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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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Kline JE, Illapani VSP, Li H, He L, Yuan W, Parikh NA. Diffuse white matter abnormality in very preterm infants at term reflects reduced brain network efficiency. NEUROIMAGE-CLINICAL 2021; 31:102739. [PMID: 34237685 PMCID: PMC8378797 DOI: 10.1016/j.nicl.2021.102739] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Revised: 05/28/2021] [Accepted: 06/21/2021] [Indexed: 01/23/2023]
Abstract
Most preterm infants exhibit regions of high signal
intensity on T2 MRI at term. Debate remains as to whether this signal (DWMA) is
pathological. We quantified DWMA and used graph theory to measure
brain network efficiency. Whole-brain and regional network efficiency at term
decreased with greater DWMA. DWMA in very preterm infants is associated with
reduced brain efficiency at term.
Between 50 and 80% of very preterm infants (<32 weeks
gestational age) exhibit increased white matter signal intensity on T2-weighted
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 related DWMA extent to graph theory
measures of network efficiency at term in a representative cohort of 343 very
preterm infants. We performed anatomic and diffusion MRI at term and quantified
DWMA volume with our novel, semi-automated algorithm. From diffusion-weighted
structural connectomes, we calculated the 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 very preterm brain and decreased local efficiency and clustering
coefficient in a variety of regions supporting cognitive, linguistic, and motor
function. We show that DWMA is associated with widespread decreased brain
network efficiency, suggesting that it is pathologic and likely has adverse
developmental consequences.
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Affiliation(s)
- Julia E Kline
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | | | - Hailong Li
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Lili He
- 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
| | - Weihong Yuan
- Pediatric Neuroimaging Research Consortium, 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
- 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; Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States.
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3
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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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4
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Laptook AR, Shankaran S, Barnes P, Rollins N, Do BT, Parikh NA, Hamrick S, Hintz SR, Tyson JE, Bell EF, Ambalavanan N, Goldberg RN, Pappas A, Huitema C, Pedroza C, Chaudhary AS, Hensman AM, Das A, Wyckoff M, Khan A, Walsh MC, Watterberg KL, Faix R, Truog W, Guillet R, Sokol GM, Poindexter BB, Higgins RD. Limitations of Conventional Magnetic Resonance Imaging as a Predictor of Death or Disability Following Neonatal Hypoxic-Ischemic Encephalopathy in the Late Hypothermia Trial. J Pediatr 2021; 230:106-111.e6. [PMID: 33189747 PMCID: PMC7914162 DOI: 10.1016/j.jpeds.2020.11.015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2020] [Revised: 11/04/2020] [Accepted: 11/09/2020] [Indexed: 01/20/2023]
Abstract
OBJECTIVE To investigate if magnetic resonance imaging (MRI) is an accurate predictor for death or moderate-severe disability at 18-22 months of age among infants with neonatal encephalopathy in a trial of cooling initiated at 6-24 hours. STUDY DESIGN Subgroup analysis of infants ≥36 weeks of gestation with moderate-severe neonatal encephalopathy randomized at 6-24 postnatal hours to hypothermia or usual care in a multicenter trial of late hypothermia. MRI scans were performed per each center's practice and interpreted by 2 central readers using the Eunice Kennedy Shriver National Institute of Child Health and Human Development injury score (6 levels, normal to hemispheric devastation). Neurodevelopmental outcomes were assessed at 18-22 months of age. RESULTS Of 168 enrollees, 128 had an interpretable MRI and were seen in follow-up (n = 119) or died (n = 9). MRI findings were predominantly acute injury and did not differ by cooling treatment. At 18-22 months, death or severe disability occurred in 20.3%. No infant had moderate disability. Agreement between central readers was moderate (weighted kappa 0.56, 95% CI 0.45-0.67). The adjusted odds of death or severe disability increased 3.7-fold (95% CI 1.8-7.9) for each increment of injury score. The area under the curve for severe MRI patterns to predict death or severe disability was 0.77 and the positive and negative predictive values were 36% and 100%, respectively. CONCLUSIONS MRI injury scores were associated with neurodevelopmental outcome at 18-22 months among infants in the Late Hypothermia Trial. However, the results suggest caution when using qualitative interpretations of MRI images to provide prognostic information to families following perinatal hypoxia-ischemia. TRIAL REGISTRATION Clinicaltrials.gov: NCT00614744.
