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Pezeshgi S, Ghaderi S, Mohammadi S, Karimi N, Ziaadini B, Mohammadi M, Fatehi F. Diffusion tensor imaging biomarkers and clinical assessments in amyotrophic lateral sclerosis (ALS) patients: an exploratory study. Ann Med Surg (Lond) 2024; 86:5080-5090. [PMID: 39239063 PMCID: PMC11374192 DOI: 10.1097/ms9.0000000000002332] [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: 05/03/2024] [Accepted: 06/21/2024] [Indexed: 09/07/2024] Open
Abstract
Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterized by progressive loss of upper and lower motor neurons. Biomarkers are needed to improve diagnosis, gauge progression, and evaluate treatment. Diffusion tensor imaging (DTI) is a promising biomarker for detecting microstructural alterations in the white matter tracts. This study aimed to assess DTI metrics as biomarkers and to examine their relationship with clinical assessments in patients with ALS. Eleven patients with ALS and 21 healthy controls (HCs) underwent 3T MRI with DTI. DTI metrics, including fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD), were compared between key motor and extra-motor tract groups. Group comparisons and correlations between DTI metrics also correlated with clinical scores of disability (ALSFRS-R), muscle strength (dynamometry), and motor unit loss (MUNIX). Widespread differences were found between patients with ALS and HCs in DTI metrics, including decreased FA and increased diffusivity metrics. However, MD and RD are more sensitive metrics for detecting white matter changes in patients with ALS. Significant interhemispheric correlations between the tract DTI metrics were also observed. DTI metrics showed symmetry between the hemispheres and correlated with the clinical assessments. MD, RD, and AD increases significantly correlated with lower ALSFRS-R and MUNIX scores and weaker dynamometry results. DTI reveals microstructural damage along the motor and extra-motor regions in ALS patients. DTI metrics can serve as quantitative neuroimaging biomarkers for diagnosis, prognosis, monitoring of progression, and treatment. Combined analysis of imaging, electrodiagnostic, and functional biomarkers shows potential for characterizing disease pathophysiology and progression.
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Affiliation(s)
- Saharnaz Pezeshgi
- Neuromuscular Research Center, Department of Neurology, Shariati Hospital
| | - Sadegh Ghaderi
- Neuromuscular Research Center, Department of Neurology, Shariati Hospital
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine
| | - Sana Mohammadi
- Neuromuscular Research Center, Department of Neurology, Shariati Hospital
| | - Narges Karimi
- Neuromuscular Research Center, Department of Neurology, Shariati Hospital
| | | | - Mahdi Mohammadi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Farzad Fatehi
- Neuromuscular Research Center, Department of Neurology, Shariati Hospital
- Department of Neurology, University Hospitals of Leicester NHS Trust, Leicester, UK
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Konell HG, Junior LOM, Dos Santos AC, Salmon CEG. Assessment of U-Net in the segmentation of short tracts: Transferring to clinical MRI routine. Magn Reson Imaging 2024; 111:217-228. [PMID: 38754751 DOI: 10.1016/j.mri.2024.05.009] [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: 04/11/2024] [Revised: 05/09/2024] [Accepted: 05/12/2024] [Indexed: 05/18/2024]
Abstract
Accurately studying structural connectivity requires precise tract segmentation strategies. The U-Net network has been widely recognized for its exceptional capacity in image segmentation tasks and provides remarkable results in large tract segmentation when high-quality diffusion-weighted imaging (DWI) data are used. However, short tracts, which are associated with various neurological diseases, pose specific challenges, particularly when high-quality DWI data acquisition within clinical settings is concerned. Here, we aimed to evaluate the U-Net network ability to segment short tracts by using DWI data acquired in different experimental conditions. To this end, we conducted three types of training experiments involving 350 healthy subjects and 11 white matter tracts, including the anterior, posterior, and hippocampal commissure, fornix, and uncinated fasciculus. In the first experiment, the model was exclusively trained with high-quality data of the Human Connectome Project (HCP) dataset. The second experiment focused on images of healthy subjects acquired from a local hospital dataset, representing a typical clinical routine acquisition. In the third experiment, a hybrid training approach was employed, combining data of the HCP and local hospital datasets. Then, the best model was also tested in unseen DWIs of 10 epilepsy patients of the local hospital and 10 healthy subjects