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Vanderhasselt T, Naeyaert M, Buls N, Allemeersch GJ, Raeymaeckers S, Raeymaekers H, Smeets N, Cools F, de Mey J, Dudink J. Synthetic magnetic resonance-based relaxometry and brain volume: cutoff values for predicting neurocognitive outcomes in very preterm infants. Pediatr Radiol 2024; 54:1523-1531. [PMID: 38980354 PMCID: PMC11324712 DOI: 10.1007/s00247-024-05981-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Revised: 06/20/2024] [Accepted: 06/23/2024] [Indexed: 07/10/2024]
Abstract
BACKGROUND Early neurorehabilitation can enhance neurocognitive outcomes in very preterm infants (<32 weeks), and conventional magnetic resonance imaging (MRI) is commonly used to assess neonatal brain injury; however, the predictive value for neurodevelopmental delay is limited. Timely predictive quantitative biomarkers are needed to improve early identification and management of infants at risk of neurodevelopmental delay. OBJECTIVE To evaluate the potential of quantitative synthetic MRI measurements at term-equivalent age as predictive biomarkers of neurodevelopmental impairment and establish practical cutoff values to guide clinical decision-making. MATERIALS AND METHODS This retrospective study included 93 very preterm infants who underwent synthetic MRI at term-equivalent age between January 2017 and September 2020. Clinical outcomes were assessed using the Bayley-III scale of infant development (mean age 2.1 years). The predictive value for impaired development was analyzed using receiver operating characteristic curves for synthetic MRI-based volumetry and T1 and T2 relaxation measurements. RESULTS The T1 relaxation time in the posterior limb of the internal capsule was a potent predictor of severe (sensitivity, 92%; specificity, 80%; area under the curve (AUC), 0.91) and mild or severe (AUC, 0.75) developmental impairment. T2 relaxation time in the posterior limb of the internal capsule was a significant predictor of severe impairment (AUC, 0.76), whereas the brain parenchymal volume was a significant predictor of severe (AUC, 0.72) and mild or severe impairment (AUC, 0.71) outperforming the reported qualitative MRI scores (AUC, 0.66). CONCLUSION The proposed cutoff values for T1 relaxation time in the posterior limb of the internal capsule and for total brain volume measurements, derived from synthetic MRI, show promise as predictors of both mild and severe neurodevelopmental impairment in very preterm infants.
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Affiliation(s)
- Tim Vanderhasselt
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium.
| | - Maarten Naeyaert
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium
| | - Nico Buls
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium
| | - Gert-Jan Allemeersch
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium
| | - Steven Raeymaeckers
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium
| | - Hubert Raeymaekers
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium
| | - Nathalie Smeets
- Department of Pediatric Neurology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Filip Cools
- Department of Neonatology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Brussels, Belgium
| | - Johan de Mey
- Department of Radiology, Vrije Universiteit Brussel, Universitair Ziekenhuis Brussel, Laarbeeklaan 101, 1090, Brussels, Belgium
| | - Jeroen Dudink
- Department of Neonatology, Wilhelmina Children's Hospital, University Medical Center Utrecht, Utrecht, the Netherlands
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Kline JE, Dudley J, Illapani VSP, Li H, Kline-Fath B, Tkach J, He L, Yuan W, Parikh NA. Diffuse excessive high signal intensity in the preterm brain on advanced MRI represents widespread neuropathology. Neuroimage 2022; 264:119727. [PMID: 36332850 PMCID: PMC9908008 DOI: 10.1016/j.neuroimage.2022.119727] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 10/26/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022] Open
Abstract
Preterm brains commonly exhibit elevated signal intensity in the white matter on T2-weighted MRI at term-equivalent age. This signal, known as diffuse excessive high signal intensity (DEHSI) or diffuse white matter abnormality (DWMA) when quantitatively assessed, is associated with abnormal microstructure on diffusion tensor imaging. However, postmortem data are largely lacking and difficult to obtain, and the pathological significance of DEHSI remains in question. In a cohort of 202 infants born preterm at ≤32 weeks gestational age, we leveraged two newer diffusion MRI models - Constrained Spherical Deconvolution (CSD) and neurite orientation dispersion and density index (NODDI) - to better characterize the macro and microstructural properties of DWMA and inform the ongoing debate around the clinical significance of DWMA. With increasing DWMA volume, fiber density broadly decreased throughout the white matter and fiber cross-section decreased in the major sensorimotor tracts. Neurite orientation dispersion decreased in the centrum semiovale, corona radiata, and temporal lobe. These findings provide insight into DWMA's biological underpinnings and demonstrate that it is a serious pathology.
