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Li Q, Xia M, Zeng D, Xu Y, Sun L, Liang X, Xu Z, Zhao T, Liao X, Yuan H, Liu Y, Huo R, Li S, He Y. Development of segregation and integration of functional connectomes during the first 1,000 days. Cell Rep 2024; 43:114168. [PMID: 38700981 DOI: 10.1016/j.celrep.2024.114168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 02/26/2024] [Accepted: 04/15/2024] [Indexed: 05/05/2024] Open
Abstract
The first 1,000 days of human life lay the foundation for brain development and later cognitive growth. However, the developmental rules of the functional connectome during this critical period remain unclear. Using high-resolution, longitudinal, task-free functional magnetic resonance imaging data from 930 scans of 665 infants aged 28 postmenstrual weeks to 3 years, we report the early maturational process of connectome segregation and integration. We show the dominant development of local connections alongside a few global connections, the shift of brain hubs from primary regions to high-order association cortices, the developmental divergence of network segregation and integration along the anterior-posterior axis, the prediction of neurocognitive outcomes, and their associations with gene expression signatures of microstructural development and neuronal metabolic pathways. These findings advance our understanding of the principles of connectome remodeling during early life and its neurobiological underpinnings and have implications for studying typical and atypical development.
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Affiliation(s)
- Qiongling Li
- 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
| | - Debin Zeng
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing 100083, 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
| | - Lianglong Sun
- 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
| | - Xinyuan Liang
- 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
| | - Zhilei 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
| | - Tengda 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
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing 100875, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Ying Liu
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Ran Huo
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China.
| | - 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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2
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Ma D, Badve C, Sun JEP, Hu S, Wang X, Chen Y, Nayate A, Wien M, Martin D, Singer LT, Durieux JC, Flask C, Costello DW. Motion Robust MR Fingerprinting Scan to Image Neonates With Prenatal Opioid Exposure. J Magn Reson Imaging 2024; 59:1758-1768. [PMID: 37515516 PMCID: PMC10823040 DOI: 10.1002/jmri.28907] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 07/04/2023] [Accepted: 07/05/2023] [Indexed: 07/31/2023] Open
Abstract
PURPOSE To explore whether MR fingerprinting (MRF) scans provide motion-robust and quantitative brain tissue measurements for non-sedated infants with prenatal opioid exposure (POE). STUDY TYPE Prospective. POPULATION 13 infants with POE (3 male; 12 newborns (age 7-65 days) and 1 infant aged 9-months). FIELD STRENGTH/SEQUENCE 3T, 3D T1-weighted MPRAGE, 3D T2-weighted TSE and MRF sequences. ASSESSMENT The image quality of MRF and MRI was assessed in a fully crossed, multiple-reader, multiple-case study. Sixteen image quality features in three types-image artifacts, structure and myelination visualization-were ranked by four neuroradiologists (8, 7, 5, and 8 years of experience respectively), using a 3-point scale. MRF T1 and T2 values in 8 white matter brain regions were compared between babies younger than 1 month and babies between 1 and 2 months. STATISTICAL TESTS Generalized estimating equations model to test the significance of differences of regional T1 and T2 values of babies under 1 month and those older. MRI and MRF image quality was assessed using Gwet's second order auto-correlation coefficient (AC2) with confidence levels. The Cochran-Mantel-Haenszel test was used to assess the difference in proportions between MRF and MRI for all features and stratified by the type of features. A P value <0.05 was considered statistically significant. RESULTS The MRF of two infants were excluded in T1 and T2 value analysis due to severe motion artifact but were included in the image quality assessment. In infants under 1 month of age (N = 6), the T1 and T2 values were significantly higher compared to those between 1 and 2 months of age (N = 4). MRF images showed significantly higher image quality ratings in all three feature types compared to MRI images. CONCLUSIONS MR Fingerprinting scans have potential to be a motion-robust and efficient method for nonsedated infants. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Dan Ma
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Chaitra Badve
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Jessie EP Sun
- Radiology, Case Western Reserve University, Cleveland, OH
| | - Siyuan Hu
- Biomedical Engineering, Case Western Reserve University, Cleveland, OH
| | - Xiaofeng Wang
- Quantitative Health Science, Cleveland Clinic, Cleveland, OH
| | - Yong Chen
- Radiology, Case Western Reserve University, Cleveland, OH
| | - Ameya Nayate
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Michael Wien
