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Li W, Wang Z, Chen S, Zuo M, Xiang Y, Yuan Y, He Y, Zhang S, Liu Y. Metabolic checkpoints in glioblastomas: targets for new therapies and non-invasive detection. Front Oncol 2024; 14:1462424. [PMID: 39678512 PMCID: PMC11638224 DOI: 10.3389/fonc.2024.1462424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2024] [Accepted: 11/11/2024] [Indexed: 12/17/2024] Open
Abstract
Glioblastoma (GBM) is a highly malignant tumor of the central nervous system that remains intractable despite advancements in current tumor treatment modalities, including immunotherapy. In recent years, metabolic checkpoints (aberrant metabolic pathways underlying the immunosuppressive tumor microenvironment) have gained attention as promising therapeutic targets and sensitive biomarkers across various cancers. Here, we briefly review the existing understanding of tumor metabolic checkpoints and their implications in the biology and management of GBM. Additionally, we discuss techniques that could evaluate metabolic checkpoints of GBM non-invasively, thereby potentially facilitating neo-adjuvant treatment and dynamic surveillance.
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Affiliation(s)
- Wenhao Li
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Zhihao Wang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Siliang Chen
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Mingrong Zuo
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
- Department of Pediatric Neurosurgery, West China Second University Hospital, Chengdu, China
| | - Yufan Xiang
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yunbo Yuan
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Yuze He
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
| | - Shuxin Zhang
- Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Yanhui Liu
- Department of Neurosurgery, West China Hospital, Sichuan University, Chengdu, China
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2
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Zong F, Wang L, Liu H, Xue B, Bai R, Liu Y. A genetic optimisation and iterative reconstruction framework for sparse multi-dimensional diffusion-relaxation correlation MRI. Comput Biol Med 2024; 175:108508. [PMID: 38678941 DOI: 10.1016/j.compbiomed.2024.108508] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 04/11/2024] [Accepted: 04/21/2024] [Indexed: 05/01/2024]
Abstract
Multi-dimensional diffusion-relaxation correlation (DRC) magnetic resonance imaging (MRI) techniques have recently been developed to investigate tissue microstructures. Sub-voxel tissue heterogeneity is resolved from the local correlation distributions of relaxation time and molecular diffusivity. However, the implementation of these techniques considerably increases the total acquisition time, and simply reducing the scan time may be at the expense of detailed structural resolution. To overcome these limitations, an optimised framework was proposed for acquiring microstructural maps of the human brain on a clinically feasible timescale. First, the acquisition parameters of the multi-dimensional DRC MRI method were sparsely optimised using a genetic algorithm with a fitness function according to the spectral resolution of the correlation map, hardware requirements, and total scan time. Next, the acquired DRC MRI data were processed using a proposed numerical algorithm based on the dynamic inverse Laplace transform (ILT). Prior knowledge from one-dimensional data was then utilised in the iterative procedure to improve the spectral resolution. Finally, the proposed framework was validated using Monte Carlo simulations and experimental data acquired from healthy participants on an MRI scanner. The results demonstrated that the suggested approach is feasible for offering high-resolution DRC maps that correspond to distinct microstructures with a limited amount of optimised acquisition data from two-dimensional DRC sampling space. By significantly reducing scan time while retaining structural resolution, this approach may enable multi-dimensional DRC MRI to be more widely used for quantitative evaluation in biological and medical settings.
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Affiliation(s)
- Fangrong Zong
- School of Artificial Intelligence, Beijing University of Post and Telecommunication, Beijing, 100876, China.
| | - Lixian Wang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Huabing Liu
- Beijing Limecho Technology Co., Ltd., Beijing, 102200, China
| | - Bing Xue
- School of Engineering and Computer Science, Victoria University of Wellington, Victoria, 6140, New Zealand
| | - Ruiliang Bai
- Interdisciplinary Institute of Neuroscience and Technology, Zhejiang University School of Medicine, Hangzhou, 310020, China; MOE Frontier Science Center for Brain Science and Brain-machine Integration, School of Brain Science and Brain Medicine, Zhejiang University, Hangzhou, 310030, China
| | - Yong Liu
- School of Artificial Intelligence, Beijing University of Post and Telecommunication, Beijing, 100876, China.
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3
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Planchuelo-Gómez Á, Descoteaux M, Larochelle H, Hutter J, Jones DK, Tax CMW. Optimisation of quantitative brain diffusion-relaxation MRI acquisition protocols with physics-informed machine learning. Med Image Anal 2024; 94:103134. [PMID: 38471339 DOI: 10.1016/j.media.2024.103134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 02/26/2024] [Accepted: 03/04/2024] [Indexed: 03/14/2024]
Abstract
Diffusion-relaxation MRI aims to extract quantitative measures that characterise microstructural tissue properties such as orientation, size, and shape, but long acquisition times are typically required. This work proposes a physics-informed learning framework to extract an optimal subset of diffusion-relaxation MRI measurements for enabling shorter acquisition times, predict non-measured signals, and estimate quantitative parameters. In vivo and synthetic brain 5D-Diffusion-T1-T2∗-weighted MRI data obtained from five healthy subjects were used for training and validation, and from a sixth participant for testing. One fully data-driven and two physics-informed machine learning methods were implemented and compared to two manual selection procedures and Cramér-Rao lower bound optimisation. The physics-informed approaches could identify measurement-subsets that yielded more consistently accurate parameter estimates in simulations than other approaches, with similar signal prediction error. Five-fold shorter protocols yielded error distributions of estimated quantitative parameters with very small effect sizes compared to estimates from the full protocol. Selected subsets commonly included a denser sampling of the shortest and longest inversion time, lowest echo time, and high b-value. The proposed framework combining machine learning and MRI physics offers a promising approach to develop shorter imaging protocols without compromising the quality of parameter estimates and signal predictions.
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Affiliation(s)
- Álvaro Planchuelo-Gómez
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom; Imaging Processing Laboratory, Universidad de Valladolid, Valladolid, Spain
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | | | - Jana Hutter
- Centre for Medical Engineering, Centre for the Developing Brain, King's College London, London, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom.
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4
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Duan M, Pan R, Gao Q, Wu X, Lin H, Yuan J, Zhang Y, Liu L, Tian Y, Fu T. A rapid multi-parametric quantitative MR imaging method to assess Parkinson's disease: a feasibility study. BMC Med Imaging 2024; 24:58. [PMID: 38443786 PMCID: PMC10916029 DOI: 10.1186/s12880-024-01229-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 02/15/2024] [Indexed: 03/07/2024] Open
Abstract
BACKGROUND MULTIPLEX is a single-scan three-dimensional multi-parametric MRI technique that provides 1 mm isotropic T1-, T2*-, proton density- and susceptibility-weighted images and the corresponding quantitative maps. This study aimed to investigate its feasibility of clinical application in Parkinson's disease (PD). METHODS 27 PD patients and 23 healthy control (HC) were recruited and underwent a MULTIPLEX scanning. All image reconstruction and processing were automatically performed with in-house C + + programs on the Automatic Differentiation using Expression Template platform. According to the HybraPD atlas consisting of 12 human brain subcortical nuclei, the region-of-interest (ROI) based analysis was conducted to extract quantitative parameters, then identify PD-related abnormalities from the T1, T2* and proton density maps and quantitative susceptibility mapping (QSM), by comparing patients and HCs. RESULTS The ROI-based analysis revealed significantly decreased mean T1 values in substantia nigra pars compacta and habenular nuclei, mean T2* value in subthalamic nucleus and increased mean QSM value in subthalamic nucleus in PD patients, compared to HCs (all p values < 0.05 after FDR correction). The receiver operating characteristic analysis showed all these four quantitative parameters significantly contributed to PD diagnosis (all p values < 0.01 after FDR correction). Furthermore, the two quantitative parameters in subthalamic nucleus showed hemicerebral differences in regard to the clinically dominant side among PD patients. CONCLUSIONS MULTIPLEX might be feasible for clinical application to assist in PD diagnosis and provide possible pathological information of PD patients' subcortical nucleus and dopaminergic midbrain regions.
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Affiliation(s)
- Min Duan
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, China
| | - Rongrong Pan
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, Jiangsu Province, China
| | - Qing Gao
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, Jiangsu Province, China
| | - Xinying Wu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, Jiangsu Province, China
| | - Hai Lin
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Jianmin Yuan
- Central Research Institute, United Imaging Healthcare, Shanghai, China
| | - Yamei Zhang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, Jiangsu Province, China
| | - Lindong Liu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, Jiangsu Province, China
| | - Youyong Tian
- Department of Neurology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, Jiangsu Province, China.
| | - Tong Fu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, 210006, Nanjing, Jiangsu Province, China.
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5
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Kruggel F, Solodkin A. Analyzing the cortical fine structure as revealed by ex-vivo anatomical MRI. J Comp Neurol 2023; 531:2146-2161. [PMID: 37522626 DOI: 10.1002/cne.25532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 04/15/2023] [Accepted: 06/21/2023] [Indexed: 08/01/2023]
Abstract
The human cortex has a rich fiber structure as revealed by myelin-staining of histological slices. Myelin also contributes to the image contrast in Magnetic Resonance Imaging (MRI). Recent advances in Magnetic Resonance (MR) scanner and imaging technology allowed the acquisition of an ex-vivo data set at an isotropic resolution of 100 µm. This study focused on a computational analysis of this data set with the aim of bridging between histological knowledge and MRI-based results. This work highlights: (1) the design and implementation of a processing chain that extracts intracortical features from a high-resolution MR image; (2) a demonstration of the correspondence between MRI-based cortical intensity profiles and the myelo-architectonic layering of the cortex; (3) the characterization and classification of four basic myelo-architectonic profile types; (4) the distinction of cortical regions based on myelo-architectonic features; and (5) the segmentation of cortical modules in the entorhinal cortex.