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Affiliation(s)
- Abbot R. Laptook
- Department of Pediatrics, Women and Infants Hospital, Brown
University, Providence, RI
| | | | - Patrick Barnes
- Department of Radiology and Pediatrics, Stanford University
School of Medicine, Palo Alto, CA
| | - Nancy Rollins
- Department of Radiology, University of Texas Southwestern
Medical Center, Dallas, TX
| | - Barbara T. Do
- Biostatistics and Epidemiology Division, RTI International,
Research Triangle Park, NC
| | - Nehal A. Parikh
- Perinatal Institute, Cincinnati Children’s Hospital
Medical Center, Cincinnati, OH
| | - Shannon Hamrick
- Emory University School of Medicine, Department of
Pediatrics, Children’s Healthcare of Atlanta, Atlanta, GA
| | - Susan R. Hintz
- Department of Pediatrics, Division of Neonatal and
Developmental Medicine, Stanford University School of Medicine and Lucile Packard
Children’s Hospital, Palo Alto, CA
| | - Jon E. Tyson
- Department of Pediatrics, McGovern Medical School at The
University of Texas Health Science Center at Houston, Houston, TX
| | - Edward F. Bell
- Department of Pediatrics, University of Iowa, Iowa City,
IA
| | | | | | - Athina Pappas
- Department of Pediatrics, Wayne State University, Detroit,
MI
| | - Carolyn Huitema
- Social, Statistical and Environmental Sciences Unit, RTI
International, Rockville, MD
| | - Claudia Pedroza
- Department of Pediatrics, McGovern Medical School at The
University of Texas Health Science Center at Houston, Houston, TX
| | | | - Angelita M. Hensman
- Department of Pediatrics, Women and Infants Hospital, Brown
University, Providence, RI
| | - Abhik Das
- Social, Statistical and Environmental Sciences Unit, RTI
International, Rockville, MD
| | - Myra Wyckoff
- Department of Pediatrics, University of Texas
Southwestern Medical Center, Dallas, TX
| | - Amir Khan
- Department of Pediatrics, McGovern Medical School at The
University of Texas Health Science Center at Houston, Houston, TX
| | - Michelle C. Walsh
- Department of Pediatrics, Rainbow Babies &
Children’s Hospital, Case Western Reserve University, Cleveland, OH
| | | | - Roger Faix
- Department of Pediatrics, Division of Neonatology,
University of Utah School of Medicine, Salt Lake City, UT
| | - William Truog
- Department of Pediatrics, Children’s Mercy
Hospital and University of Missouri Kansas City School of Medicine, Kansas City,
MO
| | - Ronnie Guillet
- University of Rochester School of Medicine and Dentistry,
Rochester, NY
| | - Gregory M. Sokol
- Department of Pediatrics, Indiana University School of
Medicine, Indianapolis, IN
| | - Brenda B. Poindexter
- Department of Pediatrics, Indiana University School of
Medicine, Indianapolis, IN,Cincinnati Children’s Hospital Medical Center,
Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati,
OH
| | - Rosemary D. Higgins
- Eunice Kennedy Shriver National Institute of Child Health
and Human Development, Pregnancy and Perinatology Branch,George Mason University, Fairfax, VA
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Rath CP, Desai S, Rao SC, Patole S. Diffuse excessive high signal intensity on term equivalent MRI does not predict disability: a systematic review and meta-analysis. Arch Dis Child Fetal Neonatal Ed 2021; 106:9-16. [PMID: 32451357 DOI: 10.1136/archdischild-2019-318207] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2019] [Revised: 03/25/2020] [Accepted: 04/22/2020] [Indexed: 02/01/2023]
Abstract
