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Zhao Z, Shuai Y, Wu Y, Xu X, Li M, Wu D. Age-dependent functional development pattern in neonatal brain: An fMRI-based brain entropy study. Neuroimage 2024; 297:120669. [PMID: 38852805 DOI: 10.1016/j.neuroimage.2024.120669] [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: 12/11/2023] [Revised: 04/01/2024] [Accepted: 06/05/2024] [Indexed: 06/11/2024] Open
Abstract
The relationship between brain entropy (BEN) and early brain development has been established through animal studies. However, it remains unclear whether the BEN can be used to identify age-dependent functional changes in human neonatal brains and the genetic underpinning of the new neuroimaging marker remains to be elucidated. In this study, we analyzed resting-state fMRI data from the Developing Human Connectome Project, including 280 infants who were scanned at 37.5-43.5 weeks postmenstrual age. The BEN maps were calculated for each subject, and a voxel-wise analysis was conducted using a general linear model to examine the effects of age, sex, and preterm birth on BEN. Additionally, we evaluated the correlation between regional BEN and gene expression levels. Our results demonstrated that the BEN in the sensorimotor-auditory and association cortices, along the 'S-A' axis, was significantly positively correlated with postnatal age (PNA), and negatively correlated with gestational age (GA), respectively. Meanwhile, the BEN in the right rolandic operculum correlated significantly with both GA and PNA. Preterm-born infants exhibited increased BEN values in widespread cortical areas, particularly in the visual-motor cortex, when compared to term-born infants. Moreover, we identified five BEN-related genes (DNAJC12, FIG4, STX12, CETN2, and IRF2BP2), which were involved in protein folding, synaptic vesicle transportation and cell division. These findings suggest that the fMRI-based BEN can serve as an indicator of age-dependent brain functional development in human neonates, which may be influenced by specific genes.
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Affiliation(s)
- Zhiyong Zhao
- Department of Radiology, Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China
| | - Yifan Shuai
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Yihan Wu
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Mingyang Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
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2
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Xu Y, Liao X, Lei T, Cao M, Zhao J, Zhang J, Zhao T, Li Q, Jeon T, Ouyang M, Chalak L, Rollins N, Huang H, He Y. Development of neonatal connectome dynamics and its prediction for cognitive and language outcomes at age 2. Cereb Cortex 2024; 34:bhae204. [PMID: 38771241 DOI: 10.1093/cercor/bhae204] [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: 12/15/2023] [Revised: 04/23/2024] [Accepted: 05/01/2024] [Indexed: 05/22/2024] Open
Abstract
The functional brain connectome is highly dynamic over time. However, how brain connectome dynamics evolves during the third trimester of pregnancy and is associated with later cognitive growth remains unknown. Here, we use resting-state functional Magnetic Resonance Imaging (MRI) data from 39 newborns aged 32 to 42 postmenstrual weeks to investigate the maturation process of connectome dynamics and its role in predicting neurocognitive outcomes at 2 years of age. Neonatal brain dynamics is assessed using a multilayer network model. Network dynamics decreases globally but increases in both modularity and diversity with development. Regionally, module switching decreases with development primarily in the lateral precentral gyrus, medial temporal lobe, and subcortical areas, with a higher growth rate in primary regions than in association regions. Support vector regression reveals that neonatal connectome dynamics is predictive of individual cognitive and language abilities at 2 years of age. Our findings highlight network-level neural substrates underlying early cognitive development.
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Affiliation(s)
- Yuehua Xu
- School of Systems Science, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tianyuan Lei
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Miao Cao
- Institution of Science and Technology for Brain-Inspired Intelligence, Fudan University, No. 220 Handan Road, Shanghai 200433, China
| | - Jianlong Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Jiaying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
| | - Tina Jeon
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States
| | - Nancy Rollins
- Department of Radiology, University of Texas Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, TX 75390, United States
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA 19104, United States
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA 19104, United States
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, No. 19 Xinjiekouwai Street, Beijing 100875, China
- Chinese Institute for Brain Research, No. 26 Kexueyuan Road, Beijing 102206, China
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3
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Xia Y, Zhao J, Xu Y, Duan D, Xia M, Jeon T, Ouyang M, Chalak L, Rollins N, Huang H, He Y. Development of sensorimotor-visual connectome gradient at birth predicts neurocognitive outcomes at 2 years of age. iScience 2024; 27:108981. [PMID: 38327782 PMCID: PMC10847735 DOI: 10.1016/j.isci.2024.108981] [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: 08/18/2023] [Revised: 12/13/2023] [Accepted: 01/17/2024] [Indexed: 02/09/2024] Open
Abstract
Functional connectome gradients represent fundamental organizing principles of the brain. Here, we report the development of the connectome gradients in preterm and term babies aged 31-42 postmenstrual weeks using task-free functional MRI and its association with postnatal cognitive growth. We show that the principal sensorimotor-to-visual gradient is present during the late preterm period and continuously evolves toward a term-like pattern. The global measurements of this gradient, characterized by explanation ratio, gradient range, and gradient variation, increased with age (p < 0.05, corrected). Focal gradient development mainly occurs in the sensorimotor, lateral, and medial parietal regions, and visual regions (p < 0.05, corrected). The connectome gradient at birth predicts cognitive and language outcomes at 2-year follow-up (p < 0.005). These results are replicated using an independent dataset from the Developing Human Connectome Project. Our findings highlight early emergent rules of the brain connectome gradient and their implications for later cognitive growth.
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Affiliation(s)
- Yunman Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai 200433, China
| | - Jianlong Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yuehua Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Dingna Duan
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Tina Jeon
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Minhui Ouyang
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Nancy Rollins
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Hao Huang
- Department of Radiology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA
- Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Chinese Institute for Brain Research, Beijing 102206, China
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4
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Calixto C, Machado-Rivas F, Cortes-Albornoz MC, Karimi D, Velasco-Annis C, Afacan O, Warfield SK, Gholipour A, Jaimes C. Characterizing microstructural development in the fetal brain using diffusion MRI from 23 to 36 weeks of gestation. Cereb Cortex 2024; 34:bhad409. [PMID: 37948665 PMCID: PMC10793585 DOI: 10.1093/cercor/bhad409] [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: 08/30/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/12/2023] Open
Abstract
We utilized motion-corrected diffusion tensor imaging (DTI) to evaluate microstructural changes in healthy fetal brains during the late second and third trimesters. Data were derived from fetal magnetic resonance imaging scans conducted as part of a prospective study spanning from 2013 March to 2019 May. The study included 44 fetuses between the gestational ages (GAs) of 23 and 36 weeks. We reconstructed fetal brain DTI using a motion-tracked slice-to-volume registration framework. Images were segmented into 14 regions of interest (ROIs) through label propagation using a fetal DTI atlas, with expert refinement. Statistical analysis involved assessing changes in fractional anisotropy (FA) and mean diffusivity (MD) throughout gestation using mixed-effects models, and identifying points of change in trajectory for ROIs with nonlinear trends. Results showed significant GA-related changes in FA and MD in all ROIs except in the thalamus' FA and corpus callosum's MD. Hemispheric asymmetries were found in the FA of the periventricular white matter (pvWM), intermediate zone, and subplate and in the MD of the ganglionic eminence and pvWM. This study provides valuable insight into the normal patterns of development of MD and FA in the fetal brain. These changes are closely linked with cytoarchitectonic changes and display indications of early functional specialization.
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Affiliation(s)
- Camilo Calixto
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Fedel Machado-Rivas
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Maria C Cortes-Albornoz
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Camilo Jaimes
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States
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5
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Taymourtash A, Schwartz E, Nenning KH, Sobotka D, Licandro R, Glatter S, Diogo MC, Golland P, Grant E, Prayer D, Kasprian G, Langs G. Fetal development of functional thalamocortical and cortico-cortical connectivity. Cereb Cortex 2023; 33:5613-5624. [PMID: 36520481 PMCID: PMC10152101 DOI: 10.1093/cercor/bhac446] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 12/23/2022] Open
Abstract
Measuring and understanding functional fetal brain development in utero is critical for the study of the developmental foundations of our cognitive abilities, possible early detection of disorders, and their prevention. Thalamocortical connections are an intricate component of shaping the cortical layout, but so far, only ex-vivo studies provide evidence of how axons enter the sub-plate and cortex during this highly dynamic phase. Evidence for normal in-utero development of the functional thalamocortical connectome in humans is missing. Here, we modeled fetal functional thalamocortical connectome development using in-utero functional magnetic resonance imaging in fetuses observed from 19th to 40th weeks of gestation (GW). We observed a peak increase of thalamocortical functional connectivity strength between 29th and 31st GW, right before axons establish synapses in the cortex. The cortico-cortical connectivity increases in a similar time window, and exhibits significant functional laterality in temporal-superior, -medial, and -inferior areas. Homologous regions exhibit overall similar mirrored connectivity profiles, but this similarity decreases during gestation giving way to a more diverse cortical interconnectedness. Our results complement the understanding of structural development of the human connectome and may serve as the basis for the investigation of disease and deviations from a normal developmental trajectory of connectivity development.
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Affiliation(s)
- Athena Taymourtash
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Ernst Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140, Old Orangeburg Road, Orangeburg, NY 10962, United States
| | - Daniel Sobotka
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Roxane Licandro
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Laboratory for Computational Neuroimaging, A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Bldg. 149, 13th Street, Charlestown, MA 02129, United States
| | - Sarah Glatter
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Mariana Cardoso Diogo
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Radiology Department, Hospital CUF Tejo, Av. 24 de Julho 171A, 1350-352 Lisboa, Portugal
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77, Massachusetts Avenue, Cambridge, MA 02139, United States
| | - Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300, Longwood Avenue, Boston, MA 02115, United States
| | - Daniela Prayer
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Gregor Kasprian
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77, Massachusetts Avenue, Cambridge, MA 02139, United States
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Yu Q, Ouyang M, Detre J, Kang H, Hu D, Hong B, Fang F, Peng Y, Huang H. Infant brain regional cerebral blood flow increases supporting emergence of the default-mode network. eLife 2023; 12:e78397. [PMID: 36693116 PMCID: PMC9873253 DOI: 10.7554/elife.78397] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2022] [Accepted: 01/12/2023] [Indexed: 01/25/2023] Open
Abstract
Human infancy is characterized by most rapid regional cerebral blood flow (rCBF) increases across lifespan and emergence of a fundamental brain system default-mode network (DMN). However, how infant rCBF changes spatiotemporally across the brain and how the rCBF increase supports emergence of functional networks such as DMN remains unknown. Here, by acquiring cutting-edge multi-modal MRI including pseudo-continuous arterial-spin-labeled perfusion MRI and resting-state functional MRI of 48 infants cross-sectionally, we elucidated unprecedented 4D spatiotemporal infant rCBF framework and region-specific physiology-function coupling across infancy. We found that faster rCBF increases in the DMN than visual and sensorimotor networks. We also found strongly coupled increases of rCBF and network strength specifically in the DMN, suggesting faster local blood flow increase to meet extraneuronal metabolic demands in the DMN maturation. These results offer insights into the physiological mechanism of brain functional network emergence and have important implications in altered network maturation in brain disorders.
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Affiliation(s)
- Qinlin Yu
- Department of Radiology, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Department of Radiology, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Minhui Ouyang
- Department of Radiology, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Department of Radiology, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - John Detre
- Department of Radiology, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
- Department of Neurology, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
| | - Huiying Kang
- Department of Radiology, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Department of Radiology, Beijing Children’s Hospital, Capital Medical UniversityBeijingChina
| | - Di Hu
- Department of Radiology, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Department of Radiology, Beijing Children’s Hospital, Capital Medical UniversityBeijingChina
| | - Bo Hong
- Department of Biomedical Engineering, Tsinghua UniversityBeijingChina
| | - Fang Fang
- School of Psychological and Cognitive Sciences, Peking UniversityBeijingChina
| | - Yun Peng
- Department of Radiology, Beijing Children’s Hospital, Capital Medical UniversityBeijingChina
| | - Hao Huang
- Department of Radiology, Children’s Hospital of PhiladelphiaPhiladelphiaUnited States
- Department of Radiology, Perelman School of Medicine, University of PennsylvaniaPhiladelphiaUnited States
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7
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Developmental Inhibitory Changes in the Primary Somatosensory Cortex of the Stargazer Mouse Model of Absence Epilepsy. Biomolecules 2023; 13:biom13010186. [PMID: 36671571 PMCID: PMC9856073 DOI: 10.3390/biom13010186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2022] [Revised: 01/08/2023] [Accepted: 01/11/2023] [Indexed: 01/19/2023] Open
Abstract
Childhood absence epilepsy seizures arise in the cortico-thalamocortical network due to multiple cellular and molecular mechanisms, which are still under investigation. Understanding the precise mechanisms is imperative given that treatment fails in ~30% of patients while adverse neurological sequelae remain common. Impaired GABAergic neurotransmission is commonly reported in research models investigating these mechanisms. Recently, we reported a region-specific reduction in the whole-tissue and synaptic GABAA receptor (GABAAR) α1 subunit and an increase in whole-tissue GAD65 in the primary somatosensory cortex (SoCx) of the adult epileptic stargazer mouse compared with its non-epileptic (NE) littermate. The current study investigated whether these changes occurred prior to the onset of seizures on postnatal days (PN) 17-18, suggesting a causative role. Synaptic and cytosolic fractions were biochemically isolated from primary SoCx lysates followed by semiquantitative Western blot analyses for GABAAR α1 and GAD65. We found no significant changes in synaptic GABAAR α1 and cytosolic GAD65 in the primary SoCx of the stargazer mice at the critical developmental stages of PN 7-9, 13-15, and 17-18. This indicates that altered levels of GABAAR α1 and GAD65 in adult mice do not directly contribute to the initial onset of absence seizures but are a later consequence of seizure activity.
