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Ren Q, Zhao S, Yu R, Xu Z, Liu S, Zhang B, Sun Q, Jiang Q, Zhao C, Meng X. Thalamic-limbic circuit dysfunction and white matter topological alteration in Parkinson's disease are correlated with gait disturbance. Front Aging Neurosci 2024; 16:1426754. [PMID: 39295640 PMCID: PMC11408845 DOI: 10.3389/fnagi.2024.1426754] [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: 05/02/2024] [Accepted: 07/02/2024] [Indexed: 09/21/2024] Open
Abstract
Background Limbic structures have recently garnered increased attention in Parkinson's disease (PD) research. This study aims to explore changes at the whole-brain level in the structural network, specifically the white matter fibres connecting the thalamus and limbic system, and their correlation with the clinical characteristics of patients with PD. Methods Between December 2020 and November 2021, we prospectively enrolled 42 patients with PD and healthy controls at the movement disorder centre. All participants underwent diffusion tensor imaging (DTI), 3D T1-weighted imaging (3D-T1WI), and routine brain magnetic resonance imaging on a 3.0 T MR scanner. We employed the tract-based spatial statistical (TBSS) analytic approach, examined structural network properties, and conducted probabilistic fibre tractography to identify alterations in white matter pathways and the topological organisation associated with PD. Results In patients with PD, significant changes were observed in the fibrous tracts of the prefrontal lobe, corpus callosum, and thalamus. Notably, the fibrous tracts in the prefrontal lobe and corpus callosum showed a moderate negative correlation with the Freezing of Gait Questionnaire (FOG-Q) scores (r = -0.423, p = 0.011). The hippocampus and orbitofrontal gyrus exhibited more fibre bundle parameter changes than other limbic structures. The mean streamline length between the thalamus and the orbitofrontal gyrus demonstrated a moderate negative correlation with Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) III (r = -0.435, p = 0.006). Topological parameters, including characteristic path length (L p), global efficiency (E g), normalised shortest path length (λ) and nodal local efficiency (N le), correlated moderately with the MDS-UPDRS, HAMA, MoCA, PDQ-39, and FOG-Q, respectively. Conclusion DTI is a valuable tool for detecting changes in water molecule dispersion and the topological structure of the brain in patients with PD. The thalamus may play a significant role in the gait abnormalities observed in PD.
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Affiliation(s)
- Qingguo Ren
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China
- Medical Imaging and Engineering Intersection Key Laboratory of Qingdao, Qingdao, China
| | - Shuai Zhao
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China
| | - Rong Yu
- Shandong University of Traditional Chinese Medicine, Jinan, China
| | - Ziliang Xu
- The First Affiliated Hospital of Air Force Military Medical University, Xi'an, China
| | - Shuangwu Liu
- School of Nursing and Rehabilitation, Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Bin Zhang
- Department of Neurology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China
| | - Qicai Sun
- Department of Radiology, Xuecheng District People's Hospital, Zaozhuang, China
| | - Qingjun Jiang
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China
| | - Cuiping Zhao
- Department of Neurology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China
| | - Xiangshui Meng
- Department of Radiology, Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University, Qingdao, China
- Medical Imaging and Engineering Intersection Key Laboratory of Qingdao, Qingdao, China
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2
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Owens-Walton C, Nir TM, Al-Bachari S, Ambrogi S, Anderson TJ, Aventurato ÍK, Cendes F, Chen YL, Ciullo V, Cook P, Dalrymple-Alford JC, Dirkx MF, Druzgal J, Emsley HCA, Guimarães R, Haroon HA, Helmich RC, Hu MT, Johansson ME, Kim HB, Klein JC, Laansma M, Lawrence KE, Lochner C, Mackay C, McMillan CT, Melzer TR, Nabulsi L, Newman B, Opriessnig P, Parkes LM, Pellicano C, Piras F, Piras F, Pirpamer L, Pitcher TL, Poston KL, Roos A, Silva LS, Schmidt R, Schwingenschuh P, Shahid-Besanti M, Spalletta G, Stein DJ, Thomopoulos SI, Tosun D, Tsai CC, van den Heuvel OA, van Heese E, Vecchio D, Villalón-Reina JE, Vriend C, Wang JJ, Wu YR, Yasuda CL, Thompson PM, Jahanshad N, van der Werf Y. A worldwide study of white matter microstructural alterations in people living with Parkinson's disease. NPJ Parkinsons Dis 2024; 10:151. [PMID: 39128907 PMCID: PMC11317500 DOI: 10.1038/s41531-024-00758-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 07/22/2024] [Indexed: 08/13/2024] Open
Abstract
The progression of Parkinson's disease (PD) is associated with microstructural alterations in neural pathways, contributing to both motor and cognitive decline. However, conflicting findings have emerged due to the use of heterogeneous methods in small studies. Here we performed a large diffusion MRI study in PD, integrating data from 17 cohorts worldwide, to identify stage-specific profiles of white matter differences. Diffusion-weighted MRI data from 1654 participants diagnosed with PD (age: 20-89 years; 33% female) and 885 controls (age: 19-84 years; 47% female) were analyzed using the ENIGMA-DTI protocol to evaluate white matter microstructure. Skeletonized maps of fractional anisotropy (FA) and mean diffusivity (MD) were compared across Hoehn and Yahr (HY) disease groups and controls to reveal the profile of white matter alterations at different stages. We found an enhanced, more widespread pattern of microstructural alterations with each stage of PD, with eventually lower FA and higher MD in almost all regions of interest: Cohen's d effect sizes reached d = -1.01 for FA differences in the fornix at PD HY Stage 4/5. The early PD signature in HY stage 1 included higher FA and lower MD across the entire white matter skeleton, in a direction opposite to that typical of other neurodegenerative diseases. FA and MD were associated with motor and non-motor clinical dysfunction. While overridden by degenerative changes in the later stages of PD, early PD is associated with paradoxically higher FA and lower MD in PD, consistent with early compensatory changes associated with the disorder.
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Grants
- R01 AG058854 NIA NIH HHS
- P41 EB015922 NIBIB NIH HHS
- R01NS107513 U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)
- R01 MH117601 NIMH NIH HHS
- R01 NS107513 NINDS NIH HHS
- U19 AG062418 NIA NIH HHS
- F32 MH122057 NIMH NIH HHS
- R01 AG059874 NIA NIH HHS
- U.S. Alzheimer’s Association (AARG-23-1149996)
- Health Research Council of New Zealand (20/538; 21/165)
- São Paulo Research Foundation FAPESP-BRAINN Grants# 2013-07559-3 / FAPESP #2022-1178-4
- São Paulo Research Foundation FAPESP-BRAINN Grant # 2013–07559-3.
- Health Research Council of New Zealand (20/538); Marsden Fund New Zealand (UOC2105); Neurological Foundation of New Zealand (2232 PRG); Research and Education Trust Pacific Radiology (MRIJDA).
- Grant from ParkinsonNL (P2023-14); Honoraria from Movement Disorders Society Quebec.
- NINDS R01NS107513
- Engineering and Physical Sciences Research Council (EPSRC) UK
- Parkinson's UK, Cure Parkinsons Trust, Oxford Biomedical Research Centre, GSK-Oxford IMCM.
- JK is supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC), and the NIHR Oxford Health Clinical Research Facility. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health.
- NIMH 32MH122057
- U19 AG062418
- Health Research Council of New Zealand (20/538); Neurological Foundation of New Zealand (2232 PRG); Research and Education Trust Pacific Radiology (MRIJDA).
- EPSRC UK, MRC UK, GE medical systems, Academy of Medical Sciences UK
- Italian Ministry of Health, grant number RF-2019-12370182
- Health Research Council of New Zealand (21/165)
- Personal fees from Bial, AbbVie and Boston Scientific.
- NIH/NIA
- São Paulo Research Foundation FAPESP-BRAINN Grant # 2013–07559-3; CNPQ (#315953/2021-7) National Council for Scientific and Technological Development
- U.S. Department of Health & Human Services | NIH | National Institute of Neurological Disorders and Stroke (NINDS)
- R01AG059874, R01MH117601, R01NS107513, R01AG058854, P41EB015922
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Affiliation(s)
- Conor Owens-Walton
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA.