OBJECTIVE To evaluate whether diffuse excessive high signal intensity (DEHSI) on term equivalent age MRI (TEA-MRI) predicts disability in preterm infants. DESIGN This is a systematic review and meta-analysis. Medline, EMBASE, Cochrane Library, EMCARE, Google Scholar and MedNar databases were searched in July 2019. Studies comparing developmental outcomes of isolated DEHSI on TEA-MRI versus normal TEA-MRI were included. Two reviewers independently extracted data and assessed the risk of bias. Meta-analysis was undertaken where data were available in a format suitable for pooling. MAIN OUTCOME MEASURES Neurodevelopmental outcomes ≥1 year of corrected age based on validated tools. RESULTS A total of 15 studies (n=1832) were included, of which data from 9 studies were available for meta-analysis. The pooled estimate (n=7) for sensitivity of DEHSI in predicting cognitive/mental disability was 0.58 (95% CI 0.34 to 0.79) and for specificity was 0.46 (95% CI 0.20 to 0.74). The summary area under the receiver operating characteristics (ROC) curve was low at 0.54 (CI 0.50 to 0.58). A pooled diagnostic OR (DOR) of 1 indicated that DEHSI does not discriminate preterm infants with and without mental disability. The pooled estimate (n=8) for sensitivity of DEHSI in predicting cerebral palsy (CP) was 0.57 (95% CI 0.37 to 0.75) and for specificity was 0.41 (95% CI 0.24 to 0.62). The summary area under the ROC curve was low at 0.51 (CI 0.46 to 0.55). A pooled DOR of 1 indicated that DEHSI does not discriminate between preterm infants with and without CP. CONCLUSIONS DEHSI on TEA-MRI did not predict future development of cognitive/mental disabilities or CP. PROSPERO REGISTRATION NUMBER CRD42019130576.
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Affiliation(s)
- Chandra Prakash Rath
- Neonatal Intensive Care Unit, Perth Children's Hospital, Nedlands, Western Australia, Australia.,Neonatal Intensive Care Unit, King Edward Memorial Hospital for Women Perth, Subiaco, Western Australia, Australia
| | - Saumil Desai
- Neonatal Intensive Care Unit, Perth Children's Hospital, Nedlands, Western Australia, Australia.,Neonatal Intensive Care Unit, King Edward Memorial Hospital for Women Perth, Subiaco, Western Australia, Australia
| | - Shripada C Rao
- Neonatal Intensive Care Unit, Perth Children's Hospital, Nedlands, Western Australia, Australia .,Neonatal Intensive Care Unit, King Edward Memorial Hospital for Women Perth, Subiaco, Western Australia, Australia.,School of Medicine, University of Western Australia, Perth, Western Australia, Australia
| | - Sanjay Patole
- Neonatal Intensive Care Unit, King Edward Memorial Hospital for Women Perth, Subiaco, Western Australia, Australia.,School of Medicine, University of Western Australia, Perth, Western Australia, Australia
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Automated brain MRI metrics in the EPIRMEX cohort of preterm newborns: Correlation with the neurodevelopmental outcome at 2 years. Diagn Interv Imaging 2020; 102:225-232. [PMID: 33187906 DOI: 10.1016/j.diii.2020.10.009] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/30/2020] [Accepted: 10/21/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE The purpose of this study was to identify in the EPIRMEX cohort the correlations between MRI brain metrics, including diffuse excessive high signal intensities (DEHSI) obtained with an automated quantitative method and neurodevelopmental outcomes at 2 years. MATERIALS AND METHODS A total of 390 very preterm infants (gestational age at birth≤32 weeks) who underwent brain MRI at term equivalent age at 1.5T (n=338) or 3T (n=52) were prospectively included. Using a validated algorithm, automated metrics of the main brain surfaces (cortical and deep gray matter, white matter, cerebrospinal fluid) and DEHSI with three thresholds were obtained. Linear adjust regressions were performed to assess the correlation between brain metrics with the ages and stages questionnaire (ASQ) score at 2 years. RESULTS Basal ganglia and thalami, cortex and white matter surfaces positively and significantly correlated with the global ASQ score. For all ASQ sub-domains, basal ganglia and thalami surfaces significantly correlated with the scores. DEHSI was present in 289 premature newborns (74%) without any correlation with the ASQ score. Metrics of DEHSI were greater at 3T than at 1.5T. CONCLUSION Brain MRI metrics obtained in our multicentric cohort correlate with the neurodevelopmental outcome at 2 years of age. The quantitative detection of DEHSI is not predictive of adverse outcomes. Our automated algorithm might easily provide useful predictive information in daily practice.
