Preterm neonates show a “catch-up” pattern toward full-term in motor development during the neonatal period: a diffusion tensor imaging study

doi:10.21203/rs.3.rs-1862358/v1

Download PDF

Research Article

Preterm neonates show a “catch-up” pattern toward full-term in motor development during the neonatal period: a diffusion tensor imaging study

https://doi.org/10.21203/rs.3.rs-1862358/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Preterm infants show high risk for subsequent neurodevelopmental disability. Early developmental characterization of brain and neurobehavioral function is critical for identifying the high-risk infants, and thus guiding the necessary intervention. This paper aims to elaborate the early evolution of preterm sensorimotor function by exploring the postnatal age-related changes of brain white matter (WM) microstructure and neurobehavioral abilities during the neonatal stage. One hundred eighteen neonates without abnormality on MRI were included. Diffusion tensor imaging (DTI)-derived fractional anisotropy (FA) and neonatal neurobehavioral assessment were separately used to characterize the brain WM microstructure and neurobehavioral development levels. The scatterplot with linear fitting and Pearson correlation was used to investigate the relationships of WM FA and neurobehavioral scores (active tone and behavior) with postnatal age separately for preterm and term neonates. Here, OR (optical radiation), AR (auditory radiation), CST (corticospinal tract), PTR (posterior thalamic radiation) and thal-PSC (thalamus-primary somatosensory cortex) were selected as WMs of interest. Significant correlations of FA with postnatal age were found in preterm CST (P = 0.042), term OR (P = 0.018) and term PTR (P = 0.002). In contrast to term neonates, preterm showed an obviously higher correlation in CST (r = 0.29 vs 0.08), while lower correlations in visual, auditory and somatosensory-associated WMs. Similarly, relatively higher and lower correlations in preterm active tone (r = 0.48 vs 0.35) and behavioral scores (r = 0.36 vs 0.52) were respectively observed. Although being delayed, preterm neonates show a “catch-up” pattern toward term neonates in motor development in the newborn stage, suggesting early intervention may work well in motor function improvement.

Preterm neonates

White matter

Neurobehavioral

Postnatal development

Diffusion tensor imaging

Preterm birth with increasing rates has been a major public health concern worldwide (Woodward and Anderson et al., 2006; Ment and Vohr, 2008; Volpe, 2009). Notably, infants born preterm present high risk for neurodevelopmental impairments in cognitive, behavioral and sensorimotor that appear to be related to early brain abnormalities (Volpe, 2009; Groeschel and Tournier et al., 2014). Converging evidence suggests that preterm infants at term-equivalent age (TEA) exhibit white matter (WM) microstructural abnormalities (Anjari and Srinivasan et al., 2007; Berman and Mukherjee et al., 2005) and numerous follow-up studies consistently find reduced cognitive performance and increased behavioral problems at school years (Ment and Hirtz et al., 2009; Bhutta and Cleves et al., 2002; Anderson, 2003). From this, a major issue confronting clinicians is the early identification of brain abnormalities and thus gives the necessary intervention.

Along with the rapid growth of neonatal brain, early experience could facilitate the brain anatomical remodeling (i.e. plasticity) which implies that early intervention may alter preterm developmental paths and improve neurobehavioral outcomes (Kolb and Gibb, 2011; Bach-y-Rita, 1990). Although the developing brain is more plastic and would be expected to have better recovery mechanism, immature brain presents more vulnerability response to significant insults (Kidokoro and Anderson et al., 2014). In this regard, detailing the developmental status of brain structure is of clinically significance for early intervention. As we know, human brain initiates a rapid growth trajectory following birth in both preterm and term neonates (Dubois and Dehaene-Lambertz et al., 2014). However, both delayed WMs (e.g. corpus callosum) (Thompson and Inder et al., 2011; Jo and Cho et al., 2012) and more mature WMs (e.g. optic radiations) (Groppo and Ricci et al., 2014) in preterm infants at TEA were found relative to the term controls. It raises concern about the intervention effects, i.e. how to make targeting intervention depending on the developmental capacity of preterm brain. From this, early maturity and evolution characteristics of preterm neurobehavioral functions and its associated brain structure may provide possible solutions to such issues.

