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Schuurmans IK, Mulder RH, Baltramonaityte V, Lahtinen A, Qiuyu F, Rothmann LM, Staginnus M, Tuulari J, Burt SA, Buss C, Craig JM, Donald KA, Felix JF, Freeman TP, Grassi-Oliveira R, Huels A, Hyde LW, Jones SA, Karlsson H, Karlsson L, Koen N, Lawn W, Mitchell C, Monk CS, Mooney MA, Muetzel R, Nigg JT, Belangero SIN, Notterman D, O'Connor T, O'Donnell KJ, Pan PM, Paunio T, Ryabinin P, Saffery R, Salum GA, Seal M, Silk TJ, Stein DJ, Zar H, Walton E, Cecil CAM. Consortium Profile: The Methylation, Imaging and NeuroDevelopment (MIND) Consortium. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.06.23.24309353. [PMID: 38978656 PMCID: PMC11230303 DOI: 10.1101/2024.06.23.24309353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Epigenetic processes, such as DNA methylation, show potential as biological markers and mechanisms underlying gene-environment interplay in the prediction of mental health and other brain-based phenotypes. However, little is known about how peripheral epigenetic patterns relate to individual differences in the brain itself. An increasingly popular approach to address this is by combining epigenetic and neuroimaging data; yet, research in this area is almost entirely comprised of cross-sectional studies in adults. To bridge this gap, we established the Methylation, Imaging and NeuroDevelopment (MIND) Consortium, which aims to bring a developmental focus to the emerging field of Neuroimaging Epigenetics by (i) promoting collaborative, adequately powered developmental research via multi-cohort analyses; (ii) increasing scientific rigor through the establishment of shared pipelines and open science practices; and (iii) advancing our understanding of DNA methylation-brain dynamics at different developmental periods (from birth to emerging adulthood), by leveraging data from prospective, longitudinal pediatric studies. MIND currently integrates 15 cohorts worldwide, comprising (repeated) measures of DNA methylation in peripheral tissues (blood, buccal cells, and saliva) and neuroimaging by magnetic resonance imaging across up to five time points over a period of up to 21 years (Npooled DNAm = 11,299; Npooled neuroimaging = 10,133; Npooled combined = 4,914). By triangulating associations across multiple developmental time points and study types, we hope to generate new insights into the dynamic relationships between peripheral DNA methylation and the brain, and how these ultimately relate to neurodevelopmental and psychiatric phenotypes.
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Abate F, Adu-Amankwah A, Ae-Ngibise KA, Agbokey F, Agyemang VA, Agyemang CT, Akgun C, Ametepe J, Arichi T, Asante KP, Balaji S, Baljer L, Basser PJ, Beauchemin J, Bennallick C, Berhane Y, Boateng-Mensah Y, Bourke NJ, Bradford L, Bruchhage M, Lorente RC, Cawley P, Cercignani M, D Sa V, Canha AD, Navarro ND, Dean DC, Delarosa J, Donald KA, Dvorak A, Edwards AD, Field D, Frail H, Freeman B, George T, Gholam J, Guerrero-Gonzalez J, Hajnal JV, Haque R, Hollander W, Hoodbhoy Z, Huentelman M, Jafri SK, Jones DK, Joubert F, Karaulanov T, Kasaro MP, Knackstedt S, Kolind S, Koshy B, Kravitz R, Lafayette SL, Lee AC, Lena B, Lepore N, Linguraru M, Ljungberg E, Lockart Z, Loth E, Mannam P, Masemola KM, Moran R, Murphy D, Nakwa FL, Nankabirwa V, Nelson CA, North K, Nyame S, O Halloran R, O'Muircheartaigh J, Oakley BF, Odendaal H, Ongeti CM, Onyango D, Oppong SA, Padormo F, Parvez D, Paus T, Pepper MS, Phiri KS, Poorman M, Ringshaw JE, Rogers J, Rutherford M, Sabir H, Sacolick L, Seal M, Sekoli ML, Shama T, Siddiqui K, Sindano N, Spelke MB, Springer PE, Suleman FE, Sundgren PC, Teixeira R, Terekegn W, Traughber M, Tuuli MG, Rensburg JV, Váša F, Velaphi S, Velasco P, Viljoen IM, Vokhiwa M, Webb A, Weiant C, Wiley N, Wintermark P, Yibetal K, Deoni S, Williams S. UNITY: A low-field magnetic resonance neuroimaging initiative to characterize neurodevelopment in low and middle-income settings. Dev Cogn Neurosci 2024; 69:101397. [PMID: 39029330 PMCID: PMC11315107 DOI: 10.1016/j.dcn.2024.101397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 05/28/2024] [Accepted: 05/30/2024] [Indexed: 07/21/2024] Open
Abstract
Measures of physical growth, such as weight and height have long been the predominant outcomes for monitoring child health and evaluating interventional outcomes in public health studies, including those that may impact neurodevelopment. While physical growth generally reflects overall health and nutritional status, it lacks sensitivity and specificity to brain growth and developing cognitive skills and abilities. Psychometric tools, e.g., the Bayley Scales of Infant and Toddler Development, may afford more direct assessment of cognitive development but they require language translation, cultural adaptation, and population norming. Further, they are not always reliable predictors of future outcomes when assessed within the first 12-18 months of a child's life. Neuroimaging may provide more objective, sensitive, and predictive measures of neurodevelopment but tools such as magnetic resonance (MR) imaging are not readily available in many low and middle-income countries (LMICs). MRI systems that operate at lower magnetic fields (< 100mT) may offer increased accessibility, but their use for global health studies remains nascent. The UNITY project is envisaged as a global partnership to advance neuroimaging in global health studies. Here we describe the UNITY project, its goals, methods, operating procedures, and expected outcomes in characterizing neurodevelopment in sub-Saharan Africa and South Asia.
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Affiliation(s)
- F Abate
- Addis Continental Institute of Public Health, Addis Ababa, Ethiopia; Waisman Research Center, Madison, WI, USA
| | - A Adu-Amankwah
- Korle-Bu Teaching Hospital, Accra, Ghana; Waisman Research Center, Madison, WI, USA
| | - K A Ae-Ngibise
- Kintampo Health Research Centre, Research and Development Division, Ghana Health Service, Kintampo North Municipality, Bono East Region, Ghana; Waisman Research Center, Madison, WI, USA
| | - F Agbokey
- Kintampo Health Research Centre, Research and Development Division, Ghana Health Service, Kintampo North Municipality, Bono East Region, Ghana; Waisman Research Center, Madison, WI, USA
| | - V A Agyemang
- Kintampo Health Research Centre, Research and Development Division, Ghana Health Service, Kintampo North Municipality, Bono East Region, Ghana; Waisman Research Center, Madison, WI, USA
| | - C T Agyemang
- Kintampo Health Research Centre, Research and Development Division, Ghana Health Service, Kintampo North Municipality, Bono East Region, Ghana; Waisman Research Center, Madison, WI, USA
| | - C Akgun
- flywheel.io Minneapolis, MN, USA; Waisman Research Center, Madison, WI, USA
| | - J Ametepe
- Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK; Waisman Research Center, Madison, WI, USA
| | - T Arichi
- Centre for the Developing Brain, Kings College London, London, UK; Waisman Research Center, Madison, WI, USA
| | - K P Asante
- Kintampo Health Research Centre, Research and Development Division, Ghana Health Service, Kintampo North Municipality, Bono East Region, Ghana; Waisman Research Center, Madison, WI, USA
| | - S Balaji
- Dept. of Neurology, University of British Columbia, Vancouver, BC, Canada; Waisman Research Center, Madison, WI, USA
| | - L Baljer
- Centre for Neuroimaging Sciences, King's College London, London, UK; Waisman Research Center, Madison, WI, USA
| | - P J Basser
- National Institutes of Health, Washington, DC, USA; Waisman Research Center, Madison, WI, USA
| | - J Beauchemin
- Advanced Baby Imaging Lab, Providence, RI, USA; Waisman Research Center, Madison, WI, USA
| | - C Bennallick
- Centre for Neuroimaging Sciences, King's College London, London, UK; Waisman Research Center, Madison, WI, USA
| | - Y Berhane
- Addis Continental Institute of Public Health, Addis Ababa, Ethiopia; Waisman Research Center, Madison, WI, USA
| | - Y Boateng-Mensah
- Korle-Bu Teaching Hospital, Accra, Ghana; Waisman Research Center, Madison, WI, USA
| | - N J Bourke
- Centre for Neuroimaging Sciences, King's College London, London, UK; Waisman Research Center, Madison, WI, USA
| | - L Bradford
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital and the Neuroscience Institute, University of Cape Town, Cape Town, South Africa; Waisman Research Center, Madison, WI, USA
| | - Mmk Bruchhage
- Dept. of Psychology, Stavanger University, Norway; Waisman Research Center, Madison, WI, USA
| | - R Cano Lorente
- Advanced Baby Imaging Lab, Providence, RI, USA; Waisman Research Center, Madison, WI, USA
| | - P Cawley
- Centre for the Developing Brain, Kings College London, London, UK; Waisman Research Center, Madison, WI, USA
| | - M Cercignani
- Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK; Waisman Research Center, Madison, WI, USA
| | - V D Sa
- Advanced Baby Imaging Lab, Providence, RI, USA; Waisman Research Center, Madison, WI, USA
| | - A de Canha
- Institute for Cellular and Molecular Medicine, Department of Medical Immunology, University of Pretoria, Pretoria, South Africa; Waisman Research Center, Madison, WI, USA
| | - N de Navarro
- Collective Minds Radiology, Sweden; Waisman Research Center, Madison, WI, USA
| | - D C Dean
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA; Waisman Research Center, Madison, WI, USA
| | - J Delarosa
- PATH, Seattle, WA, USA; Waisman Research Center, Madison, WI, USA
| | - K A Donald
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital and the Neuroscience Institute, University of Cape Town, Cape Town, South Africa; Waisman Research Center, Madison, WI, USA
| | - A Dvorak
- Dept. of Neurology, University of British Columbia, Vancouver, BC, Canada; Waisman Research Center, Madison, WI, USA
| | - A D Edwards
- Centre for the Developing Brain, Kings College London, London, UK; Waisman Research Center, Madison, WI, USA
| | - D Field
- Collective Minds Radiology, Sweden; Waisman Research Center, Madison, WI, USA
| | - H Frail
- Hyperfine.io, Guilford, CT, USA; Waisman Research Center, Madison, WI, USA
| | - B Freeman
- University of North Carolina, Department of Obstetrics and Gynecology, Chapel Hill, USA; Waisman Research Center, Madison, WI, USA
| | - T George
- Department of Radiology, Faculty of Health Sciences, Chris Hani Baragwanath Academic Hospital, University; Waisman Research Center, Madison, WI, USA
| | - J Gholam
- Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK; Waisman Research Center, Madison, WI, USA
| | - J Guerrero-Gonzalez
- School of Medicine and Public Health, University of Wisconsin, Madison, WI, USA; Waisman Research Center, Madison, WI, USA
| | - J V Hajnal
- Centre for the Developing Brain, Kings College London, London, UK; Waisman Research Center, Madison, WI, USA
| | - R Haque
- International Centre for Diarrheal Disease Research, Bangladesh (Icddr,b), Dhaka, Bangladesh; Waisman Research Center, Madison, WI, USA
| | - W Hollander
- CaliberMRI, Boulder CO USA; Waisman Research Center, Madison, WI, USA
| | - Z Hoodbhoy
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan; Waisman Research Center, Madison, WI, USA
| | - M Huentelman
- TGen, Phoenix, AZ, USA; Waisman Research Center, Madison, WI, USA
| | - S K Jafri
- Department of Pediatrics and Child Health, Aga Khan University, Karachi, Pakistan; Waisman Research Center, Madison, WI, USA
| | - D K Jones
- Cardiff University Brain Research Imaging Center, Cardiff University, Cardiff, UK; Waisman Research Center, Madison, WI, USA
| | - F Joubert
- Centre for Bioinformatics and Computational Biology, Department of Biochemistry, Microbiology and Genetics, University of Pretoria, Pretoria, South Africa; Waisman Research Center, Madison, WI, USA
| | - T Karaulanov
- CaliberMRI, Boulder CO USA; Waisman Research Center, Madison, WI, USA
| | - M P Kasaro
- University of North Carolina - Global Projects Zambia, Lusaka, Zambia; Waisman Research Center, Madison, WI, USA
| | - S Knackstedt
- PATH, Seattle, WA, USA; Waisman Research Center, Madison, WI, USA
| | - S Kolind
- Dept. of Neurology, University of British Columbia, Vancouver, BC, Canada; Waisman Research Center, Madison, WI, USA
| | - B Koshy
- Developmental Paediatrics, Christian Medical College, Vellore, India; Waisman Research Center, Madison, WI, USA
| | - R Kravitz
- International Society for Magnetic Resonance in Medicine, San Fransisco, CA, USA; Waisman Research Center, Madison, WI, USA
| | - S Lecurieux Lafayette
- Centre for the Developing Brain, Kings College London, London, UK; Waisman Research Center, Madison, WI, USA
| | - A C Lee
- Brigham and Women's Hospital, Department of Pediatrics; Harvard Medical School; Boston, MA, USA; Waisman Research Center, Madison, WI, USA
| | - B Lena
- Dept. of Radiology, Leiden University, Leiden, the Netherlands; Waisman Research Center, Madison, WI, USA
| | - N Lepore
- Dept. of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA; Waisman Research Center, Madison, WI, USA
| | - M Linguraru
- Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA; Waisman Research Center, Madison, WI, USA
| | - E Ljungberg
- Medical Radiation Physics, Lund University, Lund, Sweden; Waisman Research Center, Madison, WI, USA
| | - Z Lockart
- Department of Radiology, Faculty of Health Sciences, Steve Biko Academic Hospital, University of Pretoria, Pretoria, South Africa; Waisman Research Center, Madison, WI, USA
| | - E Loth
- Department of Forensic and Neurodevelopemental Science, Institute of Psychatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Waisman Research Center, Madison, WI, USA
| | - P Mannam
- Developmental Paediatrics, Christian Medical College, Vellore, India; Waisman Research Center, Madison, WI, USA
| | - K M Masemola
- Department of Paediatrics and Child Health, Kalafong Hospital and Faculty of Health Sciences, University of Pretoria, South Africa; Waisman Research Center, Madison, WI, USA
| | - R Moran
- Centre for Neuroimaging Sciences, King's College London, London, UK; Waisman Research Center, Madison, WI, USA
| | - D Murphy
- Department of Forensic and Neurodevelopemental Science, Institute of Psychatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Waisman Research Center, Madison, WI, USA
| | - F L Nakwa
- Department of Paediatrics and Child Health, Chris Hani Baragwanath Academic Hospital and Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Waisman Research Center, Madison, WI, USA
| | - V Nankabirwa
- Department of Epidemiology and Biostatistics, School of Public Health, Makerere University. Kampala, Uganda; Waisman Research Center, Madison, WI, USA
| | - C A Nelson
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, USA; Waisman Research Center, Madison, WI, USA
| | - K North
- Brigham and Women's Hospital, Department of Pediatrics; Harvard Medical School; Boston, MA, USA; Waisman Research Center, Madison, WI, USA
| | - S Nyame
- Kintampo Health Research Centre, Research and Development Division, Ghana Health Service, Kintampo North Municipality, Bono East Region, Ghana; Waisman Research Center, Madison, WI, USA
