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Verschuur AS, King R, Tax CMW, Boomsma MF, van Wezel-Meijler G, Leemans A, Leijser LM. Methodological considerations on diffusion MRI tractography in infants aged 0-2 years: a scoping review. Pediatr Res 2024:10.1038/s41390-024-03463-2. [PMID: 39143201 DOI: 10.1038/s41390-024-03463-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 07/20/2024] [Accepted: 07/24/2024] [Indexed: 08/16/2024]
Abstract
Diffusion MRI (dMRI) enables studying the complex architectural organization of the brain's white matter (WM) through virtual reconstruction of WM fiber tracts (tractography). Despite the anticipated clinical importance of applying tractography to study structural connectivity and tract development during the critical period of rapid infant brain maturation, detailed descriptions on how to approach tractography in young infants are limited. Over the past two decades, tractography from infant dMRI has mainly been applied in research settings and focused on diffusion tensor imaging (DTI). Only few studies used techniques superior to DTI in terms of disentangling information on the brain's organizational complexity, including crossing fibers. While more advanced techniques may enhance our understanding of the intricate processes of normal and abnormal brain development and extensive knowledge has been gained from application on adult scans, their applicability in infants has remained underexplored. This may partially be due to the higher technical requirements versus the need to limit scan time in young infants. We review various previously described methodological practices for tractography in the infant brain (0-2 years-of-age) and provide recommendations to optimize advanced tractography approaches to enable more accurate reconstructions of the brain WM's complexity. IMPACT: Diffusion tensor imaging is the technique most frequently used for fiber tracking in the developing infant brain but is limited in capability to disentangle the complex white matter organization. Advanced tractography techniques allow for reconstruction of crossing fiber bundles to better reflect the brain's complex organization. Yet, they pose practical and technical challenges in the fast developing young infant's brain. Methods on how to approach advanced tractography in the young infant's brain have hardly been described. Based on a literature review, recommendations are provided to optimize tractography for the developing infant brain, aiming to advance early diagnosis and neuroprotective strategies.
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Affiliation(s)
- Anouk S Verschuur
- Department of Radiology, Isala Hospital Zwolle, Zwolle, The Netherlands.
- Department of Pediatrics, Section of Newborn Critical Care, University of Calgary, Calgary, Canada.
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Regan King
- Department of Pediatrics, Section of Newborn Critical Care, University of Calgary, Calgary, Canada
| | - Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- CUBRIC, School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Martijn F Boomsma
- Department of Radiology, Isala Hospital Zwolle, Zwolle, The Netherlands
- Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Gerda van Wezel-Meijler
- Department of Neonatology, Isala Women and Children's Hospital Zwolle, Zwolle, The Netherlands
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lara M Leijser
- Department of Pediatrics, Section of Newborn Critical Care, University of Calgary, Calgary, Canada
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Lin S, Guo M, Liang Q, Lin X, Chen S, Li Y, Chen P, Qiu Y. Evaluation of Glymphatic System Development in Neonatal Brain via Diffusion Analysis along the Perivascular Space Index. Ann Neurol 2024. [PMID: 39096048 DOI: 10.1002/ana.27047] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2023] [Revised: 06/22/2024] [Accepted: 07/15/2024] [Indexed: 08/04/2024]
Abstract
OBJECTIVE Glymphatic system is a recently discovered macroscopic waste clearance system associated with numerous neurological diseases. However, little is known about glymphatic system development in neonates. We sought to evaluate diffusion along the perivascular space (ALPS) index, a proxy for glymphatic system function, in neonates and investigate its potential associations with maturation, sex, and preterm birth. METHODS Diffusion magnetic resonance imaging (MRI) data in 418 neonates, including 92 preterm neonates (57 males) and 326 term neonates (175 males), from the Developing Human Connectome Project were used for evaluating ALPS index. Linear regression modeling was performed to assess group differences in the ALPS index according to preterm birth and sex. Pearson's and partial correlation analysis were performed to assess the association between the ALPS index and gestational age (GA) as well as postmenstrual age (PMA) at MRI. Moderation analysis was performed to assess the moderation effect of preterm birth on the relationship between the ALPS index and PMA. RESULTS Compared to term neonates, preterm neonates exhibited lower ALPS indices (p < 0.001). The ALPS index positively correlated with PMA (p = 0.004) and GA (p < 0.001). Preterm birth (p = 0.013) had a significant moderation effect on the relationship between the ALPS index and PMA. Sex had no significant direct effect (p = 0.639) or moderation effect (p = 0.333) on ALPS index. INTERPRETATION Glymphatic system development is a dynamic process in neonates, which can be moderated by preterm birth, the ALPS index could serve as a sensitive biomarker for monitoring this process. ANN NEUROL 2024.
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Affiliation(s)
- Shiwei Lin
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Meifen Guo
- Department of Radiology, the Affiliated TCM Hospital of Guangzhou Medical University, Guangzhou, China
| | - Qunjun Liang
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Xiaoshan Lin
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Shengli Chen
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Ying Li
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Peiqi Chen
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
| | - Yingwei Qiu
- Department of Radiology, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China
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da S Senra Filho AC, Murta Junior LO, Monteiro Paschoal A. Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01185-4. [PMID: 39068635 DOI: 10.1007/s10334-024-01185-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/24/2024] [Accepted: 06/28/2024] [Indexed: 07/30/2024]
Abstract
OBJECT Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are well-known and powerful imaging techniques for MRI. Although DTI evaluation has evolved continually in recent years, there are still struggles regarding quantitative measurements that can benefit brain areas that are consistently difficult to measure via diffusion-based methods, e.g., gray matter (GM). The present study proposes a new image processing technique based on diffusion distribution evaluation of López-Ruiz, Mancini and Calbet (LMC) complexity called diffusion complexity (DC). MATERIALS AND METHODS The OASIS-3 and TractoInferno open-science databases for healthy individuals were used, and all the codes are provided as open-source materials. RESULTS The DC map showed relevant signal characterization in brain tissues and structures, achieving contrast-to-noise ratio (CNR) gains of approximately 39% and 93%, respectively, compared to those of the FA and ADC maps. DISCUSSION In the special case of GM tissue, the DC map obtains its maximum signal level, showing the possibility of studying cortical and subcortical structures challenging for classical DTI quantitative formalism. The ability to apply the DC technique, which requires the same imaging acquisition for DTI and its potential to provide complementary information to study the brain's GM structures, can be a rich source of information for further neuroscience research and clinical practice.
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Wang Y, Zhu D, Zhao L, Wang X, Zhang Z, Hu B, Wu D, Zheng W. Profiling cortical morphometric similarity in perinatal brains: Insights from development, sex difference, and inter-individual variation. Neuroimage 2024; 295:120660. [PMID: 38815676 DOI: 10.1016/j.neuroimage.2024.120660] [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: 02/23/2024] [Revised: 05/17/2024] [Accepted: 05/28/2024] [Indexed: 06/01/2024] Open
Abstract
The topological organization of the macroscopic cortical networks important for the development of complex brain functions. However, how the cortical morphometric organization develops during the third trimester and whether it demonstrates sexual and individual differences at this particular stage remain unclear. Here, we constructed the morphometric similarity network (MSN) based on morphological and microstructural features derived from multimodal MRI of two independent cohorts (cross-sectional and longitudinal) scanned at 30-44 postmenstrual weeks (PMW). Sex difference and inter-individual variations of the MSN were also examined on these cohorts. The cross-sectional analysis revealed that both network integration and segregation changed in a nonlinear biphasic trajectory, which was supported by the results obtained from longitudinal analysis. The community structure showed remarkable consistency between bilateral hemispheres and maintained stability across PMWs. Connectivity within the primary cortex strengthened faster than that within high-order communities. Compared to females, male neonates showed a significant reduction in the participation coefficient within prefrontal and parietal cortices, while their overall network organization and community architecture remained comparable. Furthermore, by using the morphometric similarity as features, we achieved over 65 % accuracy in identifying an individual at term-equivalent age from images acquired after birth, and vice versa. These findings provide comprehensive insights into the development of morphometric similarity throughout the perinatal cortex, enhancing our understanding of the establishment of neuroanatomical organization during early life.
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Affiliation(s)
- Ying Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Dalin Zhu
- Department of Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital, Lanzhou, China
| | - Leilei Zhao
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Xiaomin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China
| | - Zhe Zhang
- Institute of Brain Science, Hangzhou Normal University, Hangzhou, China; School of Physics, Hangzhou Normal University, Hangzhou, China
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China; School of Medical Technology, Beijing Institute of Technology, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China.
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China.
| | - Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University, Lanzhou, China.
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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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Verschuur AS, Tax CMW, Boomsma MF, Carlson HL, van Wezel-Meijler G, King R, Leemans A, Leijser LM. Feasibility study to unveil the potential: considerations of constrained spherical deconvolution tractography with unsedated neonatal diffusion brain MRI data. FRONTIERS IN RADIOLOGY 2024; 4:1416672. [PMID: 39007078 PMCID: PMC11239519 DOI: 10.3389/fradi.2024.1416672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Accepted: 06/10/2024] [Indexed: 07/16/2024]
Abstract
Purpose The study aimed to (1) assess the feasibility constrained spherical deconvolution (CSD) tractography to reconstruct crossing fiber bundles with unsedated neonatal diffusion MRI (dMRI), and (2) demonstrate the impact of spatial and angular resolution and processing settings on tractography and derived quantitative measures. Methods For the purpose of this study, the term-equivalent dMRIs (single-shell b800, and b2000, both 5 b0, and 45 gradient directions) of two moderate-late preterm infants (with and without motion artifacts) from a local cohort [Brain Imaging in Moderate-late Preterm infants (BIMP) study; Calgary, Canada] and one infant from the developing human connectome project with high-quality dMRI (using the b2600 shell, comprising 20 b0 and 128 gradient directions, from the multi-shell dataset) were selected. Diffusion tensor imaging (DTI) and CSD tractography were compared on b800 and b2000 dMRI. Varying image resolution modifications, (pre-)processing and tractography settings were tested to assess their impact on tractography. Each experiment involved visualizing local modeling and tractography for the corpus callosum and corticospinal tracts, and assessment of morphological and diffusion measures. Results Contrary to DTI, CSD enabled reconstruction of crossing fibers. Tractography was susceptible to image resolution, (pre-) processing and tractography settings. In addition to visual variations, settings were found to affect streamline count, length, and diffusion measures (fractional anisotropy and mean diffusivity). Diffusion measures exhibited variations of up to 23%. Conclusion Reconstruction of crossing fiber bundles using CSD tractography with unsedated neonatal dMRI data is feasible. Tractography settings affected streamline reconstruction, warranting careful documentation of methods for reproducibility and comparison of cohorts.
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Affiliation(s)
- Anouk S Verschuur
- Department of Radiology, Isala Hospital, Zwolle, Netherlands
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
- Department of Pediatrics, Section of Newborn Critical Care, University of Calgary, Calgary, AB, Canada
| | - Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
- CUBRIC, School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Martijn F Boomsma
- Department of Radiology, Isala Hospital, Zwolle, Netherlands
- Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Helen L Carlson
- Department of Pediatrics, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | | | - Regan King
- Department of Pediatrics, Section of Newborn Critical Care, University of Calgary, Calgary, AB, Canada
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Lara M Leijser
- Department of Pediatrics, Section of Newborn Critical Care, University of Calgary, Calgary, AB, Canada
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Lautarescu A, Bonthrone AF, Bos B, Barratt B, Counsell SJ. Advances in fetal and neonatal neuroimaging and everyday exposures. Pediatr Res 2024:10.1038/s41390-024-03294-1. [PMID: 38877283 DOI: 10.1038/s41390-024-03294-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 04/25/2024] [Accepted: 04/29/2024] [Indexed: 06/16/2024]
Abstract
The complex, tightly regulated process of prenatal brain development may be adversely affected by "everyday exposures" such as stress and environmental pollutants. Researchers are only just beginning to understand the neural sequelae of such exposures, with advances in fetal and neonatal neuroimaging elucidating structural, microstructural, and functional correlates in the developing brain. This narrative review discusses the wide-ranging literature investigating the influence of parental stress on fetal and neonatal brain development as well as emerging literature assessing the impact of exposure to environmental toxicants such as lead and air pollution. These 'everyday exposures' can co-occur with other stressors such as social and financial deprivation, and therefore we include a brief discussion of neuroimaging studies assessing the effect of social disadvantage. Increased exposure to prenatal stressors is associated with alterations in the brain structure, microstructure and function, with some evidence these associations are moderated by factors such as infant sex. However, most studies examine only single exposures and the literature on the relationship between in utero exposure to pollutants and fetal or neonatal brain development is sparse. Large cohort studies are required that include evaluation of multiple co-occurring exposures in order to fully characterize their impact on early brain development. IMPACT: Increased prenatal exposure to parental stress and is associated with altered functional, macro and microstructural fetal and neonatal brain development. Exposure to air pollution and lead may also alter brain development in the fetal and neonatal period. Further research is needed to investigate the effect of multiple co-occurring exposures, including stress, environmental toxicants, and socioeconomic deprivation on early brain development.
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Affiliation(s)
- Alexandra Lautarescu
- Department of Perinatal Imaging and Health, Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
- Department of Psychology, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Alexandra F Bonthrone
- Department of Perinatal Imaging and Health, Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Brendan Bos
- MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Ben Barratt
- MRC Centre for Environment and Health, Imperial College London, London, UK
| | - Serena J Counsell
- Department of Perinatal Imaging and Health, Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
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Lin R, Gholipour A, Thiran JP, Karimi D, Kebiri H, Cuadra MB. CROSS-AGE AND CROSS-SITE DOMAIN SHIFT IMPACTS ON DEEP LEARNING-BASED WHITE MATTER FIBER ESTIMATION IN NEWBORN AND BABY BRAINS. ARXIV 2023:arXiv:2312.14773v1. [PMID: 38196752 PMCID: PMC10775359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Deep learning models have shown great promise in estimating tissue microstructure from limited diffusion magnetic resonance imaging data. However, these models face domain shift challenges when test and train data are from different scanners and protocols, or when the models are applied to data with inherent variations such as the developing brains of infants and children scanned at various ages. Several techniques have been proposed to address some of these challenges, such as data harmonization or domain adaptation in the adult brain. However, those techniques remain unexplored for the estimation of fiber orientation distribution functions in the rapidly developing brains of infants. In this work, we extensively investigate the age effect and domain shift within and across two different cohorts of 201 newborns and 165 babies using the Method of Moments and fine-tuning strategies. Our results show that reduced variations in the microstructural development of babies in comparison to newborns directly impact the deep learning models' cross-age performance. We also demonstrate that a small number of target domain samples can significantly mitigate domain shift problems.
