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Ma Q, Wang H, Rolls ET, Xiang S, Li J, Li Y, Zhou Q, Cheng W, Li F. Lower gestational age is associated with lower cortical volume and cognitive and educational performance in adolescence. BMC Med 2022; 20:424. [PMID: 36329481 PMCID: PMC9635194 DOI: 10.1186/s12916-022-02627-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Gestational age (GA) is associated with later cognition and behavior. However, it is unclear how specific cognitive domains and brain structural development varies with the stepwise change of gestational duration. METHODS This large-scale longitudinal cohort study analyzed 11,878 early adolescents' brain volume maps at 9-10 years (baseline) and 5685 at 11-12 years (a 2-year follow-up) from the Adolescent Brain Cognitive Development (ABCD) study. According to gestational age, adolescents were divided into five categorical groups: ≤ 33 weeks, 34-35 weeks, 36 weeks, 37-39 weeks, and ≥ 40 weeks. The NIH Toolbox was used to estimate neurocognitive performance, including crystallized and fluid intelligence, which was measured for 11,878 adolescents at baseline with crystallized intelligence and relevant subscales obtained at 2-year follow-up (with participant numbers ranging from 6185 to 6310 depending on the cognitive domain). An additional large population-based cohort of 618,070 middle adolescents at ninth-grade (15-16 years) from the Danish national register was utilized to validate the association between gestational age and academic achievements. A linear mixed model was used to examine the group differences between gestational age and neurocognitive performance, school achievements, and grey matter volume. A mediation analysis was performed to examine whether brain structural volumes mediated the association between GA and neurocognition, followed with a longitudinal analysis to track the changes. RESULTS Significant group differences were found in all neurocognitive scores, school achievements, and twenty-five cortical regional volumes (P < 0.05, Bonferroni corrected). Specifically, lower gestational ages were associated with graded lower cognition and school achievements and with smaller brain volumes of the fronto-parieto-temporal, fusiform, cingulate, insula, postcentral, hippocampal, thalamic, and pallidal regions. These lower brain volumes mediated the association between gestational age and cognitive function (P = 1 × 10-8, β = 0.017, 95% CI: 0.007-0.028). Longitudinal analysis showed that compared to full term adolescents, preterm adolescents still had smaller brain volumes and crystallized intelligence scores at 11-12 years. CONCLUSIONS These results emphasize the relationships between gestational age at birth and adolescents' lower brain volume, and lower cognitive and educational performance, measured many years later when 9-10 and 11-12 years old. The study indicates the importance of early screening and close follow-up for neurocognitive and behavioral development for children and adolescents born with gestational ages that are even a little lower than full term.
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Affiliation(s)
- Qing Ma
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China
| | - Hui Wang
- Department of Developmental and Behavioral Pediatric & Child Primary Care/MOE-Shanghai Key Laboratory of Children's Environmental Health, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200082, China
| | - Edmund T Rolls
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.,Department of Computer Science, University of Warwick, Coventry CV4 7AL, Conventry, UK.,Oxford Centre for Computational Neuroscience, Oxford, UK
| | - Shitong Xiang
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China
| | - Jiong Li
- Department of Clinical Medicine, Aarhus University, Aarhus, 8000, Denmark
| | - Yuzhu Li
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China
| | - Qiongjie Zhou
- Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200011, China.,Shanghai Key Laboratory of Female Reproductive Endocrine-Related Diseases, Shanghai, 200011, China
| | - Wei Cheng
- Department of Neurology, Huashan Hospital, Institute of Science and Technology for Brain-Inspired Intelligence, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China. .,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, 200433, China. .,Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspired Intelligence, Zhejiang Normal University, Jinhua, 321004, China. .,Shanghai Medical College and Zhongshan Hospital Immunotherapy Technology Transfer Center, Shanghai, 200032, China.
| | - Fei Li
- Department of Developmental and Behavioral Pediatric & Child Primary Care/MOE-Shanghai Key Laboratory of Children's Environmental Health, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 200082, China.
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2
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Scheeren MFDC, Fiori HH, Machado LU, Volkmer DDFV, Oliveira MGD. Exact Gestational Age, Term Versus Early Term, Is Associated with Different Breastfeeding Success Rates in Mothers Delivered by Elective Cesarean Section. Breastfeed Med 2022; 17:825-831. [PMID: 36103275 DOI: 10.1089/bfm.2022.0068] [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] [Indexed: 11/12/2022]
Abstract
Objectives: The study was intended to verify the association between the gestational age of newborns classified as term and the success of breastfeeding in babies born by elective cesarean section. Also, to analyze how the variability of gestational age within the term influences breastfeeding. Materials and Methods: Retrospective study of a cohort, which included full-term newborns and their mothers, whose deliveries occurred by elective cesarean section. Among the inclusion criteria are delivery due to elective cesarean section and minimum gestational age of 37 weeks. The database consisted of medical birth information and interviews with mothers. Results: This study included 954 full-term newborns born by elective cesarean section. Exclusive breastfeeding at 3 months and being breastfed at 6 months showed a statistically significant association in the correlation with the variability of gestational age. There was a statistically significant association between exclusive breastfeeding at 3 months and breastfeeding at 6 months in the correlation between early term and full term. The trend test showed a significant progressive in the breastfeeding curve versus gestational age. Conclusion: Full-term newborns show better results of exclusive breastfeeding at 3 months and continued breastfeeding at 6 months, compared with those born at early term. Gestational age, even in term, has an influence on the success of breastfeeding in newborns undergoing elective cesarean delivery.
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Affiliation(s)
- Marôla Flores da Cunha Scheeren
- Moinhos de Vento Hospital, Porto Alegre, Brazil.,Department of Pediatrics and Children's Health, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil
| | - Humberto Holmer Fiori
- Department of Pediatrics and Children's Health, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil
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3
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Moody JF, Aggarwal N, Dean DC, Tromp DPM, Kecskemeti SR, Oler JA, Kalin NH, Alexander AL. Longitudinal assessment of early-life white matter development with quantitative relaxometry in nonhuman primates. Neuroimage 2022; 251:118989. [PMID: 35151851 PMCID: PMC8940652 DOI: 10.1016/j.neuroimage.2022.118989] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 01/13/2022] [Accepted: 02/09/2022] [Indexed: 12/01/2022] Open
Abstract
Alterations in white matter (WM) development are associated with many neuropsychiatric and neurodevelopmental disorders. Most MRI studies examining WM development employ diffusion tensor imaging (DTI), which relies on estimating diffusion patterns of water molecules as a reflection of WM microstructure. Quantitative relaxometry, an alternative method for characterizing WM microstructural changes, is based on molecular interactions associated with the magnetic relaxation of protons. In a longitudinal study of 34 infant non-human primates (NHP) (Macaca mulatta) across the first year of life, we implement a novel, high-resolution, T1-weighted MPnRAGE sequence to examine WM trajectories of the longitudinal relaxation rate (qR1) in relation to DTI metrics and gestational age at scan. To the best of our knowledge, this is the first study to assess developmental WM trajectories in NHPs using quantitative relaxometry and the first to directly compare DTI and relaxometry metrics during infancy. We demonstrate that qR1 exhibits robust logarithmic growth, unfolding in a posterior-anterior and medial-lateral fashion, similar to DTI metrics. On a within-subject level, DTI metrics and qR1 are highly correlated, but are largely unrelated on a between-subject level. Unlike DTI metrics, gestational age at birth (time in utero) is a strong predictor of early postnatal qR1 levels. Whereas individual differences in DTI metrics are maintained across the first year of life, this is not the case for qR1. These results point to the similarities and differences in using quantitative relaxometry and DTI in developmental studies, providing a basis for future studies to characterize the unique processes that these measures reflect at the cellular and molecular level.