| | - Talia M Nir
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | | | - Sonia Ambrogi
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Tim J Anderson
- Department of Medicine, University of Otago, Christchurch, Christchurch, New Zealand
- New Zealand Brain Research Institute, Christchurch, New Zealand
- Neurology Department, Te Whatu Ora-Health New Zealand Waitaha Canterbury, Christchurch, New Zealand
| | - Ítalo Karmann Aventurato
- Department of Neurology, University of Campinas-UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Fernando Cendes
- Department of Neurology, University of Campinas-UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Yao-Liang Chen
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Keelung, Taiwan, ROC
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City, Taiwan, ROC
| | - Valentina Ciullo
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Phil Cook
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - John C Dalrymple-Alford
- New Zealand Brain Research Institute, Christchurch, New Zealand
- Te Kura Mahi ā- Hirikapo | School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Michiel F Dirkx
- Department of Neurology and Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Jason Druzgal
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA
| | - Hedley C A Emsley
- Lancaster Medical School, Lancaster University, Lancaster, UK
- Department of Neurology, Lancashire Teaching Hospitals NHS Foundation Trust, Preston, UK
| | - Rachel Guimarães
- Department of Neurology, University of Campinas-UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Hamied A Haroon
- Division of Psychology, Communication & Human Neuroscience, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Northern Care Alliance & University of Manchester, Manchester, UK
| | - Rick C Helmich
- Department of Neurology and Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Michele T Hu
- Oxford Parkinson's Disease Centre, Nuffield, Department of Clinical Neurosciences, Division of Clinical Neurology, University of Oxford, Oxford, UK
| | - Martin E Johansson
- Department of Neurology and Center of Expertise for Parkinson & Movement Disorders, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
- Centre for Cognitive Neuroimaging, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Ho Bin Kim
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA, USA
| | - Johannes C Klein
- Oxford Parkinson's Disease Centre, Nuffield, Department of Clinical Neurosciences, Division of Clinical Neurology, University of Oxford, Oxford, UK
| | - Max Laansma
- Amsterdam UMC, Dept. Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Neurodegeneration, Amsterdam, The Netherlands
| | - Katherine E Lawrence
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Christine Lochner
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry, Stellenbosch University, Cape Town, South Africa
| | - Clare Mackay
- Oxford Parkinson's Disease Centre, Nuffield, Department of Clinical Neurosciences, Division of Clinical Neurology, University of Oxford, Oxford, UK
| | - Corey T McMillan
- University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Tracy R Melzer
- Department of Medicine, University of Otago, Christchurch, Christchurch, New Zealand
- New Zealand Brain Research Institute, Christchurch, New Zealand
- Te Kura Mahi ā- Hirikapo | School of Psychology, Speech and Hearing, University of Canterbury, Christchurch, New Zealand
| | - Leila Nabulsi
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Ben Newman
- Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA
| | - Peter Opriessnig
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Laura M Parkes
- Division of Psychology, Communication & Human Neuroscience, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, Manchester Academic Health Science Centre, Northern Care Alliance & University of Manchester, Manchester, UK
| | - Clelia Pellicano
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Fabrizio Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Federica Piras
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Lukas Pirpamer
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Toni L Pitcher
- Department of Medicine, University of Otago, Christchurch, Christchurch, New Zealand
- New Zealand Brain Research Institute, Christchurch, New Zealand
| | - Kathleen L Poston
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA, USA
| | - Annerine Roos
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Lucas Scárdua Silva
- Department of Neurology, University of Campinas-UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Petra Schwingenschuh
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Marian Shahid-Besanti
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA, USA
| | | | - Dan J Stein
- SA MRC Unit on Risk and Resilience in Mental Disorders, Department of Psychiatry and Neuroscience Institute, University of Cape Town, Cape Town, South Africa
| | - Sophia I Thomopoulos
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Duygu Tosun
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Chih-Chien Tsai
- Healthy Aging Research Center, Chang Gung University, Taoyuan City, Taiwan, ROC
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan City, Taiwan, ROC
| | - Odile A van den Heuvel
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA, USA
- Amsterdam UMC, Dept. Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam UMC, Department of Psychiatry, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Eva van Heese
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA, USA
- Amsterdam UMC, Dept. Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Daniela Vecchio
- Laboratory of Neuropsychiatry, IRCCS Santa Lucia Foundation, Rome, Italy
| | - Julio E Villalón-Reina
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Chris Vriend
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA, USA
- Amsterdam UMC, Department of Psychiatry, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Amsterdam Neuroscience, Brain Imaging program, Amsterdam, The Netherlands
| | - Jiun-Jie Wang
- Department of Diagnostic Radiology, Chang Gung Memorial Hospital, Keelung, Taiwan, ROC
- Healthy Aging Research Center, Chang Gung University, Taoyuan City, Taiwan, ROC
- Department of Medical Imaging and Radiological Sciences, Chang Gung University, Taoyuan City, Taiwan, ROC
- Department of Chemical Engineering, Ming-Chi University of Technology, New Taipei City, Taiwan, ROC
| | - Yih-Ru Wu
- Department of Neurology, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan City, Taiwan, ROC
- Department of Neurology, College of Medicine, Chang Gung University, Taoyuan City, Taiwan, ROC
| | - Clarissa Lin Yasuda
- Department of Neurology, University of Campinas-UNICAMP, Campinas, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas, Brazil
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Neda Jahanshad
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Marina del Rey, CA, USA
| | - Ysbrand van der Werf
- Department of Neurology & Neurological Sciences, Stanford University, Palo Alto, CA, USA
- Amsterdam UMC, Dept. Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
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3
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Yan S, Lu J, Li Y, Tian T, Zhou Y, Zhu H, Qin Y, Zhu W. Impaired topological properties of cortical morphological brain networks correlate with motor symptoms in Parkinson's disease. J Neuroradiol 2024; 51:101155. [PMID: 37774912 DOI: 10.1016/j.neurad.2023.09.007] [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: 03/02/2023] [Revised: 09/25/2023] [Accepted: 09/26/2023] [Indexed: 10/01/2023]
Abstract
BACKGROUND Parkinson's disease (PD) is characterized by loss of selectively vulnerable neurons within the basal ganglia circuit and progressive atrophy in subcortical and cortical regions. However, the impact of neurodegenerative pathology on the topological organization of cortical morphological networks has not been explored. The aims of this study were to investigate altered network patterns of covariance in cortical thickness and complexity, and to evaluate how morphological network integrity in PD is related to motor impairment. METHODS Individual morphological networks were constructed for 50 PD patients and 46 healthy controls (HCs) by estimating interregional similarity distributions in surface-based indices. We performed graph theoretical analysis and network-based statistics to detect PD-related alterations and further examined the correlation of network metrics with clinical scores. Furthermore, support vector regression based on topological characteristics was applied to predict the severity of motor impairment in PD. RESULTS Compared with HCs, PD patients showed lower local efficiency (p = 0.004), normalized characteristic path length (p = 0.022), and clustering coefficient (p = 0.005) for gyrification index-based morphological brain networks. Nodal topological abnormalities were mainly in the frontal, parietal and temporal regions, and impaired morphological connectivity was involved in the sensorimotor and default mode networks. The support vector regression model using network-based features allowed prediction of motor symptom severity with a correlation coefficient of 0.606. CONCLUSIONS This study identified a disrupted topological organization of cortical morphological networks that could substantially advance our understanding of the network degeneration mechanism of PD and might offer indicators for monitoring disease progression.
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Affiliation(s)
- Su Yan
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jun Lu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; Department of CT & MRI, The First Affiliated Hospital, College of Medicine, Shihezi University, Shihezi, China, 107 North Second Road
| | - Yuanhao Li
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Tian Tian
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yiran Zhou
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hongquan Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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4
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Droby A, Thaler A, Mirelman A. Imaging Markers in Genetic Forms of Parkinson's Disease. Brain Sci 2023; 13:1212. [PMID: 37626568 PMCID: PMC10452191 DOI: 10.3390/brainsci13081212] [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: 07/19/2023] [Revised: 08/13/2023] [Accepted: 08/14/2023] [Indexed: 08/27/2023] Open
Abstract
Parkinson's disease (PD) is a complex neurodegenerative disorder characterized by motor symptoms such as bradykinesia, rigidity, and resting tremor. While the majority of PD cases are sporadic, approximately 15-20% of cases have a genetic component. Advances in neuroimaging techniques have provided valuable insights into the pathophysiology of PD, including the different genetic forms of the disease. This literature review aims to summarize the current state of knowledge regarding neuroimaging findings in genetic PD, focusing on the most prevalent known genetic forms: mutations in the GBA1, LRRK2, and Parkin genes. In this review, we will highlight the contributions of various neuroimaging modalities, including positron emission tomography (PET), single-photon emission computed tomography (SPECT), and magnetic resonance imaging (MRI), in elucidating the underlying pathophysiological mechanisms and potentially identifying candidate biomarkers for genetic forms of PD.
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Affiliation(s)
- Amgad Droby
- Laboratory for Early Markers of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Medical Center, Tel Aviv 6801298, Israel; (A.T.); (A.M.)
- Movement Disorders Unit, Neurological Institute, Tel Aviv Medical Center, Tel Aviv 6423906, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv 39040, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 39040, Israel
| | - Avner Thaler
- Laboratory for Early Markers of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Medical Center, Tel Aviv 6801298, Israel; (A.T.); (A.M.)
- Movement Disorders Unit, Neurological Institute, Tel Aviv Medical Center, Tel Aviv 6423906, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv 39040, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 39040, Israel
| | - Anat Mirelman
- Laboratory for Early Markers of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Medical Center, Tel Aviv 6801298, Israel; (A.T.); (A.M.)
- Movement Disorders Unit, Neurological Institute, Tel Aviv Medical Center, Tel Aviv 6423906, Israel
- Faculty of Medicine, Tel Aviv University, Tel Aviv 39040, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 39040, Israel
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5
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Rashidi F, Khanmirzaei MH, Hosseinzadeh F, Kolahchi Z, Jafarimehrabady N, Moghisseh B, Aarabi MH. Cingulum and Uncinate Fasciculus Microstructural Abnormalities in Parkinson's Disease: A Systematic Review of Diffusion Tensor Imaging Studies. BIOLOGY 2023; 12:biology12030475. [PMID: 36979166 PMCID: PMC10045759 DOI: 10.3390/biology12030475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/12/2023] [Accepted: 03/15/2023] [Indexed: 03/30/2023]
Abstract
Diffusion tensor imaging (DTI) is gaining traction in neuroscience research as a tool for evaluating neural fibers. The technique can be used to assess white matter (WM) microstructure in neurodegenerative disorders, including Parkinson disease (PD). There is evidence that the uncinate fasciculus and the cingulum bundle are involved in the pathogenesis of PD. These fasciculus and bundle alterations correlate with the symptoms and stages of PD. PRISMA 2022 was used to search PubMed and Scopus for relevant articles. Our search revealed 759 articles. Following screening of titles and abstracts, a full-text review, and implementing the inclusion criteria, 62 papers were selected for synthesis. According to the review of selected studies, WM integrity in the uncinate fasciculus and cingulum bundles can vary according to symptoms and stages of Parkinson disease. This article provides structural insight into the heterogeneous PD subtypes according to their cingulate bundle and uncinate fasciculus changes. It also examines if there is any correlation between these brain structures' structural changes with cognitive impairment or depression scales like Geriatric Depression Scale-Short (GDS). The results showed significantly lower fractional anisotropy values in the cingulum bundle compared to healthy controls as well as significant correlations between FA and GDS scores for both left and right uncinate fasciculus regions suggesting that structural damage from disease progression may be linked to cognitive impairments seen in advanced PD patients. This review help in developing more targeted treatments for different types of Parkinson's disease, as well as providing a better understanding of how cognitive impairments may be related to these structural changes. Additionally, using DTI scans can provide clinicians with valuable information about white matter tracts which is useful for diagnosing and monitoring disease progression over time.
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Affiliation(s)
- Fatemeh Rashidi
- School of Medicine, Tehran University of Medical Science, Tehran 1417613151, Iran
| | | | - Farbod Hosseinzadeh
- School of Medicine, Tehran University of Medical Science, Tehran 1417613151, Iran
| | - Zahra Kolahchi
- School of Medicine, Tehran University of Medical Science, Tehran 1417613151, Iran
| | - Niloofar Jafarimehrabady
- Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy
| | - Bardia Moghisseh
- School of Medicine, Arak University of Medical Science, Arak 3848176941, Iran
| | - Mohammad Hadi Aarabi
- Department of Neuroscience (DNS), Padova Neuroscience Center, University of Padova, 35128 Padua, Italy
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6
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MNC-Net: Multi-task graph structure learning based on node clustering for early Parkinson's disease diagnosis. Comput Biol Med 2023; 152:106308. [PMID: 36462371 DOI: 10.1016/j.compbiomed.2022.106308] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2022] [Revised: 10/27/2022] [Accepted: 11/13/2022] [Indexed: 11/27/2022]
Abstract
PURPOSE The identification of early-stage Parkinson's disease (PD) is important for the effective management of patients, affecting their treatment and prognosis. Recently, structural brain networks (SBNs) have been used to diagnose PD. However, how to mine abnormal patterns from high-dimensional SBNs has been a challenge due to the complex topology of the brain. Meanwhile, the existing prediction mechanisms of deep learning models are often complicated, and it is difficult to extract effective interpretations. In addition, most works only focus on the classification of imaging and ignore clinical scores in practical applications, which limits the ability of the model. Inspired by the regional modularity of SBNs, we adopted graph learning from the perspective of node clustering to construct an interpretable framework for PD classification. METHODS In this study, a multi-task graph structure learning framework based on node clustering (MNC-Net) is proposed for the early diagnosis of PD. Specifically, we modeled complex SBNs into modular graphs that facilitated the representation learning of abnormal patterns. Traditional graph neural networks are optimized through graph structure learning based on node clustering, which identifies potentially abnormal brain regions and reduces the impact of irrelevant noise. Furthermore, we employed a regression task to link clinical scores to disease classification, and incorporated latent domain information into model training through multi-task learning. RESULTS We validated the proposed approach on the Parkinsons Progression Markers Initiative dataset. Experimental results showed that our MNC-Net effectively separated the early-stage PD from healthy controls(HC) with an accuracy of 95.5%. The t-SNE figures have showed that our graph structure learning method can capture more efficient and discriminatory features. Furthermore, node clustering parameters were used as important weights to extract salient task-related brain regions(ROIs). These ROIs are involved in the development of mood disorders, tremors, imbalances and other symptoms, highlighting the importance of memory, language and mild motor function in early PD. In addition, statistical results from clinical scores confirmed that our model could capture abnormal connectivity that was significantly different between PD and HC. These results are consistent with previous studies, demonstrating the interpretability of our methods.
