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Jang M, Gupta A, Kovanlikaya A, Scholl JE, Zun Z. High-resolution anatomical imaging of the fetal brain with a reduced field of view using outer volume suppression. Magn Reson Med 2024; 92:1556-1567. [PMID: 38702999 PMCID: PMC11262973 DOI: 10.1002/mrm.30147] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 04/04/2024] [Accepted: 04/19/2024] [Indexed: 05/06/2024]
Abstract
PURPOSE To achieve high-resolution fetal brain anatomical imaging without introducing image artifacts by reducing the FOV, and to demonstrate improved image quality compared to conventional full-FOV fetal brain imaging. METHODS Reduced FOV was achieved by applying outer volume suppression (OVS) pulses immediately prior to standard single-shot fast spin echo (SSFSE) imaging. In the OVS preparation, a saturation RF pulse followed by a gradient spoiler was repeated three times with optimized flip-angle weightings and a variable spoiler scheme to enhance signal suppression. Simulations and phantom and in-vivo experiments were performed to evaluate OVS performance. In-vivo high-resolution SSFSE images acquired using the proposed approach were compared with conventional and high-resolution SSFSE images with a full FOV, using image quality scores assessed by neuroradiologists and calculated image metrics. RESULTS Excellent signal suppression in the saturation bands was confirmed in phantom and in-vivo experiments. High-resolution SSFSE images with a reduced FOV acquired using OVS demonstrated the improved depiction of brain structures without significant motion and blurring artifacts. The proposed method showed the highest image quality scores in the criteria of sharpness, contrast, and artifact and was selected as the best method based on overall image quality. The calculated image sharpness and tissue contrast ratio were also the highest with the proposed method. CONCLUSION High-resolution fetal brain anatomical images acquired using a reduced FOV with OVS demonstrated improved image quality both qualitatively and quantitatively, suggesting the potential for enhanced diagnostic accuracy in detecting fetal brain abnormalities in utero.
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Affiliation(s)
- MinJung Jang
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Arzu Kovanlikaya
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Jessica E. Scholl
- Department of Obstetrics and Gynecology, Weill Cornell Medicine, New York, New York, USA
| | - Zungho Zun
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
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2
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Weichert J, Scharf JL. Advancements in Artificial Intelligence for Fetal Neurosonography: A Comprehensive Review. J Clin Med 2024; 13:5626. [PMID: 39337113 PMCID: PMC11432922 DOI: 10.3390/jcm13185626] [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: 07/30/2024] [Revised: 09/04/2024] [Accepted: 09/16/2024] [Indexed: 09/30/2024] Open
Abstract
The detailed sonographic assessment of the fetal neuroanatomy plays a crucial role in prenatal diagnosis, providing valuable insights into timely, well-coordinated fetal brain development and detecting even subtle anomalies that may impact neurodevelopmental outcomes. With recent advancements in artificial intelligence (AI) in general and medical imaging in particular, there has been growing interest in leveraging AI techniques to enhance the accuracy, efficiency, and clinical utility of fetal neurosonography. The paramount objective of this focusing review is to discuss the latest developments in AI applications in this field, focusing on image analysis, the automation of measurements, prediction models of neurodevelopmental outcomes, visualization techniques, and their integration into clinical routine.
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Affiliation(s)
- Jan Weichert
- Division of Prenatal Medicine, Department of Gynecology and Obstetrics, University Hospital of Schleswig-Holstein, Ratzeburger Allee 160, 23538 Luebeck, Germany;
- Elbe Center of Prenatal Medicine and Human Genetics, Willy-Brandt-Str. 1, 20457 Hamburg, Germany
| | - Jann Lennard Scharf
- Division of Prenatal Medicine, Department of Gynecology and Obstetrics, University Hospital of Schleswig-Holstein, Ratzeburger Allee 160, 23538 Luebeck, Germany;
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3
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Xu X, Sun C, Yu H, Yan G, Zhu Q, Kong X, Pan Y, Xu H, Zheng T, Zhou C, Wang Y, Xiao J, Chen R, Li M, Zhang S, Hu H, Zou Y, Wang J, Wang G, Wu D. Site effects in multisite fetal brain MRI: morphological insights into early brain development. Eur Radiol 2024:10.1007/s00330-024-11084-w. [PMID: 39299951 DOI: 10.1007/s00330-024-11084-w] [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: 03/21/2024] [Revised: 06/06/2024] [Accepted: 08/26/2024] [Indexed: 09/22/2024]
Abstract
OBJECTIVE To evaluate multisite effects on fetal brain MRI. Specifically, to identify crucial acquisition factors affecting fetal brain structural measurements and developmental patterns, while assessing the effectiveness of existing harmonization methods in mitigating site effects. MATERIALS AND METHODS Between May 2017 and March 2022, T2-weighted fast spin-echo sequences in-utero MRI were performed on healthy fetuses from retrospectively recruited pregnant volunteers on four different scanners at four sites. A generalized additive model (GAM) was used to quantitatively assess site effects, including field strength (FS), manufacturer (M), in-plane resolution (R), and slice thickness (ST), on subcortical volume and cortical morphological measurements, including cortical thickness, curvature, and sulcal depth. Growth models were selected to elucidate the developmental trajectories of these morphological measurements. Welch's test was performed to evaluate the influence of site effects on developmental trajectories. The comBat-GAM harmonization method was applied to mitigate site-related biases. RESULTS The final analytic sample consisted of 340 MRI scans from 218 fetuses (mean GA, 30.1 weeks ± 4.4 [range, 21.7-40 weeks]). GAM results showed that lower FS and lower spatial resolution led to overestimations in selected brain regions of subcortical volumes and cortical morphological measurements. Only the peak cortical thickness in developmental trajectories was significantly influenced by the effects of FS and R. Notably, ComBat-GAM harmonization effectively removed site effects while preserving developmental patterns. CONCLUSION Our findings pinpointed the key acquisition factors in in-utero fetal brain MRI and underscored the necessity of data harmonization when pooling multisite data for fetal brain morphology investigations. KEY POINTS Question How do specific site MRI acquisition factors affect fetal brain imaging? Finding Lower FS and spatial resolution overestimated subcortical volumes and cortical measurements. Cortical thickness in developmental trajectories was influenced by FS and in-plane resolution. Clinical relevance This study provides important guidelines for the fetal MRI community when scanning fetal brains and underscores the necessity of data harmonization of cross-center fetal studies.
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Affiliation(s)
- Xinyi Xu
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Cong Sun
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Hong Yu
- Dalian Municipal Women and Children's Medical Center (Group), Dalian, China
| | - Guohui Yan
- Department of Radiology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Qingqing Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xianglei Kong
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yibin Pan
- Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Reproductive Dysfunction Management of Zhejiang Province, Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Hangzhou, China
| | - Haoan Xu
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Tianshu Zheng
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Chi Zhou
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Yutian Wang
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Jiaxin Xiao
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
- School of Biomedical Engineering & Imaging Sciences, Faculty of Life Sciences and Medicine, King's College London, London, UK
| | - Ruike Chen
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Mingyang Li
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Songying Zhang
- Department of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Reproductive Dysfunction Management of Zhejiang Province, Zhejiang Provincial Clinical Research Center for Obstetrics and Gynecology, Hangzhou, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Yu Zou
- Department of Radiology, Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
| | - Jingshi Wang
- Dalian Municipal Women and Children's Medical Center (Group), Dalian, China.
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
| | - Dan Wu
- Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
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Neves Silva S, McElroy S, Aviles Verdera J, Colford K, St Clair K, Tomi-Tricot R, Uus A, Ozenne V, Hall M, Story L, Pushparajah K, Rutherford MA, Hajnal JV, Hutter J. Fully automated planning for anatomical fetal brain MRI on 0.55T. Magn Reson Med 2024; 92:1263-1276. [PMID: 38650351 DOI: 10.1002/mrm.30122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/08/2024] [Accepted: 04/02/2024] [Indexed: 04/25/2024]
Abstract
PURPOSE Widening the availability of fetal MRI with fully automatic real-time planning of radiological brain planes on 0.55T MRI. METHODS Deep learning-based detection of key brain landmarks on a whole-uterus echo planar imaging scan enables the subsequent fully automatic planning of the radiological single-shot Turbo Spin Echo acquisitions. The landmark detection pipeline was trained on over 120 datasets from varying field strength, echo times, and resolutions and quantitatively evaluated. The entire automatic planning solution was tested prospectively in nine fetal subjects between 20 and 37 weeks. A comprehensive evaluation of all steps, the distance between manual and automatic landmarks, the planning quality, and the resulting image quality was conducted. RESULTS Prospective automatic planning was performed in real-time without latency in all subjects. The landmark detection accuracy was 4.2± $$ \pm $$ 2.6 mm for the fetal eyes and 6.5± $$ \pm $$ 3.2 for the cerebellum, planning quality was 2.4/3 (compared to 2.6/3 for manual planning) and diagnostic image quality was 2.2 compared to 2.1 for manual planning. CONCLUSIONS Real-time automatic planning of all three key fetal brain planes was successfully achieved and will pave the way toward simplifying the acquisition of fetal MRI thereby widening the availability of this modality in nonspecialist centers.
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Affiliation(s)
- Sara Neves Silva
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Sarah McElroy
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | - Jordina Aviles Verdera
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Kathleen Colford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Kamilah St Clair
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Raphael Tomi-Tricot
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- MR Research Collaborations, Siemens Healthcare Limited, Camberley, UK
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Valéry Ozenne
- CNRS, CRMSB, UMR 5536, IHU Liryc, Université de Bordeaux, Bordeaux, France
| | - Megan Hall
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Women & Children's Health, King's College London, London, UK
| | - Lisa Story
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Women & Children's Health, King's College London, London, UK
| | - Kuberan Pushparajah
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Smart Imaging Lab, Radiological Institute, Friedrich-Alexander University Erlangen-Nuremberg, Erlangen, Germany
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5
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Liu W, Calixto C, Warfield SK, Karimi D. Streamline tractography of the fetal brain in utero with machine learning. ARXIV 2024:arXiv:2408.14326v1. [PMID: 39253631 PMCID: PMC11383324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/11/2024]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is the only non-invasive tool for studying white matter tracts and structural connectivity of the brain. These assessments rely heavily on tractography techniques, which reconstruct virtual streamlines representing white matter fibers. Much effort has been devoted to improving tractography methodology for adult brains, while tractography of the fetal brain has been largely neglected. Fetal tractography faces unique difficulties due to low dMRI signal quality, immature and rapidly developing brain structures, and paucity of reference data. To address these challenges, this work presents the first machine learning model, based on a deep neural network, for fetal tractography. The model input consists of five different sources of information: (1) Voxel-wise fiber orientation, inferred from a diffusion tensor fit to the dMRI signal; (2) Directions of recent propagation steps; (3) Global spatial information, encoded as normalized distances to keypoints in the brain cortex; (4) Tissue segmentation information; and (5) Prior information about the expected local fiber orientations supplied with an atlas. In order to mitigate the local tensor estimation error, a large spatial context around the current point in the diffusion tensor image is encoded using convolutional and attention neural network modules. Moreover, the diffusion tensor information at a hypothetical next point is included in the model input. Filtering rules based on anatomically constrained tractography are applied to prune implausible streamlines. We trained the model on manually-refined whole-brain fetal tractograms and validated the trained model on an independent set of 11 test scans with gestational ages between 23 and 36 weeks. Results show that our proposed method achieves superior performance across all evaluated tracts. The new method can significantly advance the capabilities of dMRI for studying normal and abnormal brain development in utero.
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Affiliation(s)
- Weide Liu
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Camilo Calixto
- Boston Children's Hospital and Harvard Medical School, Boston, MA
- Elmhurst Hospital Center and Icahn School of Medicine at Mount Sinai, New York, NY
| | - Simon K Warfield
- Boston Children's Hospital and Harvard Medical School, Boston, MA
| | - Davood Karimi
- Boston Children's Hospital and Harvard Medical School, Boston, MA
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Calixto C, Taymourtash A, Karimi D, Snoussi H, Velasco-Annis C, Jaimes C, Gholipour A. Advances in Fetal Brain Imaging. Magn Reson Imaging Clin N Am 2024; 32:459-478. [PMID: 38944434 PMCID: PMC11216711 DOI: 10.1016/j.mric.2024.03.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/01/2024]
Abstract
Over the last 20 years, there have been remarkable developments in fetal brain MR imaging analysis methods. This article delves into the specifics of structural imaging, diffusion imaging, functional MR imaging, and spectroscopy, highlighting the latest advancements in motion correction, fetal brain development atlases, and the challenges and innovations. Furthermore, this article explores the clinical applications of these advanced imaging techniques in comprehending and diagnosing fetal brain development and abnormalities.
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Affiliation(s)
- Camilo Calixto
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA.
| | - Athena Taymourtash
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Spitalgasse 23, Wien 1090, Austria
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Haykel Snoussi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Camilo Jaimes
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA; Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02215, USA
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, 401 Park Dr, 7th Floor West, Boston, MA 02215, USA; Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
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Zhang X, Chen Z, Li Y, Xie C, Liu Z, Wu Q, Kuang M, Yan R, Wu F, Liu H. Volume development changes in the occipital lobe gyrus assessed by MRI in fetuses with isolated ventriculomegaly correlate with neurological development in infancy and early childhood. J Perinatol 2024; 44:1178-1185. [PMID: 38802655 DOI: 10.1038/s41372-024-02012-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Revised: 05/09/2024] [Accepted: 05/16/2024] [Indexed: 05/29/2024]
Abstract
OBJECTIVE This study was to systematically assess the occipital lobe gray and white matter volume of isolated ventriculomegaly (IVM) fetuses with MRI and to follow up the neurodevelopment of participants. METHOD MRI was used to evaluate 37 IVM fetuses and 37 control fetuses. The volume of gray and white matter in each fetal occipital gyrus was manually segmented and compared, and neurodevelopment was followed up and assessed in infancy and early childhood. RESULT Compared with the control group, the volume of gray matter in occipital lobe increased in the IVM group, and the incidence of neurodevelopmental delay increased. CONCLUSION We tested the hypothesis that prenatal diagnosis IVM represents a biological marker for development in fetal occipital lobe. Compared with the control group, the IVM group showed differences in occipital gray matter development and had a higher risk of neurodevelopmental delay.
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Affiliation(s)
- Xin Zhang
- Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China
| | - Zhaoji Chen
- Department of Radiology, Hexian Memorial Hospital of PanYu District, Guangzhou, China
| | - Yuchao Li
- Department of Radiology, Longhua District People's Hospital, Shenzhen, China
| | - Chenxin Xie
- Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China
| | - Zhenqing Liu
- Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China
| | - Qianqian Wu
- Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China
| | - Minwei Kuang
- Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China
| | - Ren Yan
- Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China
| | - Fan Wu
- Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China.
| | - Hongsheng Liu
- Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangdong Provincial Clinical Research Center for Child Health, Guangzhou, China.