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Parikh NA, Harpster K, He L, Illapani VSP, Khalid FC, Klebanoff MA, O'Shea TM, Altaye M. Novel diffuse white matter abnormality biomarker at term-equivalent age enhances prediction of long-term motor development in very preterm children. Sci Rep 2020; 10:15920. [PMID: 32985533 PMCID: PMC7523012 DOI: 10.1038/s41598-020-72632-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Accepted: 08/31/2020] [Indexed: 01/09/2023] Open
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 ([Formula: see text]= -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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Affiliation(s)
- Nehal A Parikh
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, Cincinnati, USA. .,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA. .,Center for Perinatal Research, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.
| | - Karen Harpster
- Division of Occupational Therapy and Physical Therapy, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Lili He
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, Cincinnati, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | | | - Fatima Chughtai Khalid
- Perinatal Institute, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, Cincinnati, USA
| | - Mark A Klebanoff
- Center for Perinatal Research, The Research Institute at Nationwide Children's Hospital, Columbus, OH, USA.,Departments of Pediatrics and Obstetrics and Gynecology, The Ohio State University, Columbus, OH, USA
| | - T Michael O'Shea
- Departments of Pediatrics, University of North Carolina At Chapel Hill, Chapel Hill, NC, USA
| | - Mekibib Altaye
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA.,Division of Biostatistics, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
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Antecedents of Objectively Diagnosed Diffuse White Matter Abnormality in Very Preterm Infants. Pediatr Neurol 2020; 106:56-62. [PMID: 32139164 PMCID: PMC7500641 DOI: 10.1016/j.pediatrneurol.2020.01.011] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 01/22/2020] [Accepted: 01/26/2020] [Indexed: 12/25/2022]
Abstract
BACKGROUND Diffuse white matter abnormality (diffuse excessive high signal intensity) is the most common finding on structural brain magnetic resonance imaging (MRI) at term-equivalent age in very preterm infants. Yet, there remains a large gap in our understanding of the etiology of diffuse white matter abnormality. Our objective was to evaluate perinatal and neonatal inflammation-associated antecedents of diffuse white matter abnormality on MRI. METHODS We prospectively enrolled 110 very preterm infants born at ≤31 weeks gestational age and collected data on multiple perinatal/neonatal exposures, especially inflammation initiating-illnesses. We performed structural MRI at term-equivalent age and quantified the volume of diffuse white matter abnormality objectively. Multivariable regression was used to identify clinical antecedents of diffuse white matter abnormality. RESULTS The mean (S.D.) birth gestational age of the final study sample of 98 very preterm infants was 28.3 (2.5) weeks. Multiple inflammation initiating-illnesses were associated with diffuse white matter abnormality in univariate analyses. In multivariable linear regression analyses controlling for gestational age, severe retinopathy of prematurity (P < 0.001) and bronchopulmonary dysplasia (P = 0.006) were independent risk factors, whereas maternal treatment with 17-hydroxyprogesterone (P < 0.001) was protective of later development of objectively quantified diffuse white matter abnormality. CONCLUSIONS We identified several perinatal and neonatal antecedent clinical factors associated with diffuse white matter abnormality. Although we found some support for inflammation as a common underlying mechanism, larger studies are needed to validate inflammation as a potential common pathway to the development of diffuse white matter abnormality in very preterm infants.
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Parikh NA, He L, Illapani VSP, Altaye M, Folger AT, Yeates KO. Objectively Diagnosed Diffuse White Matter Abnormality at Term Is an Independent Predictor of Cognitive and Language Outcomes in Infants Born Very Preterm. J Pediatr 2020; 220:56-63. [PMID: 32147220 PMCID: PMC7583652 DOI: 10.1016/j.jpeds.2020.01.034] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2019] [Revised: 12/07/2019] [Accepted: 01/14/2020] [Indexed: 12/29/2022]
Abstract
OBJECTIVE To externally validate the independent value of objectively diagnosed diffuse white matter abnormality (DWMA; also known as diffuse excessive high signal intensity) volume to predict neurodevelopmental outcomes in very preterm infants (≤31 weeks of gestational age). STUDY DESIGN A prospective, multicenter, regional population-based cohort study in 98 very preterm infants without severe brain injury on magnetic resonance imaging (MRI). DWMA volume was diagnosed objectively on structural MRI at term-equivalent age using our published algorithm. Multivariable linear regression was used to assess the value of DWMA volume to predict cognitive and language scores on the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III) at 2 years corrected age. RESULTS Of the infants who returned for follow-up (n = 74), the mean (SD) gestational age was 28.2 (2.4) weeks, and 42 (56.8%) were boys. In bivariable analyses, DWMA volume was a significant predictor of Bayley-III cognitive and language scores. In multivariable analyses, controlling for known predictors of Bayley-III scores (ie, socioeconomic status, gestational age, sex, and global brain abnormality score), DWMA volume remained a significant predictor of cognitive (P < .001) and language (P = .04) scores at 2 years. When dichotomized, objectively diagnosed severe DWMA was a significant predictor of cognitive and language impairments, whereas visual qualitative diagnosis of DWMA was a poor predictor. CONCLUSIONS In this multicenter, prospective cohort study, we externally validated our previous findings that objectively diagnosed DWMA is an independent predictor of cognitive and language development in very preterm infants. We also demonstrated again that visually-diagnosed DWMA is not predictive of neurodevelopmental outcomes.
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Affiliation(s)
- Nehal A. Parikh
- Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH,Center for Perinatal Research, The Research Institute at Nationwide Children’s Hospital, Columbus, OH,Imaging Research Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH,Corresponding author’s contact information: Nehal A. Parikh, DO, MS, Professor of Pediatrics, Cincinnati Children’s Hospital, 3333 Burnet Ave, MLC 7009, Cincinnati, OH 45229, (513) 636-7584 (Business), (513) 803-0969 (Fax),
| | - Lili He
- Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH,Imaging Research Center, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Venkata Sita Priyanka Illapani
- Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Mekibib Altaye
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH,Divison of Biostatistics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Alonzo T. Folger
- Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH,Divison of Biostatistics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH
| | - Keith O. Yeates
- Department of Psychology, AlbertaChildren’s Hospital Research Institute and Hotchkiss Brain Institute, and University of Calgary, Alberta, Canada
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10
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Abstract
Significant advances in the field of neonatal imaging has resulted in the generation of large complex data sets of relevant information for routine daily clinical practice, and basic and translational research. The evaluation of this data is a complex task for the neonatal imager who must distinguish normal and incidental findings from clinically significant abnormalities which are often adjunctive data points applicable to clinical evaluation and treatment. This review provides an overview of the imaging manifestations of disease processes commonly encountered in the neonatal brain. Since MRI is currently the highest yield technique for the diagnosis and characterization of the normal and abnormal brain, it is therefore the focus of the majority of this review. When applicable, discussion of some of the pertinent known pathophysiology and neuropathological aspects of disease processes are reviewed.
