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Calixto C, Soldatelli MD, Jaimes C, Warfield SK, Gholipour A, Karimi D. A detailed spatio-temporal atlas of the white matter tracts for the fetal brain. bioRxiv 2024:2024.04.26.590815. [PMID: 38712296 PMCID: PMC11071632 DOI: 10.1101/2024.04.26.590815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
This study presents the construction of a comprehensive spatiotemporal atlas detailing the development of white matter tracts in the fetal brain using diffusion magnetic resonance imaging (dMRI). Our research leverages data collected from fetal MRI scans conducted between 22 and 37 weeks of gestation, capturing the dynamic changes in the brain's microstructure during this critical period. The atlas includes 60 distinct white matter tracts, including commissural, projection, and association fibers. We employed advanced fetal dMRI processing techniques and tractography to map and characterize the developmental trajectories of these tracts. Our findings reveal that the development of these tracts is characterized by complex patterns of fractional anisotropy (FA) and mean diffusivity (MD), reflecting key neurodevelopmental processes such as axonal growth, involution of the radial-glial scaffolding, and synaptic pruning. This atlas can serve as a useful resource for neuroscience research and clinical practice, improving our understanding of the fetal brain and potentially aiding in the early diagnosis of neurodevelopmental disorders. By detailing the normal progression of white matter tract development, the atlas can be used as a benchmark for identifying deviations that may indicate neurological anomalies or predispositions to disorders.
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Wallace TE, Piccini D, Kober T, Warfield SK, Afacan O. Rapid motion estimation and correction using self-encoded FID navigators in 3D radial MRI. Magn Reson Med 2024; 91:1057-1066. [PMID: 37929608 PMCID: PMC10843402 DOI: 10.1002/mrm.29899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 09/15/2023] [Accepted: 10/06/2023] [Indexed: 11/07/2023]
Abstract
PURPOSE To develop a self-navigated motion compensation strategy for 3D radial MRI that can compensate for continuous head motion by measuring rigid body motion parameters with high temporal resolution from the central k-space acquisition point (self-encoded FID navigator) in each radial spoke. METHODS A forward model was created from low-resolution calibration data to simulate the effect of relative motion between the coil sensitivity profiles and the underlying object on the self-encoded FID navigator signal. Trajectory deviations were included in the model as low spatial-order field variations. Three volunteers were imaged at 3 T using a modified 3D gradient-echo sequence acquired with a Kooshball trajectory while performing abrupt and continuous head motion. Rigid body-motion parameters were estimated from the central k-space signal of each spoke using a least-squares fitting algorithm. The accuracy of self-navigated motion parameters was assessed relative to an established external tracking system. Quantitative image quality metrics were computed for images with and without retrospective correction using external and self-navigated motion measurements. RESULTS Self-encoded FID navigators achieved mean absolute errors of 0.69 ± 0.82 mm and 0.73 ± 0.87° relative to external tracking for maximum motion amplitudes of 12 mm and 10°. Retrospective correction of the 3D radial data resulted in substantially improved image quality for both abrupt and continuous motion paradigms, comparable to external tracking results. CONCLUSIONS Accurate rigid body motion parameters can be rapidly obtained from self-encoded FID navigator signals in 3D radial MRI to continuously correct for head movements. This approach is suitable for robust neuroanatomical imaging in subjects that exhibit patterns of large and frequent motion.
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Affiliation(s)
| | - Davide Piccini
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Simon K. Warfield
- Department of Radiology, Boston Children’s Hospital, Boston, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Onur Afacan
- Department of Radiology, Boston Children’s Hospital, Boston, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
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Ahtam B, Yun HJ, Vyas R, Pienaar R, Wilson JH, Goswami CP, Berto LF, Warfield SK, Sahin M, Grant PE, Peters JM, Im K. Publisher Correction to: Morphological Features of Language Regions in Individuals with Tuberous Sclerosis Complex. J Autism Dev Disord 2024; 54:1232. [PMID: 38300505 DOI: 10.1007/s10803-023-06098-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2024]
Affiliation(s)
- Banu Ahtam
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA.
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA.
| | - Hyuk Jin Yun
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Rutvi Vyas
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Rudolph Pienaar
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Josephine H Wilson
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Caroline P Goswami
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Laura F Berto
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Mustafa Sahin
- Rosamund Stone Zander Translational Neuroscience Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, 02115, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Neuroradiology, Department of Radiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Jurriaan M Peters
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Kiho Im
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
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Masse O, Brumfield O, Ahmad E, Velasco-Annis C, Zhang J, Rollins CK, Connolly S, Barnewolt C, Shamshirsaz AA, Qaderi S, Javinani A, Warfield SK, Yang E, Gholipour A, Feldman HA, Grant PE, Mulliken JB, Pierotich L, Estroff J. Divergent growth of the transient brain compartments in fetuses with nonsyndromic isolated clefts involving the primary and secondary palate. Cereb Cortex 2024; 34:bhae024. [PMID: 38365268 PMCID: PMC10872676 DOI: 10.1093/cercor/bhae024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/29/2023] [Accepted: 12/30/2023] [Indexed: 02/18/2024] Open
Abstract
Cleft lip/palate is a common orofacial malformation that often leads to speech/language difficulties as well as developmental delays in affected children, despite surgical repair. Our understanding of brain development in these children is limited. This study aimed to analyze prenatal brain development in fetuses with cleft lip/palate and controls. We examined in utero MRIs of 30 controls and 42 cleft lip/palate fetal cases and measured regional brain volumes. Cleft lip/palate was categorized into groups A (cleft lip or alveolus) and B (any combination of clefts involving the primary and secondary palates). Using a repeated-measures regression model with relative brain hemisphere volumes (%), and after adjusting for multiple comparisons, we did not identify significant differences in regional brain growth between group A and controls. Group B clefts had significantly slower weekly cerebellar growth compared with controls. We also observed divergent brain growth in transient brain structures (cortical plate, subplate, ganglionic eminence) within group B clefts, depending on severity (unilateral or bilateral) and defect location (hemisphere ipsilateral or contralateral to the defect). Further research is needed to explore the association between regional fetal brain growth and cleft lip/palate severity, with the potential to inform early neurodevelopmental biomarkers and personalized diagnostics.
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Affiliation(s)
- Olivia Masse
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Olivia Brumfield
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Esha Ahmad
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Clemente Velasco-Annis
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Jennings Zhang
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Caitlin K Rollins
- Department of Neurology Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Susan Connolly
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Carol Barnewolt
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Alireza A Shamshirsaz
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Shohra Qaderi
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Ali Javinani
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Simon K Warfield
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Edward Yang
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Ali Gholipour
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Henry A Feldman
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Institutional Centers for Clinical and Translational Research, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Patricia E Grant
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - John B Mulliken
- Department of Plastic and Oral Surgery, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Lana Pierotich
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Judy Estroff
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
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Kertes N, Zaffrani-Reznikov Y, Afacan O, Kurugol S, Warfield SK, Freiman M. IVIM-Morph: Motion-compensated quantitative Intra-voxel Incoherent Motion (IVIM) analysis for functional fetal lung maturity assessment from diffusion-weighted MRI data. ArXiv 2024:arXiv:2401.07126v2. [PMID: 38313196 PMCID: PMC10836081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 02/06/2024]
Abstract
Quantitative analysis of pseudo-diffusion in diffusion-weighted magnetic resonance imaging (DWI) data shows potential for assessing fetal lung maturation and generating valuable imaging biomarkers. Yet, the clinical utility of DWI data is hindered by unavoidable fetal motion during acquisition. We present IVIM-morph, a self-supervised deep neural network model for motion-corrected quantitative analysis of DWI data using the Intra-voxel Incoherent Motion (IVIM) model. IVIM-morph combines two sub-networks, a registration sub-network, and an IVIM model fitting sub-network, enabling simultaneous estimation of IVIM model parameters and motion. To promote physically plausible image registration, we introduce a biophysically informed loss function that effectively balances registration and model-fitting quality. We validated the efficacy of IVIM-morph by establishing a correlation between the predicted IVIM model parameters of the lung and gestational age (GA) using fetal DWI data of 39 subjects. Our approach was compared against six baseline methods: 1) no motion compensation, 2) affine registration of all DWI images to the initial image, 3) deformable registration of all DWI images to the initial image, 4) deformable registration of each DWI image to its preceding image in the sequence, 5) iterative deformable motion compensation combined with IVIM model parameter estimation, and 6) self-supervised deep-learning-based deformable registration. IVIM-morph exhibited a notably improved correlation with gestational age (GA) when performing in-vivo quantitative analysis of fetal lung DWI data during the canalicular phase. Specifically, over 2 test groups of cases, it achieved an R f 2 of 0.44 and 0.52, outperforming the values of 0.27 and 0.25, 0.25 and 0.00, 0.00 and 0.00, 0.38 and 0.00, and 0.07 and 0.14 obtained by other methods. IVIM-morph shows potential in developing valuable biomarkers for non-invasive assessment of fetal lung maturity with DWI data. Moreover, its adaptability opens the door to potential applications in other clinical contexts where motion compensation is essential for quantitative DWI analysis. The IVIM-morph code is readily available at:https://github.com/TechnionComputationalMRILab/qDWI-Morph.
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Affiliation(s)
- Noga Kertes
- Faculty of Biomedical Engineering, Technion, Haifa, Israel
| | | | | | | | | | - Moti Freiman
- Faculty of Biomedical Engineering, Technion, Haifa, Israel
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Calixto C, Machado-Rivas F, Cortes-Albornoz MC, Karimi D, Velasco-Annis C, Afacan O, Warfield SK, Gholipour A, Jaimes C. Characterizing microstructural development in the fetal brain using diffusion MRI from 23 to 36 weeks of gestation. Cereb Cortex 2024; 34:bhad409. [PMID: 37948665 PMCID: PMC10793585 DOI: 10.1093/cercor/bhad409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 10/09/2023] [Accepted: 10/10/2023] [Indexed: 11/12/2023] Open
Abstract
We utilized motion-corrected diffusion tensor imaging (DTI) to evaluate microstructural changes in healthy fetal brains during the late second and third trimesters. Data were derived from fetal magnetic resonance imaging scans conducted as part of a prospective study spanning from 2013 March to 2019 May. The study included 44 fetuses between the gestational ages (GAs) of 23 and 36 weeks. We reconstructed fetal brain DTI using a motion-tracked slice-to-volume registration framework. Images were segmented into 14 regions of interest (ROIs) through label propagation using a fetal DTI atlas, with expert refinement. Statistical analysis involved assessing changes in fractional anisotropy (FA) and mean diffusivity (MD) throughout gestation using mixed-effects models, and identifying points of change in trajectory for ROIs with nonlinear trends. Results showed significant GA-related changes in FA and MD in all ROIs except in the thalamus' FA and corpus callosum's MD. Hemispheric asymmetries were found in the FA of the periventricular white matter (pvWM), intermediate zone, and subplate and in the MD of the ganglionic eminence and pvWM. This study provides valuable insight into the normal patterns of development of MD and FA in the fetal brain. These changes are closely linked with cytoarchitectonic changes and display indications of early functional specialization.
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Affiliation(s)
- Camilo Calixto
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Fedel Machado-Rivas
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Maria C Cortes-Albornoz
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA 02115, United States
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
| | - Camilo Jaimes
- Department of Radiology, Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, United States
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Vasylechko SD, Warfield SK, Kurugol S, Afacan O. Improved myelin water fraction mapping with deep neural networks using synthetically generated 3D data. Med Image Anal 2024; 91:102966. [PMID: 37844473 PMCID: PMC10847969 DOI: 10.1016/j.media.2023.102966] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 07/14/2023] [Accepted: 09/11/2023] [Indexed: 10/18/2023]
Abstract
We introduce a generative model for synthesis of large scale 3D datasets for quantitative parameter mapping of myelin water fraction (MWF). Our model combines a MR physics signal decay model with an accurate probabilistic multi-component parametric T2 model. We synthetically generate a wide variety of high quality signals and corresponding parameters from a wide range of naturally occurring prior parameter values. To capture spatial variation, the generative signal decay model is combined with a generative spatial model conditioned on generic tissue segmentations. Synthesized 3D datasets can be used to train any convolutional neural network (CNN) based architecture for MWF estimation. Our source code is available at: https://github.com/quin-med-harvard-edu/synthmap Reduction of acquisition time at the expense of lower SNR, as well as accuracy and repeatability of MWF estimation techniques, are key factors that affect the adoption of MWF mapping in clinical practice. We demonstrate that the synthetically trained CNN provides superior accuracy over the competing methods under the constraints of naturally occurring noise levels as well as on the synthetically generated images at low SNR levels. Normalized root mean squared error (nRMSE) is less than 7% on synthetic data, which is significantly lower than competing methods. Additionally, the proposed method yields a coefficient of variation (CoV) that is at least 4x better than the competing method on intra-session test-retest reference dataset.
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Affiliation(s)
- Serge Didenko Vasylechko
- Computational Radiology Laboratory, Boston Children's Hospital, Boston 02115, MA, USA; Harvard Medical School, Boston 02115, MA, USA.
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston 02115, MA, USA; Harvard Medical School, Boston 02115, MA, USA
| | - Sila Kurugol
- Computational Radiology Laboratory, Boston Children's Hospital, Boston 02115, MA, USA; Harvard Medical School, Boston 02115, MA, USA
| | - Onur Afacan
- Computational Radiology Laboratory, Boston Children's Hospital, Boston 02115, MA, USA; Harvard Medical School, Boston 02115, MA, USA
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Calixto C, Machado-Rivas F, Karimi D, Velasco C, Cortes-Albornoz MC, Afacan O, Warfield SK, Gholipour A, Jaimes C. Population Atlas Analysis of Emerging Brain Structural Connections in the Human Fetus. J Magn Reson Imaging 2023:10.1002/jmri.29057. [PMID: 37842932 PMCID: PMC11018715 DOI: 10.1002/jmri.29057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/17/2023] Open
Abstract
BACKGROUND A lack of in utero imaging data hampers our understanding of the connections in the human fetal brain. Generalizing observations from postmortem subjects and premature newborns is inaccurate due to technical and biological differences. PURPOSE To evaluate changes in fetal brain structural connectivity between 23 and 35 weeks postconceptional age using a spatiotemporal atlas of diffusion tensor imaging (DTI). STUDY TYPE Retrospective. POPULATION Publicly available diffusion atlases, based on 60 healthy women (age 18-45 years) with normal prenatal care, from 23 and 35 weeks of gestation. FIELD STRENGTH/SEQUENCE 3.0 Tesla/DTI acquired with diffusion-weighted echo planar imaging (EPI). ASSESSMENT We performed whole-brain fiber tractography from DTI images. The cortical plate of each diffusion atlas was segmented and parcellated into 78 regions derived from the Edinburgh Neonatal Atlas (ENA33). Connectivity matrices were computed, representing normalized fiber connections between nodes. We examined the relationship between global efficiency (GE), local efficiency (LE), small-worldness (SW), nodal efficiency (NE), and betweenness centrality (BC) with gestational age (GA) and with laterality. STATISTICAL TESTS Linear regression was used to analyze changes in GE, LE, NE, and BC throughout gestation, and to assess changes in laterality. The t-tests were used to assess SW. P-values were corrected using Holm-Bonferroni method. A corrected P-value <0.05 was considered statistically significant. RESULTS Network analysis revealed a significant weekly increase in GE (5.83%/week, 95% CI 4.32-7.37), LE (5.43%/week, 95% CI 3.63-7.25), and presence of SW across GA. No significant hemisphere differences were found in GE (P = 0.971) or LE (P = 0.458). Increasing GA was significantly associated with increasing NE in 41 nodes, increasing BC in 3 nodes, and decreasing BC in 2 nodes. DATA CONCLUSION Extensive network development and refinement occur in the second and third trimesters, marked by a rapid increase in global integration and local segregation. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Camilo Calixto
- Computational Radiology Laboratory. Department of Radiology. Boston Children’s Hospital. Boston, MA
- Harvard Medical School. Boston, MA
| | - Fedel Machado-Rivas
- Harvard Medical School. Boston, MA
- Massachusetts General Hospital. Boston, MA
| | - Davood Karimi
- Computational Radiology Laboratory. Department of Radiology. Boston Children’s Hospital. Boston, MA
- Harvard Medical School. Boston, MA
| | - Clemente Velasco
- Computational Radiology Laboratory. Department of Radiology. Boston Children’s Hospital. Boston, MA
- Harvard Medical School. Boston, MA
| | | | - Onur Afacan
- Computational Radiology Laboratory. Department of Radiology. Boston Children’s Hospital. Boston, MA
- Harvard Medical School. Boston, MA
| | - Simon K. Warfield
- Computational Radiology Laboratory. Department of Radiology. Boston Children’s Hospital. Boston, MA
- Harvard Medical School. Boston, MA
| | - Ali Gholipour
- Computational Radiology Laboratory. Department of Radiology. Boston Children’s Hospital. Boston, MA
- Harvard Medical School. Boston, MA
| | - Camilo Jaimes
- Harvard Medical School. Boston, MA
- Massachusetts General Hospital. Boston, MA
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9
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Ahmad E, Brumfield O, Masse O, Velasco-Annis C, Zhang J, Rollins CK, Connolly S, Barnewolt C, Shamshirsaz AA, Qaderi S, Javinani A, Warfield SK, Yang E, Gholipour A, Feldman HA, Estroff J, Grant PE, Vasung L. Atypical fetal brain development in fetuses with non-syndromic isolated musculoskeletal birth defects (niMSBDs). Cereb Cortex 2023; 33:10793-10801. [PMID: 37697904 PMCID: PMC10629896 DOI: 10.1093/cercor/bhad323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 08/08/2023] [Accepted: 08/09/2023] [Indexed: 09/13/2023] Open
Abstract
Non-syndromic, isolated musculoskeletal birth defects (niMSBDs) are among the leading causes of pediatric hospitalization. However, little is known about brain development in niMSBDs. Our study aimed to characterize prenatal brain development in fetuses with niMSBDs and identify altered brain regions compared to controls. We retrospectively analyzed in vivo structural T2-weighted MRIs of 99 fetuses (48 controls and 51 niMSBDs cases). For each group (19-31 and >31 gestational weeks (GW)), we conducted repeated-measures regression analysis with relative regional volume (% brain hemisphere) as a dependent variable (adjusted for age, side, and interactions). Between 19 and 31GW, fetuses with niMSBDs had a significantly (P < 0.001) smaller relative volume of the intermediate zone (-22.9 ± 3.2%) and cerebellum (-16.1 ± 3.5%,) and a larger relative volume of proliferative zones (38.3 ± 7.2%), the ganglionic eminence (34.8 ± 7.3%), and the ventricles (35.8 ± 8.0%). Between 32 and 37 GW, compared to the controls, niMSBDs showed significantly smaller volumes of central regions (-9.1 ± 2.1%) and larger volumes of the cortical plate. Our results suggest there is altered brain development in fetuses with niMSBDs compared to controls (13.1 ± 4.2%). Further basic and translational neuroscience research is needed to better visualize these differences and to characterize the altered development in fetuses with specific niMSBDs.
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Affiliation(s)
- Esha Ahmad
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Olivia Brumfield
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Olivia Masse
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Clemente Velasco-Annis
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Jennings Zhang
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Caitlin K Rollins
- Department of Neurology Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Susan Connolly
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Carol Barnewolt
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Alireza A Shamshirsaz
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Shohra Qaderi
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Ali Javinani
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Simon K Warfield
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Edward Yang
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Ali Gholipour
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Henry A Feldman
- Institutional Centers for Clinical and Translational Research, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Judy Estroff
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Maternal Fetal Care Center, Boston Children’s Hospital, Boston, MA 02115, United States
| | - Patricia E Grant
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
- Department of Radiology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
| | - Lana Vasung
- Division of Newborn Medicine, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, United States
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10
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Safdar S, Zwick BF, Yu Y, Bourantas GC, Joldes GR, Warfield SK, Hyde DE, Frisken S, Kapur T, Kikinis R, Golby A, Nabavi A, Wittek A, Miller K. SlicerCBM: automatic framework for biomechanical analysis of the brain. Int J Comput Assist Radiol Surg 2023; 18:1925-1940. [PMID: 37004646 PMCID: PMC10497672 DOI: 10.1007/s11548-023-02881-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Accepted: 03/17/2023] [Indexed: 04/04/2023]
Abstract
PURPOSE Brain shift that occurs during neurosurgery disturbs the brain's anatomy. Prediction of the brain shift is essential for accurate localisation of the surgical target. Biomechanical models have been envisaged as a possible tool for such predictions. In this study, we created a framework to automate the workflow for predicting intra-operative brain deformations. METHODS We created our framework by uniquely combining our meshless total Lagrangian explicit dynamics (MTLED) algorithm for computing soft tissue deformations, open-source software libraries and built-in functions within 3D Slicer, an open-source software package widely used for medical research. Our framework generates the biomechanical brain model from the pre-operative MRI, computes brain deformation using MTLED and outputs results in the form of predicted warped intra-operative MRI. RESULTS Our framework is used to solve three different neurosurgical brain shift scenarios: craniotomy, tumour resection and electrode placement. We evaluated our framework using nine patients. The average time to construct a patient-specific brain biomechanical model was 3 min, and that to compute deformations ranged from 13 to 23 min. We performed a qualitative evaluation by comparing our predicted intra-operative MRI with the actual intra-operative MRI. For quantitative evaluation, we computed Hausdorff distances between predicted and actual intra-operative ventricle surfaces. For patients with craniotomy and tumour resection, approximately 95% of the nodes on the ventricle surfaces are within two times the original in-plane resolution of the actual surface determined from the intra-operative MRI. CONCLUSION Our framework provides a broader application of existing solution methods not only in research but also in clinics. We successfully demonstrated the application of our framework by predicting intra-operative deformations in nine patients undergoing neurosurgical procedures.
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Affiliation(s)
- Saima Safdar
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia.
| | - Benjamin F Zwick
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - Yue Yu
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - George C Bourantas
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
- Department of Agriculture, University of Patras Nea Ktiria, 30200, Campus Mesologhi, Greece
| | - Grand R Joldes
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Damon E Hyde
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sarah Frisken
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Tina Kapur
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Ron Kikinis
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Alexandra Golby
- Brigham and Women's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Arya Nabavi
- Department of Neurosurgery, KRH Klinikum Nordstadt, Hannover, Germany
| | - Adam Wittek
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - Karol Miller
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
- Harvard Medical School, Boston, MA, USA
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11
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Levine A, Davis P, Zhang B, Peters J, Filip-Dhima R, Warfield SK, Prohl A, Capal J, Krueger D, Bebin EM, Northrup H, Wu JY, Sahin M. Epilepsy Severity Is Associated With Head Circumference and Growth Rate in Infants With Tuberous Sclerosis Complex. Pediatr Neurol 2023; 144:26-32. [PMID: 37119787 PMCID: PMC10330061 DOI: 10.1016/j.pediatrneurol.2023.03.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Revised: 02/05/2023] [Accepted: 03/23/2023] [Indexed: 05/01/2023]
Abstract
BACKGROUND Abnormal brain growth in tuberous sclerosis complex (TSC) reflects abnormalities in cellular proliferation and differentiation and results in epilepsy and other neurological manifestations. Head circumference (HC) as a proxy for brain volume may provide an easily tracked clinical measure of brain overgrowth and neurological disease burden. This study investigated the relationship between HC and epilepsy severity in infants with TSC. METHODS Prospective multicenter observational study of children from birth to three years with TSC. Epilepsy data were collected from clinical history, and HC was collected at study visits at age three, six, nine, 12, 18, 24, and 36 months. Epilepsy severity was classified as no epilepsy, low epilepsy severity (one seizure type and one or two antiepileptic drugs [AEDs]), moderate epilepsy severity (either two to three seizure types and one to two AEDs or one seizure type and more than three AEDs), or high epilepsy severity (two to three seizure types and more than three AEDs). RESULTS As a group, children with TSC had HCs approximately 1 S.D. above the mean World Health Organization (WHO) reference by age one year and demonstrated more rapid growth than the normal population reference. Males with epilepsy had larger HCs than those without. Compared with the WHO reference population, infants with TSC and no epilepsy or low or moderate epilepsy had an increased early HC growth rate, whereas those with severe epilepsy had an early larger HC but did not have a faster growth rate. CONCLUSIONS Infants and young children with TSC have larger HCs than typical growth norms and have differing rates of head growth depending on the severity of epilepsy.
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Affiliation(s)
- Alexis Levine
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts.
| | - Peter Davis
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Bo Zhang
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Jurriaan Peters
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts; Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Rajna Filip-Dhima
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Anna Prohl
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jamie Capal
- Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Darcy Krueger
- Department of Neurology and Rehabilitation Medicine, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
| | - E Martina Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Hope Northrup
- Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, Texas
| | - Joyce Y Wu
- Division of Pediatric Neurology, University of California at Los Angeles Mattel Children's Hospital, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California
| | - Mustafa Sahin
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts; F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts; Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, Massachusetts.
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12
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Machado-Rivas F, Choi JJ, Bedoya MA, Buitrago LA, Velasco-Annis C, Afacan O, Barnewolt C, Estroff J, Warfield SK, Gholipour A, Jaimes C. Brain growth in fetuses with congenital diaphragmatic hernia. J Neuroimaging 2023; 33:617-624. [PMID: 36813467 PMCID: PMC10363187 DOI: 10.1111/jon.13096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 02/08/2023] [Accepted: 02/09/2023] [Indexed: 02/24/2023] Open
Abstract
BACKGROUND AND PURPOSE To perform a volumetric evaluation of the brain in fetuses with right or left congenital diaphragmatic hernia (CDH), and to compare brain growth trajectories to normal fetuses. METHODS We identified fetal MRIs performed between 2015 and 2020 in fetuses with a diagnosis of CDH. Gestational age (GA) range was 19-40 weeks. Control subjects consisted of normally developing fetuses between 19 and 40 weeks recruited for a separate prospective study. All images were acquired at 3 Tesla and were processed with retrospective motion correction and slice-to-volume reconstruction to generate super-resolution 3-dimensional volumes. These volumes were registered to a common atlas space and segmented in 29 anatomic parcellations. RESULTS A total of 174 fetal MRIs in 149 fetuses were analyzed (99 controls [mean GA: 29.2 ± 5.2 weeks], 34 fetuses left-sided CDH [mean GA: 28.4 ± 5.3 weeks], and 16 fetuses right-sided CDH [mean GA: 27 ± 5.4 weeks]). In fetuses with left-sided CDH, brain parenchymal volume was -8.0% (95% confidence interval [CI] [-13.1, -2.5]; p = .005) lower than normal controls. Differences ranged from -11.4% (95% CI [-18, -4.3]; p < .001) in the corpus callosum to -4.6% (95% CI [-8.9, -0.1]; p = .044) in the hippocampus. In fetuses with right-sided CDH, brain parenchymal volume was -10.1% (95% CI [-16.8, -2.7]; p = .008) lower than controls. Differences ranged from -14.1% (95% CI [-21, -6.5]; p < .001) in the ventricular zone to -5.6% (95% CI [-9.3, -1.8]; p = .025) in the brainstem. CONCLUSION Left and right CDH are associated with lower fetal brain volumes.
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Affiliation(s)
- Fedel Machado-Rivas
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
| | | | - Maria Alejandra Bedoya
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
| | | | | | - Onur Afacan
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
| | - Carol Barnewolt
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
| | - Judy Estroff
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
| | - Simon K. Warfield
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
| | - Ali Gholipour
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
| | - Camilo Jaimes
- Radiology, Boston Children’s Hospital, Boston, MA 02115
- Radiology, Harvard Medical School, Boston, MA 02115
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13
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Mallela AN, Deng H, Gholipour A, Warfield SK, Goldschmidt E. Heterogeneous growth of the insula shapes the human brain. Proc Natl Acad Sci U S A 2023; 120:e2220200120. [PMID: 37279278 PMCID: PMC10268209 DOI: 10.1073/pnas.2220200120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2022] [Accepted: 04/13/2023] [Indexed: 06/08/2023] Open
Abstract
The human cerebrum consists of a precise and stereotyped arrangement of lobes, primary gyri, and connectivity that underlies human cognition [P. Rakic, Nat. Rev. Neurosci. 10, 724-735 (2009)]. The development of this arrangement is less clear. Current models explain individual primary gyrification but largely do not account for the global configuration of the cerebral lobes [T. Tallinen, J. Y. Chung, J. S. Biggins, L. Mahadevan, Proc. Natl. Acad. Sci. U.S.A. 111, 12667-12672 (2014) and D. C. Van Essen, Nature 385, 313-318 (1997)]. The insula, buried in the depths of the Sylvian fissure, is unique in terms of gyral anatomy and size. Here, we quantitatively show that the insula has unique morphology and location in the cerebrum and that these key differences emerge during fetal development. Finally, we identify quantitative differences in developmental migration patterns to the insula that may underlie these differences. We calculated morphologic data in the insula and other lobes in adults (N = 107) and in an in utero fetal brain atlas (N = 81 healthy fetuses). In utero, the insula grows an order of magnitude slower than the other lobes and demonstrates shallower sulci, less curvature, and less surface complexity both in adults and progressively throughout fetal development. Spherical projection analysis demonstrates that the lenticular nuclei obstruct 60 to 70% of radial pathways from the ventricular zone (VZ) to the insula, forcing a curved migration to the insula in contrast to a direct radial pathway. Using fetal diffusion tractography, we identify radial glial fascicles that originate from the VZ and curve around the lenticular nuclei to form the insula. These results confirm existing models of radial migration to the cortex and illustrate findings that suggest differential insular and cerebral development, laying the groundwork to understand cerebral malformations and insular function and pathologies.
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Affiliation(s)
- Arka N. Mallela
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA15213
| | - Hansen Deng
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA15213
| | - Ali Gholipour
- Department of Radiology, Harvard Medical School, Boston, MA02115
- Department of Radiology, Boston Children’s Hospital, Boston, MA02115
| | - Simon K. Warfield
- Department of Radiology, Harvard Medical School, Boston, MA02115
- Department of Radiology, Boston Children’s Hospital, Boston, MA02115
| | - Ezequiel Goldschmidt
- Department of Radiology, Harvard Medical School, Boston, MA02115
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA94143
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14
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Hassanzadeh E, Hornak A, Hassanzadeh M, Warfield SK, Pearl PL, Bolton J, Suarez R, Stone S, Stufflebeam S, Ailion AS. Comparison of fMRI language laterality with and without sedation in pediatric epilepsy. Neuroimage Clin 2023; 38:103448. [PMID: 37285796 PMCID: PMC10250119 DOI: 10.1016/j.nicl.2023.103448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 05/07/2023] [Accepted: 05/30/2023] [Indexed: 06/09/2023]
Abstract
Functional MRI is an essential component of presurgical language mapping. In clinical settings, young children may be sedated for the MRI with the functional stimuli presented passively. Research has found that sedation changes language activation in healthy adults and children. However, there is limited research comparing sedated and unsedated functional MRI in pediatric epilepsy patients. We compared language activation patterns in children with epilepsy who received sedation for functional MRI to the ones who did not. We retrospectively identified the patients with focal epilepsy who underwent presurgical functional MRI including Auditory Descriptive Decision Task at Boston Children's Hospital from 2014 to 2022. Patients were divided into sedated and awake groups, based on their sedation status during functional MRI. Auditory Descriptive Decision Task stimuli were presented passively to the sedated group per clinical protocol. We extracted language activation maps contrasted against a control task (reverse speech) in the Frontal and Temporal language regions and calculated separate language laterality indexes for each region. We considered positive laterality indexes as left dominant, negative laterality indexes as right dominant, and absolute laterality indexes <0.2 as bilateral. We defined 2 language patterns: typical (i.e., primarily left-sided) and atypical. Typical pattern required at least one left dominant region (either frontal or temporal) and no right dominant region. We then compared the language patterns between the sedated and awake groups. Seventy patients met the inclusion criteria, 25 sedated, and 45 awake. Using the Auditory Descriptive Decision Task paradigm, when adjusted for age, handedness, gender, and laterality of lesion in a weighted logistic regression model, the odds of the atypical pattern were 13.2 times higher in the sedated group compared to the awake group (Confidence Interval: 2.55-68.41, p-value < 0.01). Sedation may alter language activation patterns in pediatric epilepsy patients. Language patterns on sedated functional MRI with passive tasks may not represent language networks during wakefulness, sedation may differentially suppress some networks, or require a different task or method of analysis to capture the awake language network. Given the critical surgical implication of these findings, additional studies are needed to better understand how sedation impacts the functional MRI blood oxygenation level-dependent signal. Consistent with current practice, sedated functional MRI should be interpreted with greater caution and requires additional validation as well as research on post-surgical language outcomes.
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Affiliation(s)
- Elmira Hassanzadeh
- Neuroradiology, Massachusetts General Hospital, Harvard Medical School, 02116, USA.
| | - Alena Hornak
- Neurology, Boston Children's Hospital, Harvard Medical School, 02116, USA
| | | | - Simon K Warfield
- Radiology, Boston Children's Hospital, Harvard Medical School, 02116, USA
| | - Phillip L Pearl
- Neurology, Boston Children's Hospital, Harvard Medical School, 02116, USA
| | - Jeffrey Bolton
- Neurology, Boston Children's Hospital, Harvard Medical School, 02116, USA
| | - Ralph Suarez
- Radiology, Boston Children's Hospital, Harvard Medical School, 02116, USA
| | - Scellig Stone
- Neurosurgery, Boston Children's Hospital, Harvard Medical School, 02116, USA
| | - Steve Stufflebeam
- Neuroradiology, Massachusetts General Hospital, Harvard Medical School, 02116, USA
| | - Alyssa S Ailion
- Neurology, Boston Children's Hospital, Harvard Medical School, 02116, USA
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15
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Zwick BF, Safdar S, Bourantas GC, Joldes GR, Hyde DE, Warfield SK, Wittek A, Miller K. Image data and computational grids for computing brain shift and solving the electrocorticography forward problem. Data Brief 2023; 48:109122. [PMID: 37128587 PMCID: PMC10147975 DOI: 10.1016/j.dib.2023.109122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 03/30/2023] [Accepted: 03/31/2023] [Indexed: 05/03/2023] Open
Abstract
This article describes the dataset applied in the research reported in NeuroImage article "Patient-specific solution of the electrocorticography forward problem in deforming brain" [1] that is available for download from the Zenodo data repository (https://zenodo.org/record/7687631) [2]. Preoperative structural and diffusion-weighted magnetic resonance (MR) and postoperative computed tomography (CT) images of a 12-year-old female epilepsy patient under evaluation for surgical intervention were obtained retrospectively from Boston Children's Hospital. We used these images to conduct the analysis at The University of Western Australia's Intelligent Systems for Medicine Laboratory using SlicerCBM [3], our open-source software extension for the 3D Slicer medical imaging platform. As part of the analysis, we processed the images to extract the patient-specific brain geometry; created computational grids, including a tetrahedral grid for the meshless solution of the biomechanical model and a regular hexahedral grid for the finite element solution of the electrocorticography forward problem; predicted the postoperative MRI and DTI that correspond to the brain configuration deformed by the placement of subdural electrodes using biomechanics-based image warping; and solved the patient-specific electrocorticography forward problem to compute the electric potential distribution within the patient's head using the original preoperative and predicted postoperative image data. The well-established and open-source file formats used in this dataset, including Nearly Raw Raster Data (NRRD) files for images, STL files for surface geometry, and Visualization Toolkit (VTK) files for computational grids, allow other research groups to easily reuse the data presented herein to solve the electrocorticography forward problem accounting for the brain shift caused by implantation of subdural grid electrodes.
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Affiliation(s)
- Benjamin F. Zwick
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
- Corresponding author.
| | - Saima Safdar
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - George C. Bourantas
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - Grand R. Joldes
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - Damon E. Hyde
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Simon K. Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Adam Wittek
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
| | - Karol Miller
- Intelligent Systems for Medicine Laboratory, The University of Western Australia, 35 Stirling Highway, Perth, WA, Australia
- Harvard Medical School, Boston, MA, USA
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16
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Ahtam B, Yun HJ, Vyas R, Pienaar R, Wilson JH, Goswami CP, Berto LF, Warfield SK, Sahin M, Grant PE, Peters JM, Im K. Morphological Features of Language Regions in Individuals with Tuberous Sclerosis Complex. J Autism Dev Disord 2023:10.1007/s10803-023-06004-8. [PMID: 37222965 DOI: 10.1007/s10803-023-06004-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/21/2023] [Indexed: 05/25/2023]
Abstract
A significant number of individuals with tuberous sclerosis complex (TSC) exhibit language difficulties. Here, we examined the language-related brain morphometry in 59 participants (7 participants with TSC and comorbid autism spectrum disorder (ASD) (TSC + ASD), 13 with TSC but no ASD (TSC-ASD), 10 with ASD-only (ASD), and 29 typically developing (TD) controls). A hemispheric asymmetry was noted in surface area and gray matter volume of several cortical language areas in TD, ASD, and TSC-ASD groups, but not in TSC + ASD group. TSC + ASD group demonstrated increased cortical thickness and curvature values in multiple language regions for both hemispheres, compared to other groups. After controlling for tuber load in the TSC groups, within-group differences stayed the same but the differences between TSC-ASD and TSC + ASD were no longer statistically significant. These preliminary findings suggest that comorbid ASD in TSC as well as tuber load in TSC is associated with changes in the morphometry of language regions. Future studies with larger sample sizes will be needed to confirm these findings.
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Affiliation(s)
- Banu Ahtam
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA.
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA.
| | - Hyuk Jin Yun
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Rutvi Vyas
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Rudolph Pienaar
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Josephine H Wilson
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Caroline P Goswami
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Laura F Berto
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Mustafa Sahin
- Rosamund Stone Zander Translational Neuroscience Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- F.M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, 02115, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Neuroradiology, Department of Radiology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Jurriaan M Peters
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
| | - Kiho Im
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA
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17
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Cohen AL, Kroeck MR, Wall J, McManus P, Ovchinnikova A, Sahin M, Krueger DA, Bebin EM, Northrup H, Wu JY, Warfield SK, Peters JM, Fox MD. Tubers Affecting the Fusiform Face Area Are Associated with Autism Diagnosis. Ann Neurol 2023; 93:577-590. [PMID: 36394118 PMCID: PMC9974824 DOI: 10.1002/ana.26551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 11/11/2022] [Accepted: 11/13/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVE Tuberous sclerosis complex (TSC) is associated with focal brain "tubers" and a high incidence of autism spectrum disorder (ASD). The location of brain tubers associated with autism may provide insight into the neuroanatomical substrate of ASD symptoms. METHODS We delineated tuber locations for 115 TSC participants with ASD (n = 31) and without ASD (n = 84) from the Tuberous Sclerosis Complex Autism Center of Excellence Research Network. We tested for associations between ASD diagnosis and tuber burden within the whole brain, specific lobes, and at 8 regions of interest derived from the ASD neuroimaging literature, including the anterior cingulate, orbitofrontal and posterior parietal cortices, inferior frontal and fusiform gyri, superior temporal sulcus, amygdala, and supplemental motor area. Next, we performed an unbiased data-driven voxelwise lesion symptom mapping (VLSM) analysis. Finally, we calculated the risk of ASD associated with positive findings from the above analyses. RESULTS There were no significant ASD-related differences in tuber burden across the whole brain, within specific lobes, or within a priori regions derived from the ASD literature. However, using VLSM analysis, we found that tubers involving the right fusiform face area (FFA) were associated with a 3.7-fold increased risk of developing ASD. INTERPRETATION Although TSC is a rare cause of ASD, there is a strong association between tuber involvement of the right FFA and ASD diagnosis. This highlights a potentially causative mechanism for developing autism in TSC that may guide research into ASD symptoms more generally. ANN NEUROL 2023;93:577-590.
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Affiliation(s)
- Alexander L Cohen
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mallory R Kroeck
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Juliana Wall
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Peter McManus
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Arina Ovchinnikova
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mustafa Sahin
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA, USA
| | - Darcy A Krueger
- Department of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - E Martina Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Hope Northrup
- Department of Pediatrics, McGovern Medical School at University of Texas Health Science Center at Houston and Children's Memorial Hermann Hospital, Houston, TX, USA
| | - Joyce Y Wu
- Division of Neurology & Epilepsy, Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jurriaan M Peters
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael D Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
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18
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Calixto C, Machado‐Rivas F, Karimi D, Cortes‐Albornoz MC, Acosta‐Buitrago LM, Gallo‐Bernal S, Afacan O, Warfield SK, Gholipour A, Jaimes C. Detailed anatomic segmentations of a fetal brain diffusion tensor imaging atlas between 23 and 30 weeks of gestation. Hum Brain Mapp 2023; 44:1593-1602. [PMID: 36421003 PMCID: PMC9921217 DOI: 10.1002/hbm.26160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 11/02/2022] [Accepted: 11/12/2022] [Indexed: 11/25/2022] Open
Abstract
This work presents detailed anatomic labels for a spatiotemporal atlas of fetal brain Diffusion Tensor Imaging (DTI) between 23 and 30 weeks of post-conceptional age. Additionally, we examined developmental trajectories in fractional anisotropy (FA) and mean diffusivity (MD) across gestational ages (GA). We performed manual segmentations on a fetal brain DTI atlas. We labeled 14 regions of interest (ROIs): cortical plate (CP), subplate (SP), Intermediate zone-subventricular zone-ventricular zone (IZ/SVZ/VZ), Ganglionic Eminence (GE), anterior and posterior limbs of the internal capsule (ALIC, PLIC), genu (GCC), body (BCC), and splenium (SCC) of the corpus callosum (CC), hippocampus, lentiform Nucleus, thalamus, brainstem, and cerebellum. A series of linear regressions were used to assess GA as a predictor of FA and MD for each ROI. The combination of MD and FA allowed the identification of all ROIs. Increasing GA was significantly associated with decreasing FA in the CP, SP, IZ/SVZ/IZ, GE, ALIC, hippocampus, and BCC (p < .03, for all), and with increasing FA in the PLIC and SCC (p < .002, for both). Increasing GA was significantly associated with increasing MD in the CP, SP, IZ/SVZ/IZ, GE, ALIC, and CC (p < .03, for all). We developed a set of expert-annotated labels for a DTI spatiotemporal atlas of the fetal brain and presented a pilot analysis of developmental changes in cerebral microstructure between 23 and 30 weeks of GA.
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Affiliation(s)
- Camilo Calixto
- Computational Radiology Laboratory, Department of RadiologyBoston Children's HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - Fedel Machado‐Rivas
- Computational Radiology Laboratory, Department of RadiologyBoston Children's HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - Davood Karimi
- Computational Radiology Laboratory, Department of RadiologyBoston Children's HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - Maria C. Cortes‐Albornoz
- Computational Radiology Laboratory, Department of RadiologyBoston Children's HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | | | - Sebastian Gallo‐Bernal
- Harvard Medical SchoolBostonMassachusettsUSA
- Massachusetts General HospitalBostonMassachusettsUSA
| | - Onur Afacan
- Computational Radiology Laboratory, Department of RadiologyBoston Children's HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - Simon K. Warfield
- Computational Radiology Laboratory, Department of RadiologyBoston Children's HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of RadiologyBoston Children's HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - Camilo Jaimes
- Computational Radiology Laboratory, Department of RadiologyBoston Children's HospitalBostonMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
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19
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Tsai JW, Choi JJ, Ouaalam H, Murillo EA, Yeo KK, Vogelzang J, Sousa C, Woods JK, Ligon KL, Warfield SK, Bandopadhayay P, Cooney TM. Integrated response analysis of pediatric low-grade gliomas during and after targeted therapy treatment. Neurooncol Adv 2023; 5:vdac182. [PMID: 36926246 PMCID: PMC10011805 DOI: 10.1093/noajnl/vdac182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022] Open
Abstract
Background Pediatric low-grade gliomas (pLGGs) are the most common central nervous system tumor in children, characterized by RAS/MAPK pathway driver alterations. Genomic advances have facilitated the use of molecular targeted therapies, however, their long-term impact on tumor behavior remains critically unanswered. Methods We performed an IRB-approved, retrospective chart and imaging review of pLGGs treated with off-label targeted therapy at Dana-Farber/Boston Children's from 2010 to 2020. Response analysis was performed for BRAFV600E and BRAF fusion/duplication-driven pLGG subsets. Results Fifty-five patients were identified (dabrafenib n = 15, everolimus n = 26, trametinib n = 11, and vemurafenib n = 3). Median duration of targeted therapy was 9.48 months (0.12-58.44). The 1-year, 3-year, and 5-year EFS from targeted therapy initiation were 62.1%, 38.2%, and 31.8%, respectively. Mean volumetric change for BRAFV600E mutated pLGG on BRAF inhibitors was -54.11%; median time to best volumetric response was 8.28 months with 9 of 12 (75%) objective RAPNO responses. Median time to largest volume post-treatment was 2.86 months (+13.49%); mean volume by the last follow-up was -14.02%. Mean volumetric change for BRAF fusion/duplication pLGG on trametinib was +7.34%; median time to best volumetric response was 6.71 months with 3 of 7 (43%) objective RAPNO responses. Median time to largest volume post-treatment was 2.38 months (+71.86%); mean volume by the last follow-up was +39.41%. Conclusions Our integrated analysis suggests variability in response by pLGG molecular subgroup and targeted therapy, as well as the transience of some tumor growth following targeted therapy cessation.
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Affiliation(s)
- Jessica W Tsai
- Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, Massachusetts, USA
| | - Jungwhan John Choi
- Department of Radiology, Boston Children’s Hospital, Boston, Massachusetts, USA
| | - Hakim Ouaalam
- Department of Radiology, Boston Children’s Hospital, Boston, Massachusetts, USA
| | - Efrain Aguilar Murillo
- Department of Radiology, Division of Neuroradiology and Neurointervention, Boston, Massachusetts, USA
| | - Kee Kiat Yeo
- Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, Massachusetts, USA
| | - Jayne Vogelzang
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Cecilia Sousa
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Jared K Woods
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Keith L Ligon
- Department of Pathology, Brigham and Women’s Hospital, Boston, Massachusetts, USA
- Department of Pathology, Boston Children’s Hospital, Boston Massachusetts, USA
- Department of Pathology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children’s Hospital, Boston, Massachusetts, USA
| | - Pratiti Bandopadhayay
- Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, Massachusetts, USA
| | - Tabitha M Cooney
- Dana-Farber/Boston Children’s Cancer and Blood Disorders Center, Boston, Massachusetts, USA
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20
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Wallace TE, Kober T, Stockmann JP, Polimeni JR, Warfield SK, Afacan O. Real-time shimming with FID navigators. Magn Reson Med 2022; 88:2548-2563. [PMID: 36093989 PMCID: PMC9529812 DOI: 10.1002/mrm.29421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/22/2022] [Accepted: 08/02/2022] [Indexed: 11/12/2022]
Abstract
PURPOSE To implement a method for real-time field control using rapid FID navigator (FIDnav) measurements and evaluate the efficacy of the proposed approach for mitigating dynamic field perturbations and improvingT 2 * $$ {\mathrm{T}}_2^{\ast } $$ -weighted image quality. METHODS FIDnavs were embedded in a gradient echo sequence and a subject-specific linear calibration model was generated on the scanner to facilitate rapid shim updates in response to measured FIDnav signals. To confirm the accuracy of FID-navigated field updates, phantom and volunteer scans were performed with online updates of the scanner B0 shim settings. To evaluate improvement inT 2 * $$ {\mathrm{T}}_2^{\ast } $$ -weighted image quality with real-time shimming, 10 volunteers were scanned at 3T while performing deep-breathing and nose-touching tasks designed to modulate the B0 field. Quantitative image quality metrics were compared with and without FID-navigated field control. An additional volunteer was scanned at 7T to evaluate performance at ultra-high field. RESULTS Applying measured FIDnav shim updates successfully compensated for applied global and linear field offsets in phantoms and across all volunteers. FID-navigated real-time shimming led to a substantial reduction in field fluctuations and a consequent improvement inT 2 * $$ {\mathrm{T}}_2^{\ast } $$ -weighted image quality in volunteers performing deep-breathing and nose-touching tasks, with 7.57% ± 6.01% and 8.21% ± 10.90% improvement in peak SNR and structural similarity, respectively. CONCLUSION FIDnavs facilitate rapid measurement and application of field coefficients for slice-wise B0 shimming. The proposed approach can successfully counteract spatiotemporal field perturbations and substantially improvesT 2 * $$ {\mathrm{T}}_2^{\ast } $$ -weighted image quality, which is important for a variety of clinical and research applications, particularly at ultra-high field.
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Affiliation(s)
- Tess E Wallace
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
- LTS5, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jason P Stockmann
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Jonathan R Polimeni
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Boston Children’s Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
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21
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van der Weijden CW, van der Hoorn A, Potze JH, Renken RJ, Borra RJ, Dierckx RA, Gutmann IW, Ouaalam H, Karimi D, Gholipour A, Warfield SK, de Vries EF, Meilof JF. Diffusion-derived parameters in lesions, peri-lesion and normal-appearing white matter in multiple sclerosis using tensor, kurtosis and fixel-based analysis. J Cereb Blood Flow Metab 2022; 42:2095-2106. [PMID: 35754351 PMCID: PMC9580168 DOI: 10.1177/0271678x221107953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Neuronal damage is the primary cause of long-term disability of multiple sclerosis (MS) patients. Assessment of axonal integrity from diffusion MRI parameters might enable better disease characterisation. 16 diffusion derived measurements from diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), and fixel-based analysis (FBA) in lesions, peri-lesion and normal appearing white matter were investigated. Diffusion MRI scans of 11 MS patients were processed to generate DTI, DKI, and FBA images. Fractional anisotropy (FA) and fibre density (FD) were used to assess axonal integrity across brain regions. Subsequently, 359 lesions were identified, and lesion and peri-lesion segmentation was performed using structural T1w, T2w, T2w-FLAIR, and T1w post-contrast MRI. The segmentations were then used to extract 16 diffusion MRI parameters from lesion, peri-lesion, and contralateral normal appearing white matter (NAWM). The measurements for axonal integrity, DTI-FA, DKI-FA, FBA-FD, produced similar results. All diffusion MRI parameters were affected in lesions as compared to NAWM (p < 0.001), confirming loss of axonal integrity in lesions. In peri-lesions, most parameters, except FBA-FD, were also significantly different from NAWM, although the effect size was smaller than in lesions. The reduction in axonal integrity in peri-lesions, despite unaffected fibre density estimates, suggests an effect of Wallerian degeneration.
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Affiliation(s)
- Chris Wj van der Weijden
- Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Anouk van der Hoorn
- Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jan Hendrik Potze
- Radiology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Remco J Renken
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ronald Jh Borra
- Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Rudi Ajo Dierckx
- Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | | | - Hakim Ouaalam
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, USA
| | - Davood Karimi
- Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Ali Gholipour
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, USA.,Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, USA.,Department of Radiology, Boston Children's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Erik Fj de Vries
- Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Jan F Meilof
- Department of Biomedical Sciences of Cells and Systems, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.,Intelligent Medical Imaging Research Group, Boston Children's Hospital, Boston, MA, USA
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22
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Nian R, Gao M, Zhang S, Yu J, Gholipour A, Kong S, Wang R, Sui Y, Velasco-Annis C, Tomas-Fernandez X, Li Q, Lv H, Qian Y, Warfield SK. Toward evaluation of multiresolution cortical thickness estimation with FreeSurfer, MaCRUISE, and BrainSuite. Cereb Cortex 2022; 33:5082-5096. [PMID: 36288912 DOI: 10.1093/cercor/bhac401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 09/09/2022] [Accepted: 09/11/2022] [Indexed: 11/12/2022] Open
Abstract
Abstract
Advances in Magnetic Resonance Imaging hardware and methodologies allow for promoting the cortical morphometry with submillimeter spatial resolution. In this paper, we generated 3D self-enhanced high-resolution (HR) MRI imaging, by adapting 1 deep learning architecture, and 3 standard pipelines, FreeSurfer, MaCRUISE, and BrainSuite, have been collectively employed to evaluate the cortical thickness. We systematically investigated the differences in cortical thickness estimation for MRI sequences at multiresolution homologously originated from the native image. It has been revealed that there systematically exhibited the preferences in determining both inner and outer cortical surfaces at higher resolution, yielding most deeper cortical surface placements toward GM/WM or GM/CSF boundaries, which directs a consistent reduction tendency of mean cortical thickness estimation; on the contrary, the lower resolution data will most probably provide a more coarse and rough evaluation in cortical surface reconstruction, resulting in a relatively thicker estimation. Although the differences of cortical thickness estimation at the diverse spatial resolution varied with one another, almost all led to roughly one-sixth to one-fifth significant reduction across the entire brain at the HR, independent to the pipelines we applied, which emphasizes on generally coherent improved accuracy in a data-independent manner and endeavors to cost-efficiency with quantitative opportunities.
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Affiliation(s)
- Rui Nian
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Mingshan Gao
- Citigroup Services and Technology Limited, 1000 Chenhi Road, Shanghai, China
| | | | - Junjie Yu
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Ali Gholipour
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Shuang Kong
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Ruirui Wang
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Yao Sui
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Clemente Velasco-Annis
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Xavier Tomas-Fernandez
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
| | - Qiuying Li
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Hangyu Lv
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Yuqi Qian
- School of Electronic Engineering, Ocean University of China, 238 Songling Road, Qingdao, China
| | - Simon K Warfield
- Harvard Medical School, 25 Shattuck Street, Boston, MA, United States
- Boston Children's Hospital, 300 Longwood Avenue, Boston, MA, United States
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23
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Bell KA, Matthews LG, Cherkerzian S, Prohl AK, Warfield SK, Inder TE, Onishi S, Belfort MB. Associations of body composition with regional brain volumes and white matter microstructure in very preterm infants. Arch Dis Child Fetal Neonatal Ed 2022; 107:533-538. [PMID: 35058276 PMCID: PMC9296693 DOI: 10.1136/archdischild-2021-321653] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Accepted: 12/20/2021] [Indexed: 11/03/2022]
Abstract
OBJECTIVE To determine associations between body composition and concurrent measures of brain development including (1) Tissue-specific brain volumes and (2) White matter microstructure, among very preterm infants at term equivalent age. DESIGN Prospective observational study. SETTING Single-centre academic level III neonatal intensive care unit. PATIENTS We studied 85 infants born <33 weeks' gestation. METHODS At term equivalent age, infants underwent air displacement plethysmography to determine body composition, and brain MRI from which we quantified tissue-specific brain volumes and fractional anisotropy (FA) of white matter tracts. We estimated associations of fat and lean mass Z-scores with each brain outcome, using linear mixed models adjusted for intrafamilial correlation among twins and potential confounding variables. RESULTS Median gestational age was 29 weeks (range 23.4-32.9). One unit greater lean mass Z-score was associated with larger total brain volume (10.5 cc, 95% CI 3.8 to 17.2); larger volumes of the cerebellum (1.2 cc, 95% CI 0.5 to 1.9) and white matter (4.5 cc, 95% CI 0.7 to 8.3); and greater FA in the left cingulum (0.3%, 95% CI 0.1% to 0.6%), right uncinate fasciculus (0.2%, 95% CI 0.0% to 0.5%), and right posterior limb of the internal capsule (0.3%, 95% CI 0.03% to 0.6%). Fat Z-scores were not associated with any outcome. CONCLUSIONS Lean mass-but not fat-at term was associated with larger brain volume and white matter microstructure differences that suggest improved maturation. Lean mass accrual may index brain growth and development.
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Affiliation(s)
- Katherine Ann Bell
- Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Lillian G Matthews
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia
- Victorian Infant Brain Study (VIBeS), Murdoch Childrens Research Institute, Parkville, Victoria, Australia
| | - Sara Cherkerzian
- Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Anna K Prohl
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA
| | - Terrie E Inder
- Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Shun Onishi
- Department of Pediatric Surgery, Research Field in Medical and Health Sciences, Medical and Dental Area, Research and Education Assembly, Kagoshima University, Kagoshima, Japan
| | - Mandy B Belfort
- Department of Pediatric Newborn Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
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24
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Machado-Rivas F, Jaimes C, Scherrer B, Benson LA, Gorman MP, Warfield SK, Afacan O. Evaluation of white matter microstructure in pediatric onset multiple sclerosis with diffusion compartment imaging. J Neuroimaging 2022; 32:1098-1108. [PMID: 36036739 DOI: 10.1111/jon.13038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 07/20/2022] [Accepted: 08/05/2022] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND AND PURPOSE Pediatric-onset multiple sclerosis (POMS) shows earlier axonal involvement and greater axonal loss than in adults. We aim to characterize the white matter (WM) microstructural changes in POMS using a diffusion compartment imaging (DCI) model and compare it to standard diffusion tensor imaging (DTI). METHODS Eleven patients (2 males, mean age 18.8 ± 3.9 years) with a diagnosis of relapsing and remitting POMS (mean age at disease onset 13.8 ± 2.9 years, mean duration 5.1 ± 1.9 years) and healthy controls (8 males, mean age 26.4 ± 6.5 years) were recruited and imaged at 3 T. A 90-gradient set Cube and Sphere acquisition and a novel DCI model known as DIstribution of Anisotropic MicrOstructural eNvironments with Diffusion-weighted imaging (DIAMOND) were used to calculate a single anisotropic compartment, an isotropic compartment, and a free diffusion compartment. Lesions and contralateral normal-appearing white matter (NAWM) in patients and whole brain WM for controls were labeled. RESULTS Eleven patients and 11 controls were recruited. When comparing lesions and contralateral NAWM in patients using DCI, compartmental axial diffusivity, radial diffusivity (cRD), and mean diffusivity (cMD) were higher in lesions. Conversely, compartmental fractional anisotropy (cFA) and heterogeneity index were lower in lesions. An analysis of DTI equivalents showed the same trends. In whole-brain NAWM of patients compared to controls, cRD and cMD were higher and cFA was lower in patients. CONCLUSION Lesions in POMS can be accurately characterized by a DCI model. Incipient changes in NAWM seen in DCI may not be readily observable by DTI.
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Affiliation(s)
- Fedel Machado-Rivas
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Camilo Jaimes
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Benoit Scherrer
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Leslie A Benson
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Mark P Gorman
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Onur Afacan
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Nijman M, Yang E, Jaimes C, Prohl AK, Sahin M, Krueger DA, Wu JY, Northrup H, Stone SSD, Madsen JR, Fallah A, Blount JP, Weiner HL, Grayson L, Bebin EM, Porter BE, Warfield SK, Prabhu SP, Peters JM. Limited utility of structural MRI to identify the epileptogenic zone in young children with tuberous sclerosis. J Neuroimaging 2022; 32:991-1000. [PMID: 35729081 DOI: 10.1111/jon.13016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND AND PURPOSE The success of epilepsy surgery in children with tuberous sclerosis complex (TSC) hinges on identification of the epileptogenic zone (EZ). We studied structural MRI markers of epileptogenic lesions in young children with TSC. METHODS We included 26 children with TSC who underwent epilepsy surgery before the age of 3 years at five sites, with 12 months or more follow-up. Two neuroradiologists, blinded to surgical outcome data, reviewed 10 candidate lesions on preoperative MRI for characteristics of the tuber (large affected area, calcification, cyst-like properties) and of focal cortical dysplasia (FCD) features (cortical malformation, gray-white matter junction blurring, transmantle sign). They selected lesions suspect for the EZ based on structural MRI, and reselected after unblinding to seizure onset location on electroencephalography (EEG). RESULTS None of the tuber characteristics and FCD features were distinctive for the EZ, indicated by resected lesions in seizure-free children. With structural MRI alone, the EZ was identified out of 10 lesions in 31%, and with addition of EEG data, this increased to 48%. However, rates of identification of resected lesions in non-seizure-free children were similar. Across 251 lesions, interrater agreement was moderate for large size (κ = .60), and fair (κ = .24) for all other features. CONCLUSIONS In young children with TSC, the utility of structural MRI features is limited in the identification of the epileptogenic tuber, but improves when combined with EEG data.
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Affiliation(s)
- Maaike Nijman
- Localization Laboratory, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Edward Yang
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Camilo Jaimes
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Anna K Prohl
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Mustafa Sahin
- Rosamund Stone Zander Translational Neuroscience Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Darcy A Krueger
- Division of Neurology, Cincinnati Children's Hospital Medical Center, and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Joyce Y Wu
- Division of Neurology, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA.,Departments of Pediatrics and Neurology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Hope Northrup
- Department of Pediatrics, McGovern Medical School, at University of Texas Health Science Center at Houston (UTHealth) and Children's Memorial Hermann Hospital, Houston, Texas, USA
| | - Scellig S D Stone
- Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Joseph R Madsen
- Department of Neurosurgery, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Aria Fallah
- Department of Neurosurgery, Division of Pediatric Neurosurgery, University of California Los Angeles Mattel Children's Hospital, David Geffen School of Medicine, University of California at Los Angeles, Los Angeles, California, USA
| | - Jeffrey P Blount
- Department of Neurosurgery, Division of Pediatric Neurosurgery, Children's of Alabama, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Howard L Weiner
- Department of Surgery, Division of Pediatric Neurosurgery, Texas Children's Hospital, Houston, Texas, USA.,Department of Neurosurgery, Baylor College of Medicine, Houston, Texas, USA
| | - Leslie Grayson
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - E Martina Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Brenda E Porter
- Department of Neurology, Stanford University Medical Center, Stanford, California, USA
| | - Simon K Warfield
- Rosamund Stone Zander Translational Neuroscience Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Sanjay P Prabhu
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jurriaan M Peters
- Localization Laboratory, Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Tsai JW, Choi JJ, Ouaalam H, Murrillo EA, Yeo KK, Vogelzang J, Sousa C, Woods JK, Ligon KL, Warfield SK, Bandopadhayay P, Cooney TM. LGG-32. Integrated biologic, radiologic and clinical analysis of pediatric low-grade gliomas during and after targeted therapy treatment. Neuro Oncol 2022. [PMCID: PMC9165407 DOI: 10.1093/neuonc/noac079.344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND: Pediatric low grade gliomas (pLGGs) are the most common central nervous system tumor in children, characterized by driver alterations in the RAS and MAPK pathways. Genomic advances have facilitated use of molecular targeted therapies, however their long-term impact on tumor behavior remains critically unanswered. METHODS: We performed an IRB-approved, retrospective chart and imaging review of pLGGs treated with off-label targeted therapy at Dana-Farber/Boston Children’s Cancer and Blood Disorders Center from 2010 to 2020. Volumetric analysis was performed for BRAFV600E and BRAF fusion/duplication driven pLGG subsets. RESULTS: Fifty-five patients were identified (dabrafenib n = 15, everolimus n = 26, trametinib n = 11, and vemurafenib n = 3). Targeted agent was used as first or second-line therapy for 58% (32/55). Median duration of targeted therapy was 0.79 years (0.01 – 4.87), and overall median follow-up was 2.50 years (0.01 – 7.39). The 1-year, 3-year, and 5-year EFS from targeted therapy initiation were 62.1%, 38.2%, and 31.8%, respectively. Mean volumetric change for BRAFV600E mutated pLGG on BRAF inhibitors was -54.11%, and median time to best volumetric response was 8.28 months (n = 12). Median time to largest volume post-treatment was 2.86 months. Mean volumetric change for BRAF fusion/duplication pLGG on MEK inhibitors was +7.34% with median time to best volumetric response of 6.71 months (n = 7). Median time to largest volume post-treatment was 2.38 months. CONCLUSIONS: Our integrated clinical and volumetric data suggest the majority of patients receiving BRAF inhibitors or trametinib achieve reduction in tumor volume while on therapy and that tumor stability can be achieved following targeted therapy cessation. Moreover, volumetric analysis shows promise as a tool to assess targeted therapeutic response in pLGGs.
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Affiliation(s)
- Jessica W Tsai
- Dana-Farber/Boston Children’s Cancer and Blood Disorder Center , Boston, MA , USA
| | - Jungwhan John Choi
- Department of Radiology, Boston Children's Hospital , Boston, MA , USA
- Department of Radiology and Medical Imaging, Cincinnati Children’s Hospital , Cincinnati, OH , USA
| | - Hakim Ouaalam
- Department of Radiology, Boston Children's Hospital , Boston, MA , USA
| | - Efrain Aguilar Murrillo
- Department of Radiology, Division of Neuroradiology and Neurointervention, , Brigham and Women’s Hospital , Boston, MA , USA
| | - Kee Kiat Yeo
- Dana-Farber/Boston Children’s Cancer and Blood Disorder Center , Boston, MA , USA
| | - Jayne Vogelzang
- Department of Pathology, Brigham and Women's Hospital , Boston, MA , USA
| | - Cecilia Sousa
- Department of Pathology, Brigham and Women's Hospital , Boston, MA , USA
| | - Jared K Woods
- Department of Pathology, Brigham and Women's Hospital , Boston, MA , USA
| | - Keith L Ligon
- Department of Pathology, Brigham and Women's Hospital , Boston, MA , USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital , Boston, MA , USA
| | | | - Tabitha M Cooney
- Dana-Farber/Boston Children’s Cancer and Blood Disorder Center , Boston, MA , USA
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Sui Y, Afacan O, Jaimes C, Gholipour A, Warfield SK. Scan-Specific Generative Neural Network for MRI Super-Resolution Reconstruction. IEEE Trans Med Imaging 2022; 41:1383-1399. [PMID: 35020591 PMCID: PMC9208763 DOI: 10.1109/tmi.2022.3142610] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The interpretation and analysis of Magnetic resonance imaging (MRI) benefit from high spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is time-consuming and costly, which increases the potential for motion artifact, and suffers from reduced signal-to-noise ratio (SNR). Super-resolution reconstruction (SRR) is one of the most widely used methods in MRI since it allows for the trade-off between high spatial resolution, high SNR, and reduced scan times. Deep learning has emerged for improved SRR as compared to conventional methods. However, current deep learning-based SRR methods require large-scale training datasets of high-resolution images, which are practically difficult to obtain at a suitable SNR. We sought to develop a methodology that allows for dataset-free deep learning-based SRR, through which to construct images with higher spatial resolution and of higher SNR than can be practically obtained by direct Fourier encoding. We developed a dataset-free learning method that leverages a generative neural network trained for each specific scan or set of scans, which in turn, allows for SRR tailored to the individual patient. With the SRR from three short duration scans, we achieved high quality brain MRI at an isotropic spatial resolution of 0.125 cubic mm with six minutes of imaging time for T2 contrast and an average increase of 7.2 dB (34.2%) in SNR to these short duration scans. Motion compensation was achieved by aligning the three short duration scans together. We assessed our technique on simulated MRI data and clinical data acquired from 15 subjects. Extensive experimental results demonstrate that our approach achieved superior results to state-of-the-art methods, while in parallel, performed at reduced cost as scans delivered with direct high-resolution acquisition.
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Sadhwani A, Wypij D, Rofeberg V, Gholipour A, Mittleman M, Rohde J, Velasco-Annis C, Calderon J, Friedman KG, Tworetzky W, Grant PE, Soul JS, Warfield SK, Newburger JW, Ortinau CM, Rollins CK. Fetal Brain Volume Predicts Neurodevelopment in Congenital Heart Disease. Circulation 2022; 145:1108-1119. [PMID: 35143287 PMCID: PMC9007882 DOI: 10.1161/circulationaha.121.056305] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Neurodevelopmental impairment is common in children with congenital heart disease (CHD), but postnatal variables explain only 30% of the variance in outcomes. To explore whether the antecedents for neurodevelopmental disabilities might begin in utero, we analyzed whether fetal brain volume predicted subsequent neurodevelopmental outcome in children with CHD. METHODS Fetuses with isolated CHD and sociodemographically comparable healthy control fetuses underwent fetal brain magnetic resonance imaging and 2-year neurodevelopmental evaluation with the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III) and the Adaptive Behavior Assessment System, Third Edition (ABAS-3). Hierarchical regression evaluated potential predictors of Bayley-III and ABAS-3 outcomes in the CHD group, including fetal total brain volume adjusted for gestational age and sex, sociodemographic characteristics, birth measures, and medical history. RESULTS The CHD group (n=52) had lower Bayley-III cognitive, language, and motor scores than the control group (n=26), but fetal brain volumes were similar. Within the CHD group, larger fetal total brain volume correlated with higher Bayley-III cognitive, language, and motor scores and ABAS-3 adaptive functioning scores (r=0.32-0.47; all P<0.05), but this was not noted in the control group. Fetal brain volume predicted 10% to 21% of the variance in neurodevelopmental outcome measures in univariate analyses. Multivariable models that also included social class and postnatal factors explained 18% to 45% of the variance in outcome, depending on developmental domain. Moreover, in final multivariable models, fetal brain volume was the most consistent predictor of neurodevelopmental outcome across domains. CONCLUSIONS Small fetal brain volume is a strong independent predictor of 2-year neurodevelopmental outcomes and may be an important imaging biomarker of future neurodevelopmental risk in CHD. Future studies are needed to support this hypothesis. Our findings support inclusion of fetal brain volume in risk stratification models and as a possible outcome in fetal neuroprotective intervention studies.
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Affiliation(s)
- Anjali Sadhwani
- Department of Psychiatry, Boston Children’s Hospital, Boston, MA
- Department of Psychiatry, Harvard Medical School, Boston, MA
| | - David Wypij
- Department of Cardiology, Boston Children’s Hospital, Boston, MA
- Department of Pediatrics, Harvard Medical School, Boston, MA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA
| | - Valerie Rofeberg
- Department of Cardiology, Boston Children’s Hospital, Boston, MA
| | - Ali Gholipour
- Department of Radiology, Boston Children’s Hospital, Boston, MA
- Department of Radiology, Harvard Medical School, Boston, MA
| | | | - Julia Rohde
- Department of Neurology, Boston Children’s Hospital, Boston, MA
| | | | - Johanna Calderon
- Department of Psychiatry, Boston Children’s Hospital, Boston, MA
- Department of Psychiatry, Harvard Medical School, Boston, MA
| | - Kevin G. Friedman
- Department of Cardiology, Boston Children’s Hospital, Boston, MA
- Department of Pediatrics, Harvard Medical School, Boston, MA
| | - Wayne Tworetzky
- Department of Cardiology, Boston Children’s Hospital, Boston, MA
- Department of Pediatrics, Harvard Medical School, Boston, MA
| | - P. Ellen Grant
- Department of Radiology, Boston Children’s Hospital, Boston, MA
- Department of Radiology, Harvard Medical School, Boston, MA
| | - Janet S. Soul
- Department of Neurology, Boston Children’s Hospital, Boston, MA
- Department of Neurology, Harvard Medical School, Boston, MA
| | | | - Jane W. Newburger
- Department of Cardiology, Boston Children’s Hospital, Boston, MA
- Department of Pediatrics, Harvard Medical School, Boston, MA
| | | | - Caitlin K. Rollins
- Department of Neurology, Boston Children’s Hospital, Boston, MA
- Department of Neurology, Harvard Medical School, Boston, MA
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Machado-Rivas F, Gandhi J, Choi JJ, Velasco-Annis C, Afacan O, Warfield SK, Gholipour A, Jaimes C. Normal Growth, Sexual Dimorphism, and Lateral Asymmetries at Fetal Brain MRI. Radiology 2022; 303:162-170. [PMID: 34931857 PMCID: PMC8962825 DOI: 10.1148/radiol.211222] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Tools in image reconstruction, motion correction, and segmentation have enabled the accurate volumetric characterization of fetal brain growth at MRI. Purpose To evaluate the volumetric growth of intracranial structures in healthy fetuses, accounting for gestational age (GA), sex, and laterality with use of a spatiotemporal MRI atlas of fetal brain development. Materials and Methods T2-weighted 3.0-T half-Fourier acquired single-shot turbo spin-echo sequence MRI was performed in healthy fetuses from prospectively recruited pregnant volunteers from March 2013 to May 2019. A previously validated section-to-volume reconstruction algorithm was used to generate intensity-normalized superresolution three-dimensional volumes that were registered to a fetal brain MRI atlas with 28 anatomic regions of interest. Atlas-based segmentation was performed and manually refined. Labels included the bilateral hippocampus, amygdala, caudate nucleus, lentiform nucleus, thalamus, lateral ventricle, cerebellum, cortical plate, hemispheric white matter, internal capsule, ganglionic eminence, ventricular zone, corpus callosum, brainstem, hippocampal commissure, and extra-axial cerebrospinal fluid. For fetuses younger than 31 weeks of GA, the subplate and intermediate zones were delineated. A linear regression analysis was used to determine weekly age-related change adjusted for sex and laterality. Results The final analytic sample consisted of 122 MRI scans in 98 fetuses (mean GA, 29 weeks ± 5 [range, 20-38 weeks]). All structures had significant volume growth with increasing GA (P < .001). Weekly age-related change for individual structures in the brain parenchyma ranged from 2.0% (95% CI: 0.9, 3.1; P < .001) in the hippocampal commissure to 19.4% (95% CI: 18.7, 20.1; P < .001) in the cerebellum. The largest sex-related differences were 22.1% higher volume in male fetuses for the lateral ventricles (95% CI: 10.9, 34.4; P < .001). There was rightward volumetric asymmetry of 15.6% for the hippocampus (95% CI: 14.2, 17.2; P < .001) and leftward volumetric asymmetry of 8.1% for the lateral ventricles (95% CI: 3.7, 12.2; P < .001). Conclusion With use of a spatiotemporal MRI atlas, volumetric growth of the fetal brain showed complex trajectories dependent on structure, gestational age, sex, and laterality. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Rollins in this issue.
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Affiliation(s)
- Fedel Machado-Rivas
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
| | - Jasmine Gandhi
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
| | - Jungwhan John Choi
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
| | - Clemente Velasco-Annis
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
| | - Onur Afacan
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
| | - Simon K Warfield
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
| | - Ali Gholipour
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
| | - Camilo Jaimes
- From the Department of Radiology, Boston Children's Hospital, 300 Longwood Ave, Boston, MA 02115 (F.M.R., J.G., J.J.C., C.V.A., O.A., S.K.W., A.G., C.J.); and Department of Radiology, Harvard Medical School, Boston, Mass (F.M.R., J.G., J.J.C., O.A., S.K.W., A.G., C.J.)
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Bethlehem RAI, Seidlitz J, White SR, Vogel JW, Anderson KM, Adamson C, Adler S, Alexopoulos GS, Anagnostou E, Areces-Gonzalez A, Astle DE, Auyeung B, Ayub M, Bae J, Ball G, Baron-Cohen S, Beare R, Bedford SA, Benegal V, Beyer F, Blangero J, Blesa Cábez M, Boardman JP, Borzage M, Bosch-Bayard JF, Bourke N, Calhoun VD, Chakravarty MM, Chen C, Chertavian C, Chetelat G, Chong YS, Cole JH, Corvin A, Costantino M, Courchesne E, Crivello F, Cropley VL, Crosbie J, Crossley N, Delarue M, Delorme R, Desrivieres S, Devenyi GA, Di Biase MA, Dolan R, Donald KA, Donohoe G, Dunlop K, Edwards AD, Elison JT, Ellis CT, Elman JA, Eyler L, Fair DA, Feczko E, Fletcher PC, Fonagy P, Franz CE, Galan-Garcia L, Gholipour A, Giedd J, Gilmore JH, Glahn DC, Goodyer IM, Grant PE, Groenewold NA, Gunning FM, Gur RE, Gur RC, Hammill CF, Hansson O, Hedden T, Heinz A, Henson RN, Heuer K, Hoare J, Holla B, Holmes AJ, Holt R, Huang H, Im K, Ipser J, Jack CR, Jackowski AP, Jia T, Johnson KA, Jones PB, Jones DT, Kahn RS, Karlsson H, Karlsson L, Kawashima R, Kelley EA, Kern S, Kim KW, Kitzbichler MG, Kremen WS, Lalonde F, Landeau B, Lee S, Lerch J, Lewis JD, Li J, Liao W, Liston C, Lombardo MV, Lv J, Lynch C, Mallard TT, Marcelis M, Markello RD, Mathias SR, Mazoyer B, McGuire P, Meaney MJ, Mechelli A, Medic N, Misic B, Morgan SE, Mothersill D, Nigg J, Ong MQW, Ortinau C, Ossenkoppele R, Ouyang M, Palaniyappan L, Paly L, Pan PM, Pantelis C, Park MM, Paus T, Pausova Z, Paz-Linares D, Pichet Binette A, Pierce K, Qian X, Qiu J, Qiu A, Raznahan A, Rittman T, Rodrigue A, Rollins CK, Romero-Garcia R, Ronan L, Rosenberg MD, Rowitch DH, Salum GA, Satterthwaite TD, Schaare HL, Schachar RJ, Schultz AP, Schumann G, Schöll M, Sharp D, Shinohara RT, Skoog I, Smyser CD, Sperling RA, Stein DJ, Stolicyn A, Suckling J, Sullivan G, Taki Y, Thyreau B, Toro R, Traut N, Tsvetanov KA, Turk-Browne NB, Tuulari JJ, Tzourio C, Vachon-Presseau É, Valdes-Sosa MJ, Valdes-Sosa PA, Valk SL, van Amelsvoort T, Vandekar SN, Vasung L, Victoria LW, Villeneuve S, Villringer A, Vértes PE, Wagstyl K, Wang YS, Warfield SK, Warrier V, Westman E, Westwater ML, Whalley HC, Witte AV, Yang N, Yeo B, Yun H, Zalesky A, Zar HJ, Zettergren A, Zhou JH, Ziauddeen H, Zugman A, Zuo XN, Bullmore ET, Alexander-Bloch AF. Brain charts for the human lifespan. Nature 2022; 604:525-533. [PMID: 35388223 PMCID: PMC9021021 DOI: 10.1038/s41586-022-04554-y] [Citation(s) in RCA: 372] [Impact Index Per Article: 186.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 02/16/2022] [Indexed: 02/02/2023]
Abstract
Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes.
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Affiliation(s)
- R A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK.
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK.
| | - J Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA.
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA.
| | - S R White
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- MRC Biostatistics Unit, University of Cambridge, Cambridge, UK
| | - J W Vogel
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Informatics & Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - K M Anderson
- Department of Psychology, Yale University, New Haven, CT, USA
| | - C Adamson
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - S Adler
- UCL Great Ormond Street Institute for Child Health, London, UK
| | - G S Alexopoulos
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, USA
| | - E Anagnostou
- Department of Pediatrics University of Toronto, Toronto, Canada
- Holland Bloorview Kids Rehabilitation Hospital, Toronto, Canada
| | - A Areces-Gonzalez
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
- University of Pinar del Río "Hermanos Saiz Montes de Oca", Pinar del Río, Cuba
| | - D E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - B Auyeung
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychology, School of Philosophy, Psychology and Language Sciences, University of Edinburgh, Edinburgh, UK
| | - M Ayub
- Queen's University, Department of Psychiatry, Centre for Neuroscience Studies, Kingston, Ontario, Canada
- University College London, Mental Health Neuroscience Research Department, Division of Psychiatry, London, UK
| | - J Bae
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, Korea
| | - G Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - S Baron-Cohen
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridge Lifetime Asperger Syndrome Service (CLASS), Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - R Beare
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Victoria, Australia
- Department of Medicine, Monash University, Melbourne, Victoria, Australia
| | - S A Bedford
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - V Benegal
- Centre for Addiction Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Bengaluru, India
| | - F Beyer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - J Blangero
- Department of Human Genetics, South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Edinburg, TX, USA
| | - M Blesa Cábez
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - J P Boardman
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - M Borzage
- Fetal and Neonatal Institute, Division of Neonatology, Children's Hospital Los Angeles, Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - J F Bosch-Bayard
- McGill Centre for Integrative Neuroscience, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, Montreal, Quebec, Canada
- McGill University, Montreal, Quebec, Canada
| | - N Bourke
- Department of Brain Sciences, Imperial College London, London, UK
- Care Research and Technology Centre, Dementia Research Institute, London, UK
| | - V D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA
| | - M M Chakravarty
- McGill University, Montreal, Quebec, Canada
- Computational Brain Anatomy (CoBrA) Laboratory, Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - C Chen
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - C Chertavian
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - G Chetelat
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - Y S Chong
- Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research, Singapore, Singapore
- Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - J H Cole
- Centre for Medical Image Computing (CMIC), University College London, London, UK
- Dementia Research Centre (DRC), University College London, London, UK
| | - A Corvin
- Department of Psychiatry, Trinity College, Dublin, Ireland
| | - M Costantino
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Verdun, Quebec, Canada
- Undergraduate program in Neuroscience, McGill University, Montreal, Quebec, Canada
| | - E Courchesne
- Department of Neuroscience, University of California, San Diego, San Diego, CA, USA
- Autism Center of Excellence, University of California, San Diego, San Diego, CA, USA
| | - F Crivello
- Institute of Neurodegenerative Disorders, CNRS UMR5293, CEA, University of Bordeaux, Bordeaux, France
| | - V L Cropley
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
| | - J Crosbie
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - N Crossley
- Department of Psychiatry, School of Medicine, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- Instituto Milenio Intelligent Healthcare Engineering, Santiago, Chile
| | - M Delarue
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - R Delorme
- Child and Adolescent Psychiatry Department, Robert Debré University Hospital, AP-HP, Paris, France
- Human Genetics and Cognitive Functions, Institut Pasteur, Paris, France
| | - S Desrivieres
- Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - G A Devenyi
- Cerebral Imaging Centre, McGill Department of Psychiatry, Douglas Mental Health University Institute, Montreal, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - M A Di Biase
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - R Dolan
- Max Planck UCL Centre for Computational Psychiatry and Ageing Research, University College London, London, UK
- Wellcome Centre for Human Neuroimaging, London, UK
| | - K A Donald
- Division of Developmental Paediatrics, Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, Cape Town, South Africa
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - G Donohoe
- Center for Neuroimaging, Cognition & Genomics (NICOG), School of Psychology, National University of Ireland Galway, Galway, Ireland
| | - K Dunlop
- Weil Family Brain and Mind Research Institute, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - A D Edwards
- Centre for the Developing Brain, King's College London, London, UK
- Evelina London Children's Hospital, London, UK
- MRC Centre for Neurodevelopmental Disorders, London, UK
| | - J T Elison
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - C T Ellis
- Department of Psychology, Yale University, New Haven, CT, USA
- Haskins Laboratories, New Haven, CT, USA
| | - J A Elman
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - L Eyler
- Desert-Pacific Mental Illness Research Education and Clinical Center, VA San Diego Healthcare, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, Los Angeles, CA, USA
| | - D A Fair
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - E Feczko
- Institute of Child Development, Department of Pediatrics, Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA
| | - P C Fletcher
- Department of Psychiatry, University of Cambridge, and Wellcome Trust MRC Institute of Metabolic Science, Cambridge Biomedical Campus, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - P Fonagy
- Department of Clinical, Educational and Health Psychology, University College London, London, UK
- Anna Freud National Centre for Children and Families, London, UK
| | - C E Franz
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | | | - A Gholipour
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
| | - J Giedd
- Department of Child and Adolescent Psychiatry, University of California, San Diego, San Diego, CA, USA
- Department of Psychiatry, University of California San Diego, San Diego, CA, USA
| | - J H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - D C Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - I M Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - P E Grant
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - N A Groenewold
- Neuroscience Institute, University of Cape Town, Cape Town, South Africa
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - F M Gunning
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - R E Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - R C Gur
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
| | - C F Hammill
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Mouse Imaging Centre, Toronto, Ontario, Canada
| | - O Hansson
- Clinical Memory Research Unit, Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
- Memory Clinic, Skåne University Hospital, Malmö, Sweden
| | - T Hedden
- Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - A Heinz
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Psychiatry and Psychotherapy, Charité Campus Mitte, Berlin, Germany
| | - R N Henson
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - K Heuer
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Université de Paris, Paris, France
| | - J Hoare
- Department of Psychiatry, University of Cape Town, Cape Town, South Africa
| | - B Holla
- Department of Integrative Medicine, NIMHANS, Bengaluru, India
- Accelerator Program for Discovery in Brain disorders using Stem cells (ADBS), Department of Psychiatry, NIMHANS, Bengaluru, India
| | - A J Holmes
- Departments of Psychology and Psychiatry, Yale University, New Haven, CT, USA
| | - R Holt
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - H Huang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
- The Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - K Im
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - J Ipser
- Department of Psychiatry and Mental Health, Clinical Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - C R Jack
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
| | - A P Jackowski
- Department of Psychiatry, Universidade Federal de São Paulo, São Paulo, Brazil
- National Institute of Developmental Psychiatry, Beijing, China
| | - T Jia
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and BrainInspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
- Centre for Population Neuroscience and Precision Medicine (PONS), Institute of Psychiatry, Psychology and Neuroscience, SGDP Centre, King's College London, London, UK
| | - K A Johnson
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - P B Jones
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - D T Jones
- Department of Radiology, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
| | - R S Kahn
- Department of Psychiatry, Icahn School of Medicine, Mount Sinai, NY, USA
| | - H Karlsson
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - L Karlsson
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
| | - R Kawashima
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - E A Kelley
- Queen's University, Departments of Psychology and Psychiatry, Centre for Neuroscience Studies, Kingston, Ontario, Canada
| | - S Kern
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - K W Kim
- Department of Brain and Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
- Department of Neuropsychiatry, Seoul National University Bundang Hospital, Seongnam, South Korea
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, South Korea
- Institute of Human Behavioral Medicine, SNU-MRC, Seoul, South Korea
| | - M G Kitzbichler
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - W S Kremen
- Department of Psychiatry, Center for Behavior Genetics of Aging, University of California, San Diego, La Jolla, CA, USA
| | - F Lalonde
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - B Landeau
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - S Lee
- Department of Brain & Cognitive Sciences, Seoul National University College of Natural Sciences, Seoul, South Korea
| | - J Lerch
- Mouse Imaging Centre, Toronto, Ontario, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - J D Lewis
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - J Li
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - W Liao
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
| | - C Liston
- Department of Psychiatry and Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY, USA
| | - M V Lombardo
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Laboratory for Autism and Neurodevelopmental Disorders, Center for Neuroscience and Cognitive Systems @UniTn, Istituto Italiano di Tecnologia, Rovereto, Italy
| | - J Lv
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- School of Biomedical Engineering and Brain and Mind Centre, The University of Sydney, Sydney, New South Wales, Australia
| | - C Lynch
- Weil Family Brain and Mind Research Institute, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - T T Mallard
- Department of Psychology, University of Texas, Austin, TX, USA
| | - M Marcelis
- Department of Psychiatry and Neuropsychology, School of Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, Maastricht, The Netherlands
- Institute for Mental Health Care Eindhoven (GGzE), Eindhoven, The Netherlands
| | - R D Markello
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - S R Mathias
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - B Mazoyer
- Institute of Neurodegenerative Disorders, CNRS UMR5293, CEA, University of Bordeaux, Bordeaux, France
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - P McGuire
- Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - M J Meaney
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Mental Health University Institute, Montreal, Quebec, Canada
- Singapore Institute for Clinical Sciences, Singapore, Singapore
| | - A Mechelli
- Bordeaux University Hospital, Bordeaux, France
| | - N Medic
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - B Misic
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - S E Morgan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - D Mothersill
- Department of Psychology, School of Business, National College of Ireland, Dublin, Ireland
- School of Psychology and Center for Neuroimaging and Cognitive Genomics, National University of Ireland Galway, Galway, Ireland
- Department of Psychiatry, Trinity College Dublin, Dublin, Ireland
| | - J Nigg
- Department of Psychiatry, School of Medicine, Oregon Health and Science University, Portland, OR, USA
| | - M Q W Ong
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - C Ortinau
- Department of Pediatrics, Washington University in St Louis, St Louis, MO, USA
| | - R Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Lund University, Clinical Memory Research Unit, Lund, Sweden
| | - M Ouyang
- Radiology Research, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - L Palaniyappan
- Robarts Research Institute and The Brain and Mind Institute, University of Western Ontario, London, Ontario, Canada
| | - L Paly
- Normandie Univ, UNICAEN, INSERM, U1237, PhIND "Physiopathology and Imaging of Neurological Disorders", Institut Blood and Brain @ Caen-Normandie, Cyceron, Caen, France
| | - P M Pan
- Department of Psychiatry, Federal University of Sao Poalo (UNIFESP), Sao Poalo, Brazil
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), Sao Poalo, Brazil
| | - C Pantelis
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
- Melbourne School of Engineering, The University of Melbourne, Parkville, Victoria, Australia
- Florey Institute of Neuroscience and Mental Health, Parkville, Victoria, Australia
| | - M M Park
- Department of Psychiatry, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - T Paus
- Department of Psychiatry, Faculty of Medicine and Centre Hospitalier Universitaire Sainte-Justine, University of Montreal, Montreal, Quebec, Canada
- Departments of Psychiatry and Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Z Pausova
- The Hospital for Sick Children, Toronto, Ontario, Canada
- Departments of Physiology and Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada
| | - D Paz-Linares
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
- Cuban Neuroscience Center, Havana, Cuba
| | - A Pichet Binette
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - K Pierce
- Department of Neuroscience, University of California, San Diego, San Diego, CA, USA
| | - X Qian
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - J Qiu
- School of Psychology, Southwest University, Chongqing, China
| | - A Qiu
- Department of Biomedical Engineering, The N.1 Institute for Health, National University of Singapore, Singapore, Singapore
| | - A Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, MD, USA
| | - T Rittman
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - A Rodrigue
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - C K Rollins
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Boston Children's Hospital, Boston, MA, USA
| | - R Romero-Garcia
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Instituto de Biomedicina de Sevilla (IBiS) HUVR/CSIC/Universidad de Sevilla, Dpto. de Fisiología Médica y Biofísica, Seville, Spain
| | - L Ronan
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - M D Rosenberg
- Department of Psychology and Neuroscience Institute, University of Chicago, Chicago, IL, USA
| | - D H Rowitch
- Department of Paediatrics and Wellcome-MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - G A Salum
- Department of Psychiatry, Universidade Federal do Rio Grande do Sul (UFRGS), Hospital de Clinicas de Porto Alegre, Porto Alegre, Brazil
- National Institute of Developmental Psychiatry (INPD), São Paulo, Brazil
| | - T D Satterthwaite
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Lifespan Informatics & Neuroimaging Center, University of Pennsylvania, Philadelphia, PA, USA
| | - H L Schaare
- Otto Hahn Group Cognitive Neurogenetics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Juelich, Juelich, Germany
| | - R J Schachar
- The Hospital for Sick Children, Toronto, Ontario, Canada
| | - A P Schultz
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - G Schumann
- Centre for Population Neuroscience and Stratified Medicine (PONS), Institute for Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
- PONS-Centre, Charite Mental Health, Dept of Psychiatry and Psychotherapy, Charite Campus Mitte, Berlin, Germany
| | - M Schöll
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden
- Department of Psychiatry and Neurochemistry, University of Gothenburg, Gothenburg, Sweden
- Dementia Research Centre, Queen's Square Institute of Neurology, University College London, London, UK
| | - D Sharp
- Department of Brain Sciences, Imperial College London, London, UK
- Care Research and Technology Centre, UK Dementia Research Institute, London, UK
| | - R T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
- Center for Biomedical Image Computing and Analytics, Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - I Skoog
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
- Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, Gothenburg, Sweden
| | - C D Smyser
- Departments of Neurology, Pediatrics, and Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - R A Sperling
- Harvard Medical School, Boston, MA, USA
- Harvard Aging Brain Study, Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - D J Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Dept of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - A Stolicyn
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - J Suckling
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
| | - G Sullivan
- MRC Centre for Reproductive Health, University of Edinburgh, Edinburgh, UK
| | - Y Taki
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - B Thyreau
- Institute of Development, Aging and Cancer, Tohoku University, Seiryocho, Aobaku, Sendai, Japan
| | - R Toro
- Université de Paris, Paris, France
- Department of Neuroscience, Institut Pasteur, Paris, France
| | - N Traut
- Department of Neuroscience, Institut Pasteur, Paris, France
- Center for Research and Interdisciplinarity (CRI), Université Paris Descartes, Paris, France
| | - K A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - N B Turk-Browne
- Department of Psychology, Yale University, New Haven, CT, USA
- Wu Tsai Institute, Yale University, New Haven, CT, USA
| | - J J Tuulari
- Department of Clinical Medicine, Department of Psychiatry and Turku Brain and Mind Center, FinnBrain Birth Cohort Study, University of Turku and Turku University Hospital, Turku, Finland
- Department of Clinical Medicine, University of Turku, Turku, Finland
- Turku Collegium for Science, Medicine and Technology, University of Turku, Turku, Finland
| | - C Tzourio
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, U1219, CHU Bordeaux, Bordeaux, France
| | - É Vachon-Presseau
- Faculty of Dental Medicine and Oral Health Sciences, McGill University, Montreal, Quebec, Canada
| | | | - P A Valdes-Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, University of Electronic Science and Technology of China, Chengdu, China
- Alan Edwards Centre for Research on Pain (AECRP), McGill University, Montreal, Quebec, Canada
| | - S L Valk
- Institute for Neuroscience and Medicine 7, Forschungszentrum Jülich, Jülich, Germany
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - T van Amelsvoort
- Department of Psychiatry and Neurosychology, Maastricht University, Maastricht, The Netherlands
| | - S N Vandekar
- Department of Biostatistics, Vanderbilt University, Nashville, TN, USA
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - L Vasung
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - L W Victoria
- Weill Cornell Institute of Geriatric Psychiatry, Department of Psychiatry, Weill Cornell Medicine, New York, NY, USA
| | - S Villeneuve
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, Quebec, Canada
- Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - A Villringer
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
| | - P E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - K Wagstyl
- Wellcome Centre for Human Neuroimaging, London, UK
| | - Y S Wang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - S K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Boston, MA, USA
| | - V Warrier
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - E Westman
- Division of Clinical Geriatrics, Center for Alzheimer Research, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden
| | - M L Westwater
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - H C Whalley
- Division of Psychiatry, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - A V Witte
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Clinic for Cognitive Neurology, University of Leipzig Medical Center, Leipzig, Germany
- Faculty of Medicine, CRC 1052 'Obesity Mechanisms', University of Leipzig, Leipzig, Germany
| | - N Yang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
| | - B Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition and Centre for Translational MR Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine, National University of Singapore, Singapore, Singapore
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - H Yun
- Division of Newborn Medicine and Neuroradiology, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - A Zalesky
- Melbourne Neuropsychiatry Centre, University of Melbourne, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, University of Melbourne, Melbourne, Victoria, Australia
| | - H J Zar
- Department of Paediatrics and Child Health, Red Cross War Memorial Children's Hospital, SA-MRC Unit on Child & Adolescent Health, University of Cape Town, Cape Town, South Africa
| | - A Zettergren
- Neuropsychiatric Epidemiology Unit, Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy, Centre for Ageing and Health (AGECAP) at the University of Gothenburg, Gothenburg, Sweden
| | - J H Zhou
- Center for Sleep and Cognition, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Center for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - H Ziauddeen
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - A Zugman
- National Institute of Developmental Psychiatry for Children and Adolescents (INPD), Sao Poalo, Brazil
- National Institute of Mental Health (NIMH), National Institutes of Health (NIH), Bethesda, MD, USA
- Department of Psychiatry, Escola Paulista de Medicina, São Paulo, Brazil
| | - X N Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
- Developmental Population Neuroscience Research Center, IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
- National Basic Science Data Center, Beijing, China
- Research Center for Lifespan Development of Brain and Mind, Institute of Psychology, Chinese Academy of Sciences, Beijing, China
- Key Laboratory of Brain and Education, School of Education Science, Nanning Normal University, Nanning, China
| | - E T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - A F Alexander-Bloch
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
- Lifespan Brain Institute, The Children's Hospital of Philadelphia and Penn Medicine, Philadelphia, PA, USA
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Vasylechko SD, Warfield SK, Afacan O, Kurugol S. Self-supervised IVIM DWI parameter estimation with a physics based forward model. Magn Reson Med 2022; 87:904-914. [PMID: 34687065 PMCID: PMC8627432 DOI: 10.1002/mrm.28989] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/29/2021] [Accepted: 08/08/2021] [Indexed: 02/03/2023]
Abstract
PURPOSE To assess the robustness and repeatability of intravoxel incoherent motion model (IVIM) parameter estimation for the diffusion-weighted MRI in the abdominal organs under the constraints of noisy diffusion signal using a novel neural network method. METHODS Clinically acquired abdominal scans of Crohn's disease patients were retrospectively analyzed with regions segmented in the kidney cortex, spleen, liver, and bowel. A novel IVIM parameter fitting method based on the principle of a physics guided self-supervised convolutional neural network that does not require reference parameter estimates for training was compared to a conventional non-linear least squares (NNLS) algorithm, and a voxelwise trained artificial neural network (ANN). RESULTS Results showed substantial increase in parameter robustness to the noise corrupted signal. In an intra-session repeatability experiment, the proposed method showed reduced coefficient of variation (CoV) over multiple acquisitions in comparison to conventional NLLS method and comparable performance to ANN. The use of D and f estimates from the proposed method led to the smallest misclassification error in linear discriminant analysis for characterization between normal and abnormal Crohn's disease bowel tissue. The fitting of D∗ parameter remains to be challenging. CONCLUSION The proposed method yields robust estimates of D and f IVIM parameters under the constraints of noisy diffusion signal. This indicates a potential for the use of the proposed method in conjunction with accelerated DW-MRI acquisition strategies, which would typically result in lower signal to noise ratio.
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Affiliation(s)
- Serge Didenko Vasylechko
- Computational Radiology Laboratory, Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
- Corresponding author: Name Serge Didenko Vasylechko, Department Computational Radiology Laboratory, Institute Boston Children’s Hospital, Address 360 Longwood Avenue, Boston, MA, 02215, USA,
| | - Simon K. Warfield
- Computational Radiology Laboratory, Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Onur Afacan
- Computational Radiology Laboratory, Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Sila Kurugol
- Computational Radiology Laboratory, Boston Children’s Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
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32
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Balachandrasekaran A, Cohen AL, Afacan O, Warfield SK, Gholipour A. Reducing the Effects of Motion Artifacts in fMRI: A Structured Matrix Completion Approach. IEEE Trans Med Imaging 2022; 41:172-185. [PMID: 34432631 PMCID: PMC8934405 DOI: 10.1109/tmi.2021.3107829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Functional MRI (fMRI) is widely used to study the functional organization of normal and pathological brains. However, the fMRI signal may be contaminated by subject motion artifacts that are only partially mitigated by motion correction strategies. These artifacts lead to distance-dependent biases in the inferred signal correlations. To mitigate these spurious effects, motion-corrupted volumes are censored from fMRI time series. Censoring can result in discontinuities in the fMRI signal, which may lead to substantial alterations in functional connectivity analysis. We propose a new approach to recover the missing entries from censoring based on structured low rank matrix completion. We formulated the artifact-reduction problem as the recovery of a super-resolved matrix from unprocessed fMRI measurements. We enforced a low rank prior on a large structured matrix, formed from the samples of the time series, to recover the missing entries. The recovered time series, in addition to being motion compensated, are also slice-time corrected at a fine temporal resolution. To achieve a fast and memory-efficient solution for our proposed optimization problem, we employed a variable splitting strategy. We validated the algorithm with simulations, data acquired under different motion conditions, and datasets from the ABCD study. Functional connectivity analysis showed that the proposed reconstruction resulted in connectivity matrices with lower errors in pair-wise correlation than non-censored and censored time series based on a standard processing pipeline. In addition, seed-based correlation analyses showed improved delineation of the default mode network. These demonstrate that the method can effectively reduce the adverse effects of motion in fMRI analysis.
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Machado-Rivas F, Afacan O, Khan S, Marami B, Velasco-Annis C, Lidov H, Warfield SK, Gholipour A, Jaimes C. Spatiotemporal changes in diffusivity and anisotropy in fetal brain tractography. Hum Brain Mapp 2021; 42:5771-5784. [PMID: 34487404 PMCID: PMC8559496 DOI: 10.1002/hbm.25653] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 08/22/2021] [Accepted: 08/25/2021] [Indexed: 02/03/2023] Open
Abstract
Population averaged diffusion atlases can be utilized to characterize complex microstructural changes with less bias than data from individual subjects. In this study, a fetal diffusion tensor imaging (DTI) atlas was used to investigate tract-based changes in anisotropy and diffusivity in vivo from 23 to 38 weeks of gestational age (GA). Healthy pregnant volunteers with typically developing fetuses were imaged at 3 T. Acquisition included structural images processed with a super-resolution algorithm and DTI images processed with a motion-tracked slice-to-volume registration algorithm. The DTI from individual subjects were used to generate 16 templates, each specific to a week of GA; this was accomplished by means of a tensor-to-tensor diffeomorphic deformable registration method integrated with kernel regression in age. Deterministic tractography was performed to outline the forceps major, forceps minor, bilateral corticospinal tracts (CST), bilateral inferior fronto-occipital fasciculus (IFOF), bilateral inferior longitudinal fasciculus (ILF), and bilateral uncinate fasciculus (UF). The mean fractional anisotropy (FA) and mean diffusivity (MD) was recorded for all tracts. For a subset of tracts (forceps major, CST, and IFOF) we manually divided the tractograms into anatomy conforming segments to evaluate within-tract changes. We found tract-specific, nonlinear, age related changes in FA and MD. Early in gestation, these trends appear to be dominated by cytoarchitectonic changes in the transient white matter fetal zones while later in gestation, trends conforming to the progression of myelination were observed. We also observed significant (local) heterogeneity in within-tract developmental trajectories for the CST, IFOF, and forceps major.
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Affiliation(s)
- Fedel Machado-Rivas
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Onur Afacan
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Shadab Khan
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Bahram Marami
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Hart Lidov
- Department of Pathology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Simon K Warfield
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Camilo Jaimes
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
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Waugh JL, Hassan A, Kuster JK, Levenstein JM, Warfield SK, Makris N, Brüggemann N, Sharma N, Breiter HC, Blood AJ. An MRI method for parcellating the human striatum into matrix and striosome compartments in vivo. Neuroimage 2021; 246:118714. [PMID: 34800665 PMCID: PMC9142299 DOI: 10.1016/j.neuroimage.2021.118714] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 11/02/2021] [Accepted: 11/05/2021] [Indexed: 11/19/2022] Open
Abstract
The mammalian striatum is comprised of intermingled tissue compartments, matrix and striosome. Though indistinguishable by routine histological techniques, matrix and striosome have distinct embryologic origins, afferent/efferent connections, surface protein expression, intra-striatal location, susceptibilities to injury, and functional roles in a range of animal behaviors. Distinguishing the compartments previously required post-mortem tissue and/or genetic manipulation; we aimed to identify matrix/striosome non-invasively in living humans. We used diffusion MRI (probabilistic tractography) to identify human striatal voxels with connectivity biased towards matrix-favoring or striosome-favoring regions (determined by prior animal tract-tracing studies). Segmented striatal compartments replicated the topological segregation and somatotopic organization identified in animal matrix/striosome studies. Of brain regions mapped in prior studies, our human brain data confirmed 93% of the compartment-selective structural connectivity demonstrated in animals. Test-retest assessment on repeat scans found a voxel classification error rate of 0.14%. Fractional anisotropy was significantly higher in matrix-like voxels, while mean diffusivity did not differ between the compartments. As mapped by the Talairach human brain atlas, 460 regions were significantly biased towards either matrix or striosome. Our method allows the study of striatal compartments in human health and disease, in vivo, for the first time.
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Affiliation(s)
- J L Waugh
- Division of Pediatric Neurology, Department of Pediatrics, University of Texas Southwestern, Dallas, TX, United States; Division of Child Neurology, University of Texas Southwestern, Dallas, TX, United States; Boston Children's Hospital, Harvard Medical School, Boston, MA, United States; Mood and Motor Control Laboratory, Boston, MA, United States; Martinos Center for Biomedical Imaging, United States; Massachusetts General Hospital, Charlestown, MA, United States.
| | - Aao Hassan
- Division of Pediatric Neurology, Department of Pediatrics, University of Texas Southwestern, Dallas, TX, United States
| | - J K Kuster
- Mood and Motor Control Laboratory, Boston, MA, United States; Laboratory of Neuroimaging and Genetics, United States; Martinos Center for Biomedical Imaging, United States; Rheumatology, Allergy and Immunology Section, Massachusetts General Hospital, Boston, MA, United States.
| | - J M Levenstein
- Mood and Motor Control Laboratory, Boston, MA, United States; Martinos Center for Biomedical Imaging, United States; Yale School of Medicine, New Haven, CN, United States; Wellcome Centre for Integrative Neuroimaging, National Institutes of Health, Bethesda, MD, United States.
| | - S K Warfield
- Department of Radiology, United States; Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.
| | - N Makris
- Boston Children's Hospital, Harvard Medical School, Boston, MA, United States; Center for Morphometric Analysis, United States; Martinos Center for Biomedical Imaging, United States; Departments of Neurology and Psychiatry, Charlestown, MA, United States.
| | - N Brüggemann
- Department of Neurology, University of Oxford, Oxford, United Kingdom; Institute of Neurogenetics, University of Lübeck, Lübeck, Germany.
| | - N Sharma
- Boston Children's Hospital, Harvard Medical School, Boston, MA, United States; Massachusetts General Hospital, Charlestown, MA, United States.
| | - H C Breiter
- Laboratory of Neuroimaging and Genetics, United States; Warren Wright Adolescent Center, Department of Psychiatry and Behavioral Sciences, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
| | - A J Blood
- Mood and Motor Control Laboratory, Boston, MA, United States; Laboratory of Neuroimaging and Genetics, United States; Martinos Center for Biomedical Imaging, United States; Departments of Neurology and Psychiatry, Charlestown, MA, United States.
