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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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Zarkali A, Thomas GEC, Zetterberg H, Weil RS. Neuroimaging and fluid biomarkers in Parkinson's disease in an era of targeted interventions. Nat Commun 2024; 15:5661. [PMID: 38969680 PMCID: PMC11226684 DOI: 10.1038/s41467-024-49949-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 06/19/2024] [Indexed: 07/07/2024] Open
Abstract
A major challenge in Parkinson's disease is the variability in symptoms and rates of progression, underpinned by heterogeneity of pathological processes. Biomarkers are urgently needed for accurate diagnosis, patient stratification, monitoring disease progression and precise treatment. These were previously lacking, but recently, novel imaging and fluid biomarkers have been developed. Here, we consider new imaging approaches showing sensitivity to brain tissue composition, and examine novel fluid biomarkers showing specificity for pathological processes, including seed amplification assays and extracellular vesicles. We reflect on these biomarkers in the context of new biological staging systems, and on emerging techniques currently in development.
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Affiliation(s)
- Angeliki Zarkali
- Dementia Research Centre, Institute of Neurology, UCL, London, UK.
| | | | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
- Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA
| | - Rimona S Weil
- Dementia Research Centre, Institute of Neurology, UCL, London, UK
- Department of Advanced Neuroimaging, UCL, London, UK
- Movement Disorders Centre, UCL, London, UK
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3
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Mohammadi MS, Planty-Bonjour A, Poupon F, Uszynski I, Poupon C, Destrieux C, Andersson F. ProbaStem, a pipeline towards the first high-resolution probabilistic atlas of the whole human brainstem. Brain Struct Funct 2024; 229:115-132. [PMID: 37924354 DOI: 10.1007/s00429-023-02726-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2022] [Accepted: 10/16/2023] [Indexed: 11/06/2023]
Abstract
The brainstem plays an essential role in many vital functions, such as autonomic control, consciousness and sleep, motricity, somatic afferent function, and cognition. Its involvement in several neurological diseases and the definition of brainstem targets for deep brain stimulation (DBS) explain the need for brainstem atlases describing its structural organization and connectivity from several modalities, from histology to ultrahigh field ex vivo MRI. Nonetheless, these atlases are often limited to a subpart of the brainstem or only include a single subject, the brainstem variability being considered low. This paper proposes a pipeline to create a high-resolution multisubject probabilistic atlas of the whole human brainstem based on four ultrahigh field ex vivo MRI datasets. The variability of the brainstem structures appears higher than usually considered, both for the volume and position of the central gray matter structures of the brainstem. This justifies the creation of atlases that capture the anatomical variability across subjects. The one we present here only included four specimens, but can easily be incremented due to its highly flexible design.
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Affiliation(s)
| | - Alexia Planty-Bonjour
- UMR 1253, Inserm, iBrain, Université de Tours, Tours, France
- CHRU de Tours, Tours, France
| | - Fabrice Poupon
- CEA, CNRS, BAOBAB, Paris-Saclay University, Gif-sur-Yvette, France
| | - Ivy Uszynski
- CEA, CNRS, BAOBAB, Paris-Saclay University, Gif-sur-Yvette, France
| | - Cyril Poupon
- CEA, CNRS, BAOBAB, Paris-Saclay University, Gif-sur-Yvette, France
| | - Christophe Destrieux
- UMR 1253, Inserm, iBrain, Université de Tours, Tours, France.
- CHRU de Tours, Tours, France.
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Welton T, Hartono S, Shih YC, Schwarz ST, Xing Y, Tan EK, Auer DP, Harel N, Chan LL. Ultra-high-field 7T MRI in Parkinson's disease: ready for clinical use?-a narrative review. Quant Imaging Med Surg 2023; 13:7607-7620. [PMID: 37969629 PMCID: PMC10644128 DOI: 10.21037/qims-23-509] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 09/15/2023] [Indexed: 11/17/2023]
Abstract
Background and Objective The maturation of ultra-high-field magnetic resonance imaging (MRI) [≥7 Tesla (7T)] has improved our capability to depict and characterise brain structures efficiently, with better signal-to-noise ratio (SNR) and spatial resolution. We evaluated whether these improvements benefit the clinical detection and management of Parkinson's disease (PD). Methods We performed a literature search in March 2023 in PubMed (MEDLINE), EMBASE and Google Scholar for articles on "7T MRI" AND "Parkinson*", written in English, published between inception and 1st March, 2023, which we synthesised in narrative form. Key Content and Findings In deep-brain stimulation (DBS) surgical planning, early studies show that 7T MRI can distinguish anatomical substructures, and that this results in reduced adverse effects. In other areas, while there is strong evidence for improved accuracy and precision of 7T MRI-based measurements for PD, there is limited evidence for meaningful clinical translation. In particular, neuromelanin-iron complex quantification and visualisation in midbrain nuclei is enhanced, enabling depiction of nigrosomes 1-5, improved morphometry and vastly improved radiological assessments; however, studies on the related clinical outcomes, diagnosis, subtyping, differentiation of atypical parkinsonisms, and monitoring of treatment response using 7T MRI are lacking. Moreover, improvements in clinical utility must be great enough to justify the additional costs. Conclusions Together, current evidence supports feasible future clinical implementation of 7T MRI for PD. Future impacts to clinical decision making for diagnosis, differentiation, and monitoring of progression or treatment response are likely; however, to achieve this, further longitudinal studies using 7T MRI are needed in prodromal, early-stage PD and parkinsonism cohorts focusing on clinical translational potential.
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Affiliation(s)
- Thomas Welton
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Septian Hartono
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
| | - Yao-Chia Shih
- Duke-NUS Medical School, Singapore, Singapore
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
- Graduate Institute of Medicine, Yuan Ze University and National Taiwan University, Taipei
| | - Stefan T. Schwarz
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Radiology, Cardiff and Vale University Health Board, Cardiff, Wales, UK
| | - Yue Xing
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Eng-King Tan
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Dorothee P. Auer
- Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
- NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Ling-Ling Chan
- National Neuroscience Institute, Singapore, Singapore
- Duke-NUS Medical School, Singapore, Singapore
- Department of Diagnostic Radiology, Singapore General Hospital, Singapore, Singapore
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Andrews L, Keller SS, Osