ArticleRadiology2021
Voxelwise and Regional Brain Apparent Diffusion Coefficient Changes on MRI from Birth to 6 Years of Age.
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Article in Radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22citing papers in PubMed
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22 citing papers in PubMed, 27 citations in OpenAlex.
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- From Infancy to Aging: Precise Brain Age Estimation via Hybrid CoTResNet3D and CrossViT Models on T1-Weighted Imaging.Bioengineering (Basel, Switzerland) · 2026Article
- PARADISE: Personalized and regional adaptation for HIE disease identification and segmentation.Medical image analysis · 2025Article
- BOston Neonatal Brain Injury Data for Hypoxic Ischemic Encephalopathy (BONBID-HIE): I. MRI and Lesion Labeling.Scientific data · 2025Article
- Imaging of developing human brains with ex vivo PSOCT and dMRI.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Assessing brain maturation on neonatal MRI-do we need a more quantitative approach?European radiology · 2024Article
- Characterizing microstructural development in the fetal brain using diffusion MRI from 23 to 36 weeks of gestation.Cerebral cortex (New York, N.Y. : 1991) · 2024Article
- Sex differences in normal fetal regional brain apparent diffusion coefficient changes assessed byFrontiers in pediatrics · 2024Article
- Maternal Diet Quality during Pregnancy Is Associated with Neonatal Brain White Matter Development.Nutrients · 2023Article
- Relaxation-Diffusion Spectrum Imaging for Probing Tissue Microarchitecture.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Glutamate alterations in the premature infant brain during different gestational ages with glutamate chemical exchange saturation transfer imaging: a pilot study.European radiology · 2023Article
- Human-to-monkey transfer learning identifies the frontal white matter as a key determinant for predicting monkey brain age.Frontiers in aging neuroscience · 2023Article
- Regional ADC values of the morphologically normal canine brain.Frontiers in veterinary science · 2023Article
- Structural networking of the developing brain: from maturation to neurosurgical implications.Frontiers in neuroanatomy · 2023Review
- How Machine Learning is Powering Neuroimaging to Improve Brain Health.Neuroinformatics · 2022Review
- Associations of Human Milk Oligosaccharides with Infant Brain Tissue Organization and Regional Blood Flow at 1 Month of Age.Nutrients · 2022Article
- Investigated regional apparent diffusion coefficient values of the morphologically normal feline brain.Journal of feline medicine and surgery · 2022Article
- DWI Hyperintensity in the Fornix Fimbria on MRI in Children.AJNR. American journal of neuroradiology · 2022Article
- Article
- A Role for Data Science in Precision Nutrition and Early Brain Development.Frontiers in psychiatry · 2022Review
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Authors and funding
7 authors at 2 institutions in 1 country.
Susan SotardiFrom the Departments of Radiology (S.S.) and Psychiatry (R.L.G.), Athinoula A. Martinos Center for Biomedical Imaging (R.L.G.), Division of Newborn Medicine, Department of Pediatrics (S.V.B., R.W.), and Laboratory of Computer Science (S.N.M.), Massachusetts General Hospital, Harvard Medical School, Boston, Mass; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pa (S.S.); and Fetal-Neonatal Neuroimaging and Developmental Science Center (P.E.G., Y.O.), Computational Health Informatics Program (Y.O.), Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115 .ORCID 0000-0001-8389-2419
Randy L GollubFrom the Departments of Radiology (S.S.) and Psychiatry (R.L.G.), Athinoula A. Martinos Center for Biomedical Imaging (R.L.G.), Division of Newborn Medicine, Department of Pediatrics (S.V.B., R.W.), and Laboratory of Computer Science (S.N.M.), Massachusetts General Hospital, Harvard Medical School, Boston, Mass; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pa (S.S.); and Fetal-Neonatal Neuroimaging and Developmental Science Center (P.E.G., Y.O.), Computational Health Informatics Program (Y.O.), Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115 .ORCID 0000-0002-9434-4044
Sara V BatesFrom the Departments of Radiology (S.S.) and Psychiatry (R.L.G.), Athinoula A. Martinos Center for Biomedical Imaging (R.L.G.), Division of Newborn Medicine, Department of Pediatrics (S.V.B., R.W.), and Laboratory of Computer Science (S.N.M.), Massachusetts General Hospital, Harvard Medical School, Boston, Mass; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pa (S.S.); and Fetal-Neonatal Neuroimaging and Developmental Science Center (P.E.G., Y.O.), Computational Health Informatics Program (Y.O.), Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115 .