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Affiliation(s)
- Rizhong Lin
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- College of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Switzerland
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Hamza Kebiri
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Switzerland
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Switzerland
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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Zheng W, Wang X, Liu T, Hu B, Wu D. Preterm-birth alters the development of nodal clustering and neural connection pattern in brain structural network at term-equivalent age. Hum Brain Mapp 2023; 44:5372-5386. [PMID: 37539754 PMCID: PMC10543115 DOI: 10.1002/hbm.26442] [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: 03/09/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 08/05/2023] Open
Abstract
Preterm-born neonates are prone to impaired neurodevelopment that may be associated with disrupted whole-brain structural connectivity. The present study aimed to investigate the longitudinal developmental pattern of the structural network from preterm birth to term-equivalent age (TEA), and identify how prematurity influences the network topological organization and properties of local brain regions. Multi-shell diffusion-weighted MRI of 28 preterm-born scanned a short time after birth (PB-AB) and at TEA (PB-TEA), and 28 matched term-born (TB) neonates in the Developing Human Connectome Project (dHCP) were used to construct structural networks through constrained spherical deconvolution tractography. Structural network development from preterm birth to TEA showed reduced shortest path length, clustering coefficient, and modularity, and more "connector" hubs linking disparate communities. Furthermore, compared with TB newborns, premature birth significantly altered the nodal properties (i.e., clustering coefficient, within-module degree, and participation coefficient) in the limbic/paralimbic, default-mode, and subcortical systems but not global topology at TEA, and we were able to distinguish the PB from TB neonates at TEA based on the nodal properties with 96.43% accuracy. Our findings demonstrated a topological reorganization of the structural network occurs during the perinatal period that may prioritize the optimization of global network organization to form a more efficient architecture; and local topology was more vulnerable to premature birth-related factors than global organization of the structural network, which may underlie the impaired cognition and behavior in PB infants.
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Affiliation(s)
- Weihao Zheng
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and EngineeringLanzhou UniversityLanzhouChina
| | - Xiaomin Wang
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and EngineeringLanzhou UniversityLanzhouChina
| | - Tingting Liu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument ScienceZhejiang UniversityHangzhouChina
| | - Bin Hu
- Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and EngineeringLanzhou UniversityLanzhouChina
- School of Medical TechnologyBeijing Institute of TechnologyBeijingChina
- CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiChina
- Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University & Institute of SemiconductorsChinese Academy of SciencesLanzhouChina
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering & Instrument ScienceZhejiang UniversityHangzhouChina
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Kebiri H, Gholipour A, Vasung L, Krsnik Ž, Karimi D, Cuadra MB. Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.01.547351. [PMID: 37425859 PMCID: PMC10327173 DOI: 10.1101/2023.07.01.547351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
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 of FODs 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 to standard methods such as Constrained Spherical Deconvolution. We demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical datasets of newborns and fetuses. Additionally, we compute agreement metrics within the HARDI newborn dataset, and validate fetal FODs with post-mortem histological data. The results of this study show the advantage of deep learning in inferring the microstructure of the developing brain from in-vivo dMRI measurements that are often very limited due to subject motion and limited acquisition times, but also highlight the intrinsic limitations of dMRI in the analysis of the developing brain microstructure. These findings, therefore, advocate for the need for improved methods that are tailored to studying the early development of human brain.
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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, Massachusetts, USA
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, 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, Massachusetts, 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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11
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DiPiero M, Rodrigues PG, Gromala A, Dean DC. Applications of advanced diffusion MRI in early brain development: a comprehensive review. Brain Struct Funct 2023; 228:367-392. [PMID: 36585970 PMCID: PMC9974794 DOI: 10.1007/s00429-022-02605-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Accepted: 12/21/2022] [Indexed: 01/01/2023]
Abstract
Brain development follows a protracted developmental timeline with foundational processes of neurodevelopment occurring from the third trimester of gestation into the first decade of life. Defining structural maturational patterns of early brain development is a critical step in detecting divergent developmental trajectories associated with neurodevelopmental and psychiatric disorders that arise later in life. While considerable advancements have already been made in diffusion magnetic resonance imaging (dMRI) for pediatric research over the past three decades, the field of neurodevelopment is still in its infancy with remarkable scientific and clinical potential. This comprehensive review evaluates the application, findings, and limitations of advanced dMRI methods beyond diffusion tensor imaging, including diffusion kurtosis imaging (DKI), constrained spherical deconvolution (CSD), neurite orientation dispersion and density imaging (NODDI) and composite hindered and restricted model of diffusion (CHARMED) to quantify the rapid and dynamic changes supporting the underlying microstructural architectural foundations of the brain in early life.
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Affiliation(s)
- Marissa DiPiero
- Department of Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, 53705, USA
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | | | - Alyssa Gromala
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Douglas C Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, 53705, USA.
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, 53705, USA.
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12
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Wang W, Yu Q, Liang W, Xu F, Li Z, Tang Y, Liu S. Altered cortical microstructure in preterm infants at term-equivalent age relative to term-born neonates. Cereb Cortex 2023; 33:651-662. [PMID: 35259759 DOI: 10.1093/cercor/bhac091] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 01/11/2022] [Accepted: 02/08/2022] [Indexed: 02/03/2023] Open
Abstract
Preterm (PT) birth is a potential factor for abnormal brain development. Although various alterations of cortical structure and functional connectivity in preterm infants have been reported, the underlying microstructural foundation is still undetected thoroughly in PT infants relative to full-term (FT) neonates. To detect the very early cortical microstructural alteration noninvasively with advanced neurite orientation dispersion and density imaging (NODDI) on a whole-brain basis, we used multi-shell diffusion MRI of healthy newborns selected from the Developing Human Connectome Project. 73 PT infants and 69 FT neonates scanned at term-equivalent age were included in this study. By extracting the core voxels of gray matter (GM) using GM-based spatial statistics (GBSS), we found that comparing to FT neonates, infants born preterm showed extensive lower neurite density in both primary and higher-order association cortices (FWE corrected, P < 0.025). Higher orientation dispersion was only found in very preterm subgroup in the orbitofrontal cortex, fronto-insular cortex, entorhinal cortex, a portion of posterior cingular gyrus, and medial parieto-occipital cortex. This study provided new insights into exploring structural MR for functional and behavioral variations in preterm population, and these findings may have marked clinical importance, particularly in the guidance of ameliorating the development of premature brain.
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Affiliation(s)
- Wenjun Wang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Qiaowen Yu
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
| | - Wenjia Liang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Feifei Xu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Zhuoran Li
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
| | - Yuchun Tang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Shuwei Liu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
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13
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Cabral L, Zubiaurre-Elorza L, Wild CJ, Linke A, Cusack R. Anatomical correlates of category-selective visual regions have distinctive signatures of connectivity in neonates. Dev Cogn Neurosci 2022; 58:101179. [PMID: 36521345 PMCID: PMC9768242 DOI: 10.1016/j.dcn.2022.101179] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 11/15/2022] [Accepted: 11/21/2022] [Indexed: 11/25/2022] Open
Abstract
The ventral visual stream is shaped during development by innate proto-organization within the visual system, such as the strong input from the fovea to the fusiform face area. In adults, category-selective regions have distinct signatures of connectivity to brain regions beyond the visual system, likely reflecting cross-modal and motoric associations. We tested if this long-range connectivity is part of the innate proto-organization, or if it develops with postnatal experience, by using diffusion-weighted imaging to characterize the connectivity of anatomical correlates of category-selective regions in neonates (N = 445), 1-9 month old infants (N = 11), and adults (N = 14). Using the HCP data we identified face- and place- selective regions and a third intermediate region with a distinct profile of selectivity. Using linear classifiers, these regions were found to have distinctive connectivity at birth, to other regions in the visual system and to those outside of it. The results support an extended proto-organization that includes long-range connectivity that shapes, and is shaped by, experience-dependent development.
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Affiliation(s)
- Laura Cabral
- Department of Radiology, University of Pittsburgh, Pittsburgh 15224, PA, USA.
| | - Leire Zubiaurre-Elorza
- Department of Psychology, Faculty of Health Sciences, University of Deusto, Bilbao 48007, Spain
| | - Conor J Wild
- Western Institute for Neuroscience, Western University, London, ON N6A 3K7, Canada; Department of Physiology and Pharmacology,Western University, London, ON N6A 3K7, Canada
| | - Annika Linke
- Brain Development Imaging Laboratories, San Diego State University, San Diego 92120, CA, USA
| | - Rhodri Cusack
- Trinity College Institute of Neuroscience, Trinity College Dublin, College Green, Dublin 2, Ireland
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14
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Neumane S, Gondova A, Leprince Y, Hertz-Pannier L, Arichi T, Dubois J. Early structural connectivity within the sensorimotor network: Deviations related to prematurity and association to neurodevelopmental outcome. Front Neurosci 2022; 16:932386. [PMID: 36507362 PMCID: PMC9732267 DOI: 10.3389/fnins.2022.932386] [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: 04/29/2022] [Accepted: 11/10/2022] [Indexed: 11/27/2022] Open
Abstract
Consisting of distributed and interconnected structures that interact through cortico-cortical connections and cortico-subcortical loops, the sensorimotor (SM) network undergoes rapid maturation during the perinatal period and is thus particularly vulnerable to preterm birth. However, the impact of prematurity on the development and integrity of the emerging SM connections and their relationship to later motor and global impairments are still poorly understood. In this study we aimed to explore to which extent the early microstructural maturation of SM white matter (WM) connections at term-equivalent age (TEA) is modulated by prematurity and related with neurodevelopmental outcome at 18 months corrected age. We analyzed 118 diffusion MRI datasets from the developing Human Connectome Project (dHCP) database: 59 preterm (PT) low-risk infants scanned near TEA and a control group of full-term (FT) neonates paired for age at MRI and sex. We delineated WM connections between the primary SM cortices (S1, M1 and paracentral region) and subcortical structures using probabilistic tractography, and evaluated their microstructure with diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) models. To go beyond tract-specific univariate analyses, we computed a maturational distance related to prematurity based on the multi-parametric Mahalanobis distance of each PT infant relative to the FT group. Our results confirmed the presence of microstructural differences in SM tracts between PT and FT infants, with effects increasing with lower gestational age at birth. Maturational distance analyses highlighted that prematurity has a differential effect on SM tracts with higher distances and thus impact on (i) cortico-cortical than cortico-subcortical connections; (ii) projections involving S1 than M1 and paracentral region; and (iii) the most rostral cortico-subcortical tracts, involving the lenticular nucleus. These different alterations at TEA suggested that vulnerability follows a specific pattern coherent with the established WM caudo-rostral progression of maturation. Finally, we highlighted some relationships between NODDI-derived maturational distances of specific tracts and fine motor and cognitive outcomes at 18 months. As a whole, our results expand understanding of the significant impact of premature birth and early alterations on the emerging SM network even in low-risk infants, with possible relationship with neurodevelopmental outcomes. This encourages further exploration of these potential neuroimaging markers for prediction of neurodevelopmental disorders, with special interest for subtle neuromotor impairments frequently observed in preterm-born children.
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Affiliation(s)
- Sara Neumane
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
- School of Biomedical Engineering and Imaging Sciences, Centre for the Developing Brain, King’s College London, London, United Kingdom
| | - Andrea Gondova
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Yann Leprince
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Lucie Hertz-Pannier
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
| | - Tomoki Arichi
- School of Biomedical Engineering and Imaging Sciences, Centre for the Developing Brain, King’s College London, London, United Kingdom
- Paediatric Neurosciences, Evelina London Children’s Hospital, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Jessica Dubois
- Inserm, NeuroDiderot, Université Paris Cité, Paris, France
- CEA, NeuroSpin UNIACT, Université Paris-Saclay, Paris, France
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15
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Nicastro M, Jeurissen B, Beirinckx Q, Smekens C, Poot DHJ, Sijbers J, den Dekker AJ. To shift or to rotate? Comparison of acquisition strategies for multi-slice super-resolution magnetic resonance imaging. Front Neurosci 2022; 16:1044510. [PMID: 36440272 PMCID: PMC9694825 DOI: 10.3389/fnins.2022.1044510] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 10/18/2022] [Indexed: 07/27/2023] Open
Abstract
Multi-slice (MS) super-resolution reconstruction (SRR) methods have been proposed to improve the trade-off between resolution, signal-to-noise ratio and scan time in magnetic resonance imaging. MS-SRR consists in the estimation of an isotropic high-resolution image from a series of anisotropic MS images with a low through-plane resolution, where the anisotropic low-resolution images can be acquired according to different acquisition schemes. However, it is yet unclear how these schemes compare in terms of statistical performance criteria, especially for regularized MS-SRR. In this work, the estimation performance of two commonly adopted MS-SRR acquisition schemes based on shifted and rotated MS images respectively are evaluated in a Bayesian framework. The maximum a posteriori estimator, which introduces regularization by incorporating prior knowledge in a statistically well-defined way, is put forward as the estimator of choice and its accuracy, precision, and Bayesian mean squared error (BMSE) are used as performance criteria. Analytic calculations as well as Monte Carlo simulation experiments show that the rotated scheme outperforms the shifted scheme in terms of precision, accuracy, and BMSE. Furthermore, the superior performance of the rotated scheme is confirmed in real data experiments and in retrospective simulation experiments with and without inter-image motion. Results show that the rotated scheme allows regularized MS-SRR with a higher accuracy and precision than the shifted scheme, besides being more resilient to motion.