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Affiliation(s)
- Frithjof Kruggel
- Department of Biomedical Engineering, University of California, Irvine, Irvine, California, USA
| | - Ana Solodkin
- School of Behavioral and Brain Sciences, University of Texas, Richardson, Texas, USA
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6
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Hagiwara A, Fujita S, Kurokawa R, Andica C, Kamagata K, Aoki S. Multiparametric MRI: From Simultaneous Rapid Acquisition Methods and Analysis Techniques Using Scoring, Machine Learning, Radiomics, and Deep Learning to the Generation of Novel Metrics. Invest Radiol 2023; 58:548-560. [PMID: 36822661 PMCID: PMC10332659 DOI: 10.1097/rli.0000000000000962] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/10/2023] [Indexed: 02/25/2023]
Abstract
ABSTRACT With the recent advancements in rapid imaging methods, higher numbers of contrasts and quantitative parameters can be acquired in less and less time. Some acquisition models simultaneously obtain multiparametric images and quantitative maps to reduce scan times and avoid potential issues associated with the registration of different images. Multiparametric magnetic resonance imaging (MRI) has the potential to provide complementary information on a target lesion and thus overcome the limitations of individual techniques. In this review, we introduce methods to acquire multiparametric MRI data in a clinically feasible scan time with a particular focus on simultaneous acquisition techniques, and we discuss how multiparametric MRI data can be analyzed as a whole rather than each parameter separately. Such data analysis approaches include clinical scoring systems, machine learning, radiomics, and deep learning. Other techniques combine multiple images to create new quantitative maps associated with meaningful aspects of human biology. They include the magnetic resonance g-ratio, the inner to the outer diameter of a nerve fiber, and the aerobic glycolytic index, which captures the metabolic status of tumor tissues.
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Affiliation(s)
- Akifumi Hagiwara
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shohei Fujita
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Ryo Kurokawa
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
- Division of Neuroradiology, Department of Radiology, University of Michigan, Ann Arbor, Michigan
| | - Christina Andica
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Koji Kamagata
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- From theDepartment of Radiology, Juntendo University School of Medicine, Tokyo, Japan
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7
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Liu X, Chen H, Yao C, Xiang R, Zhou K, Du P, Liu W, Liu J, Yu Z. BTMF-GAN: A multi-modal MRI fusion generative adversarial network for brain tumors. Comput Biol Med 2023; 157:106769. [PMID: 36947904 DOI: 10.1016/j.compbiomed.2023.106769] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 03/01/2023] [Accepted: 03/07/2023] [Indexed: 03/11/2023]
Abstract
Image fusion techniques have been widely used for multi-modal medical image fusion tasks. Most existing methods aim to improve the overall quality of the fused image and do not focus on the more important textural details and contrast between the tissues of the lesion in the regions of interest (ROIs). This can lead to the distortion of important tumor ROIs information and thus limits the applicability of the fused images in clinical practice. To improve the fusion quality of ROIs relevant to medical implications, we propose a multi-modal MRI fusion generative adversarial network (BTMF-GAN) for the task of multi-modal MRI fusion of brain tumors. Unlike existing deep learning approaches which focus on improving the global quality of the fused image, the proposed BTMF-GAN aims to achieve a balance between tissue details and structural contrasts in brain tumor, which is the region of interest crucial to many medical applications. Specifically, we employ a generator with a U-shaped nested structure and residual U-blocks (RSU) to enhance multi-scale feature extraction. To enhance and recalibrate features of the encoder, the multi-perceptual field adaptive transformer feature enhancement module (MRF-ATFE) is used between the encoder and the decoder instead of a skip connection. To increase contrast between tumor tissues of the fused image, a mask-part block is introduced to fragment the source image and the fused image, based on which, we propose a novel salient loss function. Qualitative and quantitative analysis of the results on the public and clinical datasets demonstrate the superiority of the proposed approach to many other commonly used fusion methods.
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Affiliation(s)
- Xiao Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China
| | - Hongyi Chen
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China
| | - Chong Yao
- College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China
| | - Rui Xiang
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China
| | - Kun Zhou
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China
| | - Peng Du
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Weifan Liu
- College of Science, Beijing Forestry University, Beijing, 100083, China.
| | - Jie Liu
- School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China.
| | - Zekuan Yu
- Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China
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8
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Moinian S, Vegh V, Reutens D. Towards automated in vivo parcellation of the human cerebral cortex using supervised classification of magnetic resonance fingerprinting residuals. Cereb Cortex 2023; 33:1550-1565. [PMID: 35483706 PMCID: PMC9977388 DOI: 10.1093/cercor/bhac155] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Accurate parcellation of the cerebral cortex in an individual is a guide to its underlying organization. The most promising in vivo quantitative magnetic resonance (MR)-based microstructural cortical mapping methods are yet to achieve a level of parcellation accuracy comparable to quantitative histology. METHODS We scanned 6 participants using a 3D echo-planar imaging MR fingerprinting (EPI-MRF) sequence on a 7T Siemens scanner. After projecting MRF signals to the individual-specific inflated model of the cortical surface, normalized autocorrelations of MRF residuals of vertices of 8 microstructurally distinct areas (BA1, BA2, BA4a, BA6, BA44, BA45, BA17, and BA18) from 3 cortical regions were used as feature vector inputs into linear support vector machine (SVM), radial basis function SVM (RBF-SVM), random forest, and k-nearest neighbors supervised classification algorithms. The algorithms' prediction performance was compared using: (i) features from each vertex or (ii) features from neighboring vertices. RESULTS The neighborhood-based RBF-SVM classifier achieved the highest prediction score of 0.85 for classification of MRF residuals in the central region from a held-out participant. CONCLUSIONS We developed an automated method of cortical parcellation using a combination of MR fingerprinting residual analysis and machine learning classification. Our findings provide the basis for employing unsupervised learning algorithms for whole-cortex structural parcellation in individuals.
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Affiliation(s)
- Shahrzad Moinian
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
| | - Viktor Vegh
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
| | - David Reutens
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, St Lucia, QLD 4072, Australia
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9
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Baldassarro VA, Stanzani A, Giardino L, Calzà L, Lorenzini L. Neuroprotection and neuroregeneration: roles for the white matter. Neural Regen Res 2022; 17:2376-2380. [PMID: 35535874 PMCID: PMC9120696 DOI: 10.4103/1673-5374.335834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Efficient strategies for neuroprotection and repair are still an unmet medical need for neurodegenerative diseases and lesions of the central nervous system. Over the last few decades, a great deal of attention has been focused on white matter as a potential therapeutic target, mainly due to the discovery of the oligodendrocyte precursor cells in the adult central nervous system, a cell type able to fully repair myelin damage, and to the development of advanced imaging techniques to visualize and measure white matter lesions. The combination of these two events has greatly increased the body of research into white matter alterations in central nervous system lesions and neurodegenerative diseases and has identified the oligodendrocyte precursor cell as a putative target for white matter lesion repair, thus indirectly contributing to neuroprotection. This review aims to discuss the potential of white matter as a therapeutic target for neuroprotection in lesions and diseases of the central nervous system. Pivot conditions are discussed, specifically multiple sclerosis as a white matter disease; spinal cord injury, the acute lesion of a central nervous system component where white matter prevails over the gray matter, and Alzheimer's disease, where the white matter was considered an ancillary component until recently. We first describe oligodendrocyte precursor cell biology and developmental myelination, and its regulation by thyroid hormones, then briefly describe white matter imaging techniques, which are providing information on white matter involvement in central nervous system lesions and degenerative diseases. Finally, we discuss pathological mechanisms which interfere with myelin repair in adulthood.
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Affiliation(s)
| | - Agnese Stanzani
- Interdepartmental Center for Industrial Research in Life Sciences and Technologies, University of Bologna, Bologna, Italy
| | - Luciana Giardino
- Department of Veterinary Medical Science, University of Bologna, Bologna; Fondazione IRET, Ozzano Emilia, Italy
| | - Laura Calzà
- Fondazione IRET, Ozzano Emilia; Department of Pharmacy and Biotechnology, University of Bologna, Bologna; Montecatone Rehabilitation Institute, Imola, Italy
| | - Luca Lorenzini
- Department of Veterinary Medical Science, University of Bologna, Bologna, Italy
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10
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Jara H, Sakai O, Farrher E, Oros-Peusquens AM, Shah NJ, Alsop DC, Keenan KE. Primary Multiparametric Quantitative Brain MRI: State-of-the-Art Relaxometric and Proton Density Mapping Techniques. Radiology 2022; 305:5-18. [PMID: 36040334 PMCID: PMC9524578 DOI: 10.1148/radiol.211519] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 05/01/2022] [Accepted: 05/24/2022] [Indexed: 11/11/2022]
Abstract
This review on brain multiparametric quantitative MRI (MP-qMRI) focuses on the primary subset of quantitative MRI (qMRI) parameters that represent the mobile ("free") and bound ("motion-restricted") proton pools. Such primary parameters are the proton densities, relaxation times, and magnetization transfer parameters. Diffusion qMRI is also included because of its wide implementation in complete clinical MP-qMRI application. MP-qMRI advances were reviewed over the past 2 decades, with substantial progress observed toward accelerating image acquisition and increasing mapping accuracy. Areas that need further investigation and refinement are identified as follows: (a) the biologic underpinnings of qMRI parameter values and their changes with age and/or disease and (b) the theoretical limitations implicitly built into most qMRI mapping algorithms that do not distinguish between the different spatial scales of voxels versus spin packets, the central physical object of the Bloch theory. With rapidly improving image processing techniques and continuous advances in computer hardware, MP-qMRI has the potential for implementation in a wide range of clinical applications. Currently, three emerging MP-qMRI applications are synthetic MRI, macrostructural qMRI, and microstructural tissue modeling.
