
==== Front
Pediatr Res
Pediatr Res
Pediatric Research
0031-3998
1530-0447
Nature Publishing Group US New York

38225451
2994
10.1038/s41390-023-02994-4
Clinical Research Article
Corpus callosum long-term biometry in very preterm children related to cognitive and motor outcomes
Lubián-Gutiérrez Manuel 12
http://orcid.org/0000-0001-9276-1912
Benavente-Fernández Isabel isabel.benavente@uca.es

234
Marín-Almagro Yolanda 3
Jiménez-Luque Natalia 3
Zuazo-Ojeda Amaya 5
Sánchez-Sandoval Yolanda 36
Lubián-López Simón P. 34
1 grid.411342.1 0000 0004 1771 1175 Division of Neurology, Department of Paediatrics, Puerta del Mar University Hospital, Cádiz, Spain
2 https://ror.org/04mxxkb11 grid.7759.c 0000 0001 0358 0096 Area of Paediatrics, Department of Child and Mother Health and Radiology, Medical School, University of Cádiz, C/Doctor Marañón, 3, Cádiz, Spain
3 https://ror.org/02s5m5d51 grid.512013.4 Biomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, Puerta del Mar University Hospital, Cádiz, Spain
4 grid.411342.1 0000 0004 1771 1175 Division of Neonatology, Department of Paediatrics, Puerta del Mar University Hospital, Cádiz, Spain
5 grid.411342.1 0000 0004 1771 1175 Radiology Department, Puerta del Mar University Hospital, Cádiz, Spain
6 https://ror.org/04mxxkb11 grid.7759.c 0000 0001 0358 0096 Area of Developmental and Educational Psychology, Department of Psychology, University of Cádiz, Cádiz, Spain
15 1 2024
15 1 2024
2024
96 2 409417
22 5 2023
3 12 2023
15 12 2023
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Background

The corpus callosum (CC) is suggested as an indirect biomarker of white matter volume, which is often affected in preterm birth. However, diagnosing mild white matter injury is challenging.

Methods

We studied 124 children born preterm (mean age: 8.4 ± 1.1 years), using MRI to assess CC measurements and cognitive/motor outcomes based on the Wechsler Intelligence Scale for Children-V (WPPSI-V) and Movement Assessment Battery for Children-2 (MABC-2).

Results

Children with normal outcomes exhibited greater height (10.2 ± 2.1 mm vs. 9.4 ± 2.3 mm; p = 0.01) and fractional anisotropy at splenium (895[680–1000] vs 860.5[342–1000]) and total CC length (69.1 ± 4.8 mm vs. 67.3 ± 5.1 mm; p = 0.02) compared to those with adverse outcomes. All measured CC areas were smaller in the adverse outcome group. Models incorporating posterior CC measurements demonstrated the highest specificity (83.3% Sp, AUC: 0.65) for predicting neurological outcomes. CC length and splenium height were the only linear measurements associated with manual dexterity and total MABC-2 score while both the latter and genu were related with Full-Scale Intelligence Quotient.

Conclusions

CC biometry in children born very preterm at school-age is associated with outcomes and exhibits a specific subregion alteration pattern. The posterior CC may serve as an important neurodevelopmental biomarker in very preterm infants.

Impact

The corpus callosum has the potential to serve as a reliable and easily measurable biomarker of white matter integrity in very preterm children.

Estimating diffuse white matter injury in preterm infants using conventional MRI sequences is not always conclusive.

The biometry of the posterior part of the corpus callosum is associated with cognitive and certain motor outcomes at school age in children born very preterm.

Length and splenium measurements seem to serve as reliable biomarkers for assessing neurological outcomes in this population.

issue-copyright-statement© International Pediatric Research Foundation, Inc 2024
==== Body
pmcIntroduction

Corpus callosum (CC) is the largest telencephalic commissural tract and biggest white matter structure in the human central nervous system. It is present in placental mammals and constitutes the highest level of neocortical interhemispheric connection.1 Bundles of axonal fibers that cross midline forming the CC are paramount to integrate lateralized sensory-motor tasks.2 Taking into account the extensive connections of very diverse cortical areas (sensory, motor, integration areas…), it has been shown that the biometry and microarchitecture of the CC is related to cognitive and motor functions. As the most important human commissural tract, CC measurements on midsagittal plane could serve as surrogate markers to estimate total cerebral white matter volume in children with white matter diseases.3–6

Preterm birth is a major worldwide health problem.7 In the past three decades survival of preterm infants has increased and, even though moderate to severe brain injury has been reduced, the risk of neurodevelopmental impairment remains high for those preterm infant born before 32 weeks of gestation.8–10 White matter injury (WMI) is the most prevalent brain injury in very preterm infants.11,12 It is considered as a spectrum of neuropathological injuries including: focal cystic necrosis, punctate and focal microscopic necrosis and diffuse non-necrotic lesions.13 Up to 50% of very preterm infants (VPT) show some degree of WMI on magnetic resonance imaging (MRI)14,15 with diffuse WMI being the most prevalent form.16 MRI is the most sensitive tool for WMI detection in infants,17–19 however conventional MRI sequences at term equivalent age (TEA) are not able to detect mild forms of diffuse WMI. Moreover, some postmortem studies have shown that up to 82% of periventricular leukomalacia have only microscopic necrotic foci.20

CC measurements have been widely used in WMI scoring classifications. Whether by MRI or ultrasound, most of them include CC measurements in their scales (mostly thinning of CC).17,21,22 The pattern of CC development throughout childhood has been proven to be different in those born preterm from their peers at term, related to gestational age (GA) at birth.23–26 However, there is still no consensus on what the best approach to CC biometry as a biomarker for long-term neurodevelopmental outcomes is, and many classifications and subdivisions of the CC have been proposed according to its anatomy and function.27 Many of these subdivisions are not easily replicable, are time-consuming and require segmentation post-processing algorithms not available in all hospitals. Complementing conventional sequences, diffusor tensor imaging (DTI) provides a new insight into the microstructural characteristic of brain structures. DTI is based on measuring spatial diffusion of water molecules along the white matter tracts. Quantitative parameters like fractional anisotropy (FA), which is considered a biomarker related to myelination, allow the study of the differential maturational process that occur in children who were preterm compared to those who were born at TEA.28–31 Previous studies using DTI have linked lower CC thickness in preterm infants with abnormal bundles.31

Our aim is to study the CC biometry and FA in school-age children (aged 6–11) who were VPT, and their association with neurodevelopmental motor and cognitive outcomes.

Methods

Participants

This is a prospective observational cohort study including school-age children who were born at a GA equal to or less than 32 weeks and/or with a birth weight equal to or less than 1500 g, admitted to the Neonatal Intensive Care Unit (NICU) in Puerta del Mar University Hospital, Cadiz, Spain. We included a retrospective analysis of the MRI performed at TEA. This study was reviewed and approved by the local Research and Ethics Committee and with parental or legal guardian signed informed consent.

Exclusion criteria were chromosomal or genetic identified anomalies, proven metabolic or malignant disorders, congenital neurological malformation, and congenital infections.

Those children born preterm included, with a mean age of 8.3 years old, were assessed from January 2020 to December 2022. We performed MRI, motor and neuropsychological assessments. Perinatal, neonatal clinical course and neuroimaging data were collected retrospectively.

Socioeconomical status (SES) was measured using maternal level of education at the time of recruitment32,33 which was then categorized into three groups according to the number of years of maternal education: primary or secondary school, undergraduate degree, or postgraduate degree (low, medium and high, respectively). Perinatal and postnatal variables were prospectively collected. We considered moderate to severe bronchopulmonary dysplasia if there was need for supplemental oxygen and/or positive pressure at 36 weeks postmenstrual age; significant patent ductus arteriosus if requiring surgical or pharmacological closure; late onset sepsis in the presence of systemic signs of infection and isolation of a bacterial pathogen in blood culture after the first 72 h of life; confirmed necrotizing enterocolitis (Bell Stage II or higher); and severe retinopathy of prematurity (stage 3 or higher).