| | - R O Halloran
- Hyperfine.io, Guilford, CT, USA; Waisman Research Center, Madison, WI, USA
| | - J O'Muircheartaigh
- Centre for the Developing Brain, Kings College London, London, UK; Waisman Research Center, Madison, WI, USA
| | - B F Oakley
- Department of Forensic and Neurodevelopemental Science, Institute of Psychatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Waisman Research Center, Madison, WI, USA
| | - H Odendaal
- Dept Obstet Gynaecol, Stellenbosch University, South Africa; Waisman Research Center, Madison, WI, USA
| | - C M Ongeti
- Jaramogi Oginga Odinga Teaching and Referral Hospital, Kisumu, Kenya; Waisman Research Center, Madison, WI, USA
| | - D Onyango
- Jaramogi Oginga Odinga Teaching and Referral Hospital, Kisumu, Kenya; Waisman Research Center, Madison, WI, USA
| | - S A Oppong
- Korle-Bu Teaching Hospital, Accra, Ghana; Waisman Research Center, Madison, WI, USA
| | - F Padormo
- Hyperfine.io, Guilford, CT, USA; Waisman Research Center, Madison, WI, USA
| | - D Parvez
- Collective Minds Radiology, Sweden; Waisman Research Center, Madison, WI, USA
| | - T Paus
- Departments of Psychiatry and Neuroscience, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada; Waisman Research Center, Madison, WI, USA
| | - M S Pepper
- Institute for Cellular and Molecular Medicine, Department of Medical Immunology, University of Pretoria, Pretoria, South Africa; Waisman Research Center, Madison, WI, USA
| | - K S Phiri
- Training and Research Unit of Excellence (TRUE), Zomba Malawi; Waisman Research Center, Madison, WI, USA
| | - M Poorman
- Hyperfine.io, Guilford, CT, USA; Waisman Research Center, Madison, WI, USA
| | - J E Ringshaw
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital and the Neuroscience Institute, University of Cape Town, Cape Town, South Africa; Waisman Research Center, Madison, WI, USA
| | - J Rogers
- Hyperfine.io, Guilford, CT, USA; Waisman Research Center, Madison, WI, USA
| | - M Rutherford
- Centre for the Developing Brain, Kings College London, London, UK; Waisman Research Center, Madison, WI, USA
| | - H Sabir
- Experimental Neonatology, University Hospitals Bonn, Bonn, Germany; Waisman Research Center, Madison, WI, USA
| | - L Sacolick
- Hyperfine.io, Guilford, CT, USA; Waisman Research Center, Madison, WI, USA
| | - M Seal
- Murdoch Children's Research Institute, Melbourne, AUS; Waisman Research Center, Madison, WI, USA
| | - M L Sekoli
- Institute for Cellular and Molecular Medicine, Department of Medical Immunology, University of Pretoria, Pretoria, South Africa; Waisman Research Center, Madison, WI, USA
| | - T Shama
- International Centre for Diarrheal Disease Research, Bangladesh (Icddr,b), Dhaka, Bangladesh; Waisman Research Center, Madison, WI, USA
| | - K Siddiqui
- Hyperfine.io, Guilford, CT, USA; Waisman Research Center, Madison, WI, USA
| | - N Sindano
- University of North Carolina - Global Projects Zambia, Lusaka, Zambia; Waisman Research Center, Madison, WI, USA
| | - M B Spelke
- University of North Carolina, Department of Obstetrics and Gynecology, Chapel Hill, USA; Waisman Research Center, Madison, WI, USA
| | - P E Springer
- Department of Paediatrics and Child Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa; Waisman Research Center, Madison, WI, USA
| | - F E Suleman
- Department of Radiology, Faculty of Health Sciences, Kalafong Hospital, University of Pretoria, Pretoria, South Africa; Waisman Research Center, Madison, WI, USA
| | - P C Sundgren
- Section of Diagnostic Radiology,Department of Clinical Sciences Lund, Lund University, Lund, Sweden; Waisman Research Center, Madison, WI, USA
| | - R Teixeira
- Hyperfine.io, Guilford, CT, USA; Waisman Research Center, Madison, WI, USA
| | - W Terekegn
- Addis Continental Institute of Public Health, Addis Ababa, Ethiopia; Waisman Research Center, Madison, WI, USA
| | - M Traughber
- Hyperfine.io, Guilford, CT, USA; Waisman Research Center, Madison, WI, USA
| | - M G Tuuli
- Jaramogi Oginga Odinga Teaching and Referral Hospital, Kisumu, Kenya; Waisman Research Center, Madison, WI, USA
| | - J van Rensburg
- Institute for Cellular and Molecular Medicine, Department of Medical Immunology, University of Pretoria, Pretoria, South Africa; Waisman Research Center, Madison, WI, USA
| | - F Váša
- Centre for Neuroimaging Sciences, King's College London, London, UK; Waisman Research Center, Madison, WI, USA
| | - S Velaphi
- Department of Paediatrics and Child Health, Chris Hani Baragwanath Academic Hospital and Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa; Waisman Research Center, Madison, WI, USA
| | - P Velasco
- flywheel.io Minneapolis, MN, USA; Waisman Research Center, Madison, WI, USA
| | - I M Viljoen
- Department of Radiology, Faculty of Health Sciences, Chris Hani Baragwanath Academic Hospital, University; Waisman Research Center, Madison, WI, USA
| | - M Vokhiwa
- Training and Research Unit of Excellence (TRUE), Zomba Malawi; Waisman Research Center, Madison, WI, USA
| | - A Webb
- Dept. of Radiology, Leiden University, Leiden, the Netherlands; Waisman Research Center, Madison, WI, USA
| | - C Weiant
- CaliberMRI, Boulder CO USA; Waisman Research Center, Madison, WI, USA
| | - N Wiley
- Dept. of Neurology, University of British Columbia, Vancouver, BC, Canada; Waisman Research Center, Madison, WI, USA
| | - P Wintermark
- Division of Newborn Medicine, Department of Pediatrics, Montreal Children's Hospital, McGill University, Montreal, QC, Canada; Waisman Research Center, Madison, WI, USA
| | - K Yibetal
- Addis Continental Institute of Public Health, Addis Ababa, Ethiopia; Waisman Research Center, Madison, WI, USA
| | - Scl Deoni
- Bill & Melinda Gates Foundation, MNCH D&T, Seattle, WA, USA; Waisman Research Center, Madison, WI, USA
| | - Scr Williams
- Centre for Neuroimaging Sciences, King's College London, London, UK; Waisman Research Center, Madison, WI, USA.
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Abstract
Objective Accurate infant brain parcellation is crucial for understanding early brain development; however, it is challenging due to the inherent low tissue contrast, high noise, and severe partial volume effects in infant magnetic resonance images (MRIs). The aim of this study was to develop an end-to-end pipeline that enabled accurate parcellation of infant brain MRIs. Methods We proposed an end-to-end pipeline that employs a two-stage global-to-local approach for accurate parcellation of infant brain MRIs. Specifically, in the global regions of interest (ROIs) localization stage, a combination of transformer and convolution operations was employed to capture both global spatial features and fine texture features, enabling an approximate localization of the ROIs across the whole brain. In the local ROIs refinement stage, leveraging the position priors from the first stage along with the raw MRIs, the boundaries o the ROIs are refined for a more accurate parcellation. Results We utilized the Dice ratio to evaluate the accuracy of parcellation results. Results on 263 subjects from National Database for Autism Research (NDAR), Baby Connectome Project (BCP) and Cross-site datasets demonstrated the better accuracy and robustness of our method than other competing methods. Conclusion Our end-to-end pipeline may be capable of accurately parcellating 6-month-old infant brain MRIs.
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Affiliation(s)
- Limei Wang
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, New Caledonia, 27599, USA
| | - Yue Sun
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, New Caledonia, 27599, USA
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, New Caledonia, 27599, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, New Caledonia, 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, New Caledonia, 27599, USA
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He K, Peng B, Yu W, Liu Y, Liu S, Cheng J, Dai Y. A Novel Mis-Seg-Focus Loss Function Based on a Two-Stage nnU-Net Framework for Accurate Brain Tissue Segmentation. Bioengineering (Basel) 2024; 11:427. [PMID: 38790294 PMCID: PMC11118222 DOI: 10.3390/bioengineering11050427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2024] [Revised: 04/14/2024] [Accepted: 04/24/2024] [Indexed: 05/26/2024] Open
Abstract
Brain tissue segmentation plays a critical role in the diagnosis, treatment, and study of brain diseases. Accurately identifying these boundaries is essential for improving segmentation accuracy. However, distinguishing boundaries between different brain tissues can be challenging, as they often overlap. Existing deep learning methods primarily calculate the overall segmentation results without adequately addressing local regions, leading to error propagation and mis-segmentation along boundaries. In this study, we propose a novel mis-segmentation-focused loss function based on a two-stage nnU-Net framework. Our approach aims to enhance the model's ability to handle ambiguous boundaries and overlapping anatomical structures, thereby achieving more accurate brain tissue segmentation results. Specifically, the first stage targets the identification of mis-segmentation regions using a global loss function, while the second stage involves defining a mis-segmentation loss function to adaptively adjust the model, thus improving its capability to handle ambiguous boundaries and overlapping anatomical structures. Experimental evaluations on two datasets demonstrate that our proposed method outperforms existing approaches both quantitatively and qualitatively.
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Affiliation(s)
- Keyi He
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (K.H.); (B.P.); (Y.L.); (S.L.)
- The School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China;
| | - Bo Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (K.H.); (B.P.); (Y.L.); (S.L.)
| | - Weibo Yu
- The School of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China;
| | - Yan Liu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (K.H.); (B.P.); (Y.L.); (S.L.)
| | - Surui Liu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (K.H.); (B.P.); (Y.L.); (S.L.)
| | - Jian Cheng
- State Key Laboratory of Complex & Critical Software Environment, Beihang University, Beijing 100191, China
- International Innovation Institute, Beihang University, 166 Shuanghongqiao Street, Pingyao Town, Yuhang District, Hangzhou 311115, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China; (K.H.); (B.P.); (Y.L.); (S.L.)
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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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6
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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] [MESH Headings] [Grants] [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 LearningBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
- IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
- IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
| | - Lianglong Sun
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
- IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
| | - Yihe Zhang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
- IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
- IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
| | - Xuhong Liao
- School of Systems ScienceBeijing Normal UniversityBeijingChina
| | - Jiaying Zhang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
- IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
| | - Dinggang Shen
- School of Biomedical EngineeringShanghaiTech UniversityShanghaiChina
- Shanghai Clinical Research and Trial CenterShanghaiChina
- Department of Research and DevelopmentShanghai United Imaging Intelligence Co., Ltd.ShanghaiChina
| | - Li Wang
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingChina
- Beijing Key Laboratory of Brain Imaging and ConnectomicsBeijing Normal UniversityBeijingChina
- IDG/McGovern Institute for Brain Research, Beijing Normal UniversityBeijingChina
- Chinese Institute for Brain ResearchBeijingChina
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7
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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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8
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Deep learning models and traditional automated techniques for brain tumor segmentation in MRI: a review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10245-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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9
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Wang Y, Haghpanah FS, Zhang X, Santamaria K, da Costa Aguiar Alves GK, Bruno E, Aw N, Maddocks A, Duarte CS, Monk C, Laine A, Posner J. ID-Seg: an infant deep learning-based segmentation framework to improve limbic structure estimates. Brain Inform 2022; 9:12. [PMID: 35633447 PMCID: PMC9148335 DOI: 10.1186/s40708-022-00161-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 05/05/2022] [Indexed: 11/10/2022] Open
Abstract
Infant brain magnetic resonance imaging (MRI) is a promising approach for studying early neurodevelopment. However, segmenting small regions such as limbic structures is challenging due to their low inter-regional contrast and high curvature. MRI studies of the adult brain have successfully applied deep learning techniques to segment limbic structures, and similar deep learning models are being leveraged for infant studies. However, these deep learning-based infant MRI segmentation models have generally been derived from small datasets, and may suffer from generalization problems. Moreover, the accuracy of segmentations derived from these deep learning models relative to more standard Expectation-Maximization approaches has not been characterized. To address these challenges, we leveraged a large, public infant MRI dataset (n = 473) and the transfer-learning technique to first pre-train a deep convolutional neural network model on two limbic structures: amygdala and hippocampus. Then we used a leave-one-out cross-validation strategy to fine-tune the pre-trained model and evaluated it separately on two independent datasets with manual labels. We term this new approach the Infant Deep learning SEGmentation Framework (ID-Seg). ID-Seg performed well on both datasets with a mean dice similarity score (DSC) of 0.87, a mean intra-class correlation (ICC) of 0.93, and a mean average surface distance (ASD) of 0.31 mm. Compared to the Developmental Human Connectome pipeline (dHCP) pipeline, ID-Seg significantly improved segmentation accuracy. In a third infant MRI dataset (n = 50), we used ID-Seg and dHCP separately to estimate amygdala and hippocampus volumes and shapes. The estimates derived from ID-seg, relative to those from the dHCP, showed stronger associations with behavioral problems assessed in these infants at age 2. In sum, ID-Seg consistently performed well on two different datasets with an 0.87 DSC, however, multi-site testing and extension for brain regions beyond the amygdala and hippocampus are still needed.