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Affiliation(s)
- Michele Nicastro
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
- Lab for Equilibrium Investigations and Aerospace, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Quinten Beirinckx
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Céline Smekens
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
- Siemens Healthcare NV/SA, Groot-Bijgaarden, Belgium
| | - Dirk H. J. Poot
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands
| | - Jan Sijbers
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Arnold J. den Dekker
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
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16
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Liang W, Yu Q, Wang W, Dhollander T, Suluba E, Li Z, Xu F, Hu Y, Tang Y, Liu S. A comparative study of the superior longitudinal fasciculus subdivisions between neonates and young adults. Brain Struct Funct 2022; 227:2713-2730. [PMID: 36114859 PMCID: PMC9618541 DOI: 10.1007/s00429-022-02565-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 09/03/2022] [Indexed: 12/04/2022]
Abstract
The superior longitudinal fasciculus (SLF) is a complex associative tract comprising three distinct subdivisions in the frontoparietal cortex, each of which has its own anatomical connectivity and functional roles. However, many studies on white matter development, hampered by limitations of data quality and tractography methods, treated the SLF as a single entity. The exact anatomical trajectory and developmental status of each sub-bundle of the human SLF in neonates remain poorly understood. Here, we compared the morphological and microstructural characteristics of each branch of the SLF at two ages using diffusion MRI data from 40 healthy neonates and 40 adults. A multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) algorithm was used to ensure the successful separation of the three SLF branches (SLF I, SLF II and SLF III). Then, between-group differences in the diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) metrics were investigated in all the SLF branches. Meanwhile, Mahalanobis distances based on all the diffusion metrics were computed to quantify the maturation of neonatal SLF branches, considering the adult brain as the reference. The SLF branches, excluding SLF II, had similar fibre morphology and connectivity between the neonatal and adult groups. The Mahalanobis distance values further supported the notion of heterogeneous maturation among SLF branches. The greatest Mahalanobis distance was observed in SLF II, possibly indicating that it was the least mature. Our findings provide a new anatomical basis for the early diagnosis and treatment of diseases caused by abnormal neonatal SLF development.
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Affiliation(s)
- Wenjia Liang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Qiaowen Yu
- Department of Medical Imaging, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
| | - Wenjun Wang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Thijs Dhollander
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
| | - Emmanuel Suluba
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Zhuoran Li
- Department of Ultrasound, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, 250021, China
| | - Feifei Xu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Yang Hu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China
| | - Yuchun Tang
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China.
| | - Shuwei Liu
- Department of Anatomy and Neurobiology, Research Center for Sectional and Imaging Anatomy, Shandong Key Laboratory of Mental Disorders, Shandong Key Laboratory of Digital Human and Clinical Anatomy, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China.
- Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, Shandong, 250012, China.
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17
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Bonthrone AF, Chew A, Bhroin MN, Rech FM, Kelly CJ, Christiaens D, Pietsch M, Tournier JD, Cordero-Grande L, Price A, Egloff A, Hajnal JV, Pushparajah K, Simpson J, David Edwards A, Rutherford MA, Nosarti C, Batalle D, Counsell SJ. Neonatal frontal-limbic connectivity is associated with externalizing behaviours in toddlers with Congenital Heart Disease. Neuroimage Clin 2022; 36:103153. [PMID: 35987179 PMCID: PMC9403726 DOI: 10.1016/j.nicl.2022.103153] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 08/02/2022] [Accepted: 08/12/2022] [Indexed: 12/14/2022]
Abstract
Children with Congenital Heart Disease (CHD) are at increased risk of neurodevelopmental impairments. The neonatal antecedents of impaired behavioural development are unknown. 43 infants with CHD underwent presurgical brain diffusion-weighted MRI [postmenstrual age at scan median (IQR) = 39.29 (38.71-39.71) weeks] and a follow-up assessment at median age of 22.1 (IQR 22.0-22.7) months in which parents reported internalizing and externalizing problem scores on the Child Behaviour Checklist. We constructed structural brain networks from diffusion-weighted MRI and calculated edge-wise structural connectivity as well as global and local brain network features. We also calculated presurgical cerebral oxygen delivery, and extracted perioperative variables, socioeconomic status at birth and a measure of cognitively stimulating parenting. Lower degree in the right inferior frontal gyrus (partial ρ = -0.687, p < 0.001) and reduced connectivity in a frontal-limbic sub-network including the right inferior frontal gyrus were associated with higher externalizing problem scores. Externalizing problem scores were unrelated to neonatal clinical course or home environment. However, higher internalizing problem scores were associated with earlier surgery in the neonatal period (partial ρ = -0.538, p = 0.014). Our results highlight the importance of frontal-limbic networks to the development of externalizing behaviours and provide new insights into early antecedents of behavioural impairments in CHD.
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Affiliation(s)
- Alexandra F Bonthrone
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Megan Ní Bhroin
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College, Dublin, Ireland
| | - Francesca Morassutti Rech
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Christopher J Kelly
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department of Electrical Engineering (ESAT/PSI), KU Leuven, Leuven, Belgium
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - Anthony Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Alexia Egloff
- Centre for the Developing Brain, 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 and Imaging Sciences, King's College London, London, UK
| | - Kuberan Pushparajah
- Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK; Paediatric Cardiology Department, Evelina London Children's Healthcare, London, UK
| | - John Simpson
- Paediatric Cardiology Department, Evelina London Children's Healthcare, London, UK
| | - A David Edwards
- Centre for the Developing Brain, 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
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
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18
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Lautarescu A, Bonthrone AF, Pietsch M, Batalle D, Cordero-Grande L, Tournier JD, Christiaens D, Hajnal JV, Chew A, Falconer S, Nosarti C, Victor S, Craig MC, Edwards AD, Counsell SJ. Maternal depressive symptoms, neonatal white matter, and toddler social-emotional development. Transl Psychiatry 2022; 12:323. [PMID: 35945202 PMCID: PMC9363426 DOI: 10.1038/s41398-022-02073-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 07/01/2022] [Accepted: 07/18/2022] [Indexed: 11/25/2022] Open
Abstract
Maternal prenatal depression is associated with increased likelihood of neurodevelopmental and psychiatric conditions in offspring. The relationship between maternal depression and offspring outcome may be mediated by in-utero changes in brain development. Recent advances in magnetic resonance imaging (MRI) have enabled in vivo investigations of neonatal brains, minimising the effect of postnatal influences. The aim of this study was to examine associations between maternal prenatal depressive symptoms, infant white matter, and toddler behaviour. 413 mother-infant dyads enrolled in the developing Human Connectome Project. Mothers completed the Edinburgh Postnatal Depression Scale (median = 5, range = 0-28, n = 52 scores ≥ 11). Infants (n = 223 male) (median gestational age at birth = 40 weeks, range 32.14-42.29) underwent MRI (median postmenstrual age at scan = 41.29 weeks, range 36.57-44.71). Fixel-based fibre metrics (mean fibre density, fibre cross-section, and fibre density modulated by cross-section) were calculated from diffusion imaging data in the left and right uncinate fasciculi and cingulum bundle. For n = 311, internalising and externalising behaviour, and social-emotional abilities were reported at a median corrected age of 18 months (range 17-24). Statistical analysis used multiple linear regression and mediation analysis with bootstrapping. Maternal depressive symptoms were positively associated with infant fibre density in the left (B = 0.0005, p = 0.003, q = 0.027) and right (B = 0.0006, p = 0.003, q = 0.027) uncinate fasciculus, with left uncinate fasciculus fibre density, in turn, positively associated with social-emotional abilities in toddlerhood (B = 105.70, p = 0.0007, q = 0.004). In a mediation analysis, higher maternal depressive symptoms predicted toddler social-emotional difficulties (B = 0.342, t(307) = 3.003, p = 0.003), but this relationship was not mediated by fibre density in the left uncinate fasciculus (Sobel test p = 0.143, bootstrapped indirect effect = 0.035, SE = 0.02, 95% CI: [-0.01, 0.08]). There was no evidence of an association between maternal depressive and cingulum fibre properties. These findings suggest that maternal perinatal depressive symptoms are associated with neonatal uncinate fasciculi microstructure, but not fibre bundle size, and toddler behaviour.
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Affiliation(s)
- Alexandra Lautarescu
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK.
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Alexandra F Bonthrone
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Maximilian Pietsch
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Dafnis Batalle
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- CIBER de Bioingeniería, Biomateriales y Nanomedicina, Instituto de Salud Carlos III, Madrid, Spain
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain
| | - J-Donald Tournier
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Daan Christiaens
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Joseph V Hajnal
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Andrew Chew
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Shona Falconer
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
| | - Chiara Nosarti
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Suresh Victor
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- Neonatal Unit, Evelina London Children's Hospital, London, UK
| | - Michael C Craig
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- National Female Hormone Clinic, South London and Maudsley National Health Service Foundation Trust, London, UK
| | - A David Edwards
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
- Neonatal Unit, Evelina London Children's Hospital, London, UK
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
- EPSRC/Wellcome Centre for Medical Engineering, King's College London, London, UK
| | - Serena J Counsell
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King's College London, St Thomas' Hospital, London, UK
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19
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Ciarrusta J, Christiaens D, Fitzgibbon SP, Dimitrova R, Hutter J, Hughes E, Duff E, Price AN, Cordero-Grande L, Tournier JD, Rueckert D, Hajnal JV, Arichi T, McAlonan G, Edwards AD, Batalle D. The developing brain structural and functional connectome fingerprint. Dev Cogn Neurosci 2022; 55:101117. [PMID: 35662682 PMCID: PMC9344310 DOI: 10.1016/j.dcn.2022.101117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 04/14/2022] [Accepted: 05/17/2022] [Indexed: 11/03/2022] Open
Abstract
In the mature brain, structural and functional 'fingerprints' of brain connectivity can be used to identify the uniqueness of an individual. However, whether the characteristics that make a given brain distinguishable from others already exist at birth remains unknown. Here, we used neuroimaging data from the developing Human Connectome Project (dHCP) of preterm born neonates who were scanned twice during the perinatal period to assess the developing brain fingerprint. We found that 62% of the participants could be identified based on the congruence of the later structural connectome to the initial connectivity matrix derived from the earlier timepoint. In contrast, similarity between functional connectomes of the same subject at different time points was low. Only 10% of the participants showed greater self-similarity in comparison to self-to-other-similarity for the functional connectome. These results suggest that structural connectivity is more stable in early life and can represent a potential connectome fingerprint of the individual: a relatively stable structural connectome appears to support a changing functional connectome at a time when neonates must rapidly acquire new skills to adapt to their new environment.
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Affiliation(s)
- Judit Ciarrusta
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Center for Brain and Cognition (CBC), Universitat Pompeu Fabra, Barcelona, Spain
| | - Daan Christiaens
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Sean P Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Ralica Dimitrova
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Paediatric Neuroimaging Group, Department of Paediatrics, University of Oxford, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Bioengineering, Imperial College London, London SW7 2AZ, United Kingdom; Children's Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Trust, London, United Kingdom
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - Dafnis Batalle
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
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20
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Edwards AD, Rueckert D, Smith SM, Abo Seada S, Alansary A, Almalbis J, Allsop J, Andersson J, Arichi T, Arulkumaran S, Bastiani M, Batalle D, Baxter L, Bozek J, Braithwaite E, Brandon J, Carney O, Chew A, Christiaens D, Chung R, Colford K, Cordero-Grande L, Counsell SJ, Cullen H, Cupitt J, Curtis C, Davidson A, Deprez M, Dillon L, Dimitrakopoulou K, Dimitrova R, Duff E, Falconer S, Farahibozorg SR, Fitzgibbon SP, Gao J, Gaspar A, Harper N, Harrison SJ, Hughes EJ, Hutter J, Jenkinson M, Jbabdi S, Jones E, Karolis V, Kyriakopoulou V, Lenz G, Makropoulos A, Malik S, Mason L, Mortari F, Nosarti C, Nunes RG, O’Keeffe C, O’Muircheartaigh J, Patel H, Passerat-Palmbach J, Pietsch M, Price AN, Robinson EC, Rutherford MA, Schuh A, Sotiropoulos S, Steinweg J, Teixeira RPAG, Tenev T, Tournier JD, Tusor N, Uus A, Vecchiato K, Williams LZJ, Wright R, Wurie J, Hajnal JV. The Developing Human Connectome Project Neonatal Data Release. Front Neurosci 2022; 16:886772. [PMID: 35677357 PMCID: PMC9169090 DOI: 10.3389/fnins.2022.886772] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/19/2022] [Indexed: 11/24/2022] Open
Abstract
The Developing Human Connectome Project has created a large open science resource which provides researchers with data for investigating typical and atypical brain development across the perinatal period. It has collected 1228 multimodal magnetic resonance imaging (MRI) brain datasets from 1173 fetal and/or neonatal participants, together with collateral demographic, clinical, family, neurocognitive and genomic data from 1173 participants, together with collateral demographic, clinical, family, neurocognitive and genomic data. All subjects were studied in utero and/or soon after birth on a single MRI scanner using specially developed scanning sequences which included novel motion-tolerant imaging methods. Imaging data are complemented by rich demographic, clinical, neurodevelopmental, and genomic information. The project is now releasing a large set of neonatal data; fetal data will be described and released separately. This release includes scans from 783 infants of whom: 583 were healthy infants born at term; as well as preterm infants; and infants at high risk of atypical neurocognitive development. Many infants were imaged more than once to provide longitudinal data, and the total number of datasets being released is 887. We now describe the dHCP image acquisition and processing protocols, summarize the available imaging and collateral data, and provide information on how the data can be accessed.
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Affiliation(s)
- A. David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
- Institute for AI and Informatics in Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephen M. Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Samy Abo Seada
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Amir Alansary
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Jennifer Almalbis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joanna Allsop
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
| | - Sophie Arulkumaran
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Luke Baxter
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Eleanor Braithwaite
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Jacqueline Brandon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Olivia Carney
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Raymond Chung
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Kathleen Colford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Serena J. Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Harriet Cullen
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Medical and Molecular Genetics, School of Basic and Medical Biosciences, King’s College London, London, United Kingdom
| | - John Cupitt
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Charles Curtis
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Alice Davidson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Maria Deprez
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Louise Dillon
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Konstantina Dimitrakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Translational Bioinformatics Platform, NIHR Biomedical Research Centre, Guy’s and St. Thomas’ NHS Foundation Trust and King’s College London, London, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Eugene Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Seyedeh-Rezvan Farahibozorg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Sean P. Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jianliang Gao
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Andreia Gaspar
- Institute for Systems and Robotics (ISR-Lisboa)/LaRSyS, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Nicholas Harper
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Sam J. Harrison
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Emer J. Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Emily Jones
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Vyacheslav Karolis
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Antonios Makropoulos
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Shaihan Malik
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Luke Mason
- Centre for Brain and Cognitive Development, Department of Psychological Sciences, Birkbeck, University of London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, United Kingdom
| | - Rita G. Nunes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Institute for Systems and Robotics (ISR-Lisboa)/LaRSyS, Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal
| | - Camilla O’Keeffe
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Hamel Patel
- BioResource Centre, NIHR Biomedical Research Centre, South London and Maudsley NHS Trust, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Maximillian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King’s College London, London, United Kingdom
| | - Anthony N. Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Emma C. Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Mary A. Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Stamatios Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Rui Pedro Azeredo Gomes Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Alena Uus
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Logan Z. J. Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Robert Wright
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Julia Wurie
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
| | - Joseph V. Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United Kingdom
- Biomedical Engineering Department, School of Biomedical Engineering & Imaging Sciences, King’s College London, London, United Kingdom
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21
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Taoudi-Benchekroun Y, Christiaens D, Grigorescu I, Gale-Grant O, Schuh A, Pietsch M, Chew A, Harper N, Falconer S, Poppe T, Hughes E, Hutter J, Price AN, Tournier JD, Cordero-Grande L, Counsell SJ, Rueckert D, Arichi T, Hajnal JV, Edwards AD, Deprez M, Batalle D. Predicting age and clinical risk from the neonatal connectome. Neuroimage 2022; 257:119319. [PMID: 35589001 DOI: 10.1016/j.neuroimage.2022.119319] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 02/28/2022] [Accepted: 05/12/2022] [Indexed: 12/12/2022] Open
Abstract
The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-individual differences in structural brain connectivity. Individual brain connectivity maps (connectomes) are by nature high in dimensionality and complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible and can give us clues as to how and why individual developmental trajectories differ. In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large sample of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of post menstrual age (PMA) at scan in term-born infants (mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p<0.001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p<0.001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to predominantly involve frontal and temporal regions, thalami, and basal ganglia. From our models of PMA at scan for infants born at term, we computed a brain maturation index (predicted age minus actual age) of individual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome and suggest that a neural substrate of brain maturation with implications for future neurodevelopment is detectable at term equivalent age from the neonatal connectome.
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Affiliation(s)
- Yassine Taoudi-Benchekroun
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Irina Grigorescu
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Oliver Gale-Grant
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Nicholas Harper
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Tanya Poppe
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - Serena J Counsell
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Institute for Artificial Intelligence and Informatics in Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Bioengineering, Imperial College London, London, United Kingdom; Children's Neurosciences, Evelina London Children's Hospital, Guy's and St Thomas' NHS Trust, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - Maria Deprez
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom.
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22
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Kebiri H, Canales-Rodríguez EJ, Lajous H, de Dumast P, Girard G, Alemán-Gómez Y, Koob M, Jakab A, Bach Cuadra M. Through-Plane Super-Resolution With Autoencoders in Diffusion Magnetic Resonance Imaging of the Developing Human Brain. Front Neurol 2022; 13:827816. [PMID: 35585848 PMCID: PMC9109939 DOI: 10.3389/fneur.2022.827816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Accepted: 03/28/2022] [Indexed: 11/13/2022] Open
Abstract
Fetal brain diffusion magnetic resonance images (MRI) are often acquired with a lower through-plane than in-plane resolution. This anisotropy is often overcome by classical upsampling methods such as linear or cubic interpolation. In this work, we employ an unsupervised learning algorithm using an autoencoder neural network for single-image through-plane super-resolution by leveraging a large amount of data. Our framework, which can also be used for slice outliers replacement, overperformed conventional interpolations quantitatively and qualitatively on pre-term newborns of the developing Human Connectome Project. The evaluation was performed on both the original diffusion-weighted signal and the estimated diffusion tensor maps. A byproduct of our autoencoder was its ability to act as a denoiser. The network was able to generalize fetal data with different levels of motions and we qualitatively showed its consistency, hence supporting the relevance of pre-term datasets to improve the processing of fetal brain images.
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Affiliation(s)
- Hamza Kebiri
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Erick J. Canales-Rodríguez
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Hélène Lajous
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Priscille de Dumast
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
| | - Gabriel Girard
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Yasser Alemán-Gómez
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Mériam Koob
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - András Jakab
- Center for MR Research University Children's Hospital Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, 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
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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23
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Gale-Grant O, Fenn-Moltu S, França LGS, Dimitrova R, Christiaens D, Cordero-Grande L, Chew A, Falconer S, Harper N, Price AN, Hutter J, Hughes E, O'Muircheartaigh J, Rutherford M, Counsell SJ, Rueckert D, Nosarti C, Hajnal JV, McAlonan G, Arichi T, Edwards AD, Batalle D. Effects of gestational age at birth on perinatal structural brain development in healthy term-born babies. Hum Brain Mapp 2022; 43:1577-1589. [PMID: 34897872 PMCID: PMC8886657 DOI: 10.1002/hbm.25743] [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: 06/01/2021] [Revised: 11/19/2021] [Accepted: 11/30/2021] [Indexed: 11/12/2022] Open
Abstract
Infants born in early term (37-38 weeks gestation) experience slower neurodevelopment than those born at full term (40-41 weeks gestation). While this could be due to higher perinatal morbidity, gestational age at birth may also have a direct effect on the brain. Here we characterise brain volume and white matter correlates of gestational age at birth in healthy term-born neonates and their relationship to later neurodevelopmental outcome using T2 and diffusion weighted MRI acquired in the neonatal period from a cohort (n = 454) of healthy babies born at term age (>37 weeks gestation) and scanned between 1 and 41 days after birth. Images were analysed using tensor-based morphometry and tract-based spatial statistics. Neurodevelopment was assessed at age 18 months using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III). Infants born earlier had higher relative ventricular volume and lower relative brain volume in the deep grey matter, cerebellum and brainstem. Earlier birth was also associated with lower fractional anisotropy, higher mean, axial, and radial diffusivity in major white matter tracts. Gestational age at birth was positively associated with all Bayley-III subscales at age 18 months. Regression models predicting outcome from gestational age at birth were significantly improved after adding neuroimaging features associated with gestational age at birth. This work adds to the body of evidence of the impact of early term birth and highlights the importance of considering the effect of gestational age at birth in future neuroimaging studies including term-born babies.
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Affiliation(s)
- Oliver Gale-Grant
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Sunniva Fenn-Moltu
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Lucas G S França
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Ralica Dimitrova
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Daan Christiaens
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Andrew Chew
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Shona Falconer
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Nicholas Harper
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Emer Hughes
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Jonathan O'Muircheartaigh
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Mary Rutherford
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK.,Department of Medicine and Informatics, Technical University of Munich, Munich, Germany
| | - Chiara Nosarti
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Tomoki Arichi
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,Paediatric Neurosciences, Evelina London Children's Hospital Guy's and St Thomas' NHS Foundation Trust, London, UK.,Department of Bioengineering, Imperial College London, London, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK.,MRC Centre for Neurodevelopmental Disorders, King's College London, London, UK
| | - Dafnis Batalle
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,Centre for the Developing Brain, School of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
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24
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Pollatou A, Filippi CA, Aydin E, Vaughn K, Thompson D, Korom M, Dufford AJ, Howell B, Zöllei L, Martino AD, Graham A, Scheinost D, Spann MN. An ode to fetal, infant, and toddler neuroimaging: Chronicling early clinical to research applications with MRI, and an introduction to an academic society connecting the field. Dev Cogn Neurosci 2022; 54:101083. [PMID: 35184026 PMCID: PMC8861425 DOI: 10.1016/j.dcn.2022.101083] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/17/2021] [Accepted: 02/04/2022] [Indexed: 12/14/2022] Open
Abstract
Fetal, infant, and toddler neuroimaging is commonly thought of as a development of modern times (last two decades). Yet, this field mobilized shortly after the discovery and implementation of MRI technology. Here, we provide a review of the parallel advancements in the fields of fetal, infant, and toddler neuroimaging, noting the shifts from clinical to research use, and the ongoing challenges in this fast-growing field. We chronicle the pioneering science of fetal, infant, and toddler neuroimaging, highlighting the early studies that set the stage for modern advances in imaging during this developmental period, and the large-scale multi-site efforts which ultimately led to the explosion of interest in the field today. Lastly, we consider the growing pains of the community and the need for an academic society that bridges expertise in developmental neuroscience, clinical science, as well as computational and biomedical engineering, to ensure special consideration of the vulnerable mother-offspring dyad (especially during pregnancy), data quality, and image processing tools that are created, rather than adapted, for the young brain.
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Affiliation(s)
- Angeliki Pollatou
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA
| | - Courtney A Filippi
- Section on Development and Affective Neuroscience, National Institute of Mental Health, Bethesda, MD, USA; Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, USA
| | - Ezra Aydin
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA; Department of Psychology, University of Cambridge, Cambridge, UK
| | - Kelly Vaughn
- Department of Pediatrics, University of Texas Health Sciences Center, Houston, TX, USA
| | - Deanne Thompson
- Clinical Sciences, Murdoch Children's Research Institute, Parkville, Victoria, Australia
| | - Marta Korom
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA
| | - Alexander J Dufford
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA
| | - Brittany Howell
- Fralin Biomedical Research Institute at VTC, Virginia Tech, Roanoke, VA, USA; Department of Human Development and Family Science, Virginia Tech, Blacksburg, VA, USA
| | - Lilla Zöllei
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | | | - Alice Graham
- Department of Psychiatry, Oregon Health and Science University, Portland, OR, USA
| | - Dustin Scheinost
- Department of Psychological and Brain Sciences, University of Delaware, Newark, DE, USA; Department of Radiology and Biomedical Imaging, Yale School of Medicine, New Haven, CT, USA; Yale Child Study Center, Yale School of Medicine, New Haven, CT, USA
| | - Marisa N Spann
- Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA; Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA.
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25
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Fenchel D, Dimitrova R, Robinson EC, Batalle D, Chew A, Falconer S, Kyriakopoulou V, Nosarti C, Hutter J, Christiaens D, Pietsch M, Brandon J, Hughes EJ, Allsop J, O'Keeffe C, Price AN, Cordero-Grande L, Schuh A, Makropoulos A, Passerat-Palmbach J, Bozek J, Rueckert D, Hajnal JV, McAlonan G, Edwards AD, O'Muircheartaigh J. Neonatal multi-modal cortical profiles predict 18-month developmental outcomes. Dev Cogn Neurosci 2022; 54:101103. [PMID: 35364447 PMCID: PMC8971851 DOI: 10.1016/j.dcn.2022.101103] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 02/08/2022] [Accepted: 03/23/2022] [Indexed: 12/16/2022] Open
Abstract
Developmental delays in infanthood often persist, turning into life-long difficulties, and coming at great cost for the individual and community. By examining the developing brain and its relation to developmental outcomes we can start to elucidate how the emergence of brain circuits is manifested in variability of infant motor, cognitive and behavioural capacities. In this study, we examined if cortical structural covariance at birth, indexing coordinated development, is related to later infant behaviour. We included 193 healthy term-born infants from the Developing Human Connectome Project (dHCP). An individual cortical connectivity matrix derived from morphological and microstructural features was computed for each subject (morphometric similarity networks, MSNs) and was used as input for the prediction of behavioural scores at 18 months using Connectome-Based Predictive Modeling (CPM). Neonatal MSNs successfully predicted social-emotional performance. Predictive edges were distributed between and within known functional cortical divisions with a specific important role for primary and posterior cortical regions. These results reveal that multi-modal neonatal cortical profiles showing coordinated maturation are related to developmental outcomes and that network organization at birth provides an early infrastructure for future functional skills.
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Affiliation(s)
- Daphna Fenchel
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK
| | - Ralica Dimitrova
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Emma C Robinson
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EU, UK
| | - Dafnis Batalle
- Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK; Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Andrew Chew
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Shona Falconer
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Vanessa Kyriakopoulou
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Chiara Nosarti
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK; Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK
| | - Jana Hutter
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Daan Christiaens
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Maximilian Pietsch
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Jakki Brandon
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Emer J Hughes
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Joanna Allsop
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Camilla O'Keeffe
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Anthony N Price
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK
| | | | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London SW7 2AZ, UK; Institute für Artificial Intelligence and Informatics in Medicine, Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany
| | - Joseph V Hajnal
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Grainne McAlonan
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK; South London and Maudsley NHS Foundation Trust, London SE5 8AZ, UK
| | - A David Edwards
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK
| | - Jonathan O'Muircheartaigh
- MRC Centre for Neurodevelopmental Disorders, King's College London, London SE1 1UL, UK; Sackler Institute for Translational Neurodevelopment, Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London SE5 8AF, UK; Centre for the Developing Brain, Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, King's College London, London SE1 7EH UK.
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26
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Single-shell to multi-shell dMRI transformation using spatial and volumetric multilevel hierarchical reconstruction framework. Magn Reson Imaging 2022; 87:133-156. [PMID: 35017034 DOI: 10.1016/j.mri.2021.12.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 12/21/2021] [Accepted: 12/27/2021] [Indexed: 12/12/2022]
Abstract
Single or Multi-shell high angular resolution diffusion imaging (HARDI) has become an important dMRI acquisition technique for studying brain white matter fibers. Existing single-shell HARDI makes it challenging to estimate the intravoxel structure up to the desired resolution. However, multi-shell acquisition (with multiple b-values) can provide higher resolution for the intravoxel structure, which further helps in getting accurate fiber tracts; But, this comes at the cost of larger acquisition time and larger setup. Hence, we propose a novel deep learning architecture for the reconstruction of diffusion MRI volumes for different b-values (degree of diffusion weighting) using acquisitions at a fixed b-value (termed as single-shell) acquisition. This reconstruction has been performed in the spherical harmonics space to better manage varying gradient directions. In this work, we have demonstrated such a reconstruction for b = 3000 s/mm2 and b = 2000 s/mm2 from b = 1000 s/mm2. The proposed Multilevel Hierarchical Spherical Harmonics Coefficients Reconstruction (MHSH) framework takes advantage of contextual information within each slice as well as across the slices by involving Slice Level ReconNet (SLRNet) network and a Volumetric ROI Level ReconNet (VPLRNet) network, respectively. Three-loss functions have been used to optimize network learning, i.e., L1, Adversarial, and Total Variation Loss. Finally, the network is trained and validated on the publicly available HCP data-set with standard qualitative and quantitative performance measures and achieves promising results.