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Affiliation(s)
- Hernán Jara
- From the Department of Radiology, Boston University, 670 Albany St,
Boston, Mass 02118 (H.J., O.S.); Institute of Neuroscience and Medicine-4,
Forschungszentrum Jülich, Jülich, Germany (E.F., A.M.O.P.,
N.J.S.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Mass (D.C.A.); and Physical Measurement Laboratory,
National Institute of Standards and Technology, Boulder, Colo (K.E.K.)
| | - Osamu Sakai
- From the Department of Radiology, Boston University, 670 Albany St,
Boston, Mass 02118 (H.J., O.S.); Institute of Neuroscience and Medicine-4,
Forschungszentrum Jülich, Jülich, Germany (E.F., A.M.O.P.,
N.J.S.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Mass (D.C.A.); and Physical Measurement Laboratory,
National Institute of Standards and Technology, Boulder, Colo (K.E.K.)
| | - Ezequiel Farrher
- From the Department of Radiology, Boston University, 670 Albany St,
Boston, Mass 02118 (H.J., O.S.); Institute of Neuroscience and Medicine-4,
Forschungszentrum Jülich, Jülich, Germany (E.F., A.M.O.P.,
N.J.S.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Mass (D.C.A.); and Physical Measurement Laboratory,
National Institute of Standards and Technology, Boulder, Colo (K.E.K.)
| | - Ana-Maria Oros-Peusquens
- From the Department of Radiology, Boston University, 670 Albany St,
Boston, Mass 02118 (H.J., O.S.); Institute of Neuroscience and Medicine-4,
Forschungszentrum Jülich, Jülich, Germany (E.F., A.M.O.P.,
N.J.S.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Mass (D.C.A.); and Physical Measurement Laboratory,
National Institute of Standards and Technology, Boulder, Colo (K.E.K.)
| | - N. Jon Shah
- From the Department of Radiology, Boston University, 670 Albany St,
Boston, Mass 02118 (H.J., O.S.); Institute of Neuroscience and Medicine-4,
Forschungszentrum Jülich, Jülich, Germany (E.F., A.M.O.P.,
N.J.S.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Mass (D.C.A.); and Physical Measurement Laboratory,
National Institute of Standards and Technology, Boulder, Colo (K.E.K.)
| | - David C. Alsop
- From the Department of Radiology, Boston University, 670 Albany St,
Boston, Mass 02118 (H.J., O.S.); Institute of Neuroscience and Medicine-4,
Forschungszentrum Jülich, Jülich, Germany (E.F., A.M.O.P.,
N.J.S.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Mass (D.C.A.); and Physical Measurement Laboratory,
National Institute of Standards and Technology, Boulder, Colo (K.E.K.)
| | - Kathryn E. Keenan
- From the Department of Radiology, Boston University, 670 Albany St,
Boston, Mass 02118 (H.J., O.S.); Institute of Neuroscience and Medicine-4,
Forschungszentrum Jülich, Jülich, Germany (E.F., A.M.O.P.,
N.J.S.); Department of Radiology, Beth Israel Deaconess Medical Center, Harvard
Medical School, Boston, Mass (D.C.A.); and Physical Measurement Laboratory,
National Institute of Standards and Technology, Boulder, Colo (K.E.K.)
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11
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Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity. Neuroimage 2022; 262:119529. [PMID: 35926761 DOI: 10.1016/j.neuroimage.2022.119529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Revised: 07/20/2022] [Accepted: 08/01/2022] [Indexed: 11/20/2022] Open
Abstract
Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1, and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the method's sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MTsat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.
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12
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Váša F, Hobday H, Stanyard RA, Daws RE, Giampietro V, O'Daly O, Lythgoe DJ, Seidlitz J, Skare S, Williams SCR, Marquand AF, Leech R, Cole JH. Rapid processing and quantitative evaluation of structural brain scans for adaptive multimodal imaging. Hum Brain Mapp 2022; 43:1749-1765. [PMID: 34953014 PMCID: PMC8886661 DOI: 10.1002/hbm.25755] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [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: 11/21/2021] [Indexed: 12/17/2022] Open
Abstract
Current neuroimaging acquisition and processing approaches tend to be optimised for quality rather than speed. However, rapid acquisition and processing of neuroimaging data can lead to novel neuroimaging paradigms, such as adaptive acquisition, where rapidly processed data is used to inform subsequent image acquisition steps. Here we first evaluate the impact of several processing steps on the processing time and quality of registration of manually labelled T1 -weighted MRI scans. Subsequently, we apply the selected rapid processing pipeline both to rapidly acquired multicontrast EPImix scans of 95 participants (which include T1 -FLAIR, T2 , T2 *, T2 -FLAIR, DWI and ADC contrasts, acquired in ~1 min), as well as to slower, more standard single-contrast T1 -weighted scans of a subset of 66 participants. We quantify the correspondence between EPImix T1 -FLAIR and single-contrast T1 -weighted scans, using correlations between voxels and regions of interest across participants, measures of within- and between-participant identifiability as well as regional structural covariance networks. Furthermore, we explore the use of EPImix for the rapid construction of morphometric similarity networks. Finally, we quantify the reliability of EPImix-derived data using test-retest scans of 10 participants. Our results demonstrate that quantitative information can be derived from a neuroimaging scan acquired and processed within minutes, which could further be used to implement adaptive multimodal imaging and tailor neuroimaging examinations to individual patients.
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Affiliation(s)
- František Váša
- Department of NeuroimagingInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
| | - Harriet Hobday
- Department of NeuroimagingInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
| | - Ryan A. Stanyard
- Department of NeuroimagingInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
- Department of Forensic & Developmental SciencesInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
| | - Richard E. Daws
- The Computational, Cognitive and Clinical Neuroimaging Laboratory, Department of Brain SciencesImperial College LondonLondonUK
| | - Vincent Giampietro
- Department of NeuroimagingInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
| | - Owen O'Daly
- Department of NeuroimagingInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
| | - David J. Lythgoe
- Department of NeuroimagingInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral ScienceChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
- Department of PsychiatryUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Stefan Skare
- Department of NeuroradiologyKarolinska University HospitalStockholmSweden
- Department of Clinical NeuroscienceKarolinska InstitutetStockholmSweden
| | - Steven C. R. Williams
- Department of NeuroimagingInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
| | - Andre F. Marquand
- Department of NeuroimagingInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
- Donders Institute for Brain, Cognition and BehaviorRadboud University NijmegenNijmegenThe Netherlands
- Department for Cognitive NeuroscienceRadboud University Medical Center NijmegenNijmegenThe Netherlands
| | - Robert Leech
- Department of NeuroimagingInstitute of Psychiatry, Psychology & Neuroscience, King's College LondonLondonUK
| | - James H. Cole
- Department of Computer Science, Centre for Medical Image ComputingUniversity College LondonLondonUK
- Dementia Research Centre, Institute of NeurologyUniversity College LondonLondonUK
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13
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Liang Z, Lee CH, Arefin TM, Dong Z, Walczak P, Hai Shi S, Knoll F, Ge Y, Ying L, Zhang J. Virtual mouse brain histology from multi-contrast MRI via deep learning. eLife 2022; 11:72331. [PMID: 35088711 PMCID: PMC8837198 DOI: 10.7554/elife.72331] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Accepted: 01/27/2022] [Indexed: 11/16/2022] Open
Abstract
1H MRI maps brain structure and function non-invasively through versatile contrasts that exploit inhomogeneity in tissue micro-environments. Inferring histopathological information from magnetic resonance imaging (MRI) findings, however, remains challenging due to absence of direct links between MRI signals and cellular structures. Here, we show that deep convolutional neural networks, developed using co-registered multi-contrast MRI and histological data of the mouse brain, can estimate histological staining intensity directly from MRI signals at each voxel. The results provide three-dimensional maps of axons and myelin with tissue contrasts that closely mimic target histology and enhanced sensitivity and specificity compared to conventional MRI markers. Furthermore, the relative contribution of each MRI contrast within the networks can be used to optimize multi-contrast MRI acquisition. We anticipate our method to be a starting point for translation of MRI results into easy-to-understand virtual histology for neurobiologists and provide resources for validating novel MRI techniques.