Magnetic resonance imaging

We performed an MRI at elementary school age (6–11 years) on the included subjects, and we retrieved, when available, the TEA-MRI that was performed as part of the standard clinical practice at the time.

MRI scans were performed using the Magnetom Symphony 1.5 T scanner (Siemens Health Care, Erlangen, Germany) located in the radiology unit. MRI scans included T1-weighted volumetric images (Multiplanar Reconstruction (MPR)) acquired using the 1.5 Tesla scanner (slice thickness 1.0 mm; echo-time 3.53 ms; flip angle 15°; field of view 192 × 256 mm2), axial spin echo T2-weighted images and diffusion tensor imaging (DTI). Every MRI was studied by an experienced radiologist who classified the findings as abnormal if there was evidence of brain injury or normal/mild abnormalities. Neuroimaging measures were performed using Carestream (© 2019 Carestream Health, Version 12.1.5.5151) and CC biometry was assessed using MPR T1-weighted sequences. DTI was acquired with a multirepetition, single-shot echo planar sequence with 120 gradient directions (TR, 3100; TE, 98; flip angle 90; no gap), and diffusion weighting of 1000 s/mm2 (b value) and an image without diffusion weighting, resulting in an in-plane resolution of 1.3 mm. FA ranges from 0 (indicating isotropic diffusion of water molecules) to 1 (anisotropic), and it rises with the development of white matter microstructure.34 Mean FA values were obtained from 2 manually selected CC regions of interest (genu and splenium of the CC) on one selected axial plane of the color FA maps (Fig. 2).

MRI at TEA was evaluated using the scale published by ref. 21 This classification assesses development and injury of cortical and deep gray matter, white matter, and cerebellum. The overall score obtained classifies MRI findings as: normal (0–3 points) or abnormal: mild (4–7 points), moderate (8–11 points) and severe (≥12 points).

Corpus callosum measurements

We measured, according to ref. 35, five linear parameters in a T1-weighted midline sagittal section: CC length as the distance between the most anterior aspect of the genu and the most posterior aspect of the splenium. Thickness measurements of the CC at different levels (genu, body, isthmus and splenium) were taken tracing a line from the bottom to the top, perpendicular to the curvilinear axis of CC (Fig. 2). All these linear measurements were expressed in millimeters.

We assessed the total midsagittal area of the CC and performed a subdivision of this total area into five sections according to the studies by Witelson,36 Duara,37 Hofer,38 Delacoste,39 Shin40 and Westerhausen.41 In order to do this, CC length was divided in 5 sections and then tracing upward perpendicular lines. Anterior and posterior areas count as one-fifth of total CC area, respectively. The central area is the part in between both anterior and posterior, three-fifths of the total CC area. Areas were expressed in square millimeters.

Neurodevelopmental outcomes

Cognitive function was evaluated using the Wechsler Intelligence Scale for Children—Fifth Edition (WISC-V).42 This scale provides tests and composite scores (indices) reflecting intellectual functioning in five specific domains (working memory, verbal comprehension, visuospatial, processing speed, and fluid reasoning) and Full-Scale Intelligence Quotient (FSIQ), a standardized composite score with a mean of 100 and a standard deviation (SD) of 15. A cut-off value of 85 was considered to further classify participants in groups of normal versus borderline/stablished intellectual disability.

Motor abilities were evaluated using the Movement Assessment Battery for Children, Second Edition (MABC-2)43 and divided in 3 domains: manual dexterity, aiming and catching, and balance. The sum of the scores of three domains gives a total MABC-2 punctuation and percentile score. A total MABC-2 percentile score of 15 or over is considered normal. A percentile score of under 15 is considered a risk of movement disability and under 5 is considered diagnostic for developmental coordination disorder.42

Children with cerebral palsy with a Gross Motor Functional Classification System level II to III that were not able to complete all the subtests of motor testing would be considered to score under 5 on the MABC-2 total score and have an imputed percentile value of 0.1, according to similar studies.44

We considered a global adverse outcome in those children who scored under the 15th centile in MABC-2 total percentile score and/or had a FSIQ score of less than 85.

Statistical analysis

Quantitative variables were described using the median (Md) or mean value and interquartile range [IQR] or SD, according to their distribution. Bivariate analysis was performed using Pearsonʼs chi-squared test or Fisherʼs exact test for categorical data, and Studentʼs t-test or Mann–Whitney U test for continuous variables.

Multivariable linear and logistic regression models were used on perinatal variables. We performed multivariable regression and logistic models to study the relationship between CC measurements and neurodevelopmental outcome, including perinatal variables (GA, birth weight and sex), and age at scan. Variables were selected based on the theoretical background and results of bivariate analysis.

Moreover, to allow for non-hierarchical models, we also selected the best logistic model by using the method of all possible equations, which identified the best subset for logistic regression based on all the possible combinations of independent variables (linear measurements of CC and areas, adjusting by birth weight, GA and sex). For each subset area under the curve (AUC), Akaike information criterion (AIC), Schwarz Bayesian Criterion (BIC), sensitivity and specificity were studied as goodness-of-fit measures. Internal validation of the model was performed with cross-validation on the specified models in order to evaluate the model’s ability to fit out-of-sample data. Standardized beta coefficients were estimated to compare the effect size of the independent variables.

Statistical analysis was conducted using Stata 17.0 (Stata Statistical Software: Release 17. College Station, TX: StataCorp LP). A result was considered statistically significant at p < 0.05.

Results

Clinical characteristics and long-term outcome

During the study period we contacted 197 eligible subjects. We excluded thirty-nine children (19.8%): 26 declined to participate, 10 were lost to follow-up and 3 were excluded for medical conditions. Our final study population included 158 children between 6 and 11 years of age. The inclusion process and final sample size are summarized in Fig. 1. We performed an MRI on 137 (85.6%) of the included subjects, with 120 (87.6%) of them having mild abnormalities or normal conventional MRI. Moderate/severe abnormal MRI findings and/or cranial ultrasound conditions are described in Table 1s (supplementary material). Of those with an MRI, 121 (88.3%) completed motor evaluation and cognitive outcome was assessed on 124 (90.5%). The median value of total MABC-2 score was 7 [IQR 1–15], corresponding to the 16th percentile [IQR 0.1–95] and the mean FSIQ value was 93.1 (SD 12.7). A detailed description of the scores obtained in all the subtests of MABC-2 and WISC-V is included in Table 2s in the supplementary material.Fig. 1 Flowchart of prospective inclusion and final sample size of the study.

IQ intelligence quotient, MABC-2 Movement Assessment Battery for Children-2, MRI cerebral magnetic resonance imaging, VLBWI very low birth weight infant, GA weeks of gestational age, WISC-V Wechsler Intelligence Scale for Children-V.

Those with an adverse outcome (n = 58 (47.9%)) compared to those with a normal outcome (n = 63 (52.1%)) were born with lower birth weight (1165 grams [IQR 630–2345] vs 1325 grams [IQR 550–2120]; p = 0.01). No other clinical or demographic characteristics of the studied participants differed significantly between groups (Table 1).Table 1 Characteristics of the cohort classified by the normal or adverse outcome.

