Share：
Share this content in WeChat
X
[Chinese][PDF]3483
• Special Focu •
Application of grey matter-based spatial statistical analysis methods in neonatal cerebral cortical development
GE Yao  LI Xianjun  BAI Pengxuan  JIA Zhen  LIU Congcong  WANG Miaomiao  SUN Qinli  JIN Chao  YANG Jian 

Cite this article as: GE Y, LI X J, BAI P X, et al. Application of grey matter-based spatial statistical analysis methods in neonatal cerebral cortical development[J]. Chin J Magn Reson Imaging, 2024, 15(9): 11-17. DOI:10.12015/issn.1674-8034.2024.09.003.


[Abstract] Objective To explore developmental changes in microstructural properties of the cerebral cortex during the neonatal period using gray matter-based spatial statistics (GBSS).Materials and Methods From January 2011 to June 2013, 73 neonates who underwent MRI examinations from our hospital were retrospectively recruited. The cortical skeleton was extracted and the diffusion tensor imaging (DTI) covariates fractional anisotropy (FA), and mean diffusivity (MD) were projected onto the cortical skeleton using grey matter-based spatial statistics. Bilaterally symmetrical cortical regions of interest (ROIs) were selected based on neonatal T1WI atlas of brain regions and the mean values of DTI covariates in the frontal, temporal, parietal and occipital cortex as well as the mean values of the corresponding covariates within each ROI were calculated, and the values of DTI covariates in the frontal, temporal, parietal and occipital cortex as well as within each ROI were further correlated with gestational age, corrected birth weight, crown-heel length and head circumference. The correlations between gestational age and birth indicators and cortical FA and MD parameters were statistically analyzed using a general linear model.Results Cortical ROI-based analysis revealed that changes in neonatal cortical FA were based on a turning point of 38 weeks of gestational age. Before the turning point, there was no significant correlation between neonatal frontal, temporal, parietal, and occipital cortical FA and gestational age, and after the turning point, only the parietal cortical FA (r=0.424, P=0.009) increased with increasing gestational age. Based on all 48 cortical ROIs and voxel level analyses, FA values of right superior temporal gyrus, insular cortex, middle frontal gyrus, cingular gyrus, bilateral lateral fronto-orbital gyrus, inferior frontal gyrus, postcentral gyrus, precentral gyrus, and left superior parietal lobule were found to be positively correlated with gestational age, whereas MD values were negatively correlated with gestational age (P<0.05). The highest correlation between FA values and gestational age was found in the right postcentral gyrus (PoCG) (r=0.628, P=0.032), and the highest correlation between MD values and gestational age was found in the left PoCG (r=-0.598, P=0.041). Both voxel and ROI based analyses showed that no significant correlation was found between birthweight, head circumference, crown-heel length and neonatal cortical FA and MD values.Conclusions The neonatal cerebral cortex is characterized by regional heterogeneity, with primary sensory and motor cortex maturing earlier than the association cortex. Compared with birth weight, crown-heel length and head circumference, gestational age is the main index affecting the development of neonatal cerebral cortex.
[Keywords] neonate；gestational age；cerebral cortex；diffusion tensor imaging；gray matter-based spatial statistics

GE Yao1   LI Xianjun1*   BAI Pengxuan1   JIA Zhen1, 2   LIU Congcong1   WANG Miaomiao1   SUN Qinli1   JIN Chao1   YANG Jian1  

1 Department of Medical Imaging, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China

2 School of Future Technology, Xi'an Jiaotong University, Xi'an 710049, China

Corresponding author: LI X J, E-mail: xianj.li@mail.xjtu.edu.cn

Conflicts of interest   None.

Received  2024-03-08
Accepted  2024-08-07
DOI: 10.12015/issn.1674-8034.2024.09.003
Cite this article as: GE Y, LI X J, BAI P X, et al. Application of grey matter-based spatial statistical analysis methods in neonatal cerebral cortical development[J]. Chin J Magn Reson Imaging, 2024, 15(9): 11-17. DOI:10.12015/issn.1674-8034.2024.09.003.