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Affiliation(s)
- Zifei Liang
- Department of Radiology, New York University School of Medicine, New York, United States
| | - Choong H Lee
- Department of Radiology, New York University School of Medicine, New York, United States
| | - Tanzil M Arefin
- Department of Radiology, New York University School of Medicine, New York, United States
| | - Zijun Dong
- Department of Radiology, New York University School of Medicine, New York, United States
| | - Piotr Walczak
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland, Baltimore, United States
| | - Song Hai Shi
- Developmental Biology Program, Memorial Sloan Kettering Cancer Center
| | - Florian Knoll
- Department of Radiology, New York University School of Medicine, New York, United States
| | - Yulin Ge
- Department of Radiology, New York University School of Medicine, New York, United States
| | - Leslie Ying
- Departments of Biomedical Engineering, University at Buffalo, State University of New York, Buffalo, United States
| | - Jiangyang Zhang
- Department of Radiology, New York University School of Medicine, New York, United States
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14
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Moinian S, Vegh V, O'Brien K, Reutens D. Magnetic resonance fingerprinting residual signals can disassociate human grey matter regions. Brain Struct Funct 2021; 227:313-329. [PMID: 34697684 DOI: 10.1007/s00429-021-02402-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 09/29/2021] [Indexed: 10/20/2022]
Abstract
The importance of accurate structural discrimination of the human grey matter regions has motivated the development of observer-independent reproducible methods that account for inter-individual architectonic variations. We introduce a non-invasive statistical residual analysis framework, employing unique tissue-specific magnetic resonance fingerprinting (MRF) signals after adjusting for the effect of T1 and T2* MR relaxometry parameters (here termed MRF residuals). A 7 T Siemens MR scanner was used to acquire MRF signals, quantitative transmit magnetic field (B1+) maps and T1-weighted anatomical images of eleven cortical areas (5L, 5M, 5Ci, 7A, 7P, 7PC, hIP3, BA2, BA4a, BA4p and BA6) from six female participants. MRF residual signal for each voxel was calculated as the difference between the actual and best matching MRF signal evolutions from a precomputed MRF dictionary covering a range of T1, T2* and B1+ values. To compare MRF residuals between regions of interest, normalised autocorrelation was used as a shape-based statistical signal characterisation method and the Euclidean distance between autocorrelation profiles of residuals was used to measure the interareal dissimilarity. In the eleven cortical areas in both cerebral hemispheres of six participants, the proposed MRF residual analysis consistently showed interareal dissimilarity profiles that concorded with histological studies, indicating that MRF residuals potentially contain tissue microstructural information. MRF residual signals provide additional area-specific information that is complementary to the MR relaxometry-based (T1, T2*) information used previously for distinguishing microstructural differences between human cerebral cortex regions in vivo. The proposed approach led to more accurate identification of structural variations across cortical areas of interest.
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Affiliation(s)
- Shahrzad Moinian
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia.,ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia
| | - Viktor Vegh
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia.,ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia
| | - Kieran O'Brien
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia.,ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia.,Siemens Healthcare Pty Ltd, 153 Campbell Street, Brisbane, QLD, 4006, Australia
| | - David Reutens
- Centre for Advanced Imaging, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia. .,ARC Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Building 57, Research Road, Brisbane, QLD, 4072, Australia.
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15
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Huber E, Mezer A, Yeatman JD. Neurobiological underpinnings of rapid white matter plasticity during intensive reading instruction. Neuroimage 2021; 243:118453. [PMID: 34358657 DOI: 10.1016/j.neuroimage.2021.118453] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 07/24/2021] [Accepted: 08/03/2021] [Indexed: 01/18/2023] Open
Abstract
Diffusion MRI is a powerful tool for imaging brain structure, but it is challenging to discern the biological underpinnings of plasticity inferred from these and other non-invasive MR measurements. Biophysical modeling of the diffusion signal aims to render a more biologically rich image of tissue microstructure, but the application of these models comes with important caveats. A separate approach for gaining biological specificity has been to seek converging evidence from multi-modal datasets. Here we use metrics derived from diffusion kurtosis imaging (DKI) and the white matter tract integrity (WMTI) model along with quantitative MRI measurements of T1 relaxation to characterize changes throughout the white matter during an 8-week, intensive reading intervention (160 total hours of instruction). Behavioral measures, multi-shell diffusion MRI data, and quantitative T1 data were collected at regular intervals during the intervention in a group of 33 children with reading difficulties (7-12 years old), and over the same period in an age-matched non-intervention control group. Throughout the white matter, mean 'extra-axonal' diffusivity was inversely related to intervention time. In contrast, model estimated axonal water fraction (AWF), overall diffusion kurtosis, and T1 relaxation time showed no significant change over the intervention period. Both diffusion and quantitative T1 based metrics were correlated with pre-intervention reading performance, albeit with distinct anatomical distributions. These results are consistent with the view that rapid changes in diffusion properties reflect phenomena other than widespread changes in myelin density. We discuss this result in light of recent work highlighting non-axonal factors in experience-dependent plasticity and learning.
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Affiliation(s)
- Elizabeth Huber
- Institute for Learning and Brain Sciences and Department of Speech and Hearing Sciences, University of Washington, Seattle, WA 98195, USA.
| | - Aviv Mezer
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Jason D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA 94305, USA; Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA 95305, USA
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16
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Lee FT, Seed M, Sun L, Marini D. Fetal brain issues in congenital heart disease. Transl Pediatr 2021; 10:2182-2196. [PMID: 34584890 PMCID: PMC8429876 DOI: 10.21037/tp-20-224] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 08/27/2020] [Indexed: 12/17/2022] Open
Abstract
Following the improvements in the clinical management of patients with congenital heart disease (CHD) and their increased survival, neurodevelopmental outcome has become an emerging priority in pediatric cardiology. Large-scale efforts have been made to protect the brain during the postnatal, surgical, and postoperative period; however, the presence of brain immaturity and injury at birth suggests in utero and peripartum disturbances. Over the past decade, there has been considerable interest and investigations on fetal brain growth in the setting of CHD. Advancements in fetal brain imaging have identified abnormal brain development in fetuses with CHD from the macrostructural (brain volumes and cortical folding) down to the microstructural (biochemistry and water diffusivity) scale, with more severe forms of CHD showing worse disturbances and brain abnormalities starting as early as the first trimester. Anomalies in common genetic developmental pathways and diminished cerebral substrate delivery secondary to altered cardiovascular physiology are the forefront hypotheses, but other factors such as impaired placental function and maternal psychological stress have surfaced as important contributors to fetal brain immaturity in CHD. The characterization and timing of fetal brain disturbances and their associated mechanisms are important steps for determining preventative prenatal interventions, which may provide a stronger foundation for the developing brain during childhood.
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Affiliation(s)
- Fu-Tsuen Lee
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Canada.,Division of Cardiology, Department of Paediatrics, Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Mike Seed
- Division of Cardiology, Department of Paediatrics, Hospital for Sick Children, University of Toronto, Toronto, Canada.,Department of Diagnostic Imaging, Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Liqun Sun
- Division of Cardiology, Department of Paediatrics, Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Davide Marini
- Division of Cardiology, Department of Paediatrics, Hospital for Sick Children, University of Toronto, Toronto, Canada
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17
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Lee FT, Marini D, Seed M, Sun L. Maternal hyperoxygenation in congenital heart disease. Transl Pediatr 2021; 10:2197-2209. [PMID: 34584891 PMCID: PMC8429855 DOI: 10.21037/tp-20-226] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 08/27/2020] [Indexed: 01/26/2023] Open
Abstract
The importance of prenatal diagnosis and fetal intervention has been increasing as a preventative strategy for improving the morbidity and mortality in congenital heart disease (CHD). The advancements in medical imaging technology have greatly enhanced our understanding of disease progression, assessment, and impact in those with CHD. In particular, there has been a growing focus on improving the morbidity and mortality of fetuses diagnosed with left-sided lesions. The disruption of fetal hemodynamics resulting from poor structural developmental of the left outflow tract during cardiogenesis is considered a major factor in the progressive lethal underdevelopment of the left ventricle (LV). This positive feedback cycle of inadequate flow and underdevelopment of the LV leads to a disrupted fetal circulation, which has been described to impact fetal brain growth where systemic outflow is poor and, in some cases, the fetal lungs in the setting of a restrictive interatrial communication. For the past decade, maternal hyperoxygenation (MH) has been investigated as a diagnostic tool to assess the pulmonary vasculature and a therapeutic agent to improve the development of the heart and brain in fetuses with CHD with a focus on left-sided cardiac defects. This review discusses the findings of these studies as well as the utility of acute and chronic administration of MH in CHD.