N (%) or median [IQR]	Normal outcome N = 63	Adverse outcome N = 58	Total N = 121	p value	
Female sex	27 (42.9)	33 (56.9)	60 (49.6)	0.94	
Birth weight (g)	1325 [550–2120]	1165 [630–2345]	1275 [550–2345]	0.01*	
Gestational age (w)	30.3 [24–32.9]	29.3 [25.6–32.7]	30 [24–32.9]	0.13	
Age at MRI (years)	8.3 (1.0)	8.4 (1.2)	8.4 (1.1)	0.66	
Apgar 1 min	7 [0–9]	6 [3–9]	7 [0–9]	0.01*	
Apgar 5 min	8 [4–10]	8 [4–10]	8 [4–10]	0.05*	
Bronchopulmonary dysplasia	6 (10)	8 (14.8)	14 (12.2)	0.43	
Patent ductus arteriosus	10 (16.7)	12 (22.2)	22 (19.3)	0.45	
Sepsis	17 (26.9)	19 (32.8)	36 (29.8)	0.48	
Necrotizing enterocolitis	0	2 (3.4)	2 (1.6)	0.27	
Retinopathy of prematurity	14 (22.9)	13 (24.1)	27 (23.5)	0.88	
Maternal level of education	
  Low	23 (33.8)	23 (33.8)	46 (33.8)		
  Medium	20 (29.4)	23 (33.8)	43 (31.6)	0.82	
  High	25 (36.8)	22 (32.3)	47 (34.6)		
CC measurements at school age	
  Length (mm)	69.1 (4.8)	67.3 (5.1)	68.2 (5.0)	0.03*	
  Height at genu (mm)	10 [5.8–13.7]	10 [3.7–14.4]	10 [3.7–14.4]	0.71	
  Height at body (mm)	5.3 [3.5–7.5]	5.3 [1.8–7.5]	5.5 [1.8–7.5]	0.95	
  Height at isthmus (mm)	3.6 (0.8)	3.6 (0.9)	3.6 (0.9)	0.49	
  Height at splenium (mm)	10.2 (2.1)	9.4 (2.3)	9.8 (2.2)	0.02*	
  Total area (mm2)	513.5 (79.0)	481.7 (101.0)	498.3 (91.3)	0.03*	
  Anterior area (mm2)	172.2 (28.2)	165.2 (32.1)	168.9 (30.2)	0.10	
  Posterior area (mm2)	151.7 [60.1–224.1]	143.4 [31.5–202.2]	146.9 [31.5–224.1]	0.11	
  Central area (mm2)	191.7 (34.6)	178.8 (47.4)	185.5 (41.6)	0.04*	
DTI measurements	
  FA at genu	849 [626–955]	838 [553–951]	843 [553–955]	0.65	
  FA at splenium	895 [680–1000]	860.5 [342–1000]	881 [342–1000]	0.03*	
Neonatal MRI brain injury scorea (n = 63)	N = 30	N = 28	N = 58		
  Total	2 [0–10]	1 [0–12]	1 [0–12]	0.50	
  White matter	1 [0–6]	1 [0–6]	1 [0–6]	0.72	
  Gray matter	0 [0–6]	0 [0–8]	0 [0–8]	0.09	
CC corpus callosum, DTI diffusion tensor imaging, FA fractional anisotropy, IQR interquartile range. Statistically significant p-values are in bold.

*p < 0.05.

aKidokoro et al.21

School-age corpus callosum measurements related to perinatal characteristics

GA was associated with CC length (β = −0.794; p = 0.01) and isthmus thickness (β = 0.123; p = 0.01) and birth weight was related to total and segmented areas and CC length (Table 2). We found no association of sex and maternal level of education to school age CC measurements.Table 2 CC measurements at school-age that could be predicted by gestational age and birth weigth. adjusted by age at MRI.

Predictor	Gestational age	Birth weight	Total model p value	
Variable	β coefficient	p value	β coefficient	p value	R2 adj	p value	
Length (mm)	−0.794	0.01*	0.005	0.01*	0.08	0.01*	
Isthmus (mm)	0.123	0.01*	0.001	0.07	0.18	0.01*	
Total area (mm2)	−4.551	0.34	0.107	0.01*	0.16	0.01*	
Anterior area (mm2)	−1.992	0.21	0.029	0.01*	0.06	0.01*	
Posterior area (mm2)	0.306	0.86	0.028	0.01*	0.08	0.01*	
Central area (mm2)	−2.865	0.19	0.050	0.01*	0.11	0.01*	
Only measurements that were statistically significant are presented.

*P < 0.05.

Corpus callosum measurements at school age and global outcome

At school age, two linear CC measurements were different between groups of normal and adverse outcomes: height at splenium (10.2 mm (SD 2.1) vs. 9.4 mm (SD 2.3); respectively p = 0.02) and total CC length (69.1 mm (SD = 4.8) and 67.3 mm (SD 5.1), p = 0.02). Some of the CC areas were smaller among those with adverse outcomes with a total area of 513.5 mm2 (SD 79) vs 481.7 mm2 (SD 101), p = 0.03 and central area of 191.7 mm2 (SD 34.6) vs 178.8 mm2 (SD 47.4), p = 0.04. When excluding those patients with moderate/severe abnormalities in cUS or MRI (those included in Table 1s) we found no major changes to our results except for measurements of total and central areas of the CC, which would not be associated to adverse outcome in the absence of moderate/severe brain injury (Table 3s).

We observed higher FA values for the splenium in those with normal outcomes compared to those with adverse outcomes (895 [IQR 680–1000] vs 860.5 [343–1000]) (Table 1). FA at splenium and height at splenium showed similar results when accounting for GA, birth weight and age at MRI (Table 4s in supplementary material). Age at MRI (mean 8.3 years (SD 1.1)) was not different between outcome groups (p = 0.66).

When comparing different subsets obtained, those including measurements of the posterior part of CC (height at splenium: β coefficient = −0.07; p = 0.55 and FA at splenium: β = −0.01; p = 0.03; model p = 0.02) showed higher specificity (83.3%, AUC: 0.65) related to school-age global outcome.

Corpus callosum subdivision and subscales of motor and cognitive function

Each cognitive and motor subtest of WISC-V and MABC-2 and their relationship with CC measurements were studied (Fig. 3 and Table 3). Accounting for birth weight, GA, sex, and age at MRI we found that CC length and height at splenium were related to manual dexterity (β = 1.04, p = 0.01; and β = 2.23, p = 0.05, respectively) and with most of the cognitive subscales. Height at genu was also related to some of the cognitive subtests. Length of the CC and height at splenium were the only linear measurements related to the total MABC-2 score (β = 1.10, p = 0.05; and β = 2.61, p = 0.03, respectively). Both parameters were also related to FSIQ (β = 0.81, p = 0.01; and β = 1.94, p = 0.01).Table 3 CC measurements related to motor and cognitive subscales.

		Length (mm)	Height at genu (mm)	Height at body (mm)	Height at isthmus (mm)	Height at splenium (mm)	Total area (mm2)	Anterior area (mm2)	Posterior area (mm2)	Central area (mm2)	FA at genu	FA at splenium	
Motor	Total M-ABC percentile	β = 1.07 p = 0.05

Total p = 0.05

				β = 2.61 p = 0.03							
Manual dexterity percentile	β = 1.04 p = 0.01

Total p = 0.05

				β = 2.18 p = 0.05			β = 0.18 p = 0.05

Total p = 0.05

				
Aiming and catching percentile												
Balance percentile												
Cognitive	Verbal comprehension score	β = 0.72 p = 0.01

Total p = 0.01

	β = 1.77 p = 0.02

Total p = 0.01

			β = 1.60 p = 0.01

Total p = 0.01

			β = 0.09 p = 0.02

Total p = 0.01

				
Visuospatial score	β = 1.03 p = 0.01

Total p = 0.01

				β = 1.73 p = 0.01

Total p = 0.01

	β = 0.04 p = 0.01

Total p = 0.01

	β = 0.13 p = 0.01

Total p = 0.01

	β = 0.12 p = 0.01

Total p = 0.01

				
Fluid reasoning score					β = 1.31 p = 0.01

Total p = 0.01

			β = 0.10 p = 0.01

Total p = 0.01

				
Working memory score					β = 2.00 p = 0.01

Total p = 0.01

			β = 0.12 p = 0.01

Total p = 0.01

				
Processing speed score	β = 0.76 p = 0.03

Total p = 0.01

	β = 2.15 p = 0.01

Total p = 0.03

			β = 2.20 p = 0.01

Total p = 0.01

	β = 0.04 p = 0.01

Total p = 0.02

		β = 0.14 p = 0.01

Total p = 0.01

				
Full-Scale Intelligence Quotient	β = 0.81 p = 0.01

Total p = 0.01

	β = 1.82 p = 0.01

Total p = 0.01

			β = 1.94 p = 0.01

Total p = 0.01

	β = 0.03 p = 0.01

Total p = 0.01

	β = 0.08 p = 0.03

Total p = 0.01

	β = 0.13 p = 0.01

Total p = 0.01

				
All models were adjusted by GA at birth, birth weigth, sex and age at MRI. Only statistically significant results were expressed in the tables.