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Affiliation(s)
- Yun Wang
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA.,New York State Psychiatric Institute, New York, NY, USA
| | | | - Xuzhe Zhang
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | | | | | | | - Natalie Aw
- New York State Psychiatric Institute, New York, NY, USA
| | - Alexis Maddocks
- Department of Radiology, Columbia University, New York, NY, USA
| | | | - Catherine Monk
- New York State Psychiatric Institute, New York, NY, USA.,Department of Obstetrics and Gynecology, Columbia University, New York, NY, USA
| | - Andrew Laine
- Department of Biomedical Engineering, Columbia University, New York, NY, USA
| | - Jonathan Posner
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, USA. .,New York State Psychiatric Institute, New York, NY, USA.
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10
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Shiohama T, Tsujimura K. Quantitative Structural Brain Magnetic Resonance Imaging Analyses: Methodological Overview and Application to Rett Syndrome. Front Neurosci 2022; 16:835964. [PMID: 35450016 PMCID: PMC9016334 DOI: 10.3389/fnins.2022.835964] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Congenital genetic disorders often present with neurological manifestations such as neurodevelopmental disorders, motor developmental retardation, epilepsy, and involuntary movement. Through qualitative morphometric evaluation of neuroimaging studies, remarkable structural abnormalities, such as lissencephaly, polymicrogyria, white matter lesions, and cortical tubers, have been identified in these disorders, while no structural abnormalities were identified in clinical settings in a large population. Recent advances in data analysis programs have led to significant progress in the quantitative analysis of anatomical structural magnetic resonance imaging (MRI) and diffusion-weighted MRI tractography, and these approaches have been used to investigate psychological and congenital genetic disorders. Evaluation of morphometric brain characteristics may contribute to the identification of neuroimaging biomarkers for early diagnosis and response evaluation in patients with congenital genetic diseases. This mini-review focuses on the methodologies and attempts employed to study Rett syndrome using quantitative structural brain MRI analyses, including voxel- and surface-based morphometry and diffusion-weighted MRI tractography. The mini-review aims to deepen our understanding of how neuroimaging studies are used to examine congenital genetic disorders.
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Affiliation(s)
- Tadashi Shiohama
- Department of Pediatrics, Chiba University Hospital, Chiba, Japan
- *Correspondence: Tadashi Shiohama,
| | - Keita Tsujimura
- Group of Brain Function and Development, Nagoya University Neuroscience Institute of the Graduate School of Science, Nagoya, Japan
- Research Unit for Developmental Disorders, Institute for Advanced Research, Nagoya University, Nagoya, Japan
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
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11
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Pollatou A, Filippi CA, Aydin E, Vaughn K, Thompson D, Korom M, Dufford AJ, Howell B, Zöllei L, Martino AD, Graham A, Scheinost D, Spann MN. An ode to fetal, infant, and toddler neuroimaging: Chronicling early clinical to research applications with MRI, and an introduction to an academic society connecting the field. Dev Cogn Neurosci 2022; 54:101083. [PMID: 35184026 PMCID: PMC8861425 DOI: 10.1016/j.dcn.2022.101083] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/17/2021] [Accepted: 02/04/2022] [Indexed: 12/14/2022] Open
Abstract
Fetal, infant, and toddler neuroimaging is commonly thought of as a development of modern times (last two decades). Yet, this field mobilized shortly after the discovery and implementation of MRI technology. Here, we provide a review of the parallel advancements in the fields of fetal, infant, and toddler neuroimaging, noting the shifts from clinical to research use, and the ongoing challenges in this fast-growing field. We chronicle the pioneering science of fetal, infant, and toddler neuroimaging, highlighting the early studies that set the stage for modern advances in imaging during this developmental period, and the large-scale multi-site efforts which ultimately led to the explosion of interest in the field today. Lastly, we consider the growing pains of the community and the need for an academic society that bridges expertise in developmental neuroscience, clinical science, as well as computational and biomedical engineering, to ensure special consideration of the vulnerable mother-offspring dyad (especially during pregnancy), data quality, and image processing tools that are created, rather than adapted, for the young brain.
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Affiliation(s)
- Angeliki Pollatou
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Courtney A Filippi
- Section on Development and Affective Neuroscience, National Institute of Mental Health, Bethesda, MD, USA; Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
| | - Ezra Aydin
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Kelly Vaughn
- Department of Pediatrics, University of Texas Health Sciences Center, Houston, TX, USA
| | - Deanne Thompson
- Clinical Sciences, Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Marta Korom
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Alexander J Dufford
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Brittany Howell
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA; Department of Human Development and Family Science, Virginia Tech, Blacksburg, VA, USA
| | - Lilla Zöllei
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | - Alice Graham
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Dustin Scheinost
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Marisa N Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA; Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA.
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12
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Jiang L, El-Metwally D, Sours Rhodes C, Zhuo J, Almardawi R, Medina AE, Wang L, Gullapalli RP, Raghavan P. Alterations in motor functional connectivity in Neonatal Hypoxic Ischemic Encephalopathy. Brain Inj 2022; 36:287-294. [PMID: 35113755 DOI: 10.1080/02699052.2022.2034041] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Abstract
BACKGROUND Neonatal hypoxic-ischemic encephalopathy (HIE) is the result of global hypoxic-ischemic brain injury in neonates due to asphyxia during birth and is one of the most common causes of severe, long-term neurologic deficits in children. Methods: Resting state fMRI (rs-fMRI) was used to assess potential functional disruptions in the primary and association motor areas in HIE neonates (n = 16) compared to healthy controls (n = 11). RESULTS Results demonstrate reduced intra-hemispheric resting state functional connectivity (rs-FC) between primary motor regions (upper extremity and facial motor regions) as well as reduced inter-hemispheric rs-FC in the HIE group. In addition, HIE neonates demonstrated increased rs-FC between motor regions and frontal, temporal and parietal cortices but decreased rs-FC with the cerebellum. DISCUSSION These preliminary results provide initial evidence for the disruption of functional communication with the motor network in neonates with HIE. Further studies are necessary to both validate these findings in a larger dataset as well as to determine if rs-fMRI measurements collected at birth may have the potential to serve as a prognostic marker in addition to the traditional combination of clinical measurements and conventional MRI.
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Affiliation(s)
- Li Jiang
- Center for Advanced Imaging Research (Cair), 670 W Baltimore St, University of Maryland School of Medicine, Baltimore, USA.,Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, USA
| | - Dina El-Metwally
- Department of Pediatrics, University of Maryland School of Medicine, Baltimore, USA
| | - Chandler Sours Rhodes
- Center for Advanced Imaging Research (Cair), 670 W Baltimore St, University of Maryland School of Medicine, Baltimore, USA.,Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, USA.,National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, USA
| | - Jiachen Zhuo
- Center for Advanced Imaging Research (Cair), 670 W Baltimore St, University of Maryland School of Medicine, Baltimore, USA.,Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, USA
| | - Ranyah Almardawi
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, USA
| | - Alexandre E Medina
- Department of Pediatrics, University of Maryland School of Medicine, Baltimore, USA
| | - Li Wang
- Department of Radiology, University of North Carolina School of Medicine, Chapel Hill, North Carolina, USA
| | - Rao P Gullapalli
- Center for Advanced Imaging Research (Cair), 670 W Baltimore St, University of Maryland School of Medicine, Baltimore, USA.,Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, USA
| | - Prashant Raghavan
- Department of Diagnostic Radiology & Nuclear Medicine, University of Maryland School of Medicine, Baltimore, USA
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13
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Korom M, Camacho MC, Filippi CA, Licandro R, Moore LA, Dufford A, Zöllei L, Graham AM, Spann M, Howell B, Shultz S, Scheinost D. Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies. Dev Cogn Neurosci 2021; 53:101055. [PMID: 34974250 PMCID: PMC8733260 DOI: 10.1016/j.dcn.2021.101055] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 11/28/2021] [Accepted: 12/26/2021] [Indexed: 01/07/2023] Open
Abstract
The field of adult neuroimaging relies on well-established principles in research design, imaging sequences, processing pipelines, as well as safety and data collection protocols. The field of infant magnetic resonance imaging, by comparison, is a young field with tremendous scientific potential but continuously evolving standards. The present article aims to initiate a constructive dialog between researchers who grapple with the challenges and inherent limitations of a nascent field and reviewers who evaluate their work. We address 20 questions that researchers commonly receive from research ethics boards, grant, and manuscript reviewers related to infant neuroimaging data collection, safety protocols, study planning, imaging sequences, decisions related to software and hardware, and data processing and sharing, while acknowledging both the accomplishments of the field and areas of much needed future advancements. This article reflects the cumulative knowledge of experts in the FIT’NG community and can act as a resource for both researchers and reviewers alike seeking a deeper understanding of the standards and tradeoffs involved in infant neuroimaging. The field of infant MRI is young with evolving standards. We address 20 questions that researchers commonly receive reviewers. These come from research ethics boards, grant, and manuscript reviewers. This article reflects the cumulative knowledge of experts in the FIT’NG community.
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Affiliation(s)
- Marta Korom
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA.
| | - M Catalina Camacho
- Division of Biology and Biomedical Sciences (Neurosciences), Washington University School of Medicine, St. Louis, MO, USA.
| | - Courtney A Filippi
- Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - Roxane Licandro
- Institute of Visual Computing and Human-Centered Technology, Computer Vision Lab, TU Wien, Vienna, Austria; Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research, Medical University of Vienna, Vienna, Austria
| | - Lucille A Moore
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Alexander Dufford
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Alice M Graham
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Marisa Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Brittany Howell
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Department of Human Development and Family Science, Virginia Polytechnic Institute and State University, Roanoke, VA, USA
| | | | - Sarah Shultz
- Division of Autism & Department of Pediatrics, Emory University School of Medicine, Atlanta, GA, USA; Marcus Autism Center, Children's Healthcare of Atlanta, Atlanta, GA, USA.
| | - Dustin Scheinost
- Department of Radiology & Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA.
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14
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Bae I, Chae JH, Han Y. A brain extraction algorithm for infant T2 weighted magnetic resonance images based on fuzzy c-means thresholding. Sci Rep 2021; 11:23347. [PMID: 34857824 PMCID: PMC8640033 DOI: 10.1038/s41598-021-02722-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Accepted: 11/22/2021] [Indexed: 11/28/2022] Open
Abstract
It is challenging to extract the brain region from T2-weighted magnetic resonance infant brain images because conventional brain segmentation algorithms are generally optimized for adult brain images, which have different spatial resolution, dynamic changes of imaging intensity, brain size and shape from infant brain images. In this study, we propose a brain extraction algorithm for infant T2-weighted images. The proposed method utilizes histogram partitioning to separate brain regions from the background image. Then, fuzzy c-means thresholding is performed to obtain a rough brain mask for each image slice, followed by refinement steps. For slices that contain eye regions, an additional eye removal algorithm is proposed to eliminate eyes from the brain mask. By using the proposed method, accurate masks for infant T2-weighted brain images can be generated. For validation, we applied the proposed algorithm and conventional methods to T2 infant images (0–24 months of age) acquired with 2D and 3D sequences at 3T MRI. The Dice coefficients and Precision scores, which were calculated as quantitative measures, showed the highest values for the proposed method as follows: For images acquired with a 2D imaging sequence, the average Dice coefficients were 0.9650 ± 0.006 for the proposed method, 0.9262 ± 0.006 for iBEAT, and 0.9490 ± 0.006 for BET. For the data acquired with a 3D imaging sequence, the average Dice coefficient was 0.9746 ± 0.008 for the proposed method, 0.9448 ± 0.004 for iBEAT, and 0.9622 ± 0.01 for BET. The average Precision was 0.9638 ± 0.009 and 0.9565 ± 0.016 for the proposed method, 0.8981 ± 0.01 and 0.8968 ± 0.008 for iBEAT, and 0.9346 ± 0.014 and 0.9282 ± 0.019 for BET for images acquired with 2D and 3D imaging sequences, respectively, demonstrating that the proposed method could be efficiently used for brain extraction in T2-weighted infant images.
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Affiliation(s)
- Inyoung Bae
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon, Republic of Korea
| | - Jong-Hee Chae
- Department of Pediatrics, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yeji Han
- Department of Health Sciences and Technology, Gachon Advanced Institute for Health Sciences and Technology, Gachon University, Incheon, Republic of Korea. .,Department of Biomedical Engineering, College of Health Sciences, Gachon University, Incheon, Republic of Korea.