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Affiliation(s)
- Julia E Kline
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Jon Dudley
- Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Venkata Sita Priyanka Illapani
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Hailong Li
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Beth Kline-Fath
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Jean Tkach
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Lili He
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Weihong Yuan
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Imaging Research Center, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Nehal A Parikh
- Neurodevelopmental Disorders Prevention Center, Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States; Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, United States.
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Nosko D, Strindberg M, Svoboda J, Kvanta H, Broström L, Padilla N, Mårtensson G, Örtqvist M, Moreira NC, Ådén U. Discrete white matter abnormalities at age 8-11 years in children born extremely preterm are not associated with adverse cognitive or motor outcomes. Acta Paediatr 2022; 111:566-575. [PMID: 34665877 DOI: 10.1111/apa.16158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 10/12/2021] [Accepted: 10/18/2021] [Indexed: 11/27/2022]
Abstract
AIM Little is known about the prevalence of discrete white matter abnormalities (WMA) beyond the first years in children born extremely preterm (EPT) and the relation to neurodevelopmental outcomes. Our aim was to investigate the prevalence of discrete WMA in children born EPT and the relationship to neonatal white matter injuries (WMI), white matter (WM) volume, WM diffusivity and neurodevelopment. METHODS The study was a part of a longitudinal follow-up study of EPT neonates. All children were scanned at Karolinska University hospital 2004-2007 (neonates) and 2014-2015 (children at 8-11 years). WMA was qualitatively assessed by visual inspection. Developmental assessment was conducted at 12 years. RESULTS In total, 112 children (median age 10.3 years, 56 girls) underwent MRI of the brain (68 EPT, 45 controls). In the EPT group, a subset had MRI around term equivalent age (n = 61). In the EPT group, the prevalence of discrete WMA at 8-11 years was 52%. There was a positive association between WMI at TEA and 8-11 years. There was no association between WMI and WM volumes or diffusivity at 8-11 years. Discrete WMA was not related to neurodevelopmental outcomes. CONCLUSION Discrete WMA was prevalent in children born EPT at 8-11 years but were not related to neurodevelopmental outcomes.
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Affiliation(s)
- Daniela Nosko
- Department of Paediatrics Örebro University Hospital Örebro Sweden
| | - Marika Strindberg
- Department of Women's and Children's Health Karolinska Institutet Stockholm Sweden
| | - Jan Svoboda
- Department of Neuroradiology Karolinska University Hospital Stockholm Sweden
| | - Hedvig Kvanta
- Department of Women's and Children's Health Karolinska Institutet Stockholm Sweden
| | - Lina Broström
- Department of Women's and Children's Health Karolinska Institutet Stockholm Sweden
| | - Nelly Padilla
- Department of Women's and Children's Health Karolinska Institutet Stockholm Sweden
| | - Gustaf Mårtensson
- Department of Women's and Children's Health Karolinska Institutet Stockholm Sweden
| | - Maria Örtqvist
- Department of Women's and Children's Health Karolinska Institutet Stockholm Sweden
| | - Nuno Canto Moreira
- Department of Neuroradiology Karolinska University Hospital Stockholm Sweden
| | - Ulrika Ådén
- Department of Women's and Children's Health Karolinska Institutet Stockholm Sweden
- Department of Neonatal Medicine Karolinska University Hospital Stockholm Sweden
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Jeong MH, Jeong SH, Park SJ, Lee N, Bae MH, Park KH, Byun SY, Kim C, Han YM. Neurodevelopmental Outcomes of Very-Low-Birth-Weight Infants without Severe Brain Lesions and Impact of Postnatal Steroid Use: A Single-Center Korean Study. NEONATAL MEDICINE 2022. [DOI: 10.5385/nm.2022.29.1.36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022] Open
Abstract
Purpose: We used the Bayley Scales of Infant and Toddler Development (BSID)-III to analyze the incidence and risk factors of developmental delay in very-low-birth-weight infants without severe brain lesions. We further examined the correlation between the cumulative dexamethasone dose and developmental assessment results.Methods: We retrospectively analyzed data of preterm infants (birth weight <1,500 g) admitted to our neonatal intensive care unit between January 2014 to December 2020. The BSID-III scores obtained between the corrected ages of 12 and 24 months and after 24 months were analyzed. Developmental delay was defined as a composite score of <85 for the cognition, language, and motor domains. Univariate and multivariate analyses of developmental delay risk factors and developmental changes from the first to second BSID-III were performed. Correlations between the accumulated dexamethasone dose used for bronchopulmonary dysplasia (BPD) and the first and second test scores were analyzed.Results: Seventy-one and thirty-six infants completed the first and second tests, respectively. In both tests, developmental delay was most commonly observed in the language domain (26.8%, 47.2%). In multivariate analysis, mild BPD was identified as a developmental delay risk factor (P<0.05), whereas prenatal steroid use reduced the developmental delay risk (P<0.05). All domain scores were lower in the second test than in the first test. The cognition and language domain scores in the second test decreased with increasing cumulative dexamethasone doses.Conclusion: Very-low-birth-weight infants typically experience language delay, which can persist as they age.
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Parikh NA, Sharma P, He L, Li H, Altaye M, Illapani VSP. Perinatal Risk and Protective Factors in the Development of Diffuse White Matter Abnormality on Term-Equivalent Age Magnetic Resonance Imaging in Infants Born Very Preterm. J Pediatr 2021; 233:58-65.e3. [PMID: 33259857 PMCID: PMC8290900 DOI: 10.1016/j.jpeds.2020.11.058] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 10/24/2020] [Accepted: 11/24/2020] [Indexed: 12/11/2022]
Abstract
OBJECTIVE To identify perinatal clinical diseases and treatments that are associated with the development of objectively diagnosed diffuse white matter abnormality (DWMA) on structural magnetic resonance imaging (MRI) at term-equivalent age in infants born very preterm. STUDY DESIGN A prospective cohort of 392 infants born very preterm (≤32 weeks of gestational age) was enrolled from 5 level III/IV neonatal intensive care units between September 2016 and November 2019. MRIs of the brain were collected at 39 to 45 weeks of postmenstrual age to evaluate DWMA volume. A predefined list of pertinent maternal characteristics, pregnancy/delivery data, and neonatal intensive care unit data were collected for enrolled patients to identify antecedents of objectively diagnosed DWMA. RESULTS Of the 392 infants in the cohort, 377 (96%) had high-quality MRI data. Their mean (SD) gestational age was 29.3 (2.5) weeks. In multivariable linear regression analyses, pneumothorax (P = .027), severe bronchopulmonary dysplasia (BPD) (P = .009), severe retinopathy of prematurity (P < .001), and male sex (P = .041) were associated with increasing volume of DWMA. The following factors were associated with decreased risk of DWMA: postnatal dexamethasone therapy for severe BPD (P = .004), duration of caffeine therapy for severe BPD (P = .009), and exclusive maternal milk diet at neonatal intensive care unit discharge (P = .049). CONCLUSIONS Severe retinopathy of prematurity and BPD exhibited the strongest adverse association with development of DWMA. We also identified treatments and nutritional factors that appear protective against the development of DWMA that also have implications for the clinical care of infants born very preterm.