Total p: p of the full model, including adjusting variables,

CC corpus callosum, GA gestational age, MRI magnetic resonance imaging.

CC posterior area showed a relationship with all dimensions of cognitive function in WISC-V (Fig. 4 and Table 3).

We found no relationship between FA in the genu or in the splenium with any of the motor or cognitive subscales.

Corpus callosum at term equivalent age and long-term outcome

We retrospectively reviewed the available clinical MRI at TEA of 84 patients from this cohort. The clinical, neuroimaging, demographic and long-term outcome data of this subgroup are detailed in Table 5s. Accounting for GA and birth weight, only length of CC was related with all the long term motor outcomes (Fig. 1s in supplementary material). Height at genu showed relation with manual dexterity and with FSIQ. Total area and posterior area of the CC at TEA were also related with manual dexterity at school age (Fig. 1s).

Discussion

In this study of children born very preterm and assessed at elementary school age, we found smaller CC measurements related to adverse outcomes with total length of CC and splenium region size related to both cognitive and motor function. Our study showed a significant relationship of the posterior region of the CC with manual dexterity and all cognitive domains in school-age VPT. This finding could be related with impaired white matter development after preterm birth affecting tracts crossing this region of the CC that links extensive areas of sensory and motor integration, being fundamental in tasks such as bimanual function and multiple aspects of cognition.

The CC is an easily measurable structure reflecting brain connections, brain volume and white matter volume.4–6,23,45 In VPT, CC altered biometry should not be considered as an isolated disruption in its development as a consequence of preterm birth. However, this anatomical and microstructural alteration of the CC could be part of the spectrum of encephalopathy of prematurity, which includes global WMI. While easy to visualize on the midsagittal plane and easy to measure, no discernable anatomical boundaries divide CC segments. For this reason, there are many proposed subdivision schemes.27 Based on models which used DTI and connectivity maps,27,38 and considering the well-known relative importance of anterior and posterior regions, we performed a five-subdivisions model (Fig. 2) similar to Delacoste,39 Shin40 and according to Westerhausen.41 This approach is intended to be easily reproducible in clinical practice and to show results congruent with CC connectivity. In this age range, CC biometry remains relatively stable,35,46 even in VPT children.26,47Fig. 2 Linear measurements, areas and fractional anisotropy (FA) of corpus callosum.

a length; b total area; c linear measurements of genu (purple), body (green), isthmus (light blue) and splenium (red) (performed according to ref. 33. d anterior and posterior subdivision areas. e Region of interest FA measurement at the genu (blue dot) and splenium (white dot).

Altered patterns of total white matter volume and thinning of CC are common findings in preterm infants48–52 with a global reduction of the CC size in children who were born preterm, mostly in the splenium.24,30,53 Our findings are in line with previous studies, with gestational age and weight at birth being strongly related to CC length and areas at elementary school age (Table 1).23,24,50 CC biometry in these subjects is related with adverse late motor,3,51 and neuropsychological outcomes,23,24,50 and the splenium is the specific region that correlates the most with cognition.23,54,55 Our sample size has allowed us to depict different outcomes of VPT infants relating to the size of the CC, with previous studies mostly having focused on the comparison with healthy term-born children. While smaller CC has been previously demonstrated, the impact of preterm birth on different areas and related to the outcome has not been previously addressed. Our results show that the posterior part of the CC, measured as height at splenium, posterior area, and FA at splenium, is related to adverse prognosis highlighting the importance of this region in motor and cognitive pathways. The splenium of the CC is involved in transference of axons that are related to language, visuospatial integration, complex cognitive functions, behavior and consciousness.46 Accordingly, Luders et al.56 related the posterior part of the CC and some regions of the anterior part of the CC with total brain volume and FSIQ.56 Regional brain volume studies in children born preterm demonstrated reduced white and gray matter volumes in sensorimotor and parieto-occipital regions in preterm compared with full-term infants.41 Our findings may underline the relative importance of the splenium in cognitive function and all its subsets in a high-risk neurological population.

Considering the approach based on measuring areas of CC on the mid-sagittal plane, all areas were related to overall prognosis. However, this relationship did not hold when adjusting for GA, sex, birth weight and age at MRI. Although linear measurements of the CC have customarily been used in WMI classification,17,21,22 an area-based approach could provide data with higher predictive value and/or more complex information. Correlation between CC segmental areas and cognitive processes in elementary school-age children have been demonstrated in previous studies.57 However, there are not many similar studies in children who were preterm.23,24,50,58 more studies are needed to assess the usefulness of measures of CC areas at full-term age as long-term prognostic predictors.

During early life and at school age, children who were born VPT have lower FA values when compared to those born at term.30,59–61 Furthermore, children born VPT display a lower FA in CC attributable to white matter development disruption in the context of WMI.28 FA is also reduced in non-preterm children with thickened corpus callosum due to other conditions.31 Our results are in line with previous studies that found an inverse relation between FA assessed by DTI, and motor and cognitive impairment, with splenium being one of the most important regions.55,62

The association between CC abnormalities and fine motor and bimanual coordination is well-known.27,63,64 In our study, length of CC and height at splenium in school age were related to manual dexterity. On the same way, in the subgroup of patients who had an MRI at TEA, total and posterior areas and length of CC were consistently related with manual dexterity. Similar results, highlighting the importance of the CC in general, and splenium in particular, were obtained both in healthy adolescents65 and children with cerebral palsy.66 Further research, is warranted to unravel what measurements of the corpus callosum in VPT in the neonatal period may serve as long term predictor of fine motor and bimanual coordination.

Many of the CC measurements were related to WISC-V subtests, with total length, height at splenium, total area and posterior area impacting the most on cognitive outcome. However, contradictory findings have been reported relating to regional division and cognitive function. Hutchinson et al.67 found smaller posterior areas of the CC in those subjects with higher FSIQ. Nevertheless, this study was based on healthy young adults (mean age 19.2 years old) whereas an accelerated growth pattern of the CC has been described in those who were VPT.47,68 Except for this single study, our results are in line with other studies where CC subregions were found to be directly related to FSIQ and WISC-V subscales.24,47,54,55,69 It should be noted that parameters measuring the entire CC (length and total area) and posterior region (height at splenium and posterior area) at school age are broadly related to intellectual function. We also found that genu at TEA and at school-age was related to FSIQ, but not with long-term motor performance. Interestingly, the relationship of genu with FSIQ was demonstrated at TEA and at school age (Figs. 3 and 4).Fig. 3 Linear CC measurements related with motor and cognitive subscales.

All models were adjusted by GA at birth, sex and age at MRI. Only statistically significant results were expressed in the tables. CC corpus callosum, GA gestational age, MRI magnetic resonance imaging.

Fig. 4 CC areas related with motor and cognitive subscales.

All models were adjusted by GA at birth, sex and age at MRI. Only statistically significant results were expressed in the tables. CC corpus callosum, GA gestational age, MRI magnetic resonance imaging.

Interestingly, despite being suggested as a valid measure in some neuroimaging studies17,21,22 during the neonatal period, we found that the height of the corpus callosum measured at the body and central areas of the CC at elementary school age, were not statistically significant related to any of the cognitive or motor outcomes. We also found no relationship between height at body and an overall adverse prognosis.

While this is one of the studies with a larger sample size of CC biometry in school-age children who were VPT, it has certain limitations that should be addressed. We did not perform a comparison with term-born children as we did not have a control group. However, our aim was to study the differences in CC growth among VPT and our large sample size has allowed us to successfully address this objective. Another limitation of our study is the method of acquiring FA, as other methods, such as track-based spatial statistics have proven to be superior to region of interest (ROI) measurement.70 However, while other ROI in different areas are more troublesome, ROI in the CC has a high interrater agreement.71

CC biometry in school-age children who were VPT is related with both cognitive and motor outcomes. Length and splenium measurements appear to be good biomarkers of neurological outcome in this population. Future studies are needed to demonstrate the usefulness of these measures in the neonatal period as the most reliable, within the study of the CC as a white matter biomarker.