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Affiliation(s)
- Jason F Moody
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI 53705, United States.
| | - Nakul Aggarwal
- Department of Psychiatry, University of Wisconsin-Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Douglas C Dean
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI 53705, United States; Department of Pediatrics, University of Wisconsin-Madison, 600 Highland Avenue, Madison, WI 53792, United States; Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Madison, WI 53705, United States
| | - Do P M Tromp
- Department of Psychiatry, University of Wisconsin-Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Steve R Kecskemeti
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Madison, WI 53705, United States
| | - Jonathan A Oler
- Department of Psychiatry, University of Wisconsin-Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Ned H Kalin
- Department of Psychiatry, University of Wisconsin-Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States
| | - Andrew L Alexander
- Department of Medical Physics, University of Wisconsin-Madison, 1111 Highland Avenue, Madison, WI 53705, United States; Department of Psychiatry, University of Wisconsin-Madison, 6001 Research Park Boulevard, Madison, WI 53719, United States; Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Madison, WI 53705, United States
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4
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Yao Y, Li C, Meng P, Cheng B, Cheng S, Liu L, Yang X, Jia Y, Wen Y, Zhang F. An atlas of genetic correlations between gestational age and common psychiatric disorders. Autism Res 2022; 15:1008-1017. [PMID: 35384380 DOI: 10.1002/aur.2719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2021] [Revised: 03/02/2022] [Accepted: 03/21/2022] [Indexed: 11/11/2022]
Abstract
We aim to systematically explore the potential genetic correlations between five major psychiatric disorders and gestational ages. Genome-wide association study (GWAS) summary datasets of attention deficit/hyperactivity disorder (ADHD), autism spectrum disorder (ASD), bipolar disorder (BD), schizophrenia (SCZ) and major depressive disorder (MDD) in discovery were downloaded from the Psychiatric GWAS Consortium (PGC) website. Suggestive (Raw p < 0.05) genetic associations in the discovery phrase were further replicated in independent GWASs which downloaded from PGC, the FinnGen study or Integrative Psychiatric Research (iPSYCH) website. GWASs of gestational duration, preterm and post-term birth were derived from previous studies of infants from the Early Growth Genetics (EGG) Consortium, the iPSYCH study, and the Genomic and Proteomic Network for Preterm Birth Research (GPN). We calculated genetic correlations using linkage disequilibrium score (LDSC) regression. Mendelian randomization (MR) analyses were performed to investigate the causal effects. We identified four suggestive genetic correlations between psychiatric disorders and gestational age factors in discovery LDSC and two replicated in a confirmation LDSC: gestational duration and ADHD (rg = -0.1405, FDR p = 0.0406), post-term birth and SCZ (rg = -0.2003, FDR p = 0.0042). We also observed causal effect of post-term birth on SCZ by MR (PWeighted median = 0.037, PInverse variance weighted = 0.007). Our analysis suggested no significant evidence of horizontal pleiotropy and heterogeneity. This study showed the genetic correlation evidences between gestational age phenotypes and psychiatric disorders, providing novel clues for understanding the pathogenic factors of common psychiatric disorders. LAY SUMMARY: Whereas gestational age factors were reported to be associated with psychiatric disorders, the genetic relationship and causality remain to be revealed. The present study reported the first large-scale genetic correlations investigation of the associations between gestational age phenotypes and psychiatric disorders. Results indicate causal relationships between post-term birth and schizophrenia (SCZ), as well as suggestive genetic correlations between gestational duration and attention deficit/hyperactivity disorder (ADHD). This study provided novel clues for understanding the pathogenic factors of common psychiatric disorders.
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Affiliation(s)
- Yao Yao
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Chun'e Li
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Peilin Meng
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Bolun Cheng
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Shiqiang Cheng
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Li Liu
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Xuena Yang
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yumeng Jia
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Yan Wen
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
| | - Feng Zhang
- Key Laboratory of Trace Elements and Endemic Diseases, National Health and Family Planning Commission, School of Public Health, Health Science Center, Xi'an Jiaotong University, Xi'an, China
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5
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Gale-Grant O, Fenn-Moltu S, França LGS, Dimitrova R, Christiaens D, Cordero-Grande L, Chew A, Falconer S, Harper N, Price AN, Hutter J, Hughes E, O'Muircheartaigh J, Rutherford M, Counsell SJ, Rueckert D, Nosarti C, Hajnal JV, McAlonan G, Arichi T, Edwards AD, Batalle D. Effects of gestational age at birth on perinatal structural brain development in healthy term-born babies. Hum Brain Mapp 2022; 43:1577-1589. [PMID: 34897872 PMCID: PMC8886657 DOI: 10.1002/hbm.25743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 11/19/2021] [Accepted: 11/30/2021] [Indexed: 11/12/2022] Open
Abstract
Infants born in early term (37-38 weeks gestation) experience slower neurodevelopment than those born at full term (40-41 weeks gestation). While this could be due to higher perinatal morbidity, gestational age at birth may also have a direct effect on the brain. Here we characterise brain volume and white matter correlates of gestational age at birth in healthy term-born neonates and their relationship to later neurodevelopmental outcome using T2 and diffusion weighted MRI acquired in the neonatal period from a cohort (n = 454) of healthy babies born at term age (>37 weeks gestation) and scanned between 1 and 41 days after birth. Images were analysed using tensor-based morphometry and tract-based spatial statistics. Neurodevelopment was assessed at age 18 months using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III). Infants born earlier had higher relative ventricular volume and lower relative brain volume in the deep grey matter, cerebellum and brainstem. Earlier birth was also associated with lower fractional anisotropy, higher mean, axial, and radial diffusivity in major white matter tracts. Gestational age at birth was positively associated with all Bayley-III subscales at age 18 months. Regression models predicting outcome from gestational age at birth were significantly improved after adding neuroimaging features associated with gestational age at birth. This work adds to the body of evidence of the impact of early term birth and highlights the importance of considering the effect of gestational age at birth in future neuroimaging studies including term-born babies.