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15
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Woodburn M, Bricken CL, Wu Z, Li G, Wang L, Lin W, Sheridan MA, Cohen JR. The maturation and cognitive relevance of structural brain network organization from early infancy to childhood. Neuroimage 2021; 238:118232. [PMID: 34091033 PMCID: PMC8372198 DOI: 10.1016/j.neuroimage.2021.118232] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 04/30/2021] [Accepted: 06/01/2021] [Indexed: 01/14/2023] Open
Abstract
The interactions of brain regions with other regions at the network level likely provide the infrastructure necessary for cognitive processes to develop. Specifically, it has been theorized that in infancy brain networks become more modular, or segregated, to support early cognitive specialization, before integration across networks increases to support the emergence of higher-order cognition. The present study examined the maturation of structural covariance networks (SCNs) derived from longitudinal cortical thickness data collected between infancy and childhood (0–6 years). We assessed modularity as a measure of network segregation and global efficiency as a measure of network integration. At the group level, we observed trajectories of increasing modularity and decreasing global efficiency between early infancy and six years. We further examined subject-based maturational coupling networks (sbMCNs) in a subset of this cohort with cognitive outcome data at 8–10 years, which allowed us to relate the network organization of longitudinal cortical thickness maturation to cognitive outcomes in middle childhood. We found that lower global efficiency of sbMCNs throughout early development (across the first year) related to greater motor learning at 8–10 years. Together, these results provide novel evidence characterizing the maturation of brain network segregation and integration across the first six years of life, and suggest that specific trajectories of brain network maturation contribute to later cognitive outcomes.
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Affiliation(s)
- Mackenzie Woodburn
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States.
| | - Cheyenne L Bricken
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States
| | - Zhengwang Wu
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Gang Li
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Li Wang
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Weili Lin
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Margaret A Sheridan
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Carolina Institute of Developmental Disabilities, University of North Carolina, Chapel Hill, United States
| | - Jessica R Cohen
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Carolina Institute of Developmental Disabilities, University of North Carolina, Chapel Hill, United States
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16
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Liu M, Lepage C, Kim SY, Jeon S, Kim SH, Simon JP, Tanaka N, Yuan S, Islam T, Peng B, Arutyunyan K, Surento W, Kim J, Jahanshad N, Styner MA, Toga AW, Barkovich AJ, Xu D, Evans AC, Kim H. Robust Cortical Thickness Morphometry of Neonatal Brain and Systematic Evaluation Using Multi-Site MRI Datasets. Front Neurosci 2021; 15:650082. [PMID: 33815050 PMCID: PMC8010150 DOI: 10.3389/fnins.2021.650082] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 02/17/2021] [Indexed: 11/13/2022] Open
Abstract
The human brain grows the most dramatically during the perinatal and early post-natal periods, during which pre-term birth or perinatal injury that may alter brain structure and lead to developmental anomalies. Thus, characterizing cortical thickness of developing brains remains an important goal. However, this task is often complicated by inaccurate cortical surface extraction due to small-size brains. Here, we propose a novel complex framework for the reconstruction of neonatal WM and pial surfaces, accounting for large partial volumes due to small-size brains. The proposed approach relies only on T1-weighted images unlike previous T2-weighted image-based approaches while only T1-weighted images are sometimes available under the different clinical/research setting. Deep neural networks are first introduced to the neonatal magnetic resonance imaging (MRI) pipeline to address the mis-segmentation of brain tissues. Furthermore, this pipeline enhances cortical boundary delineation using combined models of the cerebrospinal fluid (CSF)/GM boundary detection with edge gradient information and a new skeletonization of sulcal folding where no CSF voxels are seen due to the limited resolution. We also proposed a systematic evaluation using three independent datasets comprising 736 pre-term and 97 term neonates. Qualitative assessment for reconstructed cortical surfaces shows that 86.9% are rated as accurate across the three site datasets. In addition, our landmark-based evaluation shows that the mean displacement of the cortical surfaces from the true boundaries was less than a voxel size (0.532 ± 0.035 mm). Evaluating the proposed pipeline (namely NEOCIVET 2.0) shows the robustness and reproducibility across different sites and different age-groups. The mean cortical thickness measured positively correlated with post-menstrual age (PMA) at scan (p < 0.0001); Cingulate cortical areas grew the most rapidly whereas the inferior temporal cortex grew the least rapidly. The range of the cortical thickness measured was biologically congruent (1.3 mm at 28 weeks of PMA to 1.8 mm at term equivalent). Cortical thickness measured on T1 MRI using NEOCIVET 2.0 was compared with that on T2 using the established dHCP pipeline. It was difficult to conclude that either T1 or T2 imaging is more ideal to construct cortical surfaces. NEOCIVET 2.0 has been open to the public through CBRAIN (https://mcin-cnim.ca/technology/cbrain/), a web-based platform for processing brain imaging data.
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Affiliation(s)
- Mengting Liu
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Claude Lepage
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sharon Y Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Seun Jeon
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sun Hyung Kim
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Julia Pia Simon
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Nina Tanaka
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Shiyu Yuan
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Tasfiya Islam
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Bailin Peng
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Knarik Arutyunyan
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Wesley Surento
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Justin Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Neda Jahanshad
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Martin A Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Arthur W Toga
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Anthony James Barkovich
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Hosung Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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17
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Turesky TK, Vanderauwera J, Gaab N. Imaging the rapidly developing brain: Current challenges for MRI studies in the first five years of life. Dev Cogn Neurosci 2021; 47:100893. [PMID: 33341534 PMCID: PMC7750693 DOI: 10.1016/j.dcn.2020.100893] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 10/21/2020] [Accepted: 12/05/2020] [Indexed: 12/20/2022] Open
Abstract
Rapid and widespread changes in brain anatomy and physiology in the first five years of life present substantial challenges for developmental structural, functional, and diffusion MRI studies. One persistent challenge is that methods best suited to earlier developmental stages are suboptimal for later stages, which engenders a trade-off between using different, but age-appropriate, methods for different developmental stages or identical methods across stages. Both options have potential benefits, but also biases, as pipelines for each developmental stage can be matched on methods or the age-appropriateness of methods, but not both. This review describes the data acquisition, processing, and analysis challenges that introduce these potential biases and attempts to elucidate decisions and make recommendations that would optimize developmental comparisons.
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Affiliation(s)
- Ted K Turesky
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Jolijn Vanderauwera
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Psychological Sciences Research Institute, Université Catholique De Louvain, Louvain-la-Neuve, Belgium; Institute of Neuroscience, Université Catholique De Louvain, Louvain-la-Neuve, Belgium
| | - Nadine Gaab
- Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
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18
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Ghribi O, Li G, Lin W, Shen D, Rekik I. Multi-Regression based supervised sample selection for predicting baby connectome evolution trajectory from neonatal timepoint. Med Image Anal 2020; 68:101853. [PMID: 33264713 DOI: 10.1016/j.media.2020.101853] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 07/27/2020] [Accepted: 09/14/2020] [Indexed: 01/06/2023]
Abstract
The connectional map of the baby brain undergoes dramatic changes over the first year of postnatal development, which makes its mapping a challenging task, let alone learning how to predict its evolution. Currently, learning models for predicting brain connectomic developmental trajectories remain broadly absent despite their great potential in spotting atypical neurodevelopmental disorders early. This is most likely due to the scarcity and often incompleteness of longitudinal infant neuroimaging studies for training such models. In this paper, we propose the first approach for progressively predicting longitudinal development of brain networks during the postnatal period solely from a baseline connectome around birth. To this end, a supervised multi-regression sample selection strategy is designed to learn how to identify the best set of neighbors of a testing baseline connectome to eventually predict its evolution trajectory at follow-up timepoints. However, given that the training dataset may have missing samples (connectomes) at certain timepoints, this may affect the training of the predictive model. To overcome this problem, we perform a low-rank tensor completion based on a robust principal component analysis to impute the missing training connectomes by linearly approximating similar complete training networks. In the prediction step, our sample selection strategy aims to preserve spatiotemporal relationships between consecutive timepoints. Therefore, the proposed method learns how to identify the set of the local closest neighbors to a target network by training an ensemble of bidirectional regressors leveraging temporal dependency between consecutive timepoints with a recall to the baseline observations to progressively predict the evolution of a testing network over time. Our method achieves the best prediction results and better captures the dynamic changes of each brain connectome over time in comparison to its ablated versions using leave-one-out cross-validation strategy.
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Affiliation(s)
- Olfa Ghribi
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; National School of Engineers of Sfax, University of Sfax, Tunisia
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
| | - Islem Rekik
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; School of Science and Engineering, Computing, University of Dundee, UK.
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19
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Zöllei L, Iglesias JE, Ou Y, Grant PE, Fischl B. Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years. Neuroimage 2020; 218:116946. [PMID: 32442637 PMCID: PMC7415702 DOI: 10.1016/j.neuroimage.2020.116946] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/03/2020] [Accepted: 05/12/2020] [Indexed: 01/23/2023] Open
Abstract
The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements (including volume, thickness, surface area, and curvature) have been associated with neuropsychiatric, neurologic, and developmental disorders in children. In this paper we present an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. The algorithm relies on a single channel of T1-weighted MR images to achieve automated segmentation of cortical and subcortical brain areas, producing volumes of subcortical structures and surface models of the cerebral cortex. We evaluated the algorithm both qualitatively and quantitatively using manually labeled datasets, relevant comparator software solutions cited in the literature, and expert evaluations. The computational tools and atlases described in this paper will be distributed to the research community as part of the FreeSurfer image analysis package.
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Affiliation(s)
- Lilla Zöllei
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA.
| | - Juan Eugenio Iglesias
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Center for Medical Image Computing, University College London, United Kingdom; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
| | - Yangming Ou
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, USA
| | - Bruce Fischl
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
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20
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Camacho MC, King LS, Ojha A, Garcia CM, Sisk LM, Cichocki AC, Humphreys KL, Gotlib IH. Cerebral blood flow in 5- to 8-month-olds: Regional tissue maturity is associated with infant affect. Dev Sci 2020; 23:e12928. [PMID: 31802580 PMCID: PMC8931704 DOI: 10.1111/desc.12928] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2019] [Revised: 11/20/2019] [Accepted: 12/02/2019] [Indexed: 12/12/2022]
Abstract
Infancy is marked by rapid neural and emotional development. The relation between brain function and emotion in infancy, however, is not well understood. Methods for measuring brain function predominantly rely on the BOLD signal; however, interpretation of the BOLD signal in infancy is challenging because the neuronal-hemodynamic relation is immature. Regional cerebral blood flow (rCBF) provides a context for the infant BOLD signal and can yield insight into the developmental maturity of brain regions that may support affective behaviors. This study aims to elucidate the relations among rCBF, age, and emotion in infancy. One hundred and seven mothers reported their infants' (infant age M ± SD = 6.14 ± 0.51 months) temperament. A subsample of infants completed MRI scans, 38 of whom produced usable perfusion MRI during natural sleep to quantify rCBF. Mother-infant dyads completed the repeated Still-Face Paradigm, from which infant affect reactivity and recovery to stress were quantified. We tested associations of infant age at scan, temperament factor scores, and observed affect reactivity and recovery with voxel-wise rCBF. Infant age was positively associated with CBF in nearly all voxels, with peaks located in sensory cortices and the ventral prefrontal cortex, supporting the formulation that rCBF is an indicator of tissue maturity. Temperamental Negative Affect and recovery of positive affect following a stressor were positively associated with rCBF in several cortical and subcortical limbic regions, including the orbitofrontal cortex and inferior frontal gyrus. This finding yields insight into the nature of affective neurodevelopment during infancy. Specifically, infants with relatively increased prefrontal cortex maturity may evidence a disposition toward greater negative affect and negative reactivity in their daily lives yet show better recovery of positive affect following a social stressor.
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Affiliation(s)
| | | | - Amar Ojha
- Stanford University, Stanford, CA, USA
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21
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Wang G, Hu Y, Li X, Wang M, Liu C, Yang J, Jin C. Impacts of skull stripping on construction of three-dimensional T1-weighted imaging-based brain structural network in full-term neonates. Biomed Eng Online 2020; 19:41. [PMID: 32493402 PMCID: PMC7268688 DOI: 10.1186/s12938-020-00785-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 05/21/2020] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Skull stripping remains a challenge for neonatal brain MR image analysis. However, little is known about the accuracy of how skull stripping affects the neonatal brain tissue segmentation and subsequent network construction. This paper therefore aimed to clarify this issue by comparing two automatic (FMRIB Software Library's Brain Extraction Tool, BET; Infant Brain Extraction and Analysis Toolbox, iBEAT) and a semiautomatic (iBEAT with manual correction) processes in constructing 3D T1-weighted imaging (T1WI)-based brain structural network. METHODS Twenty-two full-term neonates (gestational age, 37-42 weeks; boys/girls, 13/9) without abnormalities on MRI who underwent brain 3D T1WI were retrospectively recruited. Two automatic (BET and iBEAT) and a semiautomatic preprocessing (iBEAT with manual correction) workflows were separately used to perform the skull stripping. Brain tissue segmentation and volume calculation were performed by a Johns Hopkins atlas-based method. Sixty-four gray matter regions were selected as nodes; volume covariance network and its properties (clustering coefficient, Cp; characteristic path length, Lp; local efficiency, Elocal; global efficiency, Eglobal) were calculated by GRETNA. Analysis of variance (ANOVA) was used to compare the differences in the calculated volume between three workflows. RESULTS There were significant differences in volumes of 50 brain regions between the three workflows (P < 0.05). Three neonatal brain structural networks presented small-world topology. The semiautomatic workflow showed remarkably decreased Cp, increased Lp, decreased Elocal, and decreased Eglobal, in contrast to the two automatic ones. CONCLUSIONS Imperfect skull stripping indeed affected the accuracy of brain structural network in full-term neonates.