[1]
GALAKHOVA A A, HUNT S, WILBERS R, et al. Evolution of cortical neurons supporting human cognition[J]. Trends Cogn Sci, 2022, 26(11): 909-922. DOI: 10.1016/j.tics.2022.08.012.
[2]
AKULA S K, EXPOSITO-ALONSO D, WALSH C A. Shaping the brain: The emergence of cortical structure and folding[J]. Dev Cell, 2023, 58(24): 2836-2849. DOI: 10.1016/j.devcel.2023.11.004.
[3]
KOSTOVIĆ I, JOVANOV-MILOSEVIĆ N. The development of cerebral connections during the first 20-45 weeks' gestation[J]. Semin Fetal Neonatal Med, 2006, 11(6): 415-422. DOI: 10.1016/j.siny.2006.07.001.
[4]
SHIBATA M, PATTABIRAMAN K, LORENTE-GALDOS B, et al. Regulation of prefrontal patterning and connectivity by retinoic acid[J]. Nature, 2021, 598(7881): 483-488. DOI: 10.1038/s41586-021-03953-x.
[5]
YANG T, VELING M W, ZHAO X F, et al. Migrating pyramidal neurons require DSCAM to bypass the border of the developing cortical plate[J]. J Neurosci, 2022, 42(28): 5510-5521. DOI: 10.1523/jneurosci.0997-21.2022.
[6]
HANSEN A H, HIPPENMEYER S. Non-cell-autonomous mechanisms in radial projection neuron migration in the developing cerebral cortex[J/OL]. Front Cell Dev Biol, 2020, 8: 574382 [2024-03-08]. https://pubmed.ncbi.nlm.nih.gov/33102480/. DOI: 10.3389/fcell.2020.574382.
[7]
VANDERHAEGHEN P, POLLEUX F. Developmental mechanisms underlying the evolution of human cortical circuits[J]. Nat Rev Neurosci, 2023, 24(4): 213-232. DOI: 10.1038/s41583-023-00675-z.
[8]
OHTAKA-MARUYAMA C, OKAMOTO M, ENDO K, et al. Synaptic transmission from subplate neurons controls radial migration of neocortical neurons[J]. Science, 2018, 360(6386): 313-317. DOI: 10.1126/science.aar2866.
[9]
ANDREWS M G, SUBRAMANIAN L, SALMA J, et al. How mechanisms of stem cell polarity shape the human cerebral cortex[J]. Nat Rev Neurosci, 2022, 23(12): 711-724. DOI: 10.1038/s41583-022-00631-3.
[10]
ZHU T, PENG Q, OUYANG A, et al. Neuroanatomical underpinning of diffusion kurtosis measurements in the cerebral cortex of healthy macaque brains[J]. Magn Reson Med, 2021, 85(4): 1895-1908. DOI: 10.1002/mrm.28548.
[11]
VASUNG L, CHARVET C J, SHIOHAMA T, et al. Ex vivo fetal brain MRI: Recent advances, challenges, and future directions[J]. Neuroimage, 2019, 195: 23-37. DOI: 10.1016/j.neuroimage.2019.03.034.
[12]
BREU M, REISINGER D, TAO L, et al. In vivo high-resolution diffusion tensor imaging of the developing neonatal rat cortex and its relationship to glial and dendritic maturation[J]. Brain Struct Funct, 2019, 224(5): 1815-1829. DOI: 10.1007/s00429-019-01878-w.
[13]
OUYANG M, JEON T, SOTIRAS A, et al. Differential cortical microstructural maturation in the preterm human brain with diffusion kurtosis and tensor imaging[J]. Proc Natl Acad Sci U S A, 2019, 116(10): 4681-4688. DOI: 10.1073/pnas.1812156116.
[14]
YU Q, OUYANG A, CHALAK L, et al. Structural development of human fetal and preterm brain cortical plate based on population-averaged templates[J]. Cereb Cortex, 2016, 26(11): 4381-4391. DOI: 10.1093/cercor/bhv201.
[15]
CHAN W Y, YEW D T. Apoptosis and Bcl-2 oncoprotein expression in the human fetal central nervous system[J]. Anat Rec, 1998, 252(2): 165-175. DOI: 10.1002/(sici)1097-0185(199810)252:2<165::Aid-ar2>3.0.Co;2-2.
[16]
BASSER P J, MATTIELLO J, LEBIHAN D. MR diffusion tensor spectroscopy and imaging[J]. Biophys J, 1994, 66(1): 259-267. DOI: 10.1016/S0006-3495(94)80775-1.
[17]
SMYSER T A, SMYSER C D, ROGERS C E, et al. Cortical gray and adjacent white matter demonstrate synchronous maturation in very preterm infants[J]. Cereb Cortex, 2016, 26(8): 3370-3378. DOI: 10.1093/cercor/bhv164.
[18]
BOUYSSI-KOBAR M, BROSSARD-RACINE M, JACOBS M, et al. Regional microstructural organization of the cerebral cortex is affected by preterm birth[J]. Neuroimage Clin, 2018, 18: 871-880. DOI: 10.1016/j.nicl.2018.03.020.
[19]
WANG W, YU Q, LIANG W, et al. Altered cortical microstructure in preterm infants at term-equivalent age relative to term-born neonates[J]. Cereb Cortex, 2023, 33(3): 651-662. DOI: 10.1093/cercor/bhac091.
[20]
DUBOIS J, DEHAENE-LAMBERTZ G, KULIKOVA S, et al. The early development of brain white matter: a review of imaging studies in fetuses, newborns and infants[J]. Neuroscience, 2014, 276: 48-71. DOI: 10.1016/j.neuroscience.2013.12.044.
[21]
MALVIYA S, VOEPEL-LEWIS T, TAIT A R, et al. Depth of sedation in children undergoing computed tomography: validity and reliability of the University of Michigan Sedation Scale (UMSS)[J]. Br J Anaesth, 2002, 88(2): 241-245. DOI: 10.1093/bja/88.2.241.
[22]
XIAO B, CHENG X, LI Q, et al. Weakly Supervised Confidence Learning for Brain MR Image Dense Parcellation, Cham, F, 2019[C]. Springer International Publishing.
[23]
WEI J, SHI F, CUI Z, et al. Consistent Segmentation of Longitudinal Brain MR Images with Spatio-Temporal Constrained Networks, Cham, F, 2021[C]. Springer International Publishing.
[24]
WU J, XIA Y, WANG X, et al. uRP: An integrated research platform for one-stop analysis of medical images[J/OL]. Front Radiol, 2023, 3: 1153784 [2024-03-08]. https://pubmed.ncbi.nlm.nih.gov/33102480/. DOI: 10.3389/fradi.2023.1153784.
[25]
AVANTS B B, TUSTISON N J, SONG G, et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration[J]. Neuroimage, 2011, 54(3): 2033-2044. DOI: 10.1016/j.neuroimage.2010.09.025.
[26]
OISHI K, MORI S, DONOHUE P K, et al. Multi-contrast human neonatal brain atlas: application to normal neonate development analysis[J]. Neuroimage, 2011, 56(1): 8-20. DOI: 10.1016/j.neuroimage.2011.01.051.
[27]
JIN C, LI Y, LI X, et al. Associations of gestational age and birth anthropometric indicators with brain white matter maturation in full-term neonates[J]. Hum Brain Mapp, 2019, 40(12): 3620-3630. DOI: 10.1002/hbm.24620.
[28]
GALE-GRANT O, FENN-MOLTU S, FRANÇA L G S, et al. Effects of gestational age at birth on perinatal structural brain development in healthy term-born babies[J]. Hum Brain Mapp, 2022, 43(5): 1577-1589. DOI: 10.1002/hbm.25743.
[29]