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Affiliation(s)
- Oliver Gale-Grant
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Sunniva Fenn-Moltu
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Lucas G S França
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Ralica Dimitrova
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Daan Christiaens
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Andrew Chew
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Shona Falconer
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Nicholas Harper
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Jonathan O'Muircheartaigh
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Mary Rutherford
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK.,Department of Medicine and Informatics, Technical University of Munich, Munich, Germany
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,Paediatric Neurosciences, Evelina London Children's Hospital Guy's and St Thomas' NHS Foundation Trust, London, UK.,Department of Bioengineering, Imperial College London, London, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Dafnis Batalle
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
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6
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Dall'Orso S, Arichi T, Fitzgibbon SP, Edwards AD, Burdet E, Muceli S. Development of functional organization within the sensorimotor network across the perinatal period. Hum Brain Mapp 2022; 43:2249-2261. [PMID: 35088920 PMCID: PMC8996360 DOI: 10.1002/hbm.25785] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2021] [Revised: 12/30/2021] [Accepted: 01/10/2022] [Indexed: 11/18/2022] Open
Abstract
In the mature human brain, the neural processing related to different body parts is reflected in patterns of functional connectivity, which is strongest between functional homologs in opposite cortical hemispheres. To understand how this organization is first established, we investigated functional connectivity between limb regions in the sensorimotor cortex in 400 preterm and term infants aged across the equivalent period to the third trimester of gestation (32–45 weeks postmenstrual age). Masks were obtained from empirically derived functional responses in neonates from an independent data set. We demonstrate the early presence of a crude but spatially organized functional connectivity, that rapidly matures across the preterm period to achieve an adult‐like configuration by the normal time of birth. Specifically, connectivity was strongest between homolog regions, followed by connectivity between adjacent regions (different limbs but same hemisphere) already in the preterm brain, and increased with age. These changes were specific to the sensorimotor network. Crucially, these trajectories were strongly dependent on age more than age of birth. This demonstrates that during the perinatal period the sensorimotor cortex undergoes preprogrammed changes determining the functional movement organization that are not altered by preterm birth in absence of brain injury.
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Affiliation(s)
- Sofia Dall'Orso
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg.,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London.,Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, UK.,Paediatric Neurosciences, Evelina London Children's Hospital, St. Thomas' Hospital, London, UK.,Medical Research Council Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London.,Medical Research Council Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Etienne Burdet
- Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, UK
| | - Silvia Muceli
- Department of Electrical Engineering, Chalmers University of Technology, Gothenburg.,Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London
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7
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Copeland A, Silver E, Korja R, Lehtola SJ, Merisaari H, Saukko E, Sinisalo S, Saunavaara J, Lähdesmäki T, Parkkola R, Nolvi S, Karlsson L, Karlsson H, Tuulari JJ. Infant and Child MRI: A Review of Scanning Procedures. Front Neurosci 2021; 15:666020. [PMID: 34321992 PMCID: PMC8311184 DOI: 10.3389/fnins.2021.666020] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 05/04/2021] [Indexed: 12/13/2022] Open
Abstract
Magnetic resonance imaging (MRI) is a safe method to examine human brain. However, a typical MR scan is very sensitive to motion, and it requires the subject to lie still during the acquisition, which is a major challenge for pediatric scans. Consequently, in a clinical setting, sedation or general anesthesia is often used. In the research setting including healthy subjects anesthetics are not recommended for ethical reasons and potential longer-term harm. Here we review the methods used to prepare a child for an MRI scan, but also on the techniques and tools used during the scanning to enable a successful scan. Additionally, we critically evaluate how studies have reported the scanning procedure and success of scanning. We searched articles based on special subject headings from PubMed and identified 86 studies using brain MRI in healthy subjects between 0 and 6 years of age. Scan preparations expectedly depended on subject's age; infants and young children were scanned asleep after feeding and swaddling and older children were scanned awake. Comparing the efficiency of different procedures was difficult because of the heterogeneous reporting of the used methods and the success rates. Based on this review, we recommend more detailed reporting of scanning procedure to help find out which are the factors affecting the success of scanning. In the long term, this could help the research field to get high quality data, but also the clinical field to reduce the use of anesthetics. Finally, we introduce the protocol used in scanning 2 to 5-week-old infants in the FinnBrain Birth Cohort Study, and tips for calming neonates during the scans.
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Affiliation(s)
- Anni Copeland
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Eero Silver
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Riikka Korja
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychology, University of Turku, Turku, Finland
| | - Satu J. Lehtola
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Harri Merisaari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Radiology, Turku University Hospital, University of Turku, Turku, Finland
| | - Ekaterina Saukko
- Department of Radiology, Turku University Hospital, University of Turku, Turku, Finland
| | - Susanne Sinisalo
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Tuire Lähdesmäki
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Pediatric Neurology, Turku University Hospital, University of Turku, Turku, Finland
| | - Riitta Parkkola
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Radiology, Turku University Hospital, University of Turku, Turku, Finland
| | - Saara Nolvi
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychology and Speech-Language Pathology, Turku Institute for Advanced Studies, University of Turku, Turku, Finland
| | - Linnea Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital, University of Turku, Turku, Finland
| | - Hasse Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Jetro J. Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
- Department of Psychiatry, University of Oxford, Oxford, United Kingdom
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8
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McEwen LM, O'Donnell KJ, McGill MG, Edgar RD, Jones MJ, MacIsaac JL, Lin DTS, Ramadori K, Morin A, Gladish N, Garg E, Unternaehrer E, Pokhvisneva I, Karnani N, Kee MZL, Klengel T, Adler NE, Barr RG, Letourneau N, Giesbrecht GF, Reynolds JN, Czamara D, Armstrong JM, Essex MJ, de Weerth C, Beijers R, Tollenaar MS, Bradley B, Jovanovic T, Ressler KJ, Steiner M, Entringer S, Wadhwa PD, Buss C, Bush NR, Binder EB, Boyce WT, Meaney MJ, Horvath S, Kobor MS. The PedBE clock accurately estimates DNA methylation age in pediatric buccal cells. Proc Natl Acad Sci U S A 2020; 117:23329-23335. [PMID: 31611402 PMCID: PMC7519312 DOI: 10.1073/pnas.1820843116] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
The development of biological markers of aging has primarily focused on adult samples. Epigenetic clocks are a promising tool for measuring biological age that show impressive accuracy across most tissues and age ranges. In adults, deviations from the DNA methylation (DNAm) age prediction are correlated with several age-related phenotypes, such as mortality and frailty. In children, however, fewer such associations have been made, possibly because DNAm changes are more dynamic in pediatric populations as compared to adults. To address this gap, we aimed to develop a highly accurate, noninvasive, biological measure of age specific to pediatric samples using buccal epithelial cell DNAm. We gathered 1,721 genome-wide DNAm profiles from 11 different cohorts of typically developing individuals aged 0 to 20 y old. Elastic net penalized regression was used to select 94 CpG sites from a training dataset (n = 1,032), with performance assessed in a separate test dataset (n = 689). DNAm at these 94 CpG sites was highly predictive of age in the test cohort (median absolute error = 0.35 y). The Pediatric-Buccal-Epigenetic (PedBE) clock was characterized in additional cohorts, showcasing the accuracy in longitudinal data, the performance in nonbuccal tissues and adult age ranges, and the association with obstetric outcomes. The PedBE tool for measuring biological age in children might help in understanding the environmental and contextual factors that shape the DNA methylome during child development, and how it, in turn, might relate to child health and disease.