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Affiliation(s)
- Geliang Wang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China
| | - Yajie Hu
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China
| | - Xianjun Li
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China
| | - Miaomiao Wang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China
| | - Congcong Liu
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China
| | - Jian Yang
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China.
| | - Chao Jin
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, People's Republic of China.
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22
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Mostapha M, Styner M. Role of deep learning in infant brain MRI analysis. Magn Reson Imaging 2019; 64:171-189. [PMID: 31229667 PMCID: PMC6874895 DOI: 10.1016/j.mri.2019.06.009] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Revised: 06/06/2019] [Accepted: 06/08/2019] [Indexed: 12/17/2022]
Abstract
Deep learning algorithms and in particular convolutional networks have shown tremendous success in medical image analysis applications, though relatively few methods have been applied to infant MRI data due numerous inherent challenges such as inhomogenous tissue appearance across the image, considerable image intensity variability across the first year of life, and a low signal to noise setting. This paper presents methods addressing these challenges in two selected applications, specifically infant brain tissue segmentation at the isointense stage and presymptomatic disease prediction in neurodevelopmental disorders. Corresponding methods are reviewed and compared, and open issues are identified, namely low data size restrictions, class imbalance problems, and lack of interpretation of the resulting deep learning solutions. We discuss how existing solutions can be adapted to approach these issues as well as how generative models seem to be a particularly strong contender to address them.
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Affiliation(s)
- Mahmoud Mostapha
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States of America.
| | - Martin Styner
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States of America; Neuro Image Research and Analysis Lab, Department of Psychiatry, University of North Carolina at Chapel Hill, NC 27599, United States of America.
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23
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Hashempour N, Tuulari JJ, Merisaari H, Lidauer K, Luukkonen I, Saunavaara J, Parkkola R, Lähdesmäki T, Lehtola SJ, Keskinen M, Lewis JD, Scheinin NM, Karlsson L, Karlsson H. A Novel Approach for Manual Segmentation of the Amygdala and Hippocampus in Neonate MRI. Front Neurosci 2019; 13:1025. [PMID: 31616245 PMCID: PMC6768976 DOI: 10.3389/fnins.2019.01025] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2018] [Accepted: 09/09/2019] [Indexed: 12/16/2022] Open
Abstract
The gross anatomy of the infant brain at term is fairly similar to that of the adult brain, but structures are immature, and the brain undergoes rapid growth during the first 2 years of life. Neonate magnetic resonance (MR) images have different contrasts compared to adult images, and automated segmentation of brain magnetic resonance imaging (MRI) can thus be considered challenging as less software options are available. Despite this, most anatomical regions are identifiable and thus amenable to manual segmentation. In the current study, we developed a protocol for segmenting the amygdala and hippocampus in T2-weighted neonatal MR images. The participants were 31 healthy infants between 2 and 5 weeks of age. Intra-rater reliability was measured in 12 randomly selected MR images, where 6 MR images were segmented at 1-month intervals between the delineations, and another 6 MR images at 6-month intervals. The protocol was also tested by two independent raters in 20 randomly selected T2-weighted images, and finally with T1 images. Intraclass correlation coefficient (ICC) and Dice similarity coefficient (DSC) for intra-rater, inter-rater, and T1 vs. T2 comparisons were computed. Moreover, manual segmentations were compared to automated segmentations performed by iBEAT toolbox in 10 T2-weighted MR images. The intra-rater reliability was high ICC ≥ 0.91, DSC ≥ 0.89, the inter-rater reliabilities were satisfactory ICC ≥ 0.90, DSC ≥ 0.75 for hippocampus and DSC ≥ 0.52 for amygdalae. Segmentations for T1 vs. T2-weighted images showed high consistency ICC ≥ 0.90, DSC ≥ 0.74. The manual and iBEAT segmentations showed no agreement, DSC ≥ 0.39. In conclusion, there is a clear need to improve and develop the procedures for automated segmentation of infant brain MR images.
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Affiliation(s)
- Niloofar Hashempour
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Jetro J Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland.,Turku Collegium for Science and Medicine, University of Turku, Turku, Finland
| | - Harri Merisaari
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Kristian Lidauer
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - Iiris Luukkonen
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - Jani Saunavaara
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - Riitta Parkkola
- Department of Radiology, Turku University Hospital, University of Turku, Turku, Finland
| | - Tuire Lähdesmäki
- Department of Pediatric Neurology, Turku University Hospital, University of Turku, Turku, Finland
| | - Satu J Lehtola
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - Maria Keskinen
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland
| | - John D Lewis
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Noora M Scheinin
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland.,Turku PET Centre, University of Turku, Turku, Finland
| | - Linnea Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Child Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
| | - Hasse Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Turku, Finland.,Department of Psychiatry, Turku University Hospital, University of Turku, Turku, Finland
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24
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Gao Y, Li J, Xu H, Wang M, Liu C, Cheng Y, Li M, Yang J, Li X. A multi-view pyramid network for skull stripping on neonatal T1-weighted MRI. Magn Reson Imaging 2019; 63:70-79. [PMID: 31425808 DOI: 10.1016/j.mri.2019.08.025] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Revised: 07/12/2019] [Accepted: 08/15/2019] [Indexed: 11/26/2022]
Abstract
Skull stripping or brain extraction on magnetic resonance imaging is a crucial step for structure analyses. In spite of good performances of conventional methods on adult brains, the skull stripping for T1-weighted imaging (T1WI) images on the neonatal brain remains a challenge because of the low image contrast. Therefore, this paper proposes a multi-view pyramid skull stripping network (PSSNet) for neonatal T1WI. To achieve superior skull stripping performance, the conventional pyramid scene parsing network was modified through (1) adding the spatial information of raw feature maps by squeezing the channel information during the feature extraction; (2) increasing the receptive field and adding boundary repair block instead of direct up-sampling; (3) obtaining the final mask through a fusion module on multi-view 2D slices. The 3D skull stripping problem was decomposed into multi-view 2D segmentation tasks to improve the efficiency. We enrolled T1WI images of 70 neonates from the local hospital and 7 infants from the publicly available dataset NeuroBrainS12 (MICCAI 2012). Images of 51 and 26 subjects were used for model training and validation. We compared the proposed method with 7 commonly used methods by using the Dice ratio, sensitivity, specificity, and efficiency. The proposed multi-view PSSNet with the highest Dice ratio (95.44-97.33%) was superior to other methods. Meanwhile, the sensitivity (93.19-97.02%), specificity (97.52-99.68%), and efficiency (8.59-9.30 s per subject) of the proposed method were comparable with the state-of-the-art method. In conclusion, the proposed skull stripping network was robust on neonatal T1WI datasets and feasible in clinical applications.
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Affiliation(s)
- Yan Gao
- School of Electronic Engineering, Xidian University, Xi'an 710071, China
| | - Jie Li
- School of Electronic Engineering, Xidian University, Xi'an 710071, China.
| | - Haojun Xu
- School of Electronic Engineering, Xidian University, Xi'an 710071, China
| | - Miaomiao Wang
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Congcong Liu
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Yannan Cheng
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Mengxuan Li
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Jian Yang
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China
| | - Xianjun Li
- Department of Radiology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an 710061, China.
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25
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Nie D, Wang L, Adeli E, Lao C, Lin W, Shen D. 3-D Fully Convolutional Networks for Multimodal Isointense Infant Brain Image Segmentation. IEEE TRANSACTIONS ON CYBERNETICS 2019; 49:1123-1136. [PMID: 29994385 PMCID: PMC6230311 DOI: 10.1109/tcyb.2018.2797905] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Accurate segmentation of infant brain images into different regions of interest is one of the most important fundamental steps in studying early brain development. In the isointense phase (approximately 6-8 months of age), white matter and gray matter exhibit similar levels of intensities in magnetic resonance (MR) images, due to the ongoing myelination and maturation. This results in extremely low tissue contrast and thus makes tissue segmentation very challenging. Existing methods for tissue segmentation in this isointense phase usually employ patch-based sparse labeling on single modality. To address the challenge, we propose a novel 3-D multimodal fully convolutional network (FCN) architecture for segmentation of isointense phase brain MR images. Specifically, we extend the conventional FCN architectures from 2-D to 3-D, and, rather than directly using FCN, we intuitively integrate coarse (naturally high-resolution) and dense (highly semantic) feature maps to better model tiny tissue regions, in addition, we further propose a transformation module to better connect the aggregating layers; we also propose a fusion module to better serve the fusion of feature maps. We compare the performance of our approach with several baseline and state-of-the-art methods on two sets of isointense phase brain images. The comparison results show that our proposed 3-D multimodal FCN model outperforms all previous methods by a large margin in terms of segmentation accuracy. In addition, the proposed framework also achieves faster segmentation results compared to all other methods. Our experiments further demonstrate that: 1) carefully integrating coarse and dense feature maps can considerably improve the segmentation performance; 2) batch normalization can speed up the convergence of the networks, especially when hierarchical feature aggregations occur; and 3) integrating multimodal information can further boost the segmentation performance.
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26
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Wang L, Nie D, Li G, Puybareau É, Dolz J, Zhang Q, Wang F, Xia J, Wu Z, Chen J, Thung KH, Bui TD, Shin J, Zeng G, Zheng G, Fonov VS, Doyle A, Xu Y, Moeskops P, Pluim JP, Desrosiers C, Ayed IB, Sanroma G, Benkarim OM, Casamitjana A, Vilaplana V, Lin W, Li G, Shen D. Benchmark on Automatic 6-month-old Infant Brain Segmentation Algorithms: The iSeg-2017 Challenge. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:10.1109/TMI.2019.2901712. [PMID: 30835215 PMCID: PMC6754324 DOI: 10.1109/tmi.2019.2901712] [Citation(s) in RCA: 63] [Impact Index Per Article: 12.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Accurate segmentation of infant brain magnetic resonance (MR) images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) is an indispensable foundation for early studying of brain growth patterns and morphological changes in neurodevelopmental disorders. Nevertheless, in the isointense phase (approximately 6-9 months of age), due to inherent myelination and maturation process, WM and GM exhibit similar levels of intensity in both T1-weighted (T1w) and T2-weighted (T2w) MR images, making tissue segmentation very challenging. Despite many efforts were devoted to brain segmentation, only few studies have focused on the segmentation of 6-month infant brain images. With the idea of boosting methodological development in the community, iSeg-2017 challenge (http://iseg2017.web.unc.edu) provides a set of 6-month infant subjects with manual labels for training and testing the participating methods. Among the 21 automatic segmentation methods participating in iSeg-2017, we review the 8 top-ranked teams, in terms of Dice ratio, modified Hausdorff distance and average surface distance, and introduce their pipelines, implementations, as well as source codes. We further discuss limitations and possible future directions. We hope the dataset in iSeg-2017 and this review article could provide insights into methodological development for the community.
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Affiliation(s)
- Li Wang
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Dong Nie
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Guannan Li
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Élodie Puybareau
- EPITA Research and Development Laboratory (LRDE), Le Kremlin-Bicêtre, France
| | - Jose Dolz
- Laboratory for Imagery, Vision and Artificial Intelligence (LIVIA), Ecole de Technologie Supérieure, Montreal, Canada
| | - Qian Zhang
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Fan Wang
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Jing Xia
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Jiawei Chen
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Kim-Han Thung
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Toan Duc Bui
- Media System Lab., School of Electronic and Electrical Eng., Sungkyunkwan University (SKKU), Korea
| | - Jitae Shin
- Media System Lab., School of Electronic and Electrical Eng., Sungkyunkwan University (SKKU), Korea
| | - Guodong Zeng
- Information Processing in Medical Intervention Lab., University of Bern, Switzerland
| | - Guoyan Zheng
- Information Processing in Medical Intervention Lab., University of Bern, Switzerland
| | - Vladimir S. Fonov
- NeuroImaging and Surgical Technologies Lab, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Andrew Doyle
- McGill Centre for Integrative Neuroscience, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Yongchao Xu
- EPITA Research and Development Laboratory (LRDE), Le Kremlin-Bicêtre, France
| | - Pim Moeskops
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Josien P.W. Pluim
- Medical Image Analysis Group, Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands
| | - Christian Desrosiers
- Laboratory for Imagery, Vision and Artificial Intelligence (LIVIA), Ecole de Technologie Supérieure, Montreal, Canada
| | - Ismail Ben Ayed
- Laboratory for Imagery, Vision and Artificial Intelligence (LIVIA), Ecole de Technologie Supérieure, Montreal, Canada
| | - Gerard Sanroma
- Simulation, Imaging and Modelling for Biomedical Systems (SIMBIOsys), Universitat Pompeu Fabra, Spain
| | - Oualid M. Benkarim
- Simulation, Imaging and Modelling for Biomedical Systems (SIMBIOsys), Universitat Pompeu Fabra, Spain
| | | | | | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, 27599 USA
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center, UNC-Chapel Hill, NC, USA, and also Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
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27
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Lehtola SJ, Tuulari JJ, Karlsson L, Parkkola R, Merisaari H, Saunavaara J, Lähdesmäki T, Scheinin NM, Karlsson H. Associations of age and sex with brain volumes and asymmetry in 2-5-week-old infants. Brain Struct Funct 2019; 224:501-513. [PMID: 30390153 PMCID: PMC6373364 DOI: 10.1007/s00429-018-1787-x] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2018] [Accepted: 10/30/2018] [Indexed: 02/07/2023]
Abstract
Information on normal brain structure and development facilitates the recognition of abnormal developmental trajectories and thus needs to be studied in more detail. We imaged 68 healthy infants aged 2-5 weeks with high-resolution structural MRI (magnetic resonance imaging) and investigated hemispheric asymmetry as well as the associations of various total and lobar brain volumes with infant age and sex. We found similar hemispheric asymmetry in both sexes, seen as larger volumes of the right temporal lobe, and of the left parietal and occipital lobes. The degree of asymmetry did not vary with age. Regardless of controlling for gestational age, gray and white matter had different age-related growth patterns. This is a reflection of gray matter growth being greater in the first years, while white matter growth extends into early adulthood. Sex-dependent differences were seen in gray matter as larger regional absolute volumes in males and as larger regional relative volumes in females. Our results are in line with previous studies and expand our understanding of infant brain development.