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Du J, Zhou X, Liang Y, Zhao L, Dai C, Zhong Y, Liu H, Liu G, Mo L, Tan C, Liu X, Chen L. Levodopa responsiveness and white matter alterations in Parkinson's disease: A DTI-based study and brain network analysis: A cross-sectional study. Brain Behav 2022; 12:e2825. [PMID: 36423257 PMCID: PMC9759147 DOI: 10.1002/brb3.2825] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 09/24/2022] [Accepted: 11/01/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Patients with Parkinson's disease (PD) present various responsiveness to levodopa, but the cause of such differences in levodopa responsiveness is unclear. Previous studies related the damage of brain white matter (WM) to levodopa responsiveness in PD patients, but no study investigated the relationship between the structural brain network change in PD patients and their levodopa responsiveness. METHODS PD patients were recruited and evaluated using the Unified Parkinson's Disease Rating Scale (UPDRS). Each patient received a diffusion tensor imaging (DTI) scan and an acute levodopa challenge test. The improvement rate of UPDRS-III was calculated. PD patients were grouped into irresponsive group (improvement rate < 30%) and responsive group (improvement rate ≥ 30%). Tract-based spatial statistics (TBSS), deterministic tracing (DT), region of interest (ROI) analysis, and automatic fiber identification (AFQ) analyses were performed. The structural brain network was also constructed and the topological parameters were calculated. RESULTS Fifty-four PD patients were included. TBSS identified significant differences in fractional anisotropy (FA) values in the corpus callosum and other regions of the brain. DT and ROI analysis of the corpus callosum found a significant difference in FA between the two groups. Graph theory analysis showed statistical differences in global efficiency, local efficiency, and characteristic path length. CONCLUSION PD patients with poor responsiveness to levodopa had WM damage in multiple brain areas, especially the corpus callosum, which might cause disruption of information integration of the structural brain network.
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Affiliation(s)
- Juncong Du
- Department of NeurologyThe Second Affiliated Hospital of Chongqing Medical UniversityChongqingPeople's Republic of China
| | - Xuan Zhou
- Department of NeurologyThe Second Affiliated Hospital of Chongqing Medical UniversityChongqingPeople's Republic of China
| | - Yi Liang
- Department of NeurologyThe Second Affiliated Hospital of Chongqing Medical UniversityChongqingPeople's Republic of China
| | - Lili Zhao
- Department of NeurologyThe Second Affiliated Hospital of Chongqing Medical UniversityChongqingPeople's Republic of China
| | - Chengcheng Dai
- Department of NeurologyThe Second Affiliated Hospital of Chongqing Medical UniversityChongqingPeople's Republic of China
| | - Yuke Zhong
- Department of NeurologyThe Second Affiliated Hospital of Chongqing Medical UniversityChongqingPeople's Republic of China
| | - Hang Liu
- Department of NeurologyThe Second Affiliated Hospital of Chongqing Medical UniversityChongqingPeople's Republic of China
| | - Guohui Liu
- Department of NeurologyThe Second Affiliated Hospital of Chongqing Medical UniversityChongqingPeople's Republic of China
| | - Lijuan Mo
- Department of NeurologyThe Second Affiliated Hospital of Chongqing Medical UniversityChongqingPeople's Republic of China
| | - Changhong Tan
- Department of NeurologyThe Second Affiliated Hospital of Chongqing Medical UniversityChongqingPeople's Republic of China
| | - Xi Liu
- Department of NeurologyThe Second Affiliated Hospital of Chongqing Medical UniversityChongqingPeople's Republic of China
| | - Lifen Chen
- Department of NeurologyThe Second Affiliated Hospital of Chongqing Medical UniversityChongqingPeople's Republic of China
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Huang S, Dong Y, Zhao J. The mean kurtosis (MK) is more sensitive diagnostic biomarker than fractional anisotropy (FA) for Parkinson's disease: A diagnostic performance study and meta-analysis. Medicine (Baltimore) 2022; 101:e31312. [PMID: 36397320 PMCID: PMC9666087 DOI: 10.1097/md.0000000000031312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND The mean kurtosis (MK) and fractional anisotropy (FA) in patients of Parkinson's disease (PD) are usually measured by diffusion kurtosis imaging (DKI) and diffusion tensor imaging (DTI), separately. METHODS In this study we perform a meta-analysis to discuss which noninvasive biomarker is more advantageous for PD, MK, or FA. Databases including Medline via PubMed, the Cochrane Central Register of Controlled Trials, Embase via OVID and China National Knowledge Infrastructure. Databases are searched up to December 31st, 2019. Four brain regions are identified for analysis based on data extracted from articles. RESULTS The articles contain 5 trials with 274 total PD patients and 189 healthy controls (HCs). The results show not only significantly higher MK values of putamen, caudate, globus pallidus in PD compared to that of HCs (weighted mean difference [WMD] = 0.06, 95% CI = 0.02-0.09, P = .002, WMD = 0.03, 95% CI = 0.01-0.067, P = .01, WMD = 0.18, 95% CI = 0.11-0.24, P < .00001), but also a significantly higher FA in caudate of PD compared to HCs (WMD = 0.02, 95% CI = 0.00-0.03, P = .006). CONCLUSION This indicates that the sharp difference detected between PD patients and HCs can be detected by DKI and DTI. By further discussing results, we found that MK could be more sensitive diagnostic biomarker than FA toward PD diagnosis.
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Affiliation(s)
- Songtao Huang
- Department of Radiology, Guang’an People’s Hospital, Guangan, Sichuan Province, P. R. China
| | - Yanchao Dong
- Department of Interventional Treatment, Qinhuangdao Municipal, Qinhuangdao, Hebei Province, P. R. China
| | - Jiaying Zhao
- Department of Internal Medicine, Guang’an People’s Hospital, Guangan, Sichuan Province, P. R. China
- * Correspondence: Jiaying Zhao, Department of Internal Medicine, Guang’an People’s Hospital, No. 1, Section 4, Binhe Road, Guangan 638500, Sichuan Province, P. R. China (e-mail: )
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Azimi MS, Hosseini MS, Shahzadeh S, Ardekani AF, Arabi H, Zaidi H. Early Detection of Parkinson's Disease Based on Diffusion Tensor Imaging and Deep Learning. 2022 IEEE NUCLEAR SCIENCE SYMPOSIUM AND MEDICAL IMAGING CONFERENCE (NSS/MIC) 2022. [DOI: 10.1109/nss/mic44845.2022.10399248] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/05/2024]
Affiliation(s)
- Mohammad-Saber Azimi
- Shahid Beheshti University,Department of Medical Radiation Engineering,Tehran,Iran
| | | | - Sara Shahzadeh
- Shahid Beheshti University,Department of Medical Radiation Engineering,Tehran,Iran
| | | | - Hossein Arabi
- Geneva University Hospital,Division of Nuclear Medicine & Molecular Imaging,Geneva,Switzerland,CH-1211
| | - Habib Zaidi
- Geneva University Hospital,Division of Nuclear Medicine & Molecular Imaging,Geneva,Switzerland,CH-1211
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Imaging the Limbic System in Parkinson's Disease-A Review of Limbic Pathology and Clinical Symptoms. Brain Sci 2022; 12:brainsci12091248. [PMID: 36138984 PMCID: PMC9496800 DOI: 10.3390/brainsci12091248] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/05/2022] [Accepted: 09/13/2022] [Indexed: 01/09/2023] Open
Abstract
The limbic system describes a complex of brain structures central for memory, learning, as well as goal directed and emotional behavior. In addition to pathological studies, recent findings using in vivo structural and functional imaging of the brain pinpoint the vulnerability of limbic structures to neurodegeneration in Parkinson's disease (PD) throughout the disease course. Accordingly, dysfunction of the limbic system is critically related to the symptom complex which characterizes PD, including neuropsychiatric, vegetative, and motor symptoms, and their heterogeneity in patients with PD. The aim of this systematic review was to put the spotlight on neuroimaging of the limbic system in PD and to give an overview of the most important structures affected by the disease, their function, disease related alterations, and corresponding clinical manifestations. PubMed was searched in order to identify the most recent studies that investigate the limbic system in PD with the help of neuroimaging methods. First, PD related neuropathological changes and corresponding clinical symptoms of each limbic system region are reviewed, and, finally, a network integration of the limbic system within the complex of PD pathology is discussed.
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Yu J, Chen L, Cai G, Wang Y, Chen X, Hong W, Ye Q. Evaluating white matter alterations in Parkinson's disease-related parkin S/N167 mutation carriers using tract-based spatial statistics. Quant Imaging Med Surg 2022; 12:4272-4285. [PMID: 35919057 PMCID: PMC9338378 DOI: 10.21037/qims-21-1007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 05/05/2022] [Indexed: 11/30/2022]
Abstract
Background Genetic susceptibility plays an important role in the pathogenesis of Parkinson’s disease (PD). parkin S/N167 mutations may increase the risk of PD and affect white matter fibers in the brain. This cross-sectional study explored the effects of gene polymorphisms on white matter fiber damage in PD. Methods In all, 54 cases were enrolled in the study, including PD patients carrying parkin gene S/N167 mutations (G/A), PD patients without gene S/N167 mutations (G/G), and healthy controls (HC). The whole-brain white matter fiber skeleton was analyzed using the tract-based spatial statistics (TBSS) method. Two-way analysis of variance (ANOVA) and post hoc tests were used for data analyses. Results Two classification methods were used; one was based on disease classification, with 26 patients in the PD group (n=12 G/G, n=14 G/A) and 28 in the HC group (n=15 G/G, n=13 G/A), and the other was based on genetic classification, with 27 patients in the G/G group and 27 in the G/A group. In the G/A group, there was a wide range of significant changes in fractional anisotropy (FA), radial diffusivity (RD), and mean diffusivity (MD) values (P<0.05). There was also a significant decrease in FA in the PD-G/A group compared with the PD-G/G and HC-G/A groups (P<0.05). Conclusions There were more extensive brain white matter fiber damage and changes in PD patients; the G/A polymorphism may cause more extensive brain white matter damage.