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Douglas Martin
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Lynn T Singer
- Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland
| | - Jared C. Durieux
- Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Chris Flask
- Radiology, Case Western Reserve University, Cleveland, OH
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Geng X, Chan PH, Lam HS, Chu WC, Wong PC. Brain templates for Chinese babies from newborn to three months of age. Neuroimage 2024; 289:120536. [PMID: 38346529 DOI: 10.1016/j.neuroimage.2024.120536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Revised: 01/20/2024] [Accepted: 02/08/2024] [Indexed: 02/22/2024] Open
Abstract
The infant brain develops rapidly and this area of research has great clinical implications. Neurodevelopmental disorders such as autism and developmental delay have their origins, potentially, in abnormal early brain maturation. Searching for potential early neural markers requires a priori knowledge about infant brain development and anatomy. One of the most common methods of characterizing brain features requires normalization of individual images into a standard stereotactic space and conduct of group-based analyses in this space. A population representative brain template is critical for these population-based studies. Little research is available on constructing brain templates for typical developing Chinese infants. In the present work, a total of 120 babies from 5 to 89 days of age were included with high resolution structural magnetic resonance imaging scans. T1-weighted and T2-weighted templates were constructed using an unbiased registration approach for babies from newborn to 3 months of age. Age-specific templates were also estimated for babies aged at 0, 1, 2 and 3 months old. Then we conducted a series of evaluations and statistical analyses over whole tissue segmentations and brain parcellations. Compared to the use of population mismatched templates, using our established templates resulted in lower deformation energy to transform individual images into the template space and produced a smaller registration error, i.e., smaller standard deviation of the registered images. Significant volumetric growth was observed across total brain tissues and most of the brain regions within the first three months of age. The total brain tissues exhibited larger volumes in baby boys compared to baby girls. To the best of our knowledge, this is the first study focusing on the construction of Chinese infant brain templates. These templates can be used for investigating birth related conditions such as preterm birth, detecting neural biomarkers for neurological and neurodevelopmental disorders in Chinese populations, and exploring genetic and cultural effects on the brain.
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Affiliation(s)
- Xiujuan Geng
- Brain and Mind Institute The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China; Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China
| | - Peggy Hy Chan
- Department of Paediatrics, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China
| | - Hugh Simon Lam
- Department of Paediatrics, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China
| | - Winnie Cw Chu
- Department of Imaging and Interventional Radiology, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China.
| | - Patrick Cm Wong
- Brain and Mind Institute The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China; Department of Linguistics and Modern Languages, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China.
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Muñoz JS, Giles ME, Vaughn KA, Wang Y, Landry SH, Bick JR, DeMaster DM. Parenting Influences on Frontal Lobe Gray Matter and Preterm Toddlers' Problem-Solving Skills. CHILDREN (BASEL, SWITZERLAND) 2024; 11:206. [PMID: 38397318 PMCID: PMC10887128 DOI: 10.3390/children11020206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 01/28/2024] [Accepted: 01/29/2024] [Indexed: 02/25/2024]
Abstract
Children born preterm often face challenges with self-regulation during toddlerhood. This study examined the relationship between prematurity, supportive parent behaviors, frontal lobe gray matter volume (GMV), and emotion regulation (ER) among toddlers during a parent-assisted, increasingly complex problem-solving task, validated for this age range. Data were collected from preterm toddlers (n = 57) ages 15-30 months corrected for prematurity and their primary caregivers. MRI data were collected during toddlers' natural sleep. The sample contained three gestational groups: 22-27 weeks (extremely preterm; EPT), 28-33 weeks (very preterm; VPT), and 34-36 weeks (late preterm; LPT). Older toddlers became more compliant as the Tool Task increased in difficulty, but this pattern varied by gestational group. Engagement was highest for LPT toddlers, for older toddlers, and for the easiest task condition. Parents did not differentiate their support depending on task difficulty or their child's age or gestational group. Older children had greater frontal lobe GMV, and for EPT toddlers only, more parent support was related to larger right frontal lobe GMV. We found that parent support had the greatest impact on high birth risk (≤27 gestational weeks) toddler brain development, thus early parent interventions may normalize preterm child neurodevelopment and have lasting impacts.
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Affiliation(s)
- Josselyn S. Muñoz
- Department of Cognitive Sciences, Rice University, Houston, TX 77005, USA;
| | - Megan E. Giles
- Children’s Learning Institute, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (M.E.G.); (K.A.V.); (Y.W.); (S.H.L.)
| | - Kelly A. Vaughn
- Children’s Learning Institute, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (M.E.G.); (K.A.V.); (Y.W.); (S.H.L.)
| | - Ying Wang
- Children’s Learning Institute, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (M.E.G.); (K.A.V.); (Y.W.); (S.H.L.)
| | - Susan H. Landry
- Children’s Learning Institute, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (M.E.G.); (K.A.V.); (Y.W.); (S.H.L.)
| | - Johanna R. Bick
- Psychology Department, University of Houston, Houston, TX 77204, USA;
| | - Dana M. DeMaster
- Children’s Learning Institute, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; (M.E.G.); (K.A.V.); (Y.W.); (S.H.L.)
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Rasmussen JM, Wang Y, Graham AM, Fair DA, Posner J, O'Connor TG, Simhan HN, Yen E, Madan N, Entringer S, Wadhwa PD, Buss C. Segmenting hypothalamic subunits in human newborn magnetic resonance imaging data. Hum Brain Mapp 2024; 45:e26582. [PMID: 38339904 PMCID: PMC10826633 DOI: 10.1002/hbm.26582] [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: 05/22/2023] [Revised: 11/15/2023] [Accepted: 11/26/2023] [Indexed: 02/12/2024] Open
Abstract
Preclinical evidence suggests that inter-individual variation in the structure of the hypothalamus at birth is associated with variation in the intrauterine environment, with downstream implications for future disease susceptibility. However, scientific advancement in humans is limited by a lack of validated methods for the automatic segmentation of the newborn hypothalamus. N = 215 healthy full-term infants with paired T1-/T2-weighted MR images across four sites were considered for primary analyses (mean postmenstrual age = 44.3 ± 3.5 weeks, nmale /nfemale = 110/106). The outputs of FreeSurfer's hypothalamic subunit segmentation tools designed for adults (segFS) were compared against those of a novel registration-based pipeline developed here (segATLAS) and against manually edited segmentations (segMAN) as reference. Comparisons were made using Dice Similarity Coefficients (DSCs) and through expected associations with postmenstrual age at scan. In addition, we aimed to demonstrate the validity of the segATLAS pipeline by testing for the stability of inter-individual variation in hypothalamic volume across the first year of life (n = 41 longitudinal datasets available). SegFS and segATLAS segmentations demonstrated a wide spread in agreement (mean DSC = 0.65 ± 0.14 SD; range = {0.03-0.80}). SegATLAS volumes were more highly correlated with postmenstrual age at scan than segFS volumes (n = 215 infants; RsegATLAS 2 = 65% vs. RsegFS 2 = 40%), and segATLAS volumes demonstrated a higher degree of agreement with segMAN reference segmentations at the whole hypothalamus (segATLAS DSC = 0.89 ± 0.06 SD; segFS DSC = 0.68 ± 0.14 SD) and subunit levels (segATLAS DSC = 0.80 ± 0.16 SD; segFS DSC = 0.40 ± 0.26 SD). In addition, segATLAS (but not segFS) volumes demonstrated stability from near birth to ~1 years age (n = 41; R2 = 25%; p < 10-3 ). These findings highlight segATLAS as a valid and publicly available (https://github.com/jerodras/neonate_hypothalamus_seg) pipeline for the segmentation of hypothalamic subunits using human newborn MRI up to 3 months of age collected at resolutions on the order of 1 mm isotropic. Because the hypothalamus is traditionally understudied due to a lack of high-quality segmentation tools during the early life period, and because the hypothalamus is of high biological relevance to human growth and development, this tool may stimulate developmental and clinical research by providing new insight into the unique role of the hypothalamus and its subunits in shaping trajectories of early life health and disease.