Rebecca WeissFrom the Departments of Radiology (S.S.) and Psychiatry (R.L.G.), Athinoula A. Martinos Center for Biomedical Imaging (R.L.G.), Division of Newborn Medicine, Department of Pediatrics (S.V.B., R.W.), and Laboratory of Computer Science (S.N.M.), Massachusetts General Hospital, Harvard Medical School, Boston, Mass; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pa (S.S.); and Fetal-Neonatal Neuroimaging and Developmental Science Center (P.E.G., Y.O.), Computational Health Informatics Program (Y.O.), Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115 .ORCID 0000-0003-3158-5518
Shawn N MurphyFrom the Departments of Radiology (S.S.) and Psychiatry (R.L.G.), Athinoula A. Martinos Center for Biomedical Imaging (R.L.G.), Division of Newborn Medicine, Department of Pediatrics (S.V.B., R.W.), and Laboratory of Computer Science (S.N.M.), Massachusetts General Hospital, Harvard Medical School, Boston, Mass; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pa (S.S.); and Fetal-Neonatal Neuroimaging and Developmental Science Center (P.E.G., Y.O.), Computational Health Informatics Program (Y.O.), Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115 .
P Ellen GrantFrom the Departments of Radiology (S.S.) and Psychiatry (R.L.G.), Athinoula A. Martinos Center for Biomedical Imaging (R.L.G.), Division of Newborn Medicine, Department of Pediatrics (S.V.B., R.W.), and Laboratory of Computer Science (S.N.M.), Massachusetts General Hospital, Harvard Medical School, Boston, Mass; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pa (S.S.); and Fetal-Neonatal Neuroimaging and Developmental Science Center (P.E.G., Y.O.), Computational Health Informatics Program (Y.O.), Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115 .ORCID 0000-0003-1005-4013
Yangming OuFrom the Departments of Radiology (S.S.) and Psychiatry (R.L.G.), Athinoula A. Martinos Center for Biomedical Imaging (R.L.G.), Division of Newborn Medicine, Department of Pediatrics (S.V.B., R.W.), and Laboratory of Computer Science (S.N.M.), Massachusetts General Hospital, Harvard Medical School, Boston, Mass; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pa (S.S.); and Fetal-Neonatal Neuroimaging and Developmental Science Center (P.E.G., Y.O.), Computational Health Informatics Program (Y.O.), Boston Children's Hospital, Harvard Medical School, 300 Longwood Ave, Boston, MA 02115 .
Harvard University · USBoston Children's Hospital · US
Funding
Career Development Program in Substance Use and Addiction MedicineK12DA043490 · NIDA · MASSACHUSETTS GENERAL HOSPITAL · PI A EDEN EVINS, NANCY A RIGOTTI · 2017 to 2026
$4.7Mmi2b2 Enabled Pediatric Radiological Decision SupportR01EB014947 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI GRANT, PATRICIA ELLEN, HIRSCHTICK, RANDY L · 2012 to 2015
$2.8MNIBIB NIH HHS R01 EB014947NIDA NIH HHS K12 DA043490
Abstract
Background A framework for understanding rapid diffusion changes from 0 to 6 years of age is important in the detection of neurodevelopmental disorders. Purpose To quantify patterns of normal apparent diffusion coefficient (ADC) development from 0 to 6 years of age. Materials and Methods Previously constructed age-specific ADC atlases from 201 healthy full-term children (108 male; age range, 0-6 years) with MRI scans acquired from 2006 to 2013 at one large academic hospital were analyzed to quantify four patterns: ADC trajectory, rate of ADC change, age of ADC maturation, and hemispheric asymmetries of maturation ages. Patterns were quantified in whole-brain, segmented regional, and voxelwise levels by fitting a two-term exponential model. Hemispheric asymmetries in ADC maturation ages were assessed using
Indexed as
Age FactorsBrainChildChild, PreschoolFemaleHumansInfantInfant, NewbornMagnetic Resonance ImagingMale
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