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Segobin S, Haast RAM, Kumar VJ, Lella A, Alkemade A, Bach Cuadra M, Barbeau EJ, Felician O, Pergola G, Pitel AL, Saranathan M, Tourdias T, Hornberger M. A roadmap towards standardized neuroimaging approaches for human thalamic nuclei. Nat Rev Neurosci 2024; 25:792-808. [PMID: 39420114 DOI: 10.1038/s41583-024-00867-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/11/2024] [Indexed: 10/19/2024]
Abstract
The thalamus has a key role in mediating cortical-subcortical interactions but is often neglected in neuroimaging studies, which mostly focus on changes in cortical structure and activity. One of the main reasons for the thalamus being overlooked is that the delineation of individual thalamic nuclei via neuroimaging remains controversial. Indeed, neuroimaging atlases vary substantially regarding which thalamic nuclei are included and how their delineations were established. Here, we review current and emerging methods for thalamic nuclei segmentation in neuroimaging data and consider the limitations of existing techniques in terms of their research and clinical applicability. We address these challenges by proposing a roadmap to improve thalamic nuclei segmentation in human neuroimaging and, in turn, harmonize research approaches and advance clinical applications. We believe that a collective effort is required to achieve this. We hope that this will ultimately lead to the thalamic nuclei being regarded as key brain regions in their own right and not (as often currently assumed) as simply a gateway between cortical and subcortical regions.
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Affiliation(s)
- Shailendra Segobin
- Normandie University, UNICAEN, PSL Université Paris, EPHE, INSERM, U1077, CHU de Caen, Cyceron, Neuropsychologie et Imagerie de la Mémoire Humaine, Caen, France.
| | - Roy A M Haast
- Aix-Marseille University, CRMBM CNRS UMR 7339, Marseille, France
- APHM, La Timone Hospital, CEMEREM, Marseille, France
| | | | - Annalisa Lella
- Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy
| | - Anneke Alkemade
- Integrative Model-based Cognitive Neuroscience Unit, Department of Psychology, University of Amsterdam, Amsterdam, The Netherlands
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Lausanne, Switzerland
- Radiology Department, Lausanne University and University Hospital, Lausanne, Switzerland
| | - Emmanuel J Barbeau
- Centre de recherche Cerveau et Cognition (Cerco), UMR5549, CNRS - Université de Toulouse, Toulouse, France
| | - Olivier Felician
- Aix Marseille Université, INSERM INS UMR 1106, APHM, Marseille, France
| | - Giulio Pergola
- Department of Translational Biomedicine and Neuroscience (DiBraiN), University of Bari Aldo Moro, Bari, Italy
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Anne-Lise Pitel
- Normandie University, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", NeuroPresage Team, Cyceron, Caen, France
| | | | - Thomas Tourdias
- Neuroimagerie diagnostique et thérapeutique, CHU de Bordeaux, Bordeaux, France
- Neurocentre Magendie, University of Bordeaux, INSERM U1215, Bordeaux, France
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2
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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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3
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Increased Hippocampal-Inferior Temporal Gyrus White Matter Connectivity following Donepezil Treatment in Patients with Early Alzheimer's Disease: A Diffusion Tensor Probabilistic Tractography Study. J Clin Med 2023; 12:jcm12030967. [PMID: 36769615 PMCID: PMC9917574 DOI: 10.3390/jcm12030967] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/17/2023] [Accepted: 01/21/2023] [Indexed: 01/31/2023] Open
Abstract
The incidence of Alzheimer's disease (AD) has been increasing each year, and a defective hippocampus has been primarily associated with an early stage of AD. However, the effect of donepezil treatment on hippocampus-related networks is unknown. Thus, in the current study, we evaluated the hippocampal white matter (WM) connectivity in patients with early-stage AD before and after donepezil treatment using probabilistic tractography, and we further determined the WM integrity and changes in brain volume. Ten patients with early-stage AD (mean age = 72.4 ± 7.9 years; seven females and three males) and nine healthy controls (HC; mean age = 70.7 ± 3.5 years; six females and three males) underwent a magnetic resonance (MR) examination. After performing the first MR examination, the patients received donepezil treatment for 6 months. The brain volumes and diffusion tensor imaging scalars of 11 regions of interest (the superior/middle/inferior frontal gyrus, the superior/middle/inferior temporal gyrus, the amygdala, the caudate nucleus, the hippocampus, the putamen, and the thalamus) were measured using MR imaging and DTI, respectively. Seed-based structural connectivity analyses were focused on the hippocampus. The patients with early AD had a lower hippocampal volume and WM connectivity with the superior frontal gyrus and higher mean diffusivity (MD) and radial diffusivity (RD) in the amygdala than HC (p < 0.05, Bonferroni-corrected). However, brain areas with a higher (or lower) brain volume and WM connectivity were not observed in the HC compared with the patients with early AD. After six months of donepezil treatment, the patients with early AD showed increased hippocampal-inferior temporal gyrus (ITG) WM connectivity (p < 0.05, Bonferroni-corrected).
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Spatiotemporal Developmental Gradient of Thalamic Morphology, Microstructure, and Connectivity fromthe Third Trimester to Early Infancy. J Neurosci 2023; 43:559-570. [PMID: 36639904 PMCID: PMC9888512 DOI: 10.1523/jneurosci.0874-22.2022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Revised: 10/19/2022] [Accepted: 11/26/2022] [Indexed: 12/12/2022] Open
Abstract
Thalamus is a critical component of the limbic system that is extensively involved in both basic and high-order brain functions. However, how the thalamic structure and function develops at macroscopic and microscopic scales during the perinatal period development is not yet well characterized. Here, we used multishell high-angular resolution diffusion MRI of 144 preterm-born and full-term infants in both sexes scanned at 32-44 postmenstrual weeks (PMWs) from the Developing Human Connectome Project database to investigate the thalamic development in morphology, microstructure, associated connectivity, and subnucleus division. We found evident anatomic expansion and linear increases of fiber integrity in the lateral side of thalamus compared with the medial part. The tractography results indicated that thalamic connection to the frontal cortex developed later than the other thalamocortical connections (parieto-occipital, motor, somatosensory, and temporal). Using a connectivity-based segmentation strategy, we revealed that functional partitions of thalamic subdivisions were formed at 32 PMWs or earlier, and the partition developed toward the adult pattern in a lateral-to-medial pattern. Collectively, these findings revealed faster development of the lateral thalamus than the central part as well as a posterior-to-anterior developmental gradient of thalamocortical connectivity from the third trimester to early infancy.SIGNIFICANCE STATEMENT This is the first study that characterizes the spatiotemporal developmental pattern of thalamus during the third trimester to early infancy. We found that thalamus develops in a lateral-to-medial pattern for both thalamic microstructures and subdivisions; and thalamocortical connectivity develops in a posterior-to-anterior gradient that thalamofrontal connectivity appears later than the other thalamocortical connections. These findings may enrich our understanding of the developmental principles of thalamus and provide references for the atypical brain growth in neurodevelopmental disorders.
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Casamitjana A, Iglesias JE. High-resolution atlasing and segmentation of the subcortex: Review and perspective on challenges and opportunities created by machine learning. Neuroimage 2022; 263:119616. [PMID: 36084858 PMCID: PMC11534291 DOI: 10.1016/j.neuroimage.2022.119616] [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: 03/29/2022] [Revised: 08/30/2022] [Accepted: 09/05/2022] [Indexed: 11/17/2022] Open
Abstract
This paper reviews almost three decades of work on atlasing and segmentation methods for subcortical structures in human brain MRI. In writing this survey, we have three distinct aims. First, to document the evolution of digital subcortical atlases of the human brain, from the early MRI templates published in the nineties, to the complex multi-modal atlases at the subregion level that are available today. Second, to provide a detailed record of related efforts in the automated segmentation front, from earlier atlas-based methods to modern machine learning approaches. And third, to present a perspective on the future of high-resolution atlasing and segmentation of subcortical structures in in vivo human brain MRI, including open challenges and opportunities created by recent developments in machine learning.
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Affiliation(s)
- Adrià Casamitjana
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK.