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Affiliation(s)
- Fu-Tsuen Lee
- Department of Physiology, Faculty of Medicine, University of Toronto, Toronto, Canada.,Division of Cardiology, Department of Paediatrics, Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Davide Marini
- Division of Cardiology, Department of Paediatrics, Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Mike Seed
- Division of Cardiology, Department of Paediatrics, Hospital for Sick Children, University of Toronto, Toronto, Canada.,Department of Diagnostic Imaging, Hospital for Sick Children, University of Toronto, Toronto, Canada
| | - Liqun Sun
- Division of Cardiology, Department of Paediatrics, Hospital for Sick Children, University of Toronto, Toronto, Canada
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18
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De Paepe AE, Ara A, Garcia-Gorro C, Martinez-Horta S, Perez-Perez J, Kulisevsky J, Rodriguez-Dechicha N, Vaquer I, Subira S, Calopa M, Muñoz E, Santacruz P, Ruiz-Idiago J, Mareca C, de Diego-Balaguer R, Camara E. Gray Matter Vulnerabilities Predict Longitudinal Development of Apathy in Huntington's Disease. Mov Disord 2021; 36:2162-2172. [PMID: 33998063 DOI: 10.1002/mds.28638] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 04/15/2021] [Accepted: 04/16/2021] [Indexed: 11/06/2022] Open
Abstract
BACKGROUND Apathy, a common neuropsychiatric disturbance in Huntington's disease (HD), is subserved by a complex neurobiological network. However, no study has yet employed a whole-brain approach to examine underlying regional vulnerabilities that may precipitate apathy changes over time. OBJECTIVES To identify whole-brain gray matter volume (GMV) vulnerabilities that may predict longitudinal apathy development in HD. METHODS Forty-five HD individuals (31 female) were scanned and evaluated for apathy and other neuropsychiatric features using the short-Problem Behavior Assessment for a maximum total of six longitudinal visits (including baseline). In order to identify regions where changes in GMV may describe changes in apathy, we performed longitudinal voxel-based morphometry (VBM) on those 33 participants with a magnetic resonance imaging (MRI) scan on their second visit at 18 ± 6 months follow-up (78 MRI datasets). We next employed a generalized linear mixed-effects model (N = 45) to elucidate whether initial and specific GMV may predict apathy development over time. RESULTS Utilizing longitudinal VBM, we revealed a relationship between increases in apathy and specific GMV atrophy in the right middle cingulate cortex (MCC). Furthermore, vulnerability in the right MCC volume at baseline successfully predicted the severity and progression of apathy over time. CONCLUSIONS This study highlights that individual differences in apathy in HD may be explained by variability in atrophy and initial vulnerabilities in the right MCC, a region implicated in action-initiation. These findings thus serve to facilitate the prediction of an apathetic profile, permitting targeted, time-sensitive interventions in neurodegenerative disease with potential implications in otherwise healthy populations. © 2021 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Audrey E De Paepe
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, Barcelona, Spain.,Department of Cognition, Development and Educational Psychology, Universitat de Barcelona, Barcelona, Spain
| | - Alberto Ara
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, Barcelona, Spain.,Department of Cognition, Development and Educational Psychology, Universitat de Barcelona, Barcelona, Spain.,Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain
| | - Clara Garcia-Gorro
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, Barcelona, Spain.,Department of Cognition, Development and Educational Psychology, Universitat de Barcelona, Barcelona, Spain
| | - Saül Martinez-Horta
- European Huntington's Disease Network, Ulm, Germany.,Movement Disorders Unit, Department of Neurology, Biomedical Research Institute Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.,CIBERNED (Center for Networked Biomedical Research on Neurodegenerative Diseases), Carlos III Institute, Madrid, Spain
| | - Jesus Perez-Perez
- European Huntington's Disease Network, Ulm, Germany.,Movement Disorders Unit, Department of Neurology, Biomedical Research Institute Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.,CIBERNED (Center for Networked Biomedical Research on Neurodegenerative Diseases), Carlos III Institute, Madrid, Spain
| | - Jaime Kulisevsky
- European Huntington's Disease Network, Ulm, Germany.,Movement Disorders Unit, Department of Neurology, Biomedical Research Institute Sant Pau (IIB-Sant Pau), Hospital de la Santa Creu i Sant Pau, Barcelona, Spain.,CIBERNED (Center for Networked Biomedical Research on Neurodegenerative Diseases), Carlos III Institute, Madrid, Spain
| | | | - Irene Vaquer
- Hestia Duran i Reynals, Hospital Duran i Reynals, Hospitalet de Llobregat, Barcelona, Spain
| | - Susana Subira
- Hestia Duran i Reynals, Hospital Duran i Reynals, Hospitalet de Llobregat, Barcelona, Spain.,Departament de Psicologia Clínica i de la Salut, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Matilde Calopa
- Movement Disorders Unit, Neurology Service, Hospital Universitari de Bellvitge, Barcelona, Spain
| | - Esteban Muñoz
- European Huntington's Disease Network, Ulm, Germany.,Movement Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain.,IDIBAPS (Institut d'Investigacions Biomèdiques August Pi i Sunyer), Barcelona, Spain.,Facultat de Medicina, University of Barcelona, Barcelona, Spain
| | - Pilar Santacruz
- Movement Disorders Unit, Neurology Service, Hospital Clínic, Barcelona, Spain
| | - Jesus Ruiz-Idiago
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain.,Hospital Mare de Deu de la Mercè, Barcelona, Spain
| | - Celia Mareca
- Department of Psychiatry and Forensic Medicine, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Ruth de Diego-Balaguer
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, Barcelona, Spain.,Department of Cognition, Development and Educational Psychology, Universitat de Barcelona, Barcelona, Spain.,Institute of Neurosciences, Universitat de Barcelona, Barcelona, Spain.,European Huntington's Disease Network, Ulm, Germany.,ICREA (Catalan Institute for Research and Advanced Studies), Barcelona, Spain
| | - Estela Camara
- Cognition and Brain Plasticity Unit, Bellvitge Biomedical Research Institute - IDIBELL, Barcelona, Spain.,Department of Cognition, Development and Educational Psychology, Universitat de Barcelona, Barcelona, Spain.,European Huntington's Disease Network, Ulm, Germany
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19
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Oladosu O, Liu WQ, Pike BG, Koch M, Metz LM, Zhang Y. Advanced Analysis of Diffusion Tensor Imaging Along With Machine Learning Provides New Sensitive Measures of Tissue Pathology and Intra-Lesion Activity in Multiple Sclerosis. Front Neurosci 2021; 15:634063. [PMID: 34025338 PMCID: PMC8138061 DOI: 10.3389/fnins.2021.634063] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 04/15/2021] [Indexed: 12/02/2022] Open
Abstract
Tissue pathology in multiple sclerosis (MS) is highly complex, requiring multi-dimensional analysis. In this study, our goal was to test the feasibility of obtaining high angular resolution diffusion imaging (HARDI) metrics through single-shell modeling of diffusion tensor imaging (DTI) data, and investigate how advanced measures from single-shell HARDI and DTI tractography perform relative to classical DTI metrics in assessing MS pathology. We examined 52 relapsing-remitting MS patients who had 3T anatomical brain MRI and DTI. Single-shell HARDI modeling yielded 5 sub-voxel-based metrics, totalling 11 diffusion measures including 4 DTI and 2 tractography metrics. Based on machine learning of 3-dimensional regions of interest, we evaluated the importance of the measures through several tissue classification tasks. These included two within-subject comparisons: lesion versus normal appearing white matter (NAWM); and lesion core versus shell. Further, by stratifying patients as having high (above 75%ile) and low (below 25%ile) number of MS lesions, we also performed 2 classifications between subjects for lesions and NAWM respectively. Results showed that in lesion-NAWM analysis, HARDI orientation distribution function (ODF) energy, DTI fractional anisotropy (FA), and HARDI orientation dispersion index were the top three metrics, which together achieved 65.2% accuracy and 0.71 area under the receiver operating characteristic curve (AUROC). In core-shell analysis, DTI mean diffusivity (MD), radial diffusivity, and FA were the top three metrics, and MD dominated the classification, which achieved 59.3% accuracy and 0.59 AUROC alone. Between patients, FA was the leading feature in lesion comparisons, while ODF energy was the best in NAWM separation. Collectively, single-shell modeling of common diffusion data can provide robust orientation measures of lesion and NAWM pathology, and DTI metrics are most sensitive to intra-lesion abnormality. Combined analysis of both advanced and classical diffusion measures may be critical for improved understanding of MS pathology.
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Affiliation(s)
- Olayinka Oladosu
- Department of Neuroscience, Faculty of Graduate Studies, University of Calgary, Calgary, AB, Canada.,Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Wei-Qiao Liu
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Bruce G Pike
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Marcus Koch
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Luanne M Metz
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yunyan Zhang
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.,Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada.,Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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20
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Hybrid diffusion imaging reveals altered white matter tract integrity and associations with symptoms and cognitive dysfunction in chronic traumatic brain injury. NEUROIMAGE-CLINICAL 2021; 30:102681. [PMID: 34215151 PMCID: PMC8102667 DOI: 10.1016/j.nicl.2021.102681] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 03/12/2021] [Accepted: 04/18/2021] [Indexed: 11/20/2022]
Abstract
Hybrid Diffusion Imaging (HYDI) detects white matter associations in patients with cTBI. The advanced diffusion model NODDI was more sensitive in detecting between-group differences than classic DTI. DTI appeared to be just as sensitive as NODDI for detecting white matter correlations with self-reported symptoms. This study highlights the advantages of acquiring both DTI and NODDI to fully characterize white matter microstructure in cTBI.
The detection and association of in vivo biomarkers in white matter (WM) pathology after acute and chronic mild traumatic brain injury (mTBI) are needed to improve care and develop therapies. In this study, we used the diffusion MRI method of hybrid diffusion imaging (HYDI) to detect white matter alterations in patients with chronic TBI (cTBI). 40 patients with cTBI presenting symptoms at least three months post injury, and 17 healthy controls underwent magnetic resonance HYDI. cTBI patients were assessed with a battery of neuropsychological tests. A voxel-wise statistical analysis within the white matter skeleton was performed to study between group differences in the diffusion models. In addition, a partial correlation analysis controlling for age, sex, and time after injury was performed within the cTBI cohort, to test for associations between diffusion metrics and clinical outcomes. The advanced diffusion modeling technique of neurite orientation dispersion and density imaging (NODDI) showed large clusters of between-group differences resulting in lower values in the cTBI across the brain, where the single compartment diffusion tensor model failed to show any significant results. However, the diffusion tensor model appeared to be just as sensitive in detecting self-reported symptoms in the cTBI population using a within-group correlation. To the best of our knowledge this study provides the first application of HYDI in evaluation of cTBI using combined DTI and NODDI, significantly enhancing our understanding of the effects of concussion on white matter microstructure and emphasizing the utility of full characterization of complex diffusion to diagnose, monitor, and treat brain injury.
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21
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Moura LM, Ferreira VLDR, Loureiro RM, de Paiva JPQ, Rosa-Ribeiro R, Amaro E, Soares MBP, Machado BS. The Neurobiology of Zika Virus: New Models, New Challenges. Front Neurosci 2021; 15:654078. [PMID: 33897363 PMCID: PMC8059436 DOI: 10.3389/fnins.2021.654078] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 03/08/2021] [Indexed: 12/21/2022] Open
Abstract
The Zika virus (ZIKV) attracted attention due to one striking characteristic: the ability to cross the placental barrier and infect the fetus, possibly causing severe neurodevelopmental disruptions included in the Congenital Zika Syndrome (CZS). Few years after the epidemic, the CZS incidence has begun to decline. However, how ZIKV causes a diversity of outcomes is far from being understood. This is probably driven by a chain of complex events that relies on the interaction between ZIKV and environmental and physiological variables. In this review, we address open questions that might lead to an ill-defined diagnosis of CZS. This inaccuracy underestimates a large spectrum of apparent normocephalic cases that remain underdiagnosed, comprising several subtle brain abnormalities frequently masked by a normal head circumference. Therefore, new models using neuroimaging and artificial intelligence are needed to improve our understanding of the neurobiology of ZIKV and its true impact in neurodevelopment.