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Li H, Parikh NA, Wang J, Merhar S, Chen M, Parikh M, Holland S, He L. Objective and Automated Detection of Diffuse White Matter Abnormality in Preterm Infants Using Deep Convolutional Neural Networks. Front Neurosci 2019; 13:610. [PMID: 31275101 PMCID: PMC6591530 DOI: 10.3389/fnins.2019.00610] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Accepted: 05/28/2019] [Indexed: 11/19/2022] Open
Abstract
Diffuse white matter abnormality (DWMA), or diffuse excessive high signal intensity is observed in 50-80% of very preterm infants at term-equivalent age. It is subjectively defined as higher than normal signal intensity in periventricular and subcortical white matter in comparison to normal unmyelinated white matter on T2-weighted MRI images. Despite the well-documented presence of DWMA, it remains debatable whether DWMA represents pathological tissue injury or a transient developmental phenomenon. Manual tracing of DWMA exhibits poor reliability and reproducibility and unduly increases image processing time. Thus, objective and ideally automatic assessment is critical to accurately elucidate the biologic nature of DWMA. We propose a deep learning approach to automatically identify DWMA regions on T2-weighted MRI images. Specifically, we formulated DWMA detection as an image voxel classification task; that is, the voxels on T2-weighted images are treated as samples and exclusively assigned as DWMA or normal white matter voxel classes. To utilize the spatial information of individual voxels, small image patches centered on the given voxels are retrieved. A deep convolutional neural networks (CNN) model was developed to differentiate DWMA and normal voxels. We tested our deep CNN in multiple validation experiments. First, we examined DWMA detection accuracy of our CNN model using computer simulations. This was followed by in vivo assessments in a cohort of very preterm infants (N = 95) using cross-validation and holdout validation. Finally, we tested our approach on an independent preterm cohort (N = 28) to externally validate our model. Our deep CNN model achieved Dice similarity index values ranging from 0.85 to 0.99 for DWMA detection in the aforementioned validation experiments. Our proposed deep CNN model exhibited significantly better performance than other popular machine learning models. We present an objective and automated approach for accurately identifying DWMA that may facilitate the clinical diagnosis of DWMA in very preterm infants.
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Affiliation(s)
- Hailong Li
- The Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Nehal A. Parikh
- The 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
- Department of Pediatrics, Nationwide Children’s Hospital, Columbus, OH, United States
| | - Jinghua Wang
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Stephanie Merhar
- The 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
| | - Ming Chen
- The Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
- Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, OH, United States
| | - Milan Parikh
- The Perinatal Institute, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, United States
| | - Scott Holland
- Medpace Inc., Cincinnati, OH, United States
- Department of Physics, University of Cincinnati, Cincinnati, OH, United States
| | - Lili He
- The 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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12
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Mürner-Lavanchy IM, Kidokoro H, Thompson DK, Doyle LW, Cheong JL, Hunt RW, Inder TE, Anderson PJ. Thirteen-Year Outcomes in Very Preterm Children Associated with Diffuse Excessive High Signal Intensity on Neonatal Magnetic Resonance Imaging. J Pediatr 2019; 206:66-71.e1. [PMID: 30414629 PMCID: PMC8898561 DOI: 10.1016/j.jpeds.2018.10.016] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/18/2018] [Revised: 08/29/2018] [Accepted: 10/09/2018] [Indexed: 11/30/2022]
Abstract
OBJECTIVE To investigate the association between white matter diffuse excessive high signal intensity (DEHSI) on neonatal magnetic resonance imaging in very preterm infants and neurobehavioral outcomes at the age of 13 years. STUDY DESIGN Magnetic resonance images of very preterm children (<30 weeks gestational age or <1250 g birth weight) were evaluated at term-equivalent age with DEHSI classified into 5 grades. Additionally, visibility of the posterior periventricular crossroads was assessed. General intelligence, memory, attention, executive function, motor abilities, and behavior were examined in 125 children at age 13 years and related to DEHSI grades using linear regression. RESULTS DEHSI was detected in 93% of infants; 21% grade 1, 22% grade 2, 32% grade 3, and 18% grade 4. Neurobehavioral outcomes were similar for all DEHSI groups. There was weak evidence that higher DEHSI grades related to higher verbal IQ and attention and that lower DEHSI grades related to better planning ability. Adjustment for gestational age, birth weight standard score, and sex further weakened these effects. Only 12 children had invisible posterior crossroads and showed slightly poorer outcomes at 13 years of age. CONCLUSIONS There was little evidence that neonatal DEHSI serves as a sensitive biomarker for later impairment. Further investigation on the importance of invisible posterior periventricular crossroads in larger samples is needed.