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8
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Wang W, Yu Q, Liang W, Xu F, Li Z, Tang Y, Liu S. Altered cortical microstructure in preterm infants at term-equivalent age relative to term-born neonates. Cereb Cortex 2023; 33:651-662. [PMID: 35259759 DOI: 10.1093/cercor/bhac091] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 01/11/2022] [Accepted: 02/08/2022] [Indexed: 02/03/2023] Open
Abstract
Preterm (PT) birth is a potential factor for abnormal brain development. Although various alterations of cortical structure and functional connectivity in preterm infants have been reported, the underlying microstructural foundation is still undetected thoroughly in PT infants relative to full-term (FT) neonates. To detect the very early cortical microstructural alteration noninvasively with advanced neurite orientation dispersion and density imaging (NODDI) on a whole-brain basis, we used multi-shell diffusion MRI of healthy newborns selected from the Developing Human Connectome Project. 73 PT infants and 69 FT neonates scanned at term-equivalent age were included in this study. By extracting the core voxels of gray matter (GM) using GM-based spatial statistics (GBSS), we found that comparing to FT neonates, infants born preterm showed extensive lower neurite density in both primary and higher-order association cortices (FWE corrected, P < 0.025). Higher orientation dispersion was only found in very preterm subgroup in the orbitofrontal cortex, fronto-insular cortex, entorhinal cortex, a portion of posterior cingular gyrus, and medial parieto-occipital cortex. This study provided new insights into exploring structural MR for functional and behavioral variations in preterm population, and these findings may have marked clinical importance, particularly in the guidance of ameliorating the development of premature brain.
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Affiliation(s)
- Wenjun Wang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Qiaowen Yu
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
| | - Wenjia Liang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Feifei Xu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Zhuoran Li
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
| | - Yuchun Tang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Shuwei Liu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
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9
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De Asis-Cruz J, Limperopoulos C. Harnessing the Power of Advanced Fetal Neuroimaging to Understand In Utero Footprints for Later Neuropsychiatric Disorders. Biol Psychiatry 2022; 93:867-879. [PMID: 36804195 DOI: 10.1016/j.biopsych.2022.11.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 11/03/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022]
Abstract
Adverse intrauterine events may profoundly impact fetal risk for future adult diseases. The mechanisms underlying this increased vulnerability are complex and remain poorly understood. Contemporary advances in fetal magnetic resonance imaging (MRI) have provided clinicians and scientists with unprecedented access to in vivo human fetal brain development to begin to identify emerging endophenotypes of neuropsychiatric disorders such as autism spectrum disorder, attention-deficit/hyperactivity disorder, and schizophrenia. In this review, we discuss salient findings of normal fetal neurodevelopment from studies using advanced, multimodal MRI that have provided unparalleled characterization of in utero prenatal brain morphology, metabolism, microstructure, and functional connectivity. We appraise the clinical utility of these normative data in identifying high-risk fetuses before birth. We highlight available studies that have investigated the predictive validity of advanced prenatal brain MRI findings and long-term neurodevelopmental outcomes. We then discuss how ex utero quantitative MRI findings can inform in utero investigations toward the pursuit of early biomarkers of risk. Lastly, we explore future opportunities to advance our understanding of the prenatal origins of neuropsychiatric disorders using precision fetal imaging.
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10
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Zhang S, Wang R, Wang J, He Z, Wu J, Kang Y, Zhang Y, Gao H, Hu X, Zhang T. Differentiate preterm and term infant brains and characterize the corresponding biomarkers via DICCCOL-based multi-modality graph neural networks. Front Neurosci 2022; 16:951508. [PMID: 36312010 PMCID: PMC9614033 DOI: 10.3389/fnins.2022.951508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Accepted: 09/20/2022] [Indexed: 11/23/2022] Open
Abstract
Preterm birth is a worldwide problem that affects infants throughout their lives significantly. Therefore, differentiating brain disorders, and further identifying and characterizing the corresponding biomarkers are key issues to investigate the effects of preterm birth, which facilitates the interventions for neuroprotection and improves outcomes of prematurity. Until now, many efforts have been made to study the effects of preterm birth; however, most of the studies merely focus on either functional or structural perspective. In addition, an effective framework not only jointly studies the brain function and structure at a group-level, but also retains the individual differences among the subjects. In this study, a novel dense individualized and common connectivity-based cortical landmarks (DICCCOL)-based multi-modality graph neural networks (DM-GNN) framework is proposed to differentiate preterm and term infant brains and characterize the corresponding biomarkers. This framework adopts the DICCCOL system as the initialized graph node of GNN for each subject, utilizing both functional and structural profiles and effectively retaining the individual differences. To be specific, functional magnetic resonance imaging (fMRI) of the brain provides the features for the graph nodes, and brain fiber connectivity is utilized as the structural representation of the graph edges. Self-attention graph pooling (SAGPOOL)-based GNN is then applied to jointly study the function and structure of the brain and identify the biomarkers. Our results successfully demonstrate that the proposed framework can effectively differentiate the preterm and term infant brains. Furthermore, the self-attention-based mechanism can accurately calculate the attention score and recognize the most significant biomarkers. In this study, not only 87.6% classification accuracy is observed for the developing Human Connectome Project (dHCP) dataset, but also distinguishing features are explored and extracted. Our study provides a novel and uniform framework to differentiate brain disorders and characterize the corresponding biomarkers.
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Affiliation(s)
- Shu Zhang
- Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, China
- *Correspondence: Shu Zhang
| | - Ruoyang Wang
- Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Junxin Wang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Zhibin He
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Jinru Wu
- Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Yanqing Kang
- Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Yin Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Huan Gao
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xintao Hu
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
- Tuo Zhang
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11
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Iwata S, Katayama R, Tsuda K, Lin YC, Kurata T, Kinoshita M, Kawase K, Kato T, Kato S, Hisano T, Oda M, Ohmae E, Takashima S, Araki Y, Saitoh S, Iwata O. Near-infrared light scattering and water diffusion in newborn brains. Ann Clin Transl Neurol 2022; 9:1417-1427. [PMID: 35943446 PMCID: PMC9463954 DOI: 10.1002/acn3.51641] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 07/19/2022] [Indexed: 11/26/2022] Open
Abstract
Objective MRI provides useful information regarding brain maturation and injury in newborn infants. However, MRI studies are generally restricted during acute phase, resulting in uncertainty around upstream clinical events responsible for subtle cerebral injuries. Time‐resolved near‐infrared spectroscopy non‐invasively provides the reduced scattering coefficient (μs′), which theoretically reflects tissue structural complexity. This study aimed to test whether μs′ values of the newborn head reflected MRI findings. Methods Between June 2009 and January 2015, 77 hospitalised newborn infants (31.7 ± 3.8 weeks gestation) were assessed at 38.8 ± 1.3 weeks post‐conceptional age. Associations of μs′ values with MRI scores, mean diffusivity and fractional anisotropy were assessed. Results Univariable analysis showed that μs′ values were associated with gestational week (p = 0.035; regression coefficient [B], 0.065; 95% confidence interval [CI], 0.005–0.125), fractional anisotropy in the cortical grey matter (p = 0.020; B, −5.994; 95%CI, −11.032 to −0.957), average diffusivity in the cortical grey matter (p < 0.001; B, −4.728; 95%CI, −7.063 to −2.394) and subcortical white matter (p = 0.001; B, −2.071; 95%CI, −3.311 to −0.832), subarachnoid space (p < 0.001; B, −0.289; 95%CI, −0.376 to −0.201) and absence of brain abnormality (p = 0.042; B, −0.422; 95%CI, −0.829 to −0.015). The multivariable model to explain μs′ values comprised average diffusivity in the subcortical white matter (p < 0.001; B, −2.066; 95%CI, −3.200 to −0.932), subarachnoid space (p < 0.001; B, −0.314; 95%CI, −0.412 to −0.216) and absence of brain abnormality (p = 0.021; B, −0.400; 95%CI, −0.739 to −0.061). Interpretation Light scattering was associated with brain structure indicated by MRI‐assessed brain abnormality and diffusion‐tensor‐imaging‐assessed water diffusivity. When serially assessed in a larger population, μs′ values might help identify covert clinical events responsible for subtle cerebral injury.
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Affiliation(s)
- Sachiko Iwata
- Center for Human Development and Family Science, Department of Pediatrics and Neonatology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, 467-8601, Japan.,Department of Paediatrics and Child Health, Centre for Developmental and Cognitive Neuroscience, Kurume University School of Medicine, Kurume, Fukuoka, 830-0011, Japan
| | - Reiji Katayama
- Centre for the Study of Medical Education, Kurume University School of Medicine, Kurume, Fukuoka, 830-0011, Japan
| | - Kennosuke Tsuda
- Center for Human Development and Family Science, Department of Pediatrics and Neonatology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, 467-8601, Japan.,Department of Paediatrics and Child Health, Centre for Developmental and Cognitive Neuroscience, Kurume University School of Medicine, Kurume, Fukuoka, 830-0011, Japan
| | - Yung-Chieh Lin
- Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng-Kung University, Tainan, 70457, Taiwan
| | - Tsuyoshi Kurata
- Department of Paediatrics and Child Health, Centre for Developmental and Cognitive Neuroscience, Kurume University School of Medicine, Kurume, Fukuoka, 830-0011, Japan
| | - Masahiro Kinoshita
- Department of Paediatrics and Child Health, Centre for Developmental and Cognitive Neuroscience, Kurume University School of Medicine, Kurume, Fukuoka, 830-0011, Japan
| | - Koya Kawase
- Center for Human Development and Family Science, Department of Pediatrics and Neonatology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, 467-8601, Japan
| | - Takenori Kato
- Center for Human Development and Family Science, Department of Pediatrics and Neonatology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, 467-8601, Japan
| | - Shin Kato
- Center for Human Development and Family Science, Department of Pediatrics and Neonatology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, 467-8601, Japan
| | - Tadashi Hisano
- Center for Human Development and Family Science, Department of Pediatrics and Neonatology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, 467-8601, Japan
| | - Motoki Oda
- Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, Shizuoka, 434-8601, Japan
| | - Etsuko Ohmae
- Central Research Laboratory, Hamamatsu Photonics K.K., Hamamatsu, Shizuoka, 434-8601, Japan
| | - Sachio Takashima
- Yanagawa Institute for Developmental Disabilities, International University of Health and Welfare, Yanagawa, Fukuoka, 832-0813, Japan
| | - Yuko Araki
- Graduate School of Information Sciences, Tohoku University, Sendai City, Miyagi, 980-8579, Japan
| | - Shinji Saitoh
- Center for Human Development and Family Science, Department of Pediatrics and Neonatology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, 467-8601, Japan
| | - Osuke Iwata
- Center for Human Development and Family Science, Department of Pediatrics and Neonatology, Nagoya City University Graduate School of Medical Sciences, Nagoya, Aichi, 467-8601, Japan.,Department of Paediatrics and Child Health, Centre for Developmental and Cognitive Neuroscience, Kurume University School of Medicine, Kurume, Fukuoka, 830-0011, Japan
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12
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Yuan S, Liu M, Kim S, Yang J, Barkovich AJ, Xu D, Kim H. Cyto/myeloarchitecture of cortical gray matter and superficial white matter in early neurodevelopment: multimodal MRI study in preterm neonates. Cereb Cortex 2022; 33:357-373. [PMID: 35235643 PMCID: PMC9837610 DOI: 10.1093/cercor/bhac071] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 01/21/2022] [Accepted: 01/22/2022] [Indexed: 01/19/2023] Open
Abstract
The cerebral cortex undergoes rapid microstructural changes throughout the third trimester. Recently, there has been growing interest on imaging features that represent cyto/myeloarchitecture underlying intracortical myelination, cortical gray matter (GM), and its adjacent superficial whitematter (sWM). Using 92 magnetic resonance imaging scans from 78 preterm neonates, the current study used combined T1-weighted/T2-weighted (T1w/T2w) intensity ratio and diffusion tensor imaging (DTI) measurements, including fractional anisotropy (FA) and mean diffusivity (MD), to characterize the developing cyto/myeloarchitectural architecture. DTI metrics showed a linear trajectory: FA decreased in GM but increased in sWM with time; and MD decreased in both GM and sWM. Conversely, T1w/T2w measurements showed a distinctive parabolic trajectory, revealing additional cyto/myeloarchitectural signature inferred. Furthermore, the spatiotemporal courses were regionally heterogeneous: central, ventral, and temporal regions of GM and sWM exhibited faster T1w/T2w changes; anterior sWM areas exhibited faster FA increases; and central and cingulate areas in GM and sWM exhibited faster MD decreases. These results may explain cyto/myeloarchitectural processes, including dendritic arborization, synaptogenesis, glial proliferation, and radial glial cell organization and apoptosis. Finally, T1w/T2w values were significantly associated with 1-year language and cognitive outcome scores, while MD significantly decreased with intraventricular hemorrhage.
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Affiliation(s)
| | | | | | - Jingda Yang
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Anthony James Barkovich
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Duan Xu
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, San Francisco, CA 94143, USA
| | - Hosung Kim
- Corresponding author: 2025 Zonal Ave, Los Angeles, CA 90033, USA.
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13
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Machado-Rivas F, Afacan O, Khan S, Marami B, Velasco-Annis C, Lidov H, Warfield SK, Gholipour A, Jaimes C. Spatiotemporal changes in diffusivity and anisotropy in fetal brain tractography. Hum Brain Mapp 2021; 42:5771-5784. [PMID: 34487404 PMCID: PMC8559496 DOI: 10.1002/hbm.25653] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 08/22/2021] [Accepted: 08/25/2021] [Indexed: 02/03/2023] Open
Abstract
Population averaged diffusion atlases can be utilized to characterize complex microstructural changes with less bias than data from individual subjects. In this study, a fetal diffusion tensor imaging (DTI) atlas was used to investigate tract-based changes in anisotropy and diffusivity in vivo from 23 to 38 weeks of gestational age (GA). Healthy pregnant volunteers with typically developing fetuses were imaged at 3 T. Acquisition included structural images processed with a super-resolution algorithm and DTI images processed with a motion-tracked slice-to-volume registration algorithm. The DTI from individual subjects were used to generate 16 templates, each specific to a week of GA; this was accomplished by means of a tensor-to-tensor diffeomorphic deformable registration method integrated with kernel regression in age. Deterministic tractography was performed to outline the forceps major, forceps minor, bilateral corticospinal tracts (CST), bilateral inferior fronto-occipital fasciculus (IFOF), bilateral inferior longitudinal fasciculus (ILF), and bilateral uncinate fasciculus (UF). The mean fractional anisotropy (FA) and mean diffusivity (MD) was recorded for all tracts. For a subset of tracts (forceps major, CST, and IFOF) we manually divided the tractograms into anatomy conforming segments to evaluate within-tract changes. We found tract-specific, nonlinear, age related changes in FA and MD. Early in gestation, these trends appear to be dominated by cytoarchitectonic changes in the transient white matter fetal zones while later in gestation, trends conforming to the progression of myelination were observed. We also observed significant (local) heterogeneity in within-tract developmental trajectories for the CST, IFOF, and forceps major.
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Affiliation(s)
- Fedel Machado-Rivas
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Onur Afacan
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Shadab Khan
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Bahram Marami
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Hart Lidov
- Department of Pathology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Simon K Warfield
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Camilo Jaimes
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
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14
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Dimitrova R, Pietsch M, Ciarrusta J, Fitzgibbon SP, Williams LZJ, Christiaens D, Cordero-Grande L, Batalle D, Makropoulos A, Schuh A, Price AN, Hutter J, Teixeira RP, Hughes E, Chew A, Falconer S, Carney O, Egloff A, Tournier JD, McAlonan G, Rutherford MA, Counsell SJ, Robinson EC, Hajnal JV, Rueckert D, Edwards AD, O'Muircheartaigh J. Preterm birth alters the development of cortical microstructure and morphology at term-equivalent age. Neuroimage 2021; 243:118488. [PMID: 34419595 PMCID: PMC8526870 DOI: 10.1016/j.neuroimage.2021.118488] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/16/2021] [Accepted: 08/19/2021] [Indexed: 11/27/2022] Open
Abstract
INTRODUCTION The dynamic nature and complexity of the cellular events that take place during the last trimester of pregnancy make the developing cortex particularly vulnerable to perturbations. Abrupt interruption to normal gestation can lead to significant deviations to many of these processes, resulting in atypical trajectory of cortical maturation in preterm birth survivors. METHODS We sought to first map typical cortical micro- and macrostructure development using invivo MRI in a large sample of healthy term-born infants scanned after birth (n = 259). Then we offer a comprehensive characterization of the cortical consequences of preterm birth in 76 preterm infants scanned at term-equivalent age (37-44 weeks postmenstrual age). We describe the group-average atypicality, the heterogeneity across individual preterm infants, and relate individual deviations from normative development to age at birth and neurodevelopment at 18 months. RESULTS In the term-born neonatal brain, we observed heterogeneous and regionally specific associations between age at scan and measures of cortical morphology and microstructure, including rapid surface expansion, greater cortical thickness, lower cortical anisotropy and higher neurite orientation dispersion. By term-equivalent age, preterm infants had on average increased cortical tissue water content and reduced neurite density index in the posterior parts of the cortex, and greater cortical thickness anteriorly compared to term-born infants. While individual preterm infants were more likely to show extreme deviations (over 3.1 standard deviations) from normative cortical maturation compared to term-born infants, these extreme deviations were highly variable and showed very little spatial overlap between individuals. Measures of regional cortical development were associated with age at birth, but not with neurodevelopment at 18 months. CONCLUSION We showed that preterm birth alters cortical micro- and macrostructural maturation near the time of full-term birth. Deviations from normative development were highly variable between individual preterm infants.
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Affiliation(s)
- Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Judit Ciarrusta
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Sean P Fitzgibbon
- Centre for Functional MRI of the Brain (FMRIB), Welcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Logan Z J Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Belgium
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Rui Pag Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Olivia Carney
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Alexia Egloff
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom; South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Emma C Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Faculty of Informatics and Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom.
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15
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Yu Q, Peng Y, Kang H, Peng Q, Ouyang M, Slinger M, Hu D, Shou H, Fang F, Huang H. Differential White Matter Maturation from Birth to 8 Years of Age. Cereb Cortex 2021; 30:2673-2689. [PMID: 31819951 PMCID: PMC7175013 DOI: 10.1093/cercor/bhz268] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 08/23/2019] [Accepted: 09/17/2019] [Indexed: 12/14/2022] Open
Abstract
Comprehensive delineation of white matter (WM) microstructural maturation from birth to childhood is critical for understanding spatiotemporally differential circuit formation. Without a relatively large sample of datasets and coverage of critical developmental periods of both infancy and early childhood, differential maturational charts across WM tracts cannot be delineated. With diffusion tensor imaging (DTI) of 118 typically developing (TD) children aged 0–8 years and 31 children with autistic spectrum disorder (ASD) aged 2–7 years, the microstructure of every major WM tract and tract group was measured with DTI metrics to delineate differential WM maturation. The exponential model of microstructural maturation of all WM was identified. The WM developmental curves were separated into fast, intermediate, and slow phases in 0–8 years with distinctive time period of each phase across the tracts. Shorter periods of the fast and intermediate phases in certain tracts, such as the commissural tracts, indicated faster earlier development. With TD WM maturational curves as the reference, higher residual variance of WM microstructure was found in children with ASD. The presented comprehensive and differential charts of TD WM microstructural maturation of all major tracts and tract groups in 0–8 years provide reference standards for biomarker detection of neuropsychiatric disorders.
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Affiliation(s)
- Qinlin Yu
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yun Peng
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Huiying Kang
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Qinmu Peng
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Michelle Slinger
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Di Hu
- Department of Radiology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing 100045, China
| | - Haochang Shou
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Fang Fang
- School of Psychological and Cognitive Sciences and Beijing Key Laboratory of Behavior and Mental Health, Peking University, Beijing 100871, China.,Key Laboratory of Machine Perception, Peking University, Beijing 100871, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing 100871, China.,PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing 100871, China
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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16
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Dubois J, Alison M, Counsell SJ, Hertz‐Pannier L, Hüppi PS, Benders MJ. MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances. J Magn Reson Imaging 2021; 53:1318-1343. [PMID: 32420684 PMCID: PMC8247362 DOI: 10.1002/jmri.27192] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 04/24/2020] [Accepted: 04/24/2020] [Indexed: 01/04/2023] Open
Abstract
In recent years, exploration of the developing brain has become a major focus for researchers and clinicians in an attempt to understand what allows children to acquire amazing and unique abilities, as well as the impact of early disruptions (eg, prematurity, neonatal insults) that can lead to a wide range of neurodevelopmental disorders. Noninvasive neuroimaging methods such as MRI are essential to establish links between the brain and behavioral changes in newborns and infants. In this review article, we aim to highlight recent and representative studies using the various techniques available: anatomical MRI, quantitative MRI (relaxometry, diffusion MRI), multiparametric approaches, and functional MRI. Today, protocols use 1.5 or 3T MRI scanners, and specialized methodologies have been put in place for data acquisition and processing to address the methodological challenges specific to this population, such as sensitivity to motion. MR sequences must be adapted to the brains of newborns and infants to obtain relevant good soft-tissue contrast, given the small size of the cerebral structures and the incomplete maturation of tissues. The use of age-specific image postprocessing tools is also essential, as signal and contrast differ from the adult brain. Appropriate methodologies then make it possible to explore multiple neurodevelopmental mechanisms in a precise way, and assess changes with age or differences between groups of subjects, particularly through large-scale projects. Although MRI measurements only indirectly reflect the complex series of dynamic processes observed throughout development at the molecular and cellular levels, this technique can provide information on brain morphology, structural connectivity, microstructural properties of gray and white matter, and on the functional architecture. Finally, MRI measures related to clinical, behavioral, and electrophysiological markers have a key role to play from a diagnostic and prognostic perspective in the implementation of early interventions to avoid long-term disabilities in children. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Jessica Dubois
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Marianne Alison
- University of ParisNeuroDiderot, INSERM,ParisFrance
- Department of Pediatric RadiologyAPHP, Robert‐Debré HospitalParisFrance
| | - Serena J. Counsell
- Centre for the Developing BrainSchool of Biomedical Engineering & Imaging Sciences, King's College LondonLondonUK
| | - Lucie Hertz‐Pannier
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Petra S. Hüppi
- Division of Development and Growth, Department of Woman, Child and AdolescentUniversity Hospitals of GenevaGenevaSwitzerland
| | - Manon J.N.L. Benders
- Department of NeonatologyUniversity Medical Center Utrecht, Utrecht UniversityUtrechtthe Netherlands
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17
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Ouyang M, Peng Q, Jeon T, Heyne R, Chalak L, Huang H. Diffusion-MRI-based regional cortical microstructure at birth for predicting neurodevelopmental outcomes of 2-year-olds. eLife 2020; 9:58116. [PMID: 33350380 PMCID: PMC7755384 DOI: 10.7554/elife.58116] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 12/06/2020] [Indexed: 12/11/2022] Open
Abstract
Cerebral cortical architecture at birth encodes regionally differential dendritic arborization and synaptic formation. It underlies behavioral emergence of 2-year-olds. Brain changes in 0-2 years are most dynamic across the lifespan. Effective prediction of future behavior with brain microstructure at birth will reveal structural basis of behavioral emergence in typical development and identify biomarkers for early detection and tailored intervention in atypical development. Here we aimed to evaluate the neonate whole-brain cortical microstructure quantified by diffusion MRI for predicting future behavior. We found that individual cognitive and language functions assessed at the age of 2 years were robustly predicted by neonate cortical microstructure using support vector regression. Remarkably, cortical regions contributing heavily to the prediction models exhibited distinctive functional selectivity for cognition and language. These findings highlight regional cortical microstructure at birth as a potential sensitive biomarker in predicting future neurodevelopmental outcomes and identifying individual risks of brain disorders.
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Affiliation(s)
- Minhui Ouyang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States
| | - Qinmu Peng
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
| | - Tina Jeon
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States
| | - Roy Heyne
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, United States
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, United States
| | - Hao Huang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
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18
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Ball G, Seidlitz J, O’Muircheartaigh J, Dimitrova R, Fenchel D, Makropoulos A, Christiaens D, Schuh A, Passerat-Palmbach J, Hutter J, Cordero-Grande L, Hughes E, Price A, Hajnal JV, Rueckert D, Robinson EC, Edwards AD. Cortical morphology at birth reflects spatiotemporal patterns of gene expression in the fetal human brain. PLoS Biol 2020; 18:e3000976. [PMID: 33226978 PMCID: PMC7721147 DOI: 10.1371/journal.pbio.3000976] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 12/07/2020] [Accepted: 11/02/2020] [Indexed: 02/07/2023] Open
Abstract
Interruption to gestation through preterm birth can significantly impact cortical development and have long-lasting adverse effects on neurodevelopmental outcome. We compared cortical morphology captured by high-resolution, multimodal magnetic resonance imaging (MRI) in n = 292 healthy newborn infants (mean age at birth = 39.9 weeks) with regional patterns of gene expression in the fetal cortex across gestation (n = 156 samples from 16 brains, aged 12 to 37 postconceptional weeks [pcw]). We tested the hypothesis that noninvasive measures of cortical structure at birth mirror areal differences in cortical gene expression across gestation, and in a cohort of n = 64 preterm infants (mean age at birth = 32.0 weeks), we tested whether cortical alterations observed after preterm birth were associated with altered gene expression in specific developmental cell populations. Neonatal cortical structure was aligned to differential patterns of cell-specific gene expression in the fetal cortex. Principal component analysis (PCA) of 6 measures of cortical morphology and microstructure showed that cortical regions were ordered along a principal axis, with primary cortex clearly separated from heteromodal cortex. This axis was correlated with estimated tissue maturity, indexed by differential expression of genes expressed by progenitor cells and neurons, and engaged in stem cell differentiation, neuron migration, and forebrain development. Preterm birth was associated with altered regional MRI metrics and patterns of differential gene expression in glial cell populations. The spatial patterning of gene expression in the developing cortex was thus mirrored by regional variation in cortical morphology and microstructure at term, and this was disrupted by preterm birth. This work provides a framework to link molecular mechanisms to noninvasive measures of cortical development in early life and highlights novel pathways to injury in neonatal populations at increased risk of neurodevelopmental disorder. Interruption to gestation through preterm birth can significantly impact cortical development and have long-lasting adverse effects on neurodevelopmental outcome. A large neuroimaging study of newborn infants reveals how their cortical structure at birth is associated with patterns of gene expression in the fetal cortex and how this relationship is affected by preterm birth.
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Affiliation(s)
- Gareth Ball
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
- * E-mail:
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, United States of America
- Department of Psychiatry, University of Cambridge, United Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Daphna Fenchel
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Antonios Makropoulos
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Belgium
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, United Kingdom
| | | | - Jana Hutter
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Anthony Price
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Jo V. Hajnal
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, United Kingdom
| | - Emma C. Robinson
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
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19
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Zhu T, Peng Q, Ouyang A, Huang H. Neuroanatomical underpinning of diffusion kurtosis measurements in the cerebral cortex of healthy macaque brains. Magn Reson Med 2020; 85:1895-1908. [PMID: 33058286 DOI: 10.1002/mrm.28548] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 08/23/2020] [Accepted: 09/17/2020] [Indexed: 12/21/2022]
Abstract
PURPOSE To investigate the neuroanatomical underpinning of healthy macaque brain cortical microstructure measured by diffusion kurtosis imaging (DKI), which characterizes non-Gaussian water diffusion. METHODS High-resolution DKI was acquired from 6 postmortem macaque brains. Neurofilament density (ND) was quantified based on structure tensor from neurofilament histological images of a different macaque brain sample. After alignment of DKI-derived mean kurtosis (MK) maps to the histological images, MK and histology-based ND were measured at corresponding regions of interests characterized by distinguished cortical MK values in the prefrontal/precentral-postcentral and temporal cortices. Pearson correlation was performed to test significant correlation between these cortical MK and ND measurements. RESULTS Heterogeneity of cortical MK across different cortical regions was revealed, with significantly and consistently higher MK measurements in the prefrontal/precentral-postcentral cortex compared to those in the temporal cortex across all six scanned macaque brains. Corresponding higher ND measurements in the prefrontal/precentral-postcentral cortex than in the temporal cortex were also found. The heterogeneity of cortical MK is associated with heterogeneity of histology-based ND measurements, with significant correlation between cortical MK and corresponding ND measurements (P < .005). CONCLUSION These findings suggested that DKI-derived MK can potentially be an effective noninvasive biomarker quantifying underlying neuroanatomical complexity inside the cerebral cortical mantle for clinical and neuroscientific research.
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Affiliation(s)
- Tianjia Zhu
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Qinmu Peng
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Austin Ouyang
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.,Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Texas, USA
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20
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Xu Y, Cao M, Liao X, Xia M, Wang X, Jeon T, Ouyang M, Chalak L, Rollins N, Huang H, He Y. Development and Emergence of Individual Variability in the Functional Connectivity Architecture of the Preterm Human Brain. Cereb Cortex 2020; 29:4208-4222. [PMID: 30534949 DOI: 10.1093/cercor/bhy302] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2018] [Revised: 11/07/2018] [Indexed: 01/10/2023] Open
Abstract
Individual variability in human brain networks underlies individual differences in cognition and behaviors. However, researchers have not conclusively determined when individual variability patterns of the brain networks emerge and how they develop in the early phase. Here, we employed resting-state functional MRI data and whole-brain functional connectivity analyses in 40 neonates aged around 31-42 postmenstrual weeks to characterize the spatial distribution and development modes of individual variability in the functional network architecture. We observed lower individual variability in primary sensorimotor and visual areas and higher variability in association regions at the third trimester, and these patterns are generally similar to those of adult brains. Different functional systems showed dramatic differences in the development of individual variability, with significant decreases in the sensorimotor network; decreasing trends in the visual, subcortical, and dorsal and ventral attention networks, and limited change in the default mode, frontoparietal and limbic networks. The patterns of individual variability were negatively correlated with the short- to middle-range connection strength/number and this distance constraint was significantly strengthened throughout development. Our findings highlight the development and emergence of individual variability in the functional architecture of the prenatal brain, which may lay network foundations for individual behavioral differences later in life.
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Affiliation(s)
- Yuehua Xu
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875 China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875 China
| | - Miao Cao
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875 China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875 China
| | - Xuhong Liao
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875 China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875 China
| | - Mingrui Xia
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875 China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875 China
| | - Xindi Wang
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875 China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875 China
| | - Tina Jeon
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Nancy Rollins
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875 China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875 China
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21
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Peng Q, Ouyang M, Wang J, Yu Q, Zhao C, Slinger M, Li H, Fan Y, Hong B, Huang H. Regularized-Ncut: Robust and homogeneous functional parcellation of neonate and adult brain networks. Artif Intell Med 2020; 106:101872. [PMID: 32593397 DOI: 10.1016/j.artmed.2020.101872] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2019] [Revised: 03/17/2020] [Accepted: 05/02/2020] [Indexed: 11/20/2022]
Abstract
Brain network parcellation based on resting-state functional MRI (rs-fMRI) is affected by noise, resulting in spurious small patches and decreased functional homogeneity within each network. Obtaining robust and homogeneous parcellation of neonate brain is more difficult, because neonate rs-fMRI is associated with relatively higher level of noise and no prior knowledge from a functional neonate atlas is available as spatial constraints. To meet these challenges, we developed a novel data-driven Regularized Normalized-cut (RNcut) method. RNcut is formulated by adding two regularization terms, a smoothing term using Markov random fields and a small-patch removal term, to conventional normalized-cut (Ncut) method. The RNcut and competing methods were tested with simulated datasets with known ground truth and then applied to both adult and neonate rs-fMRI datasets. Based on the parcellated networks generated by RNcut, intra-network connectivity was quantified. The test results from simulated datasets demonstrated that the RNcut method is more robust (p < 0.01) to noise and can delineate parcellated functional networks with significantly better (p < 0.01) spatial contiguity and significantly higher (p < 0.01) functional homogeneity than competing methods. Application of RNcut to neonate and adult rs-fMRI dataset revealed distinctive functional brain organization of neonate brains from that of adult brains. Collectively, we developed a novel data-driven RNcut method by integrating conventional Ncut with two regularization terms, generating robust and homogeneous functional parcellation without imposing spatial constraints. A broad range of brain network applications and analyses, especially neonate and infant brain parcellation with noisy and large sample of datasets, can potentially benefit from this RNcut method.
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Affiliation(s)
- Qinmu Peng
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jiaojian Wang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Qinlin Yu
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chenying Zhao
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA
| | - Michelle Slinger
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Hongming Li
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Bo Hong
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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22
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Najafi H, Naseri M, Zahiri J, Totonchi M, Sadeghizadeh M. Identification of the Molecular Events Involved in the Development of Prefrontal Cortex Through the Analysis of RNA-Seq Data From BrainSpan. ASN Neuro 2020; 11:1759091419854627. [PMID: 31213068 PMCID: PMC6582306 DOI: 10.1177/1759091419854627] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Human brain development is a complex process that follows sequential
orchestration of gene expression, begins at conceptual stages, and continues
into adulthood. Altered profile of gene expression drives many cellular and
molecular events required for development. Here, the molecular events during
development of human prefrontal cortex (PFC) (as an important executive part of
the brain) were investigated. First, the RNA-sequencing data of BrainSpan were
used to obtain differentially expressed genes between each two developmental
stages and then, the relevant biological processes and signaling pathways were
deduced by gene set enrichment analysis. In addition, the changes in
transcriptome landscape of PFC during development were analyzed and the
potential biological processes underlie the changes were found. Comparison of
the four regions of PFC based on their biological processes showed that
additional to common biological processes and signaling pathways, each PFC
region had its own molecular characteristics, conforming their previously
reported functional roles in brain physiology. The most heterogeneity in
transcriptome between the PFC regions was observed at the time of birth which
was concurrent with the activity of some region-specific regulatory systems such
as DNA methylation, transcription regulation, RNA splicing, and presence of
different transcription factors and microRNAs. In conclusion, this study used
bioinformatics to present a comprehensive molecular overview on PFC development
which may explain the etiology of brain neuropsychiatric disorders originated
from malfunctioning of PFC.
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Affiliation(s)
- Hadi Najafi
- 1 Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohadeseh Naseri
- 2 Department of Biophysics, Bioinformatics and Computational Omics Lab (BioCOOL), Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Javad Zahiri
- 2 Department of Biophysics, Bioinformatics and Computational Omics Lab (BioCOOL), Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mehdi Totonchi
- 3 Department of Genetics and Stem Cell, Royan Institute, Tehran, Iran
| | - Majid Sadeghizadeh
- 1 Department of Genetics, Faculty of Biological Sciences, Tarbiat Modares University, Tehran, Iran
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23
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Palombo M, Ianus A, Guerreri M, Nunes D, Alexander DC, Shemesh N, Zhang H. SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI. Neuroimage 2020; 215:116835. [PMID: 32289460 PMCID: PMC8543044 DOI: 10.1016/j.neuroimage.2020.116835] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2019] [Revised: 03/26/2020] [Accepted: 04/06/2020] [Indexed: 11/29/2022] Open
Abstract
This work introduces a compartment-based model for apparent cell body (namely soma) and neurite density imaging (SANDI) using non-invasive diffusion-weighted MRI (DW-MRI). The existing conjecture in brain microstructure imaging through DW-MRI presents water diffusion in white (WM) and gray (GM) matter as restricted diffusion in neurites, modelled by infinite cylinders of null radius embedded in the hindered extra-neurite water. The extra-neurite pool in WM corresponds to water in the extra-axonal space, but in GM it combines water in the extra-cellular space with water in soma. While several studies showed that this microstructure model successfully describe DW-MRI data in WM and GM at b ≤ 3,000 s/mm2 (or 3 ms/μm2), it has been also shown to fail in GM at high b values (b≫3,000 s/mm2 or 3 ms/μm2). Here we hypothesise that the unmodelled soma compartment (i.e. cell body of any brain cell type: from neuroglia to neurons) may be responsible for this failure and propose SANDI as a new model of brain microstructure where soma of any brain cell type is explicitly included. We assess the effects of size and density of soma on the direction-averaged DW-MRI signal at high b values and the regime of validity of the model using numerical simulations and comparison with experimental data from mouse (bmax = 40,000 s/mm2, or 40 ms/μm2) and human (bmax = 10,000 s/mm2, or 10 ms/μm2) brain. We show that SANDI defines new contrasts representing complementary information on the brain cyto- and myelo-architecture. Indeed, we show maps from 25 healthy human subjects of MR soma and neurite signal fractions, that remarkably mirror contrasts of histological images of brain cyto- and myelo-architecture. Although still under validation, SANDI might provide new insight into tissue architecture by introducing a new set of biomarkers of potential great value for biomedical applications and pure neuroscience.