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Affiliation(s)
| | | | | | | | | | - Edson Amaro
- Hospital Israelita Albert Einstein, São Paulo, Brazil
| | - Milena Botelho Pereira Soares
- Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ), Bahia, Brazil.,University Center SENAI CIMATEC, SENAI Institute of Innovation (ISI) in Advanced Health Systems (CIMATEC ISI SAS), National Service of Industrial Learning - SENAI, Bahia, Brazil
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22
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Leddy S, Serra L, Esposito D, Vizzotto C, Giulietti G, Silvestri G, Petrucci A, Meola G, Lopiano L, Cercignani M, Bozzali M. Lesion distribution and substrate of white matter damage in myotonic dystrophy type 1: Comparison with multiple sclerosis. NEUROIMAGE-CLINICAL 2021; 29:102562. [PMID: 33516936 PMCID: PMC7848627 DOI: 10.1016/j.nicl.2021.102562] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 01/06/2021] [Accepted: 01/08/2021] [Indexed: 02/08/2023]
Abstract
The supratentorial distribution of lesions is similar in DM1 and MS. Patients with DM1 do not show infratentorial lesions. Quantitative magnetization transfer supports the presence of demyelination in DM1 lesions, but not in the NAWM. Anterior temporal lobe lesions in DM1 might have a different substrate than periventricular ones.
Myotonic Dystrophy type 1 (DM1) is an autosomal dominant condition caused by expansion of the CTG triplet repeats within the myotonic dystrophy protein of the kinase (DMPK) gene. The central nervous system is involved in the disease, with multiple symptoms including cognitive impairment. A typical feature of DM1 is the presence of widespread white matter (WM) lesions, whose total volume is associated with CTG triplet expansion. The aim of this study was to characterize the distribution and pathological substrate of these lesions as well as the normal appearing WM (NAWM) using quantitative magnetization transfer (qMT) MRI, and comparing data from DM1 patients with those from patients with multiple sclerosis (MS). Twenty-eight patients with DM1, 29 patients with relapsing-remitting MS, and 15 healthy controls had an MRI scan, including conventional and qMT imaging. The average pool size ratio (F), a proxy of myelination, was computed within lesions and NAWM for every participant. The lesion masks were warped into MNI space and lesion probability maps were obtained for each patient group. The lesion distribution, total lesion load and the tissue-specific mean F were compared between groups. The supratentorial distribution of lesions was similar in the 2 patient groups, although mean lesion volume was higher in MS than DM1. DM1 presented higher prevalence of anterior temporal lobe lesions, but none in the cerebellum and brainstem. Significantly reduced F values were found within DM1 lesions, suggesting a loss of myelin density. While F was reduced in the NAWM of MS patients, it did not differ between DM1 and controls. Our results provide further evidence for a need to compare histology and imaging using new MRI techniques in DM1 patients, in order to further our understanding of the underlying disease process contributing to WM disease.
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Affiliation(s)
- Sara Leddy
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, United Kingdom; Brighton and Sussex University Hospital Trust, Brighton, United Kingdom
| | - Laura Serra
- Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy
| | - Davide Esposito
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, United Kingdom
| | - Camilla Vizzotto
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, United Kingdom
| | | | - Gabriella Silvestri
- Department of Neuroscience, Fondazione Policlinico Gemelli IRCCS, Università Cattolica del S. Cuore, Rome, Italy
| | - Antonio Petrucci
- UOC Neurologia e Neurofisiopatologia, AO San Camillo Forlanini, Rome, Italy
| | - Giovanni Meola
- Department of Neurorehabilitation Sciences, Casa di Cura Policlinico, Milan, Italy; Department of Biomedical Science for Health, University of Milan, Milan, Italy
| | - Leonardo Lopiano
- 'Rita Levi Montalcini' Department of Neuroscience, University of Torino, Turin, Italy
| | - Mara Cercignani
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, United Kingdom; Neuroimaging Laboratory, Santa Lucia Foundation, Rome, Italy
| | - Marco Bozzali
- Clinical Imaging Sciences Centre, Brighton and Sussex Medical School, Brighton, United Kingdom; UOC Neurologia e Neurofisiopatologia, AO San Camillo Forlanini, Rome, Italy.
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Abstract
Hypomyelinating leukodystrophies constitute a subset of genetic white matter disorders characterized by a primary lack of myelin deposition. Most patients with severe hypomyelination present in infancy or early childhood and develop severe neurological deficits, but the clinical presentation can also be mild with onset of symptoms in adolescence or adulthood. MRI can be used to visualize the process of myelination in detail, and MRI pattern recognition can provide a clinical diagnosis in many patients. Next-generation sequencing provides a definitive diagnosis in 80-90% of patients. Genes associated with hypomyelination include those that encode structural myelin proteins but also many that encode proteins involved in RNA translation and some lysosomal proteins. The precise pathomechanisms remain to be elucidated. Improved understanding of the process of myelination, the metabolic axonal support functions of myelin and the proposed contribution of myelin to CNS plasticity provide possible explanations as to why almost all patients with hypomyelination experience slow clinical decline after a long phase of stability. In this Review, we provide an overview of the hypomyelinating leukodystrophies, the advances in our understanding of myelin biology and of the genes involved in these disorders, and the insights these advances have provided into their clinical presentations and evolution.
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Hagiwara A, Fujita S, Ohno Y, Aoki S. Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence. Invest Radiol 2020; 55:601-616. [PMID: 32209816 PMCID: PMC7413678 DOI: 10.1097/rli.0000000000000666] [Citation(s) in RCA: 90] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 01/28/2020] [Indexed: 12/19/2022]
Abstract
Radiological images have been assessed qualitatively in most clinical settings by the expert eyes of radiologists and other clinicians. On the other hand, quantification of radiological images has the potential to detect early disease that may be difficult to detect with human eyes, complement or replace biopsy, and provide clear differentiation of disease stage. Further, objective assessment by quantification is a prerequisite of personalized/precision medicine. This review article aims to summarize and discuss how the variability of quantitative values derived from radiological images are induced by a number of factors and how these variabilities are mitigated and standardization of the quantitative values are achieved. We discuss the variabilities of specific biomarkers derived from magnetic resonance imaging and computed tomography, and focus on diffusion-weighted imaging, relaxometry, lung density evaluation, and computer-aided computed tomography volumetry. We also review the sources of variability and current efforts of standardization of the rapidly evolving techniques, which include radiomics and artificial intelligence.
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Affiliation(s)
- Akifumi Hagiwara
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
| | | | - Yoshiharu Ohno
- Department of Radiology, Fujita Health University School of Medicine, Toyoake, Aichi, Japan
| | - Shigeki Aoki
- From the Department of Radiology, Juntendo University School of Medicine, Tokyo
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25
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Kamiya K, Hori M, Aoki S. NODDI in clinical research. J Neurosci Methods 2020; 346:108908. [PMID: 32814118 DOI: 10.1016/j.jneumeth.2020.108908] [Citation(s) in RCA: 172] [Impact Index Per Article: 34.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 08/08/2020] [Accepted: 08/09/2020] [Indexed: 12/11/2022]
Abstract
Diffusion MRI (dMRI) has proven to be a useful imaging approach for both clinical diagnosis and research investigating the microstructures of nervous tissues, and it has helped us to better understand the neurophysiological mechanisms of many diseases. Though diffusion tensor imaging (DTI) has long been the default tool to analyze dMRI data in clinical research, acquisition with stronger diffusion weightings beyond the DTI regimen is now possible with modern clinical scanners, potentially enabling even more detailed characterization of tissue microstructures. To take advantage of such data, neurite orientation dispersion and density imaging (NODDI) has been proposed as a way to relate the dMRI signal to tissue features via biophysically inspired modeling. The number of reports demonstrating the potential clinical utility of NODDI is rapidly increasing. At the same time, the pitfalls and limitations of NODDI, and general challenges in microstructure modeling, are becoming increasingly recognized by clinicians. dMRI microstructure modeling is a rapidly evolving field with great promise, where people from different scientific backgrounds, such as physics, medicine, biology, neuroscience, and statistics, are collaborating to build novel tools that contribute to improving human healthcare. Here, we review the applications of NODDI in clinical research and discuss future perspectives for investigations toward the implementation of dMRI microstructure imaging in clinical practice.
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Affiliation(s)
- Kouhei Kamiya
- Department of Radiology, The University of Tokyo, Tokyo, Japan; Department of Radiology, Juntendo University, Tokyo, Japan; Department of Radiology, Toho University, Tokyo, Japan.
| | - Masaaki Hori
- Department of Radiology, Juntendo University, Tokyo, Japan; Department of Radiology, Toho University, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University, Tokyo, Japan
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26
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Grussu F, Battiston M, Veraart J, Schneider T, Cohen-Adad J, Shepherd TM, Alexander DC, Fieremans E, Novikov DS, Gandini Wheeler-Kingshott CAM. Multi-parametric quantitative in vivo spinal cord MRI with unified signal readout and image denoising. Neuroimage 2020; 217:116884. [PMID: 32360689 PMCID: PMC7378937 DOI: 10.1016/j.neuroimage.2020.116884] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Revised: 03/18/2020] [Accepted: 04/23/2020] [Indexed: 12/11/2022] Open
Abstract
Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed in vivo by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout in vivo (i.e. with matched spatial encoding parameters across a range of imaging contrasts). We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via multi-contrast image denoising methods. As a proof-of-concept, here we provide a demonstration with one such method, known as Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a singular value (SV) decomposition truncation approach that relies on redundant acquisitions, i.e. such that the number of measurements is large compared to the number of components that are maintained in the truncated SV decomposition. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient MP-PCA denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 17% for mean kurtosis, 8% for bound pool fraction (myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from multi-contrast denoising methods such as MP-PCA.