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Affiliation(s)
- Lisa M McEwen
- Department of Medical Genetics, University of British Columbia-BC Children's Hospital Research Institute, Vancouver, BC, Canada V5Z 4H4
| | - Kieran J O'Donnell
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada H4H 1R3
- Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR) Institute, Toronto, ON, Canada M5G 1M1
| | - Megan G McGill
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada H4H 1R3
| | - Rachel D Edgar
- Department of Medical Genetics, University of British Columbia-BC Children's Hospital Research Institute, Vancouver, BC, Canada V5Z 4H4
| | - Meaghan J Jones
- Department of Medical Genetics, University of British Columbia-BC Children's Hospital Research Institute, Vancouver, BC, Canada V5Z 4H4
| | - Julia L MacIsaac
- Department of Medical Genetics, University of British Columbia-BC Children's Hospital Research Institute, Vancouver, BC, Canada V5Z 4H4
| | - David Tse Shen Lin
- Department of Medical Genetics, University of British Columbia-BC Children's Hospital Research Institute, Vancouver, BC, Canada V5Z 4H4
| | - Katia Ramadori
- Department of Medical Genetics, University of British Columbia-BC Children's Hospital Research Institute, Vancouver, BC, Canada V5Z 4H4
| | - Alexander Morin
- Department of Medical Genetics, University of British Columbia-BC Children's Hospital Research Institute, Vancouver, BC, Canada V5Z 4H4
| | - Nicole Gladish
- Department of Medical Genetics, University of British Columbia-BC Children's Hospital Research Institute, Vancouver, BC, Canada V5Z 4H4
| | - Elika Garg
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada H4H 1R3
| | - Eva Unternaehrer
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada H4H 1R3
| | - Irina Pokhvisneva
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada H4H 1R3
| | - Neerja Karnani
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR); Singapore 117609
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596
| | - Michelle Z L Kee
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR); Singapore 117609
| | - Torsten Klengel
- Department of Psychiatry, Harvard Medical School-McLean Hospital, Belmont, MA 02478
| | - Nancy E Adler
- Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR) Institute, Toronto, ON, Canada M5G 1M1
- Department of Psychiatry, University of California, San Francisco, CA 94143
- Department of Pediatrics, University of California, San Francisco, CA 94143
| | - Ronald G Barr
- Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR) Institute, Toronto, ON, Canada M5G 1M1
- Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada V6T 1Z4
| | - Nicole Letourneau
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada T2N 4N1
- Faculty of Nursing, University of Calgary, Calgary, AB, Canada T2N 1N4
| | - Gerald F Giesbrecht
- Alberta Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada T2N 4N1
- Department of Paediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada T2N 1N4
| | - James N Reynolds
- Department of Biomedical and Molecular Sciences, School of Medicine, Queen's University, Kingston, ON, Canada K7L 3N6
| | - Darina Czamara
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, 80804 Munich, Germany
| | - Jeffrey M Armstrong
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53706
| | - Marilyn J Essex
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI 53706
| | - Carolina de Weerth
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, 6525 HR, Nijmegen, The Netherlands
| | - Roseriet Beijers
- Behavioural Science Institute, Radboud University, 6525 HR, Nijmegen, The Netherlands
| | - Marieke S Tollenaar
- Leiden Institute for Brain and Cognition, Institute of Psychology, Leiden University, 2300 RB, Leiden, The Netherlands
| | - Bekh Bradley
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA 30322
| | - Tanja Jovanovic
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA 30322
| | - Kerry J Ressler
- Department of Psychiatry, Harvard Medical School-McLean Hospital, Belmont, MA 02478
| | - Meir Steiner
- Department of Psychiatry and Behavioural Neurosciences, St. Joseph's Healthcare Hamilton, McMaster University, Hamilton, ON, Canada L8S 4L8
| | - Sonja Entringer
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Institute of Medical Psychology, 10117 Berlin, Germany
- Development, Health, and Disease Research Program, University of California, Irvine, CA 92617
| | - Pathik D Wadhwa
- Development, Health, and Disease Research Program, University of California, Irvine, CA 92617
- Department of Psychiatry and Human Behavior, School of Medicine, University of California, Irvine, CA, 92617
- Department of Obstetrics and Gynecology, School of Medicine, University of California, Irvine, CA, 92617
- Department of Epidemiology, School of Medicine, University of California, Irvine, CA, 92617
| | - Claudia Buss
- Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health (BIH), Institute of Medical Psychology, 10117 Berlin, Germany
| | - Nicole R Bush
- Department of Psychiatry, University of California, San Francisco, CA 94143
| | - Elisabeth B Binder
- Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR) Institute, Toronto, ON, Canada M5G 1M1
- Department of Translational Research in Psychiatry, Max Planck Institute of Psychiatry, 80804 Munich, Germany
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA 30322
| | - W Thomas Boyce
- Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR) Institute, Toronto, ON, Canada M5G 1M1
- Department of Psychiatry, University of California, San Francisco, CA 94143
- Department of Pediatrics, University of California, San Francisco, CA 94143
| | - Michael J Meaney
- Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada H4H 1R3
- Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR) Institute, Toronto, ON, Canada M5G 1M1
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR); Singapore 117609
- Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596
| | - Steve Horvath
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, CA 90095;
- Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, CA 90095
| | - Michael S Kobor
- Department of Medical Genetics, University of British Columbia-BC Children's Hospital Research Institute, Vancouver, BC, Canada V5Z 4H4;
- Child and Brain Development Program, Canadian Institute for Advanced Research (CIFAR) Institute, Toronto, ON, Canada M5G 1M1
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9
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Jin C, Li Y, Li X, Liu C, Wang M, Cheng Y, Zheng J, Yang J. Associations of gestational age and birth anthropometric indicators with brain white matter maturation in full-term neonates. Hum Brain Mapp 2019; 40:3620-3630. [PMID: 31056805 DOI: 10.1002/hbm.24620] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Revised: 04/08/2019] [Accepted: 04/25/2019] [Indexed: 01/09/2023] Open
Abstract
Newborn assessments, including gestational age (GA) and anthropometric measurements (birth weight, crown-heel length, head circumference) are routinely performed in pediatric settings, being used as important indicators in assessing neonatal development. Close associations of these birth indicators with later cognitive abilities were also reported. However, specific associations of these indicators with white matter (WM) development during the neonatal period remain unclear, as well as the extent to which they influence WM maturation. To address this issue, 51 full-term neonates (GA range, 37-42 weeks) with no abnormalities on MRI were retrospectively recruited. Specific correlations between birth indicators and WM maturation, quantified by diffusion tensor imaging (DTI)-metrics (fractional anisotropy, mean diffusivity, axial diffusivity, radial diffusivity), were identified by using DTI tract-based spatial statistics and automated fiber-tract quantification. Our findings suggest that (a) higher GA, birth weight, and crown-heel length may indicate greater WM maturation in full-term neonates, while head circumference presented weak correlation with WM maturation during early newborn period; (b) among the four indicators examined, GA was the one most associated with WM maturation. We believe that this study advances our knowledge of specific correlations between birth indicators and neonatal brain development and provides a valuable reference for future neonatal studies.