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK; Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA
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6
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Kim GW, Park SE, Park K, Jeong GW. White Matter Connectivity and Gray Matter Volume Changes Following Donepezil Treatment in Patients With Mild Cognitive Impairment: A Preliminary Study Using Probabilistic Tractography. Front Aging Neurosci 2021; 12:604940. [PMID: 33796017 PMCID: PMC8007874 DOI: 10.3389/fnagi.2020.604940] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Accepted: 12/21/2020] [Indexed: 11/13/2022] Open
Abstract
The donepezil treatment is associated with improved cognitive performance in patients with mild cognitive impairment (MCI), and its clinical effectiveness is well-known. However, the impact of the donepezil treatment on the enhanced white matter connectivity in MCI is still unclear. The purpose of this study was to evaluate the thalamo-cortical white matter (WM) connectivity and cortical thickness and gray matter (GM) volume changes in the cortical regions following donepezil treatment in patients with MCI using probabilistic tractography and voxel-based morphometry. Patients with MCI underwent magnetic resonance examinations before and after 6-month donepezil treatment. Compared with healthy controls, patients with MCI showed decreased WM connectivity of the thalamo-lateral prefrontal cortex, as well as reduced thickness in the medial/lateral orbitofrontal cortices (p < 0.05). The thalamo-lateral temporal cortex connectivity in patients with MCI was negatively correlated with Alzheimer's disease assessment scale-cognitive subscale (ADAS-cog) (r = −0.76, p = 0.01). The average score of the Korean version of the mini-mental state examination (K-MMSE) in patients with MCI was improved by 7.9% after 6-months of donepezil treatment. However, the patterns of WM connectivity and brain volume change in untreated and treated patients were not significantly different from each other, resulting from multiple comparison corrections. These findings will be valuable in understanding the neurophysiopathological mechanism on MCI as a prodromal phase of Alzheimer's disease in connection with brain functional connectivity and morphometric change.
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Affiliation(s)
- Gwang-Won Kim
- Advanced Institute of Aging Science, Chonnam National University, Gwangju, South Korea.,Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Shin-Eui Park
- Advanced Institute of Aging Science, Chonnam National University, Gwangju, South Korea
| | - Kwangsung Park
- Advanced Institute of Aging Science, Chonnam National University, Gwangju, South Korea.,Department of Urology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, South Korea
| | - Gwang-Woo Jeong
- Department of Radiology, Chonnam National University Hospital, Chonnam National University Medical School, Gwangju, South Korea
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7
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Jaimes C, Machado‐Rivas F, Afacan O, Khan S, Marami B, Ortinau CM, Rollins CK, Velasco‐Annis C, Warfield SK, Gholipour A. In vivo characterization of emerging white matter microstructure in the fetal brain in the third trimester. Hum Brain Mapp 2020. [DOI: 10.1002/hbm.25006 32374063] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Affiliation(s)
- Camilo Jaimes
- Department of RadiologyBoston Children's Hospital Boston Massachusetts
- Fetal‐Neonatal Neuroimaging and Developmental Science CenterBoston Children's Hospital Boston Massachusetts
- Harvard Medical School Boston Massachusetts
| | - Fedel Machado‐Rivas
- Department of RadiologyBoston Children's Hospital Boston Massachusetts
- Harvard Medical School Boston Massachusetts
| | - Onur Afacan
- Department of RadiologyBoston Children's Hospital Boston Massachusetts
- Harvard Medical School Boston Massachusetts
| | - Shadab Khan
- Department of RadiologyBoston Children's Hospital Boston Massachusetts
- Harvard Medical School Boston Massachusetts
| | - Bahram Marami
- Department of RadiologyBoston Children's Hospital Boston Massachusetts
- Harvard Medical School Boston Massachusetts
| | - Cynthia M. Ortinau
- Department of PediatricsWashington University in St. Louis School of Medicine St. Louis Missouri
| | - Caitlin K. Rollins
- Harvard Medical School Boston Massachusetts
- Department of NeurologyBoston Children's Hospital Boston Massachusetts
| | | | - Simon K. Warfield
- Department of RadiologyBoston Children's Hospital Boston Massachusetts
- Harvard Medical School Boston Massachusetts
| | - Ali Gholipour
- Department of RadiologyBoston Children's Hospital Boston Massachusetts
- Harvard Medical School Boston Massachusetts
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8
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Jaimes C, Rofeberg V, Stopp C, Ortinau CM, Gholipour A, Friedman KG, Tworetzky W, Estroff J, Newburger JW, Wypij D, Warfield SK, Yang E, Rollins CK. Association of Isolated Congenital Heart Disease with Fetal Brain Maturation. AJNR Am J Neuroradiol 2020; 41:1525-1531. [PMID: 32646947 DOI: 10.3174/ajnr.a6635] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 04/30/2020] [Indexed: 12/15/2022]
Abstract
