The Study of Altered Brain Structure in Preterm Infants with Low Grade Intraventricular Hemorrhage Utilizing 3D T1WI Whole Brain MR Images

The Study of Altered Brain Structure in Preterm Infants with Low Grade Intraventricular Hemorrhage Utilizing 3D T1WI Whole Brain MR Images | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Study of Altered Brain Structure in Preterm Infants with Low Grade Intraventricular Hemorrhage Utilizing 3D T1WI Whole Brain MR Images Wei-Dan Kong, Qing Zhou, Li-Wei Hu, Feng Shi, Yu-Min Zhong, Qian Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7361958/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Jan, 2026 Read the published version in Chinese Journal of Academic Radiology → Version 1 posted You are reading this latest preprint version Abstract Background There are few studies on the brain structure of preterm infants based on low-grade intraventricular hemorrhage. The purpose of this study was to report changes in total and regional brain structure abnormalities in preterm neonates (PN) with low-grade intraventricular hemorrhage (grades I and II) and no other associated MRI abnormalities, and to correlate these changes with gestational age (GA). Methods We examined 76 preterm neonates (26 with low-grade IVH and 50 without IVH) who showed no focal abnormalities on magnetic resonance imaging (MRI) at term-equivalent age. The structural assessment of the brain involves measuring the surface area, thickness, average curvature, and volume of different regions. Retrospective analysis of brain magnetic resonance images of 25 healthy full-term infants and comparison with premature newborns, whose age after postmenstrual was similar. Results Compared with the control preterm infants, the infants with low-grade IVH had decreases in the following:1) Total brain surface area;2) the surface area in Orbitofrontal-Med Right; 3) brain volume in Right Orbitofrontal-Med and Right Hippocampus and Right Thalamus. Conclusions Our study reveals the potential harmful effects of low-grade IVH on surface area and volume development in preterm infants compared to those without IVH at term-equivalent age, underscoring its clinical significance for neurodevelopment in infants with low-grade IVH. Neonates Premature Mild germinal matrix-intraventricular hemorrhage Brain structure Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Recent advancements in perinatal healthcare have decreased the incidence of severe neonatal brain damage and improved survival rates among premature newborns. However, many preterm neonate survivors experience neurodevelopmental abnormalities, including significant neurological issues such as visual and hearing problems, and cerebral palsy [ 1 ] . Germinal matrix-intraventricular hemorrhage (GMH-IVH) is one of the most common neuropathologic disorders in preterm neonates, and its incidence is increasing with the decrease of gestational age(GA) and birth weight [ 2 – 4 ] . GMH is the ganglionic eminence hemorrhage, which is precursors to the production of oligodendrocytes, astrocytes, and neurons [ 5 ] . GMH may develop into IVH through ependymal rupture. The severity of GMH-IVH is categorized using the Papile grading system. grade I was GMH alone, grade II was IVH occupying lateral ventricle 50%, and grade IV was grade III plus periventricular venous infarction [ 6 ] . Literature reports that severe IVH (grade III and IV) can lead to abnormal neurodevelopmental outcomes, including cognitive impairment, and more severely, cerebral palsy [ 7 , 8 ] .The adverse outcomes caused by Low-grade IVH (grades I and II) are not yet clear, but some research evidence suggests that there are also abnormalities in neurodevelopmental outcomes [ 5 , 7 – 9 ] . Bolisetty et al. concluded in a study of an extremely premature neonates population that low-grade IVH to a higher incidence of abnormal neurodevelopmental outcomes at the age of 2–3 years old [ 10 ] . We assumed that low-grade IVH could potentially interfere with the development of gray matter (GM) and white matter (WM), thereby impacting the brain's microstructure. Voxel-based morphometry (VBM) is a reliable computational method based on brain MRI that can predict the age of the preterm brain as well as neurodevelopmental outcomes. [ 11 , 12 ] .To assess the damage of IVH to changes in brain microstructure,we utilized voxel-wise regional assessments based on DK template generated from uAI Research Portal, including measurements of surface area, thickness, mean curvature, and volume [ 13 ] . The purpose of this study was to report changes in total and regional brain structure abnormalities in preterm neonates (PN) with low-grade intraventricular hemorrhage (grades I and II) and no other associated MRI abnormalities, and to correlate these changes with gestational age (GA). Methods Ethical Review This study was approved by the Institutional Review Board at our hospital and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards (Ethical Approval Number: SCMCIRB-K2022049-1). Study population Retrospective analysis of 76 premature neonates admitted to the neonatal intensive care unit (NICU) in our hospital from October 2020 to April 2022, who underwent MR imaging examinations at term-equivalent age (Fig. 4 ). We excluded premature neonates diagnosed with severe IVH through cranial ultrasound or MRI. Any neonates with congenital malformations and poor MR image quality are excluded. Ultimately, this study included 26 premature infants with low-grade IVH (grade I and II) and 50 premature infants without IVH.The criteria for hemorrhage are low signal on SWI (magnetic sensitive weighted imaging) and high signal on DWI (diffusion weighted imaging). Meanwhile, we retrospectively reviewed 25 healthy full-term newborns within this time range as controls, who had no abnormal MRI findings and no abnormalities found in neurological examinations. Obtain detailed prenatal and neonatal information of each infant through hospitalization history,including maternal diabetes in pregnancy, hypertension in pregnancy, gestational hypothyroidism, neonatal gestational age (GA), birth weight, mode of production, gender, Apgar scores, intrauterine growth restriction, and whether prenatal/postnatal steroids and vitamin k1 are used. Infant’s outcomes included atrial septal defect(ASD), patent ductus arteriosus(PDA), necrotizing enterocolitis(NEC), hyaline membrane disease of lung, tracheal intubation, days on mechanical ventilation, and days of oxygen therapy. Magnetic resonance imaging examinations Neonatal cerebral MRI was performed on a 3.0 Tesla scanner (Discovery MR750; GE Healthcare) with the use of a 32-channel head coil. Feed and wrap non-sedation protocol was used. Oxygen saturation and heart rate were monitored during the MR scan by a pediatrician. Anatomic MR imaging was performed with a sagittal 3D T1WI sequence (TR/TE, 6.6/2.5ms; TI, 800ms; voxel size, 1×1×1 mm 3 ), axial T2-fluid attenuated inversion recovery (FLAIR) sequence (TR/TE, 9000/120ms; TI,2469ms; FOV, 200mm × 200mm; slice thickness, 4mm), axial magnetic sensitive weighted imaging (SWI, TR/TE, 69/60; FOV, 200 × 200mm; section thickness, 2.8mm)and diffusion-weighted image (DWI, TE/TR, 45/3000; FOV, 200 × 200mm; section thickness, 2.8mm). Segmentation and Evaluation of Sub-region The entire brain region of each patient was automatically extracted from a toolkit called uAI Research Portal through a pre trained deep learning model (United Imaging Intelligence, V20230515) [ 14 ] . Details of the method could be referred to previous works (Supplementary Material 1) [ 14 ] . The results of automatic segmentation included a total of 109 sub-regions (Supplementary Material 2), namely DK template. The application of predicting Alzheimer’s disease using DK template generated from uAI Research Portal is detailed illustrated in work of Shui et al [ 13 ] . Surface reconstruction and cerebral cortex analysis In our method, the cortical reconstruction algorithm embedded in Freesurfer, which is an advanced neuroimaging software, was employed for the extraction of cortical features from MRI data [ 15 – 17 ] . Our method facilitates the DK template of brain structures, allowing for the detailed measurement of cortical thickness, surface area, and volume. The process initiates with the reconstruction of the brain's cortical surface, followed by the parcellation of the cerebral cortex into distinct anatomical units based on established neuroanatomical conventions. The above DK template segmentation enables the precise measurement of various cortical attributes, crucial for understanding the neural basis of different cognitive functions and neurological conditions. Statistical Analyses Statistical analysis was conducted using SPSS 22.0.The comparison of clinical variables between groups was conducted using Student's t-test or χ2 analysis.In order to compare the differences in brain volume between different groups, after controlling gender, GA (analysis of premature infants), and postmenopausal MRI age (PMA), regression models were used, and finally the intracranial volume was adjusted for multiple comparisons. Finally, a P-value < 0.05 is statistically significant. Results patients This study included 76 premature neonates and 25 healthy full-term newborns.The average GA of the preterm neonates with and those without low-grade IVH and the full-term neonates was 32.2, 33.9, and 38.9 weeks, respectively. Compared with full-term newborns, premature neonates have a younger birth age, higher cesarean section rate, lower Apgar scores, and lower body weight for MRI examination (Table 1 ).Table 