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27
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What's New and What's Next in Diffusion MRI Preprocessing. Neuroimage 2021; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on “what’s new” since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on “Mapping the Connectome” in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on “what’s next” in dMRI preprocessing.
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28
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Dimitrova R, Pietsch M, Ciarrusta J, Fitzgibbon SP, Williams LZJ, Christiaens D, Cordero-Grande L, Batalle D, Makropoulos A, Schuh A, Price AN, Hutter J, Teixeira RP, Hughes E, Chew A, Falconer S, Carney O, Egloff A, Tournier JD, McAlonan G, Rutherford MA, Counsell SJ, Robinson EC, Hajnal JV, Rueckert D, Edwards AD, O'Muircheartaigh J. Preterm birth alters the development of cortical microstructure and morphology at term-equivalent age. Neuroimage 2021; 243:118488. [PMID: 34419595 PMCID: PMC8526870 DOI: 10.1016/j.neuroimage.2021.118488] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2021] [Revised: 07/16/2021] [Accepted: 08/19/2021] [Indexed: 11/27/2022] Open
Abstract
INTRODUCTION The dynamic nature and complexity of the cellular events that take place during the last trimester of pregnancy make the developing cortex particularly vulnerable to perturbations. Abrupt interruption to normal gestation can lead to significant deviations to many of these processes, resulting in atypical trajectory of cortical maturation in preterm birth survivors. METHODS We sought to first map typical cortical micro- and macrostructure development using invivo MRI in a large sample of healthy term-born infants scanned after birth (n = 259). Then we offer a comprehensive characterization of the cortical consequences of preterm birth in 76 preterm infants scanned at term-equivalent age (37-44 weeks postmenstrual age). We describe the group-average atypicality, the heterogeneity across individual preterm infants, and relate individual deviations from normative development to age at birth and neurodevelopment at 18 months. RESULTS In the term-born neonatal brain, we observed heterogeneous and regionally specific associations between age at scan and measures of cortical morphology and microstructure, including rapid surface expansion, greater cortical thickness, lower cortical anisotropy and higher neurite orientation dispersion. By term-equivalent age, preterm infants had on average increased cortical tissue water content and reduced neurite density index in the posterior parts of the cortex, and greater cortical thickness anteriorly compared to term-born infants. While individual preterm infants were more likely to show extreme deviations (over 3.1 standard deviations) from normative cortical maturation compared to term-born infants, these extreme deviations were highly variable and showed very little spatial overlap between individuals. Measures of regional cortical development were associated with age at birth, but not with neurodevelopment at 18 months. CONCLUSION We showed that preterm birth alters cortical micro- and macrostructural maturation near the time of full-term birth. Deviations from normative development were highly variable between individual preterm infants.
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Affiliation(s)
- Ralica Dimitrova
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Judit Ciarrusta
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Sean P Fitzgibbon
- Centre for Functional MRI of the Brain (FMRIB), Welcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Logan Z J Williams
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Belgium
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Rui Pag Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Olivia Carney
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Alexia Egloff
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Grainne McAlonan
- Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom; South London and Maudsley NHS Foundation Trust, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Emma C Robinson
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Faculty of Informatics and Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom; Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, London, United Kingdom.
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Pietsch M, Christiaens D, Hajnal JV, Tournier JD. dStripe: Slice artefact correction in diffusion MRI via constrained neural network. Med Image Anal 2021; 74:102255. [PMID: 34634644 PMCID: PMC8566280 DOI: 10.1016/j.media.2021.102255] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Revised: 09/22/2021] [Accepted: 09/24/2021] [Indexed: 11/25/2022]
Abstract
dStripe allows removing inter-slice intensity artefacts in the presence of motion. It is not tied to a particular q-space sampling scheme or motion correction method. Can be trained in the absence of ground truth data. Uses explicit constraints that locally preserve in-plane image contrast.
MRI scanner and sequence imperfections and advances in reconstruction and imaging techniques to increase motion robustness can lead to inter-slice intensity variations in Echo Planar Imaging. Leveraging deep convolutional neural networks as universal image filters, we present a data-driven method for the correction of acquisition artefacts that manifest as inter-slice inconsistencies, regardless of their origin. This technique can be applied to motion- and dropout-artefacted data by embedding it in a reconstruction pipeline. The network is trained in the absence of ground-truth data on, and finally applied to, the reconstructed multi-shell high angular resolution diffusion imaging signal to produce a corrective slice intensity modulation field. This correction can be performed in either motion-corrected or scattered source-space. We focus on gaining control over the learned filter and the image data consistency via built-in spatial frequency and intensity constraints. The end product is a corrected image reconstructed from the original raw data, modulated by a multiplicative field that can be inspected and verified to match the expected features of the artefact. In-plane, the correction approximately preserves the contrast of the diffusion signal and throughout the image series, it reduces inter-slice inconsistencies within and across subjects without biasing the data. We apply our pipeline to enhance the super-resolution reconstruction of neonatal multi-shell high angular resolution data as acquired in the developing Human Connectome Project.
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Affiliation(s)
- Maximilian Pietsch
- Centre for Medical Engineering, King's College London, London, UK; Centre for the Developing Brain, King's College London, London, UK; Department of Forensic & Neurodevelopmental Sciences, King's College London, London, UK.
| | - Daan Christiaens
- Centre for the Developing Brain, King's College London, London, UK; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Joseph V Hajnal
- Centre for Medical Engineering, King's College London, London, UK; Centre for the Developing Brain, King's College London, London, UK
| | - J-Donald Tournier
- Centre for Medical Engineering, King's College London, London, UK; Centre for the Developing Brain, King's College London, London, UK
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30
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Rajagopalan V, Deoni S, Panigrahy A, Thomason ME. Is fetal MRI ready for neuroimaging prime time? An examination of progress and remaining areas for development. Dev Cogn Neurosci 2021; 51:100999. [PMID: 34391003 PMCID: PMC8365463 DOI: 10.1016/j.dcn.2021.100999] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 07/08/2021] [Accepted: 08/03/2021] [Indexed: 11/25/2022] Open
Abstract
A major challenge in designing large-scale, multi-site studies is developing a core, scalable protocol that retains the innovation of scientific advances while also lending itself to the variability in experience and resources across sites. In the development of a common Healthy Brain and Child Development (HBCD) protocol, one of the chief questions is "is fetal MRI ready for prime-time?" While there is agreement about the value of prenatal data obtained non-invasively through MRI, questions about practicality abound. There has been rapid progress over the past years in fetal and placental MRI methodology but there is uncertainty about whether the gains afforded outweigh the challenges in supporting fetal MRI protocols at scale. Here, we will define challenges inherent in building a common protocol across sites with variable expertise and will propose a tentative framework for evaluation of design decisions. We will compare and contrast various design considerations for both normative and high-risk populations, in the setting of the post-COVID era. We will conclude with articulation of the benefits of overcoming these challenges and would lend to the primary questions articulated in the HBCD initiative.
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Affiliation(s)
- Vidya Rajagopalan
- Department of Radiology, Keck School of Medicine, University of Southern California and Childrens Hospital of Los Angeles, United States.
| | - Sean Deoni
- Department of Pediatrics, Memorial Hospital of Rhode Island, United States
| | - Ashok Panigrahy
- Department of Radiology, University of Pittsburgh Medical School and Children's Hospital of Pittsburgh, United States
| | - Moriah E Thomason
- Departments of Child and Adolescent Psychiatry and Population Health, Hassenfeld Children's Hospital at NYU Langone, United States
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31
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Uus A, Grigorescu I, Pietsch M, Batalle D, Christiaens D, Hughes E, Hutter J, Cordero Grande L, Price AN, Tournier JD, Rutherford MA, Counsell SJ, Hajnal JV, Edwards AD, Deprez M. Multi-Channel 4D Parametrized Atlas of Macro- and Microstructural Neonatal Brain Development. Front Neurosci 2021; 15:661704. [PMID: 34220423 PMCID: PMC8248811 DOI: 10.3389/fnins.2021.661704] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Accepted: 05/20/2021] [Indexed: 11/19/2022] Open
Abstract
Structural (also known as anatomical) and diffusion MRI provide complimentary anatomical and microstructural characterization of early brain maturation. However, the existing models of the developing brain in time include only either structural or diffusion MRI channels. Furthermore, there is a lack of tools for combined analysis of structural and diffusion MRI in the same reference space. In this work, we propose a methodology to generate a multi-channel (MC) continuous spatio-temporal parametrized atlas of the brain development that combines multiple MRI-derived parameters in the same anatomical space during 37-44 weeks of postmenstrual age range. We co-align structural and diffusion MRI of 170 normal term subjects from the developing Human Connectomme Project using MC registration driven by both T2-weighted and orientation distribution functions channels and fit the Gompertz model to the signals and spatial transformations in time. The resulting atlas consists of 14 spatio-temporal microstructural indices and two parcellation maps delineating white matter tracts and neonatal transient structures. In order to demonstrate applicability of the atlas for quantitative region-specific studies, a comparison analysis of 140 term and 40 preterm subjects scanned at the term-equivalent age is performed using different MRI-derived microstructural indices in the atlas reference space for multiple white matter regions, including the transient compartments. The atlas and software will be available after publication of the article.
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Affiliation(s)
- Alena Uus
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Irina Grigorescu
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Maximilian Pietsch
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
- Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium
| | - Emer Hughes
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Lucilio Cordero Grande
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
- Biomedical Image Technologies, ETSI Telecomunicacion, Universidad Politécnica de Madrid, CIBER-BBN, Madrid, Spain
| | - Anthony N. Price
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Jacques-Donald Tournier
- 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
| | - Serena J. Counsell
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - Joseph V. Hajnal
- Department of Biomedical Engineering, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
- Centre for the Developing Brain, School Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas Hospital, London, United Kingdom
| | - A. David Edwards
- 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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Dubois J, Alison M, Counsell SJ, Hertz‐Pannier L, Hüppi PS, Benders MJ. MRI of the Neonatal Brain: A Review of Methodological Challenges and Neuroscientific Advances. J Magn Reson Imaging 2021; 53:1318-1343. [PMID: 32420684 PMCID: PMC8247362 DOI: 10.1002/jmri.27192] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Revised: 04/24/2020] [Accepted: 04/24/2020] [Indexed: 01/04/2023] Open
Abstract
In recent years, exploration of the developing brain has become a major focus for researchers and clinicians in an attempt to understand what allows children to acquire amazing and unique abilities, as well as the impact of early disruptions (eg, prematurity, neonatal insults) that can lead to a wide range of neurodevelopmental disorders. Noninvasive neuroimaging methods such as MRI are essential to establish links between the brain and behavioral changes in newborns and infants. In this review article, we aim to highlight recent and representative studies using the various techniques available: anatomical MRI, quantitative MRI (relaxometry, diffusion MRI), multiparametric approaches, and functional MRI. Today, protocols use 1.5 or 3T MRI scanners, and specialized methodologies have been put in place for data acquisition and processing to address the methodological challenges specific to this population, such as sensitivity to motion. MR sequences must be adapted to the brains of newborns and infants to obtain relevant good soft-tissue contrast, given the small size of the cerebral structures and the incomplete maturation of tissues. The use of age-specific image postprocessing tools is also essential, as signal and contrast differ from the adult brain. Appropriate methodologies then make it possible to explore multiple neurodevelopmental mechanisms in a precise way, and assess changes with age or differences between groups of subjects, particularly through large-scale projects. Although MRI measurements only indirectly reflect the complex series of dynamic processes observed throughout development at the molecular and cellular levels, this technique can provide information on brain morphology, structural connectivity, microstructural properties of gray and white matter, and on the functional architecture. Finally, MRI measures related to clinical, behavioral, and electrophysiological markers have a key role to play from a diagnostic and prognostic perspective in the implementation of early interventions to avoid long-term disabilities in children. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Jessica Dubois
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Marianne Alison
- University of ParisNeuroDiderot, INSERM,ParisFrance
- Department of Pediatric RadiologyAPHP, Robert‐Debré HospitalParisFrance
| | - Serena J. Counsell
- Centre for the Developing BrainSchool of Biomedical Engineering & Imaging Sciences, King's College LondonLondonUK
| | - Lucie Hertz‐Pannier
- University of ParisNeuroDiderot, INSERM,ParisFrance
- UNIACT, NeuroSpin, CEA; Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Petra S. Hüppi
- Division of Development and Growth, Department of Woman, Child and AdolescentUniversity Hospitals of GenevaGenevaSwitzerland
| | - Manon J.N.L. Benders
- Department of NeonatologyUniversity Medical Center Utrecht, Utrecht UniversityUtrechtthe Netherlands
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Szczepankiewicz F, Westin CF, Nilsson M. Gradient waveform design for tensor-valued encoding in diffusion MRI. J Neurosci Methods 2021; 348:109007. [PMID: 33242529 PMCID: PMC8443151 DOI: 10.1016/j.jneumeth.2020.109007] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 11/17/2020] [Accepted: 11/19/2020] [Indexed: 12/13/2022]
Abstract
Diffusion encoding along multiple spatial directions per signal acquisition can be described in terms of a b-tensor. The benefit of tensor-valued diffusion encoding is that it unlocks the 'shape of the b-tensor' as a new encoding dimension. By modulating the b-tensor shape, we can control the sensitivity to microscopic diffusion anisotropy which can be used as a contrast mechanism; a feature that is inaccessible by conventional diffusion encoding. Since imaging methods based on tensor-valued diffusion encoding are finding an increasing number of applications we are prompted to highlight the challenge of designing the optimal gradient waveforms for any given application. In this review, we first establish the basic design objectives in creating field gradient waveforms for tensor-valued diffusion MRI. We also survey additional design considerations related to limitations imposed by hardware and physiology, potential confounding effects that cannot be captured by the b-tensor, and artifacts related to the diffusion encoding waveform. Throughout, we discuss the expected compromises and tradeoffs with an aim to establish a more complete understanding of gradient waveform design and its impact on accurate measurements and interpretations of data.