Supplementary information

Supplementary Information

Supplementary information

The online version contains supplementary material available at 10.1038/s41390-023-02994-4.

Author contributions

M.L.-G., I.B.-F., and S.P.L.-L. have played a fundamental role in the conception and design of the work, in the analysis and interpretation of the study data, in the editing of the paper and in the approval of its final version. A.Z.-O. has actively contributed to the data acquisition, interpretation/measurement of MRI images, and has been involved in the approval of the final version of the paper. Y.M.-A., N.J.-L. and Y.S.-S. have actively contributed to the data acquisition, psychological assessment, and has been involved in the approval of the final version of the document.

Funding

This study was funded by the Cádiz integrated territorial initiative for biomedical research, European Regional Development Fund (ERDF) 2014-2020 (ITI-0019-2019), the Department of Health and Families, Andalusian Regional Government, 2020; grant number PI-0016-2020 and the Department of Economic Transformation, Industry, Knowledge, and Universities. Andalusian Regional Government. Project co-funded by 80% by the European Union, within the framework of the ERDF Andalusia 2014-2020 Operational Program; grant number P20-00915. Manuel Lubián-Gutiérrez has a “Rio Hortega” research training contract (CM22/00100) from the Ministry of Science, Innovation and Universities (Instituto de Salud Carlos III), Spanish Government.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

Informed consent was obtained from all participants included in the study.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
==== Refs
References