1 showed the basic clinical information of the preterm neonates; 26 manifested as low-grade IVH, whereas 50 did not exhibit IVH. Compared to the without IVH group, the low-grade IVH group manifested a younger birth age and weight (p < 0.001). In this study, it was found that preterm neonates with low-grade IVH had a high rate of antenatal and postnatal corticosteroid use (61.5%,23.1%) and a high incidence of ASD (65.4%) than the without IVH group. Meanwhile, there are significant differences in the duration of mechanical ventilator use, oxygen treatment time, and incidence of pulmonary transparent membrane disease among two groups premature neonates. Table 1 Characteristics of preterm infants and term infants Characteristics Preterm with low-grade IVH (n=26) Preterm without IVH (n=50) Term (n=25) p value Maternal characteristics GDM 9/26 14/50 0 0.366 GH 6/26 14/50 0 0.431 Hypothyroidism 2/26 8/50 0 0.262 Infant characteristics Gestational age, week 32.18 ± 2.66 33.91 ± 5.20 38.94 ± 1.14 <0.001 Birth weight, g 1749.62 ± 517.38 2046.38 ± 441.14 3131.40 ± 496.59 <0.001 Cesarean section 16/26 35/50 7/25 0.002 Male sex 16/26 24/50 13/25 0.533 twins 6/26 13/50 0 0.506 Apgar 1 min 8.19 ± 2.60 8.86 ± 1.47 9.88 ± 0.33 0.010 Apgar 5 min 8.77 ± 2.29 9.44 ± 0.88 9.96 ± 0.20 0.015 IUGR 5/26 9/50 0.563 Antenatal corticosteroid 16/26 29/50 0.481 Postnatal corticosteroid 6/26 9/50 0.404 Vitamin k1 14/26 30/50 0.392 PDA 2/26 3/50 0.561 ASD 17/26 27/50 0.240 NEC 1/26 3/50 0.577 HMID 17/26 18/50 0.009 Days on mechanical ventilator 6.00 ± 5.40 1.40 ± 2.22 <0.001 Days of O 2 therapy 9.27 ± 8.25 5.40 ± 5.27 0.025 Infant characteristics at MRI Age, week 37.47 ± 0.94 36.96 ± 4.32 39.26 ± 1.06 <0.001 Weight, g 2446.08 ± 333.84 2446.6 ± 324.88 3813.40 ± 295.00 <0.001 Height, cm 45.29 ± 3.98 46.92 ± 2.15 50.68 ± 1.07 <0.001 Head circumference, cm 32.84 ± 3.36 33.10 ± 1.02 34.80 ± 0.63 <0.001 Data are the mean ± SD or n (%). MRI, magnetic resonance imaging; GDM, gestational diabetes mellitus; GH, gestational hypertension; IUGR, intrauterine growth retardation; PDA, patent ductus arteriosus; ASD, atrial septal defect; NEC, necrotizing enterocolitis; HMID, Hyaline membrane disease of newborn. Brain structure Analysis Utilizing a general linear model that accounts for gestational age (GA), sex, postmenstrual age (PMA) during the MRI scan, and applying a Bonferroni adjustment to account for multiple comparisons across different regions, it was observed that preterm neonates without IVH had significantly reduced total brain volume (p = 0.001), volume ratio of cerebral gray matter (p < 0.001), cerebral white matter (p = 0.002), subcortical gray matter (p < 0.001), cerebrospinal fluid (p = 0.021), cerebellum (p < 0.001), brainstem (p < 0.001), and overall brain surface area (p < 0.001) in comparison to full-term neonates (Table 2 ). The difference in total brain surface area between preterm neonates with low-grade IVH and those without (p = 0.04) remained statistically significant after controlling for sex, gestational age (GA), postmenstrual age (PMA) at the time of MRI using a general linear model, and applying a Bonferroni correction. Table 2 Comparison of brain structure between without IVH preterm and term infants. Brain volumes, cm 3 Preterm with low-grade IVH (n=26) Preterm IVH (n=50) Term (n = 25) Adj. p value a (preterm without IVH vs. term) Adj. p value b (preterm with vs. without low-grade IVH ) Total brain volume 348.02 ± 46.12 382.74 ± 82.02 460.85 ± 42.31 0.001 0.301 cerebral gray matter 138.11 ± 19.40 153.22 ± 36.46 208.79 ± 22.31 0.000 0.239 Cerebral white matter 148.72 ± 23.56 161.56 ± 21.82 180.69 ± 17.50 0.002 0.117 Cortical gray matter 107.00 ± 13.91 118.80 ± 28.17 161.88 ± 16.87 0.053 0.128 Subcortical gray matter 12.12 ± 2.30 13.62 ± 2.66 16.46 ± 17.44 0.000 0.638 Cerebrospin-al fluid 61.18 ± 13.07 67.96 ± 27.40 71.37 ± 10.17 0.021 0.821 Cerebellum 20.72 ± 4.66 22.15 ± 8.53 31.40 ± 5.90 0.000 0.672 Brainstem 5.24 ± 0.81 5.65 ± 1.14 6.76 ± 0.52 0.000 0.204 Total brain surface area (cm 2 ) 579.49 ± 71.49 644.90 ± 107.00 819.87 ± 70.11 0.000 0.040 Furthermore, employing the same variance analysis method and p-value correction approach to study brain sub-regions utilizing the FreeSurfer Desikan-Killiany (DK) template, it was found that low-grade IVH influences regions DK 18, 36, and 48 (Fig. 1 ). Specifically, this condition led to a reduction in the volume ratio of regions DK 18, 36, and 48 within the right hemisphere (Fig. 2 ), as well as a decrease in the surface area of region DK 18 in the right hemisphere (Fig. 3 , Table 3 ). Table 3 Comparison of DK regions between with and without low-grade IVH preterm. Brodmann atlas regions Preterm with low-grade IVH (n=26) Preterm without IVH (n=50) Adj. p valuea Area (cm 2 ) DK 18 7.07 ± 0.80 7.45 ± 1.98 0.002 Volume (cm 3 ) DK 18 DK 36 DK 48 1.14 ± 0.17 0.44 ± 0.12 1.88 ± 0.38 1.20 ± 0.37 0.52 ± 0.14 2.22 ± 0.55 0.021 0.029 0.010 Discussion Here are the key findings of this study:1)Preterm neonates showed abnormalities in both total and regional brain volumes at term-equivalent age compared to full-term neonates, regardless of PMA at the time of MRI. These findings align with those of several recent studies [ 18 – 20 ] ;2༉These impairments in preterm neonates with low-grade IVH were linked to a reduction in total brain surface area, even when no obvious brain injury was detected on MRI;3)decreased the volume ratio in the right DK 18, 36, and 48 and the surface area in the right 48 in preterm neonates with low-grade IVH compared to those without. The germinal matrix (GM) is a highly active metabolic region characterized by intense angiogenesis, surpassing that of the cerebral cortex at this stage [ 21 ] . Cerebral vessels are specialized to create a blood-brain barrier (BBB), acting as a complex interface between the brain parenchyma and the endothelial cells [ 22 ] . However, in preterm births, changes in BBB components increase its permeability, allowing toxic substances to enter the brain and raising the risk of bleeding [ 23 ] . Coagulation-related enzymes and toxic neurotransmitters can disrupt cell proliferation in the ganglion bulge for up to a month [ 24 ] . The subsequent change involves the presence of extracellular hemoglobin and the activation of microglia in the white matter surrounding the lateral ventricle [ 2 , 8 , 20 , 25 ] . The iron within extracellular hemoglobin oxidizes into a highly reactive form, causing damage to surrounding tissues [ 2 , 8 , 25 , 26 ] . In late pregnancy, brain microglia are relatively abundant and, upon activation, can secrete cytokines that induce excitotoxicity [ 2 , 20 , 27 ] . According to the literature, myelin precursor oligodendrocytes and neurons in the white matter of extremely premature newborns may eventually be damaged [ 2 , 20 , 27 ] . Whether damage can be detected depends on when GMH-IVH occurs and the brain's developmental stage during the MRI examination. In late pregnancy, the increase in cortical surface area is a significant process linked to the gyrification process [ 20 ] . In extremely premature infants with low-grade IVH, there is a reduction in total cortical gray matter volume [ 28 ] . In this study, only the differences in total brain surface area and Orbitofrontal-Med Right surface area and volume were statistically significant. Therefore, we hypothesized that damage to glial progenitor cells would also result in a decrease in cortical surface area. The small sample size may be one reason for the different results. DK 18 belongs to the right medial orbitofrontal cortex, which matures later compared to other cortical areas and continues to develop after term [ 29 ] . There were reports that damage to the right medial orbitofrontal cortex in premature newborns can lead to mental health issues and socio-emotional abnormalities, characterized by specific patterns of reduced sulci [ 30 ] . Adverse effects associated with prematurity impact brain tissue volume by disrupting the development of brain structures. The maturation time of the right medial orbitofrontal cortex is later than that of the left, and its developmental vulnerability depends on GA. The influence of GMH-IVH may be consistent with the developmental stages of these regions; This is also consistent with the results of this study [ 31 , 32 ] . The thalamus plays a role in language function, and damage to it can result in language expression disorders [ 33 ] . The medial dorsal nucleus has a memory function,so premature neonates with GMH-IVH may face poorer academic performance in the future. Between 25 and 34 weeks of development,germinal stroma aids in the production and subsequent migration of GABAergic interneurons to the association nuclei of the cerebral cortex and thalamus, both essential for higher cognitive functions [ 34 ] Therefore, it is speculated that germinal stroma hemorrhage may affect thalamic development. DK 48 belongs to the Right Thalamus, the development of the right thalamus precedes that of the left, and GMH may lead to a reduced number of late-migrating GABAergic neurons, which could account for the decreased volume observed here [ 35 , 36 ] . Hippocampus participates in the composition of medial temporal lobe memory function. The hippocampus has also been shown to play a certain role in cognitive processes, including memory function [ 37 ] . In addition, the hippocampus and its related structures are involved in the processes of reasoning, thinking, and problem-solving abilities [ 38 ] . Compared with other cortical brain regions, the hippocampus matures later in neonatal brain development [ 39 ] .In this