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Calixto C, Machado-Rivas F, Karimi D, Velasco C, Cortes-Albornoz MC, Afacan O, Warfield SK, Gholipour A, Jaimes C. Population Atlas Analysis of Emerging Brain Structural Connections in the Human Fetus. J Magn Reson Imaging 2024; 60:152-160. [PMID: 37842932 PMCID: PMC11018715 DOI: 10.1002/jmri.29057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/17/2023] Open
Abstract
BACKGROUND A lack of in utero imaging data hampers our understanding of the connections in the human fetal brain. Generalizing observations from postmortem subjects and premature newborns is inaccurate due to technical and biological differences. PURPOSE To evaluate changes in fetal brain structural connectivity between 23 and 35 weeks postconceptional age using a spatiotemporal atlas of diffusion tensor imaging (DTI). STUDY TYPE Retrospective. POPULATION Publicly available diffusion atlases, based on 60 healthy women (age 18-45 years) with normal prenatal care, from 23 and 35 weeks of gestation. FIELD STRENGTH/SEQUENCE 3.0 Tesla/DTI acquired with diffusion-weighted echo planar imaging (EPI). ASSESSMENT We performed whole-brain fiber tractography from DTI images. The cortical plate of each diffusion atlas was segmented and parcellated into 78 regions derived from the Edinburgh Neonatal Atlas (ENA33). Connectivity matrices were computed, representing normalized fiber connections between nodes. We examined the relationship between global efficiency (GE), local efficiency (LE), small-worldness (SW), nodal efficiency (NE), and betweenness centrality (BC) with gestational age (GA) and with laterality. STATISTICAL TESTS Linear regression was used to analyze changes in GE, LE, NE, and BC throughout gestation, and to assess changes in laterality. The t-tests were used to assess SW. P-values were corrected using Holm-Bonferroni method. A corrected P-value <0.05 was considered statistically significant. RESULTS Network analysis revealed a significant weekly increase in GE (5.83%/week, 95% CI 4.32-7.37), LE (5.43%/week, 95% CI 3.63-7.25), and presence of SW across GA. No significant hemisphere differences were found in GE (P = 0.971) or LE (P = 0.458). Increasing GA was significantly associated with increasing NE in 41 nodes, increasing BC in 3 nodes, and decreasing BC in 2 nodes. DATA CONCLUSION Extensive network development and refinement occur in the second and third trimesters, marked by a rapid increase in global integration and local segregation. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Camilo Calixto
- Computational Radiology Laboratory. Department of Radiology. Boston Children’s Hospital. Boston, MA
- Harvard Medical School. Boston, MA
| | - Fedel Machado-Rivas
- Harvard Medical School. Boston, MA
- Massachusetts General Hospital. Boston, MA
| | - Davood Karimi
- Computational Radiology Laboratory. Department of Radiology. Boston Children’s Hospital. Boston, MA
- Harvard Medical School. Boston, MA
| | - Clemente Velasco
- Computational Radiology Laboratory. Department of Radiology. Boston Children’s Hospital. Boston, MA
- Harvard Medical School. Boston, MA
| | | | - Onur Afacan
- Computational Radiology Laboratory. Department of Radiology. Boston Children’s Hospital. Boston, MA
- Harvard Medical School. Boston, MA
| | - Simon K. Warfield
- Computational Radiology Laboratory. Department of Radiology. Boston Children’s Hospital. Boston, MA
- Harvard Medical School. Boston, MA
| | - Ali Gholipour
- Computational Radiology Laboratory. Department of Radiology. Boston Children’s Hospital. Boston, MA
- Harvard Medical School. Boston, MA
| | - Camilo Jaimes
- Harvard Medical School. Boston, MA
- Massachusetts General Hospital. Boston, MA
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9
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Kebiri H, Gholipour A, Lin R, Vasung L, Calixto C, Krsnik Ž, Karimi D, Bach Cuadra M. Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study. Med Image Anal 2024; 95:103186. [PMID: 38701657 DOI: 10.1016/j.media.2024.103186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 02/06/2024] [Accepted: 04/22/2024] [Indexed: 05/05/2024]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results than standard methods such as Constrained Spherical Deconvolution and two state-of-the-art deep learning methods. For voxels with one and two fibers, respectively, our method shows an agreement rate in terms of the number of fibers of 77.5% and 22.2%, which is 3% and 5.4% higher than other deep learning methods, and an angular error of 10° and 20°, which is 6° and 5° lower than other deep learning methods. To determine baselines for assessing the performance of our method, we compute agreement metrics using densely sampled newborn data. Moreover, we demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical external datasets of newborns and fetuses. We validate fetal FODs, successfully estimated for the first time with deep learning, using post-mortem histological data. Our results show the advantage of deep learning in computing the fiber orientation density for the developing brain from in-vivo dMRI measurements that are often very limited due to constrained acquisition times. Our findings also highlight the intrinsic limitations of dMRI for probing the developing brain microstructure.
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Affiliation(s)
- Hamza Kebiri
- CIBM Center for Biomedical Imaging, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rizhong Lin
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Camilo Calixto
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Željka Krsnik
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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10
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Snoussi H, Karimi D, Afacan O, Utkur M, Gholipour A. HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI. ARXIV 2024:arXiv:2406.20042v1. [PMID: 38979484 PMCID: PMC11230346] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Diffusion magnetic resonance imaging (dMRI) is pivotal for probing the microstructure of the rapidly-developing fetal brain. However, fetal motion during scans and its interaction with magnetic field inhomogeneities result in artifacts and data scattering across spatial and angular domains. The effects of those artifacts are more pronounced in high-angular resolution fetal dMRI, where signal-to-noise ratio is very low. Those effects lead to biased estimates and compromise the consistency and reliability of dMRI analysis. This work presents HAITCH, the first and the only publicly available tool to correct and reconstruct multi-shell high-angular resolution fetal dMRI data. HAITCH offers several technical advances that include a blip-reversed dual-echo acquisition for dynamic distortion correction, advanced motion correction for model-free and robust reconstruction, optimized multi-shell design for enhanced information capture and increased tolerance to motion, and outlier detection for improved reconstruction fidelity. The framework is open-source, flexible, and can be used to process any type of fetal dMRI data including single-echo or single-shell acquisitions, but is most effective when used with multi-shell multi-echo fetal dMRI data that cannot be processed with any of the existing tools. Validation experiments on real fetal dMRI scans demonstrate significant improvements and accurate correction across diverse fetal ages and motion levels. HAITCH successfully removes artifacts and reconstructs high-fidelity fetal dMRI data suitable for advanced diffusion modeling, including fiber orientation distribution function estimation. These advancements pave the way for more reliable analysis of the fetal brain microstructure and tractography under challenging imaging conditions.
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Affiliation(s)
- Haykel Snoussi
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Davood Karimi
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Onur Afacan
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Mustafa Utkur
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
| | - Ali Gholipour
- Boston Children's Hospital, and Harvard Medical School, Boston, MA 02115 USA
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11
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Martins-Costa C, Wiegers A, Pham VA, Sidhaye J, Doleschall B, Novatchkova M, Lendl T, Piber M, Peer A, Möseneder P, Stuempflen M, Chow SYA, Seidl R, Prayer D, Höftberger R, Kasprian G, Ikeuchi Y, Corsini NS, Knoblich JA. ARID1B controls transcriptional programs of axon projection in an organoid model of the human corpus callosum. Cell Stem Cell 2024; 31:866-885.e14. [PMID: 38718796 DOI: 10.1016/j.stem.2024.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 02/13/2024] [Accepted: 04/17/2024] [Indexed: 06/09/2024]
Abstract
Mutations in ARID1B, a member of the mSWI/SNF complex, cause severe neurodevelopmental phenotypes with elusive mechanisms in humans. The most common structural abnormality in the brain of ARID1B patients is agenesis of the corpus callosum (ACC), characterized by the absence of an interhemispheric white matter tract that connects distant cortical regions. Here, we find that neurons expressing SATB2, a determinant of callosal projection neuron (CPN) identity, show impaired maturation in ARID1B+/- neural organoids. Molecularly, a reduction in chromatin accessibility of genomic regions targeted by TCF-like, NFI-like, and ARID-like transcription factors drives the differential expression of genes required for corpus callosum (CC) development. Through an in vitro model of the CC tract, we demonstrate that this transcriptional dysregulation impairs the formation of long-range axonal projections, causing structural underconnectivity. Our study uncovers new functions of the mSWI/SNF during human corticogenesis, identifying cell-autonomous axonogenesis defects in SATB2+ neurons as a cause of ACC in ARID1B patients.
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Affiliation(s)
- Catarina Martins-Costa
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria; Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, 1030 Vienna, Austria
| | - Andrea Wiegers
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Vincent A Pham
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Jaydeep Sidhaye
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Balint Doleschall
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria; Vienna BioCenter PhD Program, Doctoral School of the University of Vienna and Medical University of Vienna, 1030 Vienna, Austria
| | - Maria Novatchkova
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Thomas Lendl
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Marielle Piber
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Angela Peer
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Paul Möseneder
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria
| | - Marlene Stuempflen
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Siu Yu A Chow
- Institute of Industrial Science, The University of Tokyo, 153-8505 Tokyo, Japan; Institute for AI and Beyond, The University of Tokyo, 113-0032 Tokyo, Japan
| | - Rainer Seidl
- Department of Pediatrics and Adolescent Medicine, Medical University of Vienna, 1090 Vienna, Austria
| | - Daniela Prayer
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Romana Höftberger
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
| | - Gregor Kasprian
- Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Yoshiho Ikeuchi
- Institute of Industrial Science, The University of Tokyo, 153-8505 Tokyo, Japan; Institute for AI and Beyond, The University of Tokyo, 113-0032 Tokyo, Japan
| | - Nina S Corsini
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria.
| | - Jürgen A Knoblich
- Institute of Molecular Biotechnology of the Austrian Academy of Sciences (IMBA), Vienna BioCenter (VBC), 1030 Vienna, Austria; Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria.
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12
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Fidon L, Aertsen M, Kofler F, Bink A, David AL, Deprest T, Emam D, Guffens F, Jakab A, Kasprian G, Kienast P, Melbourne A, Menze B, Mufti N, Pogledic I, Prayer D, Stuempflen M, Van Elslander E, Ourselin S, Deprest J, Vercauteren T. A Dempster-Shafer Approach to Trustworthy AI With Application to Fetal Brain MRI Segmentation. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024; 46:3784-3795. [PMID: 38198270 DOI: 10.1109/tpami.2023.3346330] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2024]
Abstract
Deep learning models for medical image segmentation can fail unexpectedly and spectacularly for pathological cases and images acquired at different centers than training images, with labeling errors that violate expert knowledge. Such errors undermine the trustworthiness of deep learning models for medical image segmentation. Mechanisms for detecting and correcting such failures are essential for safely translating this technology into clinics and are likely to be a requirement of future regulations on artificial intelligence (AI). In this work, we propose a trustworthy AI theoretical framework and a practical system that can augment any backbone AI system using a fallback method and a fail-safe mechanism based on Dempster-Shafer theory. Our approach relies on an actionable definition of trustworthy AI. Our method automatically discards the voxel-level labeling predicted by the backbone AI that violate expert knowledge and relies on a fallback for those voxels. We demonstrate the effectiveness of the proposed trustworthy AI approach on the largest reported annotated dataset of fetal MRI consisting of 540 manually annotated fetal brain 3D T2w MRIs from 13 centers. Our trustworthy AI method improves the robustness of four backbone AI models for fetal brain MRIs acquired across various centers and for fetuses with various brain abnormalities.
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13
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Pandurangan K, Jayakumar J, Savoia S, Nanda R, Lata S, Kumar EH, S S, Vasudevan S, Srinivasan C, Joseph J, Sivaprakasam M, Verma R. Systematic development of immunohistochemistry protocol for large cryosections-specific to non-perfused fetal brain. J Neurosci Methods 2024; 405:110085. [PMID: 38387804 DOI: 10.1016/j.jneumeth.2024.110085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2023] [Revised: 02/01/2024] [Accepted: 02/18/2024] [Indexed: 02/24/2024]
Abstract
BACKGROUND Immunohistochemistry (IHC) is an important technique in understanding the expression of neurochemical molecules in the developing human brain. Despite its routine application in the research and clinical setup, the IHC protocol specific for soft fragile fetal brains that are fixed using the non-perfusion method is still limited in studying the whole brain. NEW METHOD This study shows that the IHC protocols, using a chromogenic detection system, used in animals and adult humans are not optimal in the fetal brains. We have optimized key steps from Antigen retrieval (AR) to chromogen visualization for formalin-fixed whole-brain cryosections (20 µm) mounted on glass slides. RESULTS We show the results from six validated, commonly used antibodies to study the fetal brain. We achieved optimal antigen retrieval with 0.1 M Boric Acid, pH 9.0 at 70°C for 20 minutes. We also present the optimal incubation duration and temperature for protein blocking and the primary antibody that results in specific antigen labeling with minimal tissue damage. COMPARISON WITH EXISTING METHODS The IHC protocol commonly used for adult human and animal brains results in significant tissue damage in the fetal brains with little or suboptimal antigen expression. Our new method with important modifications including the temperature, duration, and choice of the alkaline buffer for AR addresses these pitfalls and provides high-quality results. CONCLUSION The optimized IHC protocol for the developing human brain (13-22 GW) provides a high-quality, repeatable, and reliable method for studying chemoarchitecture in neurotypical and pathological conditions across different gestational ages.
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Affiliation(s)
- Karthika Pandurangan
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
| | - Jaikishan Jayakumar
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Center for Computational Brain Research, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
| | | | - Reetuparna Nanda
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
| | - S Lata
- Mediscan Systems, Chennai, Tamil Nadu, India.
| | | | - Suresh S
- Mediscan Systems, Chennai, Tamil Nadu, India.
| | - Sudha Vasudevan
- Department of Obstetrics & Gynaecology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India.
| | - Chitra Srinivasan
- Department of Pathology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India.
| | - Jayaraj Joseph
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Department of Electrical Engineering, Indian Institute of Technology, Madras, Chennai, Tamil Nadu, India.
| | - Mohanasankar Sivaprakasam
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India; Department of Electrical Engineering, Indian Institute of Technology, Madras, Chennai, Tamil Nadu, India.
| | - Richa Verma
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India.
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14
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Ciceri T, Casartelli L, Montano F, Conte S, Squarcina L, Bertoldo A, Agarwal N, Brambilla P, Peruzzo D. Fetal brain MRI atlases and datasets: A review. Neuroimage 2024; 292:120603. [PMID: 38588833 DOI: 10.1016/j.neuroimage.2024.120603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/28/2024] [Accepted: 04/05/2024] [Indexed: 04/10/2024] Open
Abstract
Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.
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Affiliation(s)
- Tommaso Ciceri
- NeuroImaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Luca Casartelli
- Theoretical and Cognitive Neuroscience Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Florian Montano
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Stefania Conte
- Psychology Department, State University of New York at Binghamton, New York, USA
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padua, Padua, Italy; Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Nivedita Agarwal
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Denis Peruzzo
- NeuroImaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
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15
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Miglioli C, Canini M, Vignotto E, Pecco N, Pozzoni M, Victoria-Feser MP, Guerrier S, Candiani M, Falini A, Baldoli C, Cavoretto PI, Della Rosa PA. The maternal-fetal neurodevelopmental groundings of preterm birth risk. Heliyon 2024; 10:e28825. [PMID: 38596101 PMCID: PMC11002256 DOI: 10.1016/j.heliyon.2024.e28825] [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: 10/09/2023] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 04/11/2024] Open
Abstract
Background Altered neurodevelopment is a major clinical sequela of Preterm Birth (PTB) being currently unexplored in-utero. Aims To study the link between fetal brain functional (FbF) connectivity and preterm birth, using resting-state functional magnetic resonance imaging (rs-fMRI). Study design Prospective single-centre cohort study. Subjects A sample of 31 singleton pregnancies at 28-34 weeks assigned to a low PTB risk (LR) (n = 19) or high PTB risk (HR) (n = 12) group based on a) the Maternal Frailty Inventory (MaFra) for PTB risk; b) a case-specific PTB risk gradient. Methods Fetal brain rs-fMRI was performed on 1.5T MRI scanner. First, directed causal relations representing fetal brain functional connectivity measurements were estimated using the Greedy Equivalence Search (GES) algorithm. HR vs. LR group differences were then tested with a novel ad-hoc developed Monte Carlo permutation test. Second, a MaFra-only random forest (RF) was compared against a MaFra-Neuro RF, trained by including also the most important fetal brain functional connections. Third, correlation and regression analyses were performed between MaFra-Neuro class probabilities and i) the GA at birth; ii) PTB risk gradient, iii) perinatal clinical conditions and iv) PTB below 37 weeks. Results First, fewer fetal brain functional connections were evident in the HR group. Second, the MaFra-Neuro RF improved PTB risk prediction. Third, MaFra-Neuro class probabilities showed a significant association with: i) GA at birth; ii) PTB risk gradient, iii) perinatal clinical conditions and iv) PTB below 37 weeks. Conclusion Fetal brain functional connectivity is a novel promising predictor of PTB, linked to maternal risk profiles, ahead of birth, and clinical markers of neurodevelopmental risk, at birth, thus potentially "connecting" different PTB phenotypes.