As a typical neuroimaging, diffusion tensor imaging (DTI) allows quantitative characterization of in-vivo water diffusion pattern to reveal the brain development, e.g. white matter (WM) fiber density and maturation (Hüppi and Dubois, 2006). Particularly, DTI-derived fractal anisotropy (FA) was demonstrated to closely relate with the WM’s microstructural changes that could reflect the myelination and myelination processes (Qiu and Mori et al., 2015; Pavaine and Young et al., 2016). With the aid of such quantitative metric, this study would detail the early evolution of preterm neonatal sensorimotor functions by exploring the postnatal age-related changes of brain white matter (WM) microstructure and neurobehavioral abilities during the neonatal stage.

This study was approved by the institutional review board and all the informed written parental consent was obtained for each participant.

Participants

Of 441 neonates born between October 2014 and June 2015, 230 neonates (GA range, 30–42 weeks) who underwent brain MRI examination for detecting the WM injury were enrolled from department of neonatology of the first author’s affiliation. The inclusion criteria were as follows: (1) successful completion of MRI examination; (2) postnatal age range of days 1–28 at MRI; (3) normal birth weight, crown-heel length, and head circumference for GA (> 5th and < 95th percentiles) and (4) 10-minute Apgar score > 7. Neonates with abnormalities on MRI and/or neonatal diseases, such as cerebral infection, congenital malformation, metabolic disorders, intracranial hemorrhage, small for gestational age, punctate white matter lesions, periventricular leukomalacia and cortical infarction were excluded.

MRI data acquisition

This study was permitted by the local Internal Review Board and written informed consent was obtained from all children’s parents before MRI examination. All MRI data were collected by using a 3.0 Tesla scanner (Signa HDxt, General Electric Medical System, Milwaukee, WI, USA) equipped with an 8-channel head coil. Chloral hydrate (50 mg/kg) was administered orally for sedation at about 30 minutes before the MRI examination. The neonates were swaddled with blankets, and the head was immobilized by a molded foam. Micro-earplugs and earmuffs were placed to reduce the noise exposure. The vital signs of neonates were continuously monitored throughout the examination by attending pediatrician.

The following sequences were acquired for all participants: (1) a transverse three-dimensional fast spoiled gradient-echo T1-weighted sequence (repetition time/echo time, 10ms/4.6ms; NEX, 1; matrix, 256×256; section thickness, 1mm; field of view, 240mm); (2) a transverse fast spin-echo T2-weighted sequence (repetition time/echo time, 4200ms/120ms; NEX, 1.5; matrix, 256×256; section thickness, 4mm; field of view, 180mm); (3) DTI (35 directions; b value, 1000 s/mm²; repetition time/echo time, 5500ms/95ms; NEX, 1; section thickness, 4mm; field of view, 180mm). The scanning time for data acquisition was less than 11 minutes.

Two experienced radiologists (C.C.L. and M.M.W., with 5 and 6 years of interpretation experiences in pediatric MR images) dependently reviewed the MRI data in consensus.

Neonatal neurobehavioral development assessment

Referring to neonatal behavioral neurological assessment (Chinese) (Bao XL and Yu RJ et al., 1991) and Dubowitz neurological assessment (Dubowitz LMS and Dubowitz V et al., 1999), two neurobehavioral categories i.e. behavior (six items) and active tone (four items) were employed to assess the neonatal functional abilities within 5 days before or after MRI exam. Each item has three-point scale of score (0, 1 and 2). Two neonatologists independently conducted the assessment with 25 and 30 years of experiences in consensus.

DTI processing and statistical analysis

DTI processing. All DTI images were processed using Oxford Centre for Functional Magnetic Resonance Imaging of the Brain Software Library (FSL) version 5.0. (https://fsl.fmrib.ox.ac.uk/fsl). The first step was to extract the brain using Brain Extraction Tool (BET), then data was corrected for eddy currents and motion-induced distortions. Fractional anisotropy (FA) map was calculated using the Diffusion Toolbox (FDT) of FSL. All the FA images were normalized to the neonatal Johns Hopkins template. Five typical visual, auditory and sensorimotor WMs extracted by the neonatal Johns Hopkins template were selected as regions of interest, i.e. OR (optical radiation), AR (auditory radiation), CST (corticospinal tract), thal-PSC (thalamus-primary somatosensory cortex) and PTR (posterior thalamic radiation).

Scatterplots with linear fitting between FA and postnatal age. Scatterplots with linear fitting was used to detail the postnatal age-related change of mean adjusted FA separately for preterm and term neonates. Here, FA was adjusted by multiple linear regression with GA as covariate.

Scatterplots with linear fitting between neurobehavioral scores and postnatal age. Scatterplots with linear fitting was used to detail the postnatal age-related change of mean adjusted neurobehavioral scores separately for preterm and term neonates. Here, neurobehavioral scores were adjusted by multiple linear regression with GA as covariate.

Correlations between FA and neurobehavioral scores in preterm and term neonates. Pearson correlation analyses between neurobehavioral scores and mean FA within the bilateral CST, OR, AR, thal-PSC and PTR were performed to evaluate the differences in the relationship between FA and neurological development at various postnatal age ranges.

All regression analysis, scatterplots and Pearson correlation were performed by using MATLAB software (version R2012b, Mathworks Inc, Natick, MA) and SPSS 17.0 (SPSS; Chicago, IL, USA); p < 0.05 was considered as statistically significant difference.

Participant demographics

Of 230 neonates recruited, 118 neonates with no evidence of abnormality on MRI within 28 days after birth were included. Specifically, there were 50 preterm (male/female, 28/22; GA range, 30 ~ 37weeks) and 68 term (male/female, 42/26; GA range, 37 ~ 42weeks) (Fig. 1 and Table 1). One hundred and twelve neonates with abnormal evidences on MRI were excluded; wherein specific disorders include cerebral infection (n = 2), congenital malformation (n = 1), metabolic disorders (n = 4), intracranial hemorrhage (n = 7), small for gestational age (SGA, n = 25), punctate white matter lesions (PWML, n = 69), periventricular leucumalacia (PVL, n = 2) and cortical infarction (n = 2).

Significant differences presented in GA between preterm and term, while no differences were observed in terms of gender, 10min-APGAR and background characteristics (Table 1).

Table 1

Participant demographics
	Preterm neonates (n = 50)	Term neonates (n = 68)	p value
Clinical characteristics Males/females	28/22	42/26	0.573
Gestational age (weeks)^a	34.3 ± 1.6	39.5 ± 1.2	< 0.001
10min-APGAR^a	9.72 ± 0.65	9.60 ± 0.71	0.404
Neurobehavioral scores
Active tone^a	5.55 ± 1.52	6.22 ± 1.71	0.048
Behavior^a	9.11 ± 1.11	9.81 ± 1.36	0.007
Background characteristics
Maternal age (yr)^a	28.57 ± 4.07	28.66 ± 3.78	0.907
Mother no high school education	32(44)^c	36(58)^c	0.297
Prenatal smoking exposure	0(50)	0(68)	--
Minority ethnicity	0(50)	0(68)	--
Family socioeconomic status^b			0.353
Professional/managerial	11(44)^c	19(58)	--
Technical/skilled	3(44)	7(58)	--
Semiskilled/unskilled/unemployed	30(44)	30(58)	--
^a Data represented as mean ± SD;
^b Assessed by the Elley-Irving socioeconomic index;
^c Forty-four in 55 preterm and 58 in 68 term neonates with records regarding the mother school education and family socioeconomic information.

Scatterplots between postnatal age and brain WM FA

For OR, AR, thal-PSC and PTR, term neonates showed higher Pearson correlation between adjusted FA values and postnatal age than preterm neonates (r_preterm vs r_term: OR, 0.17 vs 0.29; AR, 0.07 vs 0.22; thal-PSC, -0.05 vs 0.23; PTR, 0.14 vs 0.38). While for CST, preterm neonates presented higher linear relation than term (r_preterm=0.43, p = 0.002, r_term=0.08, p = 0.532). (Fig. 2)

Scatterplots between postnatal age and neurobehavioral scores

For active tone, preterm neonates showed higher linear relation between adjusted active tone scores and postnatal age than term (r_preterm=0.48, p = 0.002; r_term=0.35, p = 0.007) (Fig. 3a). While for behavior, term neonates presented higher linear relation than preterm (r_preterm=0.36, p = 0.024; r_term=0.52, p < 0.001). (Fig. 3b)

Correlations between FA and neurobehavioral scores in preterm and term neonates

For preterm neonates, mean CST FA showed significant correlation with active tone (r = 0.320, p = 0.044), while no significant correlations were found in other white matter tracts. In contrast, more correlations were found in term neonates between neurobehavioral scores (behavior and active tone) and mean FA in OR and PTR (Table 2).

Table 2

Pearson correlations of brain white matter fractional anisotropy (FA) with behavior and active tone scores in preterm and term neonates^a
White matter tracts	Preterm neonates (n = 40)		Term neonates (n = 58)
White matter tracts	Behavior	Active tone	Behavior	Active tone
Corticospinal tract (CST)	0.273	0.320*	0.159	0.228
Optical radiation (OR)	0.142	-0.199	0.290*	0.246
Auditory radiation (AR)	0.006	-0.187	0.144	0.119
Thalamus-primary somatosensory cortex (thal-PSC)	0.161	-0.143	0.185	0.167
Posterior thalamic radiation (PTR)	0.152	-0.146	0.329*	0.358**
^a Scores of two items, i.e., behavior and active tone, were used to correlate the FA within the typical white matter ROIs, including corticospinal tract (CST), optical radiation (OR), auditory radiation (AR) and thalamus-primary somatosensory cortex (thal-PSC).
* Significance level at P < 0.05; P values for preterm CST, term OR and PTR were 0.044, 0.027 and 0.012, respectively.
** Significance level at P < 0.01; P value for term PTR was 0.006.

The diffusion tensor imaging (DTI) parameter FA (Increases with the maturation of white matter) was applied to indirectly reflect the changes of brain white matter microstructure in this study. The results showed that the FA of CST, OR, AR and PTR in both preterm and full-term neonates increased with postnatal age. Consistently, (Rose, S. E et al., 2008) has demonstrated that the FA value of neonatal white matter increased with age after birth, indicating the gradual development of neonatal brain. However, the correlation between the OR, AR and PTR in preterm infants and postnatal age was lower than that of term infants, as well as the growth rate. Some research about preterm neonates have shown that compared with full-term infants, the FA values of many white matter in the brain are significantly reduced in preterm and term-equivalent infants. Moreover, there also exist abnormalities in white matter volume and microstructure, such as OR (Chang, L., Akazawa, K et al.,2016). This may be interpreted as delayed brain maturation, oligodendrocyte or axon damage in premature infants. So far, FA value increases more significantly in more mature white matter has been identified (Rose, S. E et al., 2008). Consequently, preterm infants presented significantly lower growth rate in certain brain regions compared with more mature full-term infants. The result above indicates that the maturity of OR, AR and PTR and the development rate of neonatal period of premature infants are significantly lower than that of full-term infants.

Notably, despite the FA of CST in preterm infants at birth is lower than term infants, the correlation between FA in preterm infants and postnatal age and its growth rate is significantly higher than that of term infants. Experimental results have shown that the CST in the brains of late-developing mice catches up with the CST in the brains of early-developing rats two days after birth, and this trend is defined as "catch-up" mode of CST (Canty and Murphy 2008). In addition, some research reported that the FA value of CST in preterm infants at term-equivalent age was higher compared with term neonates (Ment and Hirtz et al., 2009). One explanation for this unexpected result is that premature infants receive extrauterine environment stimulation earlier than full-term infants, and extrauterine environment factors can accelerate the maturation of brain tissue, leading to the trend of accelerated brain development in certain brain regions of healthy premature infants (Lebel and Deoni, 2018). Therefore, it is possible that CST in preterm infants brain may also have a "catch-up" developmental pattern, indicating a trend of catching up with more mature full-term infants in the neonatal period.

We also performed linear analysis of the correlation between active muscle tone (including motor abilities such as limbs and head movements) and neurobehavior (including visual and auditory reflection) with postnatal age (Bao XL and Yu RJ et al., 1991; Dubowitz LMS and Dubowitz V et al., 1999). The results showed that preterm infants had significantly lower active muscle tone than full-term infants, while the correlation between active muscle tone and postnatal age was higher than that of full-term infants, besides, there was also a slight trend of "catching up" to full-term infants. The corticospinal tract is an important white matter connection between the motor cortex and the spinal cord, and also carries the main information between the higher cortical structures and the dominant muscles, which plays a crucial role in autonomous movement (Holodny and Andrei I et al., 2005). Therefore, CST and active muscle tone showed the similar trend with postnatal age. The active muscle tone of preterm infants was weaker than that of full-term infants at birth, but it showed a slight "catch-up" pattern with postnatal age. Due to the critical role of CST pathway in motor function, this finding may suggest that certain intervention would be expected to work well in improving the preterm motor delay or abnormalities in the newborn period. Adversely, the correlation between behavior score of preterm infants and postnatal age was lower than that of term infants, and the increase rate with postnatal age was significantly lower than term infants. This is similar to the trend of FA values of AR and OR with postnatal age. The development of AR and OR in neonates is mainly related to auditory and visual functions (Bassi and Laura et al., 2008), indicating that the visual and auditory functions of premature infants are weaker than those of full-term infants. This point probably expressed that newborns with delayed white matter maturation lead to inferior brain function in corresponding brain region.

Some limitations remain in this study. First, the population is small. Larger sample size, especially for is desired to detail the changes of neonatal WM maturation with the varying postnatal age, and thus determine a more specified close to birth period for each WM. Second, despite taking Dubowitz neurological assessment as reference, efficiency of our neurobehavioral assessment for preterm neonates should be further evidenced by large surveys. Additionally, a longitudinal study targeting early brain development is needed to validate the motor outcomes in children.

Our study indicated that although being delayed, preterm neonates show a “catch-up” pattern toward the term neonates in motor development during the neonatal period.

Acknowledgement

This study was funded by National Key Research and Development Program of China (No. 2021YFA1003000) and Natural Science Basic Research Program of Shaanxi Province (No. 2022JM-464).

Funding: This study was funded by National Key Research and Development Program of China (No. 2021YFA1003000) and Natural Science Basic Research Program of Shaanxi Province (No. 2022JM-464).

Competing Interests: The authors have no relevant financial or non-financial interests to disclose.

Author Contributions: The whole process of this manuscript was accomplished by all authors. All authors read and approved the final manuscript.

Data Availability: The datasets generated during the current study are available from the corresponding author on reasonable request.

Ethics standards: All research was conducted in compliance with ethical standards.

Consent to participate: Informed consent was obtained from all individual participants included in the study.

Consent to publish: All authors affirm that this research participants provided informed consent for publication of all data.