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Affiliation(s)
- Jinqiu Yu
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China.,Department of Neurology, Affiliated Sanming First Hospital, Fujian Medical University, Sanming, China.,Institute of Neuroscience, Fujian Key Laboratory of Molecular Neurology, Fuzhou, China.,Institute of Clinical Neurology, Fujian Medical University, Fuzhou, China
| | - Lina Chen
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China.,Institute of Neuroscience, Fujian Key Laboratory of Molecular Neurology, Fuzhou, China.,Institute of Clinical Neurology, Fujian Medical University, Fuzhou, China
| | - Guoen Cai
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China.,Institute of Neuroscience, Fujian Key Laboratory of Molecular Neurology, Fuzhou, China.,Institute of Clinical Neurology, Fujian Medical University, Fuzhou, China
| | - Yingqing Wang
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China.,Institute of Neuroscience, Fujian Key Laboratory of Molecular Neurology, Fuzhou, China.,Institute of Clinical Neurology, Fujian Medical University, Fuzhou, China
| | - Xiaochun Chen
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China.,Institute of Neuroscience, Fujian Key Laboratory of Molecular Neurology, Fuzhou, China.,Institute of Clinical Neurology, Fujian Medical University, Fuzhou, China
| | - Weimin Hong
- Department of Neurology, Affiliated Sanming First Hospital, Fujian Medical University, Sanming, China
| | - Qinyong Ye
- Department of Neurology, Fujian Institute of Geriatrics, Fujian Medical University Union Hospital, Fuzhou, China.,Institute of Neuroscience, Fujian Key Laboratory of Molecular Neurology, Fuzhou, China.,Institute of Clinical Neurology, Fujian Medical University, Fuzhou, China
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Haghshomar M, Shobeiri P, Seyedi SA, Abbasi-Feijani F, Poopak A, Sotoudeh H, Kamali A, Aarabi MH. Cerebellar Microstructural Abnormalities in Parkinson's Disease: a Systematic Review of Diffusion Tensor Imaging Studies. CEREBELLUM (LONDON, ENGLAND) 2022; 21:545-571. [PMID: 35001330 DOI: 10.1007/s12311-021-01355-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 12/01/2021] [Indexed: 06/14/2023]
Abstract
Diffusion tensor imaging (DTI) is now having a strong momentum in research to evaluate the neural fibers of the CNS. This technique can study white matter (WM) microstructure in neurodegenerative disorders, including Parkinson's disease (PD). Previous neuroimaging studies have suggested cerebellar involvement in the pathogenesis of PD, and these cerebellum alterations can correlate with PD symptoms and stages. Using the PRISMA 2020 framework, PubMed and EMBASE were searched to retrieve relevant articles. Our search revealed 472 articles. After screening titles and abstracts, and full-text review, and implementing the inclusion criteria, 68 papers were selected for synthesis. Reviewing the selected studies revealed that the patterns of reduction in cerebellum WM integrity, assessed by fractional anisotropy, mean diffusivity, radial diffusivity, and axial diffusivity measures can differ symptoms and stages of PD. Cerebellar diffusion tensor imaging (DTI) changes in PD patients with "postural instability and gait difficulty" are significantly different from "tremor dominant" PD patients. Freezing of the gate is strongly related to cerebellar involvement depicted by DTI. The "reduced cognition," "visual disturbances," "sleep disorders," "depression," and "olfactory dysfunction" are not related to cerebellum microstructural changes on DTI, while "impulsive-compulsive behavior" can be linked to cerebellar WM alteration. Finally, higher PD stages and longer disease duration are associated with cerebellum white matter alteration depicted by DTI. Depiction of cerebellar white matter involvement in PD is feasible by DTI. There is an association with disease duration and severity and several clinical presentations with DTI findings. This clinical-imaging association may eventually improve disease management.
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Affiliation(s)
- Maryam Haghshomar
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- NeuroImaging Network (NIN), Universal Scientific Education and Research Network (USERN), Tehran, Iran
| | - Parnian Shobeiri
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran.
- NeuroImaging Network (NIN), Universal Scientific Education and Research Network (USERN), Tehran, Iran.
- Non-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, No. 10, Al-e-Ahmad and Chamran Highway intersection, Tehran, 1411713137, Iran.
| | | | | | - Amirhossein Poopak
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Houman Sotoudeh
- Department of Radiology and Neurology, University of Alabama at Birmingham (UAB), Birmingham, AL, USA
| | - Arash Kamali
- Department of Diagnostic and Interventional Radiology, University of Texas McGovern Medical School, Houston, TX, USA
| | - Mohammad Hadi Aarabi
- Department of Neuroscience (DNS), Padova Neuroscience Center-PNC, University of Padova, Padua, Italy
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Joza S, Camicioli R, Martin WRW, Wieler M, Gee M, Ba F. Pedunculopontine Nucleus Dysconnectivity Correlates With Gait Impairment in Parkinson’s Disease: An Exploratory Study. Front Aging Neurosci 2022; 14:874692. [PMID: 35875799 PMCID: PMC9304714 DOI: 10.3389/fnagi.2022.874692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2022] [Accepted: 06/20/2022] [Indexed: 11/25/2022] Open
Abstract
Background Gait impairment is a debilitating and progressive feature of Parkinson’s disease (PD). Increasing evidence suggests that gait control is partly mediated by cholinergic signaling from the pedunculopontine nucleus (PPN). Objective We investigated whether PPN structural connectivity correlated with quantitative gait measures in PD. Methods Twenty PD patients and 15 controls underwent diffusion tensor imaging to quantify structural connectivity of the PPN. Whole brain analysis using tract-based spatial statistics and probabilistic tractography were performed using the PPN as a seed region of interest for cortical and subcortical target structures. Gait metrics were recorded in subjects’ medication ON and OFF states, and were used to determine if specific features of gait dysfunction in PD were related to PPN structural connectivity. Results Tract-based spatial statistics revealed reduced structural connectivity involving the corpus callosum and right superior corona radiata, but did not correlate with gait measures. Abnormalities in PPN structural connectivity in PD were lateralized to the right hemisphere, with pathways involving the right caudate nucleus, amygdala, pre-supplementary motor area, and primary somatosensory cortex. Altered connectivity of the right PPN-caudate nucleus was associated with worsened cadence, stride time, and velocity while in the ON state; altered connectivity of the right PPN-amygdala was associated with reduced stride length in the OFF state. Conclusion Our exploratory analysis detects a potential correlation between gait dysfunction in PD and a characteristic pattern of connectivity deficits in the PPN network involving the right caudate nucleus and amygdala, which may be investigated in future larger studies.
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Affiliation(s)
- Stephen Joza
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Richard Camicioli
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | | | - Marguerite Wieler
- Department of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, AB, Canada
| | - Myrlene Gee
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, AB, Canada
| | - Fang Ba
- Division of Neurology, Department of Medicine, University of Alberta, Edmonton, AB, Canada
- *Correspondence: Fang Ba,
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Mock N, Balzer C, Gutbrod K, De Haan B, Jäncke L, Ettlin T, Trost W. Lesion-symptom mapping corroborates lateralization of verbal and nonverbal memory processes and identifies distributed brain networks responsible for memory dysfunction. Cortex 2022; 153:178-193. [DOI: 10.1016/j.cortex.2022.04.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 12/10/2021] [Accepted: 04/28/2022] [Indexed: 11/25/2022]
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Sang T, He J, Wang J, Zhang C, Zhou W, Zeng Q, Yuan Y, Yu L, Feng Y. Alterations in white matter fiber in Parkinson's disease across different cognitive stages. Neurosci Lett 2021; 769:136424. [PMID: 34958911 DOI: 10.1016/j.neulet.2021.136424] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 12/21/2021] [Accepted: 12/22/2021] [Indexed: 02/03/2023]
Affiliation(s)
- Tian Sang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Jianzhong He
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Jingqiang Wang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Chengzhe Zhang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Wenyang Zhou
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Yuan Yuan
- Department of Neurology, the First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou 310003, China
| | - Lihua Yu
- Department of Neurology, the First Affiliated Hospital, Zhejiang University School of Medicine, #79 Qingchun Road, Hangzhou 310003, China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China.
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Inguanzo A, Segura B, Sala-Llonch R, Monte-Rubio G, Abos A, Campabadal A, Uribe C, Baggio HC, Marti MJ, Valldeoriola F, Compta Y, Bargallo N, Junque C. Impaired Structural Connectivity in Parkinson's Disease Patients with Mild Cognitive Impairment: A Study Based on Probabilistic Tractography. Brain Connect 2021; 11:380-392. [PMID: 33626962 PMCID: PMC8215419 DOI: 10.1089/brain.2020.0939] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023] Open
Abstract
Background: Probabilistic tractography, in combination with graph theory, has been used to reconstruct the structural whole-brain connectome. Threshold-free network-based statistics (TFNBS) is a useful technique to study structural connectivity in neurodegenerative disorders; however, there are no previous studies using TFNBS in Parkinson's disease (PD) with and without mild cognitive impairment (MCI). Materials and Methods: Sixty-two PD patients, 27 of whom classified as PD-MCI, and 51 healthy controls (HC) underwent diffusion-weighted 3T magnetic resonance imaging. Probabilistic tractography, using FMRIB Software Library (FSL), was used to compute the number of streamlines (NOS) between regions. NOS matrices were used to find group differences with TFNBS, and to calculate global and local measures of network integrity using graph theory. A binominal logistic regression was then used to assess the discrimination between PD with and without MCI using non-overlapping significant tracts. Tract-based spatial statistics were also performed with FSL to study changes in fractional anisotropy (FA) and mean diffusivity. Results: PD-MCI showed 37 white matter connections with reduced connectivity strength compared with HC, mainly involving temporal/occipital regions. These were able to differentiate PD-MCI from PD without MCI with an area under the curve of 83-85%. PD without MCI showed disrupted connectivity in 18 connections involving frontal/temporal regions. No significant differences were found in graph measures. Only PD-MCI showed reduced FA compared with HC. Discussion: TFNBS based on whole-brain probabilistic tractography can detect structural connectivity alterations in PD with and without MCI. Reduced structural connectivity in fronto-striatal and posterior cortico-cortical connections is associated with PD-MCI.
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Affiliation(s)
- Anna Inguanzo
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain
- Medical Psychology Unit, Department of Medicine, University of Barcelona, Barcelona, Catalonia, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
| | - Barbara Segura
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain
- Medical Psychology Unit, Department of Medicine, University of Barcelona, Barcelona, Catalonia, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED: CB06/05/0018-ISCIII), Barcelona, Catalonia, Spain
| | - Roser Sala-Llonch
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
- Department of Biomedicine, University of Barcelona, Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Barcelona, Catalonia, Spain
| | - Gemma Monte-Rubio
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain
- Medical Psychology Unit, Department of Medicine, University of Barcelona, Barcelona, Catalonia, Spain
| | - Alexandra Abos
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain
- Medical Psychology Unit, Department of Medicine, University of Barcelona, Barcelona, Catalonia, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
| | - Anna Campabadal
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain
- Medical Psychology Unit, Department of Medicine, University of Barcelona, Barcelona, Catalonia, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
| | - Carme Uribe
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain
- Medical Psychology Unit, Department of Medicine, University of Barcelona, Barcelona, Catalonia, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
- Research Imaging Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health (CAMH), University of Toronto, Toronto, Canada
| | - Hugo Cesar Baggio
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain
- Medical Psychology Unit, Department of Medicine, University of Barcelona, Barcelona, Catalonia, Spain
| | - Maria Jose Marti
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED: CB06/05/0018-ISCIII), Barcelona, Catalonia, Spain
- Movement Disorders Unit, Neurology Service, Institut de Neurociències, University of Barcelona, Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain
| | - Francesc Valldeoriola
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED: CB06/05/0018-ISCIII), Barcelona, Catalonia, Spain
- Movement Disorders Unit, Neurology Service, Institut de Neurociències, University of Barcelona, Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain
| | - Yaroslau Compta
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED: CB06/05/0018-ISCIII), Barcelona, Catalonia, Spain
- Movement Disorders Unit, Neurology Service, Institut de Neurociències, University of Barcelona, Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain
| | - Nuria Bargallo
- Centre de Diagnostic per la Imatge, Hospital Clínic de Barcelona, Barcelona, Catalonia, Spain
- Magnetic Resonance Core Facility, Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
| | - Carme Junque
- Institute of Neurosciences, University of Barcelona, Barcelona, Catalonia, Spain
- Medical Psychology Unit, Department of Medicine, University of Barcelona, Barcelona, Catalonia, Spain
- Institute of Biomedical Research August Pi i Sunyer (IDIBAPS), Barcelona, Catalonia, Spain
- Centro de Investigación Biomédica en Red sobre Enfermedades Neurodegenerativas (CIBERNED: CB06/05/0018-ISCIII), Barcelona, Catalonia, Spain
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17
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Wang E, Jia Y, Ya Y, Xu J, Mao C, Luo W, Fan G, Jiang Z. Patterns of Sulcal depth and cortical thickness in Parkinson's disease. Brain Imaging Behav 2021; 15:2340-2346. [PMID: 34018166 DOI: 10.1007/s11682-020-00428-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/08/2020] [Indexed: 10/21/2022]
Abstract
Previous voxel-based morphometry (VBM) and cortical thickness (CT) studies on Parkinson's disease (PD) have mainly reported the gray matter size reduction, whereas the shape of cortical surface can also change in PD patients. For the first time, we analyzed sulcal depth (SD) patterns in PD patients by using whole brain region of interest (ROI)-based approach. In a cross-sectional study, high-resolution brain structural MRI images were collected from 60 PD patients without dementia and 56 age-and sex-matched healthy controls (HC). SD and CT were estimated using the Computational Anatomy Toolbox (CAT12) and statistically compared between groups on whole brain ROI-based level using statistical parametric mapping 12 (SPM12). Additionally, correlations between regional brain changes and clinical variables were also examined. Compared to HC, PD patients showed lower SD in widespread regions, including temporal (the bilateral transverse temporal, the left inferior temporal, the right middle temporal and the right superior temporal), insular (the left insula), frontal (the left pars triangularis, the left pars opercularis and the left precentral), parietal (the bilateral superior parietal) and occipital (the right cuneus) regions. For CT, only the left pars opercularis showed lower CT in PD patients compared to HC. No regions showed higher SD or CT in PD patients compared to HC. In PD patients, a significant positive correlation was found between SD of the left pars opercularis and MMSE scores, such that lower MMSE scores were related to lower SD of the left pars opercularis. Our results of widespread lower SD, but relatively localized lower CT, indicate that SD seems to be more sensitive to brain changes than CT and may be mainly affected by white matter damage. Hence, SD may be a more promising indicator to investigate the surface shape changes in PD patients. The significant positive correlation between SD of the left pars opercularis and MMSE scores suggests that SD may be prognostic of future cognitive decline.