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Affiliation(s)
- Cesare Miglioli
- Research Center for Statistics, University of Geneva, Boulevard Du Pont-d’Arve 40, 1205 Geneva, Switzerland
| | - Matteo Canini
- Department of Neuroradiology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, 20132, Italy
| | - Edoardo Vignotto
- Research Center for Statistics, University of Geneva, Boulevard Du Pont-d’Arve 40, 1205 Geneva, Switzerland
| | - Nicolò Pecco
- Department of Neuroradiology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, 20132, Italy
| | - Mirko Pozzoni
- Department of Obstetrics and Gynecology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60 Milan, 20132, Italy
| | - Maria-Pia Victoria-Feser
- Research Center for Statistics, University of Geneva, Boulevard Du Pont-d’Arve 40, 1205 Geneva, Switzerland
| | - Stéphane Guerrier
- Research Center for Statistics, University of Geneva, Boulevard Du Pont-d’Arve 40, 1205 Geneva, Switzerland
- Faculty of Science, University of Geneva, Quai Ernest-Ansermet 30, 1211 Geneva, Switzerland
| | - Massimo Candiani
- Department of Obstetrics and Gynecology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60 Milan, 20132, Italy
| | - Andrea Falini
- Department of Neuroradiology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, 20132, Italy
| | - Cristina Baldoli
- Department of Neuroradiology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, 20132, Italy
| | - Paolo I. Cavoretto
- Department of Obstetrics and Gynecology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60 Milan, 20132, Italy
| | - Pasquale A. Della Rosa
- Department of Neuroradiology, IRCCS San Raffaele Scientific Institute, Via Olgettina 60, Milan, 20132, Italy
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16
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Garcia KE, Wang X, Santiago SE, Bakshi S, Barnes AP, Kroenke CD. Longitudinal MRI of the developing ferret brain reveals regional variations in timing and rate of growth. Cereb Cortex 2024; 34:bhae172. [PMID: 38679479 PMCID: PMC11056283 DOI: 10.1093/cercor/bhae172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Revised: 03/22/2024] [Accepted: 04/04/2024] [Indexed: 05/01/2024] Open
Abstract
Normative ferret brain development was characterized using magnetic resonance imaging. Brain growth was longitudinally monitored in 10 ferrets (equal numbers of males and females) from postnatal day 8 (P8) through P38 in 6-d increments. Template T2-weighted images were constructed at each age, and these were manually segmented into 12 to 14 brain regions. A logistic growth model was used to fit data from whole brain volumes and 8 of the individual regions in both males and females. More protracted growth was found in males, which results in larger brains; however, sex differences were not apparent when results were corrected for body weight. Additionally, surface models of the developing cortical plate were registered to one another using the anatomically-constrained Multimodal Surface Matching algorithm. This, in turn, enabled local logistic growth parameters to be mapped across the cortical surface. A close similarity was observed between surface area expansion timing and previous reports of the transverse neurogenic gradient in ferrets. Regional variation in the extent of surface area expansion and the maximum expansion rate was also revealed. This characterization of normative brain growth over the period of cerebral cortex folding may serve as a reference for ferret studies of brain development.
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Affiliation(s)
- Kara E Garcia
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Evansville, IN 47715, United States
- Department of Mechanical Engineering and Materials Science, Washington University in St. Louis, St. Louis, MO 63130, United States
| | - Xiaojie Wang
- Division of Neuroscience, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, OR 97006, United States
| | - Sarah E Santiago
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR 97239, United States
| | - Stuti Bakshi
- Division of Neuroscience, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, OR 97006, United States
| | - Anthony P Barnes
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR 97239, United States
| | - Christopher D Kroenke
- Division of Neuroscience, Oregon National Primate Research Center, Oregon Health & Science University, Beaverton, OR 97006, United States
- Oregon Health and Science Advanced Imaging Research Center, Portland, OR 97239, United States
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17
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Ortug A, Guo Y, Feldman HA, Ou Y, Warren JLA, Dieuveuil H, Baumer NT, Faja SK, Takahashi E. Autism-associated brain differences can be observed in utero using MRI. Cereb Cortex 2024; 34:bhae117. [PMID: 38602735 PMCID: PMC11008691 DOI: 10.1093/cercor/bhae117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 03/01/2024] [Accepted: 03/02/2024] [Indexed: 04/12/2024] Open
Abstract
Developmental changes that occur before birth are thought to be associated with the development of autism spectrum disorders. Identifying anatomical predictors of early brain development may contribute to our understanding of the neurobiology of autism spectrum disorders and allow for earlier and more effective identification and treatment of autism spectrum disorders. In this study, we used retrospective clinical brain magnetic resonance imaging data from fetuses who were diagnosed with autism spectrum disorders later in life (prospective autism spectrum disorders) in order to identify the earliest magnetic resonance imaging-based regional volumetric biomarkers. Our results showed that magnetic resonance imaging-based autism spectrum disorder biomarkers can be found as early as in the fetal period and suggested that the increased volume of the insular cortex may be the most promising magnetic resonance imaging-based fetal biomarker for the future emergence of autism spectrum disorders, along with some additional, potentially useful changes in regional volumes and hemispheric asymmetries.
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Affiliation(s)
- Alpen Ortug
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Yurui Guo
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Henry A Feldman
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Yangming Ou
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Jose Luis Alatorre Warren
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Harrison Dieuveuil
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Nicole T Baumer
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
| | - Susan K Faja
- Department of Pediatrics, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Division of Developmental Medicine, Laboratories of Cognitive Neuroscience, Boston Children's Hospital, Harvard Medical School, Brookline, MA 02115, United States
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
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18
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Verma R, Jayakumar J, Folkerth R, Manger PR, Bota M, Majumder M, Pandurangan K, Savoia S, Karthik S, Kumarasami R, Joseph J, Rohini G, Vasudevan S, Srinivasan C, Lata S, Kumar EH, Rangasami R, Kumutha J, Suresh S, Šimić G, Mitra PP, Sivaprakasam M. Histological characterization and development of mesial surface sulci in the human brain at 13-15 gestational weeks through high-resolution histology. J Comp Neurol 2024; 532:e25612. [PMID: 38591638 DOI: 10.1002/cne.25612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 03/06/2024] [Accepted: 03/24/2024] [Indexed: 04/10/2024]
Abstract
Cellular-level anatomical data from early fetal brain are sparse yet critical to the understanding of neurodevelopmental disorders. We characterize the organization of the human cerebral cortex between 13 and 15 gestational weeks using high-resolution whole-brain histological data sets complimented with multimodal imaging. We observed the heretofore underrecognized, reproducible presence of infolds on the mesial surface of the cerebral hemispheres. Of note at this stage, when most of the cerebrum is occupied by lateral ventricles and the corpus callosum is incompletely developed, we postulate that these mesial infolds represent the primordial stage of cingulate, callosal, and calcarine sulci, features of mesial cortical development. Our observations are based on the multimodal approach and further include histological three-dimensional reconstruction that highlights the importance of the plane of sectioning. We describe the laminar organization of the developing cortical mantle, including these infolds from the marginal to ventricular zone, with Nissl, hematoxylin and eosin, and glial fibrillary acidic protein (GFAP) immunohistochemistry. Despite the absence of major sulci on the dorsal surface, the boundaries among the orbital, frontal, parietal, and occipital cortex were very well demarcated, primarily by the cytoarchitecture differences in the organization of the subplate (SP) and intermediate zone (IZ) in these locations. The parietal region has the thickest cortical plate (CP), SP, and IZ, whereas the orbital region shows the thinnest CP and reveals an extra cell-sparse layer above the bilaminar SP. The subcortical structures show intensely GFAP-immunolabeled soma, absent in the cerebral mantle. Our findings establish a normative neurodevelopment baseline at the early stage.
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Affiliation(s)
- Richa Verma
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Jaikishan Jayakumar
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Center for Computational Brain Research, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Rebecca Folkerth
- Department of Forensic Medicine, NYU Grossman School of Medicine, New York, New York, USA
| | - Paul R Manger
- School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Mihail Bota
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Moitrayee Majumder
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Karthika Pandurangan
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | | | - Srinivasa Karthik
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Ramdayalan Kumarasami
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
| | - Jayaraj Joseph
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Department of Electrical Engineering, Indian Institute of Technology, Madras, Chennai, Tamil Nadu, India
| | - G Rohini
- Department of Obstetrics & Gynaecology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India
| | - Sudha Vasudevan
- Department of Pathology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India
| | - Chitra Srinivasan
- Department of Pathology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India
| | - S Lata
- Mediscan Systems, Chennai, Tamil Nadu, India
| | | | - Rajeswaran Rangasami
- Department of Radiology, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India
| | - Jayaraman Kumutha
- Department of Neonatology, Saveetha Medical College, Thandalam, Chennai, Tamil Nadu, India
| | - S Suresh
- Mediscan Systems, Chennai, Tamil Nadu, India
| | - Goran Šimić
- Department of Neuroscience, Croatian Institute for Brain Research, University of Zagreb Medical School, Zagreb, Hrvatska, Croatia
| | - Partha P Mitra
- Center for Computational Brain Research, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Cold Spring Harbor Laboratory, New York, New York, USA
| | - Mohanasankar Sivaprakasam
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India
- Department of Electrical Engineering, Indian Institute of Technology, Madras, Chennai, Tamil Nadu, India
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19
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Lamon S, de Dumast P, Sanchez T, Dunet V, Pomar L, Vial Y, Koob M, Bach Cuadra M. Assessment of fetal corpus callosum biometry by 3D super-resolution reconstructed T2-weighted magnetic resonance imaging. Front Neurol 2024; 15:1358741. [PMID: 38595845 PMCID: PMC11002102 DOI: 10.3389/fneur.2024.1358741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Accepted: 03/08/2024] [Indexed: 04/11/2024] Open
Abstract
Objective To assess the accuracy of corpus callosum (CC) biometry, including sub-segments, using 3D super-resolution fetal brain MRI (SR) compared to 2D or 3D ultrasound (US) and clinical low-resolution T2-weighted MRI (T2WS). Method Fetal brain biometry was conducted by two observers on 57 subjects [21-35 weeks of gestational age (GA)], including 11 cases of partial CC agenesis. Measures were performed by a junior observer (obs1) on US, T2WS and SR and by a senior neuroradiologist (obs2) on T2WS and SR. CC biometric regression with GA was established. Statistical analysis assessed agreement within and between modalities and observers. Results This study shows robust SR to US concordance across gestation, surpassing T2WS. In obs1, SR aligns with US, except for genu and CC length (CCL), enhancing splenium visibility. In obs2, SR closely corresponds to US, differing in rostrum and CCL. The anterior CC (rostrum and genu) exhibits higher variability. SR's regression aligns better with literature (US) for CCL, splenium and body than T2WS. SR is the method with the least missing values. Conclusion SR yields CC biometry akin to US (excluding anterior CC). Thanks to superior 3D visualization and better through plane spatial resolution, SR allows to perform CC biometry more frequently than T2WS.
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Affiliation(s)
- Samuel Lamon
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Ultrasound and Fetal Medicine, Department Woman-Mother-Child, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Priscille de Dumast
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Thomas Sanchez
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Vincent Dunet
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Léo Pomar
- Ultrasound and Fetal Medicine, Department Woman-Mother-Child, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
- School of Health Sciences (HESAV), University of Applied Sciences and Arts Western Switzerland, Lausanne, Switzerland
| | - Yvan Vial
- Ultrasound and Fetal Medicine, Department Woman-Mother-Child, Lausanne University Hospital and Lausanne University, Lausanne, Switzerland
| | - Mériam Koob
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
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20
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Ball G, Oldham S, Kyriakopoulou V, Williams LZJ, Karolis V, Price A, Hutter J, Seal ML, Alexander-Bloch A, Hajnal JV, Edwards AD, Robinson EC, Seidlitz J. Molecular signatures of cortical expansion in the human fetal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.13.580198. [PMID: 38405710 PMCID: PMC10888819 DOI: 10.1101/2024.02.13.580198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/27/2024]
Abstract
The third trimester of human gestation is characterised by rapid increases in brain volume and cortical surface area. A growing catalogue of cells in the prenatal brain has revealed remarkable molecular diversity across cortical areas.1,2 Despite this, little is known about how this translates into the patterns of differential cortical expansion observed in humans during the latter stages of gestation. Here we present a new resource, μBrain, to facilitate knowledge translation between molecular and anatomical descriptions of the prenatal developing brain. Built using generative artificial intelligence, μBrain is a three-dimensional cellular-resolution digital atlas combining publicly-available serial sections of the postmortem human brain at 21 weeks gestation3 with bulk tissue microarray data, sampled across 29 cortical regions and 5 transient tissue zones.4 Using μBrain, we evaluate the molecular signatures of preferentially-expanded cortical regions during human gestation, quantified in utero using magnetic resonance imaging (MRI). We find that differences in the rates of expansion across cortical areas during gestation respect anatomical and evolutionary boundaries between cortical types5 and are founded upon extended periods of upper-layer cortical neuron migration that continue beyond mid-gestation. We identify a set of genes that are upregulated from mid-gestation and highly expressed in rapidly expanding neocortex, which are implicated in genetic disorders with cognitive sequelae. Our findings demonstrate a spatial coupling between areal differences in the timing of neurogenesis and rates of expansion across the neocortical sheet during the prenatal epoch. The μBrain atlas is available from: https://garedaba.github.io/micro-brain/ and provides a new tool to comprehensively map early brain development across domains, model systems and resolution scales.
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Affiliation(s)
- G Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - S Oldham
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - V Kyriakopoulou
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - L Z J Williams
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - V Karolis
- Centre for the Developing Brain, King's College London, London, UK
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - A Price
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - J Hutter
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - M L Seal
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
| | - A Alexander-Bloch
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children's Hospital of Philadelphia, Philadelphia, PA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA
| | - J V Hajnal
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - A D Edwards
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - E C Robinson
- Centre for the Developing Brain, King's College London, London, UK
- School of Biomedical Engineering & Imaging Science, King's College London, London, UK
| | - J Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Sciences, The Children's Hospital of Philadelphia, Philadelphia, PA
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA
- Institute of Translational Medicine and Therapeutics, University of Pennsylvania, Philadelphia, PA
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21
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Masse O, Brumfield O, Ahmad E, Velasco-Annis C, Zhang J, Rollins CK, Connolly S, Barnewolt C, Shamshirsaz AA, Qaderi S, Javinani A, Warfield SK, Yang E, Gholipour A, Feldman HA, Grant PE, Mulliken JB, Pierotich L, Estroff J. Divergent growth of the transient brain compartments in fetuses with nonsyndromic isolated clefts involving the primary and secondary palate. Cereb Cortex 2024; 34:bhae024. [PMID: 38365268 PMCID: PMC10872676 DOI: 10.1093/cercor/bhae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 02/18/2024] Open
Abstract
Cleft lip/palate is a common orofacial malformation that often leads to speech/language difficulties as well as developmental delays in affected children, despite surgical repair. Our understanding of brain development in these children is limited. This study aimed to analyze prenatal brain development in fetuses with cleft lip/palate and controls. We examined in utero MRIs of 30 controls and 42 cleft lip/palate fetal cases and measured regional brain volumes. Cleft lip/palate was categorized into groups A (cleft lip or alveolus) and B (any combination of clefts involving the primary and secondary palates). Using a repeated-measures regression model with relative brain hemisphere volumes (%), and after adjusting for multiple comparisons, we did not identify significant differences in regional brain growth between group A and controls. Group B clefts had significantly slower weekly cerebellar growth compared with controls. We also observed divergent brain growth in transient brain structures (cortical plate, subplate, ganglionic eminence) within group B clefts, depending on severity (unilateral or bilateral) and defect location (hemisphere ipsilateral or contralateral to the defect). Further research is needed to explore the association between regional fetal brain growth and cleft lip/palate severity, with the potential to inform early neurodevelopmental biomarkers and personalized diagnostics.