BACKGROUND AND PURPOSE Brain MRI of newborns with congenital heart disease show signs of immaturity relative to healthy controls. Our aim was to determine whether the semiquantitative fetal total maturation score can detect abnormalities in brain maturation in fetuses with congenital heart disease in the second and third trimesters. MATERIALS AND METHODS We analyzed data from a prospective study of fetuses with and without congenital heart disease who underwent fetal MR imaging at 25-35 weeks' gestation. Two independent neuroradiologists blinded to the clinical data reviewed and scored all images using the fetal total maturation score. Interrater reliability was evaluated by the intraclass correlation coefficient using the individual reader scores, which were also used to calculate an average score for each subject. Comparisons of the average and individual reader scores between affected and control fetuses and relationships with clinical variables were evaluated using multivariable linear regression. RESULTS Data from 69 subjects (48 cardiac, 21 controls) were included. High concordance was observed between readers with an intraclass correlation coefficient of 0.98 (95% CI, 0.97-0.99). The affected group had significantly lower fetal total maturation scores than the control group (β-estimate, -0.9 [95% CI, -1.5 to -0.4], P = .002), adjusting for gestational age and sex. Averaged fetal total maturation, germinal matrix, myelination, and superior temporal sulcus scores were significantly delayed in fetuses with congenital heart disease versus controls (P < .05 for each). The fetal total maturation score was not significantly associated with any cardiac, anatomic, or physiologic variables. CONCLUSIONS The fetal total maturation score is sensitive to differences in brain maturation between fetuses with isolated congenital heart disease and healthy controls.
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Affiliation(s)
- C Jaimes
- From the Departments of Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Cardiology (V.R., C.S., K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Fetal-Neonatal Neuroimaging and Developmental Science Center (C.J.), Boston Children's Hospital, Boston, Massachusetts.,Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Pediatrics (K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Harvard Medical School, Boston, Massachusetts
| | - V Rofeberg
- From the Departments of Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Cardiology (V.R., C.S., K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Fetal-Neonatal Neuroimaging and Developmental Science Center (C.J.), Boston Children's Hospital, Boston, Massachusetts
| | - C Stopp
- From the Departments of Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Cardiology (V.R., C.S., K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Fetal-Neonatal Neuroimaging and Developmental Science Center (C.J.), Boston Children's Hospital, Boston, Massachusetts
| | - C M Ortinau
- Pediatrics (C.M.O.), Washington University in St. Louis, St. Louis, Missouri
| | - A Gholipour
- From the Departments of Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Cardiology (V.R., C.S., K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Fetal-Neonatal Neuroimaging and Developmental Science Center (C.J.), Boston Children's Hospital, Boston, Massachusetts.,Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Pediatrics (K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Harvard Medical School, Boston, Massachusetts
| | - K G Friedman
- From the Departments of Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Cardiology (V.R., C.S., K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Fetal-Neonatal Neuroimaging and Developmental Science Center (C.J.), Boston Children's Hospital, Boston, Massachusetts.,Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Pediatrics (K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Harvard Medical School, Boston, Massachusetts
| | - W Tworetzky
- From the Departments of Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Cardiology (V.R., C.S., K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Fetal-Neonatal Neuroimaging and Developmental Science Center (C.J.), Boston Children's Hospital, Boston, Massachusetts.,Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Pediatrics (K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Harvard Medical School, Boston, Massachusetts
| | - J Estroff
- From the Departments of Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Cardiology (V.R., C.S., K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Fetal-Neonatal Neuroimaging and Developmental Science Center (C.J.), Boston Children's Hospital, Boston, Massachusetts.,Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Pediatrics (K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Harvard Medical School, Boston, Massachusetts
| | - J W Newburger
- From the Departments of Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Cardiology (V.R., C.S., K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Fetal-Neonatal Neuroimaging and Developmental Science Center (C.J.), Boston Children's Hospital, Boston, Massachusetts.,Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Pediatrics (K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Harvard Medical School, Boston, Massachusetts
| | - D Wypij
