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Conte S, Zimmerman D, Richards JE. White matter trajectories over the lifespan. PLoS One 2024; 19:e0301520. [PMID: 38758830 PMCID: PMC11101104 DOI: 10.1371/journal.pone.0301520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 03/14/2024] [Indexed: 05/19/2024] Open
Abstract
White matter (WM) changes occur throughout the lifespan at a different rate for each developmental period. We aggregated 10879 structural MRIs and 6186 diffusion-weighted MRIs from participants between 2 weeks to 100 years of age. Age-related changes in gray matter and WM partial volumes and microstructural WM properties, both brain-wide and on 29 reconstructed tracts, were investigated as a function of biological sex and hemisphere, when appropriate. We investigated the curve fit that would best explain age-related differences by fitting linear, cubic, quadratic, and exponential models to macro and microstructural WM properties. Following the first steep increase in WM volume during infancy and childhood, the rate of development slows down in adulthood and decreases with aging. Similarly, microstructural properties of WM, particularly fractional anisotropy (FA) and mean diffusivity (MD), follow independent rates of change across the lifespan. The overall increase in FA and decrease in MD are modulated by demographic factors, such as the participant's age, and show different hemispheric asymmetries in some association tracts reconstructed via probabilistic tractography. All changes in WM macro and microstructure seem to follow nonlinear trajectories, which also differ based on the considered metric. Exponential changes occurred for the WM volume and FA and MD values in the first five years of life. Collectively, these results provide novel insight into how changes in different metrics of WM occur when a lifespan approach is considered.
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Affiliation(s)
- Stefania Conte
- Department of Psychology, State University of New York at Binghamton, Vestal, NY, United States of America
| | - Dabriel Zimmerman
- Department of Biomedical Engineering, Boston University, Boston, MA, United States of America
| | - John E. Richards
- Department of Psychology, University of South Carolina, Columbia, SC, United States of America
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2
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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] [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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3
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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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4
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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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5
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Stoyell SM, Elison JT, Graupmann E, Miller NC, Emerick J, Ramey E, Sandness K, Schleiss MR, Osterholm EA. Neurobehavioral outcomes of neonatal asymptomatic congenital cytomegalovirus infection at 12-months. J Neurodev Disord 2024; 16:19. [PMID: 38637762 PMCID: PMC11025208 DOI: 10.1186/s11689-024-09533-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 04/02/2024] [Indexed: 04/20/2024] Open
Abstract
BACKGROUND Congenital cytomegalovirus (cCMV) is the most common congenital viral infection in the United States. Symptomatic infections can cause severe hearing loss and neurological disability, although ~ 90% of cCMV infections are asymptomatic at birth. Despite its prevalence, the long-term neurobehavioral risks of asymptomatic cCMV infections are not fully understood. The objective of this work was to evaluate for potential long-term neurobehavioral sequelae in infants with asymptomatic cCMV. METHODS Infants with cCMV were identified from a universal newborn cCMV screening study in a metropolitan area in the midwestern United States. Asymptomatic infants with cCMV were enrolled in a longitudinal neurodevelopmental study (N = 29). Age- and sex-matched healthy control infants (N = 193) were identified from the Baby Connectome Project (BCP), a longitudinal study of brain and behavioral development. The BCP sample supplemented an additional group of healthy control infants (N = 30), recruited from the same participant registry as the BCP specifically for comparison with infants with asymptomatic cCMV. Neurobehavioral assessments and parent questionnaires, including the Mullen Scales of Early Learning, the Repetitive Behavior Scales for Early Childhood (RBS-EC), and the Infant Toddler Social Emotional Assessment (ITSEA) were administered at 12 months of age. Neurobehavioral scores were compared between infants with asymptomatic cCMV and all identified healthy control infants. RESULTS Infants with asymptomatic cCMV performed equivalently compared to healthy control infants on the neurobehavioral measures tested at 12 months of age. CONCLUSIONS These results indicate that at 12 months of age, infants with asymptomatic cCMV are not statistically different from controls in a number of neurobehavioral domains. Although follow-up is ongoing, these observations provide reassurance about neurobehavioral outcomes for infants with asymptomatic cCMV and inform the ongoing discussion around universal screening. Additional follow-up will be necessary to understand the longer-term outcomes of these children.
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Affiliation(s)
- Sally M Stoyell
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA.
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
| | - Jed T Elison
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Emily Graupmann
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Neely C Miller
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Jessica Emerick
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Elizabeth Ramey
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Kristen Sandness
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Mark R Schleiss
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Erin A Osterholm
- Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.
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6
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Filippi CA, Winkler AM, Kanel D, Elison JT, Hardiman H, Sylvester C, Pine DS, Fox NA. Neural correlates of novelty-evoked distress in 4-month-old infants: A synthetic cohort study. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2024:S2451-9022(24)00107-1. [PMID: 38641209 DOI: 10.1016/j.bpsc.2024.03.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2023] [Revised: 03/28/2024] [Accepted: 03/28/2024] [Indexed: 04/21/2024]
Abstract
BACKGROUND Observational assessments of infant temperament have provided unparalleled insight into prediction of risk for social anxiety. Yet, it is challenging to administer and score these assessments alongside high-quality infant neuroimaging data. The current study aims to identify infant resting state functional connectivity (rsFC) associated with both parent-report and observed behavioral estimates of infant novelty-evoked distress. METHODS Using data from the Origins of Infant Temperament (OIT) study which includes deep phenotyping of infant temperament, we identified parent-report measures that were associated with observed novelty-evoked distress. These parent-report measures were then summarized into a composite score used for imaging analysis. Our infant MRI sample was a "synthetic cohort", harmonizing data from two fMRI studies of 4-month-old infants (OIT and Baby Connectome Project [BCP]; n=101) both of which included parent-reported temperament. Brain-behavior associations were evaluated using "enrichment," a statistical approach that quantifies the clustering of brain-behavior associations within network pairs. RESULTS Results demonstrated that parent-report composites of novelty-evoked distress were significantly associated with three network pairs: Dorsal Attention-Salience/Ventral Attention, Dorsal Attention-Default, and Dorsal Attention-Control. These network pairs demonstrated negative associations with novelty-evoked distress-indicating that less connectivity between these network pairs was associated with greater novelty-evoked distress. Additional analyses demonstrated that Dorsal Attention -Control network connectivity was associated with observed novelty-evoked distress in the OIT sample (n=38). CONCLUSION Overall, this work is broadly consistent with existing work and implicates dorsal attention network connectivity in novelty-evoked distress. This study provides novel data on the neural basis of infant novelty-evoked distress.
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Affiliation(s)
- Courtney A Filippi
- Department of Child and Adolescent Psychiatry, New York University Grossman School of Medicine, New York, New York, 10016.
| | - Anderson M Winkler
- Division of Human Genetics, School of Medicine, University of Texas Rio Grande Valley, Brownsville, Texas, 78520
| | - Dana Kanel
- Emotion and Development Branch, National Institute of Mental Health, Bethesda, Maryland, 20892; Department of Human Development and Quantitative Methodology, University of Maryland, College Park, Maryland, 20742
| | - Jed T Elison
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, 55455
| | - Hannah Hardiman
- Emotion and Development Branch, National Institute of Mental Health, Bethesda, Maryland, 20892; Department of Human Development and Quantitative Methodology, University of Maryland, College Park, Maryland, 20742
| | - Chad Sylvester
- Departments of Psychiatry, Radiology, and the Taylor Family Institute for Innovative Research, Washington University, St. Louis, Missouri, 63110
| | - Daniel S Pine
- Emotion and Development Branch, National Institute of Mental Health, Bethesda, Maryland, 20892
| | - Nathan A Fox
- Department of Human Development and Quantitative Methodology, University of Maryland, College Park, Maryland, 20742
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7
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Day TKM, Hermosillo R, Conan G, Randolph A, Perrone A, Earl E, Byington N, Hendrickson TJ, Elison JT, Fair DA, Feczko E. Multi-level fMRI analysis applied to hemispheric specialization in the language network, functional areas, and their behavioral correlations in the ABCD sample. Dev Cogn Neurosci 2024; 66:101355. [PMID: 38354531 PMCID: PMC10875197 DOI: 10.1016/j.dcn.2024.101355] [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: 02/14/2023] [Revised: 01/06/2024] [Accepted: 02/03/2024] [Indexed: 02/16/2024] Open
Abstract
Prior research suggests that the organization of the language network in the brain is left-dominant and becomes more lateralized with age and increasing language skill. The age at which specific components of the language network become adult-like varies depending on the abilities they subserve. So far, a large, developmental study has not included a language task paradigm, so we introduce a method to study resting-state laterality in the Adolescent Brain Cognitive Development (ABCD) study. Our approach mixes source timeseries between left and right homotopes of the (1) inferior frontal and (2) middle temporal gyri and (3) a region we term "Wernicke's area" near the supramarginal gyrus. Our large subset sample size of ABCD (n = 6153) allows improved reliability and validity compared to previous, smaller studies of brain-behavior associations. We show that behavioral metrics from the NIH Youth Toolbox and other resources are differentially related to tasks with a larger linguistic component over ones with less (e.g., executive function-dominant tasks). These baseline characteristics of hemispheric specialization in youth are critical for future work determining the correspondence of lateralization with language onset in earlier stages of development.
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Affiliation(s)
- Trevor K M Day
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
| | - Robert Hermosillo
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Gregory Conan
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Anita Randolph
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Anders Perrone
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Eric Earl
- Data Science & Sharing Team, National Institute of Mental Health, Bethesda, MD, USA
| | - Nora Byington
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - Timothy J Hendrickson
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Informatics Institute, University of Minnesota, Minneapolis, MN, USA
| | - Jed T Elison
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Damien A Fair
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA; Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
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8
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Kelley W, Ngo N, Dalca AV, Fischl B, Zöllei L, Hoffmann M. BOOSTING SKULL-STRIPPING PERFORMANCE FOR PEDIATRIC BRAIN IMAGES. ARXIV 2024:arXiv:2402.16634v1. [PMID: 38463507 PMCID: PMC10925384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the <1-minute runtime of our tool compares favorably to the fastest baselines. We distribute our model at https://w3id.org/synthstrip.
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Affiliation(s)
- William Kelley
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Nathan Ngo
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Computer Science & Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Malte Hoffmann
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA
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9
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Kim MJ, Hong E, Yum MS, Lee YJ, Kim J, Ko TS. Deep learning-based, fully automated, pediatric brain segmentation. Sci Rep 2024; 14:4344. [PMID: 38383725 PMCID: PMC10881508 DOI: 10.1038/s41598-024-54663-z] [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: 07/24/2023] [Accepted: 02/15/2024] [Indexed: 02/23/2024] Open
Abstract
The purpose of this study was to demonstrate the performance of a fully automated, deep learning-based brain segmentation (DLS) method in healthy controls and in patients with neurodevelopmental disorders, SCN1A mutation, under eleven. The whole, cortical, and subcortical volumes of previously enrolled 21 participants, under 11 years of age, with a SCN1A mutation, and 42 healthy controls, were obtained using a DLS method, and compared to volumes measured by Freesurfer with manual correction. Additionally, the volumes which were calculated with the DLS method between the patients and the control group. The volumes of total brain gray and white matter using DLS method were consistent with that volume which were measured by Freesurfer with manual correction in healthy controls. Among 68 cortical parcellated volume analysis, the volumes of only 7 areas measured by DLS methods were significantly different from that measured by Freesurfer with manual correction, and the differences decreased with increasing age in the subgroup analysis. The subcortical volume measured by the DLS method was relatively smaller than that of the Freesurfer volume analysis. Further, the DLS method could perfectly detect the reduced volume identified by the Freesurfer software and manual correction in patients with SCN1A mutations, compared with healthy controls. In a pediatric population, this new, fully automated DLS method is compatible with the classic, volumetric analysis with Freesurfer software and manual correction, and it can also well detect brain morphological changes in children with a neurodevelopmental disorder.