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Affiliation(s)
- Olivia Masse
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Olivia Brumfield
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Esha Ahmad
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Clemente Velasco-Annis
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Jennings Zhang
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Caitlin K Rollins
- Department of Neurology Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Susan Connolly
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Carol Barnewolt
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Alireza A Shamshirsaz
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Shohra Qaderi
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Ali Javinani
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Simon K Warfield
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Edward Yang
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Ali Gholipour
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Henry A Feldman
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Institutional Centers for Clinical and Translational Research, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Patricia E Grant
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - John B Mulliken
- Department of Plastic and Oral Surgery, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Lana Pierotich
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Judy Estroff
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
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22
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Demirci N, Holland MA. Scaling patterns of cortical folding and thickness in early human brain development in comparison with primates. Cereb Cortex 2024; 34:bhad462. [PMID: 38271274 DOI: 10.1093/cercor/bhad462] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 11/02/2023] [Accepted: 11/04/2023] [Indexed: 01/27/2024] Open
Abstract
Across mammalia, brain morphology follows specific scaling patterns. Bigger bodies have bigger brains, with surface area outpacing volume growth, resulting in increased foldedness. We have recently studied scaling rules of cortical thickness, both local and global, finding that the cortical thickness difference between thick gyri and thin sulci also increases with brain size and foldedness. Here, we investigate early brain development in humans, using subjects from the Developing Human Connectome Project, scanned shortly after pre-term or full-term birth, yielding magnetic resonance images of the brain from 29 to 43 postmenstrual weeks. While the global cortical thickness does not change significantly during this development period, its distribution does, with sulci thinning, while gyri thickening. By comparing our results with our recent work on humans and 11 non-human primate species, we also compare the trajectories of primate evolution with human development, noticing that the 2 trends are distinct for volume, surface area, cortical thickness, and gyrification index. Finally, we introduce the global shape index as a proxy for gyrification index; while correlating very strongly with gyrification index, it offers the advantage of being calculated only from local quantities without generating a convex hull or alpha surface.
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Affiliation(s)
- Nagehan Demirci
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, United States
| | - Maria A Holland
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, United States
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, United States
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23
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Xiao J, Sun C, Chen R, Zhao Z, Wang G, Wu D. Reproducibility of Diffusion MRI-Based Tractography in the Fetal Brain. J Magn Reson Imaging 2024. [PMID: 38284561 DOI: 10.1002/jmri.29253] [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: 08/29/2023] [Revised: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND Tractography based on diffusion MRI (dMRI) is a useful tool to study white matter of the developing brain. However, its application in fetal brains is limited due to motion artifacts and low resolution of in utero dMRI, leading to reduced reliability, which was scarcely investigated in previous studies. PURPOSE To identify reliably traceable fibers in fetal brains and assess whether reproducibility varies with gestational age (GA) and varies between brain regions. STUDY TYPE Prospective cohort study. SUBJECTS A total of 44 healthy fetuses with GAs between 25 and 37 (31 ± 6). FIELD STRENGTH/SEQUENCE 3-T, diffusion-weighted echo-planar imaging sequence (2-5 repeated dMRI scans within the same session per subject). ASSESSMENT We fitted dMRI with constrained spherical deconvolution model and conducted tractography on eight fibers. We extracted volume, fractional anisotropy, and fiber count for each fiber and assessed the reproducibility of these metrics between repeated scans within each subject. Data were divided into two age-based subgroups (≤30 weeks, N = 28, and >30 weeks, N = 16) for further tests. STATISTICAL TESTS The reproducibility were compared between fibers by analysis of variance and two-sample t tests. Multiple comparisons were corrected by the false discovery rate (5% was accepted). RESULTS The reproducibility of the anterior thalamic radiation, inferior longitudinal fasciculus (ILF), genu of the corpus callosum (GCC), and body of the corpus callosum (BCC) significantly decreased with advancing GA (correlation coefficient = 0.525-0.823), as confirmed by group comparisons between fetuses in early GA (≤30 weeks) and late GA (>30 weeks) groups. Corticospinal tract, inferior fronto-occipital fasciculus, and GCC showed high reproducibility for fiber count (weighted dice average = 0.846 vs. 0.814), while BCC and ILF exhibited the lowest reproducibility in both age groups. DATA CONCLUSION The study indicates that the reliability of fetal brain tractography depends on GA and varies among different fibers. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Jiaxin Xiao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Cong Sun
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Ruike Chen
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
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24
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Nichols ES, Grace M, Correa S, de Vrijer B, Eagleson R, McKenzie CA, de Ribaupierre S, Duerden EG. Sex- and age-based differences in fetal and early childhood hippocampus maturation: a cross-sectional and longitudinal analysis. Cereb Cortex 2024; 34:bhad421. [PMID: 37950876 PMCID: PMC10793584 DOI: 10.1093/cercor/bhad421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 10/13/2023] [Accepted: 10/14/2023] [Indexed: 11/13/2023] Open
Abstract
The hippocampus, essential for cognitive and affective processes, develops exponentially with differential trajectories seen in girls and boys, yet less is known about its development during early fetal life until early childhood. In a cross-sectional and longitudinal study, we examined the sex-, age-, and laterality-related developmental trajectories of hippocampal volumes in fetuses, infants, and toddlers associated with age. Third trimester fetuses (27-38 weeks' gestational age), newborns (0-4 weeks' postnatal age), infants (5-50 weeks' postnatal age), and toddlers (2-3 years postnatal age) were scanned with magnetic resonance imaging. A total of 133 datasets (62 female, postmenstrual age [weeks] M = 69.38, SD = 51.39, range = 27.6-195.3) were processed using semiautomatic segmentation methods. Hippocampal volumes increased exponentially during the third trimester and the first year of life, beginning to slow at approximately 2 years. Overall, boys had larger hippocampal volumes than girls. Lateralization differences were evident, with left hippocampal growth beginning to plateau sooner than the right. This period of rapid growth from the third trimester, continuing through the first year of life, may support the development of cognitive and affective function during this period.
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Affiliation(s)
- Emily S Nichols
- Department of Applied Psychology, Faculty of Education, Western University, 1137 Western Road, London, Ontario, Canada
- Western Institute for Neuroscience, Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada
| | - Michael Grace
- Department of Physiology and Pharmacology, Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada
| | - Susana Correa
- Western Institute for Neuroscience, Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada
| | - Barbra de Vrijer
- Department of Obstetrics & Gynaecology, Schulich School of Medicine & Dentistry, Western University, London Health Sciences Centre-Victoria Hospital, B2-401, London, Ontario N6H 5W9, Canada
- Division of Maternal, Fetal and Newborn Health, Children's Health Research Institute, 800 Commissioners Road East, London, Ontario N6C 2V5, Canada
| | - Roy Eagleson
- Western Institute for Neuroscience, Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada
- Department of Biomedical Engineering, Western University, Canada
- Department of Electrical and Computer Engineering, Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada
| | - Charles A McKenzie
- Division of Maternal, Fetal and Newborn Health, Children's Health Research Institute, 800 Commissioners Road East, London, Ontario N6C 2V5, Canada
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, Canada
| | - Sandrine de Ribaupierre
- Western Institute for Neuroscience, Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada
- Division of Maternal, Fetal and Newborn Health, Children's Health Research Institute, 800 Commissioners Road East, London, Ontario N6C 2V5, Canada
- Department of Biomedical Engineering, Western University, Canada
- Department of Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, Canada
- Department of Clinical Neurological Sciences, Schulich School of Medicine & Dentistry, Western University, Canada
- Department of Anatomy and Cell Biology, Schulich School of Medicine & Dentistry, Western University, Canada
| | - Emma G Duerden
- Department of Applied Psychology, Faculty of Education, Western University, 1137 Western Road, London, Ontario, Canada
- Western Institute for Neuroscience, Western University, 1151 Richmond Street, London, Ontario N6A 3K7, Canada
- Division of Maternal, Fetal and Newborn Health, Children's Health Research Institute, 800 Commissioners Road East, London, Ontario N6C 2V5, Canada
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25
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Calixto C, Machado-Rivas F, Cortes-Albornoz MC, Karimi D, Velasco-Annis C, Afacan O, Warfield SK, Gholipour A, Jaimes C. Characterizing microstructural development in the fetal brain using diffusion MRI from 23 to 36 weeks of gestation. Cereb Cortex 2024; 34:bhad409. [PMID: 37948665 PMCID: PMC10793585 DOI: 10.1093/cercor/bhad409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/12/2023] Open
Abstract
We utilized motion-corrected diffusion tensor imaging (DTI) to evaluate microstructural changes in healthy fetal brains during the late second and third trimesters. Data were derived from fetal magnetic resonance imaging scans conducted as part of a prospective study spanning from 2013 March to 2019 May. The study included 44 fetuses between the gestational ages (GAs) of 23 and 36 weeks. We reconstructed fetal brain DTI using a motion-tracked slice-to-volume registration framework. Images were segmented into 14 regions of interest (ROIs) through label propagation using a fetal DTI atlas, with expert refinement. Statistical analysis involved assessing changes in fractional anisotropy (FA) and mean diffusivity (MD) throughout gestation using mixed-effects models, and identifying points of change in trajectory for ROIs with nonlinear trends. Results showed significant GA-related changes in FA and MD in all ROIs except in the thalamus' FA and corpus callosum's MD. Hemispheric asymmetries were found in the FA of the periventricular white matter (pvWM), intermediate zone, and subplate and in the MD of the ganglionic eminence and pvWM. This study provides valuable insight into the normal patterns of development of MD and FA in the fetal brain. These changes are closely linked with cytoarchitectonic changes and display indications of early functional specialization.
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Affiliation(s)
- Camilo Calixto
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Fedel Machado-Rivas
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Maria C Cortes-Albornoz
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Camilo Jaimes
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States
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26
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Martínez de Lagrán M, Bascón-Cardozo K, Dierssen M. Neurodevelopmental disorders: 2024 update. FREE NEUROPATHOLOGY 2024; 5:5-20. [PMID: 39252863 PMCID: PMC11382549 DOI: 10.17879/freeneuropathology-2024-5734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 07/22/2024] [Accepted: 08/12/2024] [Indexed: 09/11/2024]
Abstract
Neurodevelopmental disorders encompass a range of conditions such as intellectual disability, autism spectrum disorder, rare genetic disorders and developmental and epileptic encephalopathies, all manifesting during childhood. Over 1,500 genes involved in various signaling pathways, including numerous transcriptional regulators, spliceosome elements, chromatin-modifying complexes and de novo variants have been recognized for their substantial role in these disorders. Along with new machine learning tools applied to neuroimaging, these discoveries facilitate genetic diagnoses, providing critical insights into neuropathological mechanisms and aiding in prognosis, and precision medicine. Also, new findings underscore the importance of understanding genetic contributions beyond protein-coding genes and emphasize the role of RNA and non-coding DNA molecules but also new players, such as transposable elements, whose dysregulation generates gene function disruption, epigenetic alteration, and genomic instability. Finally, recent developments in analyzing neuroimaging now offer the possibility of characterizing neuronal cytoarchitecture in vivo, presenting a viable alternative to traditional post-mortem studies. With a recently launched digital atlas of human fetal brain development, these new approaches will allow answering complex biological questions about fetal origins of cognitive function in childhood. In this review, we present ten fascinating topics where major progress has been made in the last year.
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Affiliation(s)
- María Martínez de Lagrán
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona 08003, Spain
| | - Karen Bascón-Cardozo
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona 08003, Spain
| | - Mara Dierssen
- Center for Genomic Regulation (CRG), The Barcelona Institute of Science and Technology, Barcelona 08003, Spain
- Universitat Pompeu Fabra (UPF), Barcelona 08002, Spain
- Biomedical Research Networking Center for Rare Diseases (CIBERER), Barcelona 08003, Spain
- Hospital del Mar Research Institute, Barcelona 08003, Spain
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Neves Silva S, Aviles Verdera J, Tomi‐Tricot R, Neji R, Uus A, Grigorescu I, Wilkinson T, Ozenne V, Lewin A, Story L, De Vita E, Rutherford M, Pushparajah K, Hajnal J, Hutter J. Real-time fetal brain tracking for functional fetal MRI. Magn Reson Med 2023; 90:2306-2320. [PMID: 37465882 PMCID: PMC10952752 DOI: 10.1002/mrm.29803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 07/03/2023] [Accepted: 07/03/2023] [Indexed: 07/20/2023]
Abstract
PURPOSE To improve motion robustness of functional fetal MRI scans by developing an intrinsic real-time motion correction method. MRI provides an ideal tool to characterize fetal brain development and growth. It is, however, a relatively slow imaging technique and therefore extremely susceptible to subject motion, particularly in functional MRI experiments acquiring multiple Echo-Planar-Imaging-based repetitions, for example, diffusion MRI or blood-oxygen-level-dependency MRI. METHODS A 3D UNet was trained on 125 fetal datasets to track the fetal brain position in each repetition of the scan in real time. This tracking, inserted into a Gadgetron pipeline on a clinical scanner, allows updating the position of the field of view in a modified echo-planar imaging sequence. The method was evaluated in real-time in controlled-motion phantom experiments and ten fetal MR studies (17 + 4-34 + 3 gestational weeks) at 3T. The localization network was additionally tested retrospectively on 29 low-field (0.55T) datasets. RESULTS Our method achieved real-time fetal head tracking and prospective correction of the acquisition geometry. Localization performance achieved Dice scores of 84.4% and 82.3%, respectively for both the unseen 1.5T/3T and 0.55T fetal data, with values higher for cephalic fetuses and increasing with gestational age. CONCLUSIONS Our technique was able to follow the fetal brain even for fetuses under 18 weeks GA in real-time at 3T and was successfully applied "offline" to new cohorts on 0.55T. Next, it will be deployed to other modalities such as fetal diffusion MRI and to cohorts of pregnant participants diagnosed with pregnancy complications, for example, pre-eclampsia and congenital heart disease.
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Affiliation(s)
- Sara Neves Silva
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Jordina Aviles Verdera
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Raphael Tomi‐Tricot
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- MR Research CollaborationsSiemens Healthcare LimitedCamberleyUK
| | - Radhouene Neji
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- MR Research CollaborationsSiemens Healthcare LimitedCamberleyUK
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Irina Grigorescu
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Thomas Wilkinson
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Valery Ozenne
- CNRS, CRMSB, UMR 5536, IHU LirycUniversité de BordeauxBordeauxFrance
| | - Alexander Lewin
- Institute of Neuroscience and Medicine 11, INM‐11Forschungszentrum JülichJülichGermany
- RWTHAachen UniversityAachenGermany
| | - Lisa Story
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Department of Women & Children's HealthKing's College LondonLondonUK
| | - Enrico De Vita
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- MRI Physics GroupGreat Ormond Street HospitalLondonUK
| | - Mary Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Kuberan Pushparajah
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Jo Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
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Namburete AIL, Papież BW, Fernandes M, Wyburd MK, Hesse LS, Moser FA, Ismail LC, Gunier RB, Squier W, Ohuma EO, Carvalho M, Jaffer Y, Gravett M, Wu Q, Lambert A, Winsey A, Restrepo-Méndez MC, Bertino E, Purwar M, Barros FC, Stein A, Noble JA, Molnár Z, Jenkinson M, Bhutta ZA, Papageorghiou AT, Villar J, Kennedy SH. Normative spatiotemporal fetal brain maturation with satisfactory development at 2 years. Nature 2023; 623:106-114. [PMID: 37880365 PMCID: PMC10620088 DOI: 10.1038/s41586-023-06630-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 09/08/2023] [Indexed: 10/27/2023]
Abstract
Maturation of the human fetal brain should follow precisely scheduled structural growth and folding of the cerebral cortex for optimal postnatal function1. We present a normative digital atlas of fetal brain maturation based on a prospective international cohort of healthy pregnant women2, selected using World Health Organization recommendations for growth standards3. Their fetuses were accurately dated in the first trimester, with satisfactory growth and neurodevelopment from early pregnancy to 2 years of age4,5. The atlas was produced using 1,059 optimal quality, three-dimensional ultrasound brain volumes from 899 of the fetuses and an automated analysis pipeline6-8. The atlas corresponds structurally to published magnetic resonance images9, but with finer anatomical details in deep grey matter. The between-study site variability represented less than 8.0% of the total variance of all brain measures, supporting pooling data from the eight study sites to produce patterns of normative maturation. We have thereby generated an average representation of each cerebral hemisphere between 14 and 31 weeks' gestation with quantification of intracranial volume variability and growth patterns. Emergent asymmetries were detectable from as early as 14 weeks, with peak asymmetries in regions associated with language development and functional lateralization between 20 and 26 weeks' gestation. These patterns were validated in 1,487 three-dimensional brain volumes from 1,295 different fetuses in the same cohort. We provide a unique spatiotemporal benchmark of fetal brain maturation from a large cohort with normative postnatal growth and neurodevelopment.