acquired on a scanner from another company. The outcomes of the third experiment demonstrated a notable enhancement in performance when contrasted with the preceding trials. Specifically, the short tracts within the local hospital dataset achieved Dice scores ranging between 0.60 and 0.65. Similar intervals were obtained with HCP data in the first experiment, and a substantial improvement compared to the scores between 0.37 and 0.50 obtained with the local hospital dataset at the same experiment. This improvement persisted when the method was applied to diverse scenarios, including different scanner acquisitions and epilepsy patients. These results indicate that combining datasets from different sources, coupled with resolution standardization strengthens the neural network ability to generalize predictions across a spectrum of datasets. Nevertheless, short tract segmentation performance is intricately linked to the training composition, to validation, and to testing data. Moreover, curved tracts have intricate structural nature, which adds complexities to their segmenting. Although the network training approach tested herein has provided promising results, caution must be taken when extrapolating its application to datasets acquired under distinct experimental conditions, even in the case of higher-quality data or analysis of long or short tracts.
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Affiliation(s)
- Hohana Gabriela Konell
- Inbrain Lab, Department of Physics, Faculty of Philosophy, Sciences and Letters, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Luiz Otávio Murta Junior
- Medical Signals and Imaging Computing Lab, Department of Computing and Mathematics, Faculty of Philosophy, Sciences and Letters, University of São Paulo, Ribeirão Preto, SP, Brazil
| | - Antônio Carlos Dos Santos
- Department of Medical Imaging, Hematology and Clinical Oncology, Faculty of Medicine of Ribeirão Preto, SP, Brazil
| | - Carlos Ernesto Garrido Salmon
- Inbrain Lab, Department of Physics, Faculty of Philosophy, Sciences and Letters, University of São Paulo, Ribeirão Preto, SP, Brazil; Department of Medical Imaging, Hematology and Clinical Oncology, Faculty of Medicine of Ribeirão Preto, SP, Brazil.
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Takemura H, Kruper JA, Miyata T, Rokem A. Tractometry of Human Visual White Matter Pathways in Health and Disease. Magn Reson Med Sci 2024; 23:316-340. [PMID: 38866532 PMCID: PMC11234945 DOI: 10.2463/mrms.rev.2024-0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024] Open
Abstract
Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.
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Affiliation(s)
- Hiromasa Takemura
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Graduate Institute for Advanced Studies, SOKENDAI, Hayama, Kanagawa, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - John A Kruper
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Toshikazu Miyata
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
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Kamagata K, Andica C, Uchida W, Takabayashi K, Saito Y, Lukies M, Hagiwara A, Fujita S, Akashi T, Wada A, Hori M, Kamiya K, Zalesky A, Aoki S. Advancements in Diffusion MRI Tractography for Neurosurgery. Invest Radiol 2024; 59:13-25. [PMID: 37707839 DOI: 10.1097/rli.0000000000001015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
ABSTRACT Diffusion magnetic resonance imaging tractography is a noninvasive technique that enables the visualization and quantification of white matter tracts within the brain. It is extensively used in preoperative planning for brain tumors, epilepsy, and functional neurosurgical procedures such as deep brain stimulation. Over the past 25 years, significant advancements have been made in imaging acquisition, fiber direction estimation, and tracking methods, resulting in considerable improvements in tractography accuracy. The technique enables the mapping of functionally critical pathways around surgical sites to avoid permanent functional disability. When the limitations are adequately acknowledged and considered, tractography can serve as a valuable tool to safeguard critical white matter tracts and provides insight regarding changes in normal white matter and structural connectivity of the whole brain beyond local lesions. In functional neurosurgical procedures such as deep brain stimulation, it plays a significant role in optimizing stimulation sites and parameters to maximize therapeutic efficacy and can be used as a direct target for therapy. These insights can aid in patient risk stratification and prognosis. This article aims to discuss state-of-the-art tractography methodologies and their applications in preoperative planning and highlight the challenges and new prospects for the use of tractography in daily clinical practice.