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Affiliation(s)
- Francesco Grussu
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
| | - Marco Battiston
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | | | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, Canada
| | - Timothy M Shepherd
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Brain MRI 3T Research Centre, IRCCS Mondino Foundation, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
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27
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Palacios EM, Owen JP, Yuh EL, Wang MB, Vassar MJ, Ferguson AR, Diaz-Arrastia R, Giacino JT, Okonkwo DO, Robertson CS, Stein MB, Temkin N, Jain S, McCrea M, MacDonald CL, Levin HS, Manley GT, Mukherjee P, TRACK-TBI Investigators. The evolution of white matter microstructural changes after mild traumatic brain injury: A longitudinal DTI and NODDI study. SCIENCE ADVANCES 2020; 6:eaaz6892. [PMID: 32821816 PMCID: PMC7413733 DOI: 10.1126/sciadv.aaz6892] [Citation(s) in RCA: 107] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 06/26/2020] [Indexed: 05/11/2023]
Abstract
Neuroimaging biomarkers that can detect white matter (WM) pathology after mild traumatic brain injury (mTBI) and predict long-term outcome are needed to improve care and develop therapies. We used diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) to investigate WM microstructure cross-sectionally and longitudinally after mTBI and correlate these with neuropsychological performance. Cross-sectionally, early decreases of fractional anisotropy and increases of mean diffusivity corresponded to WM regions with elevated free water fraction on NODDI. This elevated free water was more extensive in the patient subgroup reporting more early postconcussive symptoms. The longer-term longitudinal WM changes consisted of declining neurite density on NODDI, suggesting axonal degeneration from diffuse axonal injury for which NODDI is more sensitive than DTI. Therefore, NODDI is a more sensitive and specific biomarker than DTI for WM microstructural changes due to mTBI that merits further study for mTBI diagnosis, prognosis, and treatment monitoring.
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Affiliation(s)
- E. M. Palacios
- Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - J. P. Owen
- Department of Radiology, University of Washington, Seattle, WA, USA
| | - E. L. Yuh
- Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, USA
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
| | - M. B. Wang
- Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, USA
| | - M. J. Vassar
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
| | - A. R. Ferguson
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
- San Francisco Veterans Affairs Medical Center, San Francisco, CA, USA
| | - R. Diaz-Arrastia
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, USA
| | - J. T. Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Charlestown, MA, USA
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, MA, USA
| | - D. O. Okonkwo
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - C. S. Robertson
- Department of Neurosurgery, Baylor College of Medicine, Houston, TX, USA
| | - M. B. Stein
- Department of Psychiatry, University of California, San Diego, La Jolla, CA, USA
- Department of Family Medicine & Public Health, University of California, San Diego, La Jolla, CA, USA
| | - N. Temkin
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - S. Jain
- Department of Family Medicine & Public Health, University of California, San Diego, La Jolla, CA, USA
| | - M. McCrea
- Departments of Neurosurgery and Neurology, Medical College of Wisconsin, Milwaukee, WI, USA
| | - C. L. MacDonald
- Department of Neurological Surgery, University of Washington, Seattle, WA, USA
| | - H. S. Levin
- Department of Neurology, Baylor College of Medicine, Houston, TX, USA
| | - G. T. Manley
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Neurological Surgery, UCSF, San Francisco, CA, USA
| | - P. Mukherjee
- Department of Radiology & Biomedical Imaging, UCSF, San Francisco, CA, USA
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA, USA
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA, USA
- Corresponding author.
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28
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Bao Q, Ma L, Liberman G, Solomon E, Martinho RP, Frydman L. Dynamic T
2
mapping by multi‐spin‐echo spatiotemporal encoding. Magn Reson Med 2020; 84:895-907. [DOI: 10.1002/mrm.28158] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2019] [Revised: 11/28/2019] [Accepted: 12/11/2019] [Indexed: 12/12/2022]
Affiliation(s)
- Qingjia Bao
- Department of Chemical and Biological Physics Weizmann Institute Rehovot Israel
| | - Lingceng Ma
- Department of Chemical and Biological Physics Weizmann Institute Rehovot Israel
| | - Gilad Liberman
- Department of Chemical and Biological Physics Weizmann Institute Rehovot Israel
| | - Eddy Solomon
- Department of Chemical and Biological Physics Weizmann Institute Rehovot Israel
| | - Ricardo P. Martinho
- Department of Chemical and Biological Physics Weizmann Institute Rehovot Israel
| | - Lucio Frydman
- Department of Chemical and Biological Physics Weizmann Institute Rehovot Israel
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29
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Buchanan CR, Bastin ME, Ritchie SJ, Liewald DC, Madole JW, Tucker-Drob EM, Deary IJ, Cox SR. The effect of network thresholding and weighting on structural brain networks in the UK Biobank. Neuroimage 2020; 211:116443. [PMID: 31927129 DOI: 10.1016/j.neuroimage.2019.116443] [Citation(s) in RCA: 72] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 12/04/2019] [Indexed: 12/11/2022] Open
Abstract
Whole-brain structural networks can be constructed using diffusion MRI and probabilistic tractography. However, measurement noise and the probabilistic nature of the tracking procedure result in an unknown proportion of spurious white matter connections. Faithful disentanglement of spurious and genuine connections is hindered by a lack of comprehensive anatomical information at the network-level. Therefore, network thresholding methods are widely used to remove ostensibly false connections, but it is not yet clear how different thresholding strategies affect basic network properties and their associations with meaningful demographic variables, such as age. In a sample of 3153 generally healthy volunteers from the UK Biobank Imaging Study (aged 44-77 years), we constructed whole-brain structural networks and applied two principled network thresholding approaches (consistency and proportional thresholding). These were applied over a broad range of threshold levels across six alternative network weightings (streamline count, fractional anisotropy, mean diffusivity and three novel weightings from neurite orientation dispersion and density imaging) and for four common network measures (mean edge weight, characteristic path length, network efficiency and network clustering coefficient). We compared network measures against age associations and found that: 1) measures derived from unthresholded matrices yielded the weakest age-associations (0.033 ≤ |β| ≤ 0.409); and 2) the most commonly-used level of proportional-thresholding from the literature (retaining 68.7% of all possible connections) yielded significantly weaker age-associations (0.070 ≤ |β| ≤ 0.406) than the consistency-based approach which retained only 30% of connections (0.140 ≤ |β| ≤ 0.409). However, we determined that the stringency of the threshold was a stronger determinant of the network-age association than the choice of threshold method and the two thresholding approaches identified a highly overlapping set of connections (ICC = 0.84), when matched at 70% network sparsity. Generally, more stringent thresholding resulted in more age-sensitive network measures in five of the six network weightings, except at the highest levels of sparsity (>90%), where crucial connections were then removed. At two commonly-used threshold levels, the age-associations of the connections that were discarded (mean β ≤ |0.068|) were significantly smaller in magnitude than the corresponding age-associations of the connections that were retained (mean β ≤ |0.219|, p < 0.001, uncorrected). Given histological evidence of widespread degeneration of structural brain connectivity with increasing age, these results indicate that stringent thresholding methods may be most accurate in identifying true white matter connections.
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Affiliation(s)
- Colin R Buchanan
- Lothian Birth Cohorts group, The University of Edinburgh, Edinburgh, UK; Department of Psychology, The University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK.
| | - Mark E Bastin
- Lothian Birth Cohorts group, The University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK; Brain Research Imaging Centre, Neuroimaging Sciences, The University of Edinburgh, Edinburgh, UK
| | - Stuart J Ritchie
- Social, Genetic and Developmental Psychiatry Centre, King's College London, London, UK
| | - David C Liewald
- Lothian Birth Cohorts group, The University of Edinburgh, Edinburgh, UK; Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - James W Madole
- Department of Psychology, University of Texas at Austin, Austin, TX, USA
| | | | - Ian J Deary
- Lothian Birth Cohorts group, The University of Edinburgh, Edinburgh, UK; Department of Psychology, The University of Edinburgh, Edinburgh, UK
| | - Simon R Cox
- Lothian Birth Cohorts group, The University of Edinburgh, Edinburgh, UK; Department of Psychology, The University of Edinburgh, Edinburgh, UK; Scottish Imaging Network, A Platform for Scientific Excellence (SINAPSE) Collaboration, Edinburgh, UK
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30
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Rojas-Vite G, Coronado-Leija R, Narvaez-Delgado O, Ramírez-Manzanares A, Marroquín JL, Noguez-Imm R, Aranda ML, Scherrer B, Larriva-Sahd J, Concha L. Histological validation of per-bundle water diffusion metrics within a region of fiber crossing following axonal degeneration. Neuroimage 2019; 201:116013. [PMID: 31326575 DOI: 10.1016/j.neuroimage.2019.116013] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2019] [Revised: 06/11/2019] [Accepted: 07/11/2019] [Indexed: 12/12/2022] Open
Abstract
Micro-architectural characteristics of white matter can be inferred through analysis of diffusion-weighted magnetic resonance imaging (dMRI). The diffusion-dependent signal can be analyzed through several methods, with the tensor model being the most frequently used due to its straightforward interpretation and low requirements for acquisition parameters. While valuable information can be gained from the tensor-derived metrics in regions of homogeneous tissue organization, this model does not provide reliable microstructural information at crossing fiber regions, which are pervasive throughout human white matter. Several multiple fiber models have been proposed that seem to overcome the limitations of the tensor, with few providing per-bundle dMRI-derived metrics. However, biological interpretations of such metrics are limited by the lack of histological confirmation. To this end, we developed a straightforward biological validation framework. Unilateral retinal ischemia was induced in ten rats, which resulted in axonal (Wallerian) degeneration of the corresponding optic nerve, while the contralateral was left intact; the intact and injured axonal populations meet at the optic chiasm as they cross the midline, generating a fiber crossing region in which each population has different diffusion properties. Five rats served as controls. High-resolution ex vivo dMRI was acquired five weeks after experimental procedures. We correlated and compared histology to per-bundle descriptors derived from three methodologies for dMRI analysis (constrained spherical deconvolution and two multi-tensor representations). We found a tight correlation between axonal density (as evaluated through automatic segmentation of histological sections) with per-bundle apparent fiber density and fractional anisotropy (derived from dMRI). The multi-fiber methods explored were able to correctly identify the damaged fiber populations in a region of fiber crossings (chiasm). Our results provide validation of metrics that bring substantial and clinically useful information about white-matter tissue at crossing fiber regions. Our proposed framework is useful to validate other current and future dMRI methods.