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Affiliation(s)
- Chao Jin
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China.,Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Yanyan Li
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China.,Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Xianjun Li
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China.,Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Congcong Liu
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China.,Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Miaomiao Wang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China.,Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Yannan Cheng
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China.,Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Jie Zheng
- Clinical Research Center, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
| | - Jian Yang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China.,Center for Brain Science, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, PR China
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10
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Pivik R, Andres A, Tennal KB, Gu Y, Downs H, Bellando BJ, Jarratt K, Cleves MA, Badger TM. Resting gamma power during the postnatal critical period for GABAergic system development is modulated by infant diet and sex. Int J Psychophysiol 2019; 135:73-94. [DOI: 10.1016/j.ijpsycho.2018.11.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 11/14/2018] [Accepted: 11/19/2018] [Indexed: 12/13/2022]
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11
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Mongerson CRL, Wilcox SL, Goins SM, Pier DB, Zurakowski D, Jennings RW, Bajic D. Infant Brain Structural MRI Analysis in the Context of Thoracic Non-cardiac Surgery and Critical Care. Front Pediatr 2019; 7:315. [PMID: 31428593 PMCID: PMC6688189 DOI: 10.3389/fped.2019.00315] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/16/2019] [Accepted: 07/11/2019] [Indexed: 01/20/2023] Open
Abstract
Objective: To determine brain magnetic resonance imaging (MRI) measures of cerebrospinal fluid (CSF) and whole brain volume of full-term and premature infants following surgical treatment for thoracic non-cardiac congenital anomalies requiring critical care. Methods: Full-term (n = 13) and pre-term (n = 13) patients with long-gap esophageal atresia, and full-term naïve controls (n = 19) < 1 year corrected age, underwent non-sedated brain MRI following completion of thoracic non-cardiac surgery and critical care treatment. Qualitative MRI findings were reviewed and reported by a pediatric neuroradiologist and neurologist. Several linear brain metrics were measured using structural T1-weighted images, while T2-weighted images were required for segmentation of total CSF and whole brain tissue using the Morphologically Adaptive Neonatal Tissue Segmentation (MANTiS) tool. Group differences in absolute (mm, cm3) and normalized (%) data were analyzed using a univariate general linear model with age at scan as a covariate. Mean normalized values were assessed using one-way ANOVA. Results: Qualitative brain findings suggest brain atrophy in both full-term and pre-term patients. Both linear and volumetric MRI analyses confirmed significantly greater total CSF and extra-axial space, and decreased whole brain size in both full-term and pre-term patients compared to naïve controls. Although linear analysis suggests greater ventricular volumes in all patients, volumetric analysis showed that normalized ventricular volumes were higher only in premature patients compared to controls. Discussion: Linear brain metrics paralleled volumetric MRI analysis of total CSF and extra-axial space, but not ventricular size. Full-term infants appear to demonstrate similar brain vulnerability in the context of life-saving thoracic non-cardiac surgery requiring critical care as premature infants.
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Affiliation(s)
- Chandler R L Mongerson
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Sophie L Wilcox
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Stacy M Goins
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, United States
| | - Danielle B Pier
- Massachusetts General Hospital Child Neurology, Boston, MA, United States.,Harvard Medical School, Harvard University, Boston, MA, United States
| | - David Zurakowski
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, United States.,Harvard Medical School, Harvard University, Boston, MA, United States
| | - Russell W Jennings
- Harvard Medical School, Harvard University, Boston, MA, United States.,Department of Surgery, Boston Children's Hospital, Boston, MA, United States
| | - Dusica Bajic
- Department of Anesthesiology, Critical Care and Pain Medicine, Boston Children's Hospital, Boston, MA, United States.,Massachusetts General Hospital Child Neurology, Boston, MA, United States
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12
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Dean DC, Planalp EM, Wooten W, Kecskemeti SR, Adluru N, Schmidt CK, Frye C, Birn RM, Burghy CA, Schmidt NL, Styner MA, Short SJ, Kalin NH, Goldsmith HH, Alexander AL, Davidson RJ. Association of Prenatal Maternal Depression and Anxiety Symptoms With Infant White Matter Microstructure. JAMA Pediatr 2018; 172:973-981. [PMID: 30177999 PMCID: PMC6190835 DOI: 10.1001/jamapediatrics.2018.2132] [Citation(s) in RCA: 84] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
IMPORTANCE Maternal depression and anxiety can have deleterious and lifelong consequences on child development. However, many aspects of the association of early brain development with maternal symptoms remain unclear. Understanding the timing of potential neurobiological alterations holds inherent value for the development and evaluation of future therapies and interventions. OBJECTIVE To examine the association between exposure to prenatal maternal depression and anxiety symptoms and offspring white matter microstructure at 1 month of age. DESIGN, SETTING, AND PARTICIPANTS This cohort study of 101 mother-infant dyads used a composite of depression and anxiety symptoms measured in mothers during the third trimester of pregnancy and measures of white matter microstructure characterized in the mothers' 1-month offspring using diffusion tensor imaging and neurite orientation dispersion and density imaging performed from October 1, 2014, to November 30, 2016. Magnetic resonance imaging was performed at an academic research facility during natural, nonsedated sleep. MAIN OUTCOMES AND MEASURES Brain mapping algorithms and statistical models were used to evaluate the association between maternal depression and anxiety and 1-month infant white matter microstructure as measured by diffusion tensor imaging and neurite orientation dispersion and density imaging findings. RESULTS In the 101 mother-infant dyads (mean [SD] age of mothers, 33.22 [3.99] years; mean age of infants at magnetic resonance imaging, 33.07 days [range, 18-50 days]; 92 white mothers [91.1%]; 53 male infants [52.5%]), lower 1-month white matter microstructure (decreased neurite density and increased mean, radial, and axial diffusivity) was associated in right frontal white matter microstructure with higher prenatal maternal symptoms of depression and anxiety. Significant sex × symptom interactions with measures of white matter microstructure were also observed, suggesting that white matter development may be differentially sensitive to maternal depression and anxiety symptoms in males and females during the prenatal period. CONCLUSIONS AND RELEVANCE These data highlight the importance of the prenatal period to early brain development and suggest that the underlying white matter microstructure is associated with the continuum of prenatal maternal depression and anxiety symptoms.
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Affiliation(s)
| | - Elizabeth M. Planalp
- Waisman Center, University of Wisconsin, Madison,Department of Psychology, University of Wisconsin, Madison
| | - William Wooten
- Center for Healthy Minds, University of Wisconsin, Madison
| | | | | | - Cory K. Schmidt
- Waisman Center, University of Wisconsin, Madison,Center for Healthy Minds, University of Wisconsin, Madison
| | - Corrina Frye
- Center for Healthy Minds, University of Wisconsin, Madison
| | - Rasmus M. Birn
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison,Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison
| | - Cory A. Burghy
- Center for Healthy Minds, University of Wisconsin, Madison
| | | | - Martin A. Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill
| | - Sarah J. Short
- Center for Healthy Minds, University of Wisconsin, Madison
| | - Ned H. Kalin
- Waisman Center, University of Wisconsin, Madison,Department of Psychology, University of Wisconsin, Madison,Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison
| | - H. Hill Goldsmith
- Waisman Center, University of Wisconsin, Madison,Department of Psychology, University of Wisconsin, Madison
| | - Andrew L. Alexander
- Waisman Center, University of Wisconsin, Madison,Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison,Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison
| | - Richard J. Davidson
- Waisman Center, University of Wisconsin, Madison,Department of Psychology, University of Wisconsin, Madison,Center for Healthy Minds, University of Wisconsin, Madison,Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison
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13
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Proper timing for the evaluation of neonatal brain white matter development: a diffusion tensor imaging study. Eur Radiol 2018; 29:1527-1537. [DOI: 10.1007/s00330-018-5665-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2018] [Revised: 06/27/2018] [Accepted: 07/13/2018] [Indexed: 10/28/2022]
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14
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Computational neuroanatomy of baby brains: A review. Neuroimage 2018; 185:906-925. [PMID: 29574033 DOI: 10.1016/j.neuroimage.2018.03.042] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 02/23/2018] [Accepted: 03/19/2018] [Indexed: 12/12/2022] Open
Abstract
The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.
