Antecedents of Objectively Diagnosed Diffuse White Matter Abnormality in Very Preterm Infants

Importance: Diffuse white matter abnormality (DWMA) is the most common brain MRI finding in very preterm infants and is predictive of neurodevelopmental impairments. However, its etiology remains elusive and identifying perinatal risk and protective factors may allow clinicians to reduce the burden of DWMA. Objective: To identify perinatal clinical factors that are associated with the development of objectively diagnosed DWMA in very preterm infants. Design: A prospective cohort was enrolled between September 2016 and November 2019. Brain MRIs were collected at 39 to 45 weeks postmenstrual age (PMA) to evaluate DWMA volume. A pre-defined list of pertinent maternal characteristics, pregnancy/delivery data, and neonatal ICU data was collected for enrolled patients to identify antecedents of objectively diagnosed DWMA. Setting: Five level III/IV NICUs in the greater Cincinnati, Ohio area. Participants: A population-based sample of 392 very preterm infants born before 33 weeks gestational age. Exposure: Very preterm birth with associated diseases and treatments. Main Outcome and Measure: Objectively diagnosed DWMA volume on brain MRI at term-equivalent age. Results: 377 of the 392 very preterm infants (96%) had high quality MRI data. 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 (ROP) (p<0.001), and male sex (p=.041) were associated with increasing volume of DWMA. The following factors were associated with decreased risk of DWMA: dexamethasone for severe BPD (p=.004), duration of caffeine for severe BPD (p = 0.009), and exclusive maternal milk at NICU discharge (p=.049). Conclusions and Relevance: Severe ROP and BPD exhibited the strongest adverse association with the development of DWMA. Caffeine and dexamethasone treatments for infants with severe BPD exhibited a protective effect against development of DWMA. The beneficial association with maternal milk is also a modifiable factor that has clinical implications.

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Diffuse white matter abnormality in very preterm infants at term reflects reduced brain network efficiency

NeuroImage Clinical ◽

10.1016/j.nicl.2021.102739 ◽

2021 ◽

pp. 102739

Author(s):

Julia E. Kline ◽

Venkata Sita Priyanka Illapani ◽

Hailong Li ◽

Lili He ◽

Weihong Yuan ◽

...

Keyword(s):

White Matter ◽

Preterm Infants ◽

Brain Network ◽

White Matter Abnormality ◽

Network Efficiency ◽

Very Preterm Infants ◽

Very Preterm ◽

Diffuse White Matter

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Automatic Segmentation of Diffuse White Matter Abnormality on T2-weighted Brain MRI Using Deep Learning in Very Preterm Infants

Radiology Artificial Intelligence ◽

10.1148/ryai.2021200166 ◽

2021 ◽

pp. e200166

Author(s):

Hailong Li ◽

Ming Chen ◽

Jinghua Wang ◽

Venkata Sita Priyanka Illapani ◽

Nehal A. Parikh ◽

...

Keyword(s):

Deep Learning ◽

White Matter ◽

Preterm Infants ◽

Automatic Segmentation ◽

Brain Mri ◽

White Matter Abnormality ◽

Very Preterm Infants ◽

Very Preterm ◽

Diffuse White Matter

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Diffuse white matter abnormality in very preterm infants reflects reduced brain network efficiency

10.1101/2020.06.27.20141713 ◽

2020 ◽

Author(s):

Julia E. Kline ◽

Venkata Sita Priyanka Illapani ◽

Hailong Li ◽

Lili He ◽

Nehal A. Parikh

Keyword(s):