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Affiliation(s)
- Jerod M. Rasmussen
- Development, Health and Disease Research ProgramUniversity of CaliforniaIrvineCaliforniaUSA
- Department of PediatricsUniversity of CaliforniaIrvineCaliforniaUSA
| | - Yun Wang
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
- New York State Psychiatric InstituteNew YorkNew YorkUSA
| | - Alice M. Graham
- Department of Behavioral NeuroscienceOregon Health & Science UniversityPortlandOregonUSA
| | - Damien A. Fair
- Masonic Institute for the Developing BrainUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Jonathan Posner
- Department of Psychiatry and Behavioral SciencesDuke UniversityDurhamNorth CarolinaUSA
- New York State Psychiatric InstituteNew YorkNew YorkUSA
| | - Thomas G. O'Connor
- Departments of Psychiatry, Psychology, Neuroscience and Obstetrics and GynecologyUniversity of Rochester Medical CenterRochesterNew YorkUSA
| | - Hyagriv N. Simhan
- Department of Obstetrics and GynecologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | - Elizabeth Yen
- Department of PediatricsTufts Medical CenterBostonMassachusettsUSA
| | - Neel Madan
- Department of RadiologyTufts Medical CenterBostonMassachusettsUSA
| | - Sonja Entringer
- Development, Health and Disease Research ProgramUniversity of CaliforniaIrvineCaliforniaUSA
- Department of PediatricsUniversity of CaliforniaIrvineCaliforniaUSA
- Department of Medical PsychologyCharité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
| | - Pathik D. Wadhwa
- Development, Health and Disease Research ProgramUniversity of CaliforniaIrvineCaliforniaUSA
- Department of PediatricsUniversity of CaliforniaIrvineCaliforniaUSA
- Department of Psychiatry and Human BehaviorUniversity of CaliforniaIrvineCaliforniaUSA
- Department of Obstetrics and GynecologyUniversity of CaliforniaIrvineCaliforniaUSA
- Department of EpidemiologyUniversity of CaliforniaIrvineCaliforniaUSA
| | - Claudia Buss
- Development, Health and Disease Research ProgramUniversity of CaliforniaIrvineCaliforniaUSA
- Department of PediatricsUniversity of CaliforniaIrvineCaliforniaUSA
- Department of Medical PsychologyCharité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt‐Universität zu BerlinBerlinGermany
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Chen L, Wang Y, Wu Z, Shan Y, Li T, Hung SC, Xing L, Zhu H, Wang L, Lin W, Li G. Four-dimensional mapping of dynamic longitudinal brain subcortical development and early learning functions in infants. Nat Commun 2023; 14:3727. [PMID: 37349301 PMCID: PMC10287661 DOI: 10.1038/s41467-023-38974-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 05/23/2023] [Indexed: 06/24/2023] Open
Abstract
Brain subcortical structures are paramount in many cognitive functions and their aberrations during infancy are predisposed to various neurodevelopmental and neuropsychiatric disorders, making it highly essential to characterize the early subcortical normative growth patterns. This study investigates the volumetric development and surface area expansion of six subcortical structures and their associations with Mullen scales of early learning by leveraging 513 high-resolution longitudinal MRI scans within the first two postnatal years. Results show that (1) each subcortical structure (except for the amygdala with an approximately linear increase) undergoes rapid nonlinear volumetric growth after birth, which slows down at a structure-specific age with bilaterally similar developmental patterns; (2) Subcortical local area expansion reveals structure-specific and spatiotemporally heterogeneous patterns; (3) Positive associations between thalamus and both receptive and expressive languages and between caudate and putamen and fine motor are revealed. This study advances our understanding of the dynamic early subcortical developmental patterns.
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Affiliation(s)
- Liangjun Chen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Ya Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Yue Shan
- Department of Biostatistics, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Tengfei Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Sheng-Che Hung
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Lei Xing
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Rd, Chapel Hill, NC, 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, 130 Mason Farm Rd, Chapel Hill, NC, 27599, USA.
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Chen L, Wu Z, Zhao F, Wang Y, Lin W, Wang L, Li G. An attention-based context-informed deep framework for infant brain subcortical segmentation. Neuroimage 2023; 269:119931. [PMID: 36746299 DOI: 10.1016/j.neuroimage.2023.119931] [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: 11/05/2022] [Revised: 01/13/2023] [Accepted: 02/03/2023] [Indexed: 02/06/2023] Open
Abstract
Precise segmentation of subcortical structures from infant brain magnetic resonance (MR) images plays an essential role in studying early subcortical structural and functional developmental patterns and diagnosis of related brain disorders. However, due to the dynamic appearance changes, low tissue contrast, and tiny subcortical size in infant brain MR images, infant subcortical segmentation is a challenging task. In this paper, we propose a context-guided, attention-based, coarse-to-fine deep framework to precisely segment the infant subcortical structures. At the coarse stage, we aim to directly predict the signed distance maps (SDMs) from multi-modal intensity images, including T1w, T2w, and the ratio of T1w and T2w images, with an SDM-Unet, which can leverage the spatial context information, including the structural position information and the shape information of the target structure, to generate high-quality SDMs. At the fine stage, the predicted SDMs, which encode spatial-context information of each subcortical structure, are integrated with the multi-modal intensity images as the input to a multi-source and multi-path attention Unet (M2A-Unet) for achieving refined segmentation. Both the 3D spatial and channel attention blocks are added to guide the M2A-Unet to focus more on the important subregions and channels. We additionally incorporate the inner and outer subcortical boundaries as extra labels to help precisely estimate the ambiguous boundaries. We validate our method on an infant MR image dataset and on an unrelated neonatal MR image dataset. Compared to eleven state-of-the-art methods, the proposed framework consistently achieves higher segmentation accuracy in both qualitative and quantitative evaluations of infant MR images and also exhibits good generalizability in the neonatal dataset.
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Affiliation(s)
- Liangjun Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ya Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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8
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DiPiero M, Rodrigues PG, Gromala A, Dean DC. Applications of advanced diffusion MRI in early brain development: a comprehensive review. Brain Struct Funct 2023; 228:367-392. [PMID: 36585970 PMCID: PMC9974794 DOI: 10.1007/s00429-022-02605-8] [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: 09/02/2022] [Accepted: 12/21/2022] [Indexed: 01/01/2023]
Abstract
Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life.
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Affiliation(s)
- Marissa DiPiero
- Department of Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | | | - Alyssa Gromala
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Douglas C Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, 53705, USA.