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Affiliation(s)
- Marco Palombo
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK.
| | - Andrada Ianus
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK; Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Michele Guerreri
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
| | - Daniel Nunes
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Daniel C Alexander
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Hui Zhang
- Centre for Medical Image Computing and Dept of Computer Science, University College London, London, UK
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24
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Delannoy Q, Pham CH, Cazorla C, Tor-Díez C, Dollé G, Meunier H, Bednarek N, Fablet R, Passat N, Rousseau F. SegSRGAN: Super-resolution and segmentation using generative adversarial networks - Application to neonatal brain MRI. Comput Biol Med 2020; 120:103755. [PMID: 32421654 DOI: 10.1016/j.compbiomed.2020.103755] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Revised: 04/07/2020] [Accepted: 04/07/2020] [Indexed: 01/26/2023]
Abstract
BACKGROUND AND OBJECTIVE One of the main issues in the analysis of clinical neonatal brain MRI is the low anisotropic resolution of the data. In most MRI analysis pipelines, data are first re-sampled using interpolation or single image super-resolution techniques and then segmented using (semi-)automated approaches. In other words, image reconstruction and segmentation are then performed separately. In this article, we propose a methodology and a software solution for carrying out simultaneously high-resolution reconstruction and segmentation of brain MRI data. METHODS Our strategy mainly relies on generative adversarial networks. The network architecture is described in detail. We provide information about its implementation, focusing on the most crucial technical points (whereas complementary details are given in a dedicated GitHub repository). We illustrate the behavior of the proposed method for cortex analysis from neonatal MR images. RESULTS The results of the method, evaluated quantitatively (Dice, peak signal-to-noise ratio, structural similarity, number of connected components) and qualitatively on a research dataset (dHCP) and a clinical one (Epirmex), emphasize the relevance of the approach, and its ability to take advantage of data-augmentation strategies. CONCLUSIONS Results emphasize the potential of our proposed method/software with respect to practical medical applications. The method is provided as a freely available software tool, which allows one to carry out his/her own experiments, and involve the method for the super-resolution reconstruction and segmentation of arbitrary cerebral structures from any MR image dataset.
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Affiliation(s)
- Quentin Delannoy
- Université de Reims Champagne Ardenne, CReSTIC EA 3804, 51097 Reims, France
| | - Chi-Hieu Pham
- IMT Atlantique, LaTIM U1101 INSERM, UBL, Brest, France
| | - Clément Cazorla
- Université de Reims Champagne Ardenne, CReSTIC EA 3804, 51097 Reims, France
| | | | - Guillaume Dollé
- Université de Reims Champagne Ardenne, CNRS, LMR UMR 9008, 51097 Reims, France
| | - Hélène Meunier
- Service de médecine néonatale et réanimation pédiatrique, CHU de Reims, France
| | - Nathalie Bednarek
- Université de Reims Champagne Ardenne, CReSTIC EA 3804, 51097 Reims, France; Service de médecine néonatale et réanimation pédiatrique, CHU de Reims, France
| | - Ronan Fablet
- IMT Atlantique, Lab-STICC UMR CNRS 6285, Brest, France
| | - Nicolas Passat
- Université de Reims Champagne Ardenne, CReSTIC EA 3804, 51097 Reims, France.
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25
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Sydnor VJ, Bouix S, Pasternak O, Hartl E, Levin-Gleba L, Reid B, Tripodis Y, Guenette JP, Kaufmann D, Makris N, Fortier C, Salat DH, Rathi Y, Milberg WP, McGlinchey RE, Shenton ME, Koerte IK. Mild traumatic brain injury impacts associations between limbic system microstructure and post-traumatic stress disorder symptomatology. Neuroimage Clin 2020; 26:102190. [PMID: 32070813 PMCID: PMC7026283 DOI: 10.1016/j.nicl.2020.102190] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2019] [Revised: 01/16/2020] [Accepted: 01/19/2020] [Indexed: 12/30/2022]
Abstract
BACKGROUND Post-traumatic stress disorder (PTSD) is a psychiatric disorder that afflicts many individuals, yet the neuropathological mechanisms that contribute to this disorder remain to be fully determined. Moreover, it is unclear how exposure to mild traumatic brain injury (mTBI), a condition that is often comorbid with PTSD, particularly among military personnel, affects the clinical and neurological presentation of PTSD. To address these issues, the present study explores relationships between PTSD symptom severity and the microstructure of limbic and paralimbic gray matter brain regions, as well as the impact of mTBI comorbidity on these relationships. METHODS Structural and diffusion MRI data were acquired from 102 male veterans who were diagnosed with current PTSD. Diffusion data were analyzed with free-water imaging to quantify average CSF-corrected fractional anisotropy (FA) and mean diffusivity (MD) in 18 limbic and paralimbic gray matter regions. Associations between PTSD symptom severity and regional average dMRI measures were examined with repeated measures linear mixed models. Associations were studied separately in veterans with PTSD only, and in veterans with PTSD and a history of military mTBI. RESULTS Analyses revealed that in the PTSD only cohort, more severe symptoms were associated with higher FA in the right amygdala-hippocampus complex, lower FA in the right cingulate cortex, and lower MD in the left medial orbitofrontal cortex. In the PTSD and mTBI cohort, more severe PTSD symptoms were associated with higher FA bilaterally in the amygdala-hippocampus complex, with higher FA bilaterally in the nucleus accumbens, with lower FA bilaterally in the cingulate cortex, and with higher MD in the right amygdala-hippocampus complex. CONCLUSIONS These findings suggest that the microstructure of limbic and paralimbic brain regions may influence PTSD symptomatology. Further, given the additional associations observed between microstructure and symptom severity in veterans with head trauma, we speculate that mTBI may exacerbate the impact of brain microstructure on PTSD symptoms, especially within regions of the brain known to be vulnerable to chronic stress. A heightened sensitivity to the microstructural environment of the brain could partially explain why individuals with PTSD and mTBI comorbidity experience more severe symptoms and poorer illness prognoses than those without a history of brain injury. The relevance of these microstructural findings to the conceptualization of PTSD as being a disorder of stress-induced neuronal connectivity loss is discussed.
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Affiliation(s)
- Valerie J Sydnor
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Elisabeth Hartl
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Neurology, University Hospital, LMU Munich, Munich, Germany
| | - Laura Levin-Gleba
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA, United States
| | - Benjamin Reid
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Yorghos Tripodis
- Boston University School of Public Health, Boston University, Boston, MA, United States
| | - Jeffrey P Guenette
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - David Kaufmann
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Child and Adolescent Psychiatry, Psychosomatic, and Psychotherapy, Ludwig-Maximilian University, Munich, Germany
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Center for Morphometric Analysis, Departments of Psychiatry and Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Catherine Fortier
- Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA, United States; Department of Psychiatry, Harvard Medical School, Boston, MA, United States
| | - David H Salat
- Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA, United States; Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, MA, United States
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - William P Milberg
- Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA, United States; Department of Psychiatry, Harvard Medical School, Boston, MA, United States; Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA, United States
| | - Regina E McGlinchey
- Translational Research Center for TBI and Stress Disorders (TRACTS), VA Boston Healthcare System, Boston, MA, United States; Department of Psychiatry, Harvard Medical School, Boston, MA, United States; Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA, United States
| | - Martha E Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; VA Boston Healthcare System, Brockton Division, Brockton, MA, United States
| | - Inga K Koerte
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States; Department of Child and Adolescent Psychiatry, Psychosomatic, and Psychotherapy, Ludwig-Maximilian University, Munich, Germany.
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Vasung L, Charvet CJ, Shiohama T, Gagoski B, Levman J, Takahashi E. Ex vivo fetal brain MRI: Recent advances, challenges, and future directions. Neuroimage 2019; 195:23-37. [PMID: 30905833 PMCID: PMC6617515 DOI: 10.1016/j.neuroimage.2019.03.034] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 03/12/2019] [Accepted: 03/16/2019] [Indexed: 12/21/2022] Open
Abstract
During early development, the fetal brain undergoes dynamic morphological changes. These changes result from neurogenic events, such as neuronal proliferation, migration, axonal elongation, retraction, and myelination. The duration and intensity of these events vary across species. Comparative assessments of these neurogenic events give us insight into evolutionary changes and the complexity of human brain development. Recent advances in magnetic resonance imaging (MRI), especially ex vivo MRI, permit characterizing and comparing fetal brain development across species. Comparative ex vivo MRI studies support the detection of species-specific differences that occur during early brain development. In this review, we provide a comprehensive overview of ex vivo MRI studies that characterize early brain development in humans, monkeys, cats, as well as rats/mice. Finally, we discuss the current advantages and limitations of ex vivo fetal brain MRI.
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Affiliation(s)
- Lana Vasung
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 401 Park Dr., Boston, MA, 02215, USA
| | - Christine J Charvet
- Department of Molecular Biology and Genetics, Cornell University, 526 Campus Rd, Ithaca, NY, 14850, USA; Department of Psychology, Delaware State University, Dover, DE, 19901, USA
| | - Tadashi Shiohama
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 401 Park Dr., Boston, MA, 02215, USA; Department of Pediatrics, Chiba University Hospital, Inohana 1-8-1, Chiba-shi, Chiba, 2608670, Japan
| | - Borjan Gagoski
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, 401 Park Dr., Boston, MA, 02215, USA
| | - Jacob Levman
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 401 Park Dr., Boston, MA, 02215, USA; Department of Mathematics, Statistics and Computer Science, St. Francis Xavier University, Antigonish, NS, B2G 2W5, Canada
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, 401 Park Dr., Boston, MA, 02215, USA.
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27
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Differential cortical microstructural maturation in the preterm human brain with diffusion kurtosis and tensor imaging. Proc Natl Acad Sci U S A 2019; 116:4681-4688. [PMID: 30782802 PMCID: PMC6410816 DOI: 10.1073/pnas.1812156116] [Citation(s) in RCA: 56] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Delineating cortical microstructure differentiation is important for understanding complicated yet precisely organized patterns in early developing brain. Knowledge of cortical differentiation predominantly from histological studies is limited in localized and discrete cortical regions. We quantified the preterm brain cerebral cortical profile with microstructural complexity [indexed by mean kurtosis (MK)] and microstructural organization [indexed by fractional anisotropy (FA)] from advanced diffusion MRI. Cortical MK and FA maps revealed a heterogeneous maturation signature. Spatiotemporally distinctive disruption of radial glia and decrease of neuronal density among cortical regions were inferred by FA and MK decreases, respectively. These findings suggest that diffusion kurtosis metrics are significant imaging markers for microstructural differentiation of the developmental brain in health and disease. During the third trimester, the human brain undergoes rapid cellular and molecular processes that reshape the structural architecture of the cerebral cortex. Knowledge of cortical differentiation obtained predominantly from histological studies is limited in localized and small cortical regions. How cortical microstructure is differentiated across cortical regions in this critical period is unknown. In this study, the cortical microstructural architecture across the entire cortex was delineated with non-Gaussian diffusion kurtosis imaging as well as conventional diffusion tensor imaging of 89 preterm neonates aged 31–42 postmenstrual weeks. The temporal changes of cortical mean kurtosis (MK) or fractional anisotropy (FA) were heterogeneous across the cortical regions. Cortical MK decreases were observed throughout the studied age period, while cortical FA decrease reached its plateau around 37 weeks. More rapid decreases in MK were found in the primary visual region, while faster FA declines were observed in the prefrontal cortex. We found that distinctive cortical microstructural changes were coupled with microstructural maturation of associated white matter tracts. Both cortical MK and FA measurements predicted the postmenstrual age of preterm infants accurately. This study revealed a differential 4D spatiotemporal cytoarchitectural signature inferred by non-Gaussian diffusion barriers inside the cortical plate during the third trimester. The cytoarchitectural processes, including dendritic arborization and neuronal density decreases, were inferred by regional cortical FA and MK measurements. The presented findings suggest that cortical MK and FA measurements could be used as effective imaging markers for cortical microstructural changes in typical and potentially atypical brain development.