Graph Theory ◽

White Matter ◽

Preterm Infants ◽

Brain Network ◽

Clustering Coefficient ◽

White Matter Abnormality ◽

Very Preterm Infants ◽

Local Efficiency ◽

Very Preterm ◽

Diffuse White Matter

AbstractBetween 50-80% of very preterm infants (≤32 weeks gestational age) exhibit increased white matter signal intensity on T2 MRI at term-equivalent age, known as diffuse white matter abnormality (DWMA). A few studies have linked DWMA with microstructural abnormalities, but the exact relationship remains poorly understood. We used graph theory methods to relate DWMA extent to measures of efficient information processing at term in a representative cohort of 343 very preterm infants. We performed anatomic and diffusion MRI at term and quantified DWMA volume using our novel, semi-automated algorithm. From structural connectomes, we calculated graph theory metrics: local efficiency and clustering coefficient, which measure the ability of groups of nodes to perform specialized processing, and global efficiency, which assesses the ability of brain regions to efficiently combine information. We computed partial correlations between these measures and DWMA volume, adjusted for confounders. Increasing DWMA volume was associated with decreased global efficiency of the entire brain network (r= - 0.27, p= 8.36E-07) and decreased local efficiency and clustering coefficient within individual networks supporting cognitive, linguistic, and motor functions. We show that DWMA is associated with widespread decreased brain network connectivity in very preterm infants, suggesting it is pathologic and likely has adverse developmental consequences.

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Novel Diffuse White Matter Abnormality Biomarker at Term-Equivalent Age Enhances Prediction of Long-term Motor Development in Children Born Very Preterm

10.1101/2020.08.04.20167031 ◽

2020 ◽

Author(s):

Nehal A Parikh ◽

Karen Harpster ◽

Lili He ◽

Venkata Sita Priyanka Illapani ◽

Fatima Chughtai Khalid ◽

...

Keyword(s):

Preterm Infants ◽

Gestational Age ◽

Motor Development ◽

Prognostic Biomarker ◽

White Matter Abnormality ◽

Very Preterm Infants ◽

Equivalent Age ◽

Very Preterm ◽

Diffuse White Matter

Our objective was to evaluate the independent prognostic value of a novel MRI biomarker − objectively diagnosed diffuse white matter abnormality volume (DWMA; diffuse excessive high signal intensity) − for prediction of motor outcomes in very preterm infants. We prospectively enrolled a geographically-based cohort of very preterm infants without severe brain injury and born before 32 weeks gestational age. Structural brain MRI was obtained at term-equivalent age and DWMA volume was objectively quantified using a published validated algorithm. These results were compared with visually classified DWMA. We used multivariable linear regression to assess the value of DWMA volume, independent of known predictors, to predict motor development as assessed using the Bayley Scales of Infant & Toddler Development, Third Edition at 3 years of age. The mean (SD) gestational age of the cohort was 28.3 (2.4) weeks. In multivariable analyses, controlling for gestational age, sex, and abnormality on structural MRI, DWMA volume was an independent prognostic biomarker of Bayley Motor scores (β= −12.59 [95% CI: −18.70, −6.48] R2=0.41). Conversely, visually classified DWMA was not predictive of motor development. In conclusion, objectively quantified DWMA is an independent prognostic biomarker of long-term motor development in very preterm infants and warrants further study.

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Novel diffuse white matter abnormality biomarker at term-equivalent age enhances prediction of long-term motor development in very preterm children

Scientific Reports ◽

10.1038/s41598-020-72632-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Nehal A. Parikh ◽

Karen Harpster ◽

Lili He ◽

Venkata Sita Priyanka Illapani ◽

Fatima Chughtai Khalid ◽

...

Keyword(s):