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Affiliation(s)
- Ines M. Mürner-Lavanchy
- Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Australia,Murdoch Children’s Research Institute, Melbourne, Australia
| | - Hiroyuki Kidokoro
- Department of Pediatrics, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Deanne K. Thompson
- Murdoch Children’s Research Institute, Melbourne, Australia,Florey Institute of Neuroscience and Mental Health, Melbourne, Australia,Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - Lex W. Doyle
- Murdoch Children’s Research Institute, Melbourne, Australia,Department of Paediatrics, University of Melbourne, Melbourne, Australia,Department of Obstetrics and Gynaecology, The Royal Women’s Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Jeanie L.Y. Cheong
- Murdoch Children’s Research Institute, Melbourne, Australia,Department of Paediatrics, University of Melbourne, Melbourne, Australia,Department of Obstetrics and Gynaecology, The Royal Women’s Hospital, University of Melbourne, Melbourne, Victoria, Australia
| | - Rod W. Hunt
- Murdoch Children’s Research Institute, Melbourne, Australia,Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - Terrie E. Inder
- Department of Pediatric Newborn Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, USA
| | - Peter J. Anderson
- Monash Institute of Cognitive and Clinical Neurosciences, Monash University, Australia,Murdoch Children’s Research Institute, Melbourne, Australia
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13
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Hinojosa-Rodríguez M, Harmony T, Carrillo-Prado C, Van Horn JD, Irimia A, Torgerson C, Jacokes Z. Clinical neuroimaging in the preterm infant: Diagnosis and prognosis. Neuroimage Clin 2017; 16:355-368. [PMID: 28861337 PMCID: PMC5568883 DOI: 10.1016/j.nicl.2017.08.015] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2017] [Revised: 08/11/2017] [Accepted: 08/12/2017] [Indexed: 01/30/2023]
Abstract
Perinatal care advances emerging over the past twenty years have helped to diminish the mortality and severe neurological morbidity of extremely and very preterm neonates (e.g., cystic Periventricular Leukomalacia [c-PVL] and Germinal Matrix Hemorrhage - Intraventricular Hemorrhage [GMH-IVH grade 3-4/4]; 22 to < 32 weeks of gestational age, GA). However, motor and/or cognitive disabilities associated with mild-to-moderate white and gray matter injury are frequently present in this population (e.g., non-cystic Periventricular Leukomalacia [non-cystic PVL], neuronal-axonal injury and GMH-IVH grade 1-2/4). Brain research studies using magnetic resonance imaging (MRI) report that 50% to 80% of extremely and very preterm neonates have diffuse white matter abnormalities (WMA) which correspond to only the minimum grade of severity. Nevertheless, mild-to-moderate diffuse WMA has also been associated with significant affectations of motor and cognitive activities. Due to increased neonatal survival and the intrinsic characteristics of diffuse WMA, there is a growing need to study the brain of the premature infant using non-invasive neuroimaging techniques sensitive to microscopic and/or diffuse lesions. This emerging need has led the scientific community to try to bridge the gap between concepts or ideas from different methodologies and approaches; for instance, neuropathology, neuroimaging and clinical findings. This is evident from the combination of intense pre-clinical and clinicopathologic research along with neonatal neurology and quantitative neuroimaging research. In the following review, we explore literature relating the most frequently observed neuropathological patterns with the recent neuroimaging findings in preterm newborns and infants with perinatal brain injury. Specifically, we focus our discussions on the use of neuroimaging to aid diagnosis, measure morphometric brain damage, and track long-term neurodevelopmental outcomes.
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Affiliation(s)
- Manuel Hinojosa-Rodríguez
- Unidad de Investigación en Neurodesarrollo, Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México (UNAM), Campus Juriquilla, Mexico
| | - Thalía Harmony
- Unidad de Investigación en Neurodesarrollo, Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México (UNAM), Campus Juriquilla, Mexico
| | - Cristina Carrillo-Prado
- Unidad de Investigación en Neurodesarrollo, Departamento de Neurobiología Conductual y Cognitiva, Instituto de Neurobiología, Universidad Nacional Autónoma de México (UNAM), Campus Juriquilla, Mexico
| | - John Darrell Van Horn
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, 2025 Zonal Avenue, SHN, Los Angeles, California 90033, USA
| | - Andrei Irimia
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, 2025 Zonal Avenue, SHN, Los Angeles, California 90033, USA
| | - Carinna Torgerson
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, 2025 Zonal Avenue, SHN, Los Angeles, California 90033, USA
| | - Zachary Jacokes
- USC Mark and Mary Stevens Neuroimaging and Informatics Institute, 2025 Zonal Avenue, SHN, Los Angeles, California 90033, USA
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Broström L, Bolk J, Padilla N, Skiöld B, Eklöf E, Mårtensson G, Vollmer B, Ådén U. Clinical Implications of Diffuse Excessive High Signal Intensity (DEHSI) on Neonatal MRI in School Age Children Born Extremely Preterm. PLoS One 2016; 11:e0149578. [PMID: 26886451 PMCID: PMC4757441 DOI: 10.1371/journal.pone.0149578] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2015] [Accepted: 02/01/2016] [Indexed: 11/18/2022] Open
Abstract
OBJECTIVE Magnetic resonance imaging (MRI) of the brain carried out during the neonatal period shows that 55-80% of extremely preterm infants display white matter diffuse excessive high signal intensity (DEHSI). Our aim was to study differences in developmental outcome at the age of 6.5 years in children born extremely preterm with and without DEHSI. STUDY DESIGN This was a prospective cohort study of 83 children who were born in Stockholm, Sweden, between 2004 and 2007, born at gestational age of < 27 weeks + 0 days and who underwent an MRI scan of their brain at term equivalent age. The outcome measures at 6.5 years included testing 66 children with the modified Touwen neurology examination, the Movement Assessment Battery for Children 2, the Wechsler Intelligence Scale for Children-Fourth Edition, Beery Visual-motor Integration test-Sixth Edition, and the Strengths and Difficulties Questionnaire. Group-wise comparisons were done between children with and without DEHSI using Student t-test, Mann Whitney U test, Chi square test and regression analysis. RESULTS DEHSI was detected in 39 (59%) of the 66 children who were assessed at 6.5 years. The presence of DEHSI was not associated with mild neurological dysfunction, scores on M-ABC assessment, cognition, visual-motor integration, or behavior at 6.5 years. CONCLUSION The presence of qualitatively defined DEHSI on neonatal MRI did not prove to be a useful predictor of long-term impairment in children born extremely preterm.
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Affiliation(s)
- Lina Broström
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
- * E-mail:
| | - Jenny Bolk
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
- Neonatal Unit, Sachs’ Children and Youth Hospital, Stockholm, Sweden
| | - Nelly Padilla
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
| | - Béatrice Skiöld
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
- Neonatal Unit, Karolinska University Hospital, Stockholm, Sweden
| | - Eva Eklöf
- 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
| | - Brigitte Vollmer
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
- Clinical Neurosciences, Clinical and Experimental Sciences, University of Southampton, Southampton, United Kingdom
| | - Ulrika Ådén
- Department of Women's and Children's Health, Karolinska Institutet, Stockholm, Sweden
- Neonatal Unit, Karolinska University Hospital, Stockholm, Sweden
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15
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Neonatal brain MRI: how reliable is the radiologist’s eye? Neuroradiology 2015; 58:189-93. [DOI: 10.1007/s00234-015-1609-2] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Accepted: 10/06/2015] [Indexed: 10/22/2022]
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