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Affiliation(s)
- Erlei Wang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yujing Jia
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yang Ya
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Jin Xu
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Chengjie Mao
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Weifeng Luo
- Department of Neurology, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Guohua Fan
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
| | - Zhen Jiang
- Department of Radiology, The Second Affiliated Hospital of Soochow University, Suzhou, China.
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18
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Surkont J, Joza S, Camicioli R, Martin WRW, Wieler M, Ba F. Subcortical microstructural diffusion changes correlate with gait impairment in Parkinson's disease. Parkinsonism Relat Disord 2021; 87:111-118. [PMID: 34020302 DOI: 10.1016/j.parkreldis.2021.05.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 04/17/2021] [Accepted: 05/04/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Gait impairments are common in Parkinson's Disease (PD) and are likely caused by degeneration in multiple brain circuits, including the basal ganglia, thalamus and mesencephalic locomotion centers (MLC). Diffusion tensor imaging (DTI) assesses fractional anisotropy (FA) and mean diffusivity (MD) that reflect the integrity of neuronal microstructure. We hypothesized that DTI changes in motor circuits correlate with gait changes in PD. OBJECTIVE We aimed to identify microstructural changes of brain locomotion control centers in PD via DTI and their correlations with clinical and quantitative measures of gait. METHODS Twenty-one PD patients reporting gait impairment and 15 controls were recruited. Quantitative gait and clinical tests were recorded in PD subjects' medication ON and OFF states. Region of Interest (ROI) analysis of the thalamus, basal ganglia and MLC was performed using ExploreDTI. Correlations between FA/MD with clinical gait parameters were examined. RESULTS Microstructural changes were seen in the thalamus, caudate and MLC in the PD compared to the control group. Thalamic microstructural changes significantly correlated with gait parameters in the pace domain including the Timed Up and Go in the ON state. Caudate changes correlated with cadence and stride time in the OFF state. CONCLUSIONS Our pilot study suggests that PD is associated with a characteristic regional pattern of microstructural degradation in the thalamus, caudate and MLC. The DTI changes may represent subcortical locomotion network failure. Overall, DTI ROI analyses might provide a useful tool for assessing PD for functional status and specific motor domains, such as gait, and potentially could serve as an imaging marker.
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Affiliation(s)
- Jakub Surkont
- Division of Neurology, Department of Medicine, University of Alberta, Canada
| | - Stephen Joza
- Division of Neurology, Department of Medicine, University of Alberta, Canada
| | - Richard Camicioli
- Division of Neurology, Department of Medicine, University of Alberta, Canada
| | - W R Wayne Martin
- Department of Medicine, Faculty of Medicine and Dentistry, University of Alberta, Canada
| | - Marguerite Wieler
- Department of Physical Therapy, Faculty of Rehabilitation Medicine, University of Alberta, Canada
| | - Fang Ba
- Division of Neurology, Department of Medicine, University of Alberta, Canada.
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19
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Levodopa responsiveness in Parkinson's disease patients and white matter alterations in diffusion tensor imaging: a cross-sectional tract-based spatial statistics study. Neuroreport 2021; 32:636-642. [PMID: 33850092 DOI: 10.1097/wnr.0000000000001641] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
This study aimed to investigate the relationship between levodopa responsiveness and white matter alterations in Parkinson's disease patients using diffusion tensor imaging (DTI). Twenty-six recruited Parkinson's disease patients were evaluated using the Mini-Mental State Examination, Hoehn and Yahr scale (H&Y) and Unified Parkinson's Disease Rating Scale (UPDRS). Each patient underwent a DTI scan and an acute levodopa challenge test. The improvement rate of UPDRS-III was calculated, Parkinson's disease patients were grouped into a responsive group (improvement rate ≥30%) and a nonresponsive group (improvement rate <30%). The differences in fractional anisotropy, mean diffusivity, axial diffusivity and radial diffusivity between the two groups were measured using tract-based spatial statistics. There was no difference in demographic features or baseline evaluations between groups. The UPDRS-III score after the challenge was higher in the nonresponsive group than that in the responsive group. Compared to the responsive group, patients in the nonresponsive group exhibited decreased fractional anisotropy in the corpus callosum; cingulum; left corona radiata; left internal capsule; left middle frontal gyrus; left superior longitudinal fasciculus and right somatosensory cortex. Mean diffusivity and radial diffusivity were increased in wide-ranging areas in the nonresponsive group. No difference was observed in axial diffusivity. White matter alterations in the abovementioned areas may affect the function of the dopaminergic network and thus may be associated with the levodopa response in Parkinson's disease patients. Further studies are needed to analyze the specific mechanism and pathological changes underlying these effects.
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20
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Wei X, Luo C, Li Q, Hu N, Xiao Y, Liu N, Lui S, Gong Q. White Matter Abnormalities in Patients With Parkinson's Disease: A Meta-Analysis of Diffusion Tensor Imaging Using Tract-Based Spatial Statistics. Front Aging Neurosci 2021; 12:610962. [PMID: 33584244 PMCID: PMC7876070 DOI: 10.3389/fnagi.2020.610962] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 12/28/2020] [Indexed: 02/05/2023] Open
Abstract
Background: Tract-based spatial statistics (TBSS) studies based on diffusion tensor imaging (DTI) have revealed extensive abnormalities in white matter (WM) fibers of Parkinson's disease (PD); however, the results were inconsistent. Therefore, a meta-analytical approach was used in this study to find the most prominent and replicable WM abnormalities of PD. Methods: Online databases were systematically searched for all TBSS studies comparing fractional anisotropy (FA) between patients with PD and controls. Subsequently, we performed the meta-analysis using a coordinate-based meta-analytic software called seed-based d mapping. Meanwhile, meta-regression was performed to explore the potential correlation between the alteration of FA and the clinical characteristics of PD. Results: Out of a total of 1,701 studies that were identified, 23 studies were included. Thirty datasets, including 915 patients (543 men) with PD and 836 healthy controls (449 men), were included in the current study. FA reduction was identified in the body of the corpus callosum (CC; 245 voxels; z = -1.739; p < 0.001) and the left inferior fronto-occipital fasciculus (IFOF) 118 voxels; z = -1.182; p < 0.001). Both CC and IFOF maintained significance in the sensitivity analysis. No increase in FA was identified, but the percentage of male patients with PD was positively associated with the value of FA in the body of the CC. Conclusions: Although some limitations exist, DTI is regarded as a valid way to identify the pathophysiology of PD. It could be more beneficial to integrate DTI parameters with other MRI techniques to explore brain degeneration in PD.
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Affiliation(s)
- Xia Wei
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China.,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China.,Department of Radiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Chunyan Luo
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China.,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China.,Department of Radiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Qian Li
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China.,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China.,Department of Radiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Na Hu
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China.,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China.,Department of Radiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Yuan Xiao
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China.,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China.,Department of Radiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Nian Liu
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China.,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China.,Department of Radiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Su Lui
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China.,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China.,Department of Radiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Qiyong Gong
- Department of Radiology, Functional and Molecular Imaging Key Laboratory of Sichuan Province, Huaxi MR Research Center (HMRRC), West China Hospital, Sichuan University, Chengdu, China.,Psychoradiology Research Unit of the Chinese Academy of Medical Sciences (2018RU011), West China Hospital of Sichuan University, Chengdu, China.,Department of Radiology, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
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21
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Zhang Y, Burock MA. Diffusion Tensor Imaging in Parkinson's Disease and Parkinsonian Syndrome: A Systematic Review. Front Neurol 2020; 11:531993. [PMID: 33101169 PMCID: PMC7546271 DOI: 10.3389/fneur.2020.531993] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2020] [Accepted: 08/18/2020] [Indexed: 12/21/2022] Open
Abstract
Diffusion tensor imaging (DTI) allows measuring fractional anisotropy and similar microstructural indices of the brain white matter. Lower than normal fractional anisotropy as well as higher than normal diffusivity is associated with loss of microstructural integrity and neurodegeneration. Previous DTI studies in Parkinson's disease (PD) have demonstrated abnormal fractional anisotropy in multiple white matter regions, particularly in the dopaminergic nuclei and dopaminergic pathways. However, DTI is not considered a diagnostic marker for the earliest Parkinson's disease since anisotropic alterations present a temporally divergent pattern during the earliest Parkinson's course. This article reviews a majority of clinically employed DTI studies in PD, and it aims to prove the utilities of DTI as a marker of diagnosing PD, correlating clinical symptomatology, tracking disease progression, and treatment effects. To address the challenge of DTI being a diagnostic marker for early PD, this article also provides a comparison of the results from a longitudinal, early stage, multicenter clinical cohort of Parkinson's research with previous publications. This review provides evidences of DTI as a promising marker for monitoring PD progression and classifying atypical PD types, and it also interprets the possible pathophysiologic processes under the complex pattern of fractional anisotropic changes in the first few years of PD. Recent technical advantages, limitations, and further research strategies of clinical DTI in PD are additionally discussed.