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15
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Guillaume B, Wang C, Poh J, Shen MJ, Ong ML, Tan PF, Karnani N, Meaney M, Qiu A. Improving mass-univariate analysis of neuroimaging data by modelling important unknown covariates: Application to Epigenome-Wide Association Studies. Neuroimage 2018; 173:57-71. [PMID: 29448075 DOI: 10.1016/j.neuroimage.2018.01.073] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2017] [Revised: 01/03/2018] [Accepted: 01/28/2018] [Indexed: 10/18/2022] Open
Abstract
Statistical inference on neuroimaging data is often conducted using a mass-univariate model, equivalent to fitting a linear model at every voxel with a known set of covariates. Due to the large number of linear models, it is challenging to check if the selection of covariates is appropriate and to modify this selection adequately. The use of standard diagnostics, such as residual plotting, is clearly not practical for neuroimaging data. However, the selection of covariates is crucial for linear regression to ensure valid statistical inference. In particular, the mean model of regression needs to be reasonably well specified. Unfortunately, this issue is often overlooked in the field of neuroimaging. This study aims to adopt the existing Confounder Adjusted Testing and Estimation (CATE) approach and to extend it for use with neuroimaging data. We propose a modification of CATE that can yield valid statistical inferences using Principal Component Analysis (PCA) estimators instead of Maximum Likelihood (ML) estimators. We then propose a non-parametric hypothesis testing procedure that can improve upon parametric testing. Monte Carlo simulations show that the modification of CATE allows for more accurate modelling of neuroimaging data and can in turn yield a better control of False Positive Rate (FPR) and Family-Wise Error Rate (FWER). We demonstrate its application to an Epigenome-Wide Association Study (EWAS) on neonatal brain imaging and umbilical cord DNA methylation data obtained as part of a longitudinal cohort study. Software for this CATE study is freely available at http://www.bioeng.nus.edu.sg/cfa/Imaging_Genetics2.html.
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Affiliation(s)
- Bryan Guillaume
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Changqing Wang
- Department of Biomedical Engineering, National University of Singapore, Singapore
| | - Joann Poh
- Department of Biomedical Engineering, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Mo Jun Shen
- Department of Biomedical Engineering, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Mei Lyn Ong
- Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Pei Fang Tan
- Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Neerja Karnani
- Department of Biochemistry, Yong Loo Lin School of Medicine, National University of Singapore, 119228, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Michael Meaney
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, McGill University, Canada; Sackler Program for Epigenetics and Psychobiology at McGill University, Canada; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore
| | - Anqi Qiu
- Department of Biomedical Engineering, National University of Singapore, Singapore; Clinical Imaging Research Centre, National University of Singapore, Singapore; Singapore Institute for Clinical Sciences, Agency for Science, Technology, and Research, Singapore.
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16
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Ou X, Glasier CM, Ramakrishnaiah RH, Kanfi A, Rowell AC, Pivik RT, Andres A, Cleves MA, Badger TM. Gestational Age at Birth and Brain White Matter Development in Term-Born Infants and Children. AJNR Am J Neuroradiol 2017; 38:2373-2379. [PMID: 29025726 DOI: 10.3174/ajnr.a5408] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2017] [Accepted: 07/22/2017] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Studies on infants and children born preterm have shown that adequate gestational length is critical for brain white matter development. Less is known regarding how variations in gestational age at birth in term infants and children affect white matter development, which was evaluated in this study. MATERIALS AND METHODS Using DTI tract-based spatial statistics methods, we evaluated white matter microstructures in 2 groups of term-born (≥37 weeks of gestation) healthy subjects: 2-week-old infants (n = 44) and 8-year-old children (n = 63). DTI parameters including fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity were calculated by voxelwise and ROI methods and were correlated with gestational age at birth, with potential confounding factors such as postnatal age and sex controlled. RESULTS Fractional anisotropy values, which are markers for white matter microstructural integrity, positively correlated (P < .05, corrected) with gestational age at birth in most major white matter tracts/regions for the term infants. Mean diffusivity values, which are measures of water diffusivities in the brain, and axial and radial diffusivity values, which are markers for axonal growth and myelination, respectively, negatively correlated (P < .05, corrected) with gestational age at birth in all major white matter tracts/regions excluding the body and splenium of the corpus callosum for the term infants. No significant correlations with gestational age were observed for any tracts/regions for the term-born 8-year-old children. CONCLUSIONS Our results indicate that longer gestation during the normal term period is associated with significantly greater infant white matter development (as reflected by higher fractional anisotropy and lower mean diffusivity, axial diffusivity, and radial diffusivity values); however, similar associations were not observable in later childhood.
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Affiliation(s)
- X Ou
- From the Departments of Radiology (X.O., C.M.G., R.H.R., A.K., A.C.R.)
- Pediatrics (X.O., C.M.G., R.T.P., A.A., M.A.C., T.M.B.), University of Arkansas for Medical Sciences, Little Rock, Arkansas
- Arkansas Children's Nutrition Center (X.O., R.T.P., A.A., T.M.B.), Little Rock, Arkansas
- Department of Radiology (X.O., C.M.G., R.H.R., A.K., A.C.R.), Arkansas Children's Hospital, Little Rock, Arkansas
| | - C M Glasier
- From the Departments of Radiology (X.O., C.M.G., R.H.R., A.K., A.C.R.)
- Pediatrics (X.O., C.M.G., R.T.P., A.A., M.A.C., T.M.B.), University of Arkansas for Medical Sciences, Little Rock, Arkansas
- Department of Radiology (X.O., C.M.G., R.H.R., A.K., A.C.R.), Arkansas Children's Hospital, Little Rock, Arkansas
| | - R H Ramakrishnaiah
- From the Departments of Radiology (X.O., C.M.G., R.H.R., A.K., A.C.R.)
- Department of Radiology (X.O., C.M.G., R.H.R., A.K., A.C.R.), Arkansas Children's Hospital, Little Rock, Arkansas
| | - A Kanfi
- From the Departments of Radiology (X.O., C.M.G., R.H.R., A.K., A.C.R.)
- Department of Radiology (X.O., C.M.G., R.H.R., A.K., A.C.R.), Arkansas Children's Hospital, Little Rock, Arkansas
| | - A C Rowell
- From the Departments of Radiology (X.O., C.M.G., R.H.R., A.K., A.C.R.)