1. Raybaud C The corpus callosum, the other great forebrain commissures, and the septum pellucidum: anatomy, development, and malformation Neuroradiology 2010 52 447 477 10.1007/s00234-010-0696-3 20422408
Raybaud, C. The corpus callosum, the other great forebrain commissures, and the septum pellucidum: anatomy, development, and malformation. Neuroradiology 52, 447–477 (2010).20422408 10.1007/s00234-010-0696-3
2. Suarez R Gobius I Richards LJ Evolution and development of interhemispheric connections in the vertebrate forebrain Front. Hum. Neurosci. 2014 8 497 10.3389/fnhum.2014.00497 25071525
Suarez, R., Gobius, I. & Richards, L. J. Evolution and development of interhemispheric connections in the vertebrate forebrain. Front. Hum. Neurosci. 8, 497 (2014).25071525 10.3389/fnhum.2014.00497
3. Panigrahy A Barnes PD Robertson RL Sleeper LA Sayre JW Quantitative analysis of the corpus callosum in children with cerebral palsy and developmental delay: correlation with cerebral white matter volume Pediatr. Radio. 2005 35 1199 1207 10.1007/s00247-005-1577-5
Panigrahy, A., Barnes, P. D., Robertson, R. L., Sleeper, L. A. & Sayre, J. W. Quantitative analysis of the corpus callosum in children with cerebral palsy and developmental delay: correlation with cerebral white matter volume. Pediatr. Radio. 35, 1199–1207 (2005).10.1007/s00247-005-1577-5
4. Andronikou S Corpus callosum thickness on mid-sagittal MRI as a marker of brain volume: a pilot study in children with HIV-related brain disease and controls Pediatr. Radio. 2015 45 1016 1025 10.1007/s00247-014-3255-y
Andronikou, S. et al. Corpus callosum thickness on mid-sagittal MRI as a marker of brain volume: a pilot study in children with HIV-related brain disease and controls. Pediatr. Radio. 45, 1016–1025 (2015).10.1007/s00247-014-3255-y
5. Andronikou S Corpus callosum thickness in children: an MR pattern-recognition approach on the midsagittal image Pediatr. Radio. 2015 45 258 272 10.1007/s00247-014-2998-9
Andronikou, S. et al. Corpus callosum thickness in children: an MR pattern-recognition approach on the midsagittal image. Pediatr. Radio. 45, 258–272 (2015).10.1007/s00247-014-2998-9
6. Balevich EC Corpus callosum size and diffusion tensor anisotropy in adolescents and adults with schizophrenia Psychiatry Res. 2015 231 244 251 10.1016/j.pscychresns.2014.12.005 25637358
Balevich, E. C. et al. Corpus callosum size and diffusion tensor anisotropy in adolescents and adults with schizophrenia. Psychiatry Res. 231, 244–251 (2015).25637358 10.1016/j.pscychresns.2014.12.005
7. Purisch SE Gyamfi-Bannerman C Epidemiology of preterm birth Semin. Perinatol. 2017 41 387 391 10.1053/j.semperi.2017.07.009 28865982
Purisch, S. E. & Gyamfi-Bannerman, C. Epidemiology of preterm birth. Semin. Perinatol. 41, 387–391 (2017).28865982 10.1053/j.semperi.2017.07.009
8. Moore T Neurological and developmental outcome in extremely preterm children born in England in 1995 and 2006: the EPICure studies BMJ 2012 345 e7961 10.1136/bmj.e7961 23212880
Moore, T. et al. Neurological and developmental outcome in extremely preterm children born in England in 1995 and 2006: the EPICure studies. BMJ 345, e7961 (2012).23212880 10.1136/bmj.e7961
9. Pierrat V Neurodevelopmental outcome at 2 years for preterm children born at 22 to 34 weeks’ gestation in France in 2011: EPIPAGE-2 cohort study BMJ 2017 358 j3448 10.1136/bmj.j3448 28814566
Pierrat, V. et al. Neurodevelopmental outcome at 2 years for preterm children born at 22 to 34 weeks’ gestation in France in 2011: EPIPAGE-2 cohort study. BMJ 358, j3448 (2017).28814566 10.1136/bmj.j3448
10. Pierrat V Neurodevelopmental outcomes at age 5 among children born preterm: EPIPAGE-2 cohort study BMJ 2021 373 n741 10.1136/bmj.n741 33910920
Pierrat, V. et al. Neurodevelopmental outcomes at age 5 among children born preterm: EPIPAGE-2 cohort study. BMJ 373, n741 (2021).33910920 10.1136/bmj.n741
11. Schneider J Miller SP Preterm brain injury: white matter injury Handb. Clin. Neurol. 2019 162 155 172 10.1016/B978-0-444-64029-1.00007-2 31324309
Schneider, J. & Miller, S. P. Preterm brain injury: white matter injury. Handb. Clin. Neurol. 162, 155–172 (2019).31324309 10.1016/B978-0-444-64029-1.00007-2
12. Volpe JJ Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances Lancet Neurol. 2009 8 110 124 10.1016/S1474-4422(08)70294-1 19081519
Volpe, J. J. Brain injury in premature infants: a complex amalgam of destructive and developmental disturbances. Lancet Neurol. 8, 110–124 (2009).19081519 10.1016/S1474-4422(08)70294-1
13. Back SA White matter injury in the preterm infant: pathology and mechanisms Acta Neuropathol. 2017 134 331 349 10.1007/s00401-017-1718-6 28534077
Back, S. A. White matter injury in the preterm infant: pathology and mechanisms. Acta Neuropathol. 134, 331–349 (2017).28534077 10.1007/s00401-017-1718-6
14. Volpe JJ Cerebral white matter injury of the premature infant-more common than you think Pediatrics 2003 112 176 180 10.1542/peds.112.1.176 12837883
Volpe, J. J. Cerebral white matter injury of the premature infant-more common than you think. Pediatrics 112, 176–180 (2003).12837883 10.1542/peds.112.1.176
15. Dyet LE Natural history of brain lesions in extremely preterm infants studied with serial magnetic resonance imaging from birth and neurodevelopmental assessment Pediatrics 2006 118 536 548 10.1542/peds.2005-1866 16882805
Dyet, L. E. et al. Natural history of brain lesions in extremely preterm infants studied with serial magnetic resonance imaging from birth and neurodevelopmental assessment. Pediatrics 118, 536–548 (2006).16882805 10.1542/peds.2005-1866
16. Buser JR Arrested preoligodendrocyte maturation contributes to myelination failure in premature infants Ann. Neurol. 2012 71 93 109 10.1002/ana.22627 22275256
Buser, J. R. et al. Arrested preoligodendrocyte maturation contributes to myelination failure in premature infants. Ann. Neurol. 71, 93–109 (2012).22275256 10.1002/ana.22627
17. Woodward LJ Anderson PJ Austin NC Howard K Inder TE Neonatal MRI to predict neurodevelopmental outcomes in preterm infants N. Engl. J. Med. 2006 355 685 694 10.1056/NEJMoa053792 16914704
Woodward, L. J., Anderson, P. J., Austin, N. C., Howard, K. & Inder, T. E. Neonatal MRI to predict neurodevelopmental outcomes in preterm infants. N. Engl. J. Med. 355, 685–694 (2006).16914704 10.1056/NEJMoa053792
18. Inder TE Anderson NJ Spencer C Wells S Volpe JJ White matter injury in the premature infant: a comparison between serial cranial sonographic and MR findings at term AJNR Am. J. Neuroradiol. 2003 24 805 809 12748075
Inder, T. E., Anderson, N. J., Spencer, C., Wells, S. & Volpe, J. J. White matter injury in the premature infant: a comparison between serial cranial sonographic and MR findings at term. AJNR Am. J. Neuroradiol. 24, 805–809 (2003).12748075
19. Maalouf EF Comparison of findings on cranial ultrasound and magnetic resonance imaging in preterm infants Pediatrics 2001 107 719 727 10.1542/peds.107.4.719 11335750
Maalouf, E. F. et al. Comparison of findings on cranial ultrasound and magnetic resonance imaging in preterm infants. Pediatrics 107, 719–727 (2001).11335750 10.1542/peds.107.4.719
20. Pierson CR Gray matter injury associated with periventricular leukomalacia in the premature infant Acta Neuropathol. 2007 114 619 631 10.1007/s00401-007-0295-5 17912538
Pierson, C. R. et al. Gray matter injury associated with periventricular leukomalacia in the premature infant. Acta Neuropathol. 114, 619–631 (2007).17912538 10.1007/s00401-007-0295-5
21. Kidokoro H Neil JJ Inder TE New MR imaging assessment tool to define brain abnormalities in very preterm infants at term AJNR Am. J. Neuroradiol. 2013 34 2208 2214 10.3174/ajnr.A3521 23620070
Kidokoro, H., Neil, J. J., Inder, T. E. & New, M. R. imaging assessment tool to define brain abnormalities in very preterm infants at term. AJNR Am. J. Neuroradiol. 34, 2208–2214 (2013).23620070 10.3174/ajnr.A3521
22. Agut T Preterm white matter injury: ultrasound diagnosis and classification Pediatr. Res. 2020 87 37 49 10.1038/s41390-020-0781-1 32218534
Agut, T. et al. Preterm white matter injury: ultrasound diagnosis and classification. Pediatr. Res. 87, 37–49 (2020).32218534 10.1038/s41390-020-0781-1
23. Narberhaus A Gestational age at preterm birth in relation to corpus callosum and general cognitive outcome in adolescents J. Child Neurol. 2007 22 761 765 10.1177/0883073807304006 17641266
Narberhaus, A. et al. Gestational age at preterm birth in relation to corpus callosum and general cognitive outcome in adolescents. J. Child Neurol. 22, 761–765 (2007).17641266 10.1177/0883073807304006
24. Caldu X Corpus callosum size and neuropsychologic impairment in adolescents who were born preterm J. Child Neurol. 2006 21 406 410 10.1177/08830738060210050801 16901446
Caldu, X. et al. Corpus callosum size and neuropsychologic impairment in adolescents who were born preterm. J. Child Neurol. 21, 406–410 (2006).16901446 10.1177/08830738060210050801
25. Anderson NG Laurent I Cook N Woodward L Inder TE Growth rate of corpus callosum in very premature infants AJNR Am. J. Neuroradiol. 2005 26 2685 2690 16286423