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Affiliation(s)
- Yu Zhang
- Department of Psychiatry, War Related Illness and Injury Study Center, Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, United States
| | - Marc A Burock
- Department of Psychiatry, Mainline Health, Bryn Mawr Hospital, Bryn Mawr, PA, United States
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22
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Wang J, Zhang F, Zhao C, Zeng Q, He J, O'Donnell LJ, Feng Y. Investigation of local white matter abnormality in Parkinson's disease by using an automatic fiber tract parcellation. Behav Brain Res 2020; 394:112805. [PMID: 32673707 DOI: 10.1016/j.bbr.2020.112805] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 07/01/2020] [Accepted: 07/08/2020] [Indexed: 11/18/2022]
Abstract
The deficits of white matter (WM) microstructure are involved during Parkinson's disease (PD) progression. Most current methods identify key WM tracts relying on cortical regions of interest (ROIs). However, such ROI methods can be challenged due to low diffusion anisotropy near the gray matter (GM), which could result in a low sensitivity of tract identification. This work proposes an automatic WM parcellation method to improve the accuracy of WM tract identification and locate abnormal tracts by using sensitive features. The proposed method consists of 1) whole brain WM parcellation using an established fiber clustering method, without using any ROIs, 2) features of fasciculus were calculated to quantify diffusion measures at each equal cross-section along the whole cluster. Then, we use the proposed features to investigate the WM difference in PD compared with healthy controls (HC). We also use these features to investigate the relationship of clinical symptoms and specific fiber tracts. The novelty of the proposed method is that it automatically identifies the abnormal WM fibers in cluster degree. Experiment results indicated that the proposed method had advantage in detecting the local WM abnormality by performing between-group statistical analysis in 30 patients with PD and 28 HC. We found 13 hemisphere clusters and 8 commissural clusters had significant group difference (p < 0.05, corrected by FDR method) in local regions, which belonged to multiple fiber tracts including cingulum bundle (CB), inferior occipito-frontal fasciculus (IoFF), corpus callosum (CC), external capsule (EC), uncinate fasciculus (UF), superior longitudinal fasciculus (SLF) and thalamo front (TF). We also found clusters that had relevance with clinical indices of cognitive function (2 clusters), athletic function (6 clusters), and depressive state (2 clusters) in these significant clusters. From the experiment results, it confirmed the ability of the proposed method to identify potential WM microstructure abnormality.
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Affiliation(s)
- Jingqiang Wang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Changchen Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Qingrun Zeng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Jianzhong He
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | | | - Yuanjing Feng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
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23
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Liu L, Huang Q, Yang S, Wen Y, He W, Liu H, Meng L, Jiang H, Xia J, Liao W, Liu Y. Micro-structural white matter abnormalities and cognitive impairment in asymptomatic carotid plaque patients: A DTI study using TBSS analysis. Clin Neurol Neurosurg 2020; 197:106096. [PMID: 32717561 DOI: 10.1016/j.clineuro.2020.106096] [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: 05/04/2020] [Revised: 06/03/2020] [Accepted: 07/16/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND It has been shown that symptomatic or severe carotid atherosclerosis is closely related to cognitive impairment and brain white matter damage. However, there is still a lack of effective and non-invasive imaging biomarkers to identify early high-risk cerebrovascular diseases. Therefore, the purpose of this study is to explore the integrity of brain white matter and cognitive impairment in patients with asymptomatic carotid plaques by using imaging technology. METHODS All subjects were from a project of Stroke Risk Screening and Prevention and were defined as stroke high-risk patients (with three or more stroke risk factors). Tract-based spatial statistics (TBSS) based on diffusion tensor imaging (DTI) was used to analyze the whole brain white matter abnormalities in 61 patients with carotid artery plaque and in 40 healthy controls. At the same time, the general clinical data between the two groups were compared, such as age, gender, smoking, hypertension and cognitive function scores etc. Furthermore, the plaque group was divided into the have-hyperintensities group and the no-hyperintensities group to compare their microstructure of white matter injuries. RESULTS The cognitive scores of plaque group were significantly lower than that of control group. We found that when plaque group and control group were compared, no white matter fiber tracts with difference was found in FA, MD, AD and RD. However, the decrease of FA and the increase of RD were found in some white matter regions (P < 0.05) when comparing the have-hyperintensities group and the no-hyperintensities group. These white matter regions included anterior thalamic radiation, corticospinal tract, cingulum (cingulate gyrus), forceps minor, inferior fronto-occipital fasciculus, superior longitudinal fasciculus, uncinate fasciculus. What's more, there were significant differences in blood pressure between the two groups. CONCLUSION The cognitive function of patients with early high-risk cerebrovascular diseases (asymptomatic carotid plaques) has a downward trend. TBSS based on DTI can help to find out the actual damage of brain white matter in patients with early carotid plaque, and reflect the early pathological changes from the micro level.
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Affiliation(s)
- Lihui Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China; Cerebrovascular Disease Clinical Research Center of Hunan Province, Changsha, Hunan, China; Department of Geriatrics Stroke Center, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Qing Huang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China; Cerebrovascular Disease Clinical Research Center of Hunan Province, Changsha, Hunan, China; Department of Geriatrics Stroke Center, Xiangya Hospital, Central South University, Changsha, Hunan, China.
| | - Shuai Yang
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yanbin Wen
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China; Cerebrovascular Disease Clinical Research Center of Hunan Province, Changsha, Hunan, China; Department of Geriatrics Stroke Center, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Wei He
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China; Cerebrovascular Disease Clinical Research Center of Hunan Province, Changsha, Hunan, China; Department of Geriatrics Stroke Center, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hui Liu
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Li Meng
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Hong Jiang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China; Laboratory of Medical Genetics, Central South University, Changsha, Hunan, China; National Clinical Research Center for Geriatric Diseases, Central South University, Changsha, Huan, China; Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Changsha, Hunan, China
| | - Jian Xia
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China; Cerebrovascular Disease Clinical Research Center of Hunan Province, Changsha, Hunan, China; Department of Geriatrics Stroke Center, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Weihua Liao
- Department of Radiology, Xiangya Hospital, Central South University, Changsha, Hunan, China
| | - Yunhai Liu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, Hunan, China; Cerebrovascular Disease Clinical Research Center of Hunan Province, Changsha, Hunan, China; Department of Geriatrics Stroke Center, Xiangya Hospital, Central South University, Changsha, Hunan, China
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24
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Benear SL, Ngo CT, Olson IR. Dissecting the Fornix in Basic Memory Processes and Neuropsychiatric Disease: A Review. Brain Connect 2020; 10:331-354. [PMID: 32567331 DOI: 10.1089/brain.2020.0749] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Background: The fornix is the primary axonal tract of the hippocampus, connecting it to modulatory subcortical structures. This review reveals that fornix damage causes cognitive deficits that closely mirror those resulting from hippocampal lesions. Methods: We reviewed the literature on the fornix, spanning non-human animal lesion research, clinical case studies of human patients with fornix damage, as well as diffusion-weighted imaging (DWI) work that evaluates fornix microstructure in vivo. Results: The fornix is essential for memory formation because it serves as the conduit for theta rhythms and acetylcholine, as well as providing mnemonic representations to deep brain structures that guide motivated behavior, such as when and where to eat. In rodents and non-human primates, fornix lesions lead to deficits in conditioning, reversal learning, and navigation. In humans, damage to the fornix manifests as anterograde amnesia. DWI research reveals that the fornix plays a key role in mild cognitive impairment and Alzheimer's Disease, and can potentially predict conversion from the former to the latter. Emerging DWI findings link perturbations in this structure to schizophrenia, mood disorders, and eating disorders. Cutting-edge research has investigated how deep brain stimulation of the fornix can potentially attenuate memory loss, control epileptic seizures, and even improve mood. Conclusions: The fornix is essential to a fully functioning memory system and is implicated in nearly all neurological functions that rely on the hippocampus. Future research needs to use optimized DWI methods to study the fornix in vivo, which we discuss, given the difficult nature of fornix reconstruction. Impact Statement The fornix is a white matter tract that connects the hippocampus to several subcortical brain regions and is pivotal for episodic memory functioning. Functionally, the fornix transmits essential neurotransmitters, as well as theta rhythms, to the hippocampus. In addition, it is the conduit by which memories guide decisions. The fornix is biomedically important because lesions to this tract result in irreversible anterograde amnesia. Research using in vivo imaging methods has linked fornix pathology to cognitive aging, mild cognitive impairment, psychosis, epilepsy, and, importantly, Alzheimer's Disease.
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Affiliation(s)
- Susan L Benear
- Department of Psychology, Temple University, Philadelphia, Pennsylvania, USA
| | - Chi T Ngo
- Center for Lifespan Psychology, Max Planck Institute for Human Development, Berlin, Germany
| | - Ingrid R Olson
- Department of Psychology, Temple University, Philadelphia, Pennsylvania, USA
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25
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Won JH, Kim M, Youn J, Park H. Prediction of age at onset in Parkinson's disease using objective specific neuroimaging genetics based on a sparse canonical correlation analysis. Sci Rep 2020; 10:11662. [PMID: 32669683 PMCID: PMC7363828 DOI: 10.1038/s41598-020-68301-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 06/22/2020] [Indexed: 01/19/2023] Open
Abstract
The age at onset (AAO) is an important determinant in Parkinson’s disease (PD). Neuroimaging genetics is suitable for studying AAO in PD as it jointly analyzes imaging and genetics. We aimed to identify features associated with AAO in PD by applying the objective-specific neuroimaging genetics approach and constructing an AAO prediction model. Our objective-specific neuroimaging genetics extended the sparse canonical correlation analysis by an additional data type related to the target task to investigate possible associations of the imaging–genetic, genetic–target, and imaging–target pairs simultaneously. The identified imaging, genetic, and combined features were used to construct analytical models to predict the AAO in a nested five-fold cross-validation. We compared our approach with those from two feature selection approaches where only associations of imaging–target and genetic–target were explored. Using only imaging features, AAO prediction was accurate in all methods. Using only genetic features, the results from other methods were worse or unstable compared to our model. Using both imaging and genetic features, our proposed model predicted the AAO well (r = 0.5486). Our findings could have significant impacts on the characterization of prodromal PD and contribute to diagnosing PD early because genetic features could be measured accurately from birth.
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Affiliation(s)
- Ji Hye Won
- Department of Electronic and Computer Engineering, Sungkyunkwan University, Suwon, Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea
| | - Mansu Kim
- Department of Electronic and Computer Engineering, Sungkyunkwan University, Suwon, Korea.,Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea
| | - Jinyoung Youn
- Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Irwon-ro 81, Gangnam-gu, Seoul, 06351, Korea. .,Neuroscience Center, Samsung Medical Center, Seoul, Korea.
| | - Hyunjin Park
- Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea. .,School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, 16419, Korea.
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26
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Feng Y, Yan W, Wang J, Song J, Zeng Q, Zhao C. Local White Matter Fiber Clustering Differentiates Parkinson's Disease Diagnoses. Neuroscience 2020; 435:146-160. [PMID: 32272152 DOI: 10.1016/j.neuroscience.2020.03.049] [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: 07/20/2019] [Revised: 03/30/2020] [Accepted: 03/31/2020] [Indexed: 10/24/2022]
Abstract
Scans without evidence of dopaminergic deficit (SWEDD) patients are often misdiagnosed with Parkinson's disease (PD) but have normal dopamine transporter scans. We hypothesised that white matter tracts associated with motor and cognition functions may be affected differently by SWEDD and PD. Automatically annotated fibre clustering (AAFC) is a novel clustering method based on diffusion magnetic resonance imaging (dMRI) tractography that enables highly robust reconstruction of white matter tracts that are composed of corresponding clusters. This study aimed to investigate the white matter properties in the subdivisions of white matter tracts among SWEDD and PD groups. We applied AAFC to identify white matter tracts related to motion and cognition functions in the dataset consisting of SWEDD (n = 22), PD (n = 30) and normal control (NC) (n = 30). Then, we resampled 200 nodes along fibres of cluster, and the diffusion metric values corresponding to each node were calculated and used for comparison. Compared with NC, PD showed significant difference (p < 0.05) in two clusters in thalamo-frontal (TF), one cluster in thalamo-parietal (TP) and one cluster in thalamo-occipital (TO), whereas SWEDD presented no significant difference. Three clusters in cingulum bundle (CB) commonly exhibited significant differences in PD versus SWEDD and NC versus SWEDD. The support vector machine classifier achieved high accuracies in PD-NC, PD-SWEDD and NC-SWEDD classifications. This outcome validated these local white matter differences were useful to separate the three groups. These results suggest that PD exerts more significant effects on thalamo tracts than SWEDD, and unique microstructural changes occur in CB tract in SWEDD.