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Sui Y, Afacan O, Jaimes C, Gholipour A, Warfield SK. Gradient-Guided Isotropic MRI Reconstruction from Anisotropic Acquisitions. IEEE Trans Comput Imaging 2021; 7:1240-1253. [PMID: 35252479 PMCID: PMC8896514 DOI: 10.1109/tci.2021.3128745] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
The trade-off between image resolution, signal-to-noise ratio (SNR), and scan time in any magnetic resonance imaging (MRI) protocol is inevitable and unavoidable. Super-resolution reconstruction (SRR) has been shown effective in mitigating these factors, and thus, has become an important approach in addressing the current limitations of MRI. In this work, we developed a novel, image-based MRI SRR approach based on anisotropic acquisition schemes, which utilizes a new gradient guidance regularization method that guides the high-resolution (HR) reconstruction via a spatial gradient estimate. Further, we designed an analytical solution to propagate the spatial gradient fields from the low-resolution (LR) images to the HR image space and exploited these gradient fields over multiple scales with a dynamic update scheme for more accurate edge localization in the reconstruction. We also established a forward model of image formation and inverted it along with the proposed gradient guidance. The proposed SRR method allows subject motion between volumes and is able to incorporate various acquisition schemes where the LR images are acquired with arbitrary orientations and displacements, such as orthogonal and through-plane origin-shifted scans. We assessed our proposed approach on simulated data as well as on the data acquired on a Siemens 3T MRI scanner containing 45 MRI scans from 14 subjects. Our experimental results demonstrate that our approach achieved superior reconstructions compared to state-of-the-art methods, both in terms of local spatial smoothness and edge preservation, while, in parallel, at reduced, or at the same cost as scans delivered with direct HR acquisition.
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Affiliation(s)
- Yao Sui
- Harvard Medical School and Boston Children's Hospital, Boston, Massachusetts, United States
| | - Onur Afacan
- Harvard Medical School and Boston Children's Hospital, Boston, Massachusetts, United States
| | - Camilo Jaimes
- Harvard Medical School and Boston Children's Hospital, Boston, Massachusetts, United States
| | - Ali Gholipour
- Harvard Medical School and Boston Children's Hospital, Boston, Massachusetts, United States
| | - Simon K Warfield
- Harvard Medical School and Boston Children's Hospital, Boston, Massachusetts, United States
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Karimi D, Jaimes C, Machado-Rivas F, Vasung L, Khan S, Warfield SK, Gholipour A. Deep learning-based parameter estimation in fetal diffusion-weighted MRI. Neuroimage 2021; 243:118482. [PMID: 34455242 PMCID: PMC8573718 DOI: 10.1016/j.neuroimage.2021.118482] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 08/03/2021] [Accepted: 08/17/2021] [Indexed: 11/24/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) of fetal brain is challenged by frequent fetal motion and signal to noise ratio that is much lower than non-fetal imaging. As a result, accurate and robust parameter estimation in fetal DW-MRI remains an open problem. Recently, deep learning techniques have been successfully used for DW-MRI parameter estimation in non-fetal subjects. However, none of those prior works has addressed the fetal brain because obtaining reliable fetal training data is challenging. To address this problem, in this work we propose a novel methodology that utilizes fetal scans as well as scans from prematurely-born infants. High-quality newborn scans are used to estimate accurate maps of the parameter of interest. These parameter maps are then used to generate DW-MRI data that match the measurement scheme and noise distribution that are characteristic of fetal data. In order to demonstrate the effectiveness and reliability of the proposed data generation pipeline, we used the generated data to train a convolutional neural network (CNN) to estimate color fractional anisotropy (CFA). We evaluated the trained CNN on independent sets of fetal data in terms of reconstruction accuracy, precision, and expert assessment of reconstruction quality. Results showed significantly lower reconstruction error (n=100,p<0.001) and higher reconstruction precision (n=20,p<0.001) for the proposed machine learning pipeline compared with standard estimation methods. Expert assessments on 20 fetal test scans showed significantly better overall reconstruction quality (p<0.001) and more accurate reconstruction of 11 regions of interest (p<0.001) with the proposed method.
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Affiliation(s)
- Davood Karimi
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA.
| | - Camilo Jaimes
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Fedel Machado-Rivas
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Lana Vasung
- Department of Pediatrics at Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Shadab Khan
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Simon K Warfield
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
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Karimi D, Vasung L, Jaimes C, Machado-Rivas F, Warfield SK, Gholipour A. Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI. Neuroimage 2021; 239:118316. [PMID: 34182101 PMCID: PMC8385546 DOI: 10.1016/j.neuroimage.2021.118316] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/20/2021] [Accepted: 06/25/2021] [Indexed: 02/06/2023] Open
Abstract
Estimation of white matter fiber orientation distribution function (fODF) is the essential first step for reliable brain tractography and connectivity analysis. Most of the existing fODF estimation methods rely on sub-optimal physical models of the diffusion signal or mathematical simplifications, which can impact the estimation accuracy. In this paper, we propose a data-driven method that avoids some of these pitfalls. Our proposed method is based on a multilayer perceptron that learns to map the diffusion-weighted measurements, interpolated onto a fixed spherical grid in the q space, to the target fODF. Importantly, we also propose methods for synthesizing reliable simulated training data. We show that the model can be effectively trained with simulated or real training data. Our phantom experiments show that the proposed method results in more accurate fODF estimation and tractography than several competing methods including the multi-tensor model, Bayesian estimation, spherical deconvolution, and two other machine learning techniques. On real data, we compare our method with other techniques in terms of accuracy of estimating the ground-truth fODF. The results show that our method is more accurate than other methods, and that it performs better than the competing methods when applied to under-sampled diffusion measurements. We also compare our method with the Sparse Fascicle Model in terms of expert ratings of the accuracy of reconstruction of several commissural, projection, association, and cerebellar tracts. The results show that the tracts reconstructed with the proposed method are rated significantly higher by three independent experts. Our study demonstrates the potential of data-driven methods for improving the accuracy and robustness of fODF estimation.
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Affiliation(s)
- Davood Karimi
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA.
| | - Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Camilo Jaimes
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Fedel Machado-Rivas
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Simon K Warfield
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
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Askin Incebacak NC, Sui Y, Gui Levy L, Merlini L, Sa de Almeida J, Courvoisier S, Wallace TE, Klauser A, Afacan O, Warfield SK, Hüppi P, Lazeyras F. Super-resolution reconstruction of T2-weighted thick-slice neonatal brain MRI scans. J Neuroimaging 2021; 32:68-79. [PMID: 34506677 PMCID: PMC8752487 DOI: 10.1111/jon.12929] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 07/23/2021] [Accepted: 08/20/2021] [Indexed: 11/28/2022] Open
Abstract
Background and Purpose Super‐resolutionreconstruction (SRR) can be used to reconstruct 3‐dimensional (3D) high‐resolution (HR) volume from several 2‐dimensional (2D) low‐resolution (LR) stacks of MRI slices. The purpose is to compare lengthy 2D T2‐weighted HR image acquisition of neonatal subjects with 3D SRR from several LR stacks in terms of image quality for clinical and morphometric assessments. Methods LR brain images were acquired from neonatal subjects to reconstruct isotropic 3D HR volumes by using SRR algorithm. Quality assessments were done by an experienced pediatric radiologist using scoring criteria adapted to newborn anatomical landmarks. The Wilcoxon signed‐rank test was used to compare scoring results between HR and SRR images. For quantitative assessments, morphology‐based segmentation was performed on both HR and SRR images and Dice coefficients between the results were computed. Additionally, simple linear regression was performed to compare the tissue volumes. Results No statistical difference was found between HR and SRR structural scores using Wilcoxon signed‐rank test (p = .63, Z = .48). Regarding segmentation results, R2 values for the volumes of gray matter, white matter, cerebrospinal fluid, basal ganglia, cerebellum, and total brain volume including brain stem ranged between .95 and .99. Dice coefficients between the segmented regions from HR and SRR ranged between .83 ± .04 and .96 ± .01. Conclusion Qualitative and quantitative assessments showed that 3D SRR of several LR images produces images that are of comparable quality to standard 2D HR image acquisition for healthy neonatal imaging without loss of anatomical details with similar edge definition allowing the detection of fine anatomical structures and permitting comparable morphometric measurement.
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Affiliation(s)
| | - Yao Sui
- CRL, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Laura Gui Levy
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland
| | - Laura Merlini
- Pediatric Radiology Unit, Division of Radiology, University Hospitals of Geneva, Geneva, Switzerland
| | - Joana Sa de Almeida
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland
| | - Sebastien Courvoisier
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.,CIBM, Center of Biomedical Imaging, Geneva, Switzerland
| | - Tess E Wallace
- CRL, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Antoine Klauser
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.,CIBM, Center of Biomedical Imaging, Geneva, Switzerland
| | - Onur Afacan
- CRL, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Simon K Warfield
- CRL, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Petra Hüppi
- Division of Development and Growth, Department of Woman, Child and Adolescent, University Hospitals of Geneva, Geneva, Switzerland
| | - Francois Lazeyras
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.,CIBM, Center of Biomedical Imaging, Geneva, Switzerland
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39
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Scherrer B, Prohl AK, Taquet M, Kapur K, Peters JM, Tomas-Fernandez X, Davis PE, M Bebin E, Krueger DA, Northrup H, Y Wu J, Sahin M, Warfield SK. The Connectivity Fingerprint of the Fusiform Gyrus Captures the Risk of Developing Autism in Infants with Tuberous Sclerosis Complex. Cereb Cortex 2021; 30:2199-2214. [PMID: 31812987 DOI: 10.1093/cercor/bhz233] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2018] [Revised: 09/05/2019] [Accepted: 09/12/2019] [Indexed: 12/13/2022] Open
Abstract
Tuberous sclerosis complex (TSC) is a rare genetic disorder characterized by benign tumors throughout the body; it is generally diagnosed early in life and has a high prevalence of autism spectrum disorder (ASD), making it uniquely valuable in studying the early development of autism, before neuropsychiatric symptoms become apparent. One well-documented deficit in ASD is an impairment in face processing. In this work, we assessed whether anatomical connectivity patterns of the fusiform gyrus, a central structure in face processing, capture the risk of developing autism early in life. We longitudinally imaged TSC patients at 1, 2, and 3 years of age with diffusion compartment imaging. We evaluated whether the anatomical connectivity fingerprint of the fusiform gyrus was associated with the risk of developing autism measured by the Autism Observation Scale for Infants (AOSI). Our findings suggest that the fusiform gyrus connectivity captures the risk of developing autism as early as 1 year of age and provides evidence that abnormal fusiform gyrus connectivity increases with age. Moreover, the identified connections that best capture the risk of developing autism involved the fusiform gyrus and limbic and paralimbic regions that were consistent with the ASD phenotype, involving an increased number of left-lateralized structures with increasing age.
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Affiliation(s)
- Benoit Scherrer
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Anna K Prohl
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Maxime Taquet
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Kush Kapur
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Jurriaan M Peters
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Xavier Tomas-Fernandez
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Peter E Davis
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Elizabeth M Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL, 35233 USA
| | - Darcy A Krueger
- Department of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, 45229 USA
| | - Hope Northrup
- Department of Pediatrics, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX, 77030 USA
| | - Joyce Y Wu
- Department of Pediatrics, UCLA Mattel Children's Hospital, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, 90095 USA
| | - Mustafa Sahin
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115 USA
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40
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Duffy PB, Stemmer A, Callahan MJ, Cravero JP, Johnston PR, Warfield SK, Bixby SD. Free-breathing radial stack-of-stars three-dimensional Dixon gradient echo sequence in abdominal magnetic resonance imaging in sedated pediatric patients. Pediatr Radiol 2021; 51:1645-1653. [PMID: 33830291 DOI: 10.1007/s00247-021-05054-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/30/2021] [Accepted: 03/16/2021] [Indexed: 11/30/2022]
Abstract
BACKGROUND There is a strong need for improvements in motion robust T1-weighted abdominal imaging sequences in children to enable high-quality, free-breathing imaging. OBJECTIVE To compare imaging time and quality of a radial stack-of-stars, free-breathing T1-weighted gradient echo acquisition (volumetric interpolated breath-hold examination [VIBE]) three-dimensional (3-D) Dixon sequence in sedated pediatric patients undergoing abdominal magnetic resonance imaging (MRI) against conventional Cartesian T1-weighed sequences. MATERIALS AND METHODS This study was approved by the institutional review board with informed consent obtained from all subjects. Study subjects included 31 pediatric patients (19 male, 12 female; median age: 5 years; interquartile range: 5 years) undergoing abdominal MRI at 3 tesla with a free-breathing T1-weighted radial stack-of-stars 3-D VIBE Dixon prototype sequence, StarVIBE Dixon (radial technique), between October 2018 and June 2019 with previous abdominal MR imaging using conventional Cartesian T1-weighed imaging (traditional technique). MRI component times were recorded as well as the total number of non-contrast T1-weighted sequences. Two radiologists independently rated images for quality using a scale from 1 to 5 according to the following metrics: overall image quality, hepatic edge sharpness, hepatic vessel clarity and respiratory motion robustness. Scores were compared between the groups. RESULTS Mean T1-weighted imaging times for all subjects were 3.63 min for radial exams and 8.01 min for traditional exams (P<0.001), and total non-contrast imaging time was 32.7 min vs. 43.9 min (P=0.002). Adjusted mean total MRI time for all subjects was 60.2 min for radial exams and 65.7 min for traditional exams (P=0.387). The mean number of non-contrast T1-weighted sequences performed in radial MRI exams was 1.0 compared to 1.9 (range: 0-6) in traditional exams (P<0.001). StarVIBE Dixon outperformed Cartesian methods in all quality metrics. The mean overall image quality (scale 1-5) was 3.95 for radial exams and 3.31 for traditional exams (P<0.001). CONCLUSION Radial stack-of-stars 3-D VIBE Dixon during free-breathing abdominal MRI in pediatric patients offers improved image quality compared to Cartesian T1-weighted imaging techniques with decreased T1-weighted and total non-contrast imaging time. This has important implications for children undergoing sedation for imaging.
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Affiliation(s)
- Patrick B Duffy
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston, MA, 02115, USA
| | | | - Michael J Callahan
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston, MA, 02115, USA
| | - Joseph P Cravero
- Department of Anesthesiology, Boston Children's Hospital, Boston, MA, USA
| | - Patrick R Johnston
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston, MA, 02115, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston, MA, 02115, USA
| | - Sarah D Bixby
- Department of Radiology, Boston Children's Hospital, 300 Longwood Ave., Boston, MA, 02115, USA.
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41
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Karimi D, Vasung L, Jaimes C, Machado-Rivas F, Khan S, Warfield SK, Gholipour A. A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging. Med Image Anal 2021; 72:102129. [PMID: 34182203 PMCID: PMC8320341 DOI: 10.1016/j.media.2021.102129] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 12/29/2022]
Abstract
Accurate modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles. In this paper, we propose a machine learning-based technique that can accurately estimate the number and orientations of fascicles in a voxel. Our method can be trained with either simulated or real diffusion-weighted imaging data. Our method estimates the angle to the closest fascicle for each direction in a set of discrete directions uniformly spread on the unit sphere. This information is then processed to extract the number and orientations of fascicles in a voxel. On realistic simulated phantom data with known ground truth, our method predicts the number and orientations of crossing fascicles more accurately than several classical and machine learning methods. It also leads to more accurate tractography. On real data, our method is better than or compares favorably with other methods in terms of robustness to measurement down-sampling and also in terms of expert quality assessment of tractography results.
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Affiliation(s)
- Davood Karimi
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Lana Vasung
- Department of Pediatrics at Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Camilo Jaimes
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fedel Machado-Rivas
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shadab Khan
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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42
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Wang X, MacDougall RD, Chen P, Bouman CA, Warfield SK. Physics-based iterative reconstruction for dual-source and flying focal spot computed tomography. Med Phys 2021; 48:3595-3613. [PMID: 33982297 DOI: 10.1002/mp.14941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 04/08/2021] [Accepted: 04/16/2021] [Indexed: 12/21/2022] Open
Abstract
PURPOSE For single-source helical Computed Tomography (CT), both Filtered-Back Projection (FBP) and statistical iterative reconstruction have been investigated. However, for dual-source CT with flying focal spot (DS-FFS CT), a statistical iterative reconstruction that accurately models the scanner geometry and acquisition physics remains unknown to researchers. Therefore, our purpose is to present a novel physics-based iterative reconstruction method for DS-FFS CT and assess its image quality. METHODS Our algorithm uses precise physics models to reconstruct from the native cone-beam geometry and interleaved dual-source helical trajectory of a DS-FFS CT. To do so, we construct a noise physics model to represent data acquisition noise and a prior image model to represent image noise and texture. In addition, we design forward system models to compute the locations of deflected focal spots, the dimension, and sensitivity of voxels and detector units, as well as the length of intersection between x-rays and voxels. The forward system models further represent the coordinated movement between the dual sources by computing their x-ray coverage gaps and overlaps at an arbitrary helical pitch. With the above models, we reconstruct images by an advanced Consensus Equilibrium (CE) numerical method to compute the maximum a posteriori estimate to a joint optimization problem that simultaneously fits all models. RESULTS We compared our reconstruction with Siemens ADMIRE, which is the clinical standard hybrid iterative reconstruction (IR) method for DS-FFS CT, in terms of spatial resolution, noise profile, and image artifacts through both phantoms and clinical scan datasets. Experiments show that our reconstruction has a higher spatial resolution, with a Task-Based Modulation Transfer Function (MTFtask ) consistently higher than the clinical standard hybrid IR. In addition, our reconstruction shows a reduced magnitude of image undersampling artifacts than the clinical standard. CONCLUSIONS By modeling a precise geometry and avoiding data rebinning or interpolation, our physics-based reconstruction achieves a higher spatial resolution and fewer image artifacts with smaller magnitude than the clinical standard hybrid IR.
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Affiliation(s)
- Xiao Wang
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Robert D MacDougall
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Peng Chen
- National Institute of Advanced Industrial Science and Technology, Tokyo, Japan.,RIKEN Center for Computational Science, Kobe, Hyogo, Japan
| | - Charles A Bouman
- School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
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43
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Sui Y, Afacan O, Gholipour A, Warfield SK. Fast and High-Resolution Neonatal Brain MRI Through Super-Resolution Reconstruction From Acquisitions With Variable Slice Selection Direction. Front Neurosci 2021; 15:636268. [PMID: 34220414 PMCID: PMC8242183 DOI: 10.3389/fnins.2021.636268] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Accepted: 05/19/2021] [Indexed: 12/18/2022] Open
Abstract
The brain of neonates is small in comparison to adults. Imaging at typical resolutions such as one cubic mm incurs more partial voluming artifacts in a neonate than in an adult. The interpretation and analysis of MRI of the neonatal brain benefit from a reduction in partial volume averaging that can be achieved with high spatial resolution. Unfortunately, direct acquisition of high spatial resolution MRI is slow, which increases the potential for motion artifact, and suffers from reduced signal-to-noise ratio. The purpose of this study is thus that using super-resolution reconstruction in conjunction with fast imaging protocols to construct neonatal brain MRI images at a suitable signal-to-noise ratio and with higher spatial resolution than can be practically obtained by direct Fourier encoding. We achieved high quality brain MRI at a spatial resolution of isotropic 0.4 mm with 6 min of imaging time, using super-resolution reconstruction from three short duration scans with variable directions of slice selection. Motion compensation was achieved by aligning the three short duration scans together. We applied this technique to 20 newborns and assessed the quality of the images we reconstructed. Experiments show that our approach to super-resolution reconstruction achieved considerable improvement in spatial resolution and signal-to-noise ratio, while, in parallel, substantially reduced scan times, as compared to direct high-resolution acquisitions. The experimental results demonstrate that our approach allowed for fast and high-quality neonatal brain MRI for both scientific research and clinical studies.
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Affiliation(s)
- Yao Sui
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Onur Afacan
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
| | - Simon K. Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Boston, MA, United States
- Harvard Medical School, Boston, MA, United States
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44
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Vasung L, Rollins CK, Yun HJ, Velasco-Annis C, Zhang J, Wagstyl K, Evans A, Warfield SK, Feldman HA, Grant PE, Gholipour A. Quantitative In vivo MRI Assessment of Structural Asymmetries and Sexual Dimorphism of Transient Fetal Compartments in the Human Brain. Cereb Cortex 2021; 30:1752-1767. [PMID: 31602456 DOI: 10.1093/cercor/bhz200] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2019] [Revised: 08/02/2019] [Accepted: 08/05/2019] [Indexed: 12/19/2022] Open
Abstract
Structural asymmetries and sexual dimorphism of the human cerebral cortex have been identified in newborns, infants, children, adolescents, and adults. Some of these findings were linked with cognitive and neuropsychiatric disorders, which have roots in altered prenatal brain development. However, little is known about structural asymmetries or sexual dimorphism of transient fetal compartments that arise in utero. Thus, we aimed to identify structural asymmetries and sexual dimorphism in the volume of transient fetal compartments (cortical plate [CP] and subplate [SP]) across 22 regions. For this purpose, we used in vivo structural T2-weighted MRIs of 42 healthy fetuses (16.43-36.86 gestational weeks old, 15 females). We found significant leftward asymmetry in the volume of the CP and SP in the inferior frontal gyrus. The orbitofrontal cortex showed significant rightward asymmetry in the volume of CP merged with SP. Males had significantly larger volumes in regions belonging to limbic, occipital, and frontal lobes, which were driven by a significantly larger SP. Lastly, we did not observe sexual dimorphism in the growth trajectories of the CP or SP. In conclusion, these results support the hypothesis that structural asymmetries and sexual dimorphism in relative volumes of cortical regions are present during prenatal brain development.
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Affiliation(s)
- Lana Vasung
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA.,Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Caitlin K Rollins
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Hyuk Jin Yun
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA.,Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Clemente Velasco-Annis
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Jennings Zhang
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA.,McGill Centre for Integrative Neuroscience/Montreal Neurological Institute, McGill University, Montreal QC H3A 2B4, Canada
| | | | - Alan Evans
- McGill Centre for Integrative Neuroscience/Montreal Neurological Institute, McGill University, Montreal QC H3A 2B4, Canada
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Henry A Feldman
- Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA.,Institutional Centers for Clinical and Translational Research, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging & Developmental Science Center (FNNDSC), Boston, MA 02115, USA.,Division of Newborn Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Ali Gholipour
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA 02115, USA
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45
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Guler S, Cohen AL, Afacan O, Warfield SK. Matched neurofeedback during fMRI differentially activates reward-related circuits in active and sham groups. J Neuroimaging 2021; 31:947-955. [PMID: 34101274 DOI: 10.1111/jon.12899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Revised: 05/01/2021] [Accepted: 05/27/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE Functional MRI neurofeedback (fMRI-nf) leverages the brain's ability to self-regulate its own activity. However, self-regulation processes engaged during fMRI-nf are incompletely understood. Here, we used matched feedback in an fMRI-nf experimental protocol to investigate whether brain processes recognize true neurofeedback signals. METHODS We implemented an existing fMRI-nf protocol to train lateralized motor activity using a finger-tap task in conjunction with real-time feedback. Twelve healthy, right-handed, adult participants were assigned into age- and sex-matched active and sham study groups. Matched participant pairs received the same visual feedback, based on brain activity of the participant from the active group. We compared group-averaged activation maps before, during, and after neurofeedback, and analyzed changes in lateralized motor activity due to neurofeedback. RESULTS Active and sham groups demonstrated different brain activation to the same feedback during neurofeedback. In particular, there was higher activation in visual cortex, secondary somatosensory cortex, and right inferior frontal gyrus in the active group compared to the sham group. Conversely, sham participants demonstrated higher activation in anterior cingulate cortex, left frontal pole, and posterior superior temporal gyrus. Despite differing brain activations during neurofeedback, neither group demonstrated significant improvement in lateralized motor activity from pre to postfeedback scan in the same session. We also observed no significant difference between pre and postfeedback activation maps, suggesting that no significant finger-tap related functional reorganization had occurred. CONCLUSIONS These findings suggest that fMRI neurofeedback paradigms that monitor or incorporate activity from regions reported here would provide enhanced efficacy for research investigation and clinical intervention.
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Affiliation(s)
- Seyhmus Guler
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Massachusetts, Boston, USA
| | - Alexander L Cohen
- Computational Radiology Lab, Boston Children's Hospital, Harvard Medical School, Massachusetts, Boston, USA.,Department of Neurology, Boston Children's Hospital, Harvard Medical School, Massachusetts, Boston, USA
| | - Onur Afacan
- Computational Radiology Lab, Boston Children's Hospital, Harvard Medical School, Massachusetts, Boston, USA
| | - Simon K Warfield
- Computational Radiology Lab, Boston Children's Hospital, Harvard Medical School, Massachusetts, Boston, USA
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46
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Taquet M, Smith SM, Prohl AK, Peters JM, Warfield SK, Scherrer B, Harrison PJ. A structural brain network of genetic vulnerability to psychiatric illness. Mol Psychiatry 2021; 26:2089-2100. [PMID: 32372008 PMCID: PMC7644622 DOI: 10.1038/s41380-020-0723-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2019] [Revised: 03/17/2020] [Accepted: 03/30/2020] [Indexed: 12/31/2022]
Abstract
Psychiatry is undergoing a paradigm shift from the acceptance of distinct diagnoses to a representation of psychiatric illness that crosses diagnostic boundaries. How this transition is supported by a shared neurobiology remains largely unknown. In this study, we first identify single nucleotide polymorphisms (SNPs) associated with psychiatric disorders based on 136 genome-wide association studies. We then conduct a joint analysis of these SNPs and brain structural connectomes in 678 healthy children in the PING study. We discovered a strong, robust, and transdiagnostic mode of genome-connectome covariation which is positively and specifically correlated with genetic risk for psychiatric illness at the level of individual SNPs. Similarly, this mode is also significantly positively correlated with polygenic risk scores for schizophrenia, alcohol use disorder, major depressive disorder, a combined bipolar disorder-schizophrenia phenotype, and a broader cross-disorder phenotype, and significantly negatively correlated with a polygenic risk score for educational attainment. The resulting "vulnerability network" is shown to mediate the influence of genetic risks onto behaviors related to psychiatric vulnerability (e.g., marijuana, alcohol, and caffeine misuse, perceived stress, and impulsive behavior). Its anatomy overlaps with the default-mode network, with a network of cognitive control, and with the occipital cortex. These findings suggest that the brain vulnerability network represents an endophenotype funneling genetic risks for various psychiatric illnesses through a common neurobiological root. It may form part of the neural underpinning of the well-recognized but poorly explained overlap and comorbidity between psychiatric disorders.
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Affiliation(s)
- Maxime Taquet
- Department of Psychiatry, University of Oxford, Oxford, UK.
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, UK
| | - Anna K Prohl
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jurriaan M Peters
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Simon K Warfield
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Benoit Scherrer
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Paul J Harrison
- Department of Psychiatry, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging (WIN FMRIB), University of Oxford, Oxford, UK
- Oxford Health NHS Foundation Trust, Oxford, UK
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47
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Karimi D, Warfield SK, Gholipour A. Transfer learning in medical image segmentation: New insights from analysis of the dynamics of model parameters and learned representations. Artif Intell Med 2021; 116:102078. [PMID: 34020754 PMCID: PMC8164174 DOI: 10.1016/j.artmed.2021.102078] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/19/2021] [Accepted: 04/20/2021] [Indexed: 11/24/2022]
Abstract
We present a critical assessment of the role of transfer learning in training fully convolutional networks (FCNs) for medical image segmentation. We first show that although transfer learning reduces the training time on the target task, improvements in segmentation accuracy are highly task/data-dependent. Large improvements are observed only when the segmentation task is more challenging and the target training data is smaller. We shed light on these observations by investigating the impact of transfer learning on the evolution of model parameters and learned representations. We observe that convolutional filters change little during training and still look random at convergence. We further show that quite accurate FCNs can be built by freezing the encoder section of the network at random values and only training the decoder section. At least for medical image segmentation, this finding challenges the common belief that the encoder section needs to learn data/task-specific representations. We examine the evolution of FCN representations to gain a deeper insight into the effects of transfer learning on the training dynamics. Our analysis shows that although FCNs trained via transfer learning learn different representations than FCNs trained with random initialization, the variability among FCNs trained via transfer learning can be as high as that among FCNs trained with random initialization. Moreover, feature reuse is not restricted to the early encoder layers; rather, it can be more significant in deeper layers. These findings offer new insights and suggest alternative ways of training FCNs for medical image segmentation.
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Affiliation(s)
- Davood Karimi
- Department of Radiology at Boston Children's Hospital, and Harvard Medical School, Boston, MA, USA.
| | - Simon K Warfield
- Department of Radiology at Boston Children's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Ali Gholipour
- Department of Radiology at Boston Children's Hospital, and Harvard Medical School, Boston, MA, USA
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48
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Cohen AL, Mulder BPF, Prohl AK, Soussand L, Davis P, Kroeck MR, McManus P, Gholipour A, Scherrer B, Bebin EM, Wu JY, Northrup H, Krueger DA, Sahin M, Warfield SK, Fox MD, Peters JM. Tuber Locations Associated with Infantile Spasms Map to a Common Brain Network. Ann Neurol 2021; 89:726-739. [PMID: 33410532 PMCID: PMC7969435 DOI: 10.1002/ana.26015] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 01/04/2021] [Accepted: 01/04/2021] [Indexed: 12/18/2022]
Abstract
OBJECTIVE Approximately 50% of patients with tuberous sclerosis complex develop infantile spasms, a sudden onset epilepsy syndrome associated with poor neurological outcomes. An increased burden of tubers confers an elevated risk of infantile spasms, but it remains unknown whether some tuber locations confer higher risk than others. Here, we test whether tuber location and connectivity are associated with infantile spasms. METHODS We segmented tubers from 123 children with (n = 74) and without (n = 49) infantile spasms from a prospective observational cohort. We used voxelwise lesion symptom mapping to test for an association between spasms and tuber location. We then used lesion network mapping to test for an association between spasms and connectivity with tuber locations. Finally, we tested the discriminability of identified associations with logistic regression and cross-validation as well as statistical mediation. RESULTS Tuber locations associated with infantile spasms were heterogenous, and no single location was significantly associated with spasms. However, >95% of tuber locations associated with spasms were functionally connected to the globi pallidi and cerebellar vermis. These connections were specific compared to tubers in patients without spasms. Logistic regression found that globus pallidus connectivity was a stronger predictor of spasms (odds ratio [OR] = 1.96, 95% confidence interval [CI] = 1.10-3.50, p = 0.02) than tuber burden (OR = 1.65, 95% CI = 0.90-3.04, p = 0.11), with a mean receiver operating characteristic area under the curve of 0.73 (±0.1) during repeated cross-validation. INTERPRETATION Connectivity between tuber locations and the bilateral globi pallidi is associated with infantile spasms. Our findings lend insight into spasm pathophysiology and may identify patients at risk. ANN NEUROL 2021;89:726-739.
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Affiliation(s)
- Alexander L Cohen
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Laboratory for Brain Network Imaging and Modulation, Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Brechtje P F Mulder
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- VUmc School of Medical Sciences, VU University Medical Center Amsterdam, Amsterdam, the Netherlands
| | - Anna K Prohl
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Louis Soussand
- Laboratory for Brain Network Imaging and Modulation, Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Peter Davis
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Mallory R Kroeck
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Laboratory for Brain Network Imaging and Modulation, Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Peter McManus
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Laboratory for Brain Network Imaging and Modulation, Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Benoit Scherrer
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - E Martina Bebin
- Department of Neurology, University of Alabama at Birmingham, Birmingham, AL
| | - Joyce Y Wu
- Division of Pediatric Neurology, UCLA Mattel Children's Hospital, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA
| | - Hope Northrup
- Division of Medical Genetics, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX
| | - Darcy A Krueger
- Department of Neurology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH
| | - Mustafa Sahin
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- F. M. Kirby Neurobiology Center, Boston Children's Hospital, Harvard Medical School, Harvard University, Boston, MA
| | - Simon K Warfield
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
| | - Michael D Fox
- Laboratory for Brain Network Imaging and Modulation, Berenson-Allen Center for Noninvasive Brain Stimulation and Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA
| | - Jurriaan M Peters
- Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA
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Afacan O, Yang E, Lin AP, Coello E, DiBacco ML, Pearl PL, Warfield SK. Magnetic Resonance Imaging (MRI) and Spectroscopy in Succinic Semialdehyde Dehydrogenase Deficiency. J Child Neurol 2021; 36:1162-1168. [PMID: 33557675 PMCID: PMC8349937 DOI: 10.1177/0883073821991295] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Succinic semialdehyde dehydrogenase (SSADH) deficiency is an autosomal recessive disorder of γ-aminobutyric acid (GABA) degradation, resulting in elevations of brain GABA and γ-hydroxybutyric acid (GHB). Previous magnetic resonance (MR) spectroscopy studies have shown increased levels of Glx in SSADH deficiency patients. Here in this work, we measure brain GABA in a large cohort of SSADH deficiency patients using advanced MR spectroscopy techniques that allow separation of GABA from overlapping metabolite peaks. We observed significant increases in GABA concentrations in SSADH deficiency patients for all 3 brain regions that were evaluated. Although GABA levels were higher in all 3 regions, each region had different patterns in terms of GABA changes with respect to age. We also report results from structural magnetic resonance imaging (MRI) of the same cohort compared with age-matched controls. We consistently observed signal hyperintensities in globus pallidus and cerebellar dentate nucleus.
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Affiliation(s)
- Onur Afacan
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Edward Yang
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Alexander P. Lin
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, United States
| | - Eduardo Coello
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, United States
| | - Melissa L. DiBacco
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Phillip L. Pearl
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Simon K. Warfield
- Department of Radiology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States
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50
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Reymbaut A, Caron AV, Gilbert G, Szczepankiewicz F, Nilsson M, Warfield SK, Descoteaux M, Scherrer B. Magic DIAMOND: Multi-fascicle diffusion compartment imaging with tensor distribution modeling and tensor-valued diffusion encoding. Med Image Anal 2021; 70:101988. [PMID: 33611054 DOI: 10.1016/j.media.2021.101988] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 01/25/2021] [Accepted: 01/29/2021] [Indexed: 01/05/2023]
Abstract
Diffusion tensor imaging provides increased sensitivity to microstructural tissue changes compared to conventional anatomical imaging but also presents limited specificity. To tackle this problem, the DIAMOND model subdivides the voxel content into diffusion compartments and draws from diffusion-weighted data to estimate compartmental non-central matrix-variate Gamma distributions of diffusion tensors. It models each sub-voxel fascicle separately, resolving crossing white-matter pathways and allowing for a fascicle-element (fixel) based analysis of microstructural features. Alternatively, specific features of the intra-voxel diffusion tensor distribution can be selectively measured using tensor-valued diffusion-weighted acquisition schemes. However, the impact of such schemes on estimating brain microstructural features has only been studied in a handful of parametric single-fascicle models. In this work, we derive a general Laplace transform for the non-central matrix-variate Gamma distribution, which enables the extension of DIAMOND to tensor-valued encoded data. We then evaluate this "Magic DIAMOND" model in silico and in vivo on various combinations of tensor-valued encoded data. Assessing uncertainty on parameter estimation via stratified bootstrap, we investigate both voxel-based and fixel-based metrics by carrying out multi-peak tractography. We demonstrate using in silico evaluations that tensor-valued diffusion encoding significantly improves Magic DIAMOND's accuracy. Most importantly, we show in vivo that our estimated metrics can be robustly mapped along tracks across regions of fiber crossing, which opens new perspectives for tractometry and microstructure mapping along specific white-matter tracts.
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Affiliation(s)
| | | | - Guillaume Gilbert
- MR Clinical Science, Philips Healthcare Canada, Markham, ON L6C 2S3, Canada
| | - Filip Szczepankiewicz
- Department of Clinical Sciences, Lund University, 22184, Lund, Sweden; Random Walk Imaging AB, 22224, Lund, Sweden
| | - Markus Nilsson
- Department of Clinical Sciences, Lund University, 22184, Lund, Sweden
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Boston, MA 02115, United States
| | | | - Benoit Scherrer
- Department of Radiology, Boston Children's Hospital, Boston, MA 02115, United States
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