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Affiliation(s)
- Gilberto Rojas-Vite
- Institute of Neurobiology, Universidad Nacional Autónoma de México. Blvd. Juriquilla, 3001, Querétaro, Querétaro, Mexico
| | - Ricardo Coronado-Leija
- Institute of Neurobiology, Universidad Nacional Autónoma de México. Blvd. Juriquilla, 3001, Querétaro, Querétaro, Mexico
| | - Omar Narvaez-Delgado
- Institute of Neurobiology, Universidad Nacional Autónoma de México. Blvd. Juriquilla, 3001, Querétaro, Querétaro, Mexico
| | | | - José Luis Marroquín
- Centro de Investigación en Matemáticas, Valenciana S/N, Guanajuato, Guanajuato, Mexico
| | - Ramsés Noguez-Imm
- Institute of Neurobiology, Universidad Nacional Autónoma de México. Blvd. Juriquilla, 3001, Querétaro, Querétaro, Mexico
| | - Marcos L Aranda
- Department of Human Biochemistry, School of Medicine, University of Buenos Aires/CEFyBO, CONICET, Buenos Aires, Argentina
| | - Benoit Scherrer
- Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jorge Larriva-Sahd
- Institute of Neurobiology, Universidad Nacional Autónoma de México. Blvd. Juriquilla, 3001, Querétaro, Querétaro, Mexico
| | - Luis Concha
- Institute of Neurobiology, Universidad Nacional Autónoma de México. Blvd. Juriquilla, 3001, Querétaro, Querétaro, Mexico.
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Dimensionality reduction of diffusion MRI measures for improved tractometry of the human brain. Neuroimage 2019; 200:89-100. [PMID: 31228638 PMCID: PMC6711466 DOI: 10.1016/j.neuroimage.2019.06.020] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 06/05/2019] [Accepted: 06/07/2019] [Indexed: 12/13/2022] Open
Abstract
Various diffusion MRI (dMRI) measures have been proposed for characterising tissue microstructure over the last 15 years. Despite the growing number of experiments using different dMRI measures in assessments of white matter, there has been limited work on: 1) examining their covariance along specific pathways; and on 2) combining these different measures to study tissue microstructure. Indeed, it quickly becomes intractable for existing analysis pipelines to process multiple measurements at each voxel and at each vertex forming a streamline, highlighting the need for new ways to visualise or analyse such high-dimensional data. In a sample of 36 typically developing children aged 8–18 years, we profiled various commonly used dMRI measures across 22 brain pathways. Using a data-reduction approach, we identified two biologically-interpretable components that capture 80% of the variance in these dMRI measures. The first derived component captures properties related to hindrance and restriction in tissue microstructure, while the second component reflects characteristics related to tissue complexity and orientational dispersion. We then demonstrate that the components generated by this approach preserve the biological relevance of the original measurements by showing age-related effects across developmentally sensitive pathways. In summary, our findings demonstrate that dMRI analyses can benefit from dimensionality reduction techniques, to help disentangling the neurobiological underpinnings of white matter organisation.
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32
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Imaging the multiple sclerosis lesion: insights into pathogenesis, progression and repair. Curr Opin Neurol 2019; 32:338-345. [DOI: 10.1097/wco.0000000000000698] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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Möller HE, Bossoni L, Connor JR, Crichton RR, Does MD, Ward RJ, Zecca L, Zucca FA, Ronen I. Iron, Myelin, and the Brain: Neuroimaging Meets Neurobiology. Trends Neurosci 2019; 42:384-401. [PMID: 31047721 DOI: 10.1016/j.tins.2019.03.009] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 03/12/2019] [Accepted: 03/26/2019] [Indexed: 12/31/2022]
Abstract
Although iron is crucial for neuronal functioning, many aspects of cerebral iron biology await clarification. The ability to quantify specific iron forms in the living brain would open new avenues for diagnosis, therapeutic monitoring, and understanding pathogenesis of diseases. A modality that allows assessment of brain tissue composition in vivo, in particular of iron deposits or myelin content on a submillimeter spatial scale, is magnetic resonance imaging (MRI). Multimodal strategies combining MRI with complementary analytical techniques ex vivo have emerged, which may lead to improved specificity. Interdisciplinary collaborations will be key to advance beyond simple correlative analyses in the biological interpretation of MRI data and to gain deeper insights into key factors leading to iron accumulation and/or redistribution associated with neurodegeneration.
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Affiliation(s)
- Harald E Möller
- Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstr. 1A, Leipzig, Germany.
| | - Lucia Bossoni
- Department of Radiology, C.J. Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, The Netherlands
| | - James R Connor
- Department of Neurosurgery, Pennsylvania State University College of Medicine, Hershey, PA, USA
| | | | - Mark D Does
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Roberta J Ward
- Centre for Neuroinflammation and Neurodegeneration, Department of Medicine, Hammersmith Hospital Campus, Imperial College London, London, UK
| | - Luigi Zecca
- Institute of Biomedical Technologies, National Research Council of Italy, Segrate, Milan, Italy; Department of Psychiatry, Columbia University Medical Center, New York State Psychiatric Institute, New York, NY, USA
| | - Fabio A Zucca
- Institute of Biomedical Technologies, National Research Council of Italy, Segrate, Milan, Italy
| | - Itamar Ronen
- Department of Radiology, C.J. Gorter Center for High Field MRI, Leiden University Medical Center, Leiden, The Netherlands
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34
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Microstructural imaging of human neocortex in vivo. Neuroimage 2018; 182:184-206. [DOI: 10.1016/j.neuroimage.2018.02.055] [Citation(s) in RCA: 75] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2017] [Revised: 02/13/2018] [Accepted: 02/26/2018] [Indexed: 12/12/2022] Open
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35
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Edwards LJ, Pine KJ, Ellerbrock I, Weiskopf N, Mohammadi S. NODDI-DTI: Estimating Neurite Orientation and Dispersion Parameters from a Diffusion Tensor in Healthy White Matter. Front Neurosci 2017; 11:720. [PMID: 29326546 PMCID: PMC5742359 DOI: 10.3389/fnins.2017.00720] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2017] [Accepted: 12/11/2017] [Indexed: 11/25/2022] Open
Abstract
The NODDI-DTI signal model is a modification of the NODDI signal model that formally allows interpretation of standard single-shell DTI data in terms of biophysical parameters in healthy human white matter (WM). The NODDI-DTI signal model contains no CSF compartment, restricting application to voxels without CSF partial-volume contamination. This modification allowed derivation of analytical relations between parameters representing axon density and dispersion, and DTI invariants (MD and FA) from the NODDI-DTI signal model. These relations formally allow extraction of biophysical parameters from DTI data. NODDI-DTI parameters were estimated by applying the proposed analytical relations to DTI parameters estimated from the first shell of data, and compared to parameters estimated by fitting the NODDI-DTI model to both shells of data (reference dataset) in the WM of 14 in vivo diffusion datasets recorded with two different protocols, and in simulated data. The first two datasets were also fit to the NODDI-DTI model using only the first shell (as for DTI) of data. NODDI-DTI parameters estimated from DTI, and NODDI-DTI parameters estimated by fitting the model to the first shell of data gave similar errors compared to two-shell NODDI-DTI estimates. The simulations showed the NODDI-DTI method to be more noise-robust than the two-shell fitting procedure. The NODDI-DTI method gave unphysical parameter estimates in a small percentage of voxels, reflecting voxelwise DTI estimation error or NODDI-DTI model invalidity. In the course of evaluating the NODDI-DTI model, it was found that diffusional kurtosis strongly biased DTI-based MD values, and so, making assumptions based on healthy WM, a novel heuristic correction requiring only DTI data was derived and used to mitigate this bias. Since validations were only performed on healthy WM, application to grey matter or pathological WM would require further validation. Our results demonstrate NODDI-DTI to be a promising model and technique to interpret restricted datasets acquired for DTI analysis in healthy white matter with greater biophysical specificity, though its limitations must be borne in mind.
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Affiliation(s)
- Luke J Edwards
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Kerrin J Pine
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Isabel Ellerbrock
- Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Siawoosh Mohammadi
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, United Kingdom.,Institute of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
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