Anderson, P., Doyle, L. W., & Victorian Infant Collaborative Study Group (2003). Neurobehavioral outcomes of school-age children born extremely low birth weight or very preterm in the 1990s. JAMA, 289(24), 3264–3272. https://doi.org/10.1001/jama.289.24.3264
Anjari, M., Srinivasan, L., Allsop, J. M., Hajnal, J. V., Rutherford, M. A., Edwards, A. D., & Counsell, S. J. (2007). Diffusion tensor imaging with tract-based spatial statistics reveals local white matter abnormalities in preterm infants. NeuroImage, 35(3), 1021–1027. https://doi.org/10.1016/j.neuroimage.2007.01.035
Bach-y-Rita P. (1990). Brain plasticity as a basis for recovery of function in humans. Neuropsychologia, 28(6), 547–554. https://doi.org/10.1016/0028-3932(90)90033-k
Bao, X. L., Yu, R. J., Li, Z. S., & Zhang, B. L. (1991). Twenty-item behavioral neurological assessment for normal newborns in 12 cities of China. Chinese medical journal, 104(9), 742–746.
Bassi, L., Ricci, D., Volzone, A., Allsop, J. M., Srinivasan, L., Pai, A., Ribes, C., Ramenghi, L. A., Mercuri, E., Mosca, F., Edwards, A. D., Cowan, F. M., Rutherford, M. A., & Counsell, S. J. (2008). Probabilistic diffusion tractography of the optic radiations and visual function in preterm infants at term equivalent age. Brain: a journal of neurology, 131(Pt 2), 573–582. https://doi.org/10.1093/brain/awm327
Berman, J. I., Mukherjee, P., Partridge, S. C., Miller, S. P., Ferriero, D. M., Barkovich, A. J., Vigneron, D. B., & Henry, R. G. (2005). Quantitative diffusion tensor MRI fiber tractography of sensorimotor white matter development in premature infants. NeuroImage, 27(4), 862–871. https://doi.org/10.1016/j.neuroimage.2005.05.018
Bhutta, A. T., Cleves, M. A., Casey, P. H., Cradock, M. M., & Anand, K. J. (2002). Cognitive and behavioral outcomes of school-aged children who were born preterm: a meta-analysis. JAMA, 288(6), 728–737. https://doi.org/10.1001/jama.288.6.728
Canty, A. J., & Murphy, M. (2008). Molecular mechanisms of axon guidance in the developing corticospinal tract. Progress in neurobiology, 85(2), 214–235. https://doi.org/10.1016/j.pneurobio.2008.02.001
Chang, L., Akazawa, K., Yamakawa, R., Hayama, S., Buchthal, S., Alicata, D., Andres, T., Castillo, D., Oishi, K., Skranes, J., Ernst, T., & Oishi, K. (2016). Delayed early developmental trajectories of white matter tracts of functional pathways in preterm-born infants: Longitudinal diffusion tensor imaging data. Data in brief, 6, 1007–1015. https://doi.org/10.1016/j.dib.2016.01.064
Dubois, J., Dehaene-Lambertz, G., Kulikova, S., Poupon, C., Hüppi, P. S., & Hertz-Pannier, L. (2014). The early development of brain white matter: a review of imaging studies in fetuses, newborns and infants. Neuroscience, 276, 48–71. https://doi.org/10.1016/j.neuroscience.2013.12.044
Dubowitz LMS, Dubowitz V, Mercuri E. (1999). The neurological assessment of the preterm and full-term newborn infant. 2nd ed. London.
Groeschel, S., Tournier, J. D., Northam, G. B., Baldeweg, T., Wyatt, J., Vollmer, B., & Connelly, A. (2014). Identification and interpretation of microstructural abnormalities in motor pathways in adolescents born preterm. NeuroImage, 87, 209–219. https://doi.org/10.1016/j.neuroimage.2013.10.034
Groppo, M., Ricci, D., Bassi, L., Merchant, N., Doria, V., Arichi, T., Allsop, J. M., Ramenghi, L., Fox, M. J., Cowan, F. M., Counsell, S. J., & Edwards, A. D. (2014). Development of the optic radiations and visual function after premature birth. Cortex; a journal devoted to the study of the nervous system and behavior, 56, 30–37. https://doi.org/10.1016/j.cortex.2012.02.008
Holodny, A. I., Watts, R., Korneinko, V. N., Pronin, I. N., Zhukovskiy, M. E., Gor, D. M., & Ulug, A. (2005). Diffusion tensor tractography of the motor white matter tracts in man: Current controversies and future directions. Annals of the New York Academy of Sciences, 1064, 88–97. https://doi.org/10.1196/annals.1340.016