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Affiliation(s)
- Ana I L Namburete
- Oxford Machine Learning in Neuroimaging Laboratory, Department of Computer Science, University of Oxford, Oxford, UK.
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK.
- Department of Engineering Science, University of Oxford, Oxford, UK.
| | - Bartłomiej W Papież
- Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK
| | - Michelle Fernandes
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- MRC Lifecourse Epidemiology Centre, Human Development and Health Academic Unit, Faculty of Medicine, University of Southampton, Southampton, UK
- Oxford Maternal and Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Madeleine K Wyburd
- Oxford Machine Learning in Neuroimaging Laboratory, Department of Computer Science, University of Oxford, Oxford, UK
| | - Linde S Hesse
- Oxford Machine Learning in Neuroimaging Laboratory, Department of Computer Science, University of Oxford, Oxford, UK
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Felipe A Moser
- Oxford Machine Learning in Neuroimaging Laboratory, Department of Computer Science, University of Oxford, Oxford, UK
| | - Leila Cheikh Ismail
- Department of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates
| | - Robert B Gunier
- Center for Environmental Research and Children's Health, School of Public Health, University of California, Berkeley, CA, USA
| | - Waney Squier
- Department of Neuropathology, John Radcliffe Hospital, Oxford, UK
| | - Eric O Ohuma
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Maternal, Adolescent, Reproductive and Child Health Centre, London School of Hygiene and Tropical Medicine, London, UK
| | - Maria Carvalho
- Department of Obstetrics and Gynaecology, Faculty of Health Sciences, Aga Khan University Hospital, Nairobi, Kenya
| | - Yasmin Jaffer
- Department of Family and Community Health, Ministry of Health, Muscat, Sultanate of Oman
| | - Michael Gravett
- Departments of Obstetrics and Gynecology and of Global Health, University of Washington, Seattle, WA, USA
| | - Qingqing Wu
- School of Public Health, Peking University, Beijing, China
| | - Ann Lambert
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal and Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Adele Winsey
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
| | | | - Enrico Bertino
- Dipartimento di Scienze Pediatriche e dell' Adolescenza, SCDU Neonatologia, Universita di Torino, Turin, Italy
| | - Manorama Purwar
- Nagpur INTERGROWTH-21st Research Centre, Ketkar Hospital, Nagpur, India
| | - Fernando C Barros
- Programa de Pós-Graduação em Saúde e Comportamento, Universidade Católica de Pelotas, Pelotas, Brazil
| | - Alan Stein
- Department of Psychiatry, University of Oxford, Oxford, UK
- African Health Research Institute, KwaZulu-Natal, South Africa
- MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa
| | - J Alison Noble
- Department of Engineering Science, University of Oxford, Oxford, UK
| | - Zoltán Molnár
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Australian Institute for Machine Learning, Department of Computer Science, University of Adelaide, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute, Adelaide, South Australia, Australia
| | - Zulfiqar A Bhutta
- Center for Global Child Health, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Aris T Papageorghiou
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal and Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - José Villar
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal and Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
| | - Stephen H Kennedy
- Nuffield Department of Women's and Reproductive Health, University of Oxford, Oxford, UK
- Oxford Maternal and Perinatal Health Institute, Green Templeton College, University of Oxford, Oxford, UK
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Mufti N, Chappell J, Aertsen M, Ebner M, Fidon L, Deprest J, David AL, Melbourne A. Assessment of longitudinal brain development using super-resolution magnetic resonance imaging following fetal surgery for open spina bifida. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2023; 62:707-720. [PMID: 37161647 PMCID: PMC10947002 DOI: 10.1002/uog.26244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/04/2023] [Revised: 04/18/2023] [Accepted: 05/01/2023] [Indexed: 05/11/2023]
Abstract
OBJECTIVES Prenatal surgery is offered for selected fetuses with open spina bifida (OSB) to improve long-term outcome. We studied the effect of fetal OSB surgery on brain development using advanced magnetic resonance imaging (MRI) techniques to quantify the volume, surface area and shape of cerebral structures and to analyze surface curvature by means of parameters that correspond to gyrification. METHODS We compared MRI data from 29 fetuses with OSB before fetal surgery (mean gestational age (GA), 23 + 3 weeks) and at 1 and 6 weeks after surgery, with that of 36 GA-matched control fetuses (GA range, 21 + 2 to 36 + 2 weeks). Automated super-resolution reconstruction provided three-dimensional isotropic volumetric brain images. Unmyelinated white matter, cerebellum and ventricles were segmented automatically and refined manually, after which volume, surface area and shape parameter (volume/surface area) were quantified. Mathematical markers (shape index (SI) and curvedness) were used to measure gyrification. Parameters were assessed according to lesion type (myelomeningocele vs myeloschisis (MS)), postoperative persistence of hindbrain herniation (HH) and the presence of supratentorial anomalies, namely partial agenesis of the corpus callosum (pACC) and heterotopia (HT). RESULTS Growth in ventricular volume per week and change in shape parameter per week were higher at 6 weeks after surgery in fetuses with OSB compared with controls (median, 2500.94 (interquartile range (IQR), 1689.70-3580.80) mm3 /week vs 708.21 (IQR, 474.50-925.00) mm3 /week; P < 0.001 and 0.075 (IQR, 0.047-0.112) mm/week vs 0.022 (IQR, 0.009-0.042) mm/week; P = 0.046, respectively). Ventricular volume growth increased 6 weeks after surgery in cases with pACC (P < 0.001) and those with persistent HH (P = 0.002). During that time period, the change in unmyelinated white-matter shape parameter per week was decreased in OSB fetuses compared with controls (0.056 (IQR, 0.044-0.092) mm/week vs 0.159 (IQR, 0.100-0.247) mm/week; P = 0.002), particularly in cases with persistent HH (P = 0.011), MS (P = 0.015), HT (P = 0.022), HT with corpus callosum anomaly (P = 0.017) and persistent HH with corpus callosum anomaly (P = 0.007). At 6 weeks postoperatively, despite OSB fetuses having a lower rate of change in curvedness compared with controls (0.061 (IQR, 0.040-0.093) mm-1 /week vs 0.094 (IQR, 0.070-0.146) mm-1 /week; P < 0.001), reversing the trend seen at 1 week after surgery (0.144 (IQR, 0.099-0.236) mm-1 /week vs 0.072 (IQR, 0.059-0.081) mm-1 /week; P < 0.001), gyrification, as determined using SI, appeared to be increased in OSB fetuses overall compared with controls. This observation was more prominent in fetuses with pACC and those with severe ventriculomegaly (P-value range, < 0.001 to 0.006). CONCLUSIONS Following fetal OSB repair, volume, shape and curvedness of ventricles and unmyelinated white matter differed significantly compared with those of normal fetuses. Morphological brain changes after fetal surgery were not limited to effects on the circulation of cerebrospinal fluid. These observations may have implications for postnatal neurocognitive outcome. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- N. Mufti
- Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
- School of Biomedical Engineering and Imaging Sciences (BMEIS)King's College LondonLondonUK
| | - J. Chappell
- School of Biomedical Engineering and Imaging Sciences (BMEIS)King's College LondonLondonUK
| | - M. Aertsen
- Department of RadiologyUniversity Hospitals Katholieke Universiteit (KU) LeuvenLeuvenBelgium
| | - M. Ebner
- School of Biomedical Engineering and Imaging Sciences (BMEIS)King's College LondonLondonUK
| | - L. Fidon
- School of Biomedical Engineering and Imaging Sciences (BMEIS)King's College LondonLondonUK
| | - J. Deprest
- Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
- Department of Obstetrics and GynaecologyUniversity Hospitals Katholieke Universiteit (KU) LeuvenLeuvenBelgium
| | - A. L. David
- Elizabeth Garrett Anderson Institute for Women's HealthUniversity College LondonLondonUK
- Department of Obstetrics and GynaecologyUniversity Hospitals Katholieke Universiteit (KU) LeuvenLeuvenBelgium
- National Institute for Health and Care Research University College London Hospitals Biomedical Research CentreLondonUK
| | - A. Melbourne
- School of Biomedical Engineering and Imaging Sciences (BMEIS)King's College LondonLondonUK
- Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
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Ahmad E, Brumfield O, Masse O, Velasco-Annis C, Zhang J, Rollins CK, Connolly S, Barnewolt C, Shamshirsaz AA, Qaderi S, Javinani A, Warfield SK, Yang E, Gholipour A, Feldman HA, Estroff J, Grant PE, Vasung L. Atypical fetal brain development in fetuses with non-syndromic isolated musculoskeletal birth defects (niMSBDs). Cereb Cortex 2023; 33:10793-10801. [PMID: 37697904 PMCID: PMC10629896 DOI: 10.1093/cercor/bhad323] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 09/13/2023] Open
Abstract
Non-syndromic, isolated musculoskeletal birth defects (niMSBDs) are among the leading causes of pediatric hospitalization. However, little is known about brain development in niMSBDs. Our study aimed to characterize prenatal brain development in fetuses with niMSBDs and identify altered brain regions compared to controls. We retrospectively analyzed in vivo structural T2-weighted MRIs of 99 fetuses (48 controls and 51 niMSBDs cases). For each group (19-31 and >31 gestational weeks (GW)), we conducted repeated-measures regression analysis with relative regional volume (% brain hemisphere) as a dependent variable (adjusted for age, side, and interactions). Between 19 and 31GW, fetuses with niMSBDs had a significantly (P < 0.001) smaller relative volume of the intermediate zone (-22.9 ± 3.2%) and cerebellum (-16.1 ± 3.5%,) and a larger relative volume of proliferative zones (38.3 ± 7.2%), the ganglionic eminence (34.8 ± 7.3%), and the ventricles (35.8 ± 8.0%). Between 32 and 37 GW, compared to the controls, niMSBDs showed significantly smaller volumes of central regions (-9.1 ± 2.1%) and larger volumes of the cortical plate. Our results suggest there is altered brain development in fetuses with niMSBDs compared to controls (13.1 ± 4.2%). Further basic and translational neuroscience research is needed to better visualize these differences and to characterize the altered development in fetuses with specific niMSBDs.
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Affiliation(s)
- Esha Ahmad
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Olivia Brumfield
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Olivia Masse
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Clemente Velasco-Annis
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Jennings Zhang
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Caitlin K Rollins
- Department of Neurology Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Susan Connolly
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Carol Barnewolt
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Alireza A Shamshirsaz
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Shohra Qaderi
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Ali Javinani
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Simon K Warfield
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Edward Yang
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Ali Gholipour
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Henry A Feldman
- Institutional Centers for Clinical and Translational Research, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Judy Estroff
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Patricia E Grant
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Lana Vasung
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
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Shrot S, Hadi E, Barash Y, Hoffmann C. Effect of magnet strength on fetal brain biometry - a single-center retrospective MRI-based cohort study. Neuroradiology 2023; 65:1517-1525. [PMID: 37436475 DOI: 10.1007/s00234-023-03193-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 07/05/2023] [Indexed: 07/13/2023]
Abstract
PURPOSE Abnormal fetal brain measurements might affect clinical management and parental counseling. The effect of between-field-strength differences was not evaluated in quantitative fetal brain imaging until now. Our study aimed to compare fetal brain biometry measurements in 3.0 T with 1.5 T scanners. METHODS A retrospective cohort of 1150 low-risk fetuses scanned between 2012 and 2021, with apparently normal brain anatomy, were retrospectively evaluated for biometric measurements. The cohort included 1.5 T (442 fetuses) and 3.0 T scans (708 fetuses) of populations with comparable characteristics in the same tertiary medical center. Manually measured biometry included bi-parietal, fronto-occipital and trans-cerebellar diameters, length of the corpus-callosum, vermis height, and width. Measurements were then converted to centiles based on previously reported biometric reference charts. The 1.5 T centiles were compared with the 3.0 T centiles. RESULTS No significant differences between centiles of bi-parietal diameter, trans-cerebellar diameter, or length of the corpus callosum between 1.5 T and 3.0 T scanners were found. Small absolute differences were found in the vermis height, with higher centiles in the 3.0 T, compared to the 1.5 T scanner (54.6th-centile, vs. 39.0th-centile, p < 0.001); less significant differences were found in vermis width centiles (46.9th-centile vs. 37.5th-centile, p = 0.03). Fronto-occipital diameter was higher in 1.5 T than in the 3.0 T scanner (66.0th-centile vs. 61.8th-centile, p = 0.02). CONCLUSIONS The increasing use of 3.0 T MRI for fetal imaging poses a potential bias when using 1.5 T-based charts. We elucidate those biometric measurements are comparable, with relatively small between-field-strength differences, when using manual biometric measurements. Small inter-magnet differences can be related to higher spatial resolution with 3 T scanners and may be substantial when evaluating small brain structures, such as the vermis.
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Affiliation(s)
- Shai Shrot
- Section of Neuroradiology, Division of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, 2 Sheba Rd, 52621, Ramat Gan, Israel.
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Efrat Hadi
- Diagnostic Ultrasound Unit of the Institute of Obstetrical and Gynecological Imaging, Department of Obstetrics and Gynecology, Sheba Medical Center, 52621, Ramat Gan, Israel
| | - Yiftach Barash
- Section of Neuroradiology, Division of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, 2 Sheba Rd, 52621, Ramat Gan, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Chen Hoffmann
- Section of Neuroradiology, Division of Diagnostic Imaging, Sheba Medical Center, Tel Hashomer, 2 Sheba Rd, 52621, Ramat Gan, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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Kolnik SE, Marquard R, Brandon O, Puia-Dumitrescu M, Valentine G, Law JB, Natarajan N, Dighe M, Mourad PD, Wood TR, Mietzsch U. Preterm infants variability in cerebral near-infrared spectroscopy measurements in the first 72-h after birth. Pediatr Res 2023; 94:1408-1415. [PMID: 37138026 DOI: 10.1038/s41390-023-02618-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 03/29/2023] [Accepted: 04/06/2023] [Indexed: 05/05/2023]
Abstract
BACKGROUND Cerebral near-infrared spectroscopy is a non-invasive tool used to measure regional cerebral tissue oxygenation (rScO2) initially validated in adult and pediatric populations. Preterm neonates, vulnerable to neurologic injury, are attractive candidates for NIRS monitoring; however, normative data and the brain regions measured by the current technology have not yet been established for this population. METHODS This study's aim was to analyze continuous rScO2 readings within the first 6-72 h after birth in 60 neonates without intracerebral hemorrhage born at ≤1250 g and/or ≤30 weeks' gestational age (GA) to better understand the role of head circumference (HC) and brain regions measured. RESULTS Using a standardized brain MRI atlas, we determined that rScO2 in infants with smaller HCs likely measures the ventricular spaces. GA is linearly correlated, and HC is non-linearly correlated, with rScO2 readings. For HC, we infer that rScO2 is lower in infants with smaller HCs due to measuring the ventricular spaces, with values increasing in the smallest HCs as the deep cerebral structures are reached. CONCLUSION Clinicians should be aware that in preterm infants with small HCs, rScO2 displayed may reflect readings from the ventricular spaces and deep cerebral tissue. IMPACT Clinicians should be aware that in preterm infants with small head circumferences, cerebral near-infrared spectroscopy readings of rScO2 displayed may reflect readings from the ventricular spaces and deep cerebral tissue. This highlights the importance of rigorously re-validating technologies before extrapolating them to different populations. Standard rScO2 trajectories should only be established after determining whether the mathematical models used in NIRS equipment are appropriate in premature infants and the brain region(s) NIRS sensors captures in this population, including the influence of both gestational age and head circumference.