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Affiliation(s)
- Filip Szczepankiewicz
- Radiology, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Clinical Sciences, Lund University, Lund, Sweden.
| | - Carl-Fredrik Westin
- Radiology, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
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Ball G, Seidlitz J, O’Muircheartaigh J, Dimitrova R, Fenchel D, Makropoulos A, Christiaens D, Schuh A, Passerat-Palmbach J, Hutter J, Cordero-Grande L, Hughes E, Price A, Hajnal JV, Rueckert D, Robinson EC, Edwards AD. Cortical morphology at birth reflects spatiotemporal patterns of gene expression in the fetal human brain. PLoS Biol 2020; 18:e3000976. [PMID: 33226978 PMCID: PMC7721147 DOI: 10.1371/journal.pbio.3000976] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 12/07/2020] [Accepted: 11/02/2020] [Indexed: 02/07/2023] Open
Abstract
Interruption to gestation through preterm birth can significantly impact cortical development and have long-lasting adverse effects on neurodevelopmental outcome. We compared cortical morphology captured by high-resolution, multimodal magnetic resonance imaging (MRI) in n = 292 healthy newborn infants (mean age at birth = 39.9 weeks) with regional patterns of gene expression in the fetal cortex across gestation (n = 156 samples from 16 brains, aged 12 to 37 postconceptional weeks [pcw]). We tested the hypothesis that noninvasive measures of cortical structure at birth mirror areal differences in cortical gene expression across gestation, and in a cohort of n = 64 preterm infants (mean age at birth = 32.0 weeks), we tested whether cortical alterations observed after preterm birth were associated with altered gene expression in specific developmental cell populations. Neonatal cortical structure was aligned to differential patterns of cell-specific gene expression in the fetal cortex. Principal component analysis (PCA) of 6 measures of cortical morphology and microstructure showed that cortical regions were ordered along a principal axis, with primary cortex clearly separated from heteromodal cortex. This axis was correlated with estimated tissue maturity, indexed by differential expression of genes expressed by progenitor cells and neurons, and engaged in stem cell differentiation, neuron migration, and forebrain development. Preterm birth was associated with altered regional MRI metrics and patterns of differential gene expression in glial cell populations. The spatial patterning of gene expression in the developing cortex was thus mirrored by regional variation in cortical morphology and microstructure at term, and this was disrupted by preterm birth. This work provides a framework to link molecular mechanisms to noninvasive measures of cortical development in early life and highlights novel pathways to injury in neonatal populations at increased risk of neurodevelopmental disorder. Interruption to gestation through preterm birth can significantly impact cortical development and have long-lasting adverse effects on neurodevelopmental outcome. A large neuroimaging study of newborn infants reveals how their cortical structure at birth is associated with patterns of gene expression in the fetal cortex and how this relationship is affected by preterm birth.
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Affiliation(s)
- Gareth Ball
- Developmental Imaging, Murdoch Children’s Research Institute, Melbourne, Australia
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
- Department of Paediatrics, University of Melbourne, Melbourne, Australia
- * E-mail:
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, United States of America
- Department of Psychiatry, University of Cambridge, United Kingdom
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Ralica Dimitrova
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Daphna Fenchel
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Antonios Makropoulos
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Daan Christiaens
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Belgium
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, United Kingdom
| | | | - Jana Hutter
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Anthony Price
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Jo V. Hajnal
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, United Kingdom
| | - Emma C. Robinson
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, Department of Perinatal Imaging & Health, King’s College London, United Kingdom
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35
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Jha RR, Nigam A, Bhavsar A, Pathak SK, Schneider W, Rathish K. Multi-Shell D-MRI Reconstruction via Residual Learning utilizing Encoder-Decoder Network with Attention (MSR-Net). ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1709-1713. [PMID: 33018326 DOI: 10.1109/embc44109.2020.9175455] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Contemporary diffusion MRI based analysis with HARDI, which provides more accurate fiber orientation, can be performed using single or multiple b-values (single or multi-shell). Single shell HARDI cannot provide volume fraction for different tissue types, which can produce bias and noisier results in estimation of fiber ODF. Multi-shell acquisition can resolve this issue. However, it requires more scanning time and is therefore not very well suited in clinical setting. Considering this, we propose a novel deep learning architecture, MSR-Net, for reconstruction of diffusion MRI volumes for some b-value using acquisitions at another b-value. In this work, we demonstrate this for b = 2000 s/mm2 and b = 1000 s/mm2. We learn such a transformation in the space of spherical harmonic coefficients. The proposed network consists of encoder-decoder along-with an attention module and a feature module. We have considered L2 and Content loss for optimizing and improving the performance. We have trained and validated the network using the HCP data-set with standard qualitative and quantitative performance measures.
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36
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Christiaens D, Cordero-Grande L, Pietsch M, Hutter J, Price AN, Hughes EJ, Vecchiato K, Deprez M, Edwards AD, Hajnal JV, Tournier JD. Scattered slice SHARD reconstruction for motion correction in multi-shell diffusion MRI. Neuroimage 2020; 225:117437. [PMID: 33068713 PMCID: PMC7779423 DOI: 10.1016/j.neuroimage.2020.117437] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Revised: 09/22/2020] [Accepted: 10/01/2020] [Indexed: 11/18/2022] Open
Abstract
Subject motion in dMRI leads to a set of scattered slices with unique contrast. We introduce a slice-to-volume reconstruction framework for multi-shell HARDI data Based on a data-driven representation as spherical harmonics and radial decomposition (SHARD). The method is evaluated in test-retest scans and in the neonatal dHCP cohort. Results show robust reconstruction in severely motion-corrupted scans.
Diffusion MRI offers a unique probe into neural microstructure and connectivity in the developing brain. However, analysis of neonatal brain imaging data is complicated by inevitable subject motion, leading to a series of scattered slices that need to be aligned within and across diffusion-weighted contrasts. Here, we develop a reconstruction method for scattered slice multi-shell high angular resolution diffusion imaging (HARDI) data, jointly estimating an uncorrupted data representation and motion parameters at the slice or multiband excitation level. The reconstruction relies on data-driven representation of multi-shell HARDI data using a bespoke spherical harmonics and radial decomposition (SHARD), which avoids imposing model assumptions, thus facilitating to compare various microstructure imaging methods in the reconstructed output. Furthermore, the proposed framework integrates slice-level outlier rejection, distortion correction, and slice profile correction. We evaluate the method in the neonatal cohort of the developing Human Connectome Project (650 scans). Validation experiments demonstrate accurate slice-level motion correction across the age range and across the range of motion in the population. Results in the neonatal data show successful reconstruction even in severely motion-corrupted subjects. In addition, we illustrate how local tissue modelling can extract advanced microstructure features such as orientation distribution functions from the motion-corrected reconstructions.
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Affiliation(s)
- Daan Christiaens
- 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; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
| | - Lucilio Cordero-Grande
- 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; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - Maximilian Pietsch
- 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
| | - Anthony N Price
- 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
| | - Emer J Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Maria Deprez
- 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
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; MRC Centre for Neurodevelopmental Disorders, 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
| | - J-Donald Tournier
- 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
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Ní Bhroin M, Abo Seada S, Bonthrone AF, Kelly CJ, Christiaens D, Schuh A, Pietsch M, Hutter J, Tournier JD, Cordero-Grande L, Rueckert D, Hajnal JV, Pushparajah K, Simpson J, Edwards AD, Rutherford MA, Counsell SJ, Batalle D. Reduced structural connectivity in cortico-striatal-thalamic network in neonates with congenital heart disease. Neuroimage Clin 2020; 28:102423. [PMID: 32987301 PMCID: PMC7520425 DOI: 10.1016/j.nicl.2020.102423] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 08/17/2020] [Accepted: 09/07/2020] [Indexed: 12/15/2022]
Abstract
Impaired brain development has been observed in newborns with congenital heart disease (CHD). We performed graph theoretical analyses and network-based statistics (NBS) to assess global brain network topology and identify subnetworks of altered connectivity in infants with CHD prior to cardiac surgery. Fifty-eight infants with critical/serious CHD prior to surgery and 116 matched healthy controls as part of the developing Human Connectome Project (dHCP) underwent MRI on a 3T system and high angular resolution diffusion MRI (HARDI) was obtained. Multi-tissue constrained spherical deconvolution, anatomically constrained probabilistic tractography (ACT) and spherical-deconvolution informed filtering of tractograms (SIFT2) was used to construct weighted structural networks. Network topology was assessed and NBS was used to identify structural connectivity differences between CHD and control groups. Structural networks were partitioned into core and peripheral nodes, and edges classed as core, peripheral, or feeder. NBS identified one subnetwork with reduced structural connectivity in CHD infants involving basal ganglia, amygdala, hippocampus, cerebellum, vermis, and temporal and parieto-occipital lobe, primarily affecting core nodes and edges. However, we did not find significantly different global network characteristics in CHD neonates. This locally affected sub-network with reduced connectivity could explain, at least in part, the neurodevelopmental impairments associated with CHD.
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Affiliation(s)
- Megan Ní Bhroin
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Trinity College Institute of Neuroscience and Cognitive Systems Group, Discipline of Psychiatry, School of Medicine, Trinity College Dublin, Ireland
| | - Samy Abo Seada
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Alexandra F Bonthrone
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Christopher J Kelly
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department of Electrical Engineering (ESAT/PSI), KU Leuven, Leuven, Belgium
| | - Andreas Schuh
- Department of Computing, Imperial College London, London, UK
| | - Maximilian Pietsch
- Centre for the Developing Brain, 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
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Lucillio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid & CIBER-BBN, Madrid, Spain
| | - Daniel Rueckert
- Department of Computing, Imperial College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Kuberan Pushparajah
- Paediatric Cardiology Department, Evelina London Children's Healthcare, London, UK
| | - John Simpson
- Congenital Heart Disease, Evelina London Children's Hospital, London, UK
| | - A David Edwards
- Centre for the Developing Brain, 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
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
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38
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Ng IHX, Bonthrone AF, Kelly CJ, Cordero-Grande L, Hughes EJ, Price AN, Hutter J, Victor S, Schuh A, Rueckert D, Hajnal JV, Simpson J, Edwards AD, Rutherford MA, Batalle D, Counsell SJ. Investigating altered brain development in infants with congenital heart disease using tensor-based morphometry. Sci Rep 2020; 10:14909. [PMID: 32913193 PMCID: PMC7483731 DOI: 10.1038/s41598-020-72009-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 07/24/2020] [Indexed: 12/12/2022] Open
Abstract
Magnetic resonance (MR) imaging studies have demonstrated reduced global and regional brain volumes in infants with congenital heart disease (CHD). This study aimed to provide a more detailed evaluation of altered structural brain development in newborn infants with CHD compared to healthy controls using tensor-based morphometry (TBM). We compared brain development in 64 infants with CHD to 192 age- and sex-matched healthy controls. T2-weighted MR images obtained prior to surgery were analysed to compare voxel-wise differences in structure across the whole brain between groups. Cerebral oxygen delivery (CDO2) was measured in infants with CHD (n = 49) using phase contrast MR imaging and the relationship between CDO2 and voxel-wise brain structure was assessed using TBM. After correcting for global scaling differences, clusters of significant volume reduction in infants with CHD were demonstrated bilaterally within the basal ganglia, thalami, corpus callosum, occipital, temporal, parietal and frontal lobes, and right hippocampus (p < 0.025 after family-wise error correction). Clusters of significant volume expansion in infants with CHD were identified in cerebrospinal fluid spaces (p < 0.025). After correcting for global brain size, there was no significant association between voxel-wise brain structure and CDO2. This study localizes abnormal brain development in infants with CHD, identifying areas of particular vulnerability.
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Affiliation(s)
- Isabel H X Ng
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Alexandra F Bonthrone
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Christopher J Kelly
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK.,Biomedical Image Technologies, ETSI Telecomunicación, Universidad Politécnica de Madrid and CIBER-BBN, Madrid, Spain
| | - Emer J Hughes
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Suresh Victor
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK.,Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - John Simpson
- Paediatric Cardiology Department, Evelina London Children's Hospital, St Thomas' Hospital, London, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK.,Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, SE1 7EH, UK.
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Tournier JD, Christiaens D, Hutter J, Price AN, Cordero-Grande L, Hughes E, Bastiani M, Sotiropoulos SN, Smith SM, Rueckert D, Counsell SJ, Edwards AD, Hajnal JV. A data-driven approach to optimising the encoding for multi-shell diffusion MRI with application to neonatal imaging. NMR IN BIOMEDICINE 2020; 33:e4348. [PMID: 32632961 PMCID: PMC7116416 DOI: 10.1002/nbm.4348] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 04/23/2020] [Accepted: 05/18/2020] [Indexed: 06/11/2023]
Abstract
Diffusion MRI has the potential to provide important information about the connectivity and microstructure of the human brain during normal and abnormal development, noninvasively and in vivo. Recent developments in MRI hardware and reconstruction methods now permit the acquisition of large amounts of data within relatively short scan times. This makes it possible to acquire more informative multi-shell data, with diffusion sensitisation applied along many directions over multiple b-value shells. Such schemes are characterised by the number of shells acquired, and the specific b-value and number of directions sampled for each shell. However, there is currently no clear consensus as to how to optimise these parameters. In this work, we propose a means of optimising multi-shell acquisition schemes by estimating the information content of the diffusion MRI signal, and optimising the acquisition parameters for sensitivity to the observed effects, in a manner agnostic to any particular diffusion analysis method that might subsequently be applied to the data. This method was used to design the acquisition scheme for the neonatal diffusion MRI sequence used in the developing Human Connectome Project (dHCP), which aims to acquire high quality data and make it freely available to the research community. The final protocol selected by the algorithm, and currently in use within the dHCP, consists of 20 b=0 images and diffusion-weighted images at b = 400, 1000 and 2600 s/mm2 with 64, 88 and 128 directions per shell, respectively.
