da4abc2ec6d68b7900
37. 1. Lyall AE
    2. Shi F
    3. Geng X
    4. Woolson S
    5. Li G
    6. Wang L
    7. Hamer RM
    8. Shen D
    9. Gilmore JH

    (2015) Dynamic development of regional cortical thickness and surface area in early childhood

    Cerebral Cortex 25:2204–2212.

    https://doi.org/10.1093/cercor/bhu027

    • PubMed
    • Google Scholar
38. 1. Makropoulos A
    2. Gousias IS
    3. Ledig C
    4. Aljabar P
    5. Serag A
    6. Hajnal JV
    7. Edwards AD
    8. Counsell SJ
    9. Rueckert D

    (2014) Automatic whole brain MRI segmentation of the developing neonatal brain

    IEEE Transactions on Medical Imaging 33:1818–1831.

    https://doi.org/10.1109/TMI.2014.2322280

    • PubMed
    • Google Scholar
39. 1. Makropoulos A
    2. Robinson EC
    3. Schuh A
    4. Wright R
    5. Fitzgibbon S
    6. Bozek J
    7. Counsell SJ
    8. Steinweg J
    9. Vecchiato K
    10. Passerat-Palmbach J
    11. Lenz G
    12. Mortari F
    13. Tenev T
    14. Duff EP
    15. Bastiani M
    16. Cordero-Grande L
    17. Hughes E
    18. Tusor N
    19. Tournier J-D
    20. Hutter J
    21. Price AN
    22. Teixeira RPAG
    23. Murgasova M
    24. Victor S
    25. Kelly C
    26. Rutherford MA
    27. Smith SM
    28. Edwards AD
    29. Hajnal JV
    30. Jenkinson M
    31. Rueckert D

    (2018) The developing human connectome project: a minimal processing pipeline for neonatal cortical surface reconstruction

    NeuroImage 173:88–112.

    https://doi.org/10.1016/j.neuroimage.2018.01.054

    • PubMed
    • Google Scholar
40. 1. Natu VS
    2. Rosenke M
    3. Wu H
    4. Querdasi FR
    5. Kular H
    6. Lopez-Alvarez N
    7. Grotheer M
    8. Berman S
    9. Mezer AA
    10. Grill-Spector K

    (2021) Infants’ cortex undergoes microstructural growth coupled with myelination during development

    Communications Biology 4:1191.

    https://doi.org/10.1038/s42003-021-02706-w

    • PubMed
    • Google Scholar
41. 1. Newman MEJ

    (2006) Modularity and community structure in networks

    PNAS 103:8577–8582.

    https://doi.org/10.1073/pnas.0601602103

    • PubMed
    • Google Scholar
42. 1. Nithianantharajah J
    2. Hannan AJ

    (2006) Enriched environments, experience-dependent plasticity and disorders of the nervous system

    Nature Reviews. Neuroscience 7:697–709.

    https://doi.org/10.1038/nrn1970

    • PubMed
    • Google Scholar
43. 1. Nordt M
    2. Gomez J
    3. Natu VS
    4. Rezai AA
    5. Finzi D
    6. Kular H
    7. Grill-Spector K

    (2021) Cortical recycling in high-level visual cortex during childhood development

    Nature Human Behaviour 5:1686–1697.

    https://doi.org/10.1038/s41562-021-01141-5

    • PubMed
    • Google Scholar
44. 1. Poldrack RA
    2. Baker CI
    3. Durnez J
    4. Gorgolewski KJ
    5. Matthews PM
    6. Munafò MR
    7. Nichols TE
    8. Poline JB
    9. Vul E
    10. Yarkoni T

    (2017) Scanning the horizon: towards transparent and reproducible neuroimaging research

    Nature Reviews. Neuroscience 18:115–126.

    https://doi.org/10.1038/nrn.2016.167

    • PubMed
    • Google Scholar
45. 1. Robinson EC
    2. Garcia K
    3. Glasser MF
    4. Chen Z
    5. Coalson TS
    6. Makropoulos A
    7. Bozek J
    8. Wright R
    9. Schuh A
    10. Webster M
    11. Hutter J
    12. Price A
    13. Cordero Grande L
    14. Hughes E
    15. Tusor N
    16. Bayly PV
    17. Van Essen DC
    18. Smith SM
    19. Edwards AD
    20. Hajnal J
    21. Jenkinson M
    22. Glocker B
    23. Rueckert D

    (2018) Multimodal surface matching with higher-order smoothness constraints

    NeuroImage 167:453–465.

