Normative growth trajectories of fetal brain regions validated by satisfactory maturation of neurodevelopmental domains at 2 years of age.
Authors
Affiliations (28)
Affiliations (28)
- Oxford Machine Learning in Neuroimaging Laboratory, Department of Computer Science, University of Oxford, Oxford, United Kingdom. [email protected].
- Nuffield Department of Women's & Reproductive Health, University of Oxford, Oxford, United Kingdom.
- Department of Paediatrics, Institute of Developmental and Regenerative Medicine, University of Oxford, Oxford, United Kingdom.
- Oxford Maternal & Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, United Kingdom.
- Oxford Machine Learning in Neuroimaging Laboratory, Department of Computer Science, University of Oxford, Oxford, United Kingdom.
- School of Public Health, University of California, Berkeley, CA, United States of America.
- Department of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates.
- Faculty of Epidemiology and Population Health, London School of Hygiene & Tropical Medicine, London, United Kingdom.
- Departments of Obstetrics and Gynecology and of Global Health, University of Washington, Seattle, WA, United States of America.
- Nagpur INTERGROWTH-21st Research Centre, Ketkar Hospital, Nagpur, India.
- School of Public Health, Peking University, Beijing, China.
- Dipartimento di Scienze Pediatriche e dell'Adolescenza, SCDU Neonatologia, Universita di Torino, Turin, Italy.
- Department of Family and Community Health, Ministry of Health, Muscat, Sultanate of Oman.
- Department of Obstetrics and Gynaecology, Faculty of Health Sciences, Aga Khan University Hospital, Nairobi, Kenya.
- Post-graduate Course in Health in the Life Cycle, Universidade Católica de Pelotas, Pelotas, Brazil.
- Blavatnik School of Government, University of Oxford, Oxford, United Kingdom.
- African Health Research Institute, KwaZulu-Natal, South Africa.
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
- Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.
- Oxford Centre for Integrative Neuroimaging (OxCIN), FMRIB Centre, Nuffield Department of Clinical Neurosciences (NDCN), University of Oxford, JR Hospital, Oxford, United Kingdom.
- Australian Institute for Machine Learning (AIML), School of Mathematical and Computer Sciences, The University of Adelaide, Adelaide, Australia.
- South Australian Health and Medical Research Institute (SAHMRI), Adelaide, Australia.
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom.
- Center for Global Child Health, Hospital for Sick Children, Toronto, Ontario, Canada.
- Institute for Global Health and Development at Aga Khan University, South-Central Asia & East Africa, Karachi, Pakistan.
- Oxford Machine Learning in Neuroimaging Laboratory, Department of Computer Science, University of Oxford, Oxford, United Kingdom. [email protected].
- Oxford Centre for Integrative Neuroimaging (OxCIN), FMRIB Centre, Nuffield Department of Clinical Neurosciences (NDCN), University of Oxford, JR Hospital, Oxford, United Kingdom. [email protected].
Abstract
We previously constructed a qualitative, 3D ultrasound derived atlas of the normative spatiotemporal dynamics of fetal brain maturation. Here, using the same healthy multi-national cohort, we applied deep learning methods to 4205 fetal brain scans from 18-27 weeks' gestation, to produce an extensive, quantitative description of the growth of 16 fetal brain structures associated with satisfactory domain-specific neurodevelopmental scores at 2 years of age. The methodology, which is publicly available, takes less than 10 seconds per scan. We define 28 region-specific, functionally relevant, normative growth trajectories, a ratio between the relative volumes of the insular (rILV) and parietal (rPLV) lobes reflecting asynchronous maturation of fetal brain regions, and introduce a fetal brain maturation index that quantifies biological age and deviations from chronological age. Finally, the very low percentage of variance explained by between site differences (0.6% to 5.8% of the total variance) reinforces a fundamental biological principle: fetal growth and development across populations with diverse ancestries is similar provided that environmental constraints on growth are minimal.