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Affiliation(s)
- Jacques-Donald Tournier
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - Daan Christiaens
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - Jana Hutter
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - Anthony N Price
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - Lucilio Cordero-Grande
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - Emer Hughes
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Stamatios N Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging - Oxford Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, Oxford, UK
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Imperial College London, London, UK
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
| | - Joseph V Hajnal
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
- Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London, UK
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Grier MD, Zimmermann J, Heilbronner SR. Estimating Brain Connectivity With Diffusion-Weighted Magnetic Resonance Imaging: Promise and Peril. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:846-854. [PMID: 32513555 PMCID: PMC7483308 DOI: 10.1016/j.bpsc.2020.04.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 03/20/2020] [Accepted: 04/18/2020] [Indexed: 01/22/2023]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is a popular tool for noninvasively assessing properties of white matter in the brain. Among other uses, dMRI data can be used to produce estimates of anatomical connectivity on the basis of tractography. However, direct comparisons of anatomical connectivity as estimated through invasive neural tract-tracing experiments and dMRI-derived connectivity have shown only a moderate relationship in nonhuman primate (particularly macaque) studies. Tractography is plagued by known problems associated with resolution, crossing fibers, and curving fibers, among others. These problems lead to deficits in both sensitivity and specificity, which trade off with each other in multiple datasets. Although not yet examined quantitatively, there is reason to believe that some large white matter bundles, those with more topographic organization, may produce more accurate results than others. Moving forward, sophisticated analytical approaches and anatomical constraints may improve tractography accuracy. However, broadly speaking, dMRI-derived estimates of brain connectivity should be approached with caution.
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Affiliation(s)
- Mark D Grier
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota; Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota
| | - Sarah R Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota.
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Banihashemi L, Bertocci MA, Alkhars HM, Versace A, Northrup JB, Lee VK, Schmithorst VJ, Samolyk A, Taylor M, English GE, Stiffler RS, Aslam HA, Bonar L, Panigrahy A, Hipwell AE, Phillips ML. Limbic white matter structural integrity at 3 months prospectively predicts negative emotionality in 9-month-old infants: a preliminary study. J Affect Disord 2020; 273:538-541. [PMID: 32560951 PMCID: PMC7367553 DOI: 10.1016/j.jad.2020.04.029] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/29/2020] [Revised: 04/05/2020] [Accepted: 04/21/2020] [Indexed: 01/03/2023]
Abstract
BACKGROUND Little is known about how early alterations in white matter relate to clinically relevant behaviors such as emotional dysregulation. Thus, our goal was to examine how the white matter structural integrity of key limbic (i.e., uncinate fasciculus and cingulum) and commissural (i.e., forceps minor) bundles in 3-month-old infants prospectively predicts emotional regulation behaviors at 9 months. METHODS Three-month-old infants underwent multishell diffusion-weighted imaging. Following image processing, tractography was performed for each tract within each infant's native space (n=20). Measures of white matter integrity, including microstructure and morphology, were extracted from each tract. At 9 months, negative emotionality (NE) and positive emotionality (PE) were elicited using Laboratory Assessment of Temperament tasks. Elastic net regressions were performed for variable selection, which included white matter integrity variables from each of the 3 tracts, along with several covariates, including age, sex, use of public assistance, and the mother's depressive symptoms. Outcome variables were NE and PE composite scores evaluated in two separate models. RESULTS Notably, following hierarchical regression using elastic net-selected variables, uncinate structural integrity was the most robust predictor of NE (ß=-0.631, p=0.005). LIMITATIONS The sample size of our study is a limitation, however, as a preliminary study, our goal was to describe our findings to inform future, larger studies. CONCLUSIONS Greater uncinate structural integrity predicted lower NE, suggesting that greater uncinate structural integrity at 3 months allows greater emotional regulation capacity at 9 months. To our knowledge, this is the first study to demonstrate prospective brain-to-emotional behavior relationships in infants.
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Affiliation(s)
- Layla Banihashemi
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA.
| | - Michele A. Bertocci
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Hussain M. Alkhars
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Amelia Versace
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Jessie B. Northrup
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Vincent K. Lee
- UPMC Children’s Hospital of Pittsburgh, Department of Pediatric Radiology, Pittsburgh, PA
| | - Vincent J. Schmithorst
- UPMC Children’s Hospital of Pittsburgh, Department of Pediatric Radiology, Pittsburgh, PA
| | - Alyssa Samolyk
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Megan Taylor
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Gabrielle E. English
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Richelle S. Stiffler
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Haris A. Aslam
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Lisa Bonar
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Ashok Panigrahy
- UPMC Children’s Hospital of Pittsburgh, Department of Pediatric Radiology, Pittsburgh, PA
| | - Alison E. Hipwell
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
| | - Mary L. Phillips
- University of Pittsburgh School of Medicine, Department of Psychiatry, Pittsburgh, PA
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42
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Dimitrova R, Pietsch M, Christiaens D, Ciarrusta J, Wolfers T, Batalle D, Hughes E, Hutter J, Cordero-Grande L, Price AN, Chew A, Falconer S, Vecchiato K, Steinweg JK, Carney O, Rutherford MA, Tournier JD, Counsell SJ, Marquand AF, Rueckert D, Hajnal JV, McAlonan G, Edwards AD, O’Muircheartaigh J. Heterogeneity in Brain Microstructural Development Following Preterm Birth. Cereb Cortex 2020; 30:4800-4810. [PMID: 32306044 PMCID: PMC7391275 DOI: 10.1093/cercor/bhaa069] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 01/24/2020] [Accepted: 02/23/2020] [Indexed: 01/08/2023] Open
Abstract
Preterm-born children are at increased risk of lifelong neurodevelopmental difficulties. Group-wise analyses of magnetic resonance imaging show many differences between preterm- and term-born infants but do not reliably predict neurocognitive prognosis for individual infants. This might be due to the unrecognized heterogeneity of cerebral injury within the preterm group. This study aimed to determine whether atypical brain microstructural development following preterm birth is significantly variable between infants. Using Gaussian process regression, a technique that allows a single-individual inference, we characterized typical variation of brain microstructure using maps of fractional anisotropy and mean diffusivity in a sample of 270 term-born neonates. Then, we compared 82 preterm infants to these normative values to identify brain regions with atypical microstructure and relate observed deviations to degree of prematurity and neurocognition at 18 months. Preterm infants showed strikingly heterogeneous deviations from typical development, with little spatial overlap between infants. Greater and more extensive deviations, captured by a whole brain atypicality index, were associated with more extreme prematurity and predicted poorer cognitive and language abilities at 18 months. Brain microstructural development after preterm birth is highly variable between individual infants. This poorly understood heterogeneity likely relates to both the etiology and prognosis of brain injury.
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Affiliation(s)
- Ralica Dimitrova
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Maximilian Pietsch
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Daan Christiaens
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
- Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, 3001, Belgium
| | - Judit Ciarrusta
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Thomas Wolfers
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radbound University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radbound University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - Dafnis Batalle
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Emer Hughes
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Jana Hutter
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
- Biomedical Image Technologies, ETSI Telecomunicacion, Universidad Politecnica de Madrid and CIBER-BBN, Madrid, 28040, Spain
| | - Anthony N Price
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Andrew Chew
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Shona Falconer
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Katy Vecchiato
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
| | - Johannes K Steinweg
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Olivia Carney
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Mary A Rutherford
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - J-Donald Tournier
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Serena J Counsell
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Andre F Marquand
- Donders Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radbound University, Nijmegen, 6525EN, the Netherlands
- Department of Cognitive Neuroscience, Radbound University Medical Centre, Nijmegen, 6525EN, the Netherlands
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, SW7 2AZ, UK
| | - Joseph V Hajnal
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
| | - Grainne McAlonan
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, SE1 1UL, UK
- South London and Maudsley NHS Foundation Trust, London, SE5 8AZ, UK
| | - A David Edwards
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, SE1 1UL, UK
| | - Jonathan O’Muircheartaigh
- Centre for the Developing Brain, Department of Perinatal Imaging and Health, School of Biomedical Engineering and Imaging Sciences, King’s College London, London, SE1 7EH, UK
- Department for Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, SE5 8AF, UK
- MRC Centre for Neurodevelopmental Disorders, King’s College London, London, SE1 1UL, UK
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Kelly CJ, Christiaens D, Batalle D, Makropoulos A, Cordero-Grande L, Steinweg JK, O'Muircheartaigh J, Khan H, Lee G, Victor S, Alexander DC, Zhang H, Simpson J, Hajnal JV, Edwards AD, Rutherford MA, Counsell SJ. Abnormal Microstructural Development of the Cerebral Cortex in Neonates With Congenital Heart Disease Is Associated With Impaired Cerebral Oxygen Delivery. J Am Heart Assoc 2020; 8:e009893. [PMID: 30821171 PMCID: PMC6474935 DOI: 10.1161/jaha.118.009893] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Background Abnormal macrostructural development of the cerebral cortex has been associated with hypoxia in infants with congenital heart disease ( CHD ). Animal studies have suggested that hypoxia results in cortical dysmaturation at the cellular level. New magnetic resonance imaging techniques offer the potential to investigate the relationship between cerebral oxygen delivery and cortical microstructural development in newborn infants with CHD . Methods and Results We measured cortical macrostructural and microstructural properties in 48 newborn infants with serious or critical CHD and 48 age-matched healthy controls. Cortical volume and gyrification index were calculated from high-resolution structural magnetic resonance imaging. Neurite density and orientation dispersion indices were modeled using high-angular-resolution diffusion magnetic resonance imaging. Cerebral oxygen delivery was estimated in infants with CHD using phase contrast magnetic resonance imaging and preductal pulse oximetry. We used gray matter-based spatial statistics to examine voxel-wise group differences in cortical microstructure. Microstructural development of the cortex was abnormal in 48 infants with CHD , with regions of increased fractional anisotropy and reduced orientation dispersion index compared with 48 healthy controls, correcting for gestational age at birth and scan (family-wise error corrected for multiple comparisons at P<0.05). Regions of reduced cortical orientation dispersion index in infants with CHD were related to impaired cerebral oxygen delivery ( R2=0.637; n=39). Cortical orientation dispersion index was associated with the gyrification index ( R2=0.589; P<0.001; n=48). Conclusions This study suggests that the primary component of cerebral cortex dysmaturation in CHD is impaired dendritic arborization, which may underlie abnormal macrostructural findings reported in this population, and that the degree of impairment is related to reduced cerebral oxygen delivery.
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Affiliation(s)
- Christopher J Kelly
- 1 Centre for the Developing Brain School of Biomedical Engineering and Imaging Sciences King's College London St Thomas' Hospital London United Kingdom
| | - Daan Christiaens
- 1 Centre for the Developing Brain School of Biomedical Engineering and Imaging Sciences King's College London St Thomas' Hospital London United Kingdom
| | - Dafnis Batalle
- 1 Centre for the Developing Brain School of Biomedical Engineering and Imaging Sciences King's College London St Thomas' Hospital London United Kingdom
| | - Antonios Makropoulos
- 2 Biomedical Image Analysis Group Department of Computing Imperial College London London United Kingdom
| | - Lucilio Cordero-Grande
- 1 Centre for the Developing Brain School of Biomedical Engineering and Imaging Sciences King's College London St Thomas' Hospital London United Kingdom
| | - Johannes K Steinweg
- 1 Centre for the Developing Brain School of Biomedical Engineering and Imaging Sciences King's College London St Thomas' Hospital London United Kingdom
| | - Jonathan O'Muircheartaigh
- 1 Centre for the Developing Brain School of Biomedical Engineering and Imaging Sciences King's College London St Thomas' Hospital London United Kingdom.,3 Department of Forensic and Neurodevelopmental Sciences King's College London Institute of Psychiatry, Psychology and Neuroscience London United Kingdom.,4 Department of Neuroimaging King's College London Institute of Psychiatry, Psychology and Neuroscience London United Kingdom.,5 MRC Centre for Neurodevelopmental Disorders King's College London London United Kingdom
| | - Hammad Khan
- 6 Neonatal Intensive Care Unit St Thomas' Hospital London United Kingdom
| | - Geraint Lee
- 6 Neonatal Intensive Care Unit St Thomas' Hospital London United Kingdom
| | - Suresh Victor
- 1 Centre for the Developing Brain School of Biomedical Engineering and Imaging Sciences King's College London St Thomas' Hospital London United Kingdom
| | - Daniel C Alexander
- 7 Department of Computer Science and Centre for Medical Image Computing University College London London United Kingdom
| | - Hui Zhang
- 7 Department of Computer Science and Centre for Medical Image Computing University College London London United Kingdom
| | - John Simpson
- 8 Paediatric Cardiology Department Evelina London Children's Hospital St Thomas' Hospital London United Kingdom
| | - Joseph V Hajnal
- 1 Centre for the Developing Brain School of Biomedical Engineering and Imaging Sciences King's College London St Thomas' Hospital London United Kingdom
| | - A David Edwards
- 1 Centre for the Developing Brain School of Biomedical Engineering and Imaging Sciences King's College London St Thomas' Hospital London United Kingdom.,5 MRC Centre for Neurodevelopmental Disorders King's College London London United Kingdom
| | - Mary A Rutherford
- 1 Centre for the Developing Brain School of Biomedical Engineering and Imaging Sciences King's College London St Thomas' Hospital London United Kingdom
| | - Serena J Counsell
- 1 Centre for the Developing Brain School of Biomedical Engineering and Imaging Sciences King's College London St Thomas' Hospital London United Kingdom
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Fenchel D, Dimitrova R, Seidlitz J, Robinson EC, Batalle D, Hutter J, Christiaens D, Pietsch M, Brandon J, Hughes EJ, Allsop J, O'Keeffe C, Price AN, Cordero-Grande L, Schuh A, Makropoulos A, Passerat-Palmbach J, Bozek J, Rueckert D, Hajnal JV, Raznahan A, McAlonan G, Edwards AD, O'Muircheartaigh J. Development of Microstructural and Morphological Cortical Profiles in the Neonatal Brain. Cereb Cortex 2020; 30:5767-5779. [PMID: 32537627 PMCID: PMC7673474 DOI: 10.1093/cercor/bhaa150] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 04/17/2020] [Accepted: 05/10/2020] [Indexed: 01/19/2023] Open
Abstract
Interruptions to neurodevelopment during the perinatal period may have long-lasting consequences. However, to be able to investigate deviations in the foundation of proper connectivity and functional circuits, we need a measure of how this architecture evolves in the typically developing brain. To this end, in a cohort of 241 term-born infants, we used magnetic resonance imaging to estimate cortical profiles based on morphometry and microstructure over the perinatal period (37–44 weeks postmenstrual age, PMA). Using the covariance of these profiles as a measure of inter-areal network similarity (morphometric similarity networks; MSN), we clustered these networks into distinct modules. The resulting modules were consistent and symmetric, and corresponded to known functional distinctions, including sensory–motor, limbic, and association regions, and were spatially mapped onto known cytoarchitectonic tissue classes. Posterior regions became more morphometrically similar with increasing age, while peri-cingulate and medial temporal regions became more dissimilar. Network strength was associated with age: Within-network similarity increased over age suggesting emerging network distinction. These changes in cortical network architecture over an 8-week period are consistent with, and likely underpin, the highly dynamic processes occurring during this critical period. The resulting cortical profiles might provide normative reference to investigate atypical early brain development.