    https://doi.org/10.1016/j.neuroimage.2017.10.037

    • PubMed
    • Google Scholar
46. 1. Roy A
    2. Wang S
    3. Meschede-Krasa B
    4. Breffle J
    5. Van Hooser SD

    (2020) An early phase of instructive plasticity before the typical onset of sensory experience

    Nature Communications 11:1–15.

    https://doi.org/10.1038/s41467-019-13872-1

    • PubMed
    • Google Scholar
47. 1. Rubinov M
    2. Sporns O

    (2010) Complex network measures of brain connectivity: uses and interpretations

    NeuroImage 52:1059–1069.

    https://doi.org/10.1016/j.neuroimage.2009.10.003

    • PubMed
    • Google Scholar
48. Conference

    1. Schuh A
    2. Makropoulos A
    3. Wright R
    4. Robinson EC
    5. Tusor N
    6. Steinweg J
    7. Hughes E
    8. Cordero Grande L
    9. Price A
    10. Hutter J
    11. Hajnal JV
    12. Rueckert D

    (2017) A deformable model for the reconstruction of the neonatal cortex

    2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017. pp. 800–803.

    https://doi.org/10.1109/ISBI.2017.7950639

    • Google Scholar
49. 1. Soun JE
    2. Liu MZ
    3. Cauley KA
    4. Grinband J

    (2017) Evaluation of neonatal brain myelination using the T1- and T2-weighted MRI ratio

    Journal of Magnetic Resonance Imaging 46:690–696.

    https://doi.org/10.1002/jmri.25570

    • PubMed
    • Google Scholar
50. 1. Sowell ER
    2. Thompson PM
    3. Leonard CM
    4. Welcome SE
    5. Kan E
    6. Toga AW

    (2004) Longitudinal mapping of cortical thickness and brain growth in normal children

    The Journal of Neuroscience 24:8223–8231.

    https://doi.org/10.1523/JNEUROSCI.1798-04.2004

    • PubMed
    • Google Scholar
51. 1. Vincent JL
    2. Patel GH
    3. Fox MD
    4. Snyder AZ
    5. Baker JT
    6. Van Essen DC
    7. Zempel JM
    8. Snyder LH
    9. Corbetta M
    10. Raichle ME

    (2007) Intrinsic functional architecture in the anaesthetized monkey brain

    Nature 447:83–86.

    https://doi.org/10.1038/nature05758

    • PubMed
    • Google Scholar
52. 1. White LE
    2. Fitzpatrick D

    (2007) Vision and cortical MAP development

    Neuron 56:327–338.

    https://doi.org/10.1016/j.neuron.2007.10.011

    • PubMed
    • Google Scholar
53. 1. Wiesel TN

    (1982) The postnatal development of the visual cortex and the influence of environment

    Bioscience Reports 2:351–377.

    https://doi.org/10.1007/BF01119299

    • PubMed
    • Google Scholar
54. 1. Yan C-G
    2. Wang X-D
    3. Zuo X-N
    4. Zang Y-F

    (2016) DPABI: data processing & analysis for (resting-state) brain imaging

    Neuroinformatics 14:339–351.

    https://doi.org/10.1007/s12021-016-9299-4

    • PubMed
    • Google Scholar

Author details

1. Mingyang Li

   Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China

   Contribution

   Conceptualization, Data curation, Software, 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:" 0000-0002-1639-9891

2. Tingting Liu

   Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China

   Contribution

   Data curation, Software, Validation, Investigation, Methodology

   Competing interests

   No competing interests declared

3. Xinyi Xu

   Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China

   Contribution

   Software, Validation, Visualization, Methodology

   Competing interests

   No competing interests declared

4. Qingqing Wen

   Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China

   Contribution

   Visualization, Methodology

   Competing interests

   No competing interests declared

5. Zhiyong Zhao

   Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China

   Contribution

   Software, Methodology

   Competing interests

   No competing interests declared

6. Xixi Dang

   Department of Psychology, Zhejiang Sci-Tech University, Hangzhou, China

   Contribution

   Conceptualization, Investigation

   Competing interests

   No competing interests declared

7. Yi Zhang

   Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China

   Contribution

   Resources, Data curation

   Competing interests

   No competing interests declared

8. Dan Wu

   1. Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
   2. Children's Hospital School of Medicine, Zhejiang University, Hangzhou, China
   3. Binjiang Institute of Zhejiang University, Hangzhou, China

   Contribution

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

   For correspondence

   danwu.bme@zju.edu.cn

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-9303-5821

• 808

  views

• 126

  downloads

• 12

  citations

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