Preterm Infants ◽

Gestational Age ◽

Motor Development ◽

Prognostic Biomarker ◽

White Matter Abnormality ◽

Very Preterm Infants ◽

Equivalent Age ◽

Very Preterm ◽

Diffuse White Matter

Abstract Our objective was to evaluate the independent prognostic value of a novel MRI biomarker—objectively diagnosed diffuse white matter abnormality volume (DWMA; diffuse excessive high signal intensity)—for prediction of motor outcomes in very preterm infants. We prospectively enrolled a geographically-based cohort of very preterm infants without severe brain injury and born before 32 weeks gestational age. Structural brain MRI was obtained at term-equivalent age and DWMA volume was objectively quantified using a published validated algorithm. These results were compared with visually classified DWMA. We used multivariable linear regression to assess the value of DWMA volume, independent of known predictors, to predict motor development as assessed using the Bayley Scales of Infant & Toddler Development, Third Edition at 3 years of age. The mean (SD) gestational age of the cohort was 28.3 (2.4) weeks. In multivariable analyses, controlling for gestational age, sex, and abnormality on structural MRI, DWMA volume was an independent prognostic biomarker of Bayley Motor scores ($$\beta $$ β = −12.59 [95% CI −18.70, −6.48] R2 = 0.41). Conversely, visually classified DWMA was not predictive of motor development. In conclusion, objectively quantified DWMA is an independent prognostic biomarker of long-term motor development in very preterm infants and warrants further study.

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Aberrant Executive and Frontoparietal Functional Connectivity in Very Preterm Infants With Diffuse White Matter Abnormalities

Pediatric Neurology ◽

10.1016/j.pediatrneurol.2015.05.001 ◽

2015 ◽

Vol 53 (4) ◽

pp. 330-337 ◽

Cited By ~ 12

Author(s):

Lili He ◽

Nehal A. Parikh

Keyword(s):

White Matter ◽

Functional Connectivity ◽

Preterm Infants ◽

Very Preterm Infants ◽

White Matter Abnormalities ◽

Very Preterm ◽

Diffuse White Matter

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Postnatal Decrease in Circulating Insulin-Like Growth Factor-I and Low Brain Volumes in Very Preterm Infants

The Journal of Clinical Endocrinology & Metabolism ◽

10.1210/jc.2010-2440 ◽

2011 ◽

Vol 96 (4) ◽

pp. 1129-1135 ◽

Cited By ~ 39

Author(s):

Ingrid Hansen-Pupp ◽

Holger Hövel ◽

Ann Hellström ◽

Lena Hellström-Westas ◽

Chatarina Löfqvist ◽

...

Keyword(s):

White Matter ◽

Magnetic Resonance ◽

Preterm Infants ◽

Brain Volumes ◽

Very Preterm Infants ◽

Very Preterm ◽

Matter Volume ◽

Total Brain ◽

Igf I ◽

Igfbp 3

Abstract Context: IGF-I and IGF binding protein-3 (IGFBP-3) are essential for growth and maturation of the developing brain. Objective: The aim of this study was to evaluate the association between postnatal serum concentrations of IGF-I and IGFBP-3 and brain volumes at term in very preterm infants. Design: Fifty-one infants with a mean (sd) gestational age (GA) of 26.4 (1.9) wk and birth weight (BW) of 888 (288) g were studied, with weekly blood sampling of IGF-I and IGFBP-3 from birth until 35 gestational weeks (GW) and daily calculation of protein and caloric intake. Magnetic resonance images obtained at 40 GW were segmented into total brain, cerebellar, cerebrospinal fluid, gray matter, and unmyelinated white matter volumes. Main Outcome Measures: We evaluated brain growth by measuring brain volumes using magnetic resonance imaging. Results: Mean IGF-I concentrations from birth to 35 GW correlated with total brain volume, unmyelinated white matter volume, gray matter volume, and cerebellar volume [r = 0.55 (P < 0.001); r = 0.55 (P < 0.001); r = 0.44 (P = 0.002); and r = 0.58 (P < 0.001), respectively]. Similar correlations were observed for IGFBP-3 concentrations. Correlations remained after adjustment for GA, mean protein and caloric intakes, gender, severe brain damage, and steroid treatment. Protein and caloric intakes were not related to brain volumes. Infants with BW small for GA had lower mean concentrations of IGF-I (P = 0.006) and smaller brain volumes (P = 0.001–0.013) than infants with BW appropriate for GA. Conclusion: Postnatal IGF-I and IGFBP-3 concentrations are positively associated with brain volumes at 40 GW in very preterm infants. Normalization of the IGF-I axis, directly or indirectly, may support normal brain development in very preterm infants.

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