study, DK 36 belong to the Right hippocampus, and previous studies have found that the volume of the left hippocampus is smaller than that of the right hippocampus [ 38 , 40 ] . Hence, GMH might cause a decline in the count of GABAergic neurons engaged in late-stage migration, potentially explaining the observed reduction in right hippocampal volume in this study. This study has some limitations, we lack early MRI data because premature infants have weak immune systems and poor temperature regulation, making early MRI examinations unfeasible. Then,we had to exclude a significant number of newborns from MRI studies involving 3DT1WI sequences due to various factors, primarily motion artifacts, which may introduce selection bias. Additionally, the small sample size did not correlate neurodevelopmental outcomes, which weakened the statistical power and may mask the influence of some clinical confounding factors. In the future, research will be conducted in a larger population to clarify the correlations found in this study, improve the accuracy of low-grade IVH in early prognosis of PN, and provide higher value for clinical practice. Conclusions In conclusion,this study indicates that low-grade IVH in premature infants is an independent risk factor for reduced brain volume at postmenstrual age. Further follow-up of the newborns in this study should determine if the neurological development of premature infants with low-grade IVH is impaired due to reduced brain volume. A large cohort study is necessary to determine if the early effects of low-grade IVH on premature infants persist and to intervene early on factors that may impact neonatal development. Declarations Acknowledgements The authors wish to thank Pro.Yu-Min Zhong,Dr Qian Wang,Li-Wei Hu,Qing Zhou,Feng Shi for their help with the manuscript. Authors' Contributions: WD.K and Q.W conceived the experiment, WD.K and LW.H carried out data collection and analysis, Q.Z and F.S carried out the algorithm and analysis of the data, Q.W and YM.Z reviewed manuscript. Funding/Support: This study was supported in part by the National Key Clinical Specialties Construction Program, the Shanghai Committee of Science and Technology (17411965400) (Yu-Min Zhong). Data Availability: The dataset presented in the study is available on request from the corresponding author during submission or after publication. The data are not publicly available due to privary restriction. Conflict of Interests Statement: Authors declared no conflict of interests. Ethical Approval: We accepted all regulations of the Helsinki Protocol in our study. The Ethical Approval Number: SCMCIRB-K2022049-1 References Hack M, Fanaroff AA. Outcomes of children of extremely low birthweight and gestational age in the 1990s[J]. Seminars In Neonatology.2000; SN, 5(2), 89-106. Volpe JJ. Impaired Neurodevelopmental Outcome After Mild Germinal Matrix-Intraventricular Hemorrhage[J]. Pediatrics. 2015;136(6): 1185-1187. Mukerji A, Shah V, Shah PS. Periventricular/Intraventricular Hemorrhage and Neurodevelopmental Outcomes: A Meta-analysis[J]. Pediatrics. 2015;136(6): 1132-1143. Inder TE. Neurodevelopmental impact of low-grade intraventricular hemorrhage in very preterm infants[J]. J Pediatr. 2006;149(2): 152-154. Raybaud C, Ahmad T, Rastegar N, et al. The premature brain: developmental and lesional anatomy[J]. Neuroradiology. 2013;55 Suppl 2: 23-40. Papile LA, Burstein J, Burstein R, et al. Incidence and evolution of subependymal and intraventricular hemorrhage: a study of infants with birth weights less than 1,500 gm[J]. J Pediatr. 1978;92(4): 529-534. Mukerji A, Shah V, Shah PS. Periventricular/Intraventricular Hemorrhage and Neurodevelopmental Outcomes: A Meta-analysis[J]. Pediatrics. 2015;136(6): 1132-1143. Volpe JJ. Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances[J]. Lancet Neurol. 2009;8(1): 110-124. Steiner M, Schwarz H, Kasprian G, et al. Brain Biometry Reveals Impaired Brain Growth in Preterm Neonates with Intraventricular Hemorrhage[J]. Neonatology. 2023;120(2): 225-234. Bolisetty S, Dhawan A, Abdel-Latif M, et al. Intraventricular hemorrhage and neurodevelopmental outcomes in extreme preterm infants[J]. Pediatrics. 2014;133(1): 55-62. Krüger J, Opfer R, Spies L, et al. Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network[J]. Eur Radiol. 2024;34(6): 3578-3587. Liu M, Lu M, Kim SY, et al. Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates[J]. Eur Radiol. 2024;34(6): 3601-3611. Liu S, Jie C, Zheng W, et al. Investigation of Underlying Association Between Whole Brain Regions and Alzheimer's Disease: A Research Based on an Artificial Intelligence Model[J]. Front Aging Neurosci. 2022;14: 872530. Wu J, Xia Y, Wang X, et al. uRP: An integrated research platform for one-stop analysis of medical images[J]. Front Radiol. 2023;3: 1153784. Fischl B. FreeSurfer[J]. Neuroimage. 2012;62(2): 774-781. Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system[J]. Neuroimage. 1999;9(2): 195-207. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction[J]. Neuroimage. 1999;9(2): 179-194. Lee JM, Choi YH, Hong J, et al. Bronchopulmonary Dysplasia Is Associated with Altered Brain Volumes and White Matter Microstructure in Preterm Infants[J]. Neonatology. 1999; 116(2): 163-170. Thompson DK, Kelly CE, Chen J, et al. Early life predictors of brain development at term-equivalent age in infants born across the gestational age spectrum[J]. Neuroimage. 2019;185: 813-824. Argyropoulou MI, Astrakas LG, Xydis VG, et al. Is Low-Grade Intraventricular Hemorrhage in Very Preterm Infants an Innocent Condition? Structural and Functional Evaluation of the Brain Reveals Regional Neurodevelopmental Abnormalities[J]. AJNR Am J Neuroradiol. 2020;41(3): 542-547. Léger C, Dupré N, Aligny C, et al. Glutamate controls vessel-associated migration of GABA interneurons from the pial migratory route via NMDA receptors and endothelial protease activation[J]. Cell Mol Life Sci. 2020;77(10): 1959-1986. Bell AH, Miller SL, Castillo-Melendez M, et al. The Neurovascular Unit: Effects of Brain Insults During the Perinatal Period[J]. Front Neurosci. 2019;13: 1452. Ballabh P. Intraventricular hemorrhage in premature infants: mechanism of disease[J]. Pediatr Res. 2010;67(1): 1-8. Del Bigio MR. Cell proliferation in human ganglionic eminence and suppression after prematurity-associated haemorrhage[J]. Brain. 2011;134(Pt 5): 1344-1361. Ley D, Romantsik O, Vallius S, et al. High Presence of Extracellular Hemoglobin in the Periventricular White Matter Following Preterm Intraventricular Hemorrhage[J]. Front Physiol. 2016;7: 330. Tam EW, Miller SP, Studholme C, et al. Differential effects of intraventricular hemorrhage and white matter injury on preterm cerebellar growth[J]. J Pediatr. 2011;158(3): 366-371. Volpe JJ. The encephalopathy of prematurity--brain injury and impaired brain development inextricably intertwined[J]. Semin Pediatr Neurol. 2009;16(4): 167-178. Vasileiadis GT, Gelman N, Han VK, et al. Uncomplicated intraventricular hemorrhage is followed by reduced cortical volume at near-term age[J]. Pediatrics. 2004;114(3): e367-372. Mouka V, Drougia A, Xydis VG, et al. Functional and structural connectivity of the brain in very preterm babies: relationship with gestational age and body and brain growth[J]. Pediatr Radiol. 2019;49(8): 1078-1084. Ganella EP, Burnett A, Cheong J, et al. Abnormalities in orbitofrontal cortex gyrification and mental health outcomes in adolescents born extremely preterm and/or at an extremely low birth weight[J]. Hum Brain Mapp. 2015;36(3): 1138-1150. Tzarouchi LC, Astrakas LG, Zikou A, et al. Periventricular leukomalacia in preterm children: assessment of grey and white matter and cerebrospinal fluid changes by MRI[J]. Pediatr Radiol. 2009;39(12): 1327-1332. Mitter C, Prayer D, Brugger PC, et al. In vivo tractography of fetal association fibers[J]. PLoS One. 2015;10(3): e0119536. Annoni JM, Khateb A, Gramigna S, et al. Chronic cognitive impairment following laterothalamic infarcts: a study of 9 cases[J]. Arch Neurol. 2003;60(10): 1439-1443. Xu G, Broadbelt KG, Haynes RL, et al. Late development of the GABAergic system in the human cerebral cortex and white matter[J]. J Neuropathol Exp Neurol. 2011;70(10): 841-858. Whitehead K, Papadelis C, Laudiano-Dray MP, et al. The Emergence of Hierarchical Somatosensory Processing in Late Prematurity[J]. Cereb Cortex. 2019;29(5): 2245-2260. Tzarouchi LC, Astrakas LG, Xydis V, et al. Age-related grey matter changes in preterm infants: an MRI study[J]. Neuroimage. 2009;47(4): 1148-1153. Eichenbaum H, Amaral DG, Buffalo EA, et al. Hippocampus at 25[J]. Hippocampus. 2016;26(10): 1238-1249. Yonelinas AP. The hippocampus supports high-resolution binding in the service of perception, working memory and long-term memory[J]. Behav Brain Res. 2013;254: 34-44. Gómez RL, Edgin JO. The extended trajectory of hippocampal development: Implications for early memory development and disorder[J]. Dev Cogn Neurosci. 2016;18: 57-69. Uematsu A, Matsui M, Tanaka C, et al. Developmental trajectories of amygdala and hippocampus from infancy to early adulthood in healthy individuals[J]. PLoS One. 2012;7(10): e46970. Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx Cite Share Download PDF Status: Published Journal Publication published 14 Jan, 2026 Read the published version in Chinese Journal of Academic Radiology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7361958","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":504974567,"identity":"cce6f7c0-10d0-414c-a3f2-9ba09274335b","order_by":0,"name":"Wei-Dan Kong","email":"","orcid":"","institution":"Department of Radiology, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Wei-Dan","middleName":"","lastName":"Kong","suffix":""},{"id":504974568,"identity":"6959c221-669a-4d51-8161-ddbf9f1f679d","order_by":1,"name":"Qing Zhou","email":"","orcid":"","institution":"Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Qing","middleName":"","lastName":"Zhou","suffix":""},{"id":504974569,"identity":"add6d200-6601-446b-b910-1637300a827a","order_by":2,"name":"Li-Wei Hu","email":"","orcid":"","institution":"Department of Radiology, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Li-Wei","middleName":"","lastName":"Hu","suffix":""},{"id":504974570,"identity":"02f288d4-94a9-4dcd-a1e7-a5d14d41dc60","order_by":3,"name":"Feng Shi","email":"","orcid":"","institution":"Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Shi","suffix":""},{"id":504974571,"identity":"7d66f1b5-c614-4ee6-9174-f337f28168fc","order_by":4,"name":"Yu-Min Zhong","email":"","orcid":"","institution":"Department of Radiology, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":false,"prefix":"","firstName":"Yu-Min","middleName":"","lastName":"Zhong","suffix":""},{"id":504974572,"identity":"9aacb02d-647b-4c6d-a656-283442a24198","order_by":5,"name":"Qian Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIiWNgGAWjYNACAyj9wcDGjjQtjDMK0pJJs4yZ58MhxgaC5h8/e/jFmwK7PPn204mfbQwOMDOwHz66Aa+WM3lplnMMkosNzuRuls4xuMPHwJOWdgOfFrMDOWbGPAbMiRskeLcx5xg8Y2aQ4DHDr+X8G5CW+sT5M4BaLAwOMzYQ1HIjx/gxj8HhxIYbQC0MxGixv/HGjHGOwfHEDUC/SPYYpCWzEfKLZH+O8Yc3f6oT57ef3fjhxx8bO372w8fwagECNgkeFC4B5SDA/IGHsKJRMApGwSgYyQAALYhLR2nSWgcAAAAASUVORK5CYII=","orcid":"","institution":"Department of Radiology, Shanghai Children’s Medical Center, School of Medicine, Shanghai Jiao Tong University","correspondingAuthor":true,"prefix":"","firstName":"Qian","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-08-13 07:23:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7361958/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7361958/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s42058-025-00217-9","type":"published","date":"2026-01-14T16:31:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":90381712,"identity":"408eab22-cb68-482a-ac72-61ca545a3ef0","added_by":"auto","created_at":"2025-09-02 06:49:13","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":269912,"visible":true,"origin":"","legend":"\u003cp\u003eStudy design.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7361958/v1/5b7d2651af395a7037d2b1b5.png"},{"id":90383401,"identity":"09a49d7e-eff0-4586-96b2-0b47ba676705","added_by":"auto","created_at":"2025-09-02 06:57:13","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154903,"visible":true,"origin":"","legend":"\u003cp\u003eBased on the FreeSurfer DK template showed that low-grade IVH affects DK 18,36 and 48, and respectively correspond to Right Orbitofrontal-Med and Right Hippocampus and Right Thalamus.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7361958/v1/dcc07320c2763aa064687b79.png"},{"id":90383402,"identity":"861dc52c-08fc-4657-886b-6e39c26f287c","added_by":"auto","created_at":"2025-09-02 06:57:13","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":172592,"visible":true,"origin":"","legend":"\u003cp\u003eBrain area with decreased volume in the Right Orbitofrontal-Med (DK18) and Right Hippocampus (DK36) and Right Thalamus(DK48) of premature infants with low-grade IVH.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7361958/v1/73437635c1d8f0064e2ad7c1.png"},{"id":90381713,"identity":"b83fddac-f702-463f-9e7b-f87283659677","added_by":"auto","created_at":"2025-09-02 06:49:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":248792,"visible":true,"origin":"","legend":"\u003cp\u003eBrain area with decreased volume in the Right Orbitofrontal-Med (DK18) of premature infants with low-grade IVH.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7361958/v1/94c7b315f621f81e72e5b5b8.png"},{"id":100614911,"identity":"cb6252b9-9017-43c7-b146-7bbd539ae82b","added_by":"auto","created_at":"2026-01-19 17:28:19","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1772338,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7361958/v1/0ec237a2-6285-40a4-b999-8d8396f2a47e.pdf"},{"id":90381710,"identity":"7de186a8-d57b-4381-96b0-3cf18a3eebb6","added_by":"auto","created_at":"2025-09-02 06:49:13","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":244588,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-7361958/v1/5e10b0905c2fd2460616ed1d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Study of Altered Brain Structure in Preterm Infants with Low Grade Intraventricular Hemorrhage Utilizing 3D T1WI Whole Brain MR Images","fulltext":[{"header":"Introduction","content":"\u003cp\u003eRecent advancements in perinatal healthcare have decreased the incidence of severe neonatal brain damage and improved survival rates among premature newborns. However, many preterm neonate survivors experience neurodevelopmental abnormalities, including significant neurological issues such as visual and hearing problems, and cerebral palsy\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Germinal matrix-intraventricular hemorrhage (GMH-IVH) is one of the most common neuropathologic disorders in preterm neonates, and its incidence is increasing with the decrease of gestational age(GA) and birth weight\u003csup\u003e[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. GMH is the ganglionic eminence hemorrhage, which is precursors to the production of oligodendrocytes, astrocytes, and neurons\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. GMH may develop into IVH through ependymal rupture. The severity of GMH-IVH is categorized using the Papile grading system. grade I was GMH alone, grade II was IVH occupying lateral ventricle\u0026thinsp;\u0026lt;\u0026thinsp;50%, grade III was IVH accounted for lateral ventricle\u0026thinsp;\u0026gt;\u0026thinsp;50%, and grade IV was grade III plus periventricular venous infarction\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Literature reports that severe IVH (grade III and IV) can lead to abnormal neurodevelopmental outcomes, including cognitive impairment, and more severely, cerebral palsy\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e.The adverse outcomes caused by Low-grade IVH (grades I and II) are not yet clear, but some research evidence suggests that there are also abnormalities in neurodevelopmental outcomes\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Bolisetty et al. concluded in a study of an extremely premature neonates population that low-grade IVH to a higher incidence of abnormal neurodevelopmental outcomes at the age of 2\u0026ndash;3 years old \u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. We assumed that low-grade IVH could potentially interfere with the development of gray matter (GM) and white matter (WM), thereby impacting the brain's microstructure. Voxel-based morphometry (VBM) is a reliable computational method based on brain MRI that can predict the age of the preterm brain as well as neurodevelopmental outcomes.\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e.To assess the damage of IVH to changes in brain microstructure,we utilized voxel-wise regional assessments based on DK template generated from uAI Research Portal, including measurements of surface area, thickness, mean curvature, and volume\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe purpose of this study was to report changes in total and regional brain structure abnormalities in preterm neonates (PN) with low-grade intraventricular hemorrhage (grades I and II) and no other associated MRI abnormalities, and to correlate these changes with gestational age (GA).\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eEthical Review\u003c/h2\u003e\u003cp\u003eThis study was approved by the Institutional Review Board at our hospital and was performed in accordance with the ethical standards laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards (Ethical Approval Number: SCMCIRB-K2022049-1).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eStudy population\u003c/h3\u003e\n\u003cp\u003eRetrospective analysis of 76 premature neonates admitted to the neonatal intensive care unit (NICU) in our hospital from October 2020 to April 2022, who underwent MR imaging examinations at term-equivalent age (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e4\u003c/span\u003e). We excluded premature neonates diagnosed with severe IVH through cranial ultrasound or MRI. Any neonates with congenital malformations and poor MR image quality are excluded. Ultimately, this study included 26 premature infants with low-grade IVH (grade I and II) and 50 premature infants without IVH.The criteria for hemorrhage are low signal on SWI (magnetic sensitive weighted imaging) and high signal on DWI (diffusion weighted imaging). Meanwhile, we retrospectively reviewed 25 healthy full-term newborns within this time range as controls, who had no abnormal MRI findings and no abnormalities found in neurological examinations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eObtain detailed prenatal and neonatal information of each infant through hospitalization history,including maternal diabetes in pregnancy, hypertension in pregnancy, gestational hypothyroidism, neonatal gestational age (GA), birth weight, mode of production, gender, Apgar scores, intrauterine growth restriction, and whether prenatal/postnatal steroids and vitamin k1 are used. Infant\u0026rsquo;s outcomes included atrial septal defect(ASD), patent ductus arteriosus(PDA), necrotizing enterocolitis(NEC), hyaline membrane disease of lung, tracheal intubation, days on mechanical ventilation, and days of oxygen therapy.