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Affiliation(s)
- Nehal A. Parikh
- The Perinatal Institute, Cincinnati Children’s Hospital Medical Center, United States,Department of Pediatrics, University of Cincinnati, College of Medicine United States,Correspondence: Nehal A. Parikh, DO, MS, Professor of Pediatrics, Cincinnati Children’s Hospital Medical Center, 3333 Burnet Avenue, MLC 4009, Cincinnati, OH, 45229, United States, , Phone number: 513.803.7584
| | - Puneet Sharma
- The Perinatal Institute, Cincinnati Children’s Hospital Medical Center, United States,Department of Pediatrics, University of Cincinnati, College of Medicine United States
| | - Lili He
- The Perinatal Institute, Cincinnati Children’s Hospital Medical Center, United States,Department of Pediatrics, University of Cincinnati, College of Medicine United States
| | - Hailong Li
- The Perinatal Institute, Cincinnati Children’s Hospital Medical Center, United States
| | - Mekibib Altaye
- Department of Pediatrics, University of Cincinnati, College of Medicine United States,Division of Biostatistics and Epidemiology, Cincinnati Children’s Hospital Medical Center, United States
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Hadders-Algra M. Early Diagnostics and Early Intervention in Neurodevelopmental Disorders-Age-Dependent Challenges and Opportunities. J Clin Med 2021; 10:861. [PMID: 33669727 PMCID: PMC7922888 DOI: 10.3390/jcm10040861] [Citation(s) in RCA: 60] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 02/11/2021] [Accepted: 02/13/2021] [Indexed: 12/20/2022] Open
Abstract
This review discusses early diagnostics and early intervention in developmental disorders in the light of brain development. The best instruments for early detection of cerebral palsy (CP) with or without intellectual disability are neonatal magnetic resonance imaging, general movements assessment at 2-4 months and from 2-4 months onwards, the Hammersmith Infant Neurological Examination and Standardized Infant NeuroDevelopmental Assessment. Early detection of autism spectrum disorders (ASD) is difficult; its first signs emerge at the end of the first year. Prediction with the Modified Checklist for Autism in Toddlers and Infant Toddler Checklist is possible to some extent and improves during the second year, especially in children at familial risk of ASD. Thus, prediction improves substantially when transient brain structures have been replaced by permanent circuitries. At around 3 months the cortical subplate has dissolved in primary motor and sensory cortices; around 12 months the cortical subplate in prefrontal and parieto-temporal cortices and cerebellar external granular layer have disappeared. This review stresses that families are pivotal in early intervention. It summarizes evidence on the effectiveness of early intervention in medically fragile neonates, infants at low to moderate risk, infants with or at high risk of CP and with or at high risk of ASD.
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Affiliation(s)
- Mijna Hadders-Algra
- University of Groningen, University Medical Center Groningen, Department of Paediatrics-Section Developmental Neurology, 9713 GZ Groningen, The Netherlands
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Li H, Chen M, Wang J, Illapani VSP, Parikh NA, He L. Automatic Segmentation of Diffuse White Matter Abnormality on T2-weighted Brain MR Images Using Deep Learning in Very Preterm Infants. Radiol Artif Intell 2021; 3:e200166. [PMID: 34142089 PMCID: PMC8166113 DOI: 10.1148/ryai.2021200166] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 01/20/2021] [Accepted: 01/22/2021] [Indexed: 11/11/2022]
Abstract
About 50%-80% of very preterm infants (VPIs) (≤ 32 weeks gestational age) exhibit diffuse white matter abnormality (DWMA) on their MR images at term-equivalent age. It remains unknown if DWMA is associated with developmental impairments, and further study is warranted. To aid in the assessment of DWMA, a deep learning model for DWMA quantification on T2-weighted MR images was developed. This secondary analysis of prospective data was performed with an internal cohort of 98 VPIs (data collected from December 2014 to April 2016) and an external cohort of 28 VPIs (data collected from January 2012 to August 2014) who had already undergone MRI at term-equivalent age. Ground truth DWMA regions were manually annotated by two human experts with the guidance of a prior published semiautomated algorithm. In a twofold cross-validation experiment using the internal cohort of 98 infants, the three-dimensional (3D) ResU-Net model accurately segmented DWMA with a Dice similarity coefficient of 0.907 ± 0.041 (standard deviation) and balanced accuracy of 96.0% ± 2.1, outperforming multiple peer deep learning models. The 3D ResU-Net model that was trained with the whole internal cohort (n = 98) was further tested on an independent external test cohort (n = 28) and achieved a Dice similarity coefficient of 0.877 ± 0.059 and balanced accuracy of 92.3% ± 3.9. The externally validated 3D ResU-Net deep learning model for accurately segmenting DWMA may facilitate the clinical diagnosis of DWMA in VPIs. Supplemental material is available for this article. Keywords: Brain/Brain Stem, Convolutional Neural Network (CNN), MR-Imaging, Pediatrics, Segmentation, Supervised learning © RSNA, 2021.
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