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Affiliation(s)
- Sarah E Kolnik
- Department of Pediatrics, Division of Neonatology, University of Washington School of Medicine, Seattle, WA, USA.
| | | | - Olivia Brandon
- Department of Pediatrics, Division of Neonatology, University of Washington School of Medicine, Seattle, WA, USA
| | - Mihai Puia-Dumitrescu
- Department of Pediatrics, Division of Neonatology, University of Washington School of Medicine, Seattle, WA, USA
| | - Gregory Valentine
- Department of Pediatrics, Division of Neonatology, University of Washington School of Medicine, Seattle, WA, USA
| | - Janessa B Law
- Department of Pediatrics, Division of Neonatology, University of Washington School of Medicine, Seattle, WA, USA
| | - Niranjana Natarajan
- Department of Pediatrics, Division of Neonatology, University of Washington School of Medicine, Seattle, WA, USA
- Department of Neurology, Division of Child Neurology, University of Washington School of Medicine, Seattle, WA, USA
| | - Manjiri Dighe
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - Pierre D Mourad
- Division of Engineering and Mathematics, School of STEM, University of Washington, Bothell, WA, USA
- Department of Neurological Surgery, School of Medicine, University of Washington, Seattle, WA, USA
| | - Thomas R Wood
- Department of Pediatrics, Division of Neonatology, University of Washington School of Medicine, Seattle, WA, USA
| | - Ulrike Mietzsch
- Department of Pediatrics, Division of Neonatology, University of Washington School of Medicine, Seattle, WA, USA
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Ciceri T, Squarcina L, Giubergia A, Bertoldo A, Brambilla P, Peruzzo D. Review on deep learning fetal brain segmentation from Magnetic Resonance images. Artif Intell Med 2023; 143:102608. [PMID: 37673558 DOI: 10.1016/j.artmed.2023.102608] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Revised: 05/31/2023] [Accepted: 06/06/2023] [Indexed: 09/08/2023]
Abstract
Brain segmentation is often the first and most critical step in quantitative analysis of the brain for many clinical applications, including fetal imaging. Different aspects challenge the segmentation of the fetal brain in magnetic resonance imaging (MRI), such as the non-standard position of the fetus owing to his/her movements during the examination, rapid brain development, and the limited availability of imaging data. In recent years, several segmentation methods have been proposed for automatically partitioning the fetal brain from MR images. These algorithms aim to define regions of interest with different shapes and intensities, encompassing the entire brain, or isolating specific structures. Deep learning techniques, particularly convolutional neural networks (CNNs), have become a state-of-the-art approach in the field because they can provide reliable segmentation results over heterogeneous datasets. Here, we review the deep learning algorithms developed in the field of fetal brain segmentation and categorize them according to their target structures. Finally, we discuss the perceived research gaps in the literature of the fetal domain, suggesting possible future research directions that could impact the management of fetal MR images.
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Affiliation(s)
- Tommaso Ciceri
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alice Giubergia
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padua, Padua, Italy; University of Padua, Padova Neuroscience Center, Padua, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Denis Peruzzo
- NeuroImaging Laboratory, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
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Li C, Fleck JS, Martins-Costa C, Burkard TR, Themann J, Stuempflen M, Peer AM, Vertesy Á, Littleboy JB, Esk C, Elling U, Kasprian G, Corsini NS, Treutlein B, Knoblich JA. Single-cell brain organoid screening identifies developmental defects in autism. Nature 2023; 621:373-380. [PMID: 37704762 PMCID: PMC10499611 DOI: 10.1038/s41586-023-06473-y] [Citation(s) in RCA: 46] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 07/21/2023] [Indexed: 09/15/2023]
Abstract
The development of the human brain involves unique processes (not observed in many other species) that can contribute to neurodevelopmental disorders1-4. Cerebral organoids enable the study of neurodevelopmental disorders in a human context. We have developed the CRISPR-human organoids-single-cell RNA sequencing (CHOOSE) system, which uses verified pairs of guide RNAs, inducible CRISPR-Cas9-based genetic disruption and single-cell transcriptomics for pooled loss-of-function screening in mosaic organoids. Here we show that perturbation of 36 high-risk autism spectrum disorder genes related to transcriptional regulation uncovers their effects on cell fate determination. We find that dorsal intermediate progenitors, ventral progenitors and upper-layer excitatory neurons are among the most vulnerable cell types. We construct a developmental gene regulatory network of cerebral organoids from single-cell transcriptomes and chromatin modalities and identify autism spectrum disorder-associated and perturbation-enriched regulatory modules. Perturbing members of the BRG1/BRM-associated factor (BAF) chromatin remodelling complex leads to enrichment of ventral telencephalon progenitors. Specifically, mutating the BAF subunit ARID1B affects the fate transition of progenitors to oligodendrocyte and interneuron precursor cells, a phenotype that we confirmed in patient-specific induced pluripotent stem cell-derived organoids. Our study paves the way for high-throughput phenotypic characterization of disease susceptibility genes in organoid models with cell state, molecular pathway and gene regulatory network readouts.
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Affiliation(s)
- Chong Li
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria.
| | - Jonas Simon Fleck
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland
| | - Catarina Martins-Costa
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Thomas R Burkard
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Jan Themann
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Marlene Stuempflen
- Department of Radiodiagnostics, Medical University of Vienna, Vienna, Austria
| | - Angela Maria Peer
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Ábel Vertesy
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Jamie B Littleboy
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Christopher Esk
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
- Institute of Molecular Biology, University of Innsbruck, Innsbruck, Austria
| | - Ulrich Elling
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Gregor Kasprian
- Department of Radiodiagnostics, Medical University of Vienna, Vienna, Austria
| | - Nina S Corsini
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria
| | - Barbara Treutlein
- Department of Biosystems Science and Engineering, ETH Zürich, Basel, Switzerland.
| | - Juergen A Knoblich
- Institute of Molecular Biotechnology of the Austrian Academy of Science (IMBA), Vienna, Austria.
- Department of Neurology, Medical University of Vienna, Vienna, Austria.
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Dudley JA, Nagaraj UD, Merhar S, Mangano FT, Kline-Fath BM, Ou X, Acheson A, Yuan W. DTI of Opioid-Exposed Fetuses Using ComBat Harmonization: A Bi-Institutional Study. AJNR Am J Neuroradiol 2023; 44:1084-1089. [PMID: 37562830 PMCID: PMC10494946 DOI: 10.3174/ajnr.a7951] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 06/25/2023] [Indexed: 08/12/2023]
Abstract
BACKGROUND AND PURPOSE The underlying mechanisms leading to altered cognitive, behavioral, and vision outcomes in children with prenatal opioid exposure are yet to be fully understood. Some studies suggest WM alterations in infants and children with prenatal opioid exposure; however, the time course of WM changes is unknown. We aimed to evaluate differences in diffusion tensor imaging MRI parameters in the brain between opioid exposed fetuses and normal controls. MATERIALS AND METHODS This is a pilot, prospective cohort study in which subjects in the third trimester of pregnancy underwent fetal DTI of the brain with 20 noncolinear diffusion directions and a b-value of 500 s/mm2 at 2.5-mm isotropic resolution. RESULTS The study included a total of 26 fetuses, 11 opioid-exposed (mean gestational age, 32.61 [SD, 2.35] weeks) and 15 unexposed controls (mean gestational age, 31.77 [SD, 1.68] weeks). After we adjusted for gestational age, fractional anisotropy values were significantly higher in opioid-exposed fetuses relative to controls in 8 WM tracts: the bilateral lemniscus (left: P = .017; right: P = .020), middle cerebellar peduncle (P = .027), left inferior cerebellar peduncle (P = .026), right sagittal stratum (P = .040), right fornix stria terminalis (P = .022), right inferior fronto-occipital fasciculus (P = .011), and the right uncinate fasciculus (P = .033). Significant alteration was also identified in other DTI indices involving a series of brain regions. CONCLUSIONS Our data demonstrate initial evidence of cerebral WM microstructural differences between opioid-exposed fetuses and unexposed controls. Further studies in larger patient populations will be needed to fully understand these findings.
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Affiliation(s)
- J A Dudley
- From the Department of Radiology and Medical Imaging (J.A.D., U.D.N., B.M.K.-F., W.Y.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
- University of Cincinnati College of Medicine (J.A.D., U.D.N., S.M., F.T.M., B.M.K.-F., W.Y.), Cincinnati, Ohio
| | - U D Nagaraj
- From the Department of Radiology and Medical Imaging (J.A.D., U.D.N., B.M.K.-F., W.Y.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
- University of Cincinnati College of Medicine (J.A.D., U.D.N., S.M., F.T.M., B.M.K.-F., W.Y.), Cincinnati, Ohio
| | - S Merhar
- University of Cincinnati College of Medicine (J.A.D., U.D.N., S.M., F.T.M., B.M.K.-F., W.Y.), Cincinnati, Ohio
- Perinatal Institute, Division of Neonatology (S.M.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - F T Mangano
- University of Cincinnati College of Medicine (J.A.D., U.D.N., S.M., F.T.M., B.M.K.-F., W.Y.), Cincinnati, Ohio
- Department of Neurosurgery (F.T.M.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - B M Kline-Fath
- From the Department of Radiology and Medical Imaging (J.A.D., U.D.N., B.M.K.-F., W.Y.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
- University of Cincinnati College of Medicine (J.A.D., U.D.N., S.M., F.T.M., B.M.K.-F., W.Y.), Cincinnati, Ohio
| | - X Ou
- Departments of Radiology (X.O.), University of Arkansas for Medical Sciences, Little Rock, Arkansas
- Departments of Pediatrics (X.O.), University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - A Acheson
- Department of Psychiatry (A.A.), University of Arkansas for Medical Sciences, Little Rock, Arkansas
| | - W Yuan
- From the Department of Radiology and Medical Imaging (J.A.D., U.D.N., B.M.K.-F., W.Y.), Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
- University of Cincinnati College of Medicine (J.A.D., U.D.N., S.M., F.T.M., B.M.K.-F., W.Y.), Cincinnati, Ohio
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Cook KM, De Asis-Cruz J, Basu SK, Andescavage N, Murnick J, Spoehr E, du Plessis AJ, Limperopoulos C. Ex-utero third trimester developmental changes in functional brain network organization in infants born very and extremely preterm. Front Neurosci 2023; 17:1214080. [PMID: 37719160 PMCID: PMC10502339 DOI: 10.3389/fnins.2023.1214080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 08/22/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction The latter half of gestation is a period of rapid brain development, including the formation of fundamental functional brain network architecture. Unlike in-utero fetuses, infants born very and extremely preterm undergo these critical maturational changes in the extrauterine environment, with growing evidence suggesting this may result in altered brain networks. To date, however, the development of functional brain architecture has been unexplored. Methods From a prospective cohort of preterm infants, graph parameters were calculated for fMRI scans acquired prior to reaching term equivalent age. Eight graph properties were calculated, Clustering Coefficient (C), Characteristic Path Length (L), Modularity (Q), Local Efficiency (LE), Global Efficiency (GE), Normalized Clustering (λ), Normalized Path Length (γ), and Small-Worldness (σ). Properties were first compared to values generated from random and lattice networks and cost efficiency was evaluated. Subsequently, linear mixed effect models were used to assess relationship with postmenstrual age and infant sex. Results A total of 111 fMRI scans were acquired from 85 preterm infants born at a mean GA 28.93 ± 2.8. Infants displayed robust small world properties as well as both locally and globally efficient networks. Regression models found that GE increased while L, Q, λ, γ, and σ decreased with increasing postmenstrual age following multiple comparison correction (r2Adj range 0.143-0.401, p < 0048), with C and LE exhibited trending increases with age. Discussion This is the first direct investigation on the extra-uterine formation of functional brain architecture in preterm infants. Importantly, our results suggest that changes in functional architecture with increasing age exhibit a different trajectory relative to in utero fetus. Instead, they exhibit developmental changes more similar to the early postnatal period in term born infants.
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Affiliation(s)
- Kevin M. Cook
- Developing Brain Institute, Children’s National Hospital, Washington, DC, United States
| | | | - Sudeepta K. Basu
- Developing Brain Institute, Children’s National Hospital, Washington, DC, United States
| | - Nickie Andescavage
- Developing Brain Institute, Children’s National Hospital, Washington, DC, United States
| | - Jonathan Murnick
- Department of Diagnostic Imaging & Radiology, Children’s National Health System, Children’s National Hospital, Washington, DC, United States
| | - Emma Spoehr
- Developing Brain Institute, Children’s National Hospital, Washington, DC, United States
| | - Adré J. du Plessis
- Prenatal Pediatrics Institute, Children’s National Hospital, Washington, DC, United States
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Stuempflen M, Taymourtash A, Kienast P, Schmidbauer VU, Schwartz E, Mitter C, Binder J, Prayer D, Kasprian G. Ganglionic eminence: volumetric assessment of transient brain structure utilizing fetal magnetic resonance imaging. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2023; 62:405-413. [PMID: 37099530 DOI: 10.1002/uog.26232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Revised: 03/27/2023] [Accepted: 04/15/2023] [Indexed: 06/19/2023]
Abstract
OBJECTIVE To provide quantitative magnetic resonance imaging (MRI) super-resolution-based three-dimensional volumetric reference data on the growth dynamics of the ganglionic eminence (GE) relative to cortical and total fetal brain volumes (TBV). METHODS This was a retrospective study of fetuses without structural central nervous system anomalies or other confounding comorbidities that were referred for fetal MRI. Super-resolution reconstructions of 1.5- and 3-Tesla T2-weighted images were generated. Semiautomatic segmentation of TBV and cortical volume and manual segmentation of the GE were performed. Cortical volume, TBV and GE volume were quantified and three-dimensional reconstructions were generated to visualize the developmental dynamics of the GE. RESULTS Overall, 120 fetuses that underwent 127 MRI scans at a mean gestational age of 27.23 ± 4.81 weeks (range, 20-37 weeks) were included. In the investigated gestational-age range, GE volume ranged from 74.88 to 808.75 mm3 and was at its maximum at 21 gestational weeks, followed by a linear decrease (R2 = 0.559) throughout the late second and third trimesters. A pronounced reduction in GE volume relative to cortical volume and TBV occurred in the late second trimester, with a decline in this reduction observed in the third trimester (R2 = 0.936 and 0.924, respectively). Three-dimensional rendering allowed visualization of a continuous change in the shape and size of the GE throughout the second and third trimesters. CONCLUSIONS Even small compartments of the fetal brain, which are not easily accessible by standardized two-dimensional modalities, can be assessed precisely by super-resolution processed fetal MRI. The inverse growth dynamics of GE volume compared with TBV and cortical volume reflects the transitory nature and physiological involution of this (patho-)physiologically important brain structure. The normal development and involution of the GE is mandatory for normal cortical development. Pathological changes of this transient organ precede impairment of cortical structures, and their detection may allow an earlier diagnosis of such anomalies. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.
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Affiliation(s)
- M Stuempflen
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - A Taymourtash
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - P Kienast
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - V U Schmidbauer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - E Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - C Mitter
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - J Binder
- Department of Obstetrics and Feto-maternal Medicine, Medical University of Vienna, Vienna, Austria
| | - D Prayer
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - G Kasprian
- Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
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Payette K, Li HB, de Dumast P, Licandro R, Ji H, Siddiquee MMR, Xu D, Myronenko A, Liu H, Pei Y, Wang L, Peng Y, Xie J, Zhang H, Dong G, Fu H, Wang G, Rieu Z, Kim D, Kim HG, Karimi D, Gholipour A, Torres HR, Oliveira B, Vilaça JL, Lin Y, Avisdris N, Ben-Zvi O, Bashat DB, Fidon L, Aertsen M, Vercauteren T, Sobotka D, Langs G, Alenyà M, Villanueva MI, Camara O, Fadida BS, Joskowicz L, Weibin L, Yi L, Xuesong L, Mazher M, Qayyum A, Puig D, Kebiri H, Zhang Z, Xu X, Wu D, Liao K, Wu Y, Chen J, Xu Y, Zhao L, Vasung L, Menze B, Cuadra MB, Jakab A. Fetal brain tissue annotation and segmentation challenge results. Med Image Anal 2023; 88:102833. [PMID: 37267773 DOI: 10.1016/j.media.2023.102833] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 03/16/2023] [Accepted: 04/20/2023] [Indexed: 06/04/2023]
Abstract
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.