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Mapping developmental regionalization and patterns of cortical surface area from 29 post-menstrual weeks to 2 years of age. Proc Natl Acad Sci U S A 2022; 119:e2121748119. [PMID: 35939665 PMCID: PMC9388141 DOI: 10.1073/pnas.2121748119] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Surface area of the human cerebral cortex expands extremely dynamically and regionally heterogeneously from the third trimester of pregnancy to 2 y of age, reflecting the spatial heterogeneity of the underlying microstructural and functional development of the cerebral cortex. However, little is known about the developmental patterns and regionalization of cortical surface area during this critical stage, due to the lack of high-quality imaging data and accurate computational tools for pediatric brain MRI data. To fill this critical knowledge gap, by leveraging 1,037 high-quality MRI scans with the age between 29 post-menstrual weeks and 24 mo from 735 pediatric subjects in two complementary datasets, i.e., the Baby Connectome Project (BCP) and the developing Human Connectome Project (dHCP), and state-of-the-art dedicated image-processing tools, we unprecedentedly parcellate the cerebral cortex into a set of distinct subdivisions purely according to the developmental patterns of the cortical surface. Our discovered developmentally distinct subdivisions correspond well to structurally and functionally meaningful regions and reveal spatially contiguous, hierarchical, and bilaterally symmetric patterns of early cortical surface expansion. We also show that high-order association subdivisions, where cortical folds emerge later during prenatal stages, undergo more dramatic cortical surface expansion during infancy, compared with the central regions, especially the sensorimotor and insula cortices, thus forming a distinct central-pole division in early cortical surface expansion. These results provide an important reference for exploring and understanding dynamic early brain development in health and disease.
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Wang Y, Haghpanah FS, Zhang X, Santamaria K, da Costa Aguiar Alves GK, Bruno E, Aw N, Maddocks A, Duarte CS, Monk C, Laine A, Posner J. ID-Seg: an infant deep learning-based segmentation framework to improve limbic structure estimates. Brain Inform 2022; 9:12. [PMID: 35633447 PMCID: PMC9148335 DOI: 10.1186/s40708-022-00161-9] [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: 03/31/2022] [Accepted: 05/05/2022] [Indexed: 11/10/2022] Open
Abstract
Infant brain magnetic resonance imaging (MRI) is a promising approach for studying early neurodevelopment. However, segmenting small regions such as limbic structures is challenging due to their low inter-regional contrast and high curvature. MRI studies of the adult brain have successfully applied deep learning techniques to segment limbic structures, and similar deep learning models are being leveraged for infant studies. However, these deep learning-based infant MRI segmentation models have generally been derived from small datasets, and may suffer from generalization problems. Moreover, the accuracy of segmentations derived from these deep learning models relative to more standard Expectation-Maximization approaches has not been characterized. To address these challenges, we leveraged a large, public infant MRI dataset (n = 473) and the transfer-learning technique to first pre-train a deep convolutional neural network model on two limbic structures: amygdala and hippocampus. Then we used a leave-one-out cross-validation strategy to fine-tune the pre-trained model and evaluated it separately on two independent datasets with manual labels. We term this new approach the Infant Deep learning SEGmentation Framework (ID-Seg). ID-Seg performed well on both datasets with a mean dice similarity score (DSC) of 0.87, a mean intra-class correlation (ICC) of 0.93, and a mean average surface distance (ASD) of 0.31 mm. Compared to the Developmental Human Connectome pipeline (dHCP) pipeline, ID-Seg significantly improved segmentation accuracy. In a third infant MRI dataset (n = 50), we used ID-Seg and dHCP separately to estimate amygdala and hippocampus volumes and shapes. The estimates derived from ID-seg, relative to those from the dHCP, showed stronger associations with behavioral problems assessed in these infants at age 2. In sum, ID-Seg consistently performed well on two different datasets with an 0.87 DSC, however, multi-site testing and extension for brain regions beyond the amygdala and hippocampus are still needed.
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Affiliation(s)
- Yun Wang
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.,New York State Psychiatric Institute, New York, NY, USA
| | | | - Xuzhe Zhang
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | | | | | | | - Natalie Aw
- New York State Psychiatric Institute, New York, NY, USA
| | - Alexis Maddocks
- Department of Radiology, Columbia University, New York, NY, USA
| | | | - Catherine Monk
- New York State Psychiatric Institute, New York, NY, USA.,Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA
| | - Andrew Laine
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Jonathan Posner
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA. .,New York State Psychiatric Institute, New York, NY, USA.
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