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Affiliation(s)
- Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China.
| | - Wenxuan Yan
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jingqiang Wang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jiahao Song
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Changchen Zhao
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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Bergamino M, Keeling EG, Mishra VR, Stokes AM, Walsh RR. Assessing White Matter Pathology in Early-Stage Parkinson Disease Using Diffusion MRI: A Systematic Review. Front Neurol 2020; 11:314. [PMID: 32477235 PMCID: PMC7240075 DOI: 10.3389/fneur.2020.00314] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 03/31/2020] [Indexed: 12/15/2022] Open
Abstract
Structural brain white matter (WM) changes such as axonal caliber, density, myelination, and orientation, along with WM-dependent structural connectivity, may be impacted early in Parkinson disease (PD). Diffusion magnetic resonance imaging (dMRI) has been used extensively to understand such pathological WM changes, and the focus of this systematic review is to understand both the methods utilized and their corresponding results in the context of early-stage PD. Diffusion tensor imaging (DTI) is the most commonly utilized method to probe WM pathological changes. Previous studies have suggested that DTI metrics are sensitive in capturing early disease-associated WM changes in preclinical symptomatic regions such as olfactory regions and the substantia nigra, which is considered to be a hallmark of PD pathology and progression. Postprocessing analytic approaches include region of interest-based analysis, voxel-based analysis, skeletonized approaches, and connectome analysis, each with unique advantages and challenges. While DTI has been used extensively to study WM disorganization in early-stage PD, it has several limitations, including an inability to resolve multiple fiber orientations within each voxel and sensitivity to partial volume effects. Given the subtle changes associated with early-stage PD, these limitations result in inaccuracies that severely impact the reliability of DTI-based metrics as potential biomarkers. To overcome these limitations, advanced dMRI acquisition and analysis methods have been employed, including diffusion kurtosis imaging and q-space diffeomorphic reconstruction. The combination of improved acquisition and analysis in DTI may yield novel and accurate information related to WM-associated changes in early-stage PD. In the current article, we present a systematic and critical review of dMRI studies in early-stage PD, with a focus on recent advances in DTI methodology. Yielding novel metrics, these advanced methods have been shown to detect diffuse WM changes in early-stage PD. These findings support the notion of early axonal damage in PD and suggest that WM pathology may go unrecognized until symptoms appear. Finally, the advantages and disadvantages of different dMRI techniques, analysis methods, and software employed are discussed in the context of PD-related pathology.
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Affiliation(s)
- Maurizio Bergamino
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States
| | - Elizabeth G. Keeling
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States
- School of Life Sciences, Arizona State University, Tempe, AZ, United States
| | - Virendra R. Mishra
- Imaging Research, Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
| | - Ashley M. Stokes
- Division of Neuroimaging Research, Barrow Neurological Institute, Phoenix, AZ, United States
| | - Ryan R. Walsh
- Muhammad Ali Parkinson Center, Barrow Neurological Institute, Phoenix, AZ, United States
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Impaired Parahippocampal Gyrus-Orbitofrontal Cortex Circuit Associated with Visuospatial Memory Deficit as a Potential Biomarker and Interventional Approach for Alzheimer Disease. Neurosci Bull 2020; 36:831-844. [PMID: 32350798 DOI: 10.1007/s12264-020-00498-3] [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: 10/05/2019] [Accepted: 12/10/2019] [Indexed: 12/20/2022] Open
Abstract
The parahippocampal gyrus-orbitofrontal cortex (PHG-OFC) circuit in humans is homologous to the postrhinal cortex (POR)-ventral lateral orbitofrontal cortex (vlOFC) circuit in rodents. Both are associated with visuospatial malfunctions in Alzheimer's disease (AD). However, the underlying mechanisms remain to be elucidated. In this study, we explored the relationship between an impaired POR-vlOFC circuit and visuospatial memory deficits through retrograde tracing and in vivo local field potential recordings in 5XFAD mice, and investigated alterations of the PHG-OFC circuit by multi-domain magnetic resonance imaging (MRI) in patients on the AD spectrum. We demonstrated that an impaired glutamatergic POR-vlOFC circuit resulted in deficient visuospatial memory in 5XFAD mice. Moreover, MRI measurements of the PHG-OFC circuit had an accuracy of 77.33% for the classification of amnestic mild cognitive impairment converters versus non-converters. Thus, the PHG-OFC circuit explains the neuroanatomical basis of visuospatial memory deficits in AD, thereby providing a potential predictor for AD progression and a promising interventional approach for AD.
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van Wamelen DJ, Wan YM, Ray Chaudhuri K, Jenner P. Stress and cortisol in Parkinson's disease. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2020; 152:131-156. [PMID: 32450994 DOI: 10.1016/bs.irn.2020.01.005] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Stress is ubiquitous with many factors contributing to its effects, including psychological responses and associated biological factors, including cortisol related physiological responses, and inflammation. Also in Parkinson's disease there is growing evidence for the role of stress in some key symptoms, even stretching to the prodromal stage. Here we discuss the possible contributions of the range and nature of stress in PD and we aim to summarize the current knowledge about the role of stress-related responses on motor and non-motor symptoms, the underlying pathophysiology, and the potential implications for treatment.
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Affiliation(s)
- Daniel J van Wamelen
- King's College London, Department of Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom; Parkinson Foundation Centre of Excellence, King's College Hospital, London, United Kingdom.
| | - Yi-Min Wan
- King's College London, Department of Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom; Parkinson Foundation Centre of Excellence, King's College Hospital, London, United Kingdom; Department of Psychiatry, Ng Teng Fong General Hospital, Singapore, Singapore
| | - K Ray Chaudhuri
- King's College London, Department of Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, London, United Kingdom; Parkinson Foundation Centre of Excellence, King's College Hospital, London, United Kingdom
| | - Peter Jenner
- King's College London, Neurodegenerative Diseases Research Group, Institute of Pharmaceutical Sciences, Faculty of Health Sciences and Medicine, London, United Kingdom
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Abbasi N, Fereshtehnejad SM, Zeighami Y, Larcher KMH, Postuma RB, Dagher A. Predicting severity and prognosis in Parkinson's disease from brain microstructure and connectivity. NEUROIMAGE-CLINICAL 2019; 25:102111. [PMID: 31855654 PMCID: PMC6926369 DOI: 10.1016/j.nicl.2019.102111] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 11/26/2019] [Accepted: 11/27/2019] [Indexed: 12/25/2022]
Abstract
White matter disruption occurs in Parkinson's disease across several brain regions. DTI properties could identify clinically distinct subtypes of Parkinson's disease. Structural neural disruption can predict clinical outcomes in Parkinson's disease.
Objectives: Investigating biomarkers to demonstrate progression of Parkinson's disease (PD) is of high priority. We investigated the association of brain structural properties with progression of clinical outcomes and their ability to differentiate clinical subtypes of PD. Methods: A comprehensive set of clinical features was evaluated at baseline and 4.5-year follow-up for 144 de-novo PD patients from the Parkinson's Progression Markers Initiative. We created a global composite outcome (GCO) by combining z-scores of non-motor and motor symptoms, motor signs, overall activities of daily living and global cognition, as a single numeric indicator of prognosis. We classified patients into three subtypes based on multi-domain clinical criteria: ‘mild motor-predominant’, ‘intermediate’ and ‘diffuse-malignant’. We analyzed diffusion-weighted scans at the early drug-naïve stage and extracted fractional anisotropy and mean diffusivity (MD) of basal ganglia and cortical sub-regions. Then, we employed graph theory to calculate network properties and used network-based statistic to investigate our primary hypothesis. Results: Baseline MD of globus pallidus was associated with worsening of motor severity, cognition, and GCO after 4.5 years of follow-up. Connectivity disruption at baseline was correlated with decline in cognition, and increase in GCO. Baseline MD of nucleus accumbens, globus pallidus and basal-ganglia were linked to clinical subtypes at 4.5-year of follow-up. Disruption in sub-cortical networks associated with being subtyped as ‘diffuse-malignant’ versus ‘mild motor-predominant’ after 4.5 years. Conclusions: Diffusion imaging analysis at the early de-novo stage of PD was able to differentiate clinical sub-types of PD after 4.5 years and was highly associated with future clinical outcomes of PD.
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Affiliation(s)
- Nooshin Abbasi
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St., Montreal, Quebec H3A 2B4, Canada.
| | - Seyed-Mohammad Fereshtehnejad
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada; Division of Neurology, Department of Medicine, The Ottawa Hospital, University of Ottawa, Ottawa, Ontario, Canada; Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institute, Stockholm, Sweden
| | - Yashar Zeighami
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St., Montreal, Quebec H3A 2B4, Canada
| | | | - Ronald B Postuma
- Department of Neurology and Neurosurgery, McGill University, Montreal, Quebec, Canada; Centre for Advanced Research in Sleep Medicine, Hôpital du Sacré-Coeur de Montréal, Montreal, Quebec, Canada
| | - Alain Dagher
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 University St., Montreal, Quebec H3A 2B4, Canada
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31
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Koyama T, Uchiyama Y, Domen K. Comparison of Fractional Anisotropy from Tract-Based Spatial Statistics with and without Lesion Masking in Patients with Intracerebral Hemorrhage: A Technical Note. J Stroke Cerebrovasc Dis 2019; 28:104376. [PMID: 31530481 DOI: 10.1016/j.jstrokecerebrovasdis.2019.104376] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2019] [Revised: 06/27/2019] [Accepted: 08/26/2019] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Diffusion-tensor fractional anisotropy (FA) is an index of neural-fiber damage in patients following stroke. To better characterize FA, tract-based spatial statistics (TBSS) is frequently used, which involves spatial transformation into the standard brain space. Despite its utility, this technique is susceptible to space-occupying hematoma in patients with intracerebral hemorrhage. To correct this, "lesion making" has been proposed. Here, FA values from TBSS without lesion masking and TBSS with lesion masking were compared, and the clinical utility was evaluated. METHODS Forty patients from our previously published work were entered into the study. Diffusion-tensor imagings were acquired 14-21 days after onset and FA maps were generated. Lesion masks were produced in reference with nondiffusion (b = 0) brain images. Two types (with or without lesion masking) of TBSS were then performed. For both types, using individual data we extracted mean FA values within for the corticospinal tract (CST) and the superior longitudinal fasciculus (SLF). FA ratio (rFA) between the lesioned hemisphere and the unaffected hemisphere was then calculated. The two sets of the data were then compared by assessing residuals of mean root sum square error (RMSE). RESULTS Although rFA obtained from TBSS with lesion masking tended to be slightly smaller, the estimated RMSE was .025 for both the CST and the SLF. CONCLUSIONS The estimated FA differences between the two sets of TBSS were very small. Considering the time for manual labor for producing lesion masks, regular TBSS without lesion masking may be sufficient in terms of clinical utility.