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Affiliation(s)
- Kelly Payette
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland.
| | - Hongwei Bran Li
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland; Department of Informatics, Technical University of Munich, Munich, Germany
| | - Priscille de Dumast
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Roxane Licandro
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital/Harvard Medical School, Charlestown, MA, United States; Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab (CIR), Medical University of Vienna, Vienna, Austria
| | - Hui Ji
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
| | | | | | | | - Hao Liu
- Shanghai Jiaotong University, China
| | | | | | - Ying Peng
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Juanying Xie
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Huiquan Zhang
- School of Computer Science, Shaanxi Normal University, Xi'an 710119, China
| | - Guiming Dong
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Hao Fu
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Guotai Wang
- School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - ZunHyan Rieu
- Research Institute, NEUROPHET Inc., Seoul 06247, South Korea
| | - Donghyeon Kim
- Research Institute, NEUROPHET Inc., Seoul 06247, South Korea
| | - Hyun Gi Kim
- Department of Radiology, The Catholic University of Korea, Eunpyeong St. Mary's Hospital, Seoul 06247, South Korea
| | - Davood Karimi
- Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Ali Gholipour
- Boston Children's Hospital and Harvard Medical School, Boston, MA, United States
| | - Helena R Torres
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga Guimarães, Portugal
| | - Bruno Oliveira
- Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal; Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga Guimarães, Portugal
| | - João L Vilaça
- 2Ai - School of Technology, IPCA, Barcelos, Portugal
| | - Yang Lin
- Department of Computer Science, Hong Kong University of Science and Technology, China
| | - Netanell Avisdris
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel; Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Israel
| | - Ori Ben-Zvi
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Israel; Sagol School of Neuroscience, Tel Aviv University, Israel
| | - Dafna Ben Bashat
- Sagol School of Neuroscience, Tel Aviv University, Israel; Sackler Faculty of Medicine, Tel Aviv University, Israel
| | - Lucas Fidon
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, United Kingdom
| | - Michael Aertsen
- Department of Radiology, University Hospitals Leuven, Leuven 3000, Belgium
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, United Kingdom
| | - Daniel Sobotka
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria
| | - Mireia Alenyà
- BCN-MedTech, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Maria Inmaculada Villanueva
- Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain; Institut d'Investigacions Biomèdiques August Pi i Sunyer, Barcelona, Spain
| | - Oscar Camara
- BCN-MedTech, Department of Information and Communications Technologies, Universitat Pompeu Fabra, Barcelona, Spain
| | - Bella Specktor Fadida
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel
| | - Leo Joskowicz
- School of Computer Science and Engineering, The Hebrew University of Jerusalem, Israel
| | - Liao Weibin
- School of Computer Science, Beijing Institute of Technology, China
| | - Lv Yi
- School of Computer Science, Beijing Institute of Technology, China
| | - Li Xuesong
- School of Computer Science, Beijing Institute of Technology, China
| | - Moona Mazher
- Department of Computer Engineering and Mathematics, University Rovira i Virgili,Spain
| | | | - Domenec Puig
- Department of Computer Engineering and Mathematics, University Rovira i Virgili,Spain
| | - Hamza Kebiri
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Zelin Zhang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Xinyi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | | | - Yixuan Wu
- Zhejiang University, Hangzhou, China
| | | | - Yunzhi Xu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Li Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou, China
| | - Lana Vasung
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, United States; Department of Pediatrics, Harvard Medical School, United States
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland; CIBM, Center for Biomedical Imaging, Lausanne, Switzerland
| | - Andras Jakab
- Center for MR Research, University Children's Hospital Zurich, University of Zurich, Zurich, Switzerland; Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland; University Research Priority Project Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zürich, Zurich, Switzerland
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Rabanaque D, Regalado M, Benítez R, Rabanaque S, Agut T, Carreras N, Mata C. Semi-Automatic GUI Platform to Characterize Brain Development in Preterm Children Using Ultrasound Images. J Imaging 2023; 9:145. [PMID: 37504822 PMCID: PMC10381479 DOI: 10.3390/jimaging9070145] [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: 05/09/2023] [Revised: 07/06/2023] [Accepted: 07/12/2023] [Indexed: 07/29/2023] Open
Abstract
The third trimester of pregnancy is the most critical period for human brain development, during which significant changes occur in the morphology of the brain. The development of sulci and gyri allows for a considerable increase in the brain surface. In preterm newborns, these changes occur in an extrauterine environment that may cause a disruption of the normal brain maturation process. We hypothesize that a normalized atlas of brain maturation with cerebral ultrasound images from birth to term equivalent age will help clinicians assess these changes. This work proposes a semi-automatic Graphical User Interface (GUI) platform for segmenting the main cerebral sulci in the clinical setting from ultrasound images. This platform has been obtained from images of a cerebral ultrasound neonatal database images provided by two clinical researchers from the Hospital Sant Joan de Déu in Barcelona, Spain. The primary objective is to provide a user-friendly design platform for clinicians for running and visualizing an atlas of images validated by medical experts. This GUI offers different segmentation approaches and pre-processing tools and is user-friendly and designed for running, visualizing images, and segmenting the principal sulci. The presented results are discussed in detail in this paper, providing an exhaustive analysis of the proposed approach's effectiveness.
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Affiliation(s)
- David Rabanaque
- Barcelona East School of Engineering, Universitat Politècnica de Catalunya, 08019 Barcelona, Spain
| | - Maria Regalado
- Barcelona East School of Engineering, Universitat Politècnica de Catalunya, 08019 Barcelona, Spain
| | - Raul Benítez
- Barcelona East School of Engineering, Universitat Politècnica de Catalunya, 08019 Barcelona, Spain
- Research Centre for Biomedical Engineering (CREB), Barcelona East School of Engineering, Universitat Politècnica de Catalunya, 08028 Barcelona, Spain
- Pediatric Computational Imaging Research Group, Hospital Sant Joan de Déu Barcelona, 08950 Esplugues de Llobregat, Spain
| | - Sonia Rabanaque
- Barcelona East School of Engineering, Universitat Politècnica de Catalunya, 08019 Barcelona, Spain
| | - Thais Agut
- Institut de Recerca Sant Joan de Déu, Hospital Sant Joan de Déu Barcelona, 08950 Esplugues de Llobregat, Spain
- Neonatal Department, Hospital Sant Joan de Déu Barcelona, 08950 Esplugues de Llobregat, Spain
- Fundación NeNe, 28010 Madrid, Spain
| | - Nuria Carreras
- Institut de Recerca Sant Joan de Déu, Hospital Sant Joan de Déu Barcelona, 08950 Esplugues de Llobregat, Spain
- Neonatal Department, Hospital Sant Joan de Déu Barcelona, 08950 Esplugues de Llobregat, Spain
| | - Christian Mata
- Barcelona East School of Engineering, Universitat Politècnica de Catalunya, 08019 Barcelona, Spain
- Research Centre for Biomedical Engineering (CREB), Barcelona East School of Engineering, Universitat Politècnica de Catalunya, 08028 Barcelona, Spain
- Pediatric Computational Imaging Research Group, Hospital Sant Joan de Déu Barcelona, 08950 Esplugues de Llobregat, Spain
- Institut de Recerca Sant Joan de Déu, Hospital Sant Joan de Déu Barcelona, 08950 Esplugues de Llobregat, Spain
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40
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Correa S, Nichols ES, Mueller ME, de Vrijer B, Eagleson R, McKenzie CA, de Ribaupierre S, Duerden EG. Default mode network functional connectivity strength in utero and the association with fetal subcortical development. Cereb Cortex 2023; 33:9144-9153. [PMID: 37259175 PMCID: PMC10350815 DOI: 10.1093/cercor/bhad190] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 06/02/2023] Open
Abstract
The default mode network is essential for higher-order cognitive processes and is composed of an extensive network of functional and structural connections. Early in fetal life, the default mode network shows strong connectivity with other functional networks; however, the association with structural development is not well understood. In this study, resting-state functional magnetic resonance imaging and anatomical images were acquired in 30 pregnant women with singleton pregnancies. Participants completed 1 or 2 MR imaging sessions, on average 3 weeks apart (43 data sets), between 28- and 39-weeks postconceptional ages. Subcortical volumes were automatically segmented. Activation time courses from resting-state functional magnetic resonance imaging were extracted from the default mode network, medial temporal lobe network, and thalamocortical network. Generalized estimating equations were used to examine the association between functional connectivity strength between default mode network-medial temporal lobe, default mode network-thalamocortical network, and subcortical volumes, respectively. Increased functional connectivity strength in the default mode network-medial temporal lobe network was associated with smaller right hippocampal, left thalamic, and right caudate nucleus volumes, but larger volumes of the left caudate. Increased functional connectivity strength in the default mode network-thalamocortical network was associated with smaller left thalamic volumes. The strong associations seen among the default mode network functional connectivity networks and regionally specific subcortical volume development indicate the emergence of short-range connectivity in the third trimester.
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Affiliation(s)
- Susana Correa
- Neuroscience Program, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
| | - Emily S Nichols
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
- Applied Psychology, Faculty of Education, Western University, London, ON N6A 3K7, Canada
| | - Megan E Mueller
- Applied Psychology, Faculty of Education, Western University, London, ON N6A 3K7, Canada
| | - Barbra de Vrijer
- Obstetrics & Gynaecology, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
| | - Roy Eagleson
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
- Biomedical Engineering, Western University, London, ON N6A 3K7, Canada
- Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada
| | - Charles A McKenzie
- Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
| | - Sandrine de Ribaupierre
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
- Biomedical Engineering, Western University, London, ON N6A 3K7, Canada
- Medical Biophysics, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
- Clinical Neurological Sciences, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
- Anatomy and Cell Biology, Schulich School of Medicine & Dentistry, Western University, London, ON N6A 3K7, Canada
| | - Emma G Duerden
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada
- Applied Psychology, Faculty of Education, Western University, London, ON N6A 3K7, Canada
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41
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Uus AU, Egloff Collado A, Roberts TA, Hajnal JV, Rutherford MA, Deprez M. Retrospective motion correction in foetal MRI for clinical applications: existing methods, applications and integration into clinical practice. Br J Radiol 2023; 96:20220071. [PMID: 35834425 PMCID: PMC7614695 DOI: 10.1259/bjr.20220071] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 04/27/2022] [Accepted: 05/11/2022] [Indexed: 01/07/2023] Open
Abstract
Foetal MRI is a complementary imaging method to antenatal ultrasound. It provides advanced information for detection and characterisation of foetal brain and body anomalies. Even though modern single shot sequences allow fast acquisition of 2D slices with high in-plane image quality, foetal MRI is intrinsically corrupted by motion. Foetal motion leads to loss of structural continuity and corrupted 3D volumetric information in stacks of slices. Furthermore, the arbitrary and constantly changing position of the foetus requires dynamic readjustment of acquisition planes during scanning.
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Affiliation(s)
- Alena U. Uus
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas' Hospital, London, United Kingdom
| | - Alexia Egloff Collado
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas' Hospital, London, United Kingdom
| | | | | | - Mary A. Rutherford
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas' Hospital, London, United Kingdom
| | - Maria Deprez
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas' Hospital, London, United Kingdom
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Machado-Rivas F, Choi JJ, Bedoya MA, Buitrago LA, Velasco-Annis C, Afacan O, Barnewolt C, Estroff J, Warfield SK, Gholipour A, Jaimes C. Brain growth in fetuses with congenital diaphragmatic hernia. J Neuroimaging 2023; 33:617-624. [PMID: 36813467 PMCID: PMC10363187 DOI: 10.1111/jon.13096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND AND PURPOSE To perform a volumetric evaluation of the brain in fetuses with right or left congenital diaphragmatic hernia (CDH), and to compare brain growth trajectories to normal fetuses. METHODS We identified fetal MRIs performed between 2015 and 2020 in fetuses with a diagnosis of CDH. Gestational age (GA) range was 19-40 weeks. Control subjects consisted of normally developing fetuses between 19 and 40 weeks recruited for a separate prospective study. All images were acquired at 3 Tesla and were processed with retrospective motion correction and slice-to-volume reconstruction to generate super-resolution 3-dimensional volumes. These volumes were registered to a common atlas space and segmented in 29 anatomic parcellations. RESULTS A total of 174 fetal MRIs in 149 fetuses were analyzed (99 controls [mean GA: 29.2 ± 5.2 weeks], 34 fetuses left-sided CDH [mean GA: 28.4 ± 5.3 weeks], and 16 fetuses right-sided CDH [mean GA: 27 ± 5.4 weeks]). In fetuses with left-sided CDH, brain parenchymal volume was -8.0% (95% confidence interval [CI] [-13.1, -2.5]; p = .005) lower than normal controls. Differences ranged from -11.4% (95% CI [-18, -4.3]; p < .001) in the corpus callosum to -4.6% (95% CI [-8.9, -0.1]; p = .044) in the hippocampus. In fetuses with right-sided CDH, brain parenchymal volume was -10.1% (95% CI [-16.8, -2.7]; p = .008) lower than controls. Differences ranged from -14.1% (95% CI [-21, -6.5]; p < .001) in the ventricular zone to -5.6% (95% CI [-9.3, -1.8]; p = .025) in the brainstem. CONCLUSION Left and right CDH are associated with lower fetal brain volumes.
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Affiliation(s)
- Fedel Machado-Rivas
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
| | | | - Maria Alejandra Bedoya
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
| | | | | | - Onur Afacan
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
| | - Carol Barnewolt
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
| | - Judy Estroff
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
| | - Simon K. Warfield
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
| | - Ali Gholipour
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
| | - Camilo Jaimes
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
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43
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Mallela AN, Deng H, Gholipour A, Warfield SK, Goldschmidt E. Heterogeneous growth of the insula shapes the human brain. Proc Natl Acad Sci U S A 2023; 120:e2220200120. [PMID: 37279278 PMCID: PMC10268209 DOI: 10.1073/pnas.2220200120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 04/13/2023] [Indexed: 06/08/2023] Open
Abstract
The human cerebrum consists of a precise and stereotyped arrangement of lobes, primary gyri, and connectivity that underlies human cognition [P. Rakic, Nat. Rev. Neurosci. 10, 724-735 (2009)]. The development of this arrangement is less clear. Current models explain individual primary gyrification but largely do not account for the global configuration of the cerebral lobes [T. Tallinen, J. Y. Chung, J. S. Biggins, L. Mahadevan, Proc. Natl. Acad. Sci. U.S.A. 111, 12667-12672 (2014) and D. C. Van Essen, Nature 385, 313-318 (1997)]. The insula, buried in the depths of the Sylvian fissure, is unique in terms of gyral anatomy and size. Here, we quantitatively show that the insula has unique morphology and location in the cerebrum and that these key differences emerge during fetal development. Finally, we identify quantitative differences in developmental migration patterns to the insula that may underlie these differences. We calculated morphologic data in the insula and other lobes in adults (N = 107) and in an in utero fetal brain atlas (N = 81 healthy fetuses). In utero, the insula grows an order of magnitude slower than the other lobes and demonstrates shallower sulci, less curvature, and less surface complexity both in adults and progressively throughout fetal development. Spherical projection analysis demonstrates that the lenticular nuclei obstruct 60 to 70% of radial pathways from the ventricular zone (VZ) to the insula, forcing a curved migration to the insula in contrast to a direct radial pathway. Using fetal diffusion tractography, we identify radial glial fascicles that originate from the VZ and curve around the lenticular nuclei to form the insula. These results confirm existing models of radial migration to the cortex and illustrate findings that suggest differential insular and cerebral development, laying the groundwork to understand cerebral malformations and insular function and pathologies.