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Affiliation(s)
- Koji Kamagata
- From the Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan (K.K., C.A., W.U., K.T., Y.S., A.H., S.F., T.A., A.W., S.A.); Faculty of Health Data Science, Juntendo University, Chiba, Japan (C.A., S.A.); Department of Radiology, Alfred Health, Melbourne, Victoria, Australia (M.L.); Department of Radiology, University of Tokyo, Tokyo, Japan (S.F.); Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan (M.H., K.K.); Melbourne Neuropsychiatry Center, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia (A.Z.); and Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia (A.Z.)
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Andica C, Kamagata K, Uchida W, Saito Y, Takabayashi K, Hagiwara A, Takeshige-Amano H, Hatano T, Hattori N, Aoki S. Fiber-Specific White Matter Alterations in Parkinson's Disease Patients with GBA Gene Mutations. Mov Disord 2023; 38:2019-2030. [PMID: 37608502 DOI: 10.1002/mds.29578] [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: 06/06/2023] [Revised: 07/21/2023] [Accepted: 07/31/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND Patients with Parkinson's disease (PD) carrying GBA gene mutations (GBA-PD) have a more aggressive disease course than those with idiopathic PD (iPD). OBJECTIVE The objective of this study was to investigate fiber-specific white matter (WM) differences in nonmedicated patients with early-stage GBA-PD and iPD using fixel-based analysis, a novel technique to assess tract-specific WM microstructural and macrostructural features comprehensively. METHODS Fixel-based metrics, including microstructural fiber density (FD), macrostructural fiber-bundle cross section (FC), and a combination of FD and FC (FDC), were compared among 30 healthy control subjects, 16 patients with GBA-PD, and 35 patients with iPD. Associations between FDC and clinical evaluations were also explored using multiple linear regression analyses. RESULTS Patients with GBA-PD showed significantly lower FD in the fornix and superior longitudinal fasciculus than healthy control subjects, and lower FC in the corticospinal tract (CST) and lower FDC in the CST, middle cerebellar peduncle, and striatal-thalamo-cortical pathways than patients with iPD. Contrarily, patients with iPD showed significantly higher FC and FDC in the CST and striatal-thalamo-cortical pathways than healthy control subjects. In addition, lower FDC in patients with GBA-PD was associated with reduced glucocerebrosidase enzyme activity, lower cerebrospinal fluid total α-synuclein levels, lower Montreal Cognitive Assessment scores, lower striatal binding ratio, and higher Unified Parkinson's Disease Rating Scale Part III scores. CONCLUSIONS We report reduced fiber-specific WM density and bundle cross-sectional size in patients with GBA-PD, suggesting neurodegeneration linked to glucocerebrosidase deficiency, α-synuclein accumulation, and poorer cognition and motor functions. Conversely, patients with iPD showed increased fiber bundle size, likely because of WM reorganization. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Grants
- Grant-in-Aid for Special Research in Subsidies for ordinary expenses of private schools from The Promotion and Mutual Aid Corporation for Private Schools of Japan
- JP21wm0425006 Japan Agency for Medical Research and Development
- 23H02865 Japan Society for the Promotion of Science
- 23K14927 Japan Society for the Promotion of Science
- PPMI - a public-private partnership - is funded by the Michael J. Fox Foundation for Parkinson's Research funding partners 4D Pharma, Abbvie, Acurex Therapeutics, Allergan, Amathus Therapeutics, ASAP, Avid Radiopharmaceuticals, Bial Biotech, Biogen, BioLegend, Bristol-Myers Squibb, Calico, Celgene, Dacapo Brain Science, Denali, The Edmond J. Safra Foundation, GE Healthcare, Genentech, GlaxoSmithKline, Golub Capital, Handl Therapeutics, Insitro, Janssen Neuroscience, Lilly, Lundbeck, Merck, M
- JP18dm0307004 The Brain/MINDS Beyond program of the Japan Agency for Medical Research and Development
- JP19dm0307101 The Brain/MINDS Beyond program of the Japan Agency for Medical Research and Development
- The Juntendo Research Branding Project
- The Project for Training Experts in Statistical Sciences
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Affiliation(s)
- Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kaito Takabayashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | | | - Taku Hatano
- Department of Neurology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
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Xu B, Zhang X, Tian C, Yan W, Wang Y, Zhang D, Liao X, Cai X. Automatic segmentation of white matter hyperintensities and correlation analysis for cerebral small vessel disease. Front Neurol 2023; 14:1242685. [PMID: 37576013 PMCID: PMC10413581 DOI: 10.3389/fneur.2023.1242685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 07/06/2023] [Indexed: 08/15/2023] Open
Abstract
Objective Cerebral white matter hyperintensity can lead to cerebral small vessel disease, MRI images in the brain are used to assess the degree of pathological changes in white matter regions. In this paper, we propose a framework for automatic 3D segmentation of brain white matter hyperintensity based on MRI images to address the problems of low accuracy and segmentation inhomogeneity in 3D segmentation. We explored correlation analyses of cognitive assessment parameters and multiple comparison analyses to investigate differences in brain white matter hyperintensity volume among three cognitive states, Dementia, MCI and NCI. The study explored the correlation between cognitive assessment coefficients and brain white matter hyperintensity volume. Methods This paper proposes an automatic 3D segmentation framework for white matter hyperintensity using a deep multi-mapping encoder-decoder structure. The method introduces a 3D residual mapping structure for the encoder and decoder. Multi-layer Cross-connected Residual Mapping Module (MCRCM) is proposed in the encoding stage to enhance the expressiveness of model and perception of detailed features. Spatial Attention Weighted Enhanced Supervision Module (SAWESM) is proposed in the decoding stage to adjust the supervision strategy through a spatial attention weighting mechanism. This helps guide the decoder to perform feature reconstruction and detail recovery more effectively. Result Experimental data was obtained from a privately owned independent brain white matter dataset. The results of the automatic 3D segmentation framework showed a higher segmentation accuracy compared to nnunet and nnunet-resnet, with a p-value of <0.001 for the two cognitive assessment parameters MMSE and MoCA. This indicates that larger brain white matter are associated with lower scores of MMSE and MoCA, which in turn indicates poorer cognitive function. The order of volume size of white matter hyperintensity in the three groups of cognitive states is dementia, MCI and NCI, respectively. Conclusion The paper proposes an automatic 3D segmentation framework for brain white matter that achieves high-precision segmentation. The experimental results show that larger volumes of segmented regions have a negative correlation with lower scoring coefficients of MMSE and MoCA. This correlation analysis provides promising treatment prospects for the treatment of cerebral small vessel diseases in the brain through 3D segmentation analysis of brain white matter. The differences in the volume of white matter hyperintensity regions in subjects with three different cognitive states can help to better understand the mechanism of cognitive decline in clinical research.
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Affiliation(s)
- Bin Xu
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China
- Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Xiaofeng Zhang
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Congyu Tian
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Wei Yan
- Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yuanqing Wang
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China
| | - Doudou Zhang
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China
- Shenzhen University School of Medicine, Shenzhen, Guangdong, China
| | - Xiangyun Liao
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Xiaodong Cai
- Department of Neurosurgery, Shenzhen Second People's Hospital, The First Affiliated Hospital of Shenzhen University, Shenzhen, Guangdong, China
- Shenzhen University School of Medicine, Shenzhen, Guangdong, China
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