- From the Departments of Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Cardiology (V.R., C.S., K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Fetal-Neonatal Neuroimaging and Developmental Science Center (C.J.), Boston Children's Hospital, Boston, Massachusetts.,Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Pediatrics (K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Harvard Medical School, Boston, Massachusetts.,Biostatistics (D.W.), Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - S K Warfield
- From the Departments of Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Cardiology (V.R., C.S., K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Fetal-Neonatal Neuroimaging and Developmental Science Center (C.J.), Boston Children's Hospital, Boston, Massachusetts.,Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Pediatrics (K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Harvard Medical School, Boston, Massachusetts
| | - E Yang
- From the Departments of Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Cardiology (V.R., C.S., K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Fetal-Neonatal Neuroimaging and Developmental Science Center (C.J.), Boston Children's Hospital, Boston, Massachusetts.,Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Pediatrics (K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Harvard Medical School, Boston, Massachusetts
| | - C K Rollins
- From the Departments of Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Cardiology (V.R., C.S., K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Fetal-Neonatal Neuroimaging and Developmental Science Center (C.J.), Boston Children's Hospital, Boston, Massachusetts .,Radiology (C.J., A.G., J.E., S.K.W., E.Y.), Pediatrics (K.G.F., W.T., J.W.N., D.W.), Neurology (C.K.R.), Harvard Medical School, Boston, Massachusetts
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9
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Jaimes C, Machado-Rivas F, Afacan O, Khan S, Marami B, Ortinau CM, Rollins CK, Velasco-Annis C, Warfield SK, Gholipour A. In vivo characterization of emerging white matter microstructure in the fetal brain in the third trimester. Hum Brain Mapp 2020; 41:3177-3185. [PMID: 32374063 PMCID: PMC7375105 DOI: 10.1002/hbm.25006] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2020] [Revised: 03/26/2020] [Accepted: 04/03/2020] [Indexed: 12/13/2022] Open
Abstract
The third trimester of pregnancy is a period of rapid development of fiber bundles in the fetal white matter. Using a recently developed motion‐tracked slice‐to‐volume registration (MT‐SVR) method, we aimed to quantify tract‐specific developmental changes in apparent diffusion coefficient (ADC), fractional anisotropy (FA), and volume in third trimester healthy fetuses. To this end, we reconstructed diffusion tensor images from motion corrected fetal diffusion magnetic resonance imaging data. With an approved protocol, fetal MRI exams were performed on healthy pregnant women at 3 Tesla and included multiple (2–8) diffusion scans of the fetal head (1–2 b = 0 s/mm2 images and 12 diffusion‐sensitized images at b = 500 s/mm2). Diffusion data from 32 fetuses (13 females) with median gestational age (GA) of 33 weeks 4 days were processed with MT‐SVR and deterministic tractography seeded by regions of interest corresponding to 12 major fiber tracts. Multivariable regression analysis was used to evaluate the association of GA with volume, FA, and ADC for each tract. For all tracts, the volume and FA increased, and the ADC decreased with GA. Associations reached statistical significance for: FA and ADC of the forceps major; volume and ADC for the forceps minor; FA, ADC, and volume for the cingulum; ADC, FA, and volume for the uncinate fasciculi; ADC of the inferior fronto‐occipital fasciculi, ADC of the inferior longitudinal fasciculi; and FA and ADC for the corticospinal tracts. These quantitative results demonstrate the complex pattern and rates of tract‐specific, GA‐related microstructural changes of the developing white matter in human fetal brain.
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Affiliation(s)
- Camilo Jaimes
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Fedel Machado-Rivas
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Onur Afacan
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Shadab Khan
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Bahram Marami
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Cynthia M Ortinau
- Department of Pediatrics, Washington University in St. Louis School of Medicine, St. Louis, Missouri
| | - Caitlin K Rollins
- Harvard Medical School, Boston, Massachusetts.,Department of Neurology, Boston Children's Hospital, Boston, Massachusetts
| | | | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital, Boston, Massachusetts.,Harvard Medical School, Boston, Massachusetts
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10
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Kozana A, Boursianis T, Kalaitzakis G, Raissaki M, Maris TG. Neonatal brain: Fabrication of a tissue-mimicking phantom and optimization of clinical Τ1w and T2w MRI sequences at 1.5 T. Phys Med 2018; 55:88-97. [PMID: 30471825 DOI: 10.1016/j.ejmp.2018.10.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/02/2018] [Revised: 10/06/2018] [Accepted: 10/25/2018] [Indexed: 01/12/2023] Open
Abstract