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Affiliation(s)
- Arka N. Mallela
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA15213
| | - Hansen Deng
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA15213
| | - Ali Gholipour
- Department of Radiology, Harvard Medical School, Boston, MA02115
- Department of Radiology, Boston Children’s Hospital, Boston, MA02115
| | - Simon K. Warfield
- Department of Radiology, Harvard Medical School, Boston, MA02115
- Department of Radiology, Boston Children’s Hospital, Boston, MA02115
| | - Ezequiel Goldschmidt
- Department of Radiology, Harvard Medical School, Boston, MA02115
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA94143
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44
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Uus AU, Kyriakopoulou V, Makropoulos A, Fukami-Gartner A, Cromb D, Davidson A, Cordero-Grande L, Price AN, Grigorescu I, Williams LZJ, Robinson EC, Lloyd D, Pushparajah K, Story L, Hutter J, Counsell SJ, Edwards AD, Rutherford MA, Hajnal JV, Deprez M. BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.18.537347. [PMID: 37131820 PMCID: PMC10153133 DOI: 10.1101/2023.04.18.537347] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Fetal MRI is widely used for quantitative brain volumetry studies. However, currently, there is a lack of universally accepted protocols for fetal brain parcellation and segmentation. Published clinical studies tend to use different segmentation approaches that also reportedly require significant amounts of time-consuming manual refinement. In this work, we propose to address this challenge by developing a new robust deep learning-based fetal brain segmentation pipeline for 3D T2w motion corrected brain images. At first, we defined a new refined brain tissue parcellation protocol with 19 regions-of-interest using the new fetal brain MRI atlas from the Developing Human Connectome Project. This protocol design was based on evidence from histological brain atlases, clear visibility of the structures in individual subject 3D T2w images and the clinical relevance to quantitative studies. It was then used as a basis for developing an automated deep learning brain tissue parcellation pipeline trained on 360 fetal MRI datasets with different acquisition parameters using semi-supervised approach with manually refined labels propagated from the atlas. The pipeline demonstrated robust performance for different acquisition protocols and GA ranges. Analysis of tissue volumetry for 390 normal participants (21-38 weeks gestational age range), scanned with three different acquisition protocols, did not reveal significant differences for major structures in the growth charts. Only minor errors were present in < 15% of cases thus significantly reducing the need for manual refinement. In addition, quantitative comparison between 65 fetuses with ventriculomegaly and 60 normal control cases were in agreement with the findings reported in our earlier work based on manual segmentations. These preliminary results support the feasibility of the proposed atlas-based deep learning approach for large-scale volumetric analysis. The created fetal brain volumetry centiles and a docker with the proposed pipeline are publicly available online at https://hub.docker.com/r/fetalsvrtk/segmentation (tag brain_bounti_tissue).
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Affiliation(s)
- Alena U Uus
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | | | | | | | - Daniel Cromb
- Centre for the Developing Brain, King's College London, London, UK
| | - Alice Davidson
- Centre for the Developing Brain, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, King's College London, London, UK
- Biomedical Image Technologies, ETSI Telecomunicacion, Universidad Politécnica de Madrid and CIBER-BBN, ISCII, Madrid, Spain
| | - Anthony N Price
- Centre for the Developing Brain, King's College London, London, UK
| | - Irina Grigorescu
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Logan Z J Williams
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Emma C Robinson
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - David Lloyd
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Department of Congenital Heart Disease, Evelina London Children's Hospital, London, UK
| | - Kuberan Pushparajah
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Department of Congenital Heart Disease, Evelina London Children's Hospital, London, UK
| | - Lisa Story
- Centre for the Developing Brain, King's College London, London, UK
| | - Jana Hutter
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | | | - A David Edwards
- Centre for the Developing Brain, King's College London, London, UK
| | | | - Joseph V Hajnal
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Centre for the Developing Brain, King's College London, London, UK
| | - Maria Deprez
- School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
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45
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Taymourtash A, Schwartz E, Nenning KH, Sobotka D, Licandro R, Glatter S, Diogo MC, Golland P, Grant E, Prayer D, Kasprian G, Langs G. Fetal development of functional thalamocortical and cortico-cortical connectivity. Cereb Cortex 2023; 33:5613-5624. [PMID: 36520481 PMCID: PMC10152101 DOI: 10.1093/cercor/bhac446] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2022] [Revised: 08/22/2022] [Accepted: 08/23/2022] [Indexed: 12/23/2022] Open
Abstract
Measuring and understanding functional fetal brain development in utero is critical for the study of the developmental foundations of our cognitive abilities, possible early detection of disorders, and their prevention. Thalamocortical connections are an intricate component of shaping the cortical layout, but so far, only ex-vivo studies provide evidence of how axons enter the sub-plate and cortex during this highly dynamic phase. Evidence for normal in-utero development of the functional thalamocortical connectome in humans is missing. Here, we modeled fetal functional thalamocortical connectome development using in-utero functional magnetic resonance imaging in fetuses observed from 19th to 40th weeks of gestation (GW). We observed a peak increase of thalamocortical functional connectivity strength between 29th and 31st GW, right before axons establish synapses in the cortex. The cortico-cortical connectivity increases in a similar time window, and exhibits significant functional laterality in temporal-superior, -medial, and -inferior areas. Homologous regions exhibit overall similar mirrored connectivity profiles, but this similarity decreases during gestation giving way to a more diverse cortical interconnectedness. Our results complement the understanding of structural development of the human connectome and may serve as the basis for the investigation of disease and deviations from a normal developmental trajectory of connectivity development.
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Affiliation(s)
- Athena Taymourtash
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Ernst Schwartz
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Karl-Heinz Nenning
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Center for Biomedical Imaging and Neuromodulation, Nathan Kline Institute, 140, Old Orangeburg Road, Orangeburg, NY 10962, United States
| | - Daniel Sobotka
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Roxane Licandro
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Laboratory for Computational Neuroimaging, A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Bldg. 149, 13th Street, Charlestown, MA 02129, United States
| | - Sarah Glatter
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Mariana Cardoso Diogo
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Radiology Department, Hospital CUF Tejo, Av. 24 de Julho 171A, 1350-352 Lisboa, Portugal
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77, Massachusetts Avenue, Cambridge, MA 02139, United States
| | - Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300, Longwood Avenue, Boston, MA 02115, United States
| | - Daniela Prayer
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Gregor Kasprian
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
| | - Georg Langs
- Computational Imaging Research Lab, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Waehringer Guertel 18-20, A-1090 Vienna, Austria
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 77, Massachusetts Avenue, Cambridge, MA 02139, United States
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Masse O, Kraft E, Ahmad E, Rollins CK, Velasco-Annis C, Yang E, Warfield SK, Shamshirsaz AA, Gholipour A, Feldman HA, Estroff J, Grant PE, Vasung L. Abnormal prenatal brain development in Chiari II malformation. Front Neuroanat 2023; 17:1116948. [PMID: 37139180 PMCID: PMC10149737 DOI: 10.3389/fnana.2023.1116948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 03/13/2023] [Indexed: 05/05/2023] Open
Abstract
Introduction The Chiari II is a relatively common birth defect that is associated with open spinal abnormalities and is characterized by caudal migration of the posterior fossa contents through the foramen magnum. The pathophysiology of Chiari II is not entirely known, and the neurobiological substrate beyond posterior fossa findings remains unexplored. We aimed to identify brain regions altered in Chiari II fetuses between 17 and 26 GW. Methods We used in vivo structural T2-weighted MRIs of 31 fetuses (6 controls and 25 cases with Chiari II). Results The results of our study indicated altered development of diencephalon and proliferative zones (ventricular and subventricular zones) in fetuses with a Chiari II malformation compared to controls. Specifically, fetuses with Chiari II showed significantly smaller volumes of the diencephalon and significantly larger volumes of lateral ventricles and proliferative zones. Discussion We conclude that regional brain development should be taken into consideration when evaluating prenatal brain development in fetuses with Chiari II.
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Affiliation(s)
- Olivia Masse
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Emily Kraft
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Esha Ahmad
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Caitlin K. Rollins
- Department of Neurology Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Clemente Velasco-Annis
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Edward Yang
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Simon Keith Warfield
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | | | - Ali Gholipour
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Henry A. Feldman
- Institutional Centers for Clinical and Translational Research, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Judy Estroff
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA, United States
| | - Patricia Ellen Grant
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Lana Vasung
- Division of Newborn Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
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47
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Duy PQ, Rakic P, Alper SL, Robert SM, Kundishora AJ, Butler WE, Walsh CA, Sestan N, Geschwind DH, Jin SC, Kahle KT. A neural stem cell paradigm of pediatric hydrocephalus. Cereb Cortex 2023; 33:4262-4279. [PMID: 36097331 PMCID: PMC10110448 DOI: 10.1093/cercor/bhac341] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Revised: 07/12/2022] [Accepted: 08/02/2022] [Indexed: 12/25/2022] Open
Abstract
Pediatric hydrocephalus, the leading reason for brain surgery in children, is characterized by enlargement of the cerebral ventricles classically attributed to cerebrospinal fluid (CSF) overaccumulation. Neurosurgical shunting to reduce CSF volume is the default treatment that intends to reinstate normal CSF homeostasis, yet neurodevelopmental disability often persists in hydrocephalic children despite optimal surgical management. Here, we discuss recent human genetic and animal model studies that are shifting the view of pediatric hydrocephalus from an impaired fluid plumbing model to a new paradigm of dysregulated neural stem cell (NSC) fate. NSCs are neuroprogenitor cells that comprise the germinal neuroepithelium lining the prenatal brain ventricles. We propose that heterogenous defects in the development of these cells converge to disrupt cerebrocortical morphogenesis, leading to abnormal brain-CSF biomechanical interactions that facilitate passive pooling of CSF and secondary ventricular distention. A significant subset of pediatric hydrocephalus may thus in fact be due to a developmental brain malformation leading to secondary enlargement of the ventricles rather than a primary defect of CSF circulation. If hydrocephalus is indeed a neuroradiographic presentation of an inborn brain defect, it suggests the need to focus on optimizing neurodevelopment, rather than CSF diversion, as the primary treatment strategy for these children.
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Affiliation(s)
- Phan Q Duy
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA
- Medical Scientist Training Program, Yale University School of Medicine, New Haven, CT 06510, USA
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Pasko Rakic
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Seth L Alper
- Division of Nephrology and Vascular Biology Research Center, Beth Israel Deaconess Medical Center and Department of Medicine, Harvard Medical School, Boston, MA 02215, USA
| | - Stephanie M Robert
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Adam J Kundishora
- Department of Neurosurgery, Yale University School of Medicine, New Haven, CT 06510, USA
| | - William E Butler
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Christopher A Walsh
- Division of Genetics and Genomics, Manton Center for Orphan Disease Research, Department of Pediatrics, and Howard Hughes Medical Institute, Boston Children’s Hospital, Boston, MA 02115, USA
- Departments of Pediatrics and Neurology, Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Nenad Sestan
- Department of Neuroscience, Yale University School of Medicine, New Haven, CT 06510, USA
| | - Daniel H Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA
| | - Sheng Chih Jin
- Department of Genetics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Kristopher T Kahle
- Department of Neurosurgery, Massachusetts General Hospital, Boston, MA 02114, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Harvard Center for Hydrocephalus and Neurodevelopmental Disorders, Massachusetts General Hospital, Boston, MA 02114, USA
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48
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Wilson S, Pietsch M, Cordero-Grande L, Christiaens D, Uus A, Karolis VR, Kyriakopoulou V, Colford K, Price AN, Hutter J, Rutherford MA, Hughes EJ, Counsell SJ, Tournier JD, Hajnal JV, Edwards AD, O’Muircheartaigh J, Arichi T. Spatiotemporal tissue maturation of thalamocortical pathways in the human fetal brain. eLife 2023; 12:e83727. [PMID: 37010273 PMCID: PMC10125021 DOI: 10.7554/elife.83727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 03/31/2023] [Indexed: 04/04/2023] Open
Abstract
The development of connectivity between the thalamus and maturing cortex is a fundamental process in the second half of human gestation, establishing the neural circuits that are the basis for several important brain functions. In this study, we acquired high-resolution in utero diffusion magnetic resonance imaging (MRI) from 140 fetuses as part of the Developing Human Connectome Project, to examine the emergence of thalamocortical white matter over the second to third trimester. We delineate developing thalamocortical pathways and parcellate the fetal thalamus according to its cortical connectivity using diffusion tractography. We then quantify microstructural tissue components along the tracts in fetal compartments that are critical substrates for white matter maturation, such as the subplate and intermediate zone. We identify patterns of change in the diffusion metrics that reflect critical neurobiological transitions occurring in the second to third trimester, such as the disassembly of radial glial scaffolding and the lamination of the cortical plate. These maturational trajectories of MR signal in transient fetal compartments provide a normative reference to complement histological knowledge, facilitating future studies to establish how developmental disruptions in these regions contribute to pathophysiology.
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Affiliation(s)
- Siân Wilson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Centre for Neurodevelopmental Disorders, King’s College LondonLondonUnited Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de MadridMadridSpain
- Biomedical Research Networking Center in Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN)MadridSpain
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Department of Electrical Engineering (ESAT/PSI), Katholieke Universiteit LeuvenLeuvenBelgium
| | - Alena Uus
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King’s College London, St. Thomas' HospitalLondonUnited Kingdom
| | - Vyacheslav R Karolis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Kathleen Colford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Emer J Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Centre for Neurodevelopmental Disorders, King’s College LondonLondonUnited Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Centre for Neurodevelopmental Disorders, King’s College LondonLondonUnited Kingdom
- Department of Forensic and Neurodevelopmental Sciences, King’s College LondonLondonUnited Kingdom
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College LondonLondonUnited Kingdom
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College LondonLondonUnited Kingdom
- Centre for Neurodevelopmental Disorders, King’s College LondonLondonUnited Kingdom
- Children’s Neurosciences, Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation TrustLondonUnited Kingdom
- Department of Bioengineering, Imperial College LondonLondonUnited Kingdom
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49
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Aganj I, Fischl B. Intermediate Deformable Image Registration via Windowed Cross-Correlation. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2023; 2023:10.1109/isbi53787.2023.10230715. [PMID: 37691967 PMCID: PMC10485808 DOI: 10.1109/isbi53787.2023.10230715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
In population and longitudinal imaging studies that employ deformable image registration, more accurate results can be achieved by initializing deformable registration with the results of affine registration where global misalignments have been considerably reduced. Such affine registration, however, is limited to linear transformations and it cannot account for large nonlinear anatomical variations, such as those between pre- and post-operative images or across different subject anatomies. In this work, we introduce a new intermediate deformable image registration (IDIR) technique that recovers large deformations via windowed cross-correlation, and provide an efficient implementation based on the fast Fourier transform. We evaluate our method on 2D X-ray and 3D magnetic resonance images, demonstrating its ability to align substantial nonlinear anatomical variations within a few iterations.
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Affiliation(s)
- Iman Aganj
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School
| | - Bruce Fischl
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School
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50
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Karimi D, Rollins CK, Velasco-Annis C, Ouaalam A, Gholipour A. Learning to segment fetal brain tissue from noisy annotations. Med Image Anal 2023; 85:102731. [PMID: 36608414 PMCID: PMC9974964 DOI: 10.1016/j.media.2022.102731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 11/17/2022] [Accepted: 12/23/2022] [Indexed: 01/03/2023]
Abstract
Automatic fetal brain tissue segmentation can enhance the quantitative assessment of brain development at this critical stage. Deep learning methods represent the state of the art in medical image segmentation and have also achieved impressive results in brain segmentation. However, effective training of a deep learning model to perform this task requires a large number of training images to represent the rapid development of the transient fetal brain structures. On the other hand, manual multi-label segmentation of a large number of 3D images is prohibitive. To address this challenge, we segmented 272 training images, covering 19-39 gestational weeks, using an automatic multi-atlas segmentation strategy based on deformable registration and probabilistic atlas fusion, and manually corrected large errors in those segmentations. Since this process generated a large training dataset with noisy segmentations, we developed a novel label smoothing procedure and a loss function to train a deep learning model with smoothed noisy segmentations. Our proposed methods properly account for the uncertainty in tissue boundaries. We evaluated our method on 23 manually-segmented test images of a separate set of fetuses. Results show that our method achieves an average Dice similarity coefficient of 0.893 and 0.916 for the transient structures of younger and older fetuses, respectively. Our method generated results that were significantly more accurate than several state-of-the-art methods including nnU-Net that achieved the closest results to our method. Our trained model can serve as a valuable tool to enhance the accuracy and reproducibility of fetal brain analysis in MRI.
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Affiliation(s)
- Davood Karimi
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Caitlin K Rollins
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Clemente Velasco-Annis
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Abdelhakim Ouaalam
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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