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Affiliation(s)
- S J Lehtola
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Lemminkäisenkatu 3, 2nd floor, 20520, Turku, Finland.
| | - J J Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Lemminkäisenkatu 3, 2nd floor, 20520, Turku, Finland
| | - L Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Lemminkäisenkatu 3, 2nd floor, 20520, Turku, Finland
- Department of Child Psychiatry, University of Turku and Turku University Hospital, Turku, Finland
| | - R Parkkola
- Department of Radiology, University of Turku and Turku University Hospital, Turku, Finland
| | - H Merisaari
- Department of Future Technologies, University of Turku, Turku, Finland
| | - J Saunavaara
- Department of Medical Physics, Turku University Hospital, Turku, Finland
| | - T Lähdesmäki
- Department of Pediatric Neurology, University of Turku and Turku University Hospital, Turku, Finland
| | - N M Scheinin
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Lemminkäisenkatu 3, 2nd floor, 20520, Turku, Finland
- Department of Psychiatry, University of Turku, Turku University Hospital, Turku, Finland
| | - H Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Institute of Clinical Medicine, University of Turku, Lemminkäisenkatu 3, 2nd floor, 20520, Turku, Finland
- Department of Psychiatry, University of Turku, Turku University Hospital, Turku, Finland
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28
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Phan TV, Smeets D, Talcott JB, Vandermosten M. Processing of structural neuroimaging data in young children: Bridging the gap between current practice and state-of-the-art methods. Dev Cogn Neurosci 2018; 33:206-223. [PMID: 29033222 PMCID: PMC6969273 DOI: 10.1016/j.dcn.2017.08.009] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 07/28/2017] [Accepted: 08/17/2017] [Indexed: 11/25/2022] Open
Abstract
The structure of the brain is subject to very rapid developmental changes during early childhood. Pediatric studies based on Magnetic Resonance Imaging (MRI) over this age range have recently become more frequent, with the advantage of providing in vivo and non-invasive high-resolution images of the developing brain, toward understanding typical and atypical trajectories. However, it has also been demonstrated that application of currently standard MRI processing methods that have been developed with datasets from adults may not be appropriate for use with pediatric datasets. In this review, we examine the approaches currently used in MRI studies involving young children, including an overview of the rationale for new MRI processing methods that have been designed specifically for pediatric investigations. These methods are mainly related to the use of age-specific or 4D brain atlases, improved methods for quantifying and optimizing image quality, and provision for registration of developmental data obtained with longitudinal designs. The overall goal is to raise awareness of the existence of these methods and the possibilities for implementing them in developmental neuroimaging studies.
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Affiliation(s)
- Thanh Vân Phan
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium; icometrix, Research and Development, Leuven, Belgium.
| | - Dirk Smeets
- icometrix, Research and Development, Leuven, Belgium
| | - Joel B Talcott
- Aston Brain Centre, School of Life and Health Sciences, Aston University, Birmingham, United Kingdom
| | - Maaike Vandermosten
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium
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29
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Phan TV, Sima DM, Beelen C, Vanderauwera J, Smeets D, Vandermosten M. Evaluation of methods for volumetric analysis of pediatric brain data: The child metrix pipeline versus adult-based approaches. NEUROIMAGE-CLINICAL 2018; 19:734-744. [PMID: 30003026 PMCID: PMC6040578 DOI: 10.1016/j.nicl.2018.05.030] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/07/2017] [Revised: 05/04/2018] [Accepted: 05/22/2018] [Indexed: 12/18/2022]
Abstract
Pediatric brain volumetric analysis based on Magnetic Resonance Imaging (MRI) is of particular interest in order to understand the typical brain development and to characterize neurodevelopmental disorders at an early age. However, it has been shown that the results can be biased due to head motion, inherent to pediatric data, and due to the use of methods based on adult brain data that are not able to accurately model the anatomical disparity of pediatric brains. To overcome these issues, we proposed childmetrix, a tool developed for the analysis of pediatric neuroimaging data that uses an age-specific atlas and a probabilistic model-based approach in order to segment the gray matter (GM) and white matter (WM). The tool was extensively validated on 55 scans of children between 5 and 6 years old (including 13 children with developmental dyslexia) and 10 pairs of test-retest scans of children between 6 and 8 years old and compared with two state-of-the-art methods using an adult atlas, namely icobrain (applying a probabilistic model-based segmentation) and Freesurfer (applying a surface model-based segmentation). The results obtained with childmetrix showed a better reproducibility of GM and WM segmentations and a better robustness to head motion in the estimation of GM volume compared to Freesurfer. Evaluated on two subjects, childmetrix showed good accuracy with 82-84% overlap with manual segmentation for both GM and WM, thereby outperforming the adult-based methods (icobrain and Freesurfer), especially for the subject with poor quality data. We also demonstrated that the adult-based methods needed double the number of subjects to detect significant morphological differences between dyslexics and typical readers. Once further developed and validated, we believe that childmetrix would provide appropriate and reliable measures for the examination of children's brain.
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Affiliation(s)
- Thanh Vân Phan
- icometrix, Research and Development, Leuven, Belgium; Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium.
| | - Diana M Sima
- icometrix, Research and Development, Leuven, Belgium
| | - Caroline Beelen
- Parenting and Special Education Research Unit, Faculty of Psychology and Educational Science, KU Leuven, Leuven, Belgium
| | - Jolijn Vanderauwera
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium; Parenting and Special Education Research Unit, Faculty of Psychology and Educational Science, KU Leuven, Leuven, Belgium
| | - Dirk Smeets
- icometrix, Research and Development, Leuven, Belgium
| | - Maaike Vandermosten
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium
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30
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Adeli E, Meng Y, Li G, Lin W, Shen D. Multi-task prediction of infant cognitive scores from longitudinal incomplete neuroimaging data. Neuroimage 2018; 185:783-792. [PMID: 29709627 DOI: 10.1016/j.neuroimage.2018.04.052] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2017] [Revised: 03/26/2018] [Accepted: 04/23/2018] [Indexed: 01/13/2023] Open
Abstract
Early postnatal brain undergoes a stunning period of development. Over the past few years, research on dynamic infant brain development has received increased attention, exhibiting how important the early stages of a child's life are in terms of brain development. To precisely chart the early brain developmental trajectories, longitudinal studies with data acquired over a long-enough period of infants' early life is essential. However, in practice, missing data from different time point(s) during the data gathering procedure is often inevitable. This leads to incomplete set of longitudinal data, which poses a major challenge for such studies. In this paper, prediction of multiple future cognitive scores with incomplete longitudinal imaging data is modeled into a multi-task machine learning framework. To efficiently learn this model, we account for selection of informative features (i.e., neuroimaging morphometric measurements for different time points), while preserving the structural information and the interrelation between these multiple cognitive scores. Several experiments are conducted on a carefully acquired in-house dataset, and the results affirm that we can predict the cognitive scores measured at the age of four years old, using the imaging data of earlier time points, as early as 24 months of age, with a reasonable performance (i.e., root mean square error of 0.18).
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Affiliation(s)
- Ehsan Adeli
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, United States; Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA 94305, United States.
| | - Yu Meng
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, United States; Department of Computer Science, University of North Carolina at Chapel Hill, NC 27599, United States
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, United States
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, United States
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, United States; Department of Brain & Cognitive Eng, Korea University, Seoul, 02841, Republic of Korea.
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31
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Bednarz HM, Kana RK. Advances, challenges, and promises in pediatric neuroimaging of neurodevelopmental disorders. Neurosci Biobehav Rev 2018; 90:50-69. [PMID: 29608989 DOI: 10.1016/j.neubiorev.2018.03.025] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2017] [Revised: 02/26/2018] [Accepted: 03/22/2018] [Indexed: 10/17/2022]
Abstract
Recent years have witnessed the proliferation of neuroimaging studies of neurodevelopmental disorders (NDDs), particularly of children with autism spectrum disorder (ASD), attention-deficit/hyperactivity disorder (ADHD), and Tourette's syndrome (TS). Neuroimaging offers immense potential in understanding the biology of these disorders, and how it relates to clinical symptoms. Neuroimaging techniques, in the long run, may help identify neurobiological markers to assist clinical diagnosis and treatment. However, methodological challenges have affected the progress of clinical neuroimaging. This paper reviews the methodological challenges involved in imaging children with NDDs. Specific topics include correcting for head motion, normalization using pediatric brain templates, accounting for psychotropic medication use, delineating complex developmental trajectories, and overcoming smaller sample sizes. The potential of neuroimaging-based biomarkers and the utility of implementing neuroimaging in a clinical setting are also discussed. Data-sharing approaches, technological advances, and an increase in the number of longitudinal, prospective studies are recommended as future directions. Significant advances have been made already, and future decades will continue to see innovative progress in neuroimaging research endeavors of NDDs.
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Affiliation(s)
- Haley M Bednarz
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Rajesh K Kana
- Department of Psychology, University of Alabama at Birmingham, Birmingham, AL, USA.
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32
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Computational neuroanatomy of baby brains: A review. Neuroimage 2018; 185:906-925. [PMID: 29574033 DOI: 10.1016/j.neuroimage.2018.03.042] [Citation(s) in RCA: 109] [Impact Index Per Article: 18.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 02/23/2018] [Accepted: 03/19/2018] [Indexed: 12/12/2022] Open
Abstract
The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.
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33
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Makropoulos A, Robinson EC, Schuh A, Wright R, Fitzgibbon S, Bozek J, Counsell SJ, Steinweg J, Vecchiato K, Passerat-Palmbach J, Lenz G, Mortari F, Tenev T, Duff EP, Bastiani M, Cordero-Grande L, Hughes E, Tusor N, Tournier JD, Hutter J, Price AN, Teixeira RPAG, Murgasova M, Victor S, Kelly C, Rutherford MA, Smith SM, Edwards AD, Hajnal JV, Jenkinson M, Rueckert D. The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 2018. [PMID: 29409960 DOI: 10.1101/125526] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
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Affiliation(s)
- Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Emma C Robinson
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Robert Wright
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Sean Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Eugene P Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Rui Pedro A G Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Maria Murgasova
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Suresh Victor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher Kelly
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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34
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Makropoulos A, Robinson EC, Schuh A, Wright R, Fitzgibbon S, Bozek J, Counsell SJ, Steinweg J, Vecchiato K, Passerat-Palmbach J, Lenz G, Mortari F, Tenev T, Duff EP, Bastiani M, Cordero-Grande L, Hughes E, Tusor N, Tournier JD, Hutter J, Price AN, Teixeira RPAG, Murgasova M, Victor S, Kelly C, Rutherford MA, Smith SM, Edwards AD, Hajnal JV, Jenkinson M, Rueckert D. The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 2018; 173:88-112. [PMID: 29409960 DOI: 10.1016/j.neuroimage.2018.01.054] [Citation(s) in RCA: 230] [Impact Index Per Article: 38.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 01/19/2018] [Accepted: 01/21/2018] [Indexed: 12/11/2022] Open
Abstract
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
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Affiliation(s)
- Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Emma C Robinson
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Robert Wright
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Sean Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Eugene P Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Rui Pedro A G Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Maria Murgasova
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Suresh Victor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher Kelly
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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Wei L, Cao X, Wang Z, Gao Y, Hu S, Wang L, Wu G, Shen D. Learning-based deformable registration for infant MRI by integrating random forest with auto-context model. Med Phys 2017; 44:6289-6303. [PMID: 28902466 PMCID: PMC5734654 DOI: 10.1002/mp.12578] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Revised: 08/29/2017] [Accepted: 08/30/2017] [Indexed: 11/07/2022] Open
Abstract
PURPOSE Accurately analyzing the rapid structural evolution of human brain in the first year of life is a key step in early brain development studies, which requires accurate deformable image registration. However, due to (a) dynamic appearance and (b) large anatomical changes, very few methods in the literature can work well for the registration of two infant brain MR images acquired at two arbitrary development phases, such as birth and one-year-old. METHODS To address these challenging issues, we propose a learning-based registration method, which can handle the anatomical structures and the appearance changes between the two infant brain MR images with possible time gap. Specifically, in the training stage, we employ a multioutput random forest regression and auto-context model to learn the evolution of anatomical shape and appearance from a training set of longitudinal infant images. To make the learning procedure more robust, we further harness the multimodal MR imaging information. Then, in the testing stage, for registering the two new infant images scanned at two different development phases, the learned model will be used to predict both the deformation field and appearance changes between the images under registration. After that, it becomes much easier to deploy any conventional image registration method to complete the remaining registration since the above-mentioned challenges for state-of-the-art registration methods have been well addressed. RESULTS We have applied our proposed registration method to intersubject registration of infant brain MR images acquired at 2-week-old, 3-month-old, 6-month-old, and 9-month-old with the images acquired at 12-month-old. Promising registration results have been achieved in terms of registration accuracy, compared with the counterpart nonlearning based registration methods. CONCLUSIONS The proposed new learning-based registration method have tackled the challenging issues in registering infant brain images acquired from the first year of life, by leveraging the multioutput random forest regression with auto-context model, which can learn the evolution of shape and appearance from a training set of longitudinal infant images. Thus, for the new infant image, its deformation field to the template and also its template-like appearances can be predicted by the learned models. We have extensively compared our method with state-of-the-art deformable registration methods, as well as multiple variants of our method, which show that our method can achieve higher accuracy even for the difficult cases with large appearance and shape changes between subject and template images.