\u003c/p\u003e\n\u003ch3\u003eMagnetic resonance imaging examinations\u003c/h3\u003e\n\u003cp\u003eNeonatal cerebral MRI was performed on a 3.0 Tesla scanner (Discovery MR750; GE Healthcare) with the use of a 32-channel head coil. Feed and wrap non-sedation protocol was used. Oxygen saturation and heart rate were monitored during the MR scan by a pediatrician. Anatomic MR imaging was performed with a sagittal 3D T1WI sequence (TR/TE, 6.6/2.5ms; TI, 800ms; voxel size, 1\u0026times;1\u0026times;1 mm\u003csup\u003e3\u003c/sup\u003e), axial T2-fluid attenuated inversion recovery (FLAIR) sequence (TR/TE, 9000/120ms; TI,2469ms; FOV, 200mm \u0026times; 200mm; slice thickness, 4mm), axial magnetic sensitive weighted imaging (SWI, TR/TE, 69/60; FOV, 200 \u0026times; 200mm; section thickness, 2.8mm)and diffusion-weighted image (DWI, TE/TR, 45/3000; FOV, 200 \u0026times; 200mm; section thickness, 2.8mm).\u003c/p\u003e\n\u003ch3\u003eSegmentation and Evaluation of Sub-region\u003c/h3\u003e\n\u003cp\u003eThe entire brain region of each patient was automatically extracted from a toolkit called uAI Research Portal through a pre trained deep learning model (United Imaging Intelligence, V20230515)\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Details of the method could be referred to previous works (Supplementary Material 1)\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. The results of automatic segmentation included a total of 109 sub-regions (Supplementary Material 2), namely DK template. The application of predicting Alzheimer\u0026rsquo;s disease using DK template generated from uAI Research Portal is detailed illustrated in work of Shui et al\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eSurface reconstruction and cerebral cortex analysis\u003c/h3\u003e\n\u003cp\u003eIn our method, the cortical reconstruction algorithm embedded in Freesurfer, which is an advanced neuroimaging software, was employed for the extraction of cortical features from MRI data\u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e. Our method facilitates the DK template of brain structures, allowing for the detailed measurement of cortical thickness, surface area, and volume. The process initiates with the reconstruction of the brain's cortical surface, followed by the parcellation of the cerebral cortex into distinct anatomical units based on established neuroanatomical conventions. The above DK template segmentation enables the precise measurement of various cortical attributes, crucial for understanding the neural basis of different cognitive functions and neurological conditions.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analyses\u003c/h2\u003e\u003cp\u003eStatistical analysis was conducted using SPSS 22.0.The comparison of clinical variables between groups was conducted using Student's t-test or χ2 analysis.In order to compare the differences in brain volume between different groups, after controlling gender, GA (analysis of premature infants), and postmenopausal MRI age (PMA), regression models were used, and finally the intracranial volume was adjusted for multiple comparisons. Finally, a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003epatients\u003c/h2\u003e\u003cp\u003eThis study included 76 premature neonates and 25 healthy full-term newborns.The average GA of the preterm neonates with and those without low-grade IVH and the full-term neonates was 32.2, 33.9, and 38.9 weeks, respectively. Compared with full-term newborns, premature neonates have a younger birth age, higher cesarean section rate, lower Apgar scores, and lower body weight for MRI examination (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the basic clinical information of the preterm neonates; 26 manifested as low-grade IVH, whereas 50 did not exhibit IVH. Compared to the without IVH group, the low-grade IVH group manifested a younger birth age and weight (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In this study, it was found that preterm neonates with low-grade IVH had a high rate of antenatal and postnatal corticosteroid use (61.5%,23.1%) and a high incidence of ASD (65.4%) than the without IVH group. Meanwhile, there are significant differences in the duration of mechanical ventilator use, oxygen treatment time, and incidence of pulmonary transparent membrane disease among two groups premature neonates.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCharacteristics of preterm infants and term infants\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCharacteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePreterm with low-grade IVH\u003c/p\u003e\u003cp\u003e(n=26)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePreterm without IVH\u003c/p\u003e\u003cp\u003e(n=50)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003cp\u003e(n=25)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMaternal characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14/50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.366\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14/50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.431\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHypothyroidism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8/50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.262\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfant characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGestational age, week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.91\u0026thinsp;\u0026plusmn;\u0026thinsp;5.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e38.94\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBirth weight, g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1749.62\u0026thinsp;\u0026plusmn;\u0026thinsp;517.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2046.38\u0026thinsp;\u0026plusmn;\u0026thinsp;441.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3131.40\u0026thinsp;\u0026plusmn;\u0026thinsp;496.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCesarean section\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e35/50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7/25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale sex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24/50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e13/25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.533\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etwins\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13/50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.506\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApgar 1 min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.19\u0026thinsp;\u0026plusmn;\u0026thinsp;2.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e8.86\u0026thinsp;\u0026plusmn;\u0026thinsp;1.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eApgar 5 min\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8.77\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.015\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIUGR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9/50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.563\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAntenatal corticosteroid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e29/50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.481\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePostnatal corticosteroid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9/50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.404\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVitamin k1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30/50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.392\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePDA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3/50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.561\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eASD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e27/50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.240\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNEC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3/50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.577\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHMID\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17/26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18/50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDays on\u003c/p\u003e\u003cp\u003emechanical ventilator\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e6.00\u0026thinsp;\u0026plusmn;\u0026thinsp;5.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.40\u0026thinsp;\u0026plusmn;\u0026thinsp;2.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDays of O\u003csub\u003e2\u003c/sub\u003e therapy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9.27\u0026thinsp;\u0026plusmn;\u0026thinsp;8.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.40\u0026thinsp;\u0026plusmn;\u0026thinsp;5.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eInfant characteristics\u003c/p\u003e\u003cp\u003eat MRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, week\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.96\u0026thinsp;\u0026plusmn;\u0026thinsp;4.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e39.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight, g\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2446.08\u0026thinsp;\u0026plusmn;\u0026thinsp;333.