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Affiliation(s)
- Min-Jee Kim
- Department of Pediatrics, Asan Medical Center Children's Hospital, Ulsan University College of Medicine, 88, Olympic-ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea
| | | | - Mi-Sun Yum
- Department of Pediatrics, Asan Medical Center Children's Hospital, Ulsan University College of Medicine, 88, Olympic-ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea.
| | - Yun-Jeong Lee
- Department of Pediatrics, Kyungpook National University Hospital and School of Medicine, Kyungpook National University, Daegu, South Korea
| | | | - Tae-Sung Ko
- Department of Pediatrics, Asan Medical Center Children's Hospital, Ulsan University College of Medicine, 88, Olympic-ro 43-Gil, Songpa-Gu, Seoul, 05505, South Korea
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10
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Ball G, Oldham S, Kyriakopoulou V, Williams LZJ, Karolis V, Price A, Hutter J, Seal ML, Alexander-Bloch A, Hajnal JV, Edwards AD, Robinson EC, Seidlitz J. Molecular signatures of cortical expansion in the human fetal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580198. [PMID: 38405710 PMCID: PMC10888819 DOI: 10.1101/2024.02.13.580198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
The third trimester of human gestation is characterised by rapid increases in brain volume and cortical surface area. A growing catalogue of cells in the prenatal brain has revealed remarkable molecular diversity across cortical areas.1,2 Despite this, little is known about how this translates into the patterns of differential cortical expansion observed in humans during the latter stages of gestation. Here we present a new resource, μBrain, to facilitate knowledge translation between molecular and anatomical descriptions of the prenatal developing brain. Built using generative artificial intelligence, μBrain is a three-dimensional cellular-resolution digital atlas combining publicly-available serial sections of the postmortem human brain at 21 weeks gestation3 with bulk tissue microarray data, sampled across 29 cortical regions and 5 transient tissue zones.4 Using μBrain, we evaluate the molecular signatures of preferentially-expanded cortical regions during human gestation, quantified in utero using magnetic resonance imaging (MRI). We find that differences in the rates of expansion across cortical areas during gestation respect anatomical and evolutionary boundaries between cortical types5 and are founded upon extended periods of upper-layer cortical neuron migration that continue beyond mid-gestation. We identify a set of genes that are upregulated from mid-gestation and highly expressed in rapidly expanding neocortex, which are implicated in genetic disorders with cognitive sequelae. Our findings demonstrate a spatial coupling between areal differences in the timing of neurogenesis and rates of expansion across the neocortical sheet during the prenatal epoch. The μBrain atlas is available from: https://garedaba.github.io/micro-brain/ and provides a new tool to comprehensively map early brain development across domains, model systems and resolution scales.
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Affiliation(s)
- G Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - S Oldham
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - V Kyriakopoulou
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - L Z J Williams
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - V Karolis
- Centre for the Developing Brain, King's College London, London, UK
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - A Price
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - J Hutter
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - M L Seal
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - A Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children's Hospital of Philadelphia, Philadelphia, PA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA
| | - J V Hajnal
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - A D Edwards
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - E C Robinson
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - J Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children's Hospital of Philadelphia, Philadelphia, PA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA
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11
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Dayarathna S, Islam KT, Uribe S, Yang G, Hayat M, Chen Z. Deep learning based synthesis of MRI, CT and PET: Review and analysis. Med Image Anal 2024; 92:103046. [PMID: 38052145 DOI: 10.1016/j.media.2023.103046] [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: 06/05/2023] [Revised: 11/14/2023] [Accepted: 11/29/2023] [Indexed: 12/07/2023]
Abstract
Medical image synthesis represents a critical area of research in clinical decision-making, aiming to overcome the challenges associated with acquiring multiple image modalities for an accurate clinical workflow. This approach proves beneficial in estimating an image of a desired modality from a given source modality among the most common medical imaging contrasts, such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET). However, translating between two image modalities presents difficulties due to the complex and non-linear domain mappings. Deep learning-based generative modelling has exhibited superior performance in synthetic image contrast applications compared to conventional image synthesis methods. This survey comprehensively reviews deep learning-based medical imaging translation from 2018 to 2023 on pseudo-CT, synthetic MR, and synthetic PET. We provide an overview of synthetic contrasts in medical imaging and the most frequently employed deep learning networks for medical image synthesis. Additionally, we conduct a detailed analysis of each synthesis method, focusing on their diverse model designs based on input domains and network architectures. We also analyse novel network architectures, ranging from conventional CNNs to the recent Transformer and Diffusion models. This analysis includes comparing loss functions, available datasets and anatomical regions, and image quality assessments and performance in other downstream tasks. Finally, we discuss the challenges and identify solutions within the literature, suggesting possible future directions. We hope that the insights offered in this survey paper will serve as a valuable roadmap for researchers in the field of medical image synthesis.
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Affiliation(s)
- Sanuwani Dayarathna
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia.
| | | | - Sergio Uribe
- Department of Medical Imaging and Radiation Sciences, Faculty of Medicine, Monash University, Clayton VIC 3800, Australia
| | - Guang Yang
- Bioengineering Department and Imperial-X, Imperial College London, W12 7SL, United Kingdom
| | - Munawar Hayat
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
| | - Zhaolin Chen
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia; Monash Biomedical Imaging, Clayton VIC 3800, Australia
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12
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Cabral L, Calabro FJ, Foran W, Parr AC, Ojha A, Rasmussen J, Ceschin R, Panigrahy A, Luna B. Multivariate and regional age-related change in basal ganglia iron in neonates. Cereb Cortex 2024; 34:bhad456. [PMID: 38059685 DOI: 10.1093/cercor/bhad456] [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: 07/05/2023] [Revised: 10/31/2023] [Accepted: 11/01/2023] [Indexed: 12/08/2023] Open
Abstract
In the perinatal period, reward and cognitive systems begin trajectories, influencing later psychiatric risk. The basal ganglia is important for reward and cognitive processing but early development has not been fully characterized. To assess age-related development, we used a measure of basal ganglia physiology, specifically brain tissue iron, obtained from nT2* signal in resting-state functional magnetic resonance imaging (rsfMRI), associated with dopaminergic processing. We used data from the Developing Human Connectome Project (n = 464) to assess how moving from the prenatal to the postnatal environment affects rsfMRI nT2*, modeling gestational and postnatal age separately for basal ganglia subregions in linear models. We did not find associations with tissue iron and gestational age [range: 24.29-42.29] but found positive associations with postnatal age [range:0-17.14] in the pallidum and putamen, but not the caudate. We tested if there was an interaction between preterm birth and postnatal age, finding early preterm infants (GA < 35 wk) had higher iron levels and changed less over time. To assess multivariate change, we used support vector regression to predict age from voxel-wise-nT2* maps. We could predict postnatal but not gestational age when maps were residualized for the other age term. This provides evidence subregions differentially change with postnatal experience and preterm birth may disrupt trajectories.
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Affiliation(s)
- Laura Cabral
- Department of Radiology University of Pittsburgh, Pittsburgh, PA 15224, United States
| | - Finnegan J Calabro
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, United States
- Department of Bioengineering, University of Pittsburgh, 15213, United States
| | - Will Foran
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Ashley C Parr
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Amar Ojha
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA 15213, United States
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, PA 15213, United States
| | - Jerod Rasmussen
- Development, Health and Disease Research Program, University of California, Irvine, CA 92697, United States
- Department of Pediatrics, University of California, Irvine, CA 92697, United States
| | - Rafael Ceschin
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15224, United States
| | - Ashok Panigrahy
- Department of Radiology University of Pittsburgh, Pittsburgh, PA 15224, United States
| | - Beatriz Luna
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, United States
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13
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Zhao MY, Tong E, Armindo RD, Woodward A, Yeom KW, Moseley ME, Zaharchuk G. Measuring Quantitative Cerebral Blood Flow in Healthy Children: A Systematic Review of Neuroimaging Techniques. J Magn Reson Imaging 2024; 59:70-81. [PMID: 37170640 PMCID: PMC10638464 DOI: 10.1002/jmri.28758] [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: 02/03/2023] [Revised: 04/13/2023] [Accepted: 04/14/2023] [Indexed: 05/13/2023] Open
Abstract
Cerebral blood flow (CBF) is an important hemodynamic parameter to evaluate brain health. It can be obtained quantitatively using medical imaging modalities such as magnetic resonance imaging and positron emission tomography (PET). Although CBF in adults has been widely studied and linked with cerebrovascular and neurodegenerative diseases, CBF data in healthy children are sparse due to the challenges in pediatric neuroimaging. An understanding of the factors affecting pediatric CBF and its normal range is crucial to determine the optimal CBF measuring techniques in pediatric neuroradiology. This review focuses on pediatric CBF studies using neuroimaging techniques in 32 articles including 2668 normal subjects ranging from birth to 18 years old. A systematic literature search was conducted in PubMed, Embase, and Scopus and reported following the preferred reporting items for systematic reviews and meta-analyses (PRISMA). We identified factors (such as age, gender, mood, sedation, and fitness) that have significant effects on pediatric CBF quantification. We also investigated factors influencing the CBF measurements in infants. Based on this review, we recommend best practices to improve CBF measurements in pediatric neuroimaging. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Moss Y Zhao
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Elizabeth Tong
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Rui Duarte Armindo
- Department of Radiology, Stanford University, Stanford, CA, USA
- Department of Neuroradiology, Hospital Beatriz Ângelo, Loures, Lisbon, Portugal
| | - Amanda Woodward
- Lane Medical Library, Stanford University, Stanford, CA, USA
| | - Kristen W. Yeom
- Department of Radiology, Stanford University, Stanford, CA, USA
| | | | - Greg Zaharchuk
- Department of Radiology, Stanford University, Stanford, CA, USA
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14
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Hu Y, Chen H, Li X, Larsen RJ, Sutton BP, Gao W, McElwain NL. Associations between infant amygdala functional connectivity and social engagement following a stressor: A preliminary investigation. Dev Sci 2024; 27:e13418. [PMID: 37340633 PMCID: PMC10730773 DOI: 10.1111/desc.13418] [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: 10/06/2022] [Revised: 05/20/2023] [Accepted: 05/23/2023] [Indexed: 06/22/2023]
Abstract
Functional architecture of the infant brain, especially functional connectivity (FC) within the amygdala network and between the amygdala and other networks (i.e., default-mode [DMN] and salience [SAL] networks), provides a neural basis for infant socioemotional functioning. Yet, little is known about the extent to which early within- and between-network amygdala FC are related to infant stress recovery across the first year of life. In this study, we examined associations between amygdala FC (i.e., within-network amygdala connectivity, and between-network amygdala connectivity with the DMN and SAL) at 3 months and infant recovery from a mild social stressor at 3, 6 and 9 months. At 3 months, thirty-five infants (13 girls) underwent resting-state functional magnetic resonance imaging during natural sleep. Infants and their mothers completed the still-face paradigm at 3, 6, and 9 months, and infant stress recovery was assessed at each time point as the proportion of infant social engagement during the reunion episode. Bivariate correlations indicated that greater positive within-network amygdala FC and greater positive amygdala-SAL FC, but not amygdala-DMN FC, at 3 months predicted lower levels of stress recovery at 3 and 6 months, but were nonsignificant at 9 months. These findings provide preliminary evidence that early functional synchronization within the amygdala network, as well as segregation between the amygdala and the SAL, may contribute to infant stress recovery in the context of infant-mother interaction.