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Affiliation(s)
- Lifang Wei
- College of Computer and Information SciencesFujian Agriculture and Forestry UniversityFuzhou350002China
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNC27599USA
| | - Xiaohuan Cao
- School of AutomationNorthwestern Polytechnical UniversityXi'an710072China
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNC27599USA
| | - Zhensong Wang
- School of Automation EngineeringUniversity of Electronic Science and TechnologyChengdu611731China
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNC27599USA
| | - Yaozong Gao
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNC27599USA
| | - Shunbo Hu
- School of InformationLinyi UniversityLinyi276005China
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNC27599USA
| | - Li Wang
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNC27599USA
| | - Guorong Wu
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNC27599USA
| | - Dinggang Shen
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNC27599USA
- Department of Brain and Cognitive EngineeringKorea UniversitySeoul02841Korea
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A fast stochastic framework for automatic MR brain images segmentation. PLoS One 2017; 12:e0187391. [PMID: 29136034 PMCID: PMC5685492 DOI: 10.1371/journal.pone.0187391] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2017] [Accepted: 10/19/2017] [Indexed: 12/05/2022] Open
Abstract
This paper introduces a new framework for the segmentation of different brain structures (white matter, gray matter, and cerebrospinal fluid) from 3D MR brain images at different life stages. The proposed segmentation framework is based on a shape prior built using a subset of co-aligned training images that is adapted during the segmentation process based on first- and second-order visual appearance characteristics of MR images. These characteristics are described using voxel-wise image intensities and their spatial interaction features. To more accurately model the empirical grey level distribution of the brain signals, we use a linear combination of discrete Gaussians (LCDG) model having positive and negative components. To accurately account for the large inhomogeneity in infant MRIs, a higher-order Markov-Gibbs Random Field (MGRF) spatial interaction model that integrates third- and fourth- order families with a traditional second-order model is proposed. The proposed approach was tested and evaluated on 102 3D MR brain scans using three metrics: the Dice coefficient, the 95-percentile modified Hausdorff distance, and the absolute brain volume difference. Experimental results show better segmentation of MR brain images compared to current open source segmentation tools.
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Rekik I, Li G, Yap PT, Chen G, Lin W, Shen D. Joint prediction of longitudinal development of cortical surfaces and white matter fibers from neonatal MRI. Neuroimage 2017; 152:411-424. [PMID: 28284800 PMCID: PMC5432411 DOI: 10.1016/j.neuroimage.2017.03.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Revised: 03/05/2017] [Accepted: 03/06/2017] [Indexed: 10/20/2022] Open
Abstract
The human brain can be modeled as multiple interrelated shapes (or a multishape), each for characterizing one aspect of the brain, such as the cortex and white matter pathways. Predicting the developing multishape is a very challenging task due to the contrasting nature of the developmental trajectories of the constituent shapes: smooth for the cortical surface and non-smooth for white matter tracts due to changes such as bifurcation. We recently addressed this problem and proposed an approach for predicting the multishape developmental spatiotemporal trajectories of infant brains based only on neonatal MRI data using a set of geometric, dynamic, and fiber-to-surface connectivity features. In this paper, we propose two key innovations to further improve the prediction of multishape evolution. First, for a more accurate cortical surface prediction, instead of simply relying on one neonatal atlas to guide the prediction of the multishape, we propose to use multiple neonatal atlases to build a spatially heterogeneous atlas using the multidirectional varifold representation. This individualizes the atlas by locally maximizing its similarity to the testing baseline cortical shape for each cortical region, thereby better representing the baseline testing cortical surface, which founds the multishape prediction process. Second, for temporally consistent fiber prediction, we propose to reliably estimate spatiotemporal connectivity features using low-rank tensor completion, thereby capturing the variability and richness of the temporal development of fibers. Experimental results confirm that the proposed variants significantly improve the prediction performance of our original multishape prediction framework for both cortical surfaces and fiber tracts shape at 3, 6, and 9 months of age. Our pioneering model will pave the way for learning how to predict the evolution of anatomical shapes with abnormal changes. Ultimately, devising accurate shape evolution prediction models that can help quantify and predict the severity of a brain disorder as it progresses will be of great aid in individualized treatment planning.
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Affiliation(s)
- Islem Rekik
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA; CVIP, Computing, School of Science and Engineering, University of Dundee, UK
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Geng Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
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Qiu W, Chen Y, Kishimoto J, de Ribaupierre S, Chiu B, Fenster A, Menon BK, Yuan J. Longitudinal Analysis of Pre-Term Neonatal Cerebral Ventricles From 3D Ultrasound Images Using Spatial-Temporal Deformable Registration. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1016-1026. [PMID: 28026756 DOI: 10.1109/tmi.2016.2643635] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Preterm neonates with a very low birth weight of less than 1,500 grams are at increased risk for developing intraventricular hemorrhage (IVH), which is a major cause of brain injury in preterm neonates. Quantitative measurements of ventricular dilatation or shrinkage play an important role in monitoring patients and evaluating treatment options. 3D ultrasound (US) has been developed to monitor ventricle volume as a biomarker for ventricular changes. However, ventricle volume as a global indicator does not allow for precise analysis of local ventricular changes, which could be linked to specific neurological problems often seen in the patient population later in life. In this work, a 3D+t spatial-temporal deformable registration approachis proposed, which is applied to the analysis of the detailed local changes of preterm IVH neonatal ventricles from 3D US images. In particular, a novel sequential convex/dual optimization algorithm is introduced to extract the optimal 3D+t spatial-temporal deformable field, which simultaneously optimizes the sequence of 3D deformation fieldswhile enjoying both efficiencyand simplicity in numerics. The developed registration technique was evaluated by comparing two manually extracted ventricle surfaces from the baseline and the registered follow-up images using the metrics of Dice similarity coefficient (DSC), mean absolute surface distance (MAD), and maximum absolute surface distance (MAXD). The performed experiments using 14 patients with 5 time-point images per patient show that the proposed 3D+t registration approach accurately recovered the longitudinal deformation of ventricle surfaces from 3D US images. The proposed approach may be potentially used to analyse the change pattern of cerebral ventricles of IVH patients, their response to different treatment options, and to elucidate the deficiencies that a patient could have later in life. To the best of our knowledge, this paper reports the first study on the longitudinalanalysis of neonatal ventricular system from 3D US images.
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Dong P, Wang L, Lin W, Shen D, Wu G. Scalable Joint Segmentation and Registration Framework for Infant Brain Images. Neurocomputing 2017; 229:54-62. [PMID: 29416227 PMCID: PMC5798494 DOI: 10.1016/j.neucom.2016.05.107] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
The first year of life is the most dynamic and perhaps the most critical phase of postnatal brain development. The ability to accurately measure structure changes is critical in early brain development study, which highly relies on the performances of image segmentation and registration techniques. However, either infant image segmentation or registration, if deployed independently, encounters much more challenges than segmentation/registration of adult brains due to dynamic appearance change with rapid brain development. In fact, image segmentation and registration of infant images can assists each other to overcome the above challenges by using the growth trajectories (i.e., temporal correspondences) learned from a large set of training subjects with complete longitudinal data. Specifically, a one-year-old image with ground-truth tissue segmentation can be first set as the reference domain. Then, to register the infant image of a new subject at earlier age, we can estimate its tissue probability maps, i.e., with sparse patch-based multi-atlas label fusion technique, where only the training images at the respective age are considered as atlases since they have similar image appearance. Next, these probability maps can be fused as a good initialization to guide the level set segmentation. Thus, image registration between the new infant image and the reference image is free of difficulty of appearance changes, by establishing correspondences upon the reasonably segmented images. Importantly, the segmentation of new infant image can be further enhanced by propagating the much more reliable label fusion heuristics at the reference domain to the corresponding location of the new infant image via the learned growth trajectories, which brings image segmentation and registration to assist each other. It is worth noting that our joint segmentation and registration framework is also flexible to handle the registration of any two infant images even with significant age gap in the first year of life, by linking their joint segmentation and registration through the reference domain. Thus, our proposed joint segmentation and registration method is scalable to various registration tasks in early brain development studies. Promising segmentation and registration results have been achieved for infant brain MR images aged from 2-week-old to 1-year-old, indicating the applicability of our method in early brain development study.
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Affiliation(s)
- Pei Dong
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
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Hu S, Wei L, Gao Y, Guo Y, Wu G, Shen D. Learning-based deformable image registration for infant MR images in the first year of life. Med Phys 2017; 44:158-170. [PMID: 28102945 DOI: 10.1002/mp.12007] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2016] [Revised: 11/01/2016] [Accepted: 11/04/2016] [Indexed: 12/21/2022] Open
Abstract
PURPOSE Many brain development studies have been devoted to investigate dynamic structural and functional changes in the first year of life. To quantitatively measure brain development in such a dynamic period, accurate image registration for different infant subjects with possible large age gap is of high demand. Although many state-of-the-art image registration methods have been proposed for young and elderly brain images, very few registration methods work for infant brain images acquired in the first year of life, because of (a) large anatomical changes due to fast brain development and (b) dynamic appearance changes due to white-matter myelination. METHODS To address these two difficulties, we propose a learning-based registration method to not only align the anatomical structures but also alleviate the appearance differences between two arbitrary infant MR images (with large age gap) by leveraging the regression forest to predict both the initial displacement vector and appearance changes. Specifically, in the training stage, two regression models are trained separately, with (a) one model learning the relationship between local image appearance (of one development phase) and its displacement toward the template (of another development phase) and (b) another model learning the local appearance changes between the two brain development phases. Then, in the testing stage, to register a new infant image to the template, we first predict both its voxel-wise displacement and appearance changes by the two learned regression models. Since such initializations can alleviate significant appearance and shape differences between new infant image and the template, it is easy to just use a conventional registration method to refine the remaining registration. RESULTS We apply our proposed registration method to align 24 infant subjects at five different time points (i.e., 2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old), and achieve more accurate and robust registration results, compared to the state-of-the-art registration methods. CONCLUSIONS The proposed learning-based registration method addresses the challenging task of registering infant brain images and achieves higher registration accuracy compared with other counterpart registration methods.
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Affiliation(s)
- Shunbo Hu
- School of Information, Linyi University, Linyi, 276005, China.,Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Lifang Wei
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA.,College of Computer and Information Sciences, Fujian Agriculture and Forestry University, Fuzhou, 350002, China
| | - Yaozong Gao
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Yanrong Guo
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, 27599, USA.,Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Korea
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Devi CN, Chandrasekharan A, V.K. S, Alex ZC. Automatic segmentation of infant brain MR images: With special reference to myelinated white matter. Biocybern Biomed Eng 2017. [DOI: 10.1016/j.bbe.2016.11.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
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Peyton C, Yang E, Msall ME, Adde L, Støen R, Fjørtoft T, Bos AF, Einspieler C, Zhou Y, Schreiber MD, Marks JD, Drobyshevsky A. White Matter Injury and General Movements in High-Risk Preterm Infants. AJNR Am J Neuroradiol 2017; 38:162-169. [PMID: 27789448 PMCID: PMC7963672 DOI: 10.3174/ajnr.a4955] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2016] [Accepted: 07/20/2016] [Indexed: 11/07/2022]
Abstract
BACKGROUND AND PURPOSE Very preterm infants (birth weight, <1500 g) are at increased risk of cognitive and motor impairment, including cerebral palsy. These adverse neurodevelopmental outcomes are associated with white matter abnormalities on MR imaging at term-equivalent age. Cerebral palsy has been predicted by analysis of spontaneous movements in the infant termed "General Movement Assessment." The goal of this study was to determine the utility of General Movement Assessment in predicting adverse cognitive, language, and motor outcomes in very preterm infants and to identify brain imaging markers associated with both adverse outcomes and aberrant general movements. MATERIALS AND METHODS In this prospective study of 47 preterm infants of 24-30 weeks' gestation, brain MR imaging was performed at term-equivalent age. Infants underwent T1- and T2-weighted imaging for volumetric analysis and DTI. General movements were assessed at 10-15 weeks' postterm age, and neurodevelopmental outcomes were evaluated at 2 years by using the Bayley Scales of Infant and Toddler Development III. RESULTS Nine infants had aberrant general movements and were more likely to have adverse neurodevelopmental outcomes, compared with infants with normal movements. In infants with aberrant movements, Tract-Based Spatial Statistics analysis identified significantly lower fractional anisotropy in widespread white matter tracts, including the corpus callosum, inferior longitudinal and fronto-occipital fasciculi, internal capsule, and optic radiation. The subset of infants having both aberrant movements and abnormal neurodevelopmental outcomes in cognitive, language, and motor skills had significantly lower fractional anisotropy in specific brain regions. CONCLUSIONS Aberrant general movements at 10-15 weeks' postterm are associated with adverse neurodevelopmental outcomes and specific white matter microstructure abnormalities for cognitive, language, and motor delays.