Anderson, N. G., Laurent, I., Cook, N., Woodward, L. & Inder, T. E. Growth rate of corpus callosum in very premature infants. AJNR Am. J. Neuroradiol. 26, 2685–2690 (2005).16286423
26. Siffredi V Corpus callosum structural characteristics in very preterm children and adolescents: developmental trajectory and relationship to cognitive functioning Dev. Cogn. Neurosci. 2023 60 101211 10.1016/j.dcn.2023.101211 36780739
Siffredi, V. et al. Corpus callosum structural characteristics in very preterm children and adolescents: developmental trajectory and relationship to cognitive functioning. Dev. Cogn. Neurosci. 60, 101211 (2023).36780739 10.1016/j.dcn.2023.101211
27. Gooijers J Swinnen SP Interactions between brain structure and behavior: the corpus callosum and bimanual coordination Neurosci. Biobehav. Rev. 2014 43 1 19 10.1016/j.neubiorev.2014.03.008 24661987
Gooijers, J. & Swinnen, S. P. Interactions between brain structure and behavior: the corpus callosum and bimanual coordination. Neurosci. Biobehav. Rev. 43, 1–19 (2014).24661987 10.1016/j.neubiorev.2014.03.008
28. Malavolti AM Association between corpus callosum development on magnetic resonance imaging and diffusion tensor imaging, and neurodevelopmental outcome in neonates born very preterm Dev. Med. Child Neurol. 2017 59 433 440 10.1111/dmcn.13364 27976377
Malavolti, A. M. et al. Association between corpus callosum development on magnetic resonance imaging and diffusion tensor imaging, and neurodevelopmental outcome in neonates born very preterm. Dev. Med. Child Neurol. 59, 433–440 (2017).27976377 10.1111/dmcn.13364
29. van Pul C Quantitative fiber tracking in the corpus callosum and internal capsule reveals microstructural abnormalities in preterm infants at term-equivalent age AJNR Am. J. Neuroradiol. 2012 33 678 684 10.3174/ajnr.A2859 22194382
van Pul, C. et al. Quantitative fiber tracking in the corpus callosum and internal capsule reveals microstructural abnormalities in preterm infants at term-equivalent age. AJNR Am. J. Neuroradiol. 33, 678–684 (2012).22194382 10.3174/ajnr.A2859
30. Nagy Z Preterm children have disturbances of white matter at 11 years of age as shown by diffusion tensor imaging Pediatr. Res. 2003 54 672 679 10.1203/01.PDR.0000084083.71422.16 12904607
Nagy, Z. et al. Preterm children have disturbances of white matter at 11 years of age as shown by diffusion tensor imaging. Pediatr. Res. 54, 672–679 (2003).12904607 10.1203/01.PDR.0000084083.71422.16
31. Merlini L Anooshiravani M Kanavaki A Hanquinet S Microstructural changes in thickened corpus callosum in children: contribution of magnetic resonance diffusion tensor imaging Pediatr. Radio. 2015 45 896 901 10.1007/s00247-014-3242-3
Merlini, L., Anooshiravani, M., Kanavaki, A. & Hanquinet, S. Microstructural changes in thickened corpus callosum in children: contribution of magnetic resonance diffusion tensor imaging. Pediatr. Radio. 45, 896–901 (2015).10.1007/s00247-014-3242-3
32. Cirino PT Measuring socioeconomic status: reliability and preliminary validity for different approaches Assessment 2002 9 145 155 10.1177/10791102009002005 12066829
Cirino, P. T. et al. Measuring socioeconomic status: reliability and preliminary validity for different approaches. Assessment 9, 145–155 (2002).12066829 10.1177/10791102009002005
33. Asztalos EV Association between primary caregiver education and cognitive and language development of preterm neonates Am. J. Perinatol. 2017 34 364 371 27571484
Asztalos, E. V. et al. Association between primary caregiver education and cognitive and language development of preterm neonates. Am. J. Perinatol. 34, 364–371 (2017).27571484
34. Drobyshevsky A Developmental changes in diffusion anisotropy coincide with immature oligodendrocyte progression and maturation of compound action potential J. Neurosci. Off. J. Soc. Neurosci. 2005 25 5988 5997 10.1523/JNEUROSCI.4983-04.2005
Drobyshevsky, A. et al. Developmental changes in diffusion anisotropy coincide with immature oligodendrocyte progression and maturation of compound action potential. J. Neurosci. Off. J. Soc. Neurosci. 25, 5988–5997 (2005).10.1523/JNEUROSCI.4983-04.2005
35. Garel C Biometry of the corpus callosum in children: MR imaging reference data AJNR Am. J. Neuroradiol. 2011 32 1436 1443 10.3174/ajnr.A2542 21799035
Garel, C. et al. Biometry of the corpus callosum in children: MR imaging reference data. AJNR Am. J. Neuroradiol. 32, 1436–1443 (2011).21799035 10.3174/ajnr.A2542
36. Witelson SF Hand and sex differences in the isthmus and genu of the human corpus callosum. A postmortem morphological study Brain 1989 112 799 835 10.1093/brain/112.3.799 2731030
Witelson, S. F. Hand and sex differences in the isthmus and genu of the human corpus callosum. A postmortem morphological study. Brain 112, 799–835 (1989).2731030 10.1093/brain/112.3.799
37. Duara R Neuroanatomic differences between dyslexic and normal readers on magnetic resonance imaging scans Arch. Neurol. 1991 48 410 416 10.1001/archneur.1991.00530160078018 2012516
Duara, R. et al. Neuroanatomic differences between dyslexic and normal readers on magnetic resonance imaging scans. Arch. Neurol. 48, 410–416 (1991).2012516 10.1001/archneur.1991.00530160078018
38. Hofer S Frahm J Topography of the human corpus callosum revisited-comprehensive fiber tractography using diffusion tensor magnetic resonance imaging Neuroimage 2006 32 989 994 10.1016/j.neuroimage.2006.05.044 16854598
Hofer, S. & Frahm, J. Topography of the human corpus callosum revisited-comprehensive fiber tractography using diffusion tensor magnetic resonance imaging. Neuroimage 32, 989–994 (2006).16854598 10.1016/j.neuroimage.2006.05.044
39. de Lacoste MC Kirkpatrick JB Ross ED Topography of the human corpus callosum J. Neuropathol. Exp. Neurol. 1985 44 578 591 10.1097/00005072-198511000-00004 4056827
de Lacoste, M. C., Kirkpatrick, J. B. & Ross, E. D. Topography of the human corpus callosum. J. Neuropathol. Exp. Neurol. 44, 578–591 (1985).4056827 10.1097/00005072-198511000-00004
40. Shin YW Sex differences in the human corpus callosum: diffusion tensor imaging study Neuroreport 2005 16 795 798 10.1097/00001756-200505310-00003 15891572
Shin, Y. W. et al. Sex differences in the human corpus callosum: diffusion tensor imaging study. Neuroreport 16, 795–798 (2005).15891572 10.1097/00001756-200505310-00003
41. Westerhausen R Effects of handedness and gender on macro- and microstructure of the corpus callosum and its subregions: a combined high-resolution and diffusion-tensor MRI study Brain Res. Cogn. 2004 21 418 426 10.1016/j.cogbrainres.2004.07.002
Westerhausen, R. et al. Effects of handedness and gender on macro- and microstructure of the corpus callosum and its subregions: a combined high-resolution and diffusion-tensor MRI study. Brain Res. Cogn. 21, 418–426 (2004).10.1016/j.cogbrainres.2004.07.002
42. Wechsler, D. WISC-V: Technical and Interpretive Manual. 1-268. PsychCorp (2013).
43. Henderson S, S. D., Barnett A. Movement Assessment Battery for Children-Second Edition, (Movement ABC-2) (The Psychological Corporation, 2007).
44. Cayam-Rand D Interaction between preterm white matter injury and childhood thalamic growth Ann. Neurol. 2021 90 584 594 10.1002/ana.26201 34436793
Cayam-Rand, D. et al. Interaction between preterm white matter injury and childhood thalamic growth. Ann. Neurol. 90, 584–594 (2021).34436793 10.1002/ana.26201
45. Sheikhi S Saboory E Farjah GH Correlation of nerve fibers in corpus callosum and number of neurons in cerebral cortex: an innovative mathematical model Int J. Neurosci. 2018 128 995 1002 10.1080/00207454.2018.1458725 29619891
Sheikhi, S., Saboory, E. & Farjah, G. H. Correlation of nerve fibers in corpus callosum and number of neurons in cerebral cortex: an innovative mathematical model. Int J. Neurosci. 128, 995–1002 (2018).29619891 10.1080/00207454.2018.1458725
46. Blaauw J Meiners LC The splenium of the corpus callosum: embryology, anatomy, function and imaging with pathophysiological hypothesis Neuroradiology 2020 62 563 585 10.1007/s00234-019-02357-z 32062761
Blaauw, J. & Meiners, L. C. The splenium of the corpus callosum: embryology, anatomy, function and imaging with pathophysiological hypothesis. Neuroradiology 62, 563–585 (2020).32062761 10.1007/s00234-019-02357-z
47. Allin M Growth of the corpus callosum in adolescents born preterm Arch. Pediatr. Adolesc. Med. 2007 161 1183 1189 10.1001/archpedi.161.12.1183 18056564
Allin, M. et al. Growth of the corpus callosum in adolescents born preterm. Arch. Pediatr. Adolesc. Med. 161, 1183–1189 (2007).18056564 10.1001/archpedi.161.12.1183
48. Peterson BS Regional brain volumes and their later neurodevelopmental correlates in term and preterm infants Pediatrics 2003 111 939 948 10.1542/peds.111.5.939 12728069
Peterson, B. S. et al. Regional brain volumes and their later neurodevelopmental correlates in term and preterm infants. Pediatrics 111, 939–948 (2003).12728069 10.1542/peds.111.5.939
49. Skranes J Clinical findings and white matter abnormalities seen on diffusion tensor imaging in adolescents with very low birth weight Brain 2007 130 654 666 10.1093/brain/awm001 17347255