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2446.6\u0026thinsp;\u0026plusmn;\u0026thinsp;324.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3813.40\u0026thinsp;\u0026plusmn;\u0026thinsp;295.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight, cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45.29\u0026thinsp;\u0026plusmn;\u0026thinsp;3.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e46.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.68\u0026thinsp;\u0026plusmn;\u0026thinsp;1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHead circumference, cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32.84\u0026thinsp;\u0026plusmn;\u0026thinsp;3.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e33.10\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e34.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e\u0026lt;0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or n (%). MRI, magnetic resonance imaging; GDM, gestational diabetes mellitus; GH, gestational hypertension; IUGR, intrauterine growth retardation; PDA, patent ductus arteriosus; ASD, atrial septal defect; NEC, necrotizing enterocolitis; HMID, Hyaline membrane disease of newborn.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eBrain structure Analysis\u003c/h2\u003e\u003cp\u003eUtilizing a general linear model that accounts for gestational age (GA), sex, postmenstrual age (PMA) during the MRI scan, and applying a Bonferroni adjustment to account for multiple comparisons across different regions, it was observed that preterm neonates without IVH had significantly reduced total brain volume (p\u0026thinsp;=\u0026thinsp;0.001), volume ratio of cerebral gray matter (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cerebral white matter (p\u0026thinsp;=\u0026thinsp;0.002), subcortical gray matter (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), cerebrospinal fluid (p\u0026thinsp;=\u0026thinsp;0.021), cerebellum (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), brainstem (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and overall brain surface area (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in comparison to full-term neonates (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The difference in total brain surface area between preterm neonates with low-grade IVH and those without (p\u0026thinsp;=\u0026thinsp;0.04) remained statistically significant after controlling for sex, gestational age (GA), postmenstrual age (PMA) at the time of MRI using a general linear model, and applying a Bonferroni correction.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of brain structure between without IVH preterm and term infants.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrain volumes,\u003c/p\u003e\u003cp\u003ecm\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePreterm with low-grade IVH\u003c/p\u003e\u003cp\u003e(n=26)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePreterm IVH\u003c/p\u003e\u003cp\u003e(n=50)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;25)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u003cp\u003eAdj. \u003cem\u003ep\u003c/em\u003e value\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(preterm without IVH vs. term)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAdj. \u003cem\u003ep\u003c/em\u003e value\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003cp\u003e(preterm with vs. without\u003c/p\u003e\u003cp\u003elow-grade IVH )\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal brain volume\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e348.02\u0026thinsp;\u0026plusmn;\u0026thinsp;46.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e382.74\u0026thinsp;\u0026plusmn;\u0026thinsp;82.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e460.85\u0026thinsp;\u0026plusmn;\u0026thinsp;42.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.301\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecerebral gray matter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e138.11\u0026thinsp;\u0026plusmn;\u0026thinsp;19.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e153.22\u0026thinsp;\u0026plusmn;\u0026thinsp;36.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e208.79\u0026thinsp;\u0026plusmn;\u0026thinsp;22.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.239\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebral white matter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e148.72\u0026thinsp;\u0026plusmn;\u0026thinsp;23.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e161.56\u0026thinsp;\u0026plusmn;\u0026thinsp;21.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e180.69\u0026thinsp;\u0026plusmn;\u0026thinsp;17.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.117\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCortical gray matter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e107.00\u0026thinsp;\u0026plusmn;\u0026thinsp;13.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e118.80\u0026thinsp;\u0026plusmn;\u0026thinsp;28.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e161.88\u0026thinsp;\u0026plusmn;\u0026thinsp;16.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.053\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.128\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSubcortical gray matter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12.12\u0026thinsp;\u0026plusmn;\u0026thinsp;2.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e16.46\u0026thinsp;\u0026plusmn;\u0026thinsp;17.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.638\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebrospin-al fluid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.18\u0026thinsp;\u0026plusmn;\u0026thinsp;13.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e67.96\u0026thinsp;\u0026plusmn;\u0026thinsp;27.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e71.37\u0026thinsp;\u0026plusmn;\u0026thinsp;10.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.821\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCerebellum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20.72\u0026thinsp;\u0026plusmn;\u0026thinsp;4.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.15\u0026thinsp;\u0026plusmn;\u0026thinsp;8.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e31.40\u0026thinsp;\u0026plusmn;\u0026thinsp;5.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.672\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrainstem\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.65\u0026thinsp;\u0026plusmn;\u0026thinsp;1.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e6.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.204\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal brain surface area\u003c/p\u003e\u003cp\u003e(cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e579.49\u0026thinsp;\u0026plusmn;\u0026thinsp;71.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e644.90\u0026thinsp;\u0026plusmn;\u0026thinsp;107.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003e819.87\u0026thinsp;\u0026plusmn;\u0026thinsp;70.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e0.000\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e0.040\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFurthermore, employing the same variance analysis method and p-value correction approach to study brain sub-regions utilizing the FreeSurfer Desikan-Killiany (DK) template, it was found that low-grade IVH influences regions DK 18, 36, and 48 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Specifically, this condition led to a reduction in the volume ratio of regions DK 18, 36, and 48 within the right hemisphere (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e2\u003c/span\u003e), as well as a decrease in the surface area of region DK 18 in the right hemisphere (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of DK regions between with and without low-grade IVH preterm.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBrodmann atlas regions\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePreterm with low-grade IVH\u003c/p\u003e\u003cp\u003e(n=26)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePreterm without IVH\u003c/p\u003e\u003cp\u003e(n=50)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAdj. \u003cem\u003ep\u003c/em\u003e valuea\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eArea\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(cm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDK 18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.002\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eVolume\u003c/b\u003e\u003c/p\u003e\u003cp\u003e(cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDK 18\u003c/p\u003e\u003cp\u003eDK 36\u003c/p\u003e\u003cp\u003eDK 48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.12\u003c/p\u003e\u003cp\u003e1.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.20\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e\u003cp\u003e0.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e\u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.021\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.029\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003e0.010\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eHere are the key findings of this study:1)Preterm neonates showed abnormalities in both total and regional brain volumes at term-equivalent age compared to full-term neonates, regardless of PMA at the time of MRI. These findings align with those of several recent studies\u003csup\u003e[\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e;2༉These impairments in preterm neonates with low-grade IVH were linked to a reduction in total brain surface area, even when no obvious brain injury was detected on MRI;3)decreased the volume ratio in the right DK 18, 36, and 48 and the surface area in the right 48 in preterm neonates with low-grade IVH compared to those without.