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Affiliation(s)
- C Peyton
- From the Departments of Therapy Services (C.P.)
| | - E Yang
- Pediatrics (E.Y., M.E.M., M.D.S., J.D.M.), University of Chicago, Chicago, Illinois
| | - M E Msall
- Pediatrics (E.Y., M.E.M., M.D.S., J.D.M.), University of Chicago, Chicago, Illinois
| | - L Adde
- Department of Laboratory Medicine (L.A., T.F.), Children's and Women's Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - R Støen
- Department of Pediatrics (R.S.)
| | - T Fjørtoft
- Department of Laboratory Medicine (L.A., T.F.), Children's and Women's Health, Norwegian University of Science and Technology, Trondheim, Norway
- Clinics of Clinical Services (T.F.), St. Olav University Hospital, Trondheim, Norway
| | - A F Bos
- Division of Neonatology (A.F.B.), University of Groningen, Groningen, the Netherlands
| | - C Einspieler
- Institute of Physiology (C.E.), Center for Physiological Medicine, Medical University of Graz, Graz, Austria
| | - Y Zhou
- Center for Biomedical Research Informatics (Y.Z.)
| | - M D Schreiber
- Pediatrics (E.Y., M.E.M., M.D.S., J.D.M.), University of Chicago, Chicago, Illinois
| | - J D Marks
- Pediatrics (E.Y., M.E.M., M.D.S., J.D.M.), University of Chicago, Chicago, Illinois
| | - A Drobyshevsky
- Department of Pediatrics (A.D.), NorthShore University Health System, Evanston, Illinois
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Zhang Y, Shi F, Wu G, Wang L, Yap PT, Shen D. Consistent Spatial-Temporal Longitudinal Atlas Construction for Developing Infant Brains. IEEE TRANSACTIONS ON MEDICAL IMAGING 2016; 35:2568-2577. [PMID: 27392345 PMCID: PMC6537598 DOI: 10.1109/tmi.2016.2587628] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Brain atlases are an essential component in understanding the dynamic cerebral development, especially for the early postnatal period. However, longitudinal atlases are rare for infants, and the existing ones are generally limited by their fuzzy appearance. Moreover, since longitudinal atlas construction is typically performed independently over time, the constructed atlases often fail to preserve temporal consistency. This problem is further aggravated for infant images since they typically have low spatial resolution and insufficient tissue contrast. In this paper, we propose a novel framework for consistent spatial-temporal construction of longitudinal atlases for developing infant brain MR images. Specifically, for preserving structural details, the atlas construction is performed in spatial-temporal wavelet domain simultaneously. This is achieved by a patch-based combination of results from each frequency subband. Compared with the existing infant longitudinal atlases, our experimental results indicate that our approach is able to produce longitudinal atlases with richer structural details and also better longitudinal consistency, thus leading to higher performance when used for spatial normalization of a group of infant brain images.
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Affiliation(s)
- Yuyao Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Feng Shi
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Guorong 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
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
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Abstract
Cortical learning via sensorimotor experiences evoked by bodily movements begins as early as the foetal period. However, the learning mechanisms by which sensorimotor experiences guide cortical learning remain unknown owing to technical and ethical difficulties. To bridge this gap, we present an embodied brain model of a human foetus as a coupled brain-body-environment system by integrating anatomical/physiological data. Using this model, we show how intrauterine sensorimotor experiences related to bodily movements induce specific statistical regularities in somatosensory feedback that facilitate cortical learning of body representations and subsequent visual-somatosensory integration. We also show how extrauterine sensorimotor experiences affect these processes. Our embodied brain model can provide a novel computational approach to the mechanistic understanding of cortical learning based on sensorimotor experiences mediated by complex interactions between the body, environment and nervous system.
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Kim H, Lepage C, Maheshwary R, Jeon S, Evans AC, Hess CP, Barkovich AJ, Xu D. NEOCIVET: Towards accurate morphometry of neonatal gyrification and clinical applications in preterm newborns. Neuroimage 2016; 138:28-42. [PMID: 27184202 DOI: 10.1016/j.neuroimage.2016.05.034] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2015] [Revised: 05/03/2016] [Accepted: 05/10/2016] [Indexed: 01/18/2023] Open
Abstract
Cerebral cortical folding becomes dramatically more complex in the fetal brain during the 3rd trimester of gestation; the process continues in a similar fashion in children who are born prematurely. To quantify this morphological development, it is necessary to extract the interface between gray matter and white matter, which is particularly challenging due to changing tissue contrast during brain maturation. We employed the well-established CIVET pipeline to extract this cortical surface, with point correspondence across subjects, using a surface-based spherical registration. We then developed a variant of the pipeline, called NEOCIVET, that quantified cortical folding using mean curvature and sulcal depth while addressing the well-known problems of poor and temporally-varying gray/white contrast as well as motion artifact in neonatal MRI. NEOCIVET includes: i) a tissue classification technique that analyzed multi-atlas texture patches using the nonlocal mean estimator and subsequently applied a label fusion approach based on a joint probability between templates, ii) neonatal template construction based on age-specific sub-groups, and iii) masking of non-interesting structures using label-fusion approaches. These techniques replaced modules that might be suboptimal for regional analysis of poor-contrast neonatal cortex. The proposed segmentation method showed more accurate results in subjects with various ages and with various degrees of motion compared to state-of-the-art methods. In the analysis of 158 preterm-born neonates, many with multiple scans (n=231; 26-40weeks postmenstrual age at scan), NEOCIVET identified increases in cortical folding over time in numerous cortical regions (mean curvature: +0.003/week; sulcal depth: +0.04mm/week) while folding did not change in major sulci that are known to develop early (corrected p<0.05). The proposed pipeline successfully mapped cortical structural development, supporting current models of cerebral morphogenesis, and furthermore, revealed impairment of cortical folding in extremely preterm newborns relative to relatively late preterm newborns, demonstrating its potential to provide biomarkers of prematurity-related developmental outcome.
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Affiliation(s)
- Hosung Kim
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA.
| | - Claude Lepage
- McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Romir Maheshwary
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Seun Jeon
- McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Alan C Evans
- McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Christopher P Hess
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - A James Barkovich
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
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Rekik I, Li G, Lin W, Shen D. Multidirectional and Topography-based Dynamic-scale Varifold Representations with Application to Matching Developing Cortical Surfaces. Neuroimage 2016; 135:152-62. [PMID: 27138207 DOI: 10.1016/j.neuroimage.2016.04.037] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Revised: 04/13/2016] [Accepted: 04/15/2016] [Indexed: 01/22/2023] Open
Abstract
The human cerebral cortex is marked by great complexity as well as substantial dynamic changes during early postnatal development. To obtain a fairly comprehensive picture of its age-induced and/or disorder-related cortical changes, one needs to match cortical surfaces to one another, while maximizing their anatomical alignment. Methods that geodesically shoot surfaces into one another as currents (a distribution of oriented normals) and varifolds (a distribution of non-oriented normals) provide an elegant Riemannian framework for generic surface matching and reliable statistical analysis. However, both conventional current and varifold matching methods have two key limitations. First, they only use the normals of the surface to measure its geometry and guide the warping process, which overlooks the importance of the orientations of the inherently convoluted cortical sulcal and gyral folds. Second, the 'conversion' of a surface into a current or a varifold operates at a fixed scale under which geometric surface details will be neglected, which ignores the dynamic scales of cortical foldings. To overcome these limitations and improve varifold-based cortical surface registration, we propose two different strategies. The first strategy decomposes each cortical surface into its normal and tangent varifold representations, by integrating principal curvature direction field into the varifold matching framework, thus providing rich information of the orientation of cortical folding and better characterization of the complex cortical geometry. The second strategy explores the informative cortical geometric features to perform a dynamic-scale measurement of the cortical surface that depends on the local surface topography (e.g., principal curvature), thereby we introduce the concept of a topography-based dynamic-scale varifold. We tested the proposed varifold variants for registering 12 pairs of dynamically developing cortical surfaces from 0 to 6 months of age. Both variants improved the matching accuracy in terms of closeness to the target surface and the goodness of alignment with regional anatomical boundaries, when compared with three state-of-the-art methods: (1) diffeomorphic spectral matching, (2) conventional current-based surface matching, and (3) conventional varifold-based surface matching.
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Affiliation(s)
- Islem Rekik
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
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Wu Y, Wu G, Wang L, Munsell BC, Wang Q, Lin W, Feng Q, Chen W, Shen D. Hierarchical and symmetric infant image registration by robust longitudinal-example-guided correspondence detection. Med Phys 2016; 42:4174-89. [PMID: 26133617 DOI: 10.1118/1.4922393] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
PURPOSE To investigate anatomical differences across individual subjects, or longitudinal changes in early brain development, it is important to perform accurate image registration. However, due to fast brain development and dynamic tissue appearance changes, it is very difficult to align infant brain images acquired from birth to 1-yr-old. METHODS To solve this challenging problem, a novel image registration method is proposed to align two infant brain images, regardless of age at acquisition. The main idea is to utilize the growth trajectories, or spatial-temporal correspondences, learned from a set of longitudinal training images, for guiding the registration of two different time-point images with different image appearances. Specifically, in the training stage, an intrinsic growth trajectory is first estimated for each training subject using the longitudinal images. To register two new infant images with potentially a large age gap, the corresponding images patches between each new image and its respective training images with similar age are identified. Finally, the registration between the two new images can be assisted by the learned growth trajectories from one time point to another time point that have been established in the training stage. To further improve registration accuracy, the proposed method is combined with a hierarchical and symmetric registration framework that can iteratively add new key points in both images to steer the estimation of the deformation between the two infant brain images under registration. RESULTS To evaluate image registration accuracy, the proposed method is used to align 24 infant subjects at five different time points (2-week-old, 3-month-old, 6-month-old, 9-month-old, and 12-month-old). Compared to the state-of-the-art methods, the proposed method demonstrated superior registration performance. CONCLUSIONS The proposed method addresses the difficulties in the infant brain registration and produces better results compared to existing state-of-the-art registration methods.
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Affiliation(s)
- Yao Wu
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China and Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Brent C Munsell
- Department of Computer Science, College of Charleston, Charleston, South Carolina 29424
| | - Qian Wang
- Med-X Research Institute, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Qianjin Feng
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Wufan Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul 136-701, Republic of Korea
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Rekik I, Li G, Lin W, Shen D. Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing. Med Image Anal 2016; 28:1-12. [PMID: 26619188 PMCID: PMC4914136 DOI: 10.1016/j.media.2015.10.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Revised: 08/20/2015] [Accepted: 10/23/2015] [Indexed: 12/27/2022]
Abstract
Longitudinal neuroimaging analysis methods have remarkably advanced our understanding of early postnatal brain development. However, learning predictive models to trace forth the evolution trajectories of both normal and abnormal cortical shapes remains broadly absent. To fill this critical gap, we pioneered the first prediction model for longitudinal developing cortical surfaces in infants using a spatiotemporal current-based learning framework solely from the baseline cortical surface. In this paper, we detail this prediction model and even further improve its performance by introducing two key variants. First, we use the varifold metric to overcome the limitations of the current metric for surface registration that was used in our preliminary study. We also extend the conventional varifold-based surface registration model for pairwise registration to a spatiotemporal surface regression model. Second, we propose a morphing process of the baseline surface using its topographic attributes such as normal direction and principal curvature sign. Specifically, our method learns from longitudinal data both the geometric (vertices positions) and dynamic (temporal evolution trajectories) features of the infant cortical surface, comprising a training stage and a prediction stage. In the training stage, we use the proposed varifold-based shape regression model to estimate geodesic cortical shape evolution trajectories for each training subject. We then build an empirical mean spatiotemporal surface atlas. In the prediction stage, given an infant, we select the best learnt features from training subjects to simultaneously predict the cortical surface shapes at all later timepoints, based on similarity metrics between this baseline surface and the learnt baseline population average surface atlas. We used a leave-one-out cross validation method to predict the inner cortical surface shape at 3, 6, 9 and 12 months of age from the baseline cortical surface shape at birth. Our method attained a higher prediction accuracy and better captured the spatiotemporal dynamic change of the highly folded cortical surface than the previous proposed prediction method.
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Affiliation(s)
- Islem Rekik
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
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Neonatal brain MRI segmentation: A review. Comput Biol Med 2015; 64:163-78. [DOI: 10.1016/j.compbiomed.2015.06.016] [Citation(s) in RCA: 43] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2015] [Revised: 06/06/2015] [Accepted: 06/18/2015] [Indexed: 11/20/2022]
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de Macedo Rodrigues K, Ben-Avi E, Sliva DD, Choe MS, Drottar M, Wang R, Fischl B, Grant PE, Zöllei L. A FreeSurfer-compliant consistent manual segmentation of infant brains spanning the 0-2 year age range. Front Hum Neurosci 2015; 9:21. [PMID: 25741260 PMCID: PMC4332305 DOI: 10.3389/fnhum.2015.00021] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2014] [Accepted: 01/10/2015] [Indexed: 11/13/2022] Open
Abstract
We present a detailed description of a set of FreeSurfer compatible segmentation guidelines tailored to infant MRI scans, and a unique data set of manually segmented acquisitions, with subjects nearly evenly distributed between 0 and 2 years of age. We believe that these segmentation guidelines and this dataset will have a wide range of potential uses in medicine and neuroscience.
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Affiliation(s)
| | - Emma Ben-Avi
- Laboratory of Computational Neuroimaging, AA Martinos Center, Massachusetts General Hospital Charlestown, MA, USA
| | - Danielle D Sliva
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital Boston, MA, USA ; Laboratories of Cognitive Neuroscience, Division of Developmental Medicine, Department of Medicine, Boston Children's Hospital Boston, MA, USA
| | - Myong-Sun Choe
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital Boston, MA, USA
| | - Marie Drottar
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital Boston, MA, USA
| | - Ruopeng Wang
- Laboratory of Computational Neuroimaging, AA Martinos Center, Massachusetts General Hospital Charlestown, MA, USA
| | - Bruce Fischl
- Laboratory of Computational Neuroimaging, AA Martinos Center, Massachusetts General Hospital Charlestown, MA, USA ; Department of Electrical Engineering and Computer Science, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology Cambridge, MA, USA
| | - Patricia E Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital Boston, MA, USA
| | - Lilla Zöllei
- Laboratory of Computational Neuroimaging, AA Martinos Center, Massachusetts General Hospital Charlestown, MA, USA
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