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Affiliation(s)
- Tetsuo Koyama
- Department of Rehabilitation Medicine, Nishinomiya Kyoritsu Neurosurgical Hospital, Nishinomiya, Hyogo, Japan; Department of Rehabilitation Medicine, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan.
| | - Yuki Uchiyama
- Department of Rehabilitation Medicine, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan
| | - Kazuhisa Domen
- Department of Rehabilitation Medicine, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan
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32
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Salsone M, Caligiuri ME, Vescio V, Arabia G, Cherubini A, Nicoletti G, Morelli M, Quattrone A, Vescio B, Nisticò R, Novellino F, Cascini GL, Sabatini U, Montilla M, Rektor I, Quattrone A. Microstructural changes of normal-appearing white matter in Vascular Parkinsonism. Parkinsonism Relat Disord 2019; 63:60-65. [DOI: 10.1016/j.parkreldis.2019.02.046] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Revised: 02/24/2019] [Accepted: 02/27/2019] [Indexed: 11/25/2022]
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Ji G, Ren C, Li Y, Sun J, Liu T, Gao Y, Xue D, Shen L, Cheng W, Zhu C, Tian Y, Hu P, Chen X, Wang K. Regional and network properties of white matter function in Parkinson's disease. Hum Brain Mapp 2019; 40:1253-1263. [PMID: 30414340 PMCID: PMC6865582 DOI: 10.1002/hbm.24444] [Citation(s) in RCA: 79] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2018] [Revised: 10/05/2018] [Accepted: 10/16/2018] [Indexed: 02/01/2023] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder with dysfunction in cortices as well as white matter (WM) tracts. While the changes to WM structure have been extensively investigated in PD, the nature of the functional changes to WM remains unknown. In this study, the regional activity and functional connectivity of WM were compared between PD patients (n = 57) and matched healthy controls (n = 52), based on multimodel magnetic resonance imaging data sets. By tract-based spatial statistical analyses of regional activity, patients showed decreased structural-functional coupling in the left corticospinal tract compared to controls. This tract also displayed abnormally increased functional connectivity within the left post-central gyrus and left putamen in PD patients. At the network level, the WM functional network showed small-worldness in both controls and PD patients, yet it was abnormally increased in the latter group. Based on the features of the WM functional connectome, previously un-evaluated individuals could be classified with fair accuracy (73%) and area under the curve of the receiver operating characteristics (75%). These neuroimaging findings provide direct evidence for WM functional changes in PD, which is crucial to understand the functional role of fiber tracts in the pathology of neural circuits.
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Affiliation(s)
- Gong‐Jun Ji
- Department of Medical Psychology, Chaohu Clinical Medical CollegeAnhui Medical UniversityHefeiChina
| | - Cuiping Ren
- Department of Neurology, The First Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Ying Li
- Department of Neurology, The First Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Jinmei Sun
- Department of Neurology, The First Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Tingting Liu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Yaxiang Gao
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Dongzhang Xue
- Department of NeurologyThe 123 Hospital of People's Liberation ArmyBengbuChina
| | - Longshan Shen
- Department of ImagingThe Second Affiliated Hospital of Bengbu Medical CollegeBengbuChina
| | - Wen Cheng
- College of Literature and EducationBengbu CollegeBengbuChina
| | - Chunyan Zhu
- Department of Medical Psychology, Chaohu Clinical Medical CollegeAnhui Medical UniversityHefeiChina
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Yanghua Tian
- Department of Neurology, The First Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Panpan Hu
- Department of Neurology, The First Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Xianwen Chen
- Department of Neurology, The First Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Kai Wang
- Department of Neurology, The First Affiliated Hospital of Anhui Medical UniversityHefeiChina
- Collaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
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Guo Y, Gao F, Liu Y, Guo H, Yu W, Chen Z, Yang M, Du L, Yang D, Li J. White Matter Microstructure Alterations in Patients With Spinal Cord Injury Assessed by Diffusion Tensor Imaging. Front Hum Neurosci 2019; 13:11. [PMID: 30809136 PMCID: PMC6379286 DOI: 10.3389/fnhum.2019.00011] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Accepted: 01/10/2019] [Indexed: 12/12/2022] Open
Abstract
Compared to healthy controls, spinal cord injury (SCI) patients demonstrate white matter (WM) abnormalities in the brain. However, little progress has been made in comparing cerebral WM differences between SCI-subgroups. The purpose of this study was to investigate WM microstructure differences between paraplegia and quadriplegia using tract-based spatial statistics (TBSS) and atlas-based analysis methods. Twenty-two SCI patients (11 cervical SCI and 11 thoracic SCI) and 22 age- and sex-matched healthy controls were included in this study. TBSS and atlas-based analyses were performed between SCI and control groups and between SCI-subgroups using multiple diffusion metrics, including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). Compared to controls, SCI patients had decreased FA and increased MD and RD in the corpus callosum (CC; genu and splenium), superior longitudinal fasciculus (SLF), corona radiata (CR), posterior thalamic radiation (PTR), right cingulum (cingulate gyrus; CCG) and right superior fronto-occipital fasciculus (SFOF). Cervical SCI patients had lower FA and higher RD in the left PTR than thoracic SCI patients. Time since injury had a negative correlation with FA within the right SFOF (r = −0.452, p = 0.046) and a positive association between the FA of left PTR and the American Spinal Injury Association (ASIA) sensory score (r = 0.428, p = 0.047). In conclusion, our study suggests that multiple cerebral WM tracts are damaged in SCI patients, and WM disruption in cervical SCI is worse than thoracic injury level, especially in the PTR region.
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Affiliation(s)
- Yun Guo
- School of Rehabilitation Medicine, Capital Medical University, Beijing, China.,China Rehabilitation Research Center, Department of Spinal and Neural Functional Reconstruction, Beijing, China.,Center of Neural Injury and Repair, Beijing Institute for Brain Disorders, Beijing, China.,China Rehabilitation Science Institute, Beijing, China.,Beijing Key Laboratory of Neural Injury and Rehabilitation, Beijing, China
| | - Feng Gao
- School of Rehabilitation Medicine, Capital Medical University, Beijing, China.,China Rehabilitation Research Center, Department of Spinal and Neural Functional Reconstruction, Beijing, China.,Center of Neural Injury and Repair, Beijing Institute for Brain Disorders, Beijing, China.,China Rehabilitation Science Institute, Beijing, China.,Beijing Key Laboratory of Neural Injury and Rehabilitation, Beijing, China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hua Guo
- Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China
| | - Weiyong Yu
- China Rehabilitation Research Center, Department of Radiology, Beijing, China
| | - Zhenbo Chen
- China Rehabilitation Research Center, Department of Radiology, Beijing, China
| | - Mingliang Yang
- School of Rehabilitation Medicine, Capital Medical University, Beijing, China.,China Rehabilitation Research Center, Department of Spinal and Neural Functional Reconstruction, Beijing, China.,Center of Neural Injury and Repair, Beijing Institute for Brain Disorders, Beijing, China.,China Rehabilitation Science Institute, Beijing, China.,Beijing Key Laboratory of Neural Injury and Rehabilitation, Beijing, China
| | - Liangjie Du
- School of Rehabilitation Medicine, Capital Medical University, Beijing, China.,China Rehabilitation Research Center, Department of Spinal and Neural Functional Reconstruction, Beijing, China.,Center of Neural Injury and Repair, Beijing Institute for Brain Disorders, Beijing, China.,China Rehabilitation Science Institute, Beijing, China.,Beijing Key Laboratory of Neural Injury and Rehabilitation, Beijing, China
| | - Degang Yang
- School of Rehabilitation Medicine, Capital Medical University, Beijing, China.,China Rehabilitation Research Center, Department of Spinal and Neural Functional Reconstruction, Beijing, China.,Center of Neural Injury and Repair, Beijing Institute for Brain Disorders, Beijing, China.,China Rehabilitation Science Institute, Beijing, China.,Beijing Key Laboratory of Neural Injury and Rehabilitation, Beijing, China
| | - Jianjun Li
- School of Rehabilitation Medicine, Capital Medical University, Beijing, China.,China Rehabilitation Research Center, Department of Spinal and Neural Functional Reconstruction, Beijing, China.,Center of Neural Injury and Repair, Beijing Institute for Brain Disorders, Beijing, China.,China Rehabilitation Science Institute, Beijing, China.,Beijing Key Laboratory of Neural Injury and Rehabilitation, Beijing, China
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35
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Haghshomar M, Dolatshahi M, Ghazi Sherbaf F, Sanjari Moghaddam H, Shirin Shandiz M, Aarabi MH. Disruption of Inferior Longitudinal Fasciculus Microstructure in Parkinson's Disease: A Systematic Review of Diffusion Tensor Imaging Studies. Front Neurol 2018; 9:598. [PMID: 30093877 PMCID: PMC6070770 DOI: 10.3389/fneur.2018.00598] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2018] [Accepted: 07/05/2018] [Indexed: 12/19/2022] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder accompanied by a series of pathological mechanisms which contribute to a variety of motor and non-motor symptoms. Recently, there has been an increasing interest in structural diffusion tensor imaging (DTI) in PD which has shed light on our understanding of structural abnormalities underlying PD symptoms or its associations with pathological mechanisms. One of the white matter tracts shown to be disrupted in PD with a possible contribution to some PD symptoms is the inferior longitudinal fasciculus (ILF). On the whole, lower ILF integrity contributes to thought disorders, impaired visual emotions, cognitive impairments such as semantic fluency deficits, and mood disorders. This review outlines the microstructural changes in ILF associated with systemic inflammation and various PD symptoms like cognitive decline, facial emotion recognition deficit, depression, color discrimination deficit, olfactory dysfunction, and tremor genesis. However, few studies have investigated DTI correlates of each symptom and larger studies with standardized imaging protocols are required to extend these preliminary findings and lead to more promising results.
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Affiliation(s)
- Maryam Haghshomar
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mahsa Dolatshahi
- Faculty of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | | | | | - Mehdi Shirin Shandiz
- Department of Medical Physics, Zahedan University of Medical Sciences, Zahedan, Iran
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36
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Ghazi Sherbaf F, Rostam Abadi Y, Mojtahed Zadeh M, Ashraf-Ganjouei A, Sanjari Moghaddam H, Aarabi MH. Microstructural Changes in Patients With Parkinson's Disease Comorbid With REM Sleep Behaviour Disorder and Depressive Symptoms. Front Neurol 2018; 9:441. [PMID: 29997561 PMCID: PMC6028696 DOI: 10.3389/fneur.2018.00441] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2018] [Accepted: 05/25/2018] [Indexed: 12/14/2022] Open
Abstract
The diagnosis of Parkinson's disease (PD) is currently anchored on clinical motor symptoms, which appear more than 20 years after initiation of the neurotoxicity. Extra-nigral involvement in the onset of PD with probable nonmotor manifestations before the development of motor signs, lead us to the preclinical (asymptomatic) or prodromal stages of the disease (various nonmotor or subtle motor signs). REM sleep behavior disorder (RBD) and depression are established prodromal clinical markers of PD and predict worse motor and cognitive outcomes. Nevertheless, taken by themselves, these markers are not yet claimed to be practical in identifying high-risk individuals. Combining promising markers may be helpful in a reliable diagnosis of early PD. Therefore, we aimed to detect neural correlates of RBD and depression in 93 treatment-naïve and non-demented early PD by means of diffusion MRI connectometry. Comparing four groups of PD patients with or without comorbid RBD and/or depressive symptoms with each other and with 31 healthy controls, we found that these two non-motor symptoms are associated with lower connectivity in several white matter tracts including the cerebellar peduncles, corpus callosum and long association fibers such as cingulum, fornix, and inferior longitudinal fasciculus. For the first time, we were able to detect the involvement of short association fibers (U-fibers) in PD neurodegenerative process. Longitudinal studies on larger sample groups are needed to further investigate the reported associations.
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