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Affiliation(s)
- Daphna Fenchel
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, SE1 1UL, UK.,Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK
| | - Ralica Dimitrova
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA.,Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK
| | - Emma C Robinson
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, SE1 7EU, UK
| | - Dafnis Batalle
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Jana Hutter
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Daan Christiaens
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Maximilian Pietsch
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Jakki Brandon
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Emer J Hughes
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Joanna Allsop
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Camilla O'Keeffe
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Anthony N Price
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Lucilio Cordero-Grande
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, SW7 2AZ, UK
| | - Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, SW7 2AZ, UK
| | | | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, 10000, Croatia
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, SW7 2AZ, UK
| | - Joseph V Hajnal
- Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Armin Raznahan
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Grainne McAlonan
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, SE1 1UL, UK.,Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,South London and Maudsley NHS Foundation Trust, London, SE5 8AZ, UK
| | - A David Edwards
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, SE1 1UL, UK.,Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
| | - Jonathan O'Muircheartaigh
- MRC Centre for Neurodevelopmental Disorders, King's College London, London, SE1 1UL, UK.,Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Forensic and Neurodevelopmental Sciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.,Department of Perinatal Imaging & Health, School of Biomedical Engineering & Imaging Sciences, Centre for the Developing Brain, King's College London, London, SE1 7EH, UK
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45
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Gale-Grant O, Christiaens D, Cordero-Grande L, Chew A, Falconer S, Makropoulos A, Harper N, Price AN, Hutter J, Hughes E, Victor S, Counsell SJ, Rueckert D, Hajnal JV, Edwards AD, O'Muircheartaigh J, Batalle D. Parental age effects on neonatal white matter development. NEUROIMAGE-CLINICAL 2020; 27:102283. [PMID: 32526683 PMCID: PMC7284122 DOI: 10.1016/j.nicl.2020.102283] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 04/28/2020] [Accepted: 05/10/2020] [Indexed: 12/29/2022]
Abstract
Advanced paternal age is associated with a range of later negative outcomes. It is not known if these negative outcomes are due to genetics or environment. We use neonatal diffusion MRI to demonstrate paternal age effect on white matter. The babies of older fathers had reduced fractional anisotropy in multiple areas. These changes correlated with cognitive outcome at 18 months.
Objective Advanced paternal age is associated with poor offspring developmental outcome. Though an increase in paternal age-related germline mutations may affect offspring white matter development, outcome differences could also be due to psychosocial factors. Here we investigate possible cerebral changes prior to strong environmental influences using brain MRI in a cohort of healthy term-born neonates. Methods We used structural and diffusion MRI images acquired soon after birth from a cohort (n = 275) of healthy term-born neonates. Images were analysed using a customised tract based spatial statistics (TBSS) processing pipeline. Neurodevelopmental assessment using the Bayley-III scales was offered to all participants at age 18 months. For statistical analysis neonates were compared in two groups, representing the upper quartile (paternal age ≥38 years) and lower three quartiles. The same method was used to assess associations with maternal age. Results In infants with older fathers (≥38 years), fractional anisotropy, a marker of white matter organisation, was significantly reduced in three early maturing anatomical locations (the corticospinal tract, the corpus callosum, and the optic radiation). Fractional anisotropy in these locations correlated positively with Bayley-III cognitive composite score at 18 months in the advanced paternal age group. A small but significant reduction in total brain volume was also observed in in the infants of older fathers. No significant associations were found between advanced maternal age and neonatal imaging. Conclusions The epidemiological association between advanced paternal age and offspring outcome is extremely robust. We have for the first time demonstrated a neuroimaging phenotype of advanced paternal age before sustained parental interaction that correlates with later outcome.
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Affiliation(s)
- Oliver Gale-Grant
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, United Kingdom; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom.
| | - Daan Christiaens
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom
| | - Andrew Chew
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom
| | - Shona Falconer
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom
| | | | - Nicholas Harper
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom
| | - Suresh Victor
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom
| | - Serena J Counsell
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom
| | - Daniel Rueckert
- Department of Computing, Imperial College London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, United Kingdom
| | - Jonathan O'Muircheartaigh
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom; MRC Centre for Neurodevelopmental Disorders, King's College London, United Kingdom; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
| | - Dafnis Batalle
- Centre for the Developing Brain, School of Imaging Sciences & Biomedical Engineering, King's College London, United Kingdom; Department of Forensic and Neurodevelopmental Science, Institute of Psychiatry, Psychology & Neuroscience, King's College London, United Kingdom
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46
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Deprez M, Price A, Christiaens D, Lockwood Estrin G, Cordero-Grande L, Hutter J, Daducci A, Tournier JD, Rutherford M, Counsell SJ, Cuadra MB, Hajnal JV. Higher Order Spherical Harmonics Reconstruction of Fetal Diffusion MRI With Intensity Correction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1104-1113. [PMID: 31562073 DOI: 10.1109/tmi.2019.2943565] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
We present a novel method for higher order reconstruction of fetal diffusion MRI signal that enables detection of fiber crossings. We combine data-driven motion and intensity correction with super-resolution reconstruction and spherical harmonic parametrisation to reconstruct data scattered in both spatial and angular domains into consistent fetal dMRI signal suitable for further diffusion analysis. We show that intensity correction is essential for good performance of the method and identify anatomically plausible fiber crossings. The proposed methodology has potential to facilitate detailed investigation of developing brain connectivity and microstructure in-utero.
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47
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Grigorescu I, Uus A, Christiaens D, Cordero-Grande L, Hutter J, Edwards AD, Hajnal JV, Modat M, Deprez M. Diffusion Tensor Driven Image Registration: A Deep Learning Approach. BIOMEDICAL IMAGE REGISTRATION 2020. [PMCID: PMC7279925 DOI: 10.1007/978-3-030-50120-4_13] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
Tracking microsctructural changes in the developing brain relies on accurate inter-subject image registration. However, most methods rely on either structural or diffusion data to learn the spatial correspondences between two or more images, without taking into account the complementary information provided by using both. Here we propose a deep learning registration framework which combines the structural information provided by \documentclass[12pt]{minimal}
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\begin{document}$$T_2$$\end{document}w) images with the rich microstructural information offered by diffusion tensor imaging (DTI) scans. This allows our trained network to register pairs of images in a single pass. We perform a leave-one-out cross-validation study where we compare the performance of our multi-modality registration model with a baseline model trained on structural data only, in terms of Dice scores and differences in fractional anisotropy (FA) maps. Our results show that in terms of average Dice scores our model performs better in subcortical regions when compared to using structural data only. Moreover, average sum-of-squared differences between warped and fixed FA maps show that our proposed model performs better at aligning the diffusion data.
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48
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Christiaens D, Veraart J, Cordero-Grande L, Price AN, Hutter J, Hajnal JV, Tournier JD. On the need for bundle-specific microstructure kernels in diffusion MRI. Neuroimage 2019; 208:116460. [PMID: 31843710 PMCID: PMC7014821 DOI: 10.1016/j.neuroimage.2019.116460] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Revised: 11/18/2019] [Accepted: 12/10/2019] [Indexed: 11/23/2022] Open
Abstract
Probing microstructure with diffusion magnetic resonance imaging (dMRI) on a scale orders of magnitude below the imaging resolution relies on biophysical modelling of the signal response in the tissue. The vast majority of these biophysical models of diffusion in white matter assume that the measured dMRI signal is the sum of the signals emanating from each of the constituent compartments, each of which exhibits a distinct behaviour in the b-value and/or orientation domain. Many of these models further assume that the dMRI behaviour of the oriented compartments (e.g. the intra-axonal space) is identical between distinct fibre populations, at least at the level of a single voxel. This implicitly assumes that any potential biological differences between fibre populations are negligible, at least as far as is measurable using dMRI. Here, we validate this assumption by means of a voxel-wise, model-free signal decomposition that, under the assumption above and in the absence of noise, is shown to be rank-1. We evaluate the effect size of signal components beyond this rank-1 representation and use permutation testing to assess their significance. We conclude that in the healthy adult brain, the dMRI signal is adequately represented by a rank-1 model, implying that biologically more realistic, but mathematically more complex fascicle-specific microstructure models do not capture statistically significant or anatomically meaningful structure, even in extended high-b diffusion MRI scans.
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Affiliation(s)
- Daan Christiaens
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK; Department of Electrical Engineering, ESAT/PSI, KU Leuven, Leuven, Belgium.
| | - Jelle Veraart
- Centre for Biomedical Imaging, NYU School of Medicine, New York, NY, USA; iMinds - Vision Lab, University of Antwerp, Antwerp, Belgium
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Anthony N Price
- Centre for the Developing Brain, 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
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
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49
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Cordero-Grande L, Christiaens D, Hutter J, Price AN, Hajnal JV. Complex diffusion-weighted image estimation via matrix recovery under general noise models. Neuroimage 2019; 200:391-404. [PMID: 31226495 PMCID: PMC6711461 DOI: 10.1016/j.neuroimage.2019.06.039] [Citation(s) in RCA: 162] [Impact Index Per Article: 32.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2018] [Revised: 03/31/2019] [Accepted: 06/17/2019] [Indexed: 11/28/2022] Open
Abstract
We propose a patch-based singular value shrinkage method for diffusion magnetic resonance image estimation targeted at low signal to noise ratio and accelerated acquisitions. It operates on the complex data resulting from a sensitivity encoding reconstruction, where asymptotically optimal signal recovery guarantees can be attained by modeling the noise propagation in the reconstruction and subsequently simulating or calculating the limit singular value spectrum. Simple strategies are presented to deal with phase inconsistencies and optimize patch construction. The pertinence of our contributions is quantitatively validated on synthetic data, an in vivo adult example, and challenging neonatal and fetal cohorts. Our methodology is compared with related approaches, which generally operate on magnitude-only data and use data-based noise level estimation and singular value truncation. Visual examples are provided to illustrate effectiveness in generating denoised and debiased diffusion estimates with well preserved spatial and diffusion detail.
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Affiliation(s)
- Lucilio Cordero-Grande
- Centre for the Developing Brain and Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, UK.
| | - Daan Christiaens
- Centre for the Developing Brain and Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, UK
| | - Jana Hutter
- Centre for the Developing Brain and Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, UK
| | - Anthony N Price
- Centre for the Developing Brain and Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, UK
| | - Jo V Hajnal
- Centre for the Developing Brain and Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St Thomas' Hospital, London, SE1 7EH, UK
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50
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Szczepankiewicz F, Westin CF, Nilsson M. Maxwell-compensated design of asymmetric gradient waveforms for tensor-valued diffusion encoding. Magn Reson Med 2019; 82:1424-1437. [PMID: 31148245 PMCID: PMC6626569 DOI: 10.1002/mrm.27828] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 05/03/2019] [Accepted: 05/06/2019] [Indexed: 01/13/2023]
Abstract
PURPOSE Diffusion encoding with asymmetric gradient waveforms is appealing because the asymmetry provides superior efficiency. However, concomitant gradients may cause a residual gradient moment at the end of the waveform, which can cause significant signal error and image artifacts. The purpose of this study was to develop an asymmetric waveform designs for tensor-valued diffusion encoding that is not sensitive to concomitant gradients. METHODS The "Maxwell index" was proposed as a scalar invariant to capture the effect of concomitant gradients. Optimization of "Maxwell-compensated" waveforms was performed in which this index was constrained. Resulting waveforms were compared to waveforms from literature, in terms of the measured and predicted impact of concomitant gradients, by numerical analysis as well as experiments in a phantom and in a healthy human brain. RESULTS Maxwell-compensated waveforms with Maxwell indices below 100 (mT/m)2 ms showed negligible signal bias in both numerical analysis and experiments. By contrast, several waveforms from literature showed gross signal bias under the same conditions, leading to a signal bias that was large enough to markedly affect parameter maps. Experimental results were accurately predicted by theory. CONCLUSION Constraining the Maxwell index in the optimization of asymmetric gradient waveforms yields efficient diffusion encoding that negates the effects of concomitant fields while enabling arbitrary shapes of the b-tensor. This waveform design is especially useful in combination with strong gradients, long encoding times, thick slices, simultaneous multi-slice acquisition, and large FOVs.
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Affiliation(s)
- Filip Szczepankiewicz
- Radiology, Brigham and Women’s Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
| | - Carl-Fredrik Westin
- Radiology, Brigham and Women’s Hospital, Boston, MA, US
- Harvard Medical School, Boston, MA, US
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