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28
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Vasung L, Abaci Turk E, Ferradal SL, Sutin J, Stout JN, Ahtam B, Lin PY, Grant PE. Exploring early human brain development with structural and physiological neuroimaging. Neuroimage 2019; 187:226-254. [PMID: 30041061 PMCID: PMC6537870 DOI: 10.1016/j.neuroimage.2018.07.041] [Citation(s) in RCA: 80] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Revised: 07/16/2018] [Accepted: 07/16/2018] [Indexed: 12/11/2022] Open
Abstract
Early brain development, from the embryonic period to infancy, is characterized by rapid structural and functional changes. These changes can be studied using structural and physiological neuroimaging methods. In order to optimally acquire and accurately interpret this data, concepts from adult neuroimaging cannot be directly transferred. Instead, one must have a basic understanding of fetal and neonatal structural and physiological brain development, and the important modulators of this process. Here, we first review the major developmental milestones of transient cerebral structures and structural connectivity (axonal connectivity) followed by a summary of the contributions from ex vivo and in vivo MRI. Next, we discuss the basic biology of neuronal circuitry development (synaptic connectivity, i.e. ensemble of direct chemical and electrical connections between neurons), physiology of neurovascular coupling, baseline metabolic needs of the fetus and the infant, and functional connectivity (defined as statistical dependence of low-frequency spontaneous fluctuations seen with functional magnetic resonance imaging (fMRI)). The complementary roles of magnetic resonance imaging (MRI), electroencephalography (EEG), magnetoencephalography (MEG), and near-infrared spectroscopy (NIRS) are discussed. We include a section on modulators of brain development where we focus on the placenta and emerging placental MRI approaches. In each section we discuss key technical limitations of the imaging modalities and some of the limitations arising due to the biology of the system. Although neuroimaging approaches have contributed significantly to our understanding of early brain development, there is much yet to be done and a dire need for technical innovations and scientific discoveries to realize the future potential of early fetal and infant interventions to avert long term disease.
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Affiliation(s)
- Lana Vasung
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA.
| | - Esra Abaci Turk
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA.
| | - Silvina L Ferradal
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA.
| | - Jason Sutin
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA.
| | - Jeffrey N Stout
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA.
| | - Banu Ahtam
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA.
| | - Pei-Yi Lin
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA.
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA 02115, USA.
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29
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Pietsch M, Christiaens D, Hutter J, Cordero-Grande L, Price AN, Hughes E, Edwards AD, Hajnal JV, Counsell SJ, Tournier JD. A framework for multi-component analysis of diffusion MRI data over the neonatal period. Neuroimage 2019; 186:321-337. [PMID: 30391562 PMCID: PMC6347572 DOI: 10.1016/j.neuroimage.2018.10.060] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 10/17/2018] [Accepted: 10/22/2018] [Indexed: 12/11/2022] Open
Abstract
We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) technique. We decompose the signal into one isotropic component and two anisotropic components, with response functions estimated from cerebrospinal fluid and white matter in the youngest and oldest participant groups, respectively. We build an orientationally-resolved template of those tissue components from data acquired from 113 babies between 33 and 44 weeks postmenstrual age, imaged as part of the Developing Human Connectome Project. These data were split into weekly groups, and registered to the corresponding group average templates using a previously-proposed non-linear diffeomorphic registration framework, designed to align orientation density functions (ODF). This framework was extended to allow the use of the multiple contrasts provided by the multi-tissue decomposition, and shown to provide superior alignment. Finally, the weekly templates were registered to the same common template to facilitate investigations into the evolution of the different components as a function of age. The resulting multi-tissue atlas provides insights into brain development and accompanying changes in microstructure, and forms the basis for future longitudinal investigations into healthy and pathological white matter maturation.
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Affiliation(s)
- Maximilian Pietsch
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK.
| | - Daan Christiaens
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK; Department of Biomedical Engineering, School of Bioengineering and Imaging Sciences, Kings College London, Kings Health Partners, St. Thomas Hospital, London, SE1 7EH, UK
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30
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Feng L, Li H, Oishi K, Mishra V, Song L, Peng Q, Ouyang M, Wang J, Slinger M, Jeon T, Lee L, Heyne R, Chalak L, Peng Y, Liu S, Huang H. Age-specific gray and white matter DTI atlas for human brain at 33, 36 and 39 postmenstrual weeks. Neuroimage 2019; 185:685-698. [PMID: 29959046 PMCID: PMC6289605 DOI: 10.1016/j.neuroimage.2018.06.069] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2017] [Revised: 05/21/2018] [Accepted: 06/25/2018] [Indexed: 01/24/2023] Open
Abstract
During the 3rd trimester, dramatic structural changes take place in the human brain, underlying the neural circuit formation. The survival rate of premature infants has increased significantly in recent years. The large morphological differences of the preterm brain at 33 or 36 postmenstrual weeks (PMW) from the brain at 40PMW (full term) make it necessary to establish age-specific atlases for preterm brains. In this study, with high quality (1.5 × 1.5 × 1.6 mm3 imaging resolution) diffusion tensor imaging (DTI) data obtained from 84 healthy preterm and term-born neonates, we established age-specific preterm and term-born brain templates and atlases at 33, 36 and 39PMW. Age-specific DTI templates include a single-subject template, a population-averaged template with linear transformation and a population-averaged template with nonlinear transformation. Each of the age-specific DTI atlases includes comprehensive labeling of 126 major gray matter (GM) and white matter (WM) structures, specifically 52 cerebral cortical structures, 40 cerebral WM structures, 22 brainstem and cerebellar structures and 12 subcortical GM structures. From 33 to 39 PMW, dramatic morphological changes of delineated individual neural structures such as ganglionic eminence and uncinate fasciculus were revealed. The evaluation based on measurements of Dice ratio and L1 error suggested reliable and reproducible automated labels from the age-matched atlases compared to labels from manual delineation. Applying these atlases to automatically and effectively delineate microstructural changes of major WM tracts during the 3rd trimester was demonstrated. The established age-specific DTI templates and atlases of 33, 36 and 39 PMW brains may be used for not only understanding normal functional and structural maturational processes but also detecting biomarkers of neural disorders in the preterm brains.
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Affiliation(s)
- Lei Feng
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA; Research Center for Sectional and Imaging Anatomy, Shandong University Cheeloo College of Medicine, Shandong, China
| | - Hang Li
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA; Department of Radiology, Beijing Children's Hospital Affiliated to Capital Medical University, National Center for Children's Health, Beijing, China
| | - Kenichi Oishi
- Department of Radiology, Johns Hopkins University, MD, USA
| | - Virendra Mishra
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, TX, USA
| | - Limei Song
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA; Research Center for Sectional and Imaging Anatomy, Shandong University Cheeloo College of Medicine, Shandong, China
| | - Qinmu Peng
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA; Advanced Imaging Research Center, University of Texas Southwestern Medical Center, TX, USA
| | - Jiaojian Wang
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, USA
| | - Michelle Slinger
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA
| | - Tina Jeon
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA
| | - Lizette Lee
- Department of Pediatrics, University of Texas Southwestern Medical Center, TX, USA
| | - Roy Heyne
- Department of Pediatrics, University of Texas Southwestern Medical Center, TX, USA
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, TX, USA
| | - Yun Peng
- Department of Radiology, Beijing Children's Hospital Affiliated to Capital Medical University, National Center for Children's Health, Beijing, China
| | - Shuwei Liu
- Research Center for Sectional and Imaging Anatomy, Shandong University Cheeloo College of Medicine, Shandong, China
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, PA, USA; Advanced Imaging Research Center, University of Texas Southwestern Medical Center, TX, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, USA.
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31
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Kostović I, Sedmak G, Judaš M. Neural histology and neurogenesis of the human fetal and infant brain. Neuroimage 2018; 188:743-773. [PMID: 30594683 DOI: 10.1016/j.neuroimage.2018.12.043] [Citation(s) in RCA: 102] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 12/18/2018] [Accepted: 12/20/2018] [Indexed: 01/11/2023] Open
Abstract
The human brain develops slowly and over a long period of time which lasts for almost three decades. This enables good spatio-temporal resolution of histogenetic and neurogenetic events as well as an appropriate and clinically relevant timing of these events. In order to successfully apply in vivo neuroimaging data, in analyzing both the normal brain development and the neurodevelopmental origin of major neurological and mental disorders, it is important to correlate these neuroimaging data with the existing data on morphogenetic, histogenetic and neurogenetic events. Furthermore, when performing such correlation, the genetic, genomic, and molecular biology data on phenotypic specification of developing brain regions, areas and neurons should also be included. In this review, we focus on early developmental periods (form 8 postconceptional weeks to the second postnatal year) and describe the microstructural organization and neural circuitry elements of the fetal and early postnatal human cerebrum.
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Affiliation(s)
- I Kostović
- University of Zagreb School of Medicine, Croatian Institute for Brain Research, Centre of Excellence for Basic, Clinical and Translational Neuroscience, Šalata 12, 10000, Zagreb, Croatia.
| | - G Sedmak
- University of Zagreb School of Medicine, Croatian Institute for Brain Research, Centre of Excellence for Basic, Clinical and Translational Neuroscience, Šalata 12, 10000, Zagreb, Croatia.
| | - M Judaš
- University of Zagreb School of Medicine, Croatian Institute for Brain Research, Centre of Excellence for Basic, Clinical and Translational Neuroscience, Šalata 12, 10000, Zagreb, Croatia.
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32
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An iterative multi-atlas patch-based approach for cortex segmentation from neonatal MRI. Comput Med Imaging Graph 2018; 70:73-82. [PMID: 30296626 DOI: 10.1016/j.compmedimag.2018.09.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2018] [Revised: 08/10/2018] [Accepted: 09/13/2018] [Indexed: 11/21/2022]
Abstract
Brain structure analysis in the newborn is a major health issue. This is especially the case for preterm neonates, in order to obtain predictive information related to the child development. In particular, the cortex is a structure of interest, that can be observed in magnetic resonance imaging (MRI). However, neonatal MRI data present specific properties that make them challenging to process. In this context, multi-atlas approaches constitute an efficient strategy, taking advantage of images processed beforehand. The method proposed in this article relies on such a multi-atlas strategy. More precisely, it uses two paradigms: first, a non-local model based on patches; second, an iterative optimization scheme. Coupling both concepts allows us to consider patches related not only to the image information, but also to the current segmentation. This strategy is compared to other multi-atlas methods proposed in the literature. Experiments on dHCP datasets show that the proposed approach provides robust cortex segmentation results.
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33
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Pecheva D, Kelly C, Kimpton J, Bonthrone A, Batalle D, Zhang H, Counsell SJ. Recent advances in diffusion neuroimaging: applications in the developing preterm brain. F1000Res 2018; 7. [PMID: 30210783 PMCID: PMC6107996 DOI: 10.12688/f1000research.15073.1] [Citation(s) in RCA: 34] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/17/2018] [Indexed: 12/13/2022] Open
Abstract
Measures obtained from diffusion-weighted imaging provide objective indices of white matter development and injury in the developing preterm brain. To date, diffusion tensor imaging (DTI) has been used widely, highlighting differences in fractional anisotropy (FA) and mean diffusivity (MD) between preterm infants at term and healthy term controls; altered white matter development associated with a number of perinatal risk factors; and correlations between FA values in the white matter in the neonatal period and subsequent neurodevelopmental outcome. Recent developments, including neurite orientation dispersion and density imaging (NODDI) and fixel-based analysis (FBA), enable white matter microstructure to be assessed in detail. Constrained spherical deconvolution (CSD) enables multiple fibre populations in an imaging voxel to be resolved and allows delineation of fibres that traverse regions of fibre-crossings, such as the arcuate fasciculus and cerebellar–cortical pathways. This review summarises DTI findings in the preterm brain and discusses initial findings in this population using CSD, NODDI, and FBA.
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Affiliation(s)
- Diliana Pecheva
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Christopher Kelly
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Jessica Kimpton
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Alexandra Bonthrone
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Hui Zhang
- Department of Computer Science & Centre for Medical Image Computing, University College London, London, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
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Zhao T, Mishra V, Jeon T, Ouyang M, Peng Q, Chalak L, Wisnowski JL, Heyne R, Rollins N, Shu N, Huang H. Structural network maturation of the preterm human brain. Neuroimage 2018; 185:699-710. [PMID: 29913282 DOI: 10.1016/j.neuroimage.2018.06.047] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 06/13/2018] [Accepted: 06/14/2018] [Indexed: 12/16/2022] Open
Abstract
During the 3rd trimester, large-scale neural circuits are formed in the human brain, resulting in a highly efficient and segregated connectome at birth. Despite recent findings identifying important preterm human brain network properties such as rich-club organization, how the structural network develops differentially across brain regions and among different types of connections in this period is not yet known. Here, using high resolution diffusion MRI of 77 preterm-born and full-term neonates scanned at 31.9-41.7 postmenstrual weeks (PMW), we constructed structural connectivity matrices and performed graph-theory-based analyses. Faster increases of nodal efficiency were mainly located at the brain hubs distributed in primary sensorimotor regions, superior-middle frontal, and precuneus regions during 31.9-41.7PMW. Higher rates of edge strength increases were found in the rich-club and within-module connections, compared to other connections. The edge strength of short-range connections increased faster than that of long-range connections. Nodal efficiencies of the hubs predicted individual postmenstrual ages more accurately than those of non-hubs. Collectively, these findings revealed more rapid efficiency increases of the hub and rich-club connections as well as higher developmental rates of edge strength in short-range and within-module connections. These jointly underlie network segregation and differentiated emergence of brain functions.