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Affiliation(s)
- Yannan Hu
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Haitao Chen
- Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, California, USA
| | - Xiaomei Li
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Ryan J. Larsen
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Bradley P. Sutton
- Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
| | - Wei Gao
- Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute, Cedars Sinai Medical Center, Los Angeles, California, USA
| | - Nancy L. McElwain
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA
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15
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Lin R, Gholipour A, Thiran JP, Karimi D, Kebiri H, Cuadra MB. CROSS-AGE AND CROSS-SITE DOMAIN SHIFT IMPACTS ON DEEP LEARNING-BASED WHITE MATTER FIBER ESTIMATION IN NEWBORN AND BABY BRAINS. ARXIV 2023:arXiv:2312.14773v1. [PMID: 38196752 PMCID: PMC10775359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Deep learning models have shown great promise in estimating tissue microstructure from limited diffusion magnetic resonance imaging data. However, these models face domain shift challenges when test and train data are from different scanners and protocols, or when the models are applied to data with inherent variations such as the developing brains of infants and children scanned at various ages. Several techniques have been proposed to address some of these challenges, such as data harmonization or domain adaptation in the adult brain. However, those techniques remain unexplored for the estimation of fiber orientation distribution functions in the rapidly developing brains of infants. In this work, we extensively investigate the age effect and domain shift within and across two different cohorts of 201 newborns and 165 babies using the Method of Moments and fine-tuning strategies. Our results show that reduced variations in the microstructural development of babies in comparison to newborns directly impact the deep learning models' cross-age performance. We also demonstrate that a small number of target domain samples can significantly mitigate domain shift problems.
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Affiliation(s)
- Rizhong Lin
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- College of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Switzerland
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Hamza Kebiri
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Switzerland
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Switzerland
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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16
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Zapaishchykova A, Liu KX, Saraf A, Ye Z, Catalano PJ, Benitez V, Ravipati Y, Jain A, Huang J, Hayat H, Likitlersuang J, Vajapeyam S, Chopra RB, Familiar AM, Nabavidazeh A, Mak RH, Resnick AC, Mueller S, Cooney TM, Haas-Kogan DA, Poussaint TY, Aerts HJWL, Kann BH. Automated temporalis muscle quantification and growth charts for children through adulthood. Nat Commun 2023; 14:6863. [PMID: 37945573 PMCID: PMC10636102 DOI: 10.1038/s41467-023-42501-1] [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: 07/21/2023] [Accepted: 10/12/2023] [Indexed: 11/12/2023] Open
Abstract
Lean muscle mass (LMM) is an important aspect of human health. Temporalis muscle thickness is a promising LMM marker but has had limited utility due to its unknown normal growth trajectory and reference ranges and lack of standardized measurement. Here, we develop an automated deep learning pipeline to accurately measure temporalis muscle thickness (iTMT) from routine brain magnetic resonance imaging (MRI). We apply iTMT to 23,876 MRIs of healthy subjects, ages 4 through 35, and generate sex-specific iTMT normal growth charts with percentiles. We find that iTMT was associated with specific physiologic traits, including caloric intake, physical activity, sex hormone levels, and presence of malignancy. We validate iTMT across multiple demographic groups and in children with brain tumors and demonstrate feasibility for individualized longitudinal monitoring. The iTMT pipeline provides unprecedented insights into temporalis muscle growth during human development and enables the use of LMM tracking to inform clinical decision-making.
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Affiliation(s)
- Anna Zapaishchykova
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kevin X Liu
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anurag Saraf
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zezhong Ye
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul J Catalano
- Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Viviana Benitez
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
| | - Yashwanth Ravipati
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Arnav Jain
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Julia Huang
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Hasaan Hayat
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Michigan State University, East Lansing, MI, USA
| | - Jirapat Likitlersuang
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sridhar Vajapeyam
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Rishi B Chopra
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ariana M Familiar
- Children's Hospital of Philadelphia, Philadelphia, USA
- University of Pennsylvania, Pennsylvania, USA
| | - Ali Nabavidazeh
- Children's Hospital of Philadelphia, Philadelphia, USA
- University of Pennsylvania, Pennsylvania, USA
| | - Raymond H Mak
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Adam C Resnick
- Children's Hospital of Philadelphia, Philadelphia, USA
- University of Pennsylvania, Pennsylvania, USA
| | - Sabine Mueller
- Department of Neurology, Neurosurgery and Pediatrics, University of California, San Francisco, USA
| | - Tabitha M Cooney
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
| | - Daphne A Haas-Kogan
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Tina Y Poussaint
- Dana-Farber/Boston Children's Cancer and Blood Disorders Center, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA
| | - Hugo J W L Aerts
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology and Nuclear Medicine, CARIM & GROW, Maastricht University, Maastricht, the Netherlands
| | - Benjamin H Kann
- Artificial Intelligence in Medicine (AIM) Program, Mass General Brigham, Harvard Medical School, Boston, MA, USA.
- Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
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17
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Huang Y, Ahmad S, Han L, Wang S, Wu Z, Lin W, Li G, Wang L, Yap PT. Longitudinal Prediction of Postnatal Brain Magnetic Resonance Images via a Metamorphic Generative Adversarial Network. PATTERN RECOGNITION 2023; 143:109715. [PMID: 37425426 PMCID: PMC10327994 DOI: 10.1016/j.patcog.2023.109715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Missing scans are inevitable in longitudinal studies due to either subject dropouts or failed scans. In this paper, we propose a deep learning framework to predict missing scans from acquired scans, catering to longitudinal infant studies. Prediction of infant brain MRI is challenging owing to the rapid contrast and structural changes particularly during the first year of life. We introduce a trustworthy metamorphic generative adversarial network (MGAN) for translating infant brain MRI from one time-point to another. MGAN has three key features: (i) Image translation leveraging spatial and frequency information for detail-preserving mapping; (ii) Quality-guided learning strategy that focuses attention on challenging regions. (iii) Multi-scale hybrid loss function that improves translation of image contents. Experimental results indicate that MGAN outperforms existing GANs by accurately predicting both tissue contrasts and anatomical details.
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Affiliation(s)
- Yunzhi Huang
- School of Artificial Intelligence (School of Future Technology), Nanjing University of Information Science and Technology, Nanjing 210044, China
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Sahar Ahmad
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Luyi Han
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Geert Grooteplein 10, 6525 GA, Nijmegen, The Netherlands
| | - Shuai Wang
- Department of Computer Science, Shandong University (Weihai), China
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
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18
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Weaver JM, DiPiero M, Rodrigues PG, Cordash H, Davidson RJ, Planalp EM, Dean DC. Automated motion artifact detection in early pediatric diffusion MRI using a convolutional neural network. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:10.1162/imag_a_00023. [PMID: 38344118 PMCID: PMC10854394 DOI: 10.1162/imag_a_00023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/15/2024]
Abstract
Diffusion MRI (dMRI) is a widely used method to investigate the microstructure of the brain. Quality control (QC) of dMRI data is an important processing step that is performed prior to analysis using models such as diffusion tensor imaging (DTI) or neurite orientation dispersion and density imaging (NODDI). When processing dMRI data from infants and young children, where intra-scan motion is common, the identification and removal of motion artifacts is of the utmost importance. Manual QC of dMRI data is (1) time-consuming due to the large number of diffusion directions, (2) expensive, and (3) prone to subjective errors and observer variability. Prior techniques for automated dMRI QC have mostly been limited to adults or school-age children. Here, we propose a deep learning-based motion artifact detection tool for dMRI data acquired from infants and toddlers. The proposed framework uses a simple three-dimensional convolutional neural network (3DCNN) trained and tested on an early pediatric dataset of 2,276 dMRI volumes from 121 exams acquired at 1 month and 24 months of age. An average classification accuracy of 95% was achieved following four-fold cross-validation. A second dataset with different acquisition parameters and ages ranging from 2-36 months (consisting of 2,349 dMRI volumes from 26 exams) was used to test network generalizability, achieving 98% classification accuracy. Finally, to demonstrate the importance of motion artifact volume removal in a dMRI processing pipeline, the dMRI data were fit to the DTI and NODDI models and the parameter maps were compared with and without motion artifact removal.
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Affiliation(s)
- Jayse Merle Weaver
- Department of Medical Physics, University of Wisconsin–Madison, Madison, WI, United States
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
| | - Marissa DiPiero
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Neuroscience Training Program, University of Wisconsin–Madison, Madison, WI, United States
| | | | - Hassan Cordash
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
| | - Richard J. Davidson
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Department of Psychology, University of Wisconsin–Madison, Madison, WI, United States
- Center for Healthy Minds, University of Wisconsin–Madison, Madison WI, United States
- Department of Psychiatry, University of Wisconsin–Madison, Madison, WI, United States
| | - Elizabeth M. Planalp
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Department of Medicine, University of Wisconsin–Madison, Madison, WI, United States
| | - Douglas C. Dean
- Department of Medical Physics, University of Wisconsin–Madison, Madison, WI, United States
- Waisman Center, University of Wisconsin–Madison, Madison, WI, United States
- Department of Pediatrics, University of Wisconsin–Madison, Madison, WI, United States
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19
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Yates TS, Ellis CT, Turk-Browne NB. Functional networks in the infant brain during sleep and wake states. Cereb Cortex 2023; 33:10820-10835. [PMID: 37718160 DOI: 10.1093/cercor/bhad327] [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: 04/27/2023] [Revised: 08/18/2023] [Accepted: 08/20/2023] [Indexed: 09/19/2023] Open
Abstract
Functional brain networks are assessed differently earlier versus later in development: infants are almost universally scanned asleep, whereas adults are typically scanned awake. Observed differences between infant and adult functional networks may thus reflect differing states of consciousness rather than or in addition to developmental changes. We explore this question by comparing functional networks in functional magnetic resonance imaging (fMRI) scans of infants during natural sleep and awake movie-watching. As a reference, we also scanned adults during awake rest and movie-watching. Whole-brain functional connectivity was more similar within the same state (sleep and movie in infants; rest and movie in adults) compared with across states. Indeed, a classifier trained on patterns of functional connectivity robustly decoded infant state and even generalized to adults; interestingly, a classifier trained on adult state did not generalize as well to infants. Moreover, overall similarity between infant and adult functional connectivity was modulated by adult state (stronger for movie than rest) but not infant state (same for sleep and movie). Nevertheless, the connections that drove this similarity, particularly in the frontoparietal control network, were modulated by infant state. In sum, infant functional connectivity differs between sleep and movie states, highlighting the value of awake fMRI for studying functional networks over development.
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Affiliation(s)
- Tristan S Yates
- Department of Psychology, Yale University, New Haven, CT, United States
| | - Cameron T Ellis
- Department of Psychology, Stanford University, Stanford, CA, United States
| | - Nicholas B Turk-Browne
- Department of Psychology, Yale University, New Haven, CT, United States
- Wu Tsai Institute, Yale University, New Haven, CT, United States
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20
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Jiang W, Zhou Z, Li G, Yin W, Wu Z, Wang L, Ghanbari M, Li G, Yap PT, Howell BR, Styner MA, Yacoub E, Hazlett H, Gilmore JH, Keith Smith J, Ugurbil K, Elison JT, Zhang H, Shen D, Lin W. Mapping the evolution of regional brain network efficiency and its association with cognitive abilities during the first twenty-eight months of life. Dev Cogn Neurosci 2023; 63:101284. [PMID: 37517139 PMCID: PMC10400876 DOI: 10.1016/j.dcn.2023.101284] [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: 02/15/2023] [Revised: 06/20/2023] [Accepted: 07/23/2023] [Indexed: 08/01/2023] Open
Abstract
Human brain undergoes rapid growth during the first few years of life. While previous research has employed graph theory to study early brain development, it has mostly focused on the topological attributes of the whole brain. However, examining regional graph-theory features may provide unique insights into the development of cognitive abilities. Utilizing a large and longitudinal rsfMRI dataset from the UNC/UMN Baby Connectome Project, we investigated the developmental trajectories of regional efficiency and evaluated the relationships between these changes and cognitive abilities using Mullen Scales of Early Learning during the first twenty-eight months of life. Our results revealed a complex and spatiotemporally heterogeneous development pattern of regional global and local efficiency during this age period. Furthermore, we found that the trajectories of the regional global efficiency at the left temporal occipital fusiform and bilateral occipital fusiform gyri were positively associated with cognitive abilities, including visual reception, expressive language, receptive language, and early learning composite scores (P < 0.05, FDR corrected). However, these associations were weakened with age. These findings offered new insights into the regional developmental features of brain topologies and their associations with cognition and provided evidence of ongoing optimization of brain networks at both whole-brain and regional levels.