- Department of Radiology (X.O., C.M.G., R.H.R., A.K., A.C.R.), Arkansas Children's Hospital, Little Rock, Arkansas
| | - R T Pivik
- Pediatrics (X.O., C.M.G., R.T.P., A.A., M.A.C., T.M.B.), University of Arkansas for Medical Sciences, Little Rock, Arkansas
- Arkansas Children's Nutrition Center (X.O., R.T.P., A.A., T.M.B.), Little Rock, Arkansas
| | - A Andres
- Pediatrics (X.O., C.M.G., R.T.P., A.A., M.A.C., T.M.B.), University of Arkansas for Medical Sciences, Little Rock, Arkansas
- Arkansas Children's Nutrition Center (X.O., R.T.P., A.A., T.M.B.), Little Rock, Arkansas
| | - M A Cleves
- Pediatrics (X.O., C.M.G., R.T.P., A.A., M.A.C., T.M.B.), University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - T M Badger
- Pediatrics (X.O., C.M.G., R.T.P., A.A., M.A.C., T.M.B.), University of Arkansas for Medical Sciences, Little Rock, Arkansas
- Arkansas Children's Nutrition Center (X.O., R.T.P., A.A., T.M.B.), Little Rock, Arkansas
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17
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Ferreira I, Gbatu PT, Boreham CA. Gestational Age and Cardiorespiratory Fitness in Individuals Born At Term: A Life Course Study. J Am Heart Assoc 2017; 6:e006467. [PMID: 28954725 PMCID: PMC5721855 DOI: 10.1161/jaha.117.006467] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/22/2017] [Accepted: 08/04/2017] [Indexed: 11/20/2022]
Abstract
BACKGROUND In contrast to the effects of preterm birth, the extent to which shorter gestational age affects the cardiorespiratory fitness (CRF) levels of individuals who were born at term (ie, between 37 and 42 weeks) is largely unknown. The aim of this study was to examine whether life-course CRF levels varied across different gestational ages within the at-term range. METHODS AND RESULTS The association between gestational age (in weeks) obtained from Child Health Services records and CRF, estimated from field and laboratory tests and expressed by maximal oxygen uptake level through adolescence to young adulthood, was examined in 791 participants in the Northern Ireland Young Hearts Study, all singletons born at term. Longitudinal data were analyzed with generalized estimating equations, accounting for important potential confounders. Mean levels of CRF were 45.6, 43.7, and 33.0 mL/kg per minute when participants were aged 12, 15, and 22 years, respectively. After adjustment for confounders, each week increase in gestational age was associated with 0.46 mL/kg per minute (95% confidence interval, 0.14-0.79) in CRF. Compared with individuals born full term (39-40 weeks, n=533) or late term (41-42 weeks, n=148), those who were born early term (37-38 weeks, n=110) had a higher incidence of poor CRF (risk ratio, 1.57; 95% confidence interval, 1.14-2.16). The changes in CRF through adolescence to young adulthood were similar across groups, with those born early term consistently displaying the lowest CRF. CONCLUSIONS These findings suggest that early-term births within the at-term range are linked to poorer CRF through adolescence to young adulthood, and may have important clinical and public health implications for policies about (avoidable) early-term deliveries given their recent increasing trends.
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Affiliation(s)
- Isabel Ferreira
- School of Public Health, The University of Queensland, Herston, Queensland, Australia
| | - Pei T Gbatu
- School of Public Health, The University of Queensland, Herston, Queensland, Australia
| | - Colin A Boreham
- Institute for Sport and Health, University College Dublin, Dublin, Ireland
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18
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Dean DC, Planalp EM, Wooten W, Adluru N, Kecskemeti SR, Frye C, Schmidt CK, Schmidt NL, Styner MA, Goldsmith HH, Davidson RJ, Alexander AL. Mapping White Matter Microstructure in the One Month Human Brain. Sci Rep 2017; 7:9759. [PMID: 28852074 PMCID: PMC5575288 DOI: 10.1038/s41598-017-09915-6] [Citation(s) in RCA: 47] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 08/01/2017] [Indexed: 11/24/2022] Open
Abstract
White matter microstructure, essential for efficient and coordinated transmission of neural communications, undergoes pronounced development during the first years of life, while deviations to this neurodevelopmental trajectory likely result in alterations of brain connectivity relevant to behavior. Hence, systematic evaluation of white matter microstructure in the normative brain is critical for a neuroscientific approach to both typical and atypical early behavioral development. However, few studies have examined the infant brain in detail, particularly in infants under 3 months of age. Here, we utilize quantitative techniques of diffusion tensor imaging and neurite orientation dispersion and density imaging to investigate neonatal white matter microstructure in 104 infants. An optimized multiple b-value diffusion protocol was developed to allow for successful acquisition during non-sedated sleep. Associations between white matter microstructure measures and gestation corrected age, regional asymmetries, infant sex, as well as newborn growth measures were assessed. Results highlight changes of white matter microstructure during the earliest periods of development and demonstrate differential timing of developing regions and regional asymmetries. Our results contribute to a growing body of research investigating the neurobiological changes associated with neurodevelopment and suggest that characteristics of white matter microstructure are already underway in the weeks immediately following birth.
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Affiliation(s)
- D C Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA.
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA.
| | - E M Planalp
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - W Wooten
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
| | - N Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - S R Kecskemeti
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - C Frye
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
| | - C K Schmidt
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
| | - N L Schmidt
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
| | - M A Styner
- Department of Psychiatry, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
- Department of Computer Science, University of North Carolina-Chapel Hill, Chapel Hill, NC, USA
| | - H H Goldsmith
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - R J Davidson
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, WI, USA
- Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Psychiatry, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
| | - A L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Department of Psychiatry, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
- Department of Medical Physics, University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA
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19
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Rajagopalan V, Scott JA, Liu M, Poskitt K, Chau V, Miller S, Studholme C. Complementary cortical gray and white matter developmental patterns in healthy, preterm neonates. Hum Brain Mapp 2017; 38:4322-4336. [PMID: 28608653 DOI: 10.1002/hbm.23618] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2017] [Revised: 04/03/2017] [Accepted: 04/06/2017] [Indexed: 01/12/2023] Open
Abstract
Preterm birth is associated with brain injury and altered cognitive development. However, the consequences of extrauterine development are not clearly distinguished from perinatal brain injury. Therefore, we characterized cortical growth patterns from 30 to 46 postmenstrual weeks (PMW) in 27 preterm neonates (25-32 PMW at birth) without detectable brain injury on magnetic resonance imaging. We introduce surface-based morphometric descriptors that quantify radial (thickness) and tangential (area) change rates. Within a tensor-based morphometry framework, we use a temporally weighted formulation of regression to simultaneously model local age-related changes in cortical gray matter (GM) and underlying white matter (WM) mapped onto the cortical surface. The spatiotemporal pattern of GM and WM development corresponded to the expected gyrification time course of primary sulcal deepening and branching. In primary gyri, surface area and thickness rates were below average along sulcal pits and above average on gyral banks and crests in both GM and WM. Above average surface area rates in GM corresponded to emergence of secondary and tertiary folds. These findings map the development of neonatal cortical morphometry in the context of extrauterine brain development using a novel approach. Future studies may compare this developmental trajectory to preterm populations with brain injury. Hum Brain Mapp 38:4322-4336, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Vidya Rajagopalan