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Affiliation(s)
- Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China
| | - Virendra Mishra
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390, United States
| | - Tina Jeon
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, United States
| | - Minhui Ouyang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, United States
| | - Qinmu Peng
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, United States
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, United States
| | - Jessica Lee Wisnowski
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, 75390, United States; Department of Radiology, Children's Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, Chile
| | - Roy Heyne
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, 75390, United States
| | - Nancy Rollins
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, 19104, United States
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, 100875, China.
| | - Hao Huang
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, 75390, United States; Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, 19104, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, 19104, United States.
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35
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Bastiani M, Andersson JLR, Cordero-Grande L, Murgasova M, Hutter J, Price AN, Makropoulos A, Fitzgibbon SP, Hughes E, Rueckert D, Victor S, Rutherford M, Edwards AD, Smith SM, Tournier JD, Hajnal JV, Jbabdi S, Sotiropoulos SN. Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project. Neuroimage 2018; 185:750-763. [PMID: 29852283 PMCID: PMC6299258 DOI: 10.1016/j.neuroimage.2018.05.064] [Citation(s) in RCA: 90] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2017] [Revised: 05/25/2018] [Accepted: 05/26/2018] [Indexed: 12/29/2022] Open
Abstract
The developing Human Connectome Project is set to create and make available to the scientific community a 4-dimensional map of functional and structural cerebral connectivity from 20 to 44 weeks post-menstrual age, to allow exploration of the genetic and environmental influences on brain development, and the relation between connectivity and neurocognitive function. A large set of multi-modal MRI data from fetuses and newborn infants is currently being acquired, along with genetic, clinical and developmental information. In this overview, we describe the neonatal diffusion MRI (dMRI) image processing pipeline and the structural connectivity aspect of the project. Neonatal dMRI data poses specific challenges, and standard analysis techniques used for adult data are not directly applicable. We have developed a processing pipeline that deals directly with neonatal-specific issues, such as severe motion and motion-related artefacts, small brain sizes, high brain water content and reduced anisotropy. This pipeline allows automated analysis of in-vivo dMRI data, probes tissue microstructure, reconstructs a number of major white matter tracts, and includes an automated quality control framework that identifies processing issues or inconsistencies. We here describe the pipeline and present an exemplar analysis of data from 140 infants imaged at 38–44 weeks post-menstrual age. A comprehensive and automated pipeline to consistently analyse neonatal dMRI data. Optimised motion and distortions correction to address newborn specific challenges. The automated QC framework allows to detect issues and to quantify data quality. Automated white matter segmentation allows to extract tract-specific masks. Preliminary data analysis of 140 infants imaged at 38–44 weeks post-menstrual age.
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Affiliation(s)
- Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK.
| | - Jesper L R Andersson
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | | | | | - Jana Hutter
- Centre for the Developing Brain, King's College London, UK
| | | | | | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Emer Hughes
- Centre for the Developing Brain, King's College London, UK
| | | | - Suresh Victor
- Centre for the Developing Brain, King's College London, UK
| | | | | | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | | | | | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Stamatios N Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK
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36
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Ouyang M, Dubois J, Yu Q, Mukherjee P, Huang H. Delineation of early brain development from fetuses to infants with diffusion MRI and beyond. Neuroimage 2018; 185:836-850. [PMID: 29655938 DOI: 10.1016/j.neuroimage.2018.04.017] [Citation(s) in RCA: 134] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 04/01/2018] [Accepted: 04/08/2018] [Indexed: 02/08/2023] Open
Abstract
Dynamic macrostructural and microstructural changes take place from the mid-fetal stage to 2 years after birth. Delineating structural changes of the brain during early development provides new insights into the complicated processes of both typical development and the pathological mechanisms underlying various psychiatric and neurological disorders including autism, attention deficit hyperactivity disorder and schizophrenia. Decades of histological studies have identified strong spatial and functional maturation gradients in human brain gray and white matter. The recent improvements in magnetic resonance imaging (MRI) techniques, especially diffusion MRI (dMRI), relaxometry imaging, and magnetization transfer imaging (MTI) have provided unprecedented opportunities to non-invasively quantify and map the early developmental changes at whole brain and regional levels. Here, we review the recent advances in understanding early brain structural development during the second half of gestation and the first two postnatal years using modern MR techniques. Specifically, we review studies that delineate the emergence and microstructural maturation of white matter tracts, as well as dynamic mapping of inhomogeneous cortical microstructural organization unique to fetuses and infants. These imaging studies converge into maturational curves of MRI measurements that are distinctive across different white matter tracts and cortical regions. Furthermore, contemporary models offering biophysical interpretations of the dMRI-derived measurements are illustrated to infer the underlying microstructural changes. Collectively, this review summarizes findings that contribute to charting spatiotemporally heterogeneous gray and white matter structural development, offering MRI-based biomarkers of typical brain development and setting the stage for understanding aberrant brain development in neurodevelopmental disorders.
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Affiliation(s)
- Minhui Ouyang
- Radiology Research, Children's Hospital of Philadelphia, PA, United States
| | - Jessica Dubois
- INSERM, UMR992, CEA, NeuroSpin Center, University Paris Saclay, Gif-sur-Yvette, France
| | - Qinlin Yu
- Radiology Research, Children's Hospital of Philadelphia, PA, United States
| | - Pratik Mukherjee
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA, United States
| | - Hao Huang
- Radiology Research, Children's Hospital of Philadelphia, PA, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, United States.
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37
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Oishi K, Chang L, Huang H. Baby brain atlases. Neuroimage 2018; 185:865-880. [PMID: 29625234 DOI: 10.1016/j.neuroimage.2018.04.003] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Revised: 02/27/2018] [Accepted: 04/02/2018] [Indexed: 01/23/2023] Open
Abstract
The baby brain is constantly changing due to its active neurodevelopment, and research into the baby brain is one of the frontiers in neuroscience. To help guide neuroscientists and clinicians in their investigation of this frontier, maps of the baby brain, which contain a priori knowledge about neurodevelopment and anatomy, are essential. "Brain atlas" in this review refers to a 3D-brain image with a set of reference labels, such as a parcellation map, as the anatomical reference that guides the mapping of the brain. Recent advancements in scanners, sequences, and motion control methodologies enable the creation of various types of high-resolution baby brain atlases. What is becoming clear is that one atlas is not sufficient to characterize the existing knowledge about the anatomical variations, disease-related anatomical alterations, and the variations in time-dependent changes. In this review, the types and roles of the human baby brain MRI atlases that are currently available are described and discussed, and future directions in the field of developmental neuroscience and its clinical applications are proposed. The potential use of disease-based atlases to characterize clinically relevant information, such as clinical labels, in addition to conventional anatomical labels, is also discussed.
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Affiliation(s)
- Kenichi Oishi
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Linda Chang
- Departments of Diagnostic Radiology and Nuclear Medicine, and Neurology, University of Maryland School of Medicine, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Medicine, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Hao Huang
- Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
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38
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Bouyssi-Kobar M, Brossard-Racine M, Jacobs M, Murnick J, Chang T, Limperopoulos C. Regional microstructural organization of the cerebral cortex is affected by preterm birth. Neuroimage Clin 2018; 18:871-880. [PMID: 29876271 PMCID: PMC5988027 DOI: 10.1016/j.nicl.2018.03.020] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Revised: 03/09/2018] [Accepted: 03/15/2018] [Indexed: 10/31/2022]
Abstract
Objectives To compare regional cerebral cortical microstructural organization between preterm infants at term-equivalent age (TEA) and healthy full-term newborns, and to examine the impact of clinical risk factors on cerebral cortical micro-organization in the preterm cohort. Study design We prospectively enrolled very preterm infants (gestational age (GA) at birth<32 weeks; birthweight<1500 g) and healthy full-term controls. Using non-invasive 3T diffusion tensor imaging (DTI) metrics, we quantified regional micro-organization in ten cerebral cortical areas: medial/dorsolateral prefrontal cortex, anterior/posterior cingulate cortex, insula, posterior parietal cortex, motor/somatosensory/auditory/visual cortex. ANCOVA analyses were performed controlling for sex and postmenstrual age at MRI. Results We studied 91 preterm infants at TEA and 69 full-term controls. Preterm infants demonstrated significantly higher diffusivity in the prefrontal, parietal, motor, somatosensory, and visual cortices suggesting delayed maturation of these cortical areas. Additionally, postnatal hydrocortisone treatment was related to accelerated microstructural organization in the prefrontal and somatosensory cortices. Conclusions Preterm birth alters regional microstructural organization of the cerebral cortex in both neurocognitive brain regions and areas with primary sensory/motor functions. We also report for the first time a potential protective effect of postnatal hydrocortisone administration on cerebral cortical development in preterm infants.
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Affiliation(s)
- Marine Bouyssi-Kobar
- The Developing Brain Research Laboratory, Department of Diagnostic Imaging and Radiology, Children's National Health System, Washington, DC 20010, USA; Institute for Biomedical Sciences, George Washington University, Washington, DC 20037, USA.
| | - Marie Brossard-Racine
- Department of Pediatrics Neurology, McGill University Health Center, Montreal, QC H4A3J1, Canada.
| | - Marni Jacobs
- Division of Biostatistics and Study Methodology, Children's Research Institute, Children's National Health System, Washington, DC 20010, USA.
| | - Jonathan Murnick
- The Developing Brain Research Laboratory, Department of Diagnostic Imaging and Radiology, Children's National Health System, Washington, DC 20010, USA.
| | - Taeun Chang
- Department of Neurology, Children's National Health System, Washington, DC 20010, USA.
| | - Catherine Limperopoulos
- The Developing Brain Research Laboratory, Department of Diagnostic Imaging and Radiology, Children's National Health System, Washington, DC 20010, USA.
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39
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Song L, Mishra V, Ouyang M, Peng Q, Slinger M, Liu S, Huang H. Human Fetal Brain Connectome: Structural Network Development from Middle Fetal Stage to Birth. Front Neurosci 2017; 11:561. [PMID: 29081731 PMCID: PMC5645529 DOI: 10.3389/fnins.2017.00561] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2017] [Accepted: 09/25/2017] [Indexed: 12/25/2022] Open
Abstract
Complicated molecular and cellular processes take place in a spatiotemporally heterogeneous and precisely regulated pattern in the human fetal brain, yielding not only dramatic morphological and microstructural changes, but also macroscale connectomic transitions. As the underlying substrate of the fetal brain structural network, both dynamic neuronal migration pathways and rapid developing fetal white matter (WM) fibers could fundamentally reshape early fetal brain connectome. Quantifying structural connectome development can not only shed light on the brain reconfiguration in this critical yet rarely studied developmental period, but also reveal alterations of the connectome under neuropathological conditions. However, transition of the structural connectome from the mid-fetal stage to birth is not yet known. The contribution of different types of neural fibers to the structural network in the mid-fetal brain is not known, either. In this study, diffusion tensor magnetic resonance imaging (DT-MRI or DTI) of 10 fetal brain specimens at the age of 20 postmenstrual weeks (PMW), 12 in vivo brains at 35 PMW, and 12 in vivo brains at term (40 PMW) were acquired. The structural connectome of each brain was established with evenly parcellated cortical regions as network nodes and traced fiber pathways based on DTI tractography as network edges. Two groups of fibers were categorized based on the fiber terminal locations in the cerebral wall in the 20 PMW fetal brains. We found that fetal brain networks become stronger and more efficient during 20–40 PMW. Furthermore, network strength and global efficiency increase more rapidly during 20–35 PMW than during 35–40 PMW. Visualization of the whole brain fiber distribution by the lengths suggested that the network reconfiguration in this developmental period could be associated with a significant increase of major long association WM fibers. In addition, non-WM neural fibers could be a major contributor to the structural network configuration at 20 PMW and small-world network organization could exist as early as 20 PMW. These findings offer a preliminary record of the fetal brain structural connectome maturation from the middle fetal stage to birth and reveal the critical role of non-WM neural fibers in structural network configuration in the middle fetal stage.