PURPOSE Tο fabricate a tissue-mimicking phantom simulating the MR relaxation times of neonatal gray and white matter at 1.5 T, for the optimization of clinical Τ1 weighted (T1w) and T2 weighted (T2w) sequences. METHODS Numerous agarose gel solutions, doped with paramagnetic Gadopentetic acid (Gd-DTPA) ions, underwent quantitative relaxometry with a Turbo-Inversion-Recovery Spin-Echo (TIRSE) sequence and a Car-Purcell-Meiboom-Gill (CPMG) sequence for T1 and T2 measurements, respectively. Twenty samples which simulated the spectrum of relaxation times of neonatal brain parenchyma were selected. Reproducibility was tested by refabrication and relaxometry of the relevant samples while stability was tested by six sets of quantitative relaxometry scans during a 12-month period. RESULTS "Neonatal gray matter equivalent"(0.6%w/v agarose-0.10 mM Gd-DTPA), accurately mimicked relaxation times of neonatal gray matter: T1 = (1134 ± 7)ms, T2 = (200 ± 7)ms. "Neonatal white matter equivalent"(0.3%w/v agarose-0.03 mM Gd-DTPA), accurately mimicked relaxation times of neonatal white matter: T1 = (1654 ± 9)ms, T2 = (376 ± 4)ms. Coefficient of variation of T1 and T2 relaxation times measurements remained less than 5% during 12 months. Sequences were modified according to maximum relative contrast (RC) between neonatal gray and white matter equivalents. Optimized T2wTSE and T1wTSE parameters were TR/TE = 9500 ms/280 ms and TR/TE = 1200 ms/10 ms, respectively for a MAGNETOM Vision/Sonata Hybrid 1.5 T system. Quantitative relaxometry at different 1.5 T MR systems resulted in inter-system T1, T2 measurement deviations of 12% and 3%, respectively. CONCLUSION A precise, stable and reproducible phantom for the neonatal brain was fabricated. Subsequent optimization of clinical T1w and T2w sequences based on maximum RC between neonatal gray and white matter equivalents was scientifically supported with robust relaxometry. The procedure was applicable in different 1.5 T systems. HIGHLIGHT TR & TE optimization of neonatal brain at 1.5 T was based on relaxometry of a stable, reproducible phantom.
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Affiliation(s)
- Androniki Kozana
- Radiology Department, University Hospital of Heraklion, GR71110, Voutes, Heraklion, Crete, Greece; Department of Medical Physics, Medical School, University of Crete, GR 71201, Voutes, Heraklion, Crete, Greece
| | - Themis Boursianis
- Department of Medical Physics, Medical School, University of Crete, GR 71201, Voutes, Heraklion, Crete, Greece
| | - George Kalaitzakis
- Department of Medical Physics, Medical School, University of Crete, GR 71201, Voutes, Heraklion, Crete, Greece
| | - Maria Raissaki
- Radiology Department, University Hospital of Heraklion, GR71110, Voutes, Heraklion, Crete, Greece
| | - Thomas G Maris
- Radiology Department, University Hospital of Heraklion, GR71110, Voutes, Heraklion, Crete, Greece; Department of Medical Physics, Medical School, University of Crete, GR 71201, Voutes, Heraklion, Crete, Greece.
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11
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Cheng C, Fan L, Xia X, Eickhoff SB, Li H, Li H, Chen J, Jiang T. Rostro-caudal organization of the human posterior superior temporal sulcus revealed by connectivity profiles. Hum Brain Mapp 2018; 39:5112-5125. [PMID: 30273447 DOI: 10.1002/hbm.24349] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Revised: 07/20/2018] [Accepted: 07/30/2018] [Indexed: 01/01/2023] Open
Abstract
The posterior superior temporal sulcus (pSTS) plays an important role in biological motion perception but is also thought to be essential for speech and facial processing. However, although there are many previous investigations of distinct functional modules within the pSTS, the functional organization of the pSTS in its full functional heterogeneity has not yet been established. Here we applied a connectivity-based parcellation strategy to delineate the human pSTS subregions based on distinct anatomical connectivity profiles and divided it into rostral and caudal subregions using diffusion tensor imaging. Subsequent multimodal connection pattern analyses revealed distinct subregional connectivity profiles. From this we inferred that the two subregions are involved in distinct functional circuits, the language processing loop and the cognition attention network. These results indicate a convergent functional architecture of the pSTS that can be revealed based on different types of connectivity and is reflected in different functions and interactions. In addition, when the subregions were performing their processing in the different functional circuits, we found asymmetry in the bilateral pSTS. Our findings may improve the understanding of the functional organization of the pSTS and provide new insights into its interactions and integration of information at the subregional level.
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Affiliation(s)
- Chen Cheng
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoluan Xia
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Simon B Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.,Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Hai Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Haifang Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China.,The Queensland Brain Institute, University of Queensland, Brisbane, Queensland, Australia
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