\u003c/p\u003e\u003cp\u003eThe germinal matrix (GM) is a highly active metabolic region characterized by intense angiogenesis, surpassing that of the cerebral cortex at this stage\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. Cerebral vessels are specialized to create a blood-brain barrier (BBB), acting as a complex interface between the brain parenchyma and the endothelial cells\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. However, in preterm births, changes in BBB components increase its permeability, allowing toxic substances to enter the brain and raising the risk of bleeding \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eCoagulation-related enzymes and toxic neurotransmitters can disrupt cell proliferation in the ganglion bulge for up to a month\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The subsequent change involves the presence of extracellular hemoglobin and the activation of microglia in the white matter surrounding the lateral ventricle \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The iron within extracellular hemoglobin oxidizes into a highly reactive form, causing damage to surrounding tissues \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. In late pregnancy, brain microglia are relatively abundant and, upon activation, can secrete cytokines that induce excitotoxicity\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. According to the literature, myelin precursor oligodendrocytes and neurons in the white matter of extremely premature newborns may eventually be damaged\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e. Whether damage can be detected depends on when GMH-IVH occurs and the brain's developmental stage during the MRI examination.\u003c/p\u003e\u003cp\u003eIn late pregnancy, the increase in cortical surface area is a significant process linked to the gyrification process \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. In extremely premature infants with low-grade IVH, there is a reduction in total cortical gray matter volume\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In this study, only the differences in total brain surface area and Orbitofrontal-Med Right surface area and volume were statistically significant. Therefore, we hypothesized that damage to glial progenitor cells would also result in a decrease in cortical surface area. The small sample size may be one reason for the different results.\u003c/p\u003e\u003cp\u003eDK 18 belongs to the right medial orbitofrontal cortex, which matures later compared to other cortical areas and continues to develop after term\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. There were reports that damage to the right medial orbitofrontal cortex in premature newborns can lead to mental health issues and socio-emotional abnormalities, characterized by specific patterns of reduced sulci \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Adverse effects associated with prematurity impact brain tissue volume by disrupting the development of brain structures.\u003c/p\u003e\u003cp\u003eThe maturation time of the right medial orbitofrontal cortex is later than that of the left, and its developmental vulnerability depends on GA. The influence of GMH-IVH may be consistent with the developmental stages of these regions; This is also consistent with the results of this study\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe thalamus plays a role in language function, and damage to it can result in language expression disorders\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. The medial dorsal nucleus has a memory function,so premature neonates with GMH-IVH may face poorer academic performance in the future. Between 25 and 34 weeks of development,germinal stroma aids in the production and subsequent migration of GABAergic interneurons to the association nuclei of the cerebral cortex and thalamus, both essential for higher cognitive functions\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e Therefore, it is speculated that germinal stroma hemorrhage may affect thalamic development. DK 48 belongs to the Right Thalamus, the development of the right thalamus precedes that of the left, and GMH may lead to a reduced number of late-migrating GABAergic neurons, which could account for the decreased volume observed here \u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eHippocampus participates in the composition of medial temporal lobe memory function. The hippocampus has also been shown to play a certain role in cognitive processes, including memory function\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. In addition, the hippocampus and its related structures are involved in the processes of reasoning, thinking, and problem-solving abilities \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. Compared with other cortical brain regions, the hippocampus matures later in neonatal brain development\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e.In this study, DK 36 belong to the Right hippocampus, and previous studies have found that the volume of the left hippocampus is smaller than that of the right hippocampus \u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. Hence, GMH might cause a decline in the count of GABAergic neurons engaged in late-stage migration, potentially explaining the observed reduction in right hippocampal volume in this study.\u003c/p\u003e\u003cp\u003eThis study has some limitations, we lack early MRI data because premature infants have weak immune systems and poor temperature regulation, making early MRI examinations unfeasible. Then,we had to exclude a significant number of newborns from MRI studies involving 3DT1WI sequences due to various factors, primarily motion artifacts, which may introduce selection bias. Additionally, the small sample size did not correlate neurodevelopmental outcomes, which weakened the statistical power and may mask the influence of some clinical confounding factors. In the future, research will be conducted in a larger population to clarify the correlations found in this study, improve the accuracy of low-grade IVH in early prognosis of PN, and provide higher value for clinical practice.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion,this study indicates that low-grade IVH in premature infants is an independent risk factor for reduced brain volume at postmenstrual age. Further follow-up of the newborns in this study should determine if the neurological development of premature infants with low-grade IVH is impaired due to reduced brain volume. A large cohort study is necessary to determine if the early effects of low-grade IVH on premature infants persist and to intervene early on factors that may impact neonatal development.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank Pro.Yu-Min Zhong,Dr Qian Wang,Li-Wei Hu,Qing Zhou,Feng Shi for their help with the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWD.K and Q.W conceived the experiment, WD.K and LW.H carried out data collection and analysis, Q.Z and F.S carried out the algorithm and analysis of the data, Q.W and YM.Z reviewed manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding/Support:\u0026nbsp;\u003c/strong\u003eThis study was supported in part by the National Key Clinical Specialties Construction Program, the Shanghai Committee of Science and Technology (17411965400) (Yu-Min Zhong).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability:\u003c/strong\u003e The dataset presented in the study is available on request from the corresponding author during submission or after publication. The data are not\u003c/p\u003e\n\u003cp\u003epublicly available due to privary restriction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interests Statement:\u0026nbsp;\u003c/strong\u003eAuthors declared no conflict of interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u0026nbsp;\u003c/strong\u003eWe accepted all regulations of the Helsinki Protocol in our study. The Ethical Approval Number: SCMCIRB-K2022049-1\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHack M, Fanaroff AA. Outcomes of children of extremely low birthweight and gestational age in the 1990s[J]. Seminars In Neonatology.2000; SN, 5(2), 89-106.\u003c/li\u003e\n\u003cli\u003eVolpe JJ. Impaired Neurodevelopmental Outcome After Mild Germinal Matrix-Intraventricular Hemorrhage[J]. Pediatrics. 2015;136(6): 1185-1187.\u003c/li\u003e\n\u003cli\u003eMukerji A, Shah V, Shah PS. Periventricular/Intraventricular Hemorrhage and Neurodevelopmental Outcomes: A Meta-analysis[J]. Pediatrics. 2015;136(6): 1132-1143. \u003c/li\u003e\n\u003cli\u003eInder TE. Neurodevelopmental impact of low-grade intraventricular hemorrhage in very preterm infants[J]. J Pediatr. 2006;149(2): 152-154.\u003c/li\u003e\n\u003cli\u003eRaybaud C, Ahmad T, Rastegar N, et al. The premature brain: developmental and lesional anatomy[J]. Neuroradiology. 2013;55 Suppl 2: 23-40. \u003c/li\u003e\n\u003cli\u003ePapile LA, Burstein J, Burstein R, et al. Incidence and evolution of subependymal and intraventricular hemorrhage: a study of infants with birth weights less than 1,500 gm[J]. J Pediatr. 1978;92(4): 529-534.\u003c/li\u003e\n\u003cli\u003eMukerji A, Shah V, Shah PS. Periventricular/Intraventricular Hemorrhage and Neurodevelopmental Outcomes: A Meta-analysis[J]. Pediatrics. 2015;136(6): 1132-1143. \u003c/li\u003e\n\u003cli\u003eVolpe JJ. Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances[J]. Lancet Neurol. 2009;8(1): 110-124. \u003c/li\u003e\n\u003cli\u003eSteiner M, Schwarz H, Kasprian G, et al. Brain Biometry Reveals Impaired Brain Growth in Preterm Neonates with Intraventricular Hemorrhage[J]. Neonatology. 2023;120(2): 225-234. \u003c/li\u003e\n\u003cli\u003eBolisetty S, Dhawan A, Abdel-Latif M, et al. Intraventricular hemorrhage and neurodevelopmental outcomes in extreme preterm infants[J]. Pediatrics. 2014;133(1): 55-62. \u003c/li\u003e\n\u003cli\u003eKr\u0026uuml;ger J, Opfer R, Spies L, et al. Voxel-based morphometry in single subjects without a scanner-specific normal database using a convolutional neural network[J]. Eur Radiol. 2024;34(6): 3578-3587. \u003c/li\u003e\n\u003cli\u003eLiu M, Lu M, Kim SY, et al. Brain age predicted using graph convolutional neural network explains neurodevelopmental trajectory in preterm neonates[J]. Eur Radiol. 2024;34(6): 3601-3611. \u003c/li\u003e\n\u003cli\u003eLiu S, Jie C, Zheng W, et al. Investigation of Underlying Association Between Whole Brain Regions and Alzheimer\u0026apos;s Disease: A Research Based on an Artificial Intelligence Model[J]. Front Aging Neurosci. 2022;14: 872530. \u003c/li\u003e\n\u003cli\u003eWu J, Xia Y, Wang X, et al. uRP: An integrated research platform for one-stop analysis of medical images[J]. Front Radiol. 2023;3: 1153784.\u003c/li\u003e\n\u003cli\u003eFischl B. FreeSurfer[J]. Neuroimage. 2012;62(2): 774-781. \u003c/li\u003e\n\u003cli\u003eFischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system[J]. Neuroimage. 1999;9(2): 195-207. \u003c/li\u003e\n\u003cli\u003eDale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction[J]. Neuroimage. 1999;9(2): 179-194. \u003c/li\u003e\n\u003cli\u003eLee JM, Choi YH, Hong J, et al. Bronchopulmonary Dysplasia Is Associated with Altered Brain Volumes and White Matter Microstructure in Preterm Infants[J]. Neonatology. 1999; 116(2): 163-170. \u003c/li\u003e\n\u003cli\u003eThompson DK, Kelly CE, Chen J, et al. Early life predictors of brain development at term-equivalent age in infants born across the gestational age spectrum[J]. Neuroimage. 2019;185: 813-824.\u003c/li\u003e\n\u003cli\u003eArgyropoulou MI, Astrakas LG, Xydis VG, et al. Is Low-Grade Intraventricular Hemorrhage in Very Preterm Infants an Innocent Condition? Structural and Functional Evaluation of the Brain Reveals Regional Neurodevelopmental Abnormalities[J]. AJNR Am J Neuroradiol. 2020;41(3): 542-547. \u003c/li\u003e\n\u003cli\u003eL\u0026eacute;ger C, Dupr\u0026eacute; N, Aligny C, et al. Glutamate controls vessel-associated migration of GABA interneurons from the pial migratory route via NMDA receptors and endothelial protease activation[J]. Cell Mol Life Sci. 2020;77(10): 1959-1986.\u003c/li\u003e\n\u003cli\u003eBell AH, Miller SL, Castillo-Melendez M, et al. The Neurovascular Unit: Effects of Brain Insults During the Perinatal Period[J]. Front Neurosci. 2019;13: 1452.\u003c/li\u003e\n\u003cli\u003eBallabh P. Intraventricular hemorrhage in premature infants: mechanism of disease[J]. Pediatr Res. 2010;67(1): 1-8. \u003c/li\u003e\n\u003cli\u003eDel Bigio MR. Cell proliferation in human ganglionic eminence and suppression after prematurity-associated haemorrhage[J]. Brain. 2011;134(Pt 5): 1344-1361. \u003c/li\u003e\n\u003cli\u003eLey D, Romantsik O, Vallius S, et al. High Presence of Extracellular Hemoglobin in the Periventricular White Matter Following Preterm Intraventricular Hemorrhage[J]. Front Physiol. 2016;7: 330. \u003c/li\u003e\n\u003cli\u003eTam EW, Miller SP, Studholme C, et al. Differential effects of intraventricular hemorrhage and white matter injury on preterm cerebellar growth[J]. J Pediatr. 2011;158(3): 366-371. \u003c/li\u003e\n\u003cli\u003eVolpe JJ. The encephalopathy of prematurity--brain injury and impaired brain development inextricably intertwined[J]. Semin Pediatr Neurol. 2009;16(4): 167-178. \u003c/li\u003e\n\u003cli\u003eVasileiadis GT, Gelman N, Han VK, et al. Uncomplicated intraventricular hemorrhage is followed by reduced cortical volume at near-term age[J]. Pediatrics. 2004;114(3): e367-372. \u003c/li\u003e\n\u003cli\u003eMouka V, Drougia A, Xydis VG, et al. Functional and structural connectivity of the brain in very preterm babies: relationship with gestational age and body and brain growth[J]. Pediatr Radiol. 2019;49(8): 1078-1084. \u003c/li\u003e\n\u003cli\u003eGanella EP, Burnett A, Cheong J, et al. Abnormalities in orbitofrontal cortex gyrification and mental health outcomes in adolescents born extremely preterm and/or at an extremely low birth weight[J]. Hum Brain Mapp. 2015;36(3): 1138-1150. \u003c/li\u003e\n\u003cli\u003eTzarouchi LC, Astrakas LG, Zikou A, et al. Periventricular leukomalacia in preterm children: assessment of grey and white matter and cerebrospinal fluid changes by MRI[J]. Pediatr Radiol. 2009;39(12): 1327-1332.\u003c/li\u003e\n\u003cli\u003eMitter C, Prayer D, Brugger PC, et al. In vivo tractography of fetal association fibers[J]. PLoS One. 2015;10(3): e0119536. \u003c/li\u003e\n\u003cli\u003eAnnoni JM, Khateb A, Gramigna S, et al. Chronic cognitive impairment following laterothalamic infarcts: a study of 9 cases[J]. Arch Neurol. 2003;60(10): 1439-1443.\u003c/li\u003e\n\u003cli\u003eXu G, Broadbelt KG, Haynes RL, et al. Late development of the GABAergic system in the human cerebral cortex and white matter[J]. J Neuropathol Exp Neurol. 2011;70(10): 841-858. \u003c/li\u003e\n\u003cli\u003eWhitehead K, Papadelis C, Laudiano-Dray MP, et al. The Emergence of Hierarchical Somatosensory Processing in Late Prematurity[J]. Cereb Cortex. 2019;29(5): 2245-2260. \u003c/li\u003e\n\u003cli\u003eTzarouchi LC, Astrakas LG, Xydis V, et al. Age-related grey matter changes in preterm infants: an MRI study[J]. Neuroimage. 2009;47(4): 1148-1153.\u003c/li\u003e\n\u003cli\u003eEichenbaum H, Amaral DG, Buffalo EA, et al. Hippocampus at 25[J]. Hippocampus. 2016;26(10): 1238-1249.\u003c/li\u003e\n\u003cli\u003eYonelinas AP. The hippocampus supports high-resolution binding in the service of perception, working memory and long-term memory[J]. Behav Brain Res. 2013;254: 34-44. \u003c/li\u003e\n\u003cli\u003eG\u0026oacute;mez RL, Edgin JO. The extended trajectory of hippocampal development: Implications for early memory development and disorder[J]. Dev Cogn Neurosci. 2016;18: 57-69. \u003c/li\u003e\n\u003cli\u003eUematsu A, Matsui M, Tanaka C, et al. Developmental trajectories of amygdala and hippocampus from infancy to early adulthood in healthy individuals[J]. PLoS One. 2012;7(10): e46970. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Neonates, Premature, Mild germinal matrix-intraventricular hemorrhage, Brain structure","lastPublishedDoi":"10.21203/rs.3.rs-7361958/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7361958/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThere are few studies on the brain structure of preterm infants based on low-grade intraventricular hemorrhage. The purpose of this study was to report changes in total and regional brain structure abnormalities in preterm neonates (PN) with low-grade intraventricular hemorrhage (grades I and II) and no other associated MRI abnormalities, and to correlate these changes with gestational age (GA).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe examined 76 preterm neonates (26 with low-grade IVH and 50 without IVH) who showed no focal abnormalities on magnetic resonance imaging (MRI) at term-equivalent age. The structural assessment of the brain involves measuring the surface area, thickness, average curvature, and volume of different regions. Retrospective analysis of brain magnetic resonance images of 25 healthy full-term infants and comparison with premature newborns, whose age after postmenstrual was similar.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCompared with the control preterm infants, the infants with low-grade IVH had decreases in the following:1) Total brain surface area;2) the surface area in Orbitofrontal-Med Right; 3) brain volume in Right Orbitofrontal-Med and Right Hippocampus and Right Thalamus.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eOur study reveals the potential harmful effects of low-grade IVH on surface area and volume development in preterm infants compared to those without IVH at term-equivalent age, underscoring its clinical significance for neurodevelopment in infants with low-grade IVH.\u003c/p\u003e","manuscriptTitle":"The Study of Altered Brain Structure in Preterm Infants with Low Grade Intraventricular Hemorrhage Utilizing 3D T1WI Whole Brain MR Images","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 06:49:08","doi":"10.21203/rs.3.rs-7361958/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c003dee6-51e8-42e4-88dc-4342af2ea7fa","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-19T16:51:27+00:00","versionOfRecord":{"articleIdentity":"rs-7361958","link":"https://doi.org/10.1007/s42058-025-00217-9","journal":{"identity":"chinese-journal-of-academic-radiology","isVorOnly":false,"title":"Chinese Journal of Academic Radiology"},"publishedOn":"2026-01-14 16:31:07","publishedOnDateReadable":"January 14th, 2026"},"versionCreatedAt":"2025-09-02 06:49:08","video":"","vorDoi":"10.1007/s42058-025-00217-9","vorDoiUrl":"https://doi.org/10.1007/s42058-025-00217-9","workflowStages":[]},"version":"v1","identity":"rs-7361958","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7361958","identity":"rs-7361958","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}