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Affiliation(s)
- Weixiong Jiang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhen Zhou
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Guoshi Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Weiyan Yin
- 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
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Maryam Ghanbari
- 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
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | | | - Martin A Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, USA
| | - Heather Hazlett
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA; Department of Radiology, University of North Carolina at Chapel Hill, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | - J Keith Smith
- Department of Radiology, University of North Carolina at Chapel Hill, USA
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, USA
| | - Jed T Elison
- Institute of Child Development, University of Minnesota, USA; Department of Pediatrics, University of Minnesota, USA
| | - Han Zhang
- Biomedical Engineering, Shanghai Tech University, Shanghai, China
| | - Dinggang Shen
- Biomedical Engineering, Shanghai Tech University, Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai 201210, China
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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21
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Schilling KG, Chad JA, Chamberland M, Nozais V, Rheault F, Archer D, Li M, Gao Y, Cai L, Del'Acqua F, Newton A, Moyer D, Gore JC, Lebel C, Landman BA. White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.25.559330. [PMID: 37808645 PMCID: PMC10557619 DOI: 10.1101/2023.09.25.559330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
Characterizing how, when and where the human brain changes across the lifespan is fundamental to our understanding of developmental processes of childhood and adolescence, degenerative processes of aging, and divergence from normal patterns in disease and disorders. We aimed to provide detailed descriptions of white matter pathways across the lifespan by thoroughly characterizing white matter microstructure, white matter macrostructure, and morphology of the cortex associated with white matter pathways. We analyzed 4 large, high-quality, publicly-available datasets comprising 2789 total imaging sessions, and participants ranging from 0 to 100 years old, using advanced tractography and diffusion modeling. We first find that all microstructural, macrostructural, and cortical features of white matter bundles show unique lifespan trajectories, with rates and timing of development and degradation that vary across pathways - describing differences between types of pathways and locations in the brain, and developmental milestones of maturation of each feature. Second, we show cross-sectional relationships between different features that may help elucidate biological changes occurring during different stages of the lifespan. Third, we show unique trajectories of age-associations across features. Finally, we find that age associations during development are strongly related to those during aging. Overall, this study reports normative data for several features of white matter pathways of the human brain that will be useful for studying normal and abnormal white matter development and degeneration.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan A Chad
- Rotman Research Institute, Baycrest Academy for Research and Education, Toronto, ON, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Maxime Chamberland
- Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, The Netherlands
| | | | - Francois Rheault
- Medical Imaging and Neuroinformatic (MINi) Lab, Department of Computer Science, University of Sherbrooke, Canada
| | - Derek Archer
- Vanderbilt Memory & Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, 37212, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Muwei Li
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Leon Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Flavio Del'Acqua
- NatbrainLab, Department of Forensics and Neurodevelopmental Sciences, King's College London, London UK
| | - Allen Newton
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Daniel Moyer
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - John C Gore
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Catherine Lebel
- Alberta Children's Hospital Research Institute (ACHRI), Calgary, AB, Canada
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Bennett A Landman
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, USA
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
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22
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Torabian S, Grossman ED. When shapes are more than shapes: perceptual, developmental, and neurophysiological basis for attributions of animacy and theory of mind. Front Psychol 2023; 14:1168739. [PMID: 37744598 PMCID: PMC10513434 DOI: 10.3389/fpsyg.2023.1168739] [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: 02/18/2023] [Accepted: 07/25/2023] [Indexed: 09/26/2023] Open
Abstract
Among a variety of entities in their environment, what do humans consider alive or animate and how does this attribution of animacy promote development of more abstract levels of mentalizing? By decontextualizing the environment of bodily features, we review how physical movements give rise to perceived animacy in Heider-Simmel style animations. We discuss the developmental course of how perceived animacy shapes our interpretation of the social world, and specifically discuss when and how children transition from perceiving actions as goal-directed to attributing behaviors to unobservable mental states. This transition from a teleological stance, asserting a goal-oriented interpretation to an agent's actions, to a mentalistic stance allows older children to reason about more complex actions guided by hidden beliefs. The acquisition of these more complex cognitive behaviors happens developmentally at the same time neural systems for social cognition are coming online in young children. We review perceptual, developmental, and neural evidence to identify the joint cognitive and neural changes associated with when children begin to mentalize and how this ability is instantiated in the brain.
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Affiliation(s)
- Sajjad Torabian
- Visual Perception and Neuroimaging Lab, Department of Cognitive Sciences, University of California, Irvine, Irvine, CA, United States
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23
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Sun Y, Wang L, Gao K, Ying S, Lin W, Humphreys KL, Li G, Niu S, Liu M, Wang L. Self-supervised learning with application for infant cerebellum segmentation and analysis. Nat Commun 2023; 14:4717. [PMID: 37543620 PMCID: PMC10404262 DOI: 10.1038/s41467-023-40446-z] [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: 12/13/2021] [Accepted: 07/27/2023] [Indexed: 08/07/2023] Open
Abstract
Accurate tissue segmentation is critical to characterize early cerebellar development in the first two postnatal years. However, challenges in tissue segmentation arising from tightly-folded cortex, low and dynamic tissue contrast, and large inter-site data heterogeneity have limited our understanding of early cerebellar development. In this paper, we propose an accurate self-supervised learning framework for infant cerebellum segmentation. We validate its accuracy using 358 subjects from three datasets. Our results suggest the first six months exhibit the most rapid and dynamic changes, with gray matter (GM) playing a dominant role in cerebellar growth over white matter (WM). We also find both GM and WM volumes are larger in males than females, and GM and WM volumes are larger in autistic males than neurotypical males. Application of our method to a larger population will fuel more cerebellar studies, ultimately advancing our comprehension of its structure and function in neurotypical and disordered development.
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Affiliation(s)
- Yue Sun
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Limei Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Kun Gao
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Shihui Ying
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Kathryn L Humphreys
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, 37203, USA
- Department of Psychiatric and Behavioral Sciences, School of Medicine, Tulane University, New Orleans, LA, 70118, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Sijie Niu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Mingxia Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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24
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Wang F, Zhang H, Wu Z, Hu D, Zhou Z, Girault JB, Wang L, Lin W, Li G. Fine-grained functional parcellation maps of the infant cerebral cortex. eLife 2023; 12:e75401. [PMID: 37526293 PMCID: PMC10393291 DOI: 10.7554/elife.75401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 07/17/2023] [Indexed: 08/02/2023] Open
Abstract
Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infants and adults. Creating infant-specific cortical parcellation maps is thus highly desired but remains challenging due to difficulties in acquiring and processing infant brain MRIs. In this study, we leveraged 1064 high-resolution longitudinal rs-fMRIs from 197 typically developing infants and toddlers from birth to 24 months who participated in the Baby Connectome Project to develop the first set of infant-specific, fine-grained, surface-based cortical functional parcellation maps. To establish meaningful cortical functional correspondence across individuals, we performed cortical co-registration using both the cortical folding geometric features and the local gradient of functional connectivity (FC). Then we generated both age-related and age-independent cortical parcellation maps with over 800 fine-grained parcels during infancy based on aligned and averaged local gradient maps of FC across individuals. These parcellation maps reveal complex functional developmental patterns, such as changes in local gradient, network size, and local efficiency, especially during the first 9 postnatal months. Our generated fine-grained infant cortical functional parcellation maps are publicly available at https://www.nitrc.org/projects/infantsurfatlas/ for advancing the pediatric neuroimaging field.
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Affiliation(s)
- Fan Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong UniversityXi'anChina
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Han Zhang
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Dan Hu
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Zhen Zhou
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Jessica B Girault
- Department of Psychiatry, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
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25
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Sadler JR, Thapaliya G, Ranganath K, Gabay A, Chen L, Smith KR, Osorio RS, Convit A, Carnell S. Paediatric obesity and metabolic syndrome associations with cognition and the brain in youth: Current evidence and future directions. Pediatr Obes 2023; 18:e13042. [PMID: 37202148 PMCID: PMC10826337 DOI: 10.1111/ijpo.13042] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Revised: 03/14/2023] [Accepted: 04/18/2023] [Indexed: 05/20/2023]
Abstract
Obesity and components of the metabolic syndrome (MetS) are associated with differences in brain structure and function and in general and food-related cognition in adults. Here, we review evidence for similar phenomena in children and adolescents, with a focus on the implications of extant research for possible underlying mechanisms and potential interventions for obesity and MetS in youth. Current evidence is limited by a relative reliance on small cross-sectional studies. However, we find that youth with obesity and MetS or MetS components show differences in brain structure, including alterations in grey matter volume and cortical thickness across brain regions subserving reward, cognitive control and other functions, as well as in white matter integrity and volume. Children with obesity and MetS components also show some evidence for hyperresponsivity of food reward regions and hyporesponsivity of cognitive control circuits during food-related tasks, altered brain responses to food tastes, and altered resting-state connectivity including between cognitive control and reward processing networks. Potential mechanisms for these findings include neuroinflammation, impaired vascular reactivity, and effects of diet and obesity on myelination and dopamine function. Future observational research using longitudinal measures, improved sampling strategies and study designs, and rigorous statistical methods, promises to further illuminate dynamic relationships and causal mechanisms. Intervention studies targeted at modifiable biological and behavioural factors associated with paediatric obesity and MetS can further inform mechanisms, as well as test whether brain and behaviour can be altered for beneficial outcomes.
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Affiliation(s)
- Jennifer R. Sadler
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Gita Thapaliya
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kushi Ranganath
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Andrea Gabay
- Department of Psychiatry, New York University School of Medicine, New York, New York, USA
| | - Liuyi Chen
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kimberly R. Smith
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Ricardo S. Osorio
- Department of Psychiatry, New York University School of Medicine, New York, New York, USA
- Nathan Kline Institute, Orangeburg, New York, USA
| | - Antonio Convit
- Department of Psychiatry, New York University School of Medicine, New York, New York, USA
- Nathan Kline Institute, Orangeburg, New York, USA
| | - Susan Carnell
- Division of Child and Adolescent Psychiatry, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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26
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Wong JN, Walter JR, Conrad EC, Seshadri DR, Lee JY, Gonzalez H, Reuther W, Hong SJ, Pini N, Marsillio L, Moskalyk K, Vicenteno M, Padilla E, Gann O, Chung HU, Ryu D, du Plessis C, Odendaal HJ, Fifer WP, Wu JY, Xu S. A comprehensive wireless neurological and cardiopulmonary monitoring platform for pediatrics. PLOS DIGITAL HEALTH 2023; 2:e0000291. [PMID: 37410727 PMCID: PMC10325120 DOI: 10.1371/journal.pdig.0000291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 06/01/2023] [Indexed: 07/08/2023]
Abstract
Neurodevelopment in the first 10 years of life is a critical time window during which milestones that define an individual's functional potential are achieved. Comprehensive multimodal neurodevelopmental monitoring is particularly crucial for socioeconomically disadvantaged, marginalized, historically underserved and underrepresented communities as well as medically underserved areas. Solutions designed for use outside the traditional clinical environment represent an opportunity for addressing such health inequalities. In this work, we present an experimental platform, ANNE EEG, which adds 16-channel cerebral activity monitoring to the existing, USA FDA-cleared ANNE wireless monitoring platform which provides continuous electrocardiography, respiratory rate, pulse oximetry, motion, and temperature measurements. The system features low-cost consumables, real-time control and streaming with widely available mobile devices, and fully wearable operation to allow a child to remain in their naturalistic environment. This multi-center pilot study successfully collected ANNE EEG recordings from 91 neonatal and pediatric patients at academic quaternary pediatric care centers and in LMIC settings. We demonstrate the practicality and feasibility to conduct electroencephalography studies with high levels of accuracy, validated via both quantitative and qualitative metrics, compared against gold standard systems. An overwhelming majority of parents surveyed during studies indicated not only an overall preference for the wireless system, but also that its use would improve their children's physical and emotional health. Our findings demonstrate the potential for the ANNE system to perform multimodal monitoring to screen for a variety of neurologic diseases that have the potential to negatively impact neurodevelopment.