DIPIERO M, RODRIGUES P G, GROMALA A, et al. Applications of advanced diffusion MRI in early brain development: a comprehensive review[J]. Brain Struct Funct, 2023, 228(2): 367-392. DOI: 10.1007/s00429-022-02605-8.
[30]
CALIXTO C, MACHADO-RIVAS F, KARIMI D, et al. Detailed anatomic segmentations of a fetal brain diffusion tensor imaging atlas between 23 and 30 weeks of gestation[J]. Hum Brain Mapp, 2023, 44(4): 1593-1602. DOI: 10.1002/hbm.26160.
[31]
BATALLE D, O'MUIRCHEARTAIGH J, MAKROPOULOS A, et al. Different patterns of cortical maturation before and after 38 weeks gestational age demonstrated by diffusion MRI in vivo[J]. Neuroimage, 2019, 185: 764-775. DOI: 10.1016/j.neuroimage.2018.05.046.
[32]
SYDNOR V J, LARSEN B, BASSETT D S, et al. Neurodevelopment of the association cortices: Patterns, mechanisms, and implications for psychopathology[J]. Neuron, 2021, 109(18): 2820-2846. DOI: 10.1016/j.neuron.2021.06.016.
[33]
VAFAII H, MANDINO F, DESROSIERS-GRÉGOIRE G, et al. Multimodal measures of spontaneous brain activity reveal both common and divergent patterns of cortical functional organization[J/OL]. Nat Commun, 2024, 15(1): 229 [2024-03-08]. https://pubmed.ncbi.nlm.nih.gov/38172111/. DOI: 10.1038/s41467-023-44363-z.
[34]
GRAHAM A M, MARR M, BUSS C, et al. Understanding vulnerability and adaptation in early brain development using network neuroscience[J]. Trends Neurosci, 2021, 44(4): 276-288. DOI: 10.1016/j.tins.2021.01.008.
[35]
OUYANG M, DUBOIS J, YU Q, et al. Delineation of early brain development from fetuses to infants with diffusion MRI and beyond[J]. Neuroimage, 2019, 185: 836-850. DOI: 10.1016/j.neuroimage.2018.04.017.
[36]
GARCIA K E, ROBINSON E C, ALEXOPOULOS D, et al. Dynamic patterns of cortical expansion during folding of the preterm human brain[J]. Proc Natl Acad Sci U S A, 2018, 115(12): 3156-3161. DOI: 10.1073/pnas.1715451115.
[37]
WANG F, LIAN C, WU Z, et al. Developmental topography of cortical thickness during infancy[J]. Proc Natl Acad Sci U S A, 2019, 116(32): 15855-15860. DOI: 10.1073/pnas.1821523116.
[38]
INDER T E, WARFIELD S K, WANG H, et al. Abnormal cerebral structure is present at term in premature infants[J]. Pediatrics, 2005, 115(2): 286-294. DOI: 10.1542/peds.2004-0326.
[39]
KIRKEGAARD H, MÖLLER S, WU C, et al. Associations of birth size, infancy, and childhood growth with intelligence quotient at 5 years of age: a Danish cohort study[J]. Am J Clin Nutr, 2020, 112(1): 96-105. DOI: 10.1093/ajcn/nqaa051.
[40]
YU W H, WANG S T, CHEN L W, et al. Effect of first-month head-size growth trajectory on cognitive outcomes in preterm infants[J]. J Formos Med Assoc, 2022, 121(1Pt 2): 367-374. DOI: 10.1016/j.jfma.2021.05.013.

PREV Evaluation of glymphatic system activity and disease state by diffusion tensor image analysis along the perivascular space index in children with benign Rolandic epilepsy
NEXT Lateralization of brain volume in term newborns based on MR structural imaging
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn