Skip to main content
Brain and Behavior logoLink to Brain and Behavior
. 2023 May 29;13(7):e3068. doi: 10.1002/brb3.3068

Gestational age‐related changes in relaxation times of neonatal brain by quantitative synthetic magnetic resonance imaging

Yan Dong 1,2, Xianyu Deng 3, Meizhen Xie 1, Lan Yu 1, Long Qian 4, Ge Chen 2, Yali Zhang 2, Yanyun Tang 2, Zhipeng Zhou 2, Liling Long 1,
PMCID: PMC10338790  PMID: 37248768

Abstract

Objective

This study aimed to explore the correlation between T1 and T2 relaxation times of synthetic MRI (SyMRI) and gestational age (GA) in each hemisphere of preterm and term newborns at the initial 28 days of birth.

Methods

Seventy preterm and full‐term infants were prospectively included in this study. All subjects completed 3.0 T routine MRI and SyMRI (MAGiC) one‐stop scanning within 28 days of birth (aged 34–42 W at examination). The SyMRI postprocessing software (v8.0.4) was used to measure the T1 and T2 relaxation values of each brain region. The linear regression equations of quantitative relaxation values with GA were established to compare the variation speed in each brain region.

Results

A significant linear and negative correlation was found between relaxation times and GA in the neonate cerebral cortex and subcortical gray and white matter regions (All p<.05). The relaxation time of the left centrum semiovale decreased with maximum variance with increasing GA among all white matter regions (T1: b = –51.45, β = –0.65, p < .0001; T2: b = –8.77, β = –0.71, p < .0001), whereas the right posterior limb of internal capsule showed minimal variance (T1: b = –27.94, β = –0.60, p < .0001; T2: b = –3.25, β = –0.68, p < .0001). Among all gray matter regions, the right globus pallidus and thalamus indicated the most significant decreasing degree of T1 and T2 relaxation values with GA (right globus pallidus T1: b = –33.14, β = –0.64, p < .0001; right thalamus T2: b = –3.94, β = –0.81, p < .0001), and the right and left occipital lobes indicated the least significant decreasing degree of T1 and T2 relaxation values with GA, respectively (right occipital lobes T1: b = –11.18, β = –0.26, p = .028; left occipital lobes T2: b = –1.22, β = –0.27, p = .024).

Conclusions

SyMRI could quantitatively evaluate the linear changes of T1 and T2 relaxation values with GA in brain gray and white matter of preterm and term neonates.

Keywords: linear regression analysis, newborn, quantification of relaxation time, synthetic MRI


1. SyMRI could quantitatively evaluate the linear changes of T1 and T2 relaxation values with GA in brain gray and white matter of preterm and term neonates. 2. The relaxation times of white matter indicated faster‐decreasing trends with the increasing GA than those of gray matter. 3. The relaxation times of gray and white matters were asymmetrical between the left and right cerebral hemispheres with GA.

graphic file with name BRB3-13-e3068-g004.jpg

1. INTRODUCTION

Preterm birth is a global health problem and the leading cause of death in children under 5 years of age (Liu et al., 2016), associated with more than 30,000 infants yearly, second only to congenital abnormalities in China (He et al., 2017). Preterm birth is also the most common cause of chronic neurological diseases due to cerebral palsy and neurobehavioral disorders (Back, 2017). Considering the complexity and rapid development of the neonatal brain, the evaluation of neonatal brain structure and maturity may be the key to detecting early preterm brain injury and subsequent neurodevelopmental disorders for timely treatment and surveillance (Fenchel et al., 2020; Holland et al., 2014; Huppi et al., 1998).

Magnetic resonance imaging (MRI) is a valuable tool for neuroimaging evaluation and predicting neonatal motor and cognitive function using quantitative approaches (de Vries et al., 2015; Fenchel et al., 2020; Huppi et al., 1998). In recent years, synthetic MRI (SyMRI) has emerged with significant advantages in quantitative T1, T2, and proton density (PD) values (Hagiwara et al., 2017; Lee et al., 2018) rapid imaging and postprocessing (Hagiwara et al., 2017), and robust assessment of brain volume (Kim et al., 2022).

SyMRI is essentially a single‐period, multicontrast imaging technology. SyMRI is much faster than the multiperiod conventional MRI (Liu et al., 2021). A 6‐min scan can obtain quantitative imaging of the relaxation rate covering the whole brain and complete anatomical imaging, whereas conventional MRI brain sequence acquisition takes 20–60 min (Hagiwara et al., 2017). In addition, we can obtain multicontrast images in about 1 min using SyMRI software (Hagiwara et al., 2019).

Relaxation time has been demonstrated to correlate with neonatal brain maturity (Caiwen et al., 2021; Kim et al., 2022). The previous studies have explored the correlation between SyMRI relaxation time and gestational age (GA) in premature infants or children with the assumption of bilateral brain symmetry (Caiwen et al., 2021; McAllister et al., 2017; Vanderhasselt et al., 2021). However, there are rare cases of bilateral brain symmetry in anatomy, function, and gene expression (Dehaene‐Lambertz et al., 2006; Dehaene‐Lambertz et al., 2002; Erberich et al., 2006; Gilmore et al., 2007; Lin et al., 2013; Mahmoudzadeh et al., 2013; McCartney & Hepper, 1999; Sun & Walsh, 2006; Sun et al., 2005). Anatomically, the volume of the left hemisphere of a newborn is larger than that of the right hemisphere (McAllister et al., 2017). On the functional level, early hearing and language processing of infants have left brain advantages (Dehaene‐Lambertz et al., 2006; Gilmore et al., 2007). Asymmetry of frontal lobe activation has been observed at the sixth month of gestation (Dehaene‐Lambertz et al., 2002). Somatosensory response and movement lateralization can be detected at birth or even in the second trimester of pregnancy (Erberich et al., 2006; Mahmoudzadeh et al., 2013). In clinical work, some diseases occur specifically on one side. For example, primary progressive aphasia is characterized by the neurodegeneration of the left cerebral cortex (Mesulam et al., 2022). The lateralization of unilateral Parkinson's disease symptoms corresponds to differences in neuronal molecular biology (Li et al., 2020). Common cerebral hemispheres infarction on both sides of the human brain and bilateral subcortical vascular lesions have asymmetrical effects on attention (Fimm et al., 2001). Current studies have shown that this asymmetry may be closely related to the differences in the internal structure of neurons and tissues on both sides of the brain and the asymmetry of gene expression (Galaburda et al., 1978; Li et al., 2020; Sun & Walsh, 2006). To our knowledge, no one has reported the correlation between the T1 and T2 values of SyMRI and GA in preterm and term neonates from the respective left and right sides of the brain.

This study aimed to explore the correlation between T1 and T2 relaxation times of SyMRI and GA at the initial 28 days of birth in each hemisphere of preterm and term newborns.

2. METHODS

2.1. Patients

We prospectively included preterm and full‐term neonates admitted to the Department of Neonatology, Affiliated Hospital of Guilin Medical College, who underwent head MRI examinations between April 1, 2020, and April 23, 2022. Patients were excluded if they showed one of the following three criteria: (1) the presence of severe complications, including bilirubin encephalopathy, congenital infections, congenital metabolic diseases, craniocerebral malformations, and other organ injuries; (2) the age of MRI examination more than 28 days; (3) obvious motion artifacts or partial pixel loss in SyMRI images. A total of 84 neonates with appropriate weight for gestational age were included initially, in which 11 were excluded due to motion artifacts and 3 were excluded due to partial pixel loss. Ultimately, 70 neonates (34 full‐term and 36 preterm infants) were included in the study (Figure 1). Demographic characteristics, including sex, GA, postmenstrual age (PMA), and birth weight (BW), were included in the study (Table 1). The PMA was calculated based on the sum of GA and age at the MR examination.

FIGURE 1.

FIGURE 1

Flowchart of preterm and full‐term neonates.

TABLE 1.

Demographic parameters and clinical characteristics.

Total number (n = 70)
Quantitative variable

GA (week)

PMA (week)

37.08 ± 2.76

37.64 (36.43, 40.14)

Birth weight (kg) 2.66 ± 0.68
Categorical variable
Sex (female∖male) 22/48

The present prospective study was approved by the Ethics Committee of our hospital (Approval number: 2022WJWZCLL‐15) with the informed consent of the newborn's guardian before the MRI examination.

2.2. MRI examination

All neonates were sedated by enema (10% chloral hydrate 0.5 mL/kg) or intramuscular administration of phenobarbital (10 mg/kg) 30 min before the MRI examination for assurance of quiet and hearing protection in neonates.

Head MRI scans were performed on a GE SIGNATM Architect 3.0T device using a 48‐channel phased front coil. Conventional scans, including axial T2WI PROPELLER, T1WI‐FLAIR (fluid‐attenuated inversion recovery), T2WI‐FLAIR, DWI, sagittal T1WI‐FLAIR, and diffusion‐weighted imaging (DWI), were performed, followed by MAGiC transverse axis sequence scans. The specific magnetic resonance acquisition parameters of this study are shown in Table 2.

TABLE 2.

Magnetic resonance imaging acquisition parameters.

Axial SyMRI Axial T2WI PROPELLER Axial T1‐weighted fluid‐attenuated inversion recovery (FLAIR) Axial T2‐weighted fluid‐attenuated inversion recovery (FLAIR) Axial diffusion‐weighted imaging (DWI) Sag‐T1‐weighted fluid‐attenuated inversion recovery (FLAIR)
Matrix 288×288 320×320 320×224 256×256 160×160 320×224
Field of view (cm2) 180×180 180×180 180×135 180×135 180×135 160×160
Slice thickness (mm) 4 4 4 4 4 4
Spacing (mm) 0.4 0.4 0.4 0.4 0.4 1.0
TR (ms) 4364.0 4922.0 1750.0 4574.0 4574.0 1750.0
TE (ms) 21.8 100.1 24 145.0 90.3 24.0
Pixel size 0.6×0.6×4 0.6×0.6×4 0.6×0.8×4 0.7×0.7×4 1.1×0.8×4 0.5×0.7×4
Number of sections 20 20 20 20 20 15
Acquisition time (min) 5:32 1:24 1:35 1:57 1:02 1:51

2.3. Measurement of quantitative relaxation values in SyMRI

SyMRI sequence is a multilayer, multiecho, and multisaturation delay saturation recovery spin‐echo (SE) acquisition method. The algorithm first uses multiple TE images to determine the T2 relaxation time. Then, images with different saturation delay times (TD) are used to obtain the first estimates of T1 and PD through the exponential fitting of the equation (Warntjes et al., 2008). Finally, T1 and PD are recalculated and refined using the locally effective rollover angles of saturated and excited radio frequency (RF) pulses (Krauss et al., 2015).

Data were imported into GE SIGNATM Architect 3.0T host workstation for postprocessing (Figures 6 and 7). In this study, each brain hemisphere had eight regions of interest (ROIs), including centrum semiovale (CS), posterior limb of internal capsule (PLIC), frontal white matter (FWM), occipital gray matter (OGM), frontal gray matte (FGM), occipital gray matter (OGM), globus pallidus (GP), and thalamus (TH). Each newborn brain had a total of 16 ROIs. The left and right sides were abbreviated as L and R, respectively; for example, the left CS was present as LCS, and the right CS was present as RCS. Two hemispheres were manually depicted on the T1WI sequence of SyMRI for each subject (Figures 4 and 5). All ROIs were drawn by the first author (Yan Dong) of this manuscript. The rules of ROIs are as follows: keep size symmetry on both sides of the same anatomical region as far as possible. After avoiding edges and sulci, the ROI size was selected as the maximum range delineated in the corresponding anatomical area at the selected level. Each ROI was measured three times, and the final value was the average of the three measurements.

FIGURE 6.

FIGURE 6

Quantitative ROI (globus pallidus) mapping and T1 mapping, T2 mapping, R1 mapping, R2 mapping, and PD mapping diagram of one‐term MAGiC T1 FLAIR sequence at basal ganglia level (first figure, first row) obtained by SyMRI scan.

FIGURE 7.

FIGURE 7

Quantitative relaxation time (T1: 2348 ± 178 ms, T2: 276 ± 32 ms) of the right centrum semiovale region of a preterm newborn were measured on MAGiC T1 FLAIR sequence.

FIGURE 4.

FIGURE 4

Schematic diagram of ROI selection (MAGiC T1 FLAIR, axial view of centrum semiovale).

FIGURE 5.

FIGURE 5

Schematic diagram of ROI selection (MAGiC T1 FLAIR, axial view of basal ganglia). White matter region (1–8): 1, 2: centrum semiovale, CS (Figure 4); 3, 4: posterior limb of internal capsule, PLIC; 5, 6: frontal white matter, FWM; 7, 8: occipital white matter, OWM. Gray matter region (9–16): 13, 14: frontal gray matte, FGM; 15, 16: occipital gray matter, OGM; 9, 10: globus pallidus, GP; 11, 12: thalamus, TH.

2.4. Statistical methods

The statistical analysis was completed using IBM SPSS Statistics 26.0. A p value less than 0.05 indicated statistical significance. In this study, PMA was present with nonnormal distribution, and the distribution of BW and GA was evaluated with normality. The distribution of relaxation time variables was evaluated with normality or approximate normality that T2 values of right GP (RGP) and right TH (RTH) and T1 values of left OGM (LOGM) were present with approximately normal distribution. The remaining 13 ROIs on T1 and T2 values were normally distributed data. Single‐factor linear regression was used to explore the relationship between T1 and T2 relaxation times and GA in each hemisphere, and slope (regression coefficient b) was used to evaluate the speed of variation in quantitative relaxation value with GA for each ROI. The t test is the statistical method used to test the significance of regression coefficient values.

3. RESULTS

3.1. Correlation between quantitative relaxation times and neonatal clinical characteristics

The T1 and T2 values of each region were significantly correlated with GA, PMA, and BW (all < .05). GA indicated the strongest correlation with relaxation time among all clinical characteristics. After the adjustment of BW, relaxation time was still significantly correlated with GA in each ROI, whereas it was not significantly correlated with BW after the adjustment of GA. There was no significant correlation between sex and relaxation time (p > .05).

3.2. Single‐factor linear regression analysis between quantitative relaxation time and GA

The decreasing speed of T1 and T2 values was ranked with the increasing GA in each ROI as follows (Table 3):

TABLE 3.

Linear regression of synthetic MRI relaxation time T1, T2, and GA in neonatal brain regions.

T1 T2
ROI Side Adj‐R 2 b β p Value Adj‐R 2 b β p Value
CS R 0.35 −46.19 −0.60 <.0001 0.46 −7.83 −0.68 <.0001
L 0.41 −51.45 −0.65 <.0001 0.50 −8.77 −0.71 <.0001
PLIC R 0.35 −27.94 −0.60 <.0001 0.45 −3.25 −0.68 <.0001
L 0.43 −31.37 −0.66 <.0001 0.47 −3.28 −0.69 <.0001
GP R 0.40 −33.14 −0.64 <.0001 0.52 −3.32 −0.73 <.0001
L 0.37 −30.14 −0.61 <.0001 0.49 −3.28 −0.71 <.0001
TH R 0.45 −29.13 −0.68 <.0001 0.65 −3.94 −0.81 <.0001
L 0.47 −27.14 −0.69 <.0001 0.62 −3.59 −0.79 <.0001
FWM R 0.26 −36.87 −0.52 <.0001 0.35 −6.51 −0.60 <.0001
L 0.20 −32.51 −0.46 <.0001 0.32 −7.13 −0.58 <.0001
FGM R 0.17 −22.78 −0.42 <.0001 0.16 −1.96 −0.42 <.0001
L 0.27 −27.57 −0.53 <.0001 0.32 −2.22 −0.57 <.0001
OWM R 0.38 −39.51 −0.63 <.0001 0.38 −6.52 −0.62 <.0001
L 0.31 −34.54 −0.57 <.0001 0.35 −6.58 −0.60 <.0001
OGM R 0.06 −11.18 −0.26 .028 0.10 −1.28 −0.33 .005
L 0.09 −12.37 −0.32 .007 0.06 −1.22 −0.27 .024

T1 values for each white matter (WM) region: LCS>RCS>ROWM>RFWM>LOWM>LFWM>LPLIC>RPLIC;

T2 values for each WM region: LCS>RCS>LFWM>LOWM>ROWM>RFWM>LPLIC>RPLIC;

T1 values for each gray matter (GM) region: RGP>LGP>RTH>LFGM>LTH>RFGM>LOGM>ROGM;

T2 values for each GM region: RTH>LTH>RGP>LGP>LFGM>RFGM>ROGM>LOGM.

The residuals of the univariate regression models between relaxation times and GA in all ROIs were present with normality, variance homogeneity, and independence.

4. DISCUSSION

In the present study, we determined that SyMRI could linearly and quantitatively evaluate the associations between relaxation time and GA in both preterm and term neonates, which might be used to evaluate the brain maturity and development of white and gray matter.

In this study, gender was not found to significantly influence the quantitative relaxation time of each hemisphere in neonates, which was consistent with the quantitative relaxation study on SyMRI technology in the adult human brain (Hagiwara et al., 2021).

The accuracy and repeatability of quantitative measurements of relaxation time and PD value of brain tissue by SyMRI have been confirmed in previous studies (Krauss et al., 2015; Vanderhasselt et al., 2021; Vanderhasselt et al., 2020).

We found that T1 and T2 relaxation times of WM decreased faster than those of GM as GA increased, which might be attributed to a large amount of myelination in white matter during the neonatal period (Korogi et al., 1996; Morel et al., 2021).

Among all white matter ROIs, the relaxation time of LCS decreased maximally with the increasing GA, whereas RPLIC decreased minimally with the increasing GA (Figures 2 and 3 ). These findings were inconsistent with the study led by Vanderhasselt et al. (2021), which found the obvious significance of PLIC.

FIGURE 2.

FIGURE 2

Linear regression relationship between T1 value and GA in each brain region of the left and right sides of neonates (slope in ms/week).

FIGURE 3.

FIGURE 3

Linear regression relationship between T2 value and GA in each brain region of the left and right sides of neonates (slope in ms/week).

During the first 3 years of life, the increasing anisotropy in nondense white matter structures of CS has been proven to be significantly greater than that in dense white matter structures of PLIC (McGraw1 et al., 2002). However, the rate of myelination in nondense white matter structures has been demonstrated faster histologically compared with dense white matter structures (Brody et al., 1987; Kinney et al., 1988).

The relaxation time of neonatal CS rather than PLIC decreased significantly in a linear dependence on GA, which might be related to the faster myelination rate of nondense white matter structures in the neonatal period. Vanderhasselt et al. (2021) also pointed out no correlation between relaxation time in the frontal lobe, parietal lobe, and central semiovale with corrected GA. Nevertheless, the T1 and T2 values in all ROIs, including the frontal lobe, occipital lobe, and central semiovale, were correlated with GA significantly in the present study (all p < .05), which might have been induced by different GA periods selected in the two studies.

The decreasing T1 value in gray matter with GA was determined with obvious significance in RGP (Figure 2) as the region with the highest nonheme iron content in all ROIs with the property of shortening T1 (Hallgren & Sourander, 1958).

The most significant decreasing T2 value in gray matter with GA was found in RTH (Figure 3), and the linear regression model between thalamic T2 value and GA was identified with the strongest correlation among all ROIs (RTH: Adj‐R 2 = 0.65, LTH: Adj‐R 2 = 0.62, all p < .001), which implied that thalamic T2 value was easily affected by GA in the neonatal period. Although GP and TH belong to deep subcortical gray matter nuclei, abundant white matter tracts remain inside (Qiu et al., 2013). Nearly one‐third of the internal white matter tracts in GP are myelinated at birth (Melbourne et al., 2016), and the myelination process generates and enhances the internal connections between subcortical gray matter and its connections with the cortex (Qiu et al., 2013).

The relaxation time of subcortical gray matter (GP and TH) decreased faster with the increasing GA than that of frontal and occipital gray matter (Figures 2 and 3). The theory about myelination could not fully explain the variation of relaxation time in cortical and subcortical gray matter with GA. There might be internal microstructural changes caused by the late development of nerve cells, proliferation of axons, and glia (Korogi et al., 1996; Melbourne et al., 2016), and even changes in calcium and iron deposition (Raz et al., 2003), which formed the combined action to decrease relaxation time rapidly (Tullo et al., 2019).

Although the iron content in each brain region of the newborn is lower than that of the adult (Hallgren & Sourander, 1958), the present study found that the relaxation time of each brain region in the newborn changed with the speed of GA. The distribution of nonheme iron content in the adult brain, for example, the iron content in the core of the extrapyramidal system was higher than that in the frontal and occipital gray matter of the cerebral cortex, and the concentration of nonheme iron in white matter was higher than that in the gray matter (Hallgren & Sourander, 1958). In this study, the correlation between left and right occipital gray matter relaxation times and GA showed the minimum significance among all ROIs. The changes of T1 and T2 values in the gray matter of the frontal lobe were greater than those in the gray matter of the occipital lobe with GA (Figures 2 and 3), which might be attributed to the rapid maturity of the occipital lobe compared with the frontal lobe (Gilmore et al., 2007) inducting slight variance of relaxation time with GA. This finding was consistent with the previous results that SyMRI relaxation in the occipital lobe of the adult brain remains relatively stable with age, but the frontal lobe changes obviously with age (Badve et al., 2015).

The results of relaxation time variation with GA in the deep subcortical gray matter on both sides indicated that the right side seemed more significant than the left. However, the deep subcortical white matter results contradicted it (Figures 2 and 3). These findings were per the well‐accepted rule that the volume of the subcortical gray matter region was asymmetric to the right, and that of the subcortical white matter region was asymmetric to the left (Dean et al., 2018). It suggested that the different changing rates of relaxation time with GA in subcortical structures between the left and right hemispheres supported the existence of asymmetry in subcortical gray and white matter during the neonatal period, which was consistent with the trend of volume asymmetry. Previous studies about cortical asymmetry have confirmed gene expression asymmetry in the embryonic cortex at the third month of human pregnancy (Sun & Walsh, 2006). It was still unknown whether there is asymmetric gene expression in human subcortical structure asymmetry based on the findings about asymmetric neural coding in the guinea pig auditory cortex on both sides of the hypothalamus in previous animal experiments (King et al., 1999).

The present study found that the right side represented more obvious T1 changes with GA in the frontal and occipital white matter than on the left side (Figure 2), where the corresponding T2 value was more evident on the left side (Figure 3). However, there was no clear left and right dominance with GA in the relaxation time of the frontal and occipital gray matter. On the one hand, there might be more obvious asymmetry in the gray matter for frontal and occipital lobes than white matter (Good et al., 2001). On the other hand, a weaker correlation between the relaxation time of occipital gray matter and GA was found compared with that between white matter and GA (adj‐R 2 of occipital gray matter was the smallest in all ROIs).

4.1. Limitations

First, SyMRI images are too sensitive to generate motion artifacts. Second, the data from the cerebellum, brainstem, and corpus callosum fluctuated clearly with skewed distribution due to the anatomical location and volume effect, which was not included in the present analysis. In addition, even the latest SyMRI 11.1 segmentation algorithm could not achieve accurate distinguishing performance between GM and WM for newborns compared with MANTiS (McAllister et al., 2017). Third, a slice thickness of 4 mm for neonate SyMRI sequences is a bit thick, and ROI measurements for SyMRI are currently available only manually. Fourth, the current study did not apply the promising magnetic resonance fingerprinting technology that could quantify multiple tissue properties (T1, T2, and magnetization transfer) (Hilbert et al., 2020).

Fortunately, relaxation values in each ROI were linearly and negatively correlated with GA, which was almost consistent with the results of the latest research that found that T1 relaxation value was linearly correlated with GA in the newborn brain by quantifying regional differences and maturation of the newborn brain using a 3D magnetic resonance fingerprint (Yu et al., 2022). In addition, we obtained a linear correlation between the T2 value and GA, which was not found by magnetic resonance fingerprinting. Finally, the current study did not evaluate the prognosis of preterm and term neonates. Our research team is identifying the influence of relaxation value on the prognosis of preterm and term neonates.

In conclusion, the linear changes in T1 and T2 relaxation values of neonatal brain SyMRI related to GA reflected the differences and asymmetric differences in the maturity of bilateral cortical and subcortical gray/white matter. It could assist in the early assessment of neonatal neural development and cognitive abnormalities by evaluating the agreement between the relaxation time of each region and GA, considering the linear relationship between the relaxation time of the neonatal brain and GA.

4.2. Moral recognition

This study was approved by the Ethics Committee of the hospital (Approval number: 2022WJWZCLL‐15). Informed consent was obtained from the family members of the subjects before the MR examination.

AUTHOR CONTRIBUTIONS

Yan Dong and Xianyu Deng participated in literature reading, statistical analysis, article conception, and writing. Liling Long guided me in the direction of the project. Zhipeng Zhou and Yanyun Tang gave technical support. Long Qian contributed to postprocessing guidance. Meizhen Xie, Lan Yu, Yali Zhang, and Ge Chen collected original data.

PEER REVIEW

The peer review history for this article is available at https://publons.com/publon/10.1002/brb3.3068.

ACKNOWLEDGMENTS

We want to thank the infants and their families for their support and cooperation. We thank Medjaden Inc. for the scientific editing of this manuscript.

Dong, Y. , Deng, X. , Xie, M. , Yu, L. , Qian, L. , Chen, G. , Zhang, Y. , Tang, Y. , Zhou, Z. , & Long, L. (2023). Gestational age‐related changes in relaxation times of neonatal brain by quantitative synthetic magnetic resonance imaging. Brain and Behavior, 13, e3068. 10.1002/brb3.3068

Yan Dong and Xianyu Deng contributed equally to this study.

Contributor Information

Yan Dong, Email: dongyan@stu.gxmu.edu.cn.

Liling Long, Email: cjr.longliling@vip.163.com.

DATA AVAILABILITY STATEMENT

The data supporting the findings of this study are available from the corresponding author upon reasonable request.

REFERENCES

  1. Back, S. A. (2017). White matter injury in the preterm infant: pathology and mechanisms. Acta Neuropathologica, 134(3), 331–349. 10.1007/s00401-017-1718-6 [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Badve, C. , Yu, A. , Rogers, M. , Ma, D. , Liu, Y. , Schluchter, M. , Sunshine, J. , Griswold, M. , & Gulani, V. (2015). Simultaneous T1 and T2 brain relaxometry in asymptomatic volunteers using magnetic resonance fingerprinting. Tomography, 1(2), 136–144. 10.18383/j.tom.2015.00166 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Brody, B. A. , Kinney, H. C. , Kloman, A. S. , & Gilles, F. H. (1987). Sequence of central nervous system myelination in human infancy. I. An autopsy study of myelination. Journal of Neuropathology & Experimental Neurology, 46(3), 283–301. [DOI] [PubMed] [Google Scholar]
  4. Caiwen, Z. , Xin, Z. , Yanchao, L. , Qingna, X. , & Xiaoan, Z. (2021). Initial application of synthetic MRI in evaluating brain maturation of preterm infants. Chinese Journal of Magnetic Resonance Imaging, 12(12), 1–5. [Google Scholar]
  5. Dean, D. C., 3rd , Planalp, E. M. , Wooten, W. , Schmidt, C. K. , Kecskemeti, S. R. , Frye, C. , Schmidt, N. L. , Goldsmith, H. H. , Alexander, A. L. , & Davidson, R. J. (2018). Investigation of brain structure in the 1‐month infant. Brain Structure and Function, 223(4), 1953–1970. 10.1007/s00429-017-1600-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Dehaene‐Lambertz, G. , Dehaene, S. , & Hertz‐Pannier, L. (2002). Functional neuroimaging of speech perception in infants. Science, 298(5600), 2013–2015. 10.1126/science.1077066 [DOI] [PubMed] [Google Scholar]
  7. Dehaene‐Lambertz, G. , Hertz‐Pannier, L. , & Dubois, J. (2006). Nature and nurture in language acquisition: anatomical and functional brain‐imaging studies in infants. Trends in Neuroscience (Tins), 29(7), 367–373. 10.1016/j.tins.2006.05.011 [DOI] [PubMed] [Google Scholar]
  8. de Vries, L. S. , Benders, M. J. , & Groenendaal, F. (2015). Progress in neonatal neurology with a focus on neuroimaging in the preterm infant. Neuropediatrics, 46(4), 234–241. [DOI] [PubMed] [Google Scholar]
  9. Erberich, S. G. , Panigrahy, A. , Friedlich, P. , Seri, I. , Nelson, M. D. , & Gilles, F. (2006). Somatosensory lateralization in the newborn brain. Neuroimage, 29(1), 155–161. 10.1016/j.neuroimage.2005.07.024 [DOI] [PubMed] [Google Scholar]
  10. Fenchel, D. , Dimitrova, R. , Seidlitz, J. , Robinson, E. C. , Batalle, D. , Hutter, J. , Christiaens, D. , Pietsch, M. , Brandon, J. , Hughes, E. J. , Allsop, J. , O'Keeffe, C. , Price, A. N. , Cordero‐Grande, L. , Schuh, A. , Makropoulos, A. , Passerat‐Palmbach, J. , Bozek, J. , Rueckert, D. , … O'Muircheartaigh, J. (2020). Development of microstructural and morphological cortical profiles in the neonatal brain. Cerebral Cortex, 30(11), 5767–5779. 10.1093/cercor/bhaa150 [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Fimm, B. , Zahn, R. , Mull, M. , Kemeny, S. , Buchwald, F. , Block, F. , & Schwarz, M. (2001). Asymmetries of visual attention after circumscribed subcortical vascular lesions. Journal of Neurology, Neurosurgery, and Psychiatry, 71(5), 652–657. 10.1136/jnnp.71.5.652 [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Galaburda, A. M. , LeMay, M. , Kemper, T. L. , & Geschwind, N. (1978). Right‐left asymmetrics in the brain. Science, 199(4331), 852–856. 10.1126/science.341314 [DOI] [PubMed] [Google Scholar]
  13. Gilmore, J. H. , Lin, W. , Prastawa, M. W. , Looney, C. B. , Vetsa, Y. S. , Knickmeyer, R. C. , Evans, D. D. , Smith, J. K. , Hamer, R. M. , Lieberman, J. A. , & Gerig, G. (2007). Regional gray matter growth, sexual dimorphism, and cerebral asymmetry in the neonatal brain. Journal of Neuroscience, 27(6), 1255–1260. 10.1523/JNEUROSCI.3339-06.2007 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Good, C. D. , Johnsrude, I. , Ashburner, J. , Henson, R. N. , Friston, K. J. , & Frackowiak, R. S. (2001). Cerebral asymmetry and the effects of sex and handedness on brain structure: A voxel‐based morphometric analysis of 465 normal adult human brains. Neuroimage, 14(3), 685–700. 10.1006/nimg.2001.0857 [DOI] [PubMed] [Google Scholar]
  15. Hagiwara, A. , Fujimoto, K. , Kamagata, K. , Murata, S. , Irie, R. , Kaga, H. , Someya, Y. , Andica, C. , Fujita, S. , Kato, S. , Fukunaga, I. , Wada, A. , Hori, M. , Tamura, Y. , Kawamori, R. , Watada, H. , & Aoki, S. (2021). Age‐related changes in relaxation times, proton density, myelin, and tissue volumes in adult brain analyzed by 2‐Dimensional quantitative synthetic magnetic resonance imaging. Investigative Radiology, 56(3), 163–172. 10.1097/RLI.0000000000000720 [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hagiwara, A. , Hori, M. , Cohen‐Adad, J. , Nakazawa, M. , Suzuki, Y. , Kasahara, A. , Horita, M. , Haruyama, T. , Andica, C. , Maekawa, T. , Kamagata, K. , Kumamaru, K. K. , Abe, O. , & Aoki, S. (2019). Linearity, bias, intrascanner repeatability, and interscanner reproducibility of quantitative multidynamic multiecho sequence for rapid simultaneous relaxometry at 3 T: A validation study with a standardized phantom and healthy controls. Investigative Radiology, 54(1), 39–47. 10.1097/RLI.0000000000000510 [DOI] [PubMed] [Google Scholar]
  17. Hagiwara, A. , Warntjes, M. , Hori, M. , Andica, C. , Nakazawa, M. , Kumamaru, K. K. , Abe, O. , & Aoki, S. (2017). SyMRI of the brain: Rapid quantification of relaxation rates and proton density, with synthetic MRI, automatic brain segmentation, and myelin measurement. Investigative Radiology, 52(10), 647–657. 10.1097/RLI.0000000000000365 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Hallgren, B. , & Sourander, P. (1958). The effect of age on the non‐haemin iron in the human brain. Journal of Neurochemistry, 3(1), 41–51. 10.1111/j.1471-4159.1958.tb12607.x [DOI] [PubMed] [Google Scholar]
  19. He, C. , Liu, L. , Chu, Y. , Perin, J. , Dai, L. , Li, X. , Miao, L. , Kang, L. , Li, Q. , Scherpbier, R. , Guo, S. , Rudan, I. , Song, P. , Chan, K. Y. , Guo, Y. , Black, R. E. , Wang, Y. , & Zhu, J. (2017). National and subnational all‐cause and cause‐specific child mortality in China, 1996–2015: a systematic analysis with implications for the Sustainable Development Goals. The Lancet Global Health, 5(2), e186–e197. 10.1016/S2214-109X(16)30334-5 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hilbert, T. , Xia, D. , Block, K. T. , Yu, Z. , Lattanzi, R. , Sodickson, D. K. , Kober, T. , & Cloos, M. A. (2020). Magnetization transfer in magnetic resonance fingerprinting. Magnetic Resonance in Medicine, 84(1), 128–141. 10.1002/mrm.28096 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Holland, D. , Chang, L. , Ernst, T. M. , Curran, M. , Buchthal, S. D. , Alicata, D. , Skranes, J. , Johansen, H. , Hernandez, A. , Yamakawa, R. , Kuperman, J. M. , & Dale, A. M. (2014). Structural growth trajectories and rates of change in the first 3 months of infant brain development. JAMA Neurology, 71(10), 1266–1274. 10.1001/jamaneurol.2014.1638 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Huppi, P. S. W. S. , Kikinis, R. , Barnes, P. D. , Zientara, G. P. , Jolesz, F. A. , Tsuji, M. K. , & Volpe, J. J. (1998). Quantitative brain magnetic resonance imaging of brain development in premature and mature newborns. Annals of Neurology, 43(No 2), 224–235. 10.1002/ana.410430213 [DOI] [PubMed] [Google Scholar]
  23. Kim, H. G. , Choi, J. W. , Lee, J. H. , Jung, D. E. , & Gho, S. M. (2022). Association of cerebral blood flow and brain tissue relaxation time with neurodevelopmental outcomes of preterm neonates: Multidelay arterial spin labeling and synthetic MRI study. Investigative Radiology, 57(4), 254–262. 10.1097/RLI.0000000000000833 [DOI] [PubMed] [Google Scholar]
  24. King, C. , Nicol, T. , McGee, T. , & Kraus, N. (1999). Thalamic asymmetry is related to acoustic signal complexity. Neuroscience Letters, 267(2), 89–92. 10.1016/S0304-3940(99)00336-5 [DOI] [PubMed] [Google Scholar]
  25. Kinney, H. C. , Brody, B. A. , Kloman, A. S. , & Gilles, F. H. (1988). Sequence of central nervous system myelination in human infancy: II. Patterns of myelination in autopsied infants. Journal of Neuropathology & Experimental Neurology, 47(3), 217–234. [DOI] [PubMed] [Google Scholar]
  26. Korogi, Y. , Takahashi, M. , Sumi, M. , Hirai, T. , Sakamoto, Y. , Ikushima, I. , & Miyayama, H. (1996). MR signal intensity of the perirolandic cortex in the neonate and infant. Neuroradiology, 38(6), 578–584. 10.1007/BF00626104 [DOI] [PubMed] [Google Scholar]
  27. Krauss, W. , Gunnarsson, M. , Andersson, T. , & Thunberg, P. (2015). Accuracy and reproducibility of a quantitative magnetic resonance imaging method for concurrent measurements of tissue relaxation times and proton density. Magnetic Resonance Imaging, 33(5), 584–591. 10.1016/j.mri.2015.02.013 [DOI] [PubMed] [Google Scholar]
  28. Lee, S. M. , Choi, Y. H. , You, S. K. , Lee, W. K. , Kim, W. H. , Kim, H. J. , Lee, S. Y. , & Cheon, H. (2018). Age‐related changes in tissue value properties in children: Simultaneous quantification of relaxation times and proton density using synthetic magnetic resonance imaging. Investigative Radiology, 53(4), 236–245. 10.1097/RLI.0000000000000435 [DOI] [PubMed] [Google Scholar]
  29. Li, P. , Ensink, E. , Lang, S. , Marshall, L. , Schilthuis, M. , Lamp, J. , Vega, I. , & Labrie, V. (2020). Hemispheric asymmetry in the human brain and in Parkinson's disease is linked to divergent epigenetic patterns in neurons. Genome Biology, 21(1), 61. 10.1186/s13059-020-01960-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Lin, P. Y. , Roche‐Labarbe, N. , Dehaes, M. , Fenoglio, A. , Grant, P. E. , & Franceschini, M. A. (2013). Regional and hemispheric asymmetries of cerebral hemodynamic and oxygen metabolism in newborns. Cerebral Cortex, 23(2), 339–348. 10.1093/cercor/bhs023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Liu, L. , Oza, S. , Hogan, D. , Chu, Y. , Perin, J. , Zhu, J. , Lawn, J. E. , Cousens, S. , Mathers, C. , & Black, R. E. (2016). Global, regional, and national causes of under‐5 mortality in 2000–15: An updated systematic analysis with implications for the Sustainable Development Goals. The Lancet, 388(10063), 3027–3035. 10.1016/S0140-6736(16)31593-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Liu, S. , Meng, T. , Russo, C. , Di Ieva, A. , Berkovsky, S. , Peng, L. , Dou, W. , & Qian, L. (2021). Brain volumetric and fractal analysis of synthetic MRI: A comparative study with conventional 3D T1‐weighted images. European Journal of Radiology, 141, 109782. 10.1016/j.ejrad.2021.109782 [DOI] [PubMed] [Google Scholar]
  33. Mahmoudzadeh, M. , Dehaene‐Lambertz, G. , Fournier, M. , Kongolo, G. , Goudjil, S. , Dubois, J. , Grebe, R. , & Wallois, F. (2013). Syllabic discrimination in premature human infants prior to complete formation of cortical layers. PNAS, 110(12), 4846–4851. 10.1073/pnas.1212220110 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. McAllister, A. , Leach, J. , West, H. , Jones, B. , Zhang, B. , & Serai, S. (2017). Quantitative synthetic MRI in children: Normative intracranial tissue segmentation values during development. Ajnr American Journal of Neuroradiology, 38(12), 2364–2372. 10.3174/ajnr.A5398 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. McCartney, G. , & Hepper, P. (1999). Development of lateralized behaviour in the human fetus from 12 to 27 weeks' gestation. Developmental Medicine and Child Neurology, 41(2), 83–86. 10.1017/S0012162299000183 [DOI] [PubMed] [Google Scholar]
  36. McGraw1, P. , Liang, L. , & Provenzale, J. M. (2002). Evaluation of normal age‐related changes in anisotropy during infancy and childhood as shown by diffusion tensor imaging. AJR, 179, 1515–1522. 10.2214/ajr.179.6.1791515 [DOI] [PubMed] [Google Scholar]
  37. Melbourne, A. , Eaton‐Rosen, Z. , Orasanu, E. , Price, D. , Bainbridge, A. , Cardoso, M. J. , Kendall, G. S. , Robertson, N. J. , Marlow, N. , & Ourselin, S. (2016). Longitudinal development in the preterm thalamus and posterior white matter: MRI correlations between diffusion weighted imaging and T2 relaxometry. Human Brain Mapping, 37(7), 2479–2492. 10.1002/hbm.23188 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Mesulam, M. M. , Coventry, C. A. , Bigio, E. H. , Sridhar, J. , Gill, N. , Fought, A. J. , Zhang, H. , Thompson, C. K. , Geula, C. , Gefen, T. , Flanagan, M. , Mao, Q. , Weintraub, S. , & Rogalski, E. J. (2022). Neuropathological fingerprints of survival, atrophy and language in primary progressive aphasia. Brain, 145(6), 2133–2148. 10.1093/brain/awab410 [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Morel, B. , Piredda, G. F. , Cottier, J. P. , Tauber, C. , Destrieux, C. , Hilbert, T. , Sirinelli, D. , Thiran, J. P. , Marechal, B. , & Kober, T. (2021). Normal volumetric and T1 relaxation time values at 1.5 T in segmented pediatric brain MRI using a MP2RAGE acquisition. European Radiology, 31(3), 1505–1516. 10.1007/s00330-020-07194-w [DOI] [PubMed] [Google Scholar]
  40. Qiu, A. , Fortier, M. V. , Bai, J. , Zhang, X. , Chong, Y. S. , Kwek, K. , Saw, S. M. , Godfrey, K. M. , Gluckman, P. D. , & Meaney, M. J. (2013). Morphology and microstructure of subcortical structures at birth: a large‐scale Asian neonatal neuroimaging study. Neuroimage, 65, 315–323. 10.1016/j.neuroimage.2012.09.032 [DOI] [PubMed] [Google Scholar]
  41. Raz, N. , Rodrigue, K. M. , Kennedy, K. M. , Head, D. , Gunning‐Dixon, F. , & Acker, J. D. (2003). Differential aging of the human striatum: Longitudinal evidence. Ajnr American Journal of Neuroradiology, 24(9), 1849–1856. [PMC free article] [PubMed] [Google Scholar]
  42. Sun, T. , Patoine, C. , Abu‐Khalil, A. , Visvader, J. , Sum, E. , Cherry, T. J. , Orkin, S. H. , Geschwind, D. H. , & Walsh, C. A. (2005). Early asymmetry of gene transcription in embryonic human left and right cerebral cortex. Science, 308(5729), 1794–1798. 10.1126/science.1110324 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Sun, T. , & Walsh, C. A. (2006). Molecular approaches to brain asymmetry and handedness. Nature Reviews Neuroscience, 7(8), 655–662. 10.1038/nrn1930 [DOI] [PubMed] [Google Scholar]
  44. Tullo, S. , Patel, R. , Devenyi, G. A. , Salaciak, A. , Bedford, S. A. , Farzin, S. , Wlodarski, N. , Tardif, C. L. , Group, P.‐A. R. , Breitner, J. C. S. , & Chakravarty, M. M. (2019). MR‐based age‐related effects on the striatum, globus pallidus, and thalamus in healthy individuals across the adult lifespan. Human Brain Mapping, 40(18), 5269–5288. 10.1002/hbm.24771 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Vanderhasselt, T. , Naeyaert, M. , Watte, N. , Allemeersch, G. J. , Raeymaeckers, S. , Dudink, J. , de Mey, J. , & Raeymaekers, H. (2020). Synthetic MRI of preterm infants at term‐equivalent age: Evaluation of diagnostic image quality and automated brain volume segmentation. Ajnr American Journal of Neuroradiology, 41(5), 882–888. 10.3174/ajnr.A6533 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Vanderhasselt, T. , Zolfaghari, R. , Naeyaert, M. , Dudink, J. , Buls, N. , Allemeersch, G. J. , Raeymaekers, H. , Cools, F. , & de Mey, J. (2021). Synthetic MRI demonstrates prolonged regional relaxation times in the brain of preterm born neonates with severe postnatal morbidity. NeuroImage: Clinical | Journal, 29, 102544. 10.1016/j.nicl.2020.102544 [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Warntjes, J. B. , Leinhard, O. D. , West, J. , & Lundberg, P. (2008). Rapid magnetic resonance quantification on the brain: Optimization for clinical usage. Magnetic Resonance in Medicine, 60(2), 320–329. 10.1002/mrm.21635 [DOI] [PubMed] [Google Scholar]
  48. Yu, N. , Kim, J. Y. , Han, D. , Kim, S. Y. , Lee, H. M. , Kim, D. H. , & Kim, H. G. (2022). Three‐dimensional magnetic resonance fingerprinting in neonates: Quantifying regional difference and maturation in the brain. Investigative Radiology, 57(1), 44–51. 10.1097/RLI.0000000000000800 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data supporting the findings of this study are available from the corresponding author upon reasonable request.


Articles from Brain and Behavior are provided here courtesy of Wiley

RESOURCES