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Affiliation(s)
- Limei Song
- Shandong Provincial Key Laboratory of Mental Disorders, Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Jinan, China.,Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Virendra Mishra
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
| | - Minhui Ouyang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Qinmu Peng
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Michelle Slinger
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Shuwei Liu
- Shandong Provincial Key Laboratory of Mental Disorders, Research Center for Sectional and Imaging Anatomy, Shandong University School of Medicine, Jinan, China
| | - Hao Huang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, United States.,Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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40
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Ouyang M, Kang H, Detre JA, Roberts TPL, Huang H. Short-range connections in the developmental connectome during typical and atypical brain maturation. Neurosci Biobehav Rev 2017; 83:109-122. [PMID: 29024679 DOI: 10.1016/j.neubiorev.2017.10.007] [Citation(s) in RCA: 61] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2017] [Revised: 09/09/2017] [Accepted: 10/06/2017] [Indexed: 01/10/2023]
Abstract
The human brain is remarkably complex with connectivity constituting its basic organizing principle. Although long-range connectivity has been focused on in most research, short-range connectivity is characterized by unique and spatiotemporally heterogeneous dynamics from infancy to adulthood. Alterations in the maturational dynamics of short-range connectivity has been associated with neuropsychiatric disorders, such as autism and schizophrenia. Recent advances in neuroimaging techniques, especially diffusion magnetic resonance imaging (dMRI), resting-state functional MRI (rs-fMRI), electroencephalography (EEG) and magnetoencephalography (MEG), have made quantification of short-range connectivity possible in pediatric populations. This review summarizes findings on the development of short-range functional and structural connections at the macroscale. These findings suggest an inverted U-shaped pattern of maturation from primary to higher-order brain regions, and possible "hyper-" and "hypo-" short-range connections in autism and schizophrenia, respectively. The precisely balanced short- and long-range connections contribute to the integration and segregation of the connectome during development. The mechanistic relationship among short-range connectivity maturation, the developmental connectome and emerging brain functions needs further investigation, including the refinement of methodological approaches.
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Affiliation(s)
- Minhui Ouyang
- Radiology Research, Children's Hospital of Philadelphia, PA, United States
| | - Huiying Kang
- Radiology Research, Children's Hospital of Philadelphia, PA, United States; Department of Radiology, Beijing Children's Hospital, Capital Medical University, Beijing, China
| | - John A Detre
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, PA, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, United States
| | - Timothy P L Roberts
- Radiology Research, Children's Hospital of Philadelphia, PA, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, United States
| | - Hao Huang
- Radiology Research, Children's Hospital of Philadelphia, PA, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, United States.
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41
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Cao M, Huang H, He Y. Developmental Connectomics from Infancy through Early Childhood. Trends Neurosci 2017; 40:494-506. [PMID: 28684174 PMCID: PMC5975640 DOI: 10.1016/j.tins.2017.06.003] [Citation(s) in RCA: 125] [Impact Index Per Article: 17.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2017] [Revised: 06/05/2017] [Accepted: 06/07/2017] [Indexed: 12/14/2022]
Abstract
The human brain undergoes rapid growth in both structure and function from infancy through early childhood, and this significantly influences cognitive and behavioral development in later life. A newly emerging research framework, developmental connectomics, provides unprecedented opportunities for exploring the developing brain through non-invasive mapping of structural and functional connectivity patterns. Within this framework, we review recent neuroimaging and neurophysiological studies investigating connectome development from 20 postmenstrual weeks to 5 years of age. Specifically, we highlight five fundamental principles of brain network development during the critical first years of life, emphasizing strengthened segregation/integration balance, a remarkable hierarchical order from primary to higher-order regions, unparalleled structural and functional maturations, substantial individual variability, and high vulnerability to risk factors and developmental disorders.
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Affiliation(s)
- Miao Cao
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA; Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China.
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42
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Cao M, He Y, Dai Z, Liao X, Jeon T, Ouyang M, Chalak L, Bi Y, Rollins N, Dong Q, Huang H. Early Development of Functional Network Segregation Revealed by Connectomic Analysis of the Preterm Human Brain. Cereb Cortex 2017; 27:1949-1963. [PMID: 26941380 DOI: 10.1093/cercor/bhw038] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Human brain functional networks are topologically organized with nontrivial connectivity characteristics such as small-worldness and densely linked hubs to support highly segregated and integrated information processing. However, how they emerge and change at very early developmental phases remains poorly understood. Here, we used resting-state functional MRI and voxel-based graph theory analysis to systematically investigate the topological organization of whole-brain networks in 40 infants aged around 31 to 42 postmenstrual weeks. The functional connectivity strength and heterogeneity increased significantly in primary motor, somatosensory, visual, and auditory regions, but much less in high-order default-mode and executive-control regions. The hub and rich-club structures in primary regions were already present at around 31 postmenstrual weeks and exhibited remarkable expansions with age, accompanied by increased local clustering and shortest path length, indicating a transition from a relatively random to a more organized configuration. Moreover, multivariate pattern analysis using support vector regression revealed that individual brain maturity of preterm babies could be predicted by the network connectivity patterns. Collectively, we highlighted a gradually enhanced functional network segregation manner in the third trimester, which is primarily driven by the rapid increases of functional connectivity of the primary regions, providing crucial insights into the topological development patterns prior to birth.
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Affiliation(s)
- Miao Cao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zhengjia Dai
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Xuhong Liao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Tina Jeon
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Minhui Ouyang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | | | - Yanchao Bi
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Nancy Rollins
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Qi Dong
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Hao Huang
- Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.,Department of Radiology, University of Pennsylvania, Philadelphia, PA 19104, USA
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43
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Folding, But Not Surface Area Expansion, Is Associated with Cellular Morphological Maturation in the Fetal Cerebral Cortex. J Neurosci 2017; 37:1971-1983. [PMID: 28069920 DOI: 10.1523/jneurosci.3157-16.2017] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Revised: 12/22/2016] [Accepted: 01/04/2017] [Indexed: 01/26/2023] Open
Abstract
Altered macroscopic anatomical characteristics of the cerebral cortex have been identified in individuals affected by various neurodevelopmental disorders. However, the cellular developmental mechanisms that give rise to these abnormalities are not understood. Previously, advances in image reconstruction of diffusion magnetic resonance imaging (MRI) have made possible high-resolution in utero measurements of water diffusion anisotropy in the fetal brain. Here, diffusion anisotropy within the developing fetal cerebral cortex is longitudinally characterized in the rhesus macaque, focusing on gestation day (G85) through G135 of the 165 d term. Additionally, for subsets of animals characterized at G90 and G135, immunohistochemical staining was performed, and 3D structure tensor analyses were used to identify the cellular processes that most closely parallel changes in water diffusion anisotropy with cerebral cortical maturation. Strong correlations were found between maturation of dendritic arbors on the cellular level and the loss of diffusion anisotropy with cortical development. In turn, diffusion anisotropy changes were strongly associated both regionally and temporally with cortical folding. Notably, the regional and temporal dependence of diffusion anisotropy and folding were distinct from the patterns observed for cerebral cortical surface area expansion. These findings strengthen the link proposed in previous studies between cellular-level changes in dendrite morphology and noninvasive diffusion MRI measurements of the developing cerebral cortex and support the possibility that, in gyroencephalic species, structural differentiation within the cortex is coupled to the formation of gyri and sulci.SIGNIFICANCE STATEMENT Abnormal brain morphology has been found in populations with neurodevelopmental disorders. However, the mechanisms linking cellular level and macroscopic maturation are poorly understood, even in normal brains. This study contributes new understanding to this subject using serial in utero MRI measurements of rhesus macaque fetuses, from which macroscopic and cellular information can be derived. We found that morphological differentiation of dendrites was strongly associated both regionally and temporally with folding of the cerebral cortex. Interestingly, parallel associations were not observed with cortical surface area expansion. These findings support the possibility that perturbed morphological differentiation of cells within the cortex may underlie abnormal macroscopic characteristics of individuals affected by neurodevelopmental disorders.
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44
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Schore AN. ALL OUR SONS: THE DEVELOPMENTAL NEUROBIOLOGY AND NEUROENDOCRINOLOGY OF BOYS AT RISK. Infant Ment Health J 2017; 38:15-52. [PMID: 28042663 DOI: 10.1002/imhj.21616] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Why are boys at risk? To address this question, I use the perspective of regulation theory to offer a model of the deeper psychoneurobiological mechanisms that underlie the vulnerability of the developing male. The central thesis of this work dictates that significant gender differences are seen between male and female social and emotional functions in the earliest stages of development, and that these result from not only differences in sex hormones and social experiences but also in rates of male and female brain maturation, specifically in the early developing right brain. I present interdisciplinary research which indicates that the stress-regulating circuits of the male brain mature more slowly than those of the female in the prenatal, perinatal, and postnatal critical periods, and that this differential structural maturation is reflected in normal gender differences in right-brain attachment functions. Due to this maturational delay, developing males also are more vulnerable over a longer period of time to stressors in the social environment (attachment trauma) and toxins in the physical environment (endocrine disruptors) that negatively impact right-brain development. In terms of differences in gender-related psychopathology, I describe the early developmental neuroendocrinological and neurobiological mechanisms that are involved in the increased vulnerability of males to autism, early onset schizophrenia, attention deficit hyperactivity disorder, and conduct disorders as well as the epigenetic mechanisms that can account for the recent widespread increase of these disorders in U.S. culture. I also offer a clinical formulation of early assessments of boys at risk, discuss the impact of early childcare on male psychopathogenesis, and end with a neurobiological model of optimal adult male socioemotional functions.
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Ouyang M, Liu P, Jeon T, Chalak L, Heyne R, Rollins NK, Licht DJ, Detre JA, Roberts TPL, Lu H, Huang H. Heterogeneous increases of regional cerebral blood flow during preterm brain development: Preliminary assessment with pseudo-continuous arterial spin labeled perfusion MRI. Neuroimage 2016; 147:233-242. [PMID: 27988320 DOI: 10.1016/j.neuroimage.2016.12.034] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2016] [Revised: 12/10/2016] [Accepted: 12/13/2016] [Indexed: 01/11/2023] Open
Abstract
The human brain develops rapidly during 32-45 postmenstrual weeks (PMW), a critical stage characterized by dramatic increases of metabolic demand. The increasing metabolic demand can be inferred through measurements of regional cerebral blood flow (CBF), which might be coupled to regional metabolism in preterm brains. Arterial spin labeled (ASL) perfusion MRI is one of the few viable approaches for imaging regional CBF of preterm brains, but must be optimized for the extremely slow blood velocity unique in preterm brains. In this study, we explored the spatiotemporal CBF distribution in newborns scanned at the age of 32-45PMW using a pseudo-continuous ASL (pCASL) protocol adapted to slow blood flow in neonates. A total of 89 neonates were recruited. PCASL MRI was acquired from 34 normal newborns and phase contrast (PC) images from 19 newborns. Diffusion tensor images (DTI) were acquired from all 89 neonates for measuring cortical fractional anisotropy (FA), which characterizes cortical microstructure. Reproducible CBF measurements were obtained with the adjusted pCASL sequence. Global CBF measurement based on PC MRI was found to double its value in the 3rd trimester. Regional CBF increases were heterogeneous across the brain with a significantly higher rate of CBF increase in the frontal lobe and a lower rate of CBF increase in the occipital lobe. A significant correlation was found between frontal cortical CBF and cortical FA measurements (p<0.01). Increasing CBF values observed in the frontal lobe corresponded to lower FA values, suggesting that dendritic arborization and synaptic formation might be associated with an elevated local CBF. These results offer a preliminary account of heterogeneous regional CBF increases in a vital early developmental period and may shed the light on underlying metabolic support for cortical microstructural changes during the developmental period of 32-45PMW. Preterm effects and limitations of pCASL techniques in newborns need to be carefully considered for interpretation these results.
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Affiliation(s)
- Minhui Ouyang
- Radiology Research, Children's Hospital of Philadelphia, PA, United States; Advanced Imaging Research Center, University of Texas Southwestern Medical Center, TX, United States; Biomedical Engineering Joint Graduate Program, University of Texas at Arlington-University of Texas Southwestern Medical Center, TX, United States
| | - Peiying Liu
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, TX, United States; Department of Radiology, School of Medicine, Johns Hopkins University, MD, United States
| | - Tina Jeon
- Radiology Research, Children's Hospital of Philadelphia, PA, United States; Advanced Imaging Research Center, University of Texas Southwestern Medical Center, TX, United States; Biomedical Engineering Joint Graduate Program, University of Texas at Arlington-University of Texas Southwestern Medical Center, TX, United States
| | - Lina Chalak
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Roy Heyne
- Department of Pediatrics, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Nancy K Rollins
- Departemnt of Radiology, Children's Medical Center, Dallas, TX, United States
| | - Daniel J Licht
- Division of Neurology, Children's Hospital of Philadelphia, PA, United States
| | - John A Detre
- Department of Neurology, University of Pennsylvania, PA, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, United States
| | - Timothy P L Roberts
- Radiology Research, Children's Hospital of Philadelphia, PA, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, United States
| | - Hanzhang Lu
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, TX, United States; Department of Radiology, School of Medicine, Johns Hopkins University, MD, United States
| | - Hao Huang
- Radiology Research, Children's Hospital of Philadelphia, PA, United States; Advanced Imaging Research Center, University of Texas Southwestern Medical Center, TX, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, United States.
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