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Affiliation(s)
- Jeremy N. Wong
- Epilepsy Center, Division of Pediatric Neurology, Lurie Children’s Hospital of Chicago, Chicago, Illinois, United States of America
- Department of Pediatrics, Division of Neurology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Jessica R. Walter
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Erin C. Conrad
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | | | - Jong Yoon Lee
- Sibel Inc., Niles, Illinois, United States of America
| | | | | | - Sue J. Hong
- Department of Pediatrics, Division of Neurology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
- Division of Critical Care, Lurie Children’s Hospital of Chicago, Chicago, Illinois, United States of America
- Department of Pediatrics, Division of Critical Care, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Nicolò Pini
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, United States of America
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, New York, United States of America
| | - Lauren Marsillio
- Division of Critical Care, Lurie Children’s Hospital of Chicago, Chicago, Illinois, United States of America
- Department of Pediatrics, Division of Critical Care, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Khrystyna Moskalyk
- Epilepsy Center, Division of Pediatric Neurology, Lurie Children’s Hospital of Chicago, Chicago, Illinois, United States of America
| | - Mariana Vicenteno
- Epilepsy Center, Division of Pediatric Neurology, Lurie Children’s Hospital of Chicago, Chicago, Illinois, United States of America
| | - Erik Padilla
- Epilepsy Center, Division of Pediatric Neurology, Lurie Children’s Hospital of Chicago, Chicago, Illinois, United States of America
| | - Olivia Gann
- Sibel Inc., Niles, Illinois, United States of America
| | - Ha Uk Chung
- Sibel Inc., Niles, Illinois, United States of America
| | - Dennis Ryu
- Sibel Inc., Niles, Illinois, United States of America
| | - Carlie du Plessis
- Department of Obstetrics and Gynecology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Hein J. Odendaal
- Department of Obstetrics and Gynecology, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - William P. Fifer
- Department of Psychiatry, Columbia University Irving Medical Center, New York, New York, United States of America
- Division of Developmental Neuroscience, New York State Psychiatric Institute, New York, New York, United States of America
- Department of Pediatrics, Columbia University Irving Medical Center, New York, New York, United States of America
| | - Joyce Y. Wu
- Epilepsy Center, Division of Pediatric Neurology, Lurie Children’s Hospital of Chicago, Chicago, Illinois, United States of America
- Department of Pediatrics, Division of Neurology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
| | - Shuai Xu
- Sibel Inc., Niles, Illinois, United States of America
- Simpson Querrey Institute, Northwestern University, Chicago, Illinois, United States of America
- Department of Biomedical Engineering, Northwestern University, Evanston, Illinois, United States of America
- Department of Dermatology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States of America
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27
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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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28
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Li X, Chen H, Hu Y, Larsen RJ, Sutton BP, McElwain NL, Gao W. Functional neural network connectivity at 3 months predicts infant-mother dyadic flexibility during play at 6 months. Cereb Cortex 2023; 33:8321-8332. [PMID: 37020357 PMCID: PMC10321085 DOI: 10.1093/cercor/bhad117] [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: 08/29/2022] [Revised: 03/07/2023] [Accepted: 03/09/2023] [Indexed: 04/07/2023] Open
Abstract
Early functioning of neural networks likely underlies the flexible switching between internal and external orientation and may be key to the infant's ability to effectively engage in social interactions. To test this hypothesis, we examined the association between infants' neural networks at 3 months and infant-mother dyadic flexibility (denoting the structural variability of their interaction dynamics) at 3, 6, and 9 months. Participants included thirty-five infants (37% girls) and their mothers (87% White). At 3 months, infants participated in a resting-state functional magnetic resonance imaging session, and functional connectivity (FC) within the default mode (DMN) and salience (SN) networks, as well as DMN-SN internetwork FC, were derived using a seed-based approach. When infants were 3, 6, and 9 months, infant-mother dyads completed the Still-Face Paradigm where their individual engagement behaviors were observed and used to quantify dyadic flexibility using state space analysis. Results revealed that greater within-DMN FC, within-SN FC, and DMN-SN anticorrelation at 3 months predicted greater dyadic flexibility at 6 months, but not at 3 and 9 months. Findings suggest that early synchronization and interaction between neural networks underlying introspection and salience detection may support infants' flexible social interactions as they become increasingly active and engaged social partners.
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Affiliation(s)
- Xiaomei Li
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, 905 S. Goodwin Ave, Urbana, IL 61801, United States
| | - Haitao Chen
- Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute, Cedars Sinai Medical Center, 116 N. Robertson Blvd, Los Angeles, CA 90048, CA, United States
- David Geffen School of Medicine, University of California, Geffen Hall, 885 Tiverton Drive, Los Angeles, CA 90095, United States
| | - Yannan Hu
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, 905 S. Goodwin Ave, Urbana, IL 61801, United States
| | - Ryan J Larsen
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, United States
| | - Bradley P Sutton
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, United States
- Department of Bioengineering, University of Illinois at Urbana-Champaign, 1406 W. Green St, Urbana, IL 61801, United States
| | - Nancy L McElwain
- Department of Human Development and Family Studies, University of Illinois at Urbana-Champaign, 905 S. Goodwin Ave, Urbana, IL 61801, United States
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 405 N. Mathews Ave, Urbana, IL 61801, United States
| | - Wei Gao
- Department of Biomedical Sciences and Imaging, Biomedical Imaging Research Institute, Cedars Sinai Medical Center, 116 N. Robertson Blvd, Los Angeles, CA 90048, CA, United States
- David Geffen School of Medicine, University of California, Geffen Hall, 885 Tiverton Drive, Los Angeles, CA 90095, United States
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29
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Hendrickson TJ, Reiners P, Moore LA, Perrone AJ, Alexopoulos D, Lee EG, Styner M, Kardan O, Chamberlain TA, Mummaneni A, Caldas HA, Bower B, Stoyell S, Martin T, Sung S, Fair E, Uriarte-Lopez J, Rueter AR, Yacoub E, Rosenberg MD, Smyser CD, Elison JT, Graham A, Fair DA, Feczko E. BIBSNet: A Deep Learning Baby Image Brain Segmentation Network for MRI Scans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.22.533696. [PMID: 36993540 PMCID: PMC10055337 DOI: 10.1101/2023.03.22.533696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
Objectives Brain segmentation of infant magnetic resonance (MR) images is vitally important in studying developmental mental health and disease. The infant brain undergoes many changes throughout the first years of postnatal life, making tissue segmentation difficult for most existing algorithms. Here, we introduce a deep neural network BIBSNet (Baby and Infant Brain Segmentation Neural Network), an open-source, community-driven model that relies on data augmentation and a large sample size of manually annotated images to facilitate the production of robust and generalizable brain segmentations. Experimental Design Included in model training and testing were MR brain images on 84 participants with an age range of 0-8 months (median postmenstrual ages of 13.57 months). Using manually annotated real and synthetic segmentation images, the model was trained using a 10-fold cross-validation procedure. Testing occurred on MRI data processed with the DCAN labs infant-ABCD-BIDS processing pipeline using segmentations produced from gold standard manual annotation, joint-label fusion (JLF), and BIBSNet to assess model performance. Principal Observations Using group analyses, results suggest that cortical metrics produced using BIBSNet segmentations outperforms JLF segmentations. Additionally, when analyzing individual differences, BIBSNet segmentations perform even better. Conclusions BIBSNet segmentation shows marked improvement over JLF segmentations across all age groups analyzed. The BIBSNet model is 600x faster compared to JLF and can be easily included in other processing pipelines.
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Affiliation(s)
- Timothy J Hendrickson
- Minnesota Supercomputing Institute, University of Minnesota
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Paul Reiners
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Lucille A Moore
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Anders J Perrone
- Masonic Institute for the Developing Brain, University of Minnesota
| | | | - Erik G Lee
- Minnesota Supercomputing Institute, University of Minnesota
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Martin Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill
| | - Omid Kardan
- Department of Psychology, University of Chicago
- University of Michigan
| | | | | | | | | | - Sally Stoyell
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Tabitha Martin
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Sooyeon Sung
- Masonic Institute for the Developing Brain, University of Minnesota
| | - Ermias Fair
- Masonic Institute for the Developing Brain, University of Minnesota
| | | | | | - Essa Yacoub
- Department of Radiology, University of Minnesota
- Center for Magnetic Resonance Research, University of Minnesota
| | | | | | - Jed T Elison
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
- Department of Pediatrics, University of Minnesota
| | | | - Damien A Fair
- Masonic Institute for the Developing Brain, University of Minnesota
- Institute of Child Development, University of Minnesota
- Department of Pediatrics, University of Minnesota
| | - Eric Feczko
- Masonic Institute for the Developing Brain, University of Minnesota
- Department of Pediatrics, University of Minnesota
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30
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Planalp EM, Dowe KN, Alexander AL, Goldsmith HH, Davidson RJ, Dean DC. White matter microstructure predicts individual differences in infant fear (But not anger and sadness). Dev Sci 2023; 26:e13340. [PMID: 36367143 PMCID: PMC10079554 DOI: 10.1111/desc.13340] [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: 09/16/2021] [Revised: 08/19/2022] [Accepted: 10/18/2022] [Indexed: 11/13/2022]
Abstract
We examine neural correlates of discrete expressions of negative emotionality in infants to determine whether the microstructure of white matter tracts at 1 month of age foreshadows the expression of specific negative emotions later in infancy. Infants (n = 103) underwent neuroimaging at 1-month, and mothers reported on infant fear, sadness, and anger at 6, 12, and 18 months using the Infant Behavior Questionnaire-Revised. Levels and developmental change in fear, sadness, and anger were estimated from mother reports. Relations between MRI and infant emotion indicated that 1-month white matter microstructure was differentially associated with level and change in infant fear, but not anger or sadness, in the left stria terminalis (p < 0.05, corrected), a tract that connects frontal and tempo-parietal regions and has been implicated in emerging psychopathology in adults. More relaxed constraints on significance (p < 0.10, corrected) revealed that fear was associated with lower white matter microstructure bilaterally in the inferior portion of the stria terminalis and regions within the sagittal stratum. Results suggest the neurobehavioral uniqueness of fear as early as 1 month of age in regions that are associated with potential longer-term outcomes. This work highlights the early neural precursors of fearfulness, adding to literature explaining the psychobiological accounts of affective development. HIGHLIGHTS: Expressions of infant fear and anger, but not sadness, increase from 6 to 18 months of age. Early neural architecture in the stria terminalis is related to higher initial levels and increasing fear in infancy. After accounting for fear, anger and sadness do not appear to be associated with differences in early white matter microstructure. This work identifies early neural precursors of fearfulness as early as 1-month of age.
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Affiliation(s)
| | - Kristin N Dowe
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Andrew L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - H Hill Goldsmith
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Richard J Davidson
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Center for Healthy Minds, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Douglas C Dean
- Waisman Center, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Pediatrics, University of Wisconsin-Madison, Madison, Wisconsin, USA
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31
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Bessadok A, Mahjoub MA, Rekik I. Graph Neural Networks in Network Neuroscience. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2023; 45:5833-5848. [PMID: 36155474 DOI: 10.1109/tpami.2022.3209686] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
Noninvasive medical neuroimaging has yielded many discoveries about the brain connectivity. Several substantial techniques mapping morphological, structural and functional brain connectivities were developed to create a comprehensive road map of neuronal activities in the human brain -namely brain graph. Relying on its non-euclidean data type, graph neural network (GNN) provides a clever way of learning the deep graph structure and it is rapidly becoming the state-of-the-art leading to enhanced performance in various network neuroscience tasks. Here we review current GNN-based methods, highlighting the ways that they have been used in several applications related to brain graphs such as missing brain graph synthesis and disease classification. We conclude by charting a path toward a better application of GNN models in network neuroscience field for neurological disorder diagnosis and population graph integration. The list of papers cited in our work is available at https://github.com/basiralab/GNNs-in-Network-Neuroscience.
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32
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Wang Y, Chen L, Wu Z, Li T, Sun Y, Cheng J, Zhu H, Lin W, Wang L, Huang W, Li G. Longitudinal development of the cerebellum in human infants during the first 800 days. Cell Rep 2023; 42:112281. [PMID: 36964904 DOI: 10.1016/j.celrep.2023.112281] [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: 06/07/2022] [Revised: 11/24/2022] [Accepted: 03/03/2023] [Indexed: 03/26/2023] Open
Abstract
Revealing early dynamic development of the normative cerebellar structures contributes to exploring cerebellum-related neurodevelopmental disorders. Here, leveraging infant-tailored cerebellar image processing techniques, we studied the dynamic volumetric developmental trajectories of cerebellum and 27 cerebellar sub-regions and their relationships with behavioral scores based on 511 high-resolution structural MRI scans during the first 800 postnatal days. The ratio of the entire cerebellum to the intracranial volume increases rapidly at first and then peaks at 13 months after birth. Both the absolute and relative volumes of most cerebellar sub-structures exhibit rapid increase at first, then the relative volumes decrease slightly after arriving at peaks (except for X lobules). Each lobule depicts larger absolute volume in males than in females. The within-subject variation of the cerebellar volumetric percentile score is generally stable. The volumetric development of several lobules (e.g., V, Crus I, and Crus II) has a significantly positive correlation with fine motor skills during the age range examined.
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Affiliation(s)
- Ya Wang
- National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China; Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Liangjun Chen
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tengfei Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yue Sun
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jiale Cheng
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
| | - Wenhua Huang
- National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China.
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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33
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Phạm DĐ, McDonald DJ, Ding L, Nebel MB, Mejia AF. Less is more: balancing noise reduction and data retention in fMRI with data-driven scrubbing. Neuroimage 2023; 270:119972. [PMID: 36842522 PMCID: PMC10773988 DOI: 10.1016/j.neuroimage.2023.119972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Revised: 02/15/2023] [Accepted: 02/21/2023] [Indexed: 02/28/2023] Open
Abstract
Functional MRI (fMRI) data may be contaminated by artifacts arising from a myriad of sources, including subject head motion, respiration, heartbeat, scanner drift, and thermal noise. These artifacts cause deviations from common distributional assumptions, introduce spatial and temporal outliers, and reduce the signal-to-noise ratio of the data-all of which can have negative consequences for the accuracy and power of downstream statistical analysis. Scrubbing is a technique for excluding fMRI volumes thought to be contaminated by artifacts and generally comes in two flavors. Motion scrubbing based on subject head motion-derived measures is popular but suffers from a number of drawbacks, among them the need to choose a threshold, a lack of generalizability to multiband acquisitions, and high rates of censoring of individual volumes and entire subjects. Alternatively, data-driven scrubbing methods like DVARS are based on observed noise in the processed fMRI timeseries and may avoid some of these issues. Here we propose "projection scrubbing", a novel data-driven scrubbing method based on a statistical outlier detection framework and strategic dimension reduction, including independent component analysis (ICA), to isolate artifactual variation. We undertake a comprehensive comparison of motion scrubbing with data-driven projection scrubbing and DVARS. We argue that an appropriate metric for the success of scrubbing is maximal data retention subject to reasonable performance on typical benchmarks such as the validity, reliability, and identifiability of functional connectivity. We find that stringent motion scrubbing yields worsened validity, worsened reliability, and produced small improvements to fingerprinting. Meanwhile, data-driven scrubbing methods tend to yield greater improvements to fingerprinting while not generally worsening validity or reliability. Importantly, however, data-driven scrubbing excludes a fraction of the number of volumes or entire sessions compared to motion scrubbing. The ability of data-driven fMRI scrubbing to improve data retention without negatively impacting the quality of downstream analysis has major implications for sample sizes in population neuroscience research.
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Affiliation(s)
- Damon Đ Phạm
- Department of Statistics, Indiana University, Bloomington, IN, USA.
| | - Daniel J McDonald
- Department of Statistics, University of British Columbia, Vancouver, BC, Canada
| | - Lei Ding
- Department of Statistics, Indiana University, Bloomington, IN, USA
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University, Baltimore, MD, USA
| | - Amanda F Mejia
- Department of Statistics, Indiana University, Bloomington, IN, USA
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34
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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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35
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Xiao T, Dong X, Lu Y, Zhou W. High-Resolution and Multidimensional Phenotypes Can Complement Genomics Data to Diagnose Diseases in the Neonatal Population. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:204-215. [PMID: 37197647 PMCID: PMC10110825 DOI: 10.1007/s43657-022-00071-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 08/03/2022] [Accepted: 08/08/2022] [Indexed: 05/19/2023]
Abstract
Advances in genomic medicine have greatly improved our understanding of human diseases. However, phenome is not well understood. High-resolution and multidimensional phenotypes have shed light on the mechanisms underlying neonatal diseases in greater details and have the potential to optimize clinical strategies. In this review, we first highlight the value of analyzing traditional phenotypes using a data science approach in the neonatal population. We then discuss recent research on high-resolution, multidimensional, and structured phenotypes in neonatal critical diseases. Finally, we briefly introduce current technologies available for the analysis of multidimensional data and the value that can be provided by integrating these data into clinical practice. In summary, a time series of multidimensional phenome can improve our understanding of disease mechanisms and diagnostic decision-making, stratify patients, and provide clinicians with optimized strategies for therapeutic intervention; however, the available technologies for collecting multidimensional data and the best platform for connecting multiple modalities should be considered.
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Affiliation(s)
- Tiantian Xiao
- Division of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai, 201102 China
- Department of Neonatology, Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 610000 China
| | - Xinran Dong
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
| | - Yulan Lu
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
| | - Wenhao Zhou
- Division of Neonatology, Children’s Hospital of Fudan University, National Children’s Medical Center, 399 Wanyuan Road, Shanghai, 201102 China
- Center for Molecular Medicine, Pediatric Research Institute, Children’s Hospital of Fudan University, National Children’s Medical Center, Shanghai, 201102 China
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36
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Jaramillo V, Schoch SF, Markovic A, Kohler M, Huber R, Lustenberger C, Kurth S. An infant sleep electroencephalographic marker of thalamocortical connectivity predicts behavioral outcome in late infancy. Neuroimage 2023; 269:119924. [PMID: 36739104 DOI: 10.1016/j.neuroimage.2023.119924] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 01/24/2023] [Accepted: 02/01/2023] [Indexed: 02/05/2023] Open
Abstract
Infancy represents a critical period during which thalamocortical brain connections develop and mature. Deviations in the maturation of thalamocortical connectivity are linked to neurodevelopmental disorders. There is a lack of early biomarkers to detect and localize neuromaturational deviations, which can be overcome with mapping through high-density electroencephalography (hdEEG) assessed in sleep. Specifically, slow waves and spindles in non-rapid eye movement (NREM) sleep are generated by the thalamocortical system, and their characteristics, slow wave slope and spindle density, are closely related to neuroplasticity and learning. Spindles are often subdivided into slow (11.0-13.0 Hz) and fast (13.5-16.0 Hz) frequencies, for which not only different functions have been proposed, but for which also distinctive developmental trajectories have been reported across the first years of life. Recent studies further suggest that information processing during sleep underlying sleep-dependent learning is promoted by the temporal coupling of slow waves and spindles, yet slow wave-spindle coupling remains unexplored in infancy. Thus, we evaluated three potential biomarkers: 1) slow wave slope, 2) spindle density, and 3) the temporal coupling of slow waves with spindles. We use hdEEG to first examine the occurrence and spatial distribution of these three EEG features in healthy infants and second to evaluate a predictive relationship with later behavioral outcomes. We report four key findings: First, infants' EEG features appear locally: slow wave slope is maximal in occipital and frontal areas, whereas slow and fast spindle density is most pronounced frontocentrally. Second, slow waves and spindles are temporally coupled in infancy, with maximal coupling strength in the occipital areas of the brain. Third, slow wave slope, fast spindle density, and slow wave-spindle coupling are not associated with concurrent behavioral status (6 months). Fourth, fast spindle density in central and frontocentral regions at age 6 months predicts overall developmental status at age 12 months, and motor skills at age 12 and 24 months. Neither slow wave slope nor slow wave-spindle coupling predict later behavioral development. We further identified spindle frequency as a determinant of slow and fast spindle density, which accordingly, also predicts motor skills at 24 months. Our results propose fast spindle density, or alternatively spindle frequency, as early EEG biomarker for identifying thalamocortical maturation, which can potentially be used for early diagnosis of neurodevelopmental disorders in infants. These findings are in support of a role of sleep spindles in sensorimotor microcircuitry development. A crucial next step will be to evaluate whether early therapeutic interventions may be effective to reverse deviations in identified individuals at risk.
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Affiliation(s)
- Valeria Jaramillo
- Department of Pulmonology, University Hospital Zurich, Zurich, CH; Surrey Sleep Research Centre, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom; Neuromodulation Laboratory, School of Psychology, University of Surrey, Guildford, United Kingdom
| | - Sarah F Schoch
- Department of Pulmonology, University Hospital Zurich, Zurich, CH; Center of Competence Sleep & Health Zurich, University of Zurich, Zurich, CH; Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre, Nijmegen, NL
| | - Andjela Markovic
- Department of Pulmonology, University Hospital Zurich, Zurich, CH; Department of Psychology, University of Fribourg, Fribourg, CH
| | - Malcolm Kohler
- Department of Pulmonology, University Hospital Zurich, Zurich, CH; Center of Competence Sleep & Health Zurich, University of Zurich, Zurich, CH
| | - Reto Huber
- Child Development Center, University Children's Hospital Zurich, Zurich, CH; Children's Research Center, University Children's Hospital Zurich, University of Zurich (UZH), Zürich, Switzerland; Center of Competence Sleep & Health Zurich, University of Zurich, Zurich, CH; Department of Child and Adolescent Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, CH
| | - Caroline Lustenberger
- Center of Competence Sleep & Health Zurich, University of Zurich, Zurich, CH; Neural Control of Movement Lab, Institute of Human Movement Sciences and Sport, Department of Health Sciences and Technology, ETH Zurich, 8092 Zurich, Switzerland
| | - Salome Kurth
- Department of Pulmonology, University Hospital Zurich, Zurich, CH; Center of Competence Sleep & Health Zurich, University of Zurich, Zurich, CH; Department of Psychology, University of Fribourg, Fribourg, CH.