Hüppi, P. S., & Dubois, J. (2006). Diffusion tensor imaging of brain development. Seminars in fetal & neonatal medicine, 11(6), 489–497. https://doi.org/10.1016/j.siny.2006.07.006
Jo, H. M., Cho, H. K., Jang, S. H., Yeo, S. S., Lee, E., Kim, H. S., & Son, S. M. (2012). A comparison of microstructural maturational changes of the corpus callosum in preterm and full-term children: a diffusion tensor imaging study. Neuroradiology, 54(9), 997–1005. https://doi.org/10.1007/s00234-012-1042-8
Kidokoro, H., Anderson, P. J., Doyle, L. W., Woodward, L. J., Neil, J. J., & Inder, T. E. (2014). Brain injury and altered brain growth in preterm infants: predictors and prognosis. Pediatrics, 134(2), e444–e453. https://doi.org/10.1542/peds.2013-2336
Kolb, B., & Gibb, R. (2011). Brain plasticity and behaviour in the developing brain. Journal of the Canadian Academy of Child and Adolescent Psychiatry, 20(4), 265–276.
Lebel, C., & Deoni, S. (2018). The development of brain white matter microstructure. NeuroImage, 182, 207–218. https://doi.org/10.1016/j.neuroimage.2017.12.097
Li, X., Gao, J., Wang, M., Wan, M., & Yang, J. (2016). Rapid and reliable tract-based spatial statistics pipeline for diffusion tensor imaging in the neonatal brain: Applications to the white matter development and lesions. Magnetic resonance imaging, 34(9), 1314–1321. https://doi.org/10.1016/j.mri.2016.07.011
Ment, L. R., Hirtz, D., & Hüppi, P. S. (2009). Imaging biomarkers of outcome in the developing preterm brain. The Lancet. Neurology, 8(11), 1042–1055. https://doi.org/10.1016/S1474-4422(09)70257-1
Ment, L. R., & Vohr, B. R. (2008). Preterm birth and the developing brain. The Lancet. Neurology, 7(5), 378–379. https://doi.org/10.1016/S1474-4422(08)70073-5
Pavaine, J., Young, J. M., Morgan, B. R., Shroff, M., Raybaud, C., & Taylor, M. J. (2016). Diffusion tensor imaging-based assessment of white matter tracts and visual-motor outcomes in very preterm neonates. Neuroradiology, 58(3), 301–310. https://doi.org/10.1007/s00234-015-1625-2
Qiu, A., Mori, S., & Miller, M. I. (2015). Diffusion tensor imaging for understanding brain development in early life. Annual review of psychology, 66, 853–876. https://doi.org/10.1146/annurev-psych-010814-015340
Rose, S. E., Hatzigeorgiou, X., Strudwick, M. W., Durbridge, G., Davies, P. S., & Colditz, P. B. (2008). Altered white matter diffusion anisotropy in normal and preterm infants at term-equivalent age. Magnetic resonance in medicine, 60(4), 761–767. https://doi.org/10.1002/mrm.21689
Thompson, D. K., Inder, T. E., Faggian, N., Johnston, L., Warfield, S. K., Anderson, P. J., Doyle, L. W., & Egan, G. F. (2011). Characterization of the corpus callosum in very preterm and full-term infants utilizing MRI. NeuroImage, 55(2), 479–490. https://doi.org/10.1016/j.neuroimage.2010.12.025
Volpe J. J. (2009). Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances. The Lancet. Neurology, 8(1), 110–124. https://doi.org/10.1016/S1474-4422(08)70294-1
Woodward, L. J., Anderson, P. J., Austin, N. C., Howard, K., & Inder, T. E. (2006). Neonatal MRI to predict neurodevelopmental outcomes in preterm infants. The New England journal of medicine, 355(7), 685–694. https://doi.org/10.1056/NEJMoa053792

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Preterm neonates show a “catch-up” pattern toward full-term in motor development during the neonatal period: a diffusion tensor imaging study

Status:

Version 1

Abstract

Figures

Introduction

Materials And Methods

Participants

MRI data acquisition

Neonatal neurobehavioral development assessment

DTI processing and statistical analysis

Results

Participant demographics

Scatterplots between postnatal age and brain WM FA

Scatterplots between postnatal age and neurobehavioral scores

Correlations between FA and neurobehavioral scores in preterm and term neonates

Discussion

Conclusion

Declarations

References

Additional Declarations

Status:

Version 1