- Children's Hospital Los Angeles, and Rudi Schulte Research Institute, Santa Barbara, California
| | - Julia A Scott
- Department of Neurology, University of California Davis, Davis, California
| | - Mengyuan Liu
- Biomedical Image Computing Group, Departments of Pediatrics, Bioengineering, and Radiology, University of Washington, Seattle, Washington
| | - Kenneth Poskitt
- Department of Pediatrics, University of British Columbia, British Columbia, Canada
| | - Vann Chau
- Department of Pediatrics, The Hospital for Sick Children and the University of Toronto, Toronto, Ontario, M5G 1X8, Canada
| | - Steven Miller
- Department of Pediatrics, The Hospital for Sick Children and the University of Toronto, Toronto, Ontario, M5G 1X8, Canada
| | - Colin Studholme
- Biomedical Image Computing Group, Departments of Pediatrics, Bioengineering, and Radiology, University of Washington, Seattle, Washington
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20
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Felder JN, Epel E, Lewis JB, Cunningham SD, Tobin JN, Rising SS, Thomas M, Ickovics JR. Depressive symptoms and gestational length among pregnant adolescents: Cluster randomized control trial of CenteringPregnancy® plus group prenatal care. J Consult Clin Psychol 2017; 85:574-584. [PMID: 28287802 DOI: 10.1037/ccp0000191] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
OBJECTIVES Depressive symptoms are associated with preterm birth among adults. Pregnant adolescents have high rates of depressive symptoms and low rates of treatment; however, few interventions have targeted this vulnerable group. Objectives are to: (a) examine impact of CenteringPregnancy® Plus group prenatal care on perinatal depressive symptoms compared to individual prenatal care; and (b) determine effects of depressive symptoms on gestational age and preterm birth among pregnant adolescents. METHOD This cluster-randomized controlled trial was conducted in 14 community health centers and hospitals in New York City. Clinical sites were randomized to receive standard individual prenatal care (n = 7) or CenteringPregnancy® Plus group prenatal care (n = 7). Pregnant adolescents (ages 14-21, N = 1,135) completed the Center for Epidemiologic Studies Depression Scale during pregnancy (second and third trimesters) and postpartum (6 and 12 months). Gestational age was obtained from medical records, based on ultrasound dating. Intention to treat analyses were used to examine objectives. RESULTS Adolescents at clinical sites randomized to CenteringPregnancy® Plus experienced greater reductions in perinatal depressive symptoms compared to those at clinical sites randomized to individual care (p = .003). Increased depressive symptoms from second to third pregnancy trimester were associated with shorter gestational age at delivery and preterm birth (<37 weeks gestation). Third trimester depressive symptoms were also associated with shorter gestational age and preterm birth. All p < .05. CONCLUSIONS Pregnant adolescents should be screened for depressive symptoms prior to third trimester. Group prenatal care may be an effective nonpharmacological option for reducing depressive symptoms among perinatal adolescents. (PsycINFO Database Record
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Affiliation(s)
| | - Elissa Epel
- Department of Psychiatry, University of California, San Francisco
| | | | | | | | | | - Melanie Thomas
- Department of Psychiatry, University of California, San Francisco
| | - Jeannette R Ickovics
- Departments of Chronic Disease Epidemiology and Psychology, Yale School of Public Health
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21
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Shi J, Chang L, Wang J, Zhang S, Yao Y, Zhang S, Jiang R, Guo L, Guan H, Zhu W. Initial Application of Diffusional Kurtosis Imaging in Evaluating Brain Development of Healthy Preterm Infants. PLoS One 2016; 11:e0154146. [PMID: 27101246 PMCID: PMC4839617 DOI: 10.1371/journal.pone.0154146] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Accepted: 04/08/2016] [Indexed: 11/19/2022] Open
Abstract
Objective To explore the parametric characteristics of diffusional kurtosis imaging (DKI) in the brain development of healthy preterm infants. Materials and Methods Conventional magnetic resonance imaging (MRI) and DKI were performed in 35 preterm (29 to 36 weeks gestational age [GA]; scanned at 33 to 44 weeks postmenstrual age [PMA]) and 10 term infants (37.4 to 40.7 weeks GA; scanned at 38.3 to 42.9 weeks PMA). Fractional anisotropy (FA), mean diffusivity (MD) and mean kurtosis (MK) values from 8 regions of interest, including both white matter (WM) and gray matter (GM), were obtained. Results MK and FA values were positively correlated with PMA in most selected WM regions, such as the posterior limbs of the internal capsule (PLIC) and the splenium of the corpus callosum (SCC). The positive correlation between MK value and PMA in the deep GM region was higher than that between FA and PMA. The MK value gradually decreased from the PLIC to the cerebral lobe. In addition, DKI parameters exhibited subtle differences in the parietal WM between the preterm and term control groups. Conclusions MK may serve as a more reliable imaging marker of the normal myelination process and provide a more robust characterization of deep GM maturation.
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Affiliation(s)
- Jingjing Shi
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Liwen Chang
- Department ofneonatology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jian Wang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shun Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yihao Yao
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Shuixia Zhang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Rifeng Jiang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Linying Guo
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hanxiong Guan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- * E-mail: (HXG); (WZZ)
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- * E-mail: (HXG); (WZZ)
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22
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Maternal prenatal iron status and tissue organization in the neonatal brain. Pediatr Res 2016; 79:482-8. [PMID: 26599151 PMCID: PMC4821682 DOI: 10.1038/pr.2015.248] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2015] [Accepted: 09/09/2015] [Indexed: 01/09/2023]
Abstract
BACKGROUND Children prenatally exposed to inadequate iron have poorer motor and neurocognitive development. No prior study to our knowledge has assessed the influence of maternal prenatal iron intake on newborn brain tissue organization in full-term infants. METHODS Third trimester daily iron intake was obtained using the Automated Self-Administered 24-h Dietary Recall with n = 40 healthy pregnant adolescents (aged 14-19 y). Cord blood ferritin was collected in a subsample (n = 16). Newborn (mean = 39 gestational weeks at birth; range 37-41) magnetic resonance imaging scans were acquired on a 3.0 Tesla MR Scanner. Diffusion Tensor Imaging (DTI) slices were acquired to measure the directional diffusion of water indexed by fractional anisotropy (FA). RESULTS Reported iron intake was inversely associated with newborn FA values (P ≤ 0.0001) predominantly in cortical gray matter. FA findings were similar using cord blood ferritin values. CONCLUSION Higher maternal prenatal iron intake accentuates, and lower intake attenuates, the normal age-related decline in FA values in gray matter, perhaps representing increasing dendritic arborization and synapse formation with higher iron intake. These DTI results suggest that typical variation in maternal iron outside the scope of standard clinical surveillance exerts subtle effects on infant brain development.
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23
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Abstract
The human brain rapidly develops during the final weeks of gestation and in the first two years following birth. Diffusion tensor imaging (DTI) is a unique in vivo imaging technique that allows three-dimensional visualization of the white matter anatomy in the brain. It has been considered to be a valuable tool for studying brain development in early life. In this review, we first introduce the DTI technique. We then review DTI findings on white matter development at the fetal stage and in infancy as well as DTI applications for understanding neurocognitive development and brain abnormalities in preterm infants. Finally, we discuss limitations of DTI and potential valuable imaging techniques for studying white matter myelination.
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Affiliation(s)
- Anqi Qiu
- Department of Biomedical Engineering and Clinical Imaging Research Center, National University of Singapore, 117576 Singapore;
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