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37
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Burkhouse KL, Kujawa A. Annual Research Review: Emotion processing in offspring of mothers with depression diagnoses - a systematic review of neural and physiological research. J Child Psychol Psychiatry 2023; 64:583-607. [PMID: 36511171 DOI: 10.1111/jcpp.13734] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 11/25/2022] [Indexed: 12/15/2022]
Abstract
BACKGROUND Theories of the intergenerational transmission of depression emphasize alterations in emotion processing among offspring of depressed mothers as a key risk mechanism, raising questions about biological processes contributing to these alterations. The objective of this systematic annual research review was to examine and integrate studies of the associations between maternal depression diagnoses and offspring's emotion processing from birth through adolescence across biological measures including autonomic psychophysiology, electroencephalography (EEG), magnetoencephalography (MEG), event-related potentials (ERP), and structural and functional magnetic resonance imaging (MRI). METHODS The review was conducted in accordance with the PRISMA 2020 standards. A systematic search was conducted in PsycInfo and PubMed in 2022 for studies that included, 1) mothers with and without DSM-defined depressive disorders assessed via a clinical or diagnostic interview, and 2) measures of offspring emotion processing assessed at the psychophysiological or neural level between birth and 18 years of age. RESULTS Findings from 64 studies indicated that young offspring of mothers with depression histories exhibit heightened corticolimbic activation to negative emotional stimuli, reduced left frontal brain activation, and reduced ERP and mesocorticolimbic responses to reward cues compared to offspring of never-depressed mothers. Further, activation of resting-state networks involved in affective processing differentiate offspring of depressed relative to nondepressed mothers. Some of these alterations were only apparent among youth of depressed mothers exposed to negative environmental contexts or exhibiting current emotional problems. Further, some of these patterns were observable in infancy, reflecting very early emerging vulnerabilities. CONCLUSIONS This systematic review provides evidence that maternal depression is associated with alterations in emotion processing across several biological units of analysis in offspring. We present a preliminary conceptual model of the role of deficient emotion processing in pathways from maternal depression to offspring psychopathology and discuss future research avenues addressing limitations of the existing research and clinical implications.
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Affiliation(s)
- Katie L Burkhouse
- The Research Institute, Nationwide Children's Hospital, Columbus, OH, USA.,Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Autumn Kujawa
- Department of Psychology and Human Development, Vanderbilt University, Nashville, TN, USA
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38
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Chen G, Hong Y, Huynh KM, Yap PT. Deep learning prediction of diffusion MRI data with microstructure-sensitive loss functions. Med Image Anal 2023; 85:102742. [PMID: 36682154 PMCID: PMC9974781 DOI: 10.1016/j.media.2023.102742] [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: 02/09/2022] [Revised: 12/05/2022] [Accepted: 01/05/2023] [Indexed: 01/15/2023]
Abstract
Deep learning prediction of diffusion MRI (DMRI) data relies on the utilization of effective loss functions. Existing losses typically measure the signal-wise differences between the predicted and target DMRI data without considering the quality of derived diffusion scalars that are eventually utilized for quantification of tissue microstructure. Here, we propose two novel loss functions, called microstructural loss and spherical variance loss, to explicitly consider the quality of both the predicted DMRI data and derived diffusion scalars. We apply these loss functions to the prediction of multi-shell data and enhancement of angular resolution. Evaluation based on infant and adult DMRI data indicates that both microstructural loss and spherical variance loss improve the quality of derived diffusion scalars.
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Affiliation(s)
- Geng Chen
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA
| | - Yoonmi Hong
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA
| | - Khoi Minh Huynh
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA
| | - Pew-Thian Yap
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA.
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39
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Park J, Jang M, Heier L, Limperopoulos C, Zun Z. Rapid anatomical imaging of the neonatal brain using T 2 -prepared 3D balanced steady-state free precession. Magn Reson Med 2023; 89:1456-1468. [PMID: 36420869 PMCID: PMC10208121 DOI: 10.1002/mrm.29537] [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: 07/13/2022] [Revised: 10/18/2022] [Accepted: 11/03/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE To develop a new approach to 3D gradient echo-based anatomical imaging of the neonatal brain with a substantially shorter scan time than standard 3D fast spin echo (FSE) methods, while maintaining a high SNR. METHODS T2 -prepration was employed immediately prior to image acquisition of 3D balanced steady-state free precession (bSSFP) with a single trajectory of center-out k-space view ordering, which requires no magnetization recovery time between imaging segments during the scan. This approach was compared with 3D FSE, 2D single-shot FSE, and product 3D bSSFP imaging in numerical simulations, plus phantom and in vivo experiments. RESULTS T2 -prepared 3D bSSFP generated image contrast of gray matter, white matter, and CSF very similar to that of reference T2 -weighted imaging methods, without major image artifacts. Scan time of T2 -prepared 3D bSSFP was remarkably shorter compared to 3D FSE, whereas SNR was comparable to that of 3D FSE and higher than that of 2D single-shot FSE. Specific absorption rate of T2 -prepared 3D bSSFP remained within the safety limit. Determining an optimal imaging flip angle of T2 -prepared 3D bSSFP was critical to minimizing blurring of images. CONCLUSION T2 -prepared 3D bSSFP offers an alternative method for anatomical imaging of the neonatal brain with dramatically reduced scan time compared to standard 3D FSE and higher SNR than 2D single-shot FSE.
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Affiliation(s)
- Jinho Park
- Department of Cardiology, Yonsei University, Seoul, Korea
| | - MinJung Jang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
- Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, Korea
| | - Linda Heier
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Catherine Limperopoulos
- Developing Brain Institute, Division of Diagnostic Imaging and Radiology, Children’s National Hospital, Washington, DC, USA
- Division of Fetal and Transitional Medicine, Children’s National Hospital, Washington, DC, USA
- Department of Pediatrics, School of Medicine and Health Sciences, George Washington University, Washington, DC, USA
- Department of Radiology, School of Medicine and Health Sciences, George Washington University, Washington, DC, USA
| | - Zungho Zun
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
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40
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Wang L, Wu Z, Chen L, Sun Y, Lin W, Li G. iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction. Nat Protoc 2023; 18:1488-1509. [PMID: 36869216 DOI: 10.1038/s41596-023-00806-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 11/03/2022] [Indexed: 03/05/2023]
Abstract
The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic resonance imaging (MRI) datasets have been collected from multiple imaging sites with different scanners and imaging protocols for the investigation of normal and abnormal early brain development. However, it is extremely challenging to precisely process and quantify infant brain development with these multisite imaging data because infant brain MRI scans exhibit (a) extremely low and dynamic tissue contrast caused by ongoing myelination and maturation and (b) inter-site data heterogeneity resulting from the use of diverse imaging protocols/scanners. Consequently, existing computational tools and pipelines typically perform poorly on infant MRI data. To address these challenges, we propose a robust, multisite-applicable, infant-tailored computational pipeline that leverages powerful deep learning techniques. The main functionality of the proposed pipeline includes preprocessing, brain skull stripping, tissue segmentation, topology correction, cortical surface reconstruction and measurement. Our pipeline can handle both T1w and T2w structural infant brain MR images well in a wide age range (from birth to 6 years of age) and is effective for different imaging protocols/scanners, despite being trained only on the data from the Baby Connectome Project. Extensive comparisons with existing methods on multisite, multimodal and multi-age datasets demonstrate superior effectiveness, accuracy and robustness of our pipeline. We have maintained a website, iBEAT Cloud, for users to process their images with our pipeline ( http://www.ibeat.cloud ), which has successfully processed over 16,000 infant MRI scans from more than 100 institutions with various imaging protocols/scanners.
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Affiliation(s)
- Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Liangjun Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Yue Sun
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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41
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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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42
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Lasch C, Carlson SM, Elison JT. Responding to joint attention as a developmental catalyst: Longitudinal associations with language and social responsiveness. INFANCY 2023; 28:339-366. [PMID: 36404295 PMCID: PMC9899317 DOI: 10.1111/infa.12515] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 09/21/2022] [Accepted: 10/17/2022] [Indexed: 11/22/2022]
Abstract
Joint attention (JA), infants' ability to engage in triadic attention with another person and a separate object or event, emerges in infancy. Responding to joint attention (RJA) develops earlier than initiating joint attention (IJA) and may benefit from a reconceptualization from a competence to a skill that varies in performance. Investigating associations between RJA performance and important skills of toddlerhood such as language, social responsiveness, and executive function (EF) in typically developing samples can better elucidate how RJA may serve as a developmental precursor to later dimensional skills, with implications for both typical and atypical development. Here, 210 (82% White) infants completed the Dimensional Joint Attention Assessment (DJAA), a naturalistic play-based assessment of RJA, at 8-15 months. At 16-38 months social responsiveness, verbal ability, and EF were assessed. Multilevel models showed that DJAA scores were associated with later verbal abilities and parent-reported social responsiveness. Exploratory analyses showed trend-level associations between RJA and EF. Results establish the content validity of the DJAA as a measure of RJA, and longitudinal associations with later verbal ability and social responsiveness. Future work should examine EF emergence and consolidation, and RJA and later EF associations.
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Affiliation(s)
- Carolyn Lasch
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA
| | | | - Jed T. Elison
- Institute of Child Development, University of Minnesota, Minneapolis, MN, USA,Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
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43
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Zeng Z, Zhao T, Sun L, Zhang Y, Xia M, Liao X, Zhang J, Shen D, Wang L, He Y. 3D-MASNet: 3D mixed-scale asymmetric convolutional segmentation network for 6-month-old infant brain MR images. Hum Brain Mapp 2023; 44:1779-1792. [PMID: 36515219 PMCID: PMC9921327 DOI: 10.1002/hbm.26174] [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: 07/24/2022] [Revised: 11/04/2022] [Accepted: 11/25/2022] [Indexed: 12/15/2022] Open
Abstract
Precise segmentation of infant brain magnetic resonance (MR) images into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) are essential for studying neuroanatomical hallmarks of early brain development. However, for 6-month-old infants, the extremely low-intensity contrast caused by inherent myelination hinders accurate tissue segmentation. Existing convolutional neural networks (CNNs) based segmentation models for this task generally employ single-scale symmetric convolutions, which are inefficient for encoding the isointense tissue boundaries in baby brain images. Here, we propose a 3D mixed-scale asymmetric convolutional segmentation network (3D-MASNet) framework for brain MR images of 6-month-old infants. We replaced the traditional convolutional layer of an existing to-be-trained network with a 3D mixed-scale convolution block consisting of asymmetric kernels (MixACB) during the training phase and then equivalently converted it into the original network. Five canonical CNN segmentation models were evaluated using both T1- and T2-weighted images of 23 6-month-old infants from iSeg-2019 datasets, which contained manual labels as ground truth. MixACB significantly enhanced the average accuracy of all five models and obtained the most considerable improvement in the fully convolutional network model (CC-3D-FCN) and the highest performance in the Dense U-Net model. This approach further obtained Dice coefficient accuracies of 0.931, 0.912, and 0.961 in GM, WM, and CSF, respectively, ranking first among 30 teams on the validation dataset of the iSeg-2019 Grand Challenge. Thus, the proposed 3D-MASNet can improve the accuracy of existing CNNs-based segmentation models as a plug-and-play solution that offers a promising technique for future infant brain MRI studies.
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Affiliation(s)
- Zilong Zeng
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Yihe Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xuhong Liao
- School of Systems Science, Beijing Normal University, Beijing, China
| | - Jiaying Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.,Shanghai Clinical Research and Trial Center, Shanghai, China.,Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.,Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China.,IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.,Chinese Institute for Brain Research, Beijing, China
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44
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Digital brain atlases reveal postnatal development to 2 years of age in human infants. Nat Methods 2023; 20:38-39. [PMID: 36585457 DOI: 10.1038/s41592-022-01704-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
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45
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Ahmad S, Wu Y, Wu Z, Thung KH, Liu S, Lin W, Li G, Wang L, Yap PT. Multifaceted atlases of the human brain in its infancy. Nat Methods 2023; 20:55-64. [PMID: 36585454 PMCID: PMC9834057 DOI: 10.1038/s41592-022-01703-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 10/25/2022] [Indexed: 12/31/2022]
Abstract
Brain atlases are spatial references for integrating, processing, and analyzing brain features gathered from different individuals, sources, and scales. Here we introduce a collection of joint surface-volume atlases that chart postnatal development of the human brain in a spatiotemporally dense manner from two weeks to two years of age. Our month-specific atlases chart normative patterns and capture key traits of early brain development and are therefore conducive to identifying aberrations from normal developmental trajectories. These atlases will enhance our understanding of early structural and functional development by facilitating the mapping of diverse features of the infant brain to a common reference frame for precise multifaceted quantification of cortical and subcortical changes.