Skranes, J. et al. Clinical findings and white matter abnormalities seen on diffusion tensor imaging in adolescents with very low birth weight. Brain 130, 654–666 (2007).17347255 10.1093/brain/awm001
50. Nosarti C Corpus callosum size and very preterm birth: relationship to neuropsychological outcome Brain 2004 127 2080 2089 10.1093/brain/awh230 15289268
Nosarti, C. et al. Corpus callosum size and very preterm birth: relationship to neuropsychological outcome. Brain 127, 2080–2089 (2004).15289268 10.1093/brain/awh230
51. Moses P Regional size reduction in the human corpus callosum following pre- and perinatal brain injury Cereb. Cortex 2000 10 1200 1210 10.1093/cercor/10.12.1200 11073869
Moses, P. et al. Regional size reduction in the human corpus callosum following pre- and perinatal brain injury. Cereb. Cortex 10, 1200–1210 (2000).11073869 10.1093/cercor/10.12.1200
52. Rademaker KJ Larger corpus callosum size with better motor performance in prematurely born children Semin. Perinatol. 2004 28 279 287 10.1053/j.semperi.2004.08.005 15565788
Rademaker, K. J. et al. Larger corpus callosum size with better motor performance in prematurely born children. Semin. Perinatol. 28, 279–287 (2004).15565788 10.1053/j.semperi.2004.08.005
53. Nosarti C Preterm birth and structural brain alterations in early adulthood Neuroimage Clin. 2014 6 180 191 10.1016/j.nicl.2014.08.005 25379430
Nosarti, C. et al. Preterm birth and structural brain alterations in early adulthood. Neuroimage Clin. 6, 180–191 (2014).25379430 10.1016/j.nicl.2014.08.005
54. Peterson BS Regional brain volume abnormalities and long-term cognitive outcome in preterm infants JAMA 2000 284 1939 1947 10.1001/jama.284.15.1939 11035890
Peterson, B. S. et al. Regional brain volume abnormalities and long-term cognitive outcome in preterm infants. JAMA 284, 1939–1947 (2000).11035890 10.1001/jama.284.15.1939
55. Fryer SL Microstructural integrity of the corpus callosum linked with neuropsychological performance in adolescents Brain Cogn. 2008 67 225 233 10.1016/j.bandc.2008.01.009 18346830
Fryer, S. L. et al. Microstructural integrity of the corpus callosum linked with neuropsychological performance in adolescents. Brain Cogn. 67, 225–233 (2008).18346830 10.1016/j.bandc.2008.01.009
56. Luders E Positive correlations between corpus callosum thickness and intelligence Neuroimage 2007 37 1457 1464 10.1016/j.neuroimage.2007.06.028 17689267
Luders, E. et al. Positive correlations between corpus callosum thickness and intelligence. Neuroimage 37, 1457–1464 (2007).17689267 10.1016/j.neuroimage.2007.06.028
57. Moreno MB Concha L Gonzalez-Santos L Ortiz JJ Barrios FA Correlation between corpus callosum sub-segmental area and cognitive processes in school-age children PLoS One 2014 9 e104549 10.1371/journal.pone.0104549 25170897
Moreno, M. B., Concha, L., Gonzalez-Santos, L., Ortiz, J. J. & Barrios, F. A. Correlation between corpus callosum sub-segmental area and cognitive processes in school-age children. PLoS One 9, e104549 (2014).25170897 10.1371/journal.pone.0104549
58. Cuzzilla R Relationships between early postnatal cranial ultrasonography linear measures and neurodevelopment at 2 years in infants born at <30 weeks’ gestational age without major brain injury Arch. Dis. Child. Fetal Neonatal Ed. 2023 108 511 516 10.1136/archdischild-2022-324660 36958812
Cuzzilla, R. et al. Relationships between early postnatal cranial ultrasonography linear measures and neurodevelopment at 2 years in infants born at <30 weeks’ gestational age without major brain injury. Arch. Dis. Child. Fetal Neonatal Ed. 108, 511–516 (2023).36958812 10.1136/archdischild-2022-324660
59. Huppi PS Microstructural development of human newborn cerebral white matter assessed in vivo by diffusion tensor magnetic resonance imaging Pediatr. Res. 1998 44 584 590 10.1203/00006450-199810000-00019 9773850
Huppi, P. S. et al. Microstructural development of human newborn cerebral white matter assessed in vivo by diffusion tensor magnetic resonance imaging. Pediatr. Res. 44, 584–590 (1998).9773850 10.1203/00006450-199810000-00019
60. O’Gorman RL Tract-based spatial statistics to assess the neuroprotective effect of early erythropoietin on white matter development in preterm infants Brain 2015 138 388 397 10.1093/brain/awu363 25534356
O’Gorman, R. L. et al. Tract-based spatial statistics to assess the neuroprotective effect of early erythropoietin on white matter development in preterm infants. Brain 138, 388–397 (2015).25534356 10.1093/brain/awu363
61. Thompson DK Accelerated corpus callosum development in prematurity predicts improved outcome Hum. Brain Mapp. 2015 36 3733 3748 10.1002/hbm.22874 26108187
Thompson, D. K. et al. Accelerated corpus callosum development in prematurity predicts improved outcome. Hum. Brain Mapp. 36, 3733–3748 (2015).26108187 10.1002/hbm.22874
62. Cahill-Rowley K Prediction of gait impairment in toddlers born preterm from near-term brain microstructure assessed with DTI, using exhaustive feature selection and cross-validation Front Hum. Neurosci. 2019 13 305 10.3389/fnhum.2019.00305 31619977
Cahill-Rowley, K. et al. Prediction of gait impairment in toddlers born preterm from near-term brain microstructure assessed with DTI, using exhaustive feature selection and cross-validation. Front Hum. Neurosci. 13, 305 (2019).31619977 10.3389/fnhum.2019.00305
63. Mercuri E Evaluation of the corpus callosum in clumsy children born prematurely: a functional and morphological study Neuropediatrics 1996 27 317 322 10.1055/s-2007-973801 9050050
Mercuri, E. et al. Evaluation of the corpus callosum in clumsy children born prematurely: a functional and morphological study. Neuropediatrics 27, 317–322 (1996).9050050 10.1055/s-2007-973801
64. Rudisch J Butler J Izadi H Birtles D Green D Developmental characteristics of disparate bimanual movement skills in typically developing children J. Mot. Behav. 2018 50 8 16 10.1080/00222895.2016.1271302 28632103
Rudisch, J., Butler, J., Izadi, H., Birtles, D. & Green, D. Developmental characteristics of disparate bimanual movement skills in typically developing children. J. Mot. Behav. 50, 8–16 (2018).28632103 10.1080/00222895.2016.1271302
65. Muetzel RL The development of corpus callosum microstructure and associations with bimanual task performance in healthy adolescents Neuroimage 2008 39 1918 1925 10.1016/j.neuroimage.2007.10.018 18060810
Muetzel, R. L. et al. The development of corpus callosum microstructure and associations with bimanual task performance in healthy adolescents. Neuroimage 39, 1918–1925 (2008).18060810 10.1016/j.neuroimage.2007.10.018
66. Hung YC Robert MT Friel KM Gordon AM Relationship between integrity of the corpus callosum and bimanual coordination in children with unilateral spastic cerebral palsy Front Hum. Neurosci. 2019 13 334 10.3389/fnhum.2019.00334 31607881
Hung, Y. C., Robert, M. T., Friel, K. M. & Gordon, A. M. Relationship between integrity of the corpus callosum and bimanual coordination in children with unilateral spastic cerebral palsy. Front Hum. Neurosci. 13, 334 (2019).31607881 10.3389/fnhum.2019.00334
67. Hutchinson AD Relationship between intelligence and the size and composition of the corpus callosum Exp. Brain Res. 2009 192 455 464 10.1007/s00221-008-1604-5 18949469
Hutchinson, A. D. et al. Relationship between intelligence and the size and composition of the corpus callosum. Exp. Brain Res. 192, 455–464 (2009).18949469 10.1007/s00221-008-1604-5
68. Karolis VR Volumetric grey matter alterations in adolescents and adults born very preterm suggest accelerated brain maturation Neuroimage 2017 163 379 389 10.1016/j.neuroimage.2017.09.039 28942062
Karolis, V. R. et al. Volumetric grey matter alterations in adolescents and adults born very preterm suggest accelerated brain maturation. Neuroimage 163, 379–389 (2017).28942062 10.1016/j.neuroimage.2017.09.039
69. Young JM White matter microstructural differences identified using multi-shell diffusion imaging in six-year-old children born very preterm Neuroimage Clin. 2019 23 101855 10.1016/j.nicl.2019.101855 31103872
Young, J. M. et al. White matter microstructural differences identified using multi-shell diffusion imaging in six-year-old children born very preterm. Neuroimage Clin. 23, 101855 (2019).31103872 10.1016/j.nicl.2019.101855
70. Lebel C Treit S Beaulieu C A review of diffusion MRI of typical white matter development from early childhood to young adulthood NMR Biomed. 2019 32 e3778 10.1002/nbm.3778 28886240
Lebel, C., Treit, S. & Beaulieu, C. A review of diffusion MRI of typical white matter development from early childhood to young adulthood. NMR Biomed. 32, e3778 (2019).28886240 10.1002/nbm.3778
71. Ozturk A Regional differences in diffusion tensor imaging measurements: assessment of intrarater and interrater variability AJNR Am. J. Neuroradiol. 2008 29 1124 1127 10.3174/ajnr.A0998 18356471
Ozturk, A. et al. Regional differences in diffusion tensor imaging measurements: assessment of intrarater and interrater variability. AJNR Am. J. Neuroradiol. 29, 1124–1127 (2008).18356471 10.3174/ajnr.A0998