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Affiliation(s)
- Sahar Ahmad
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Kim-Han Thung
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Siyuan Liu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.
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46
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Bao S, Boyd BD, Kanakaraj P, Ramadass K, Meyer FAC, Liu Y, Duett WE, Huo Y, Lyu I, Zald DH, Smith SA, Rogers BP, Landman BA. Integrating the BIDS Neuroimaging Data Format and Workflow Optimization for Large-Scale Medical Image Analysis. J Digit Imaging 2022; 35:1576-1589. [PMID: 35922700 PMCID: PMC9712842 DOI: 10.1007/s10278-022-00679-8] [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: 07/31/2021] [Revised: 06/21/2022] [Accepted: 07/08/2022] [Indexed: 01/01/2023] Open
Abstract
A robust medical image computing infrastructure must host massive multimodal archives, perform extensive analysis pipelines, and execute scalable job management. An emerging data format standard, the Brain Imaging Data Structure (BIDS), introduces complexities for interfacing with XNAT archives. Moreover, workflow integration is combinatorically problematic when matching large amount of processing to large datasets. Historically, workflow engines have been focused on refining workflows themselves instead of actual job generation. However, such an approach is incompatible with data centric architecture that hosts heterogeneous medical image computing. Distributed automation for XNAT toolkit (DAX) provides large-scale image storage and analysis pipelines with an optimized job management tool. Herein, we describe developments for DAX that allows for integration of XNAT and BIDS standards. We also improve DAX's efficiencies of diverse containerized workflows in a high-performance computing (HPC) environment. Briefly, we integrate YAML configuration processor scripts to abstract workflow data inputs, data outputs, commands, and job attributes. Finally, we propose an online database-driven mechanism for DAX to efficiently identify the most recent updated sessions, thereby improving job building efficiency on large projects. We refer the proposed overall DAX development in this work as DAX-1 (DAX version 1). To validate the effectiveness of the new features, we verified (1) the efficiency of converting XNAT data to BIDS format and the correctness of the conversion using a collection of BIDS standard containerized neuroimaging workflows, (2) how YAML-based processor simplified configuration setup via a sequence of application pipelines, and (3) the productivity of DAX-1 on generating actual HPC processing jobs compared with earlier DAX baseline method. The empirical results show that (1) DAX-1 converting XNAT data to BIDS has similar speed as accessing XNAT data only; (2) YAML can integrate to the DAX-1 with shallow learning curve for users, and (3) DAX-1 reduced the job/assessor generation latency by finding recent modified sessions. Herein, we present approaches for efficiently integrating XNAT and modern image formats with a scalable workflow engine for the large-scale dataset access and processing.
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Affiliation(s)
- Shunxing Bao
- Computer Science, Vanderbilt University, Nashville, TN USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN USA
| | - Brian D. Boyd
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | | | | | | | - Yuqian Liu
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
| | - William E. Duett
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
| | - Yuankai Huo
- Computer Science, Vanderbilt University, Nashville, TN USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN USA
- Data Science Institute, Vanderbilt University, Nashville, TN USA
| | - Ilwoo Lyu
- Computer Science and Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea
| | - David H. Zald
- Department of Psychology, Vanderbilt University, Nashville, TN USA
- Department of Psychiatry, Brain Health Institute, Rutgers University, Piscataway, NJ USA
| | - Seth A. Smith
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - Baxter P. Rogers
- Department of Psychiatry and Behavioral Sciences, Vanderbilt University Medical Center, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA
| | - Bennett A. Landman
- Computer Science, Vanderbilt University, Nashville, TN USA
- Institute of Imaging Science, Vanderbilt University, Nashville, TN USA
- Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN USA
- Electrical and Computer Engineering, Vanderbilt University, Nashville, TN USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN USA
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Li Y, Zhang X, Nie J, Zhang G, Fang R, Xu X, Wu Z, Hu D, Wang L, Zhang H, Lin W, Li G. Brain Connectivity Based Graph Convolutional Networks and Its Application to Infant Age Prediction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2764-2776. [PMID: 35500083 PMCID: PMC10041448 DOI: 10.1109/tmi.2022.3171778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Infancy is a critical period for the human brain development, and brain age is one of the indices for the brain development status associated with neuroimaging data. The difference between the predicted age based on neuroimaging and the chronological age can provide an important early indicator of deviation from the normal developmental trajectory. In this study, we utilize the Graph Convolutional Network (GCN) to predict the infant brain age based on resting-state fMRI data. The brain connectivity obtained from rs-fMRI can be represented as a graph with brain regions as nodes and functional connections as edges. However, since the brain connectivity is a fully connected graph with features on edges, current GCN cannot be directly used for it is a node-based method for sparse graphs. Hence, we propose an edge-based Graph Path Convolution (GPC) method, which aggregates the information from different paths and can be naturally applied on dense graphs. We refer the whole model as Brain Connectivity Graph Convolutional Networks (BC-GCN). Further, two upgraded network structures are proposed by including the residual and attention modules, referred as BC-GCN-Res and BC-GCN-SE to emphasize the information of the original data and enhance influential channels. Moreover, we design a two-stage coarse-to-fine framework, which determines the age group first and then predicts the age using group-specific BC-GCN-SE models. To avoid accumulated errors from the first stage, a cross-group training strategy is adopted for the second stage regression models. We conduct experiments on infant fMRI scans from 6 to 811 days of age. The coarse-to-fine framework shows significant improvements when being applied to several models (reducing error over 10 days). Comparing with state-of-the-art methods, our proposed model BC-GCN-SE with coarse-to-fine framework reduces the mean absolute error of the prediction from >70 days to 49.9 days. The code is now available at https://github.com/SCUT-Xinlab/BC-GCN.
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Zhang X, He X, Guo J, Ettehadi N, Aw N, Semanek D, Posner J, Laine A, Wang Y. PTNet3D: A 3D High-Resolution Longitudinal Infant Brain MRI Synthesizer Based on Transformers. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:2925-2940. [PMID: 35560070 PMCID: PMC9529847 DOI: 10.1109/tmi.2022.3174827] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
An increased interest in longitudinal neurodevelopment during the first few years after birth has emerged in recent years. Noninvasive magnetic resonance imaging (MRI) can provide crucial information about the development of brain structures in the early months of life. Despite the success of MRI collections and analysis for adults, it remains a challenge for researchers to collect high-quality multimodal MRIs from developing infant brains because of their irregular sleep pattern, limited attention, inability to follow instructions to stay still during scanning. In addition, there are limited analytic approaches available. These challenges often lead to a significant reduction of usable MRI scans and pose a problem for modeling neurodevelopmental trajectories. Researchers have explored solving this problem by synthesizing realistic MRIs to replace corrupted ones. Among synthesis methods, the convolutional neural network-based (CNN-based) generative adversarial networks (GANs) have demonstrated promising performance. In this study, we introduced a novel 3D MRI synthesis framework- pyramid transformer network (PTNet3D)- which relies on attention mechanisms through transformer and performer layers. We conducted extensive experiments on high-resolution Developing Human Connectome Project (dHCP) and longitudinal Baby Connectome Project (BCP) datasets. Compared with CNN-based GANs, PTNet3D consistently shows superior synthesis accuracy and superior generalization on two independent, large-scale infant brain MRI datasets. Notably, we demonstrate that PTNet3D synthesized more realistic scans than CNN-based models when the input is from multi-age subjects. Potential applications of PTNet3D include synthesizing corrupted or missing images. By replacing corrupted scans with synthesized ones, we observed significant improvement in infant whole brain segmentation.
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Xu T, Wu Y, Hong Y, Ahmad S, Huynh KM, Wang Z, Lin W, Chang WT, Yap PT. Rapid Diffusion Magnetic Resonance Imaging Using Slice-Interleaved Encoding. Med Image Anal 2022; 81:102548. [PMID: 35917693 PMCID: PMC9988327 DOI: 10.1016/j.media.2022.102548] [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: 07/26/2021] [Revised: 06/24/2022] [Accepted: 07/12/2022] [Indexed: 11/28/2022]
Abstract
In this paper, we present a robust reconstruction scheme for diffusion MRI (dMRI) data acquired using slice-interleaved diffusion encoding (SIDE). When combined with SIDE undersampling and simultaneous multi-slice (SMS) imaging, our reconstruction strategy is capable of significantly reducing the amount of data that needs to be acquired, enabling high-speed diffusion imaging for pediatric, elderly, and claustrophobic individuals. In contrast to the conventional approach of acquiring a full diffusion-weighted (DW) volume per diffusion wavevector, SIDE acquires in each repetition time (TR) a volume that consists of interleaved slice groups, each group corresponding to a different diffusion wavevector. This strategy allows SIDE to rapidly acquire data covering a large number of wavevectors within a short period of time. The proposed reconstruction method uses a diffusion spectrum model and multi-dimensional total variation to recover full DW images from DW volumes that are slice-undersampled due to unacquired SIDE volumes. We formulate an inverse problem that can be solved efficiently using the alternating direction method of multipliers (ADMM). Experiment results demonstrate that DW images can be reconstructed with high fidelity even when the acquisition is accelerated by 25 folds.
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Affiliation(s)
- Tiantian Xu
- Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Ye Wu
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Yoonmi Hong
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Sahar Ahmad
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Khoi Minh Huynh
- Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Zhixing Wang
- Department of Biomedical Engineering, University of Virginia, Charlottesville, VA 22904, USA
| | - Weili Lin
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Wei-Tang Chang
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA
| | - Pew-Thian Yap
- Department of Computer Science, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Biomedical Engineering, University of North Carolina, Chapel Hill, NC 27599, USA; Department of Radiology, University of North Carolina, Chapel Hill, NC 27599, USA; Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599, USA.
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Bahathiq RA, Banjar H, Bamaga AK, Jarraya SK. Machine learning for autism spectrum disorder diagnosis using structural magnetic resonance imaging: Promising but challenging. Front Neuroinform 2022; 16:949926. [PMID: 36246393 PMCID: PMC9554556 DOI: 10.3389/fninf.2022.949926] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 08/10/2022] [Indexed: 11/13/2022] Open
Abstract
Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that affects approximately 1% of the population and causes significant burdens. ASD's pathogenesis remains elusive; hence, diagnosis is based on a constellation of behaviors. Structural magnetic resonance imaging (sMRI) studies have shown several abnormalities in volumetric and geometric features of the autistic brain. However, inconsistent findings prevented most contributions from being translated into clinical practice. Establishing reliable biomarkers for ASD using sMRI is crucial for the correct diagnosis and treatment. In recent years, machine learning (ML) and specifically deep learning (DL) have quickly extended to almost every sector, notably in disease diagnosis. Thus, this has led to a shift and improvement in ASD diagnostic methods, fulfilling most clinical diagnostic requirements. However, ASD discovery remains difficult. This review examines the ML-based ASD diagnosis literature over the past 5 years. A literature-based taxonomy of the research landscape has been mapped, and the major aspects of this topic have been covered. First, we provide an overview of ML's general classification pipeline and the features of sMRI. Next, representative studies are highlighted and discussed in detail with respect to methods, and biomarkers. Finally, we highlight many common challenges and make recommendations for future directions. In short, the limited sample size was the main obstacle; Thus, comprehensive data sets and rigorous methods are necessary to check the generalizability of the results. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians soon.
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Affiliation(s)
- Reem Ahmed Bahathiq
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Haneen Banjar
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
- Center of Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ahmed K. Bamaga
- Neuromuscular Medicine Unit, Department of Pediatric, Faculty of Medicine and King Abdulaziz University Hospital, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Salma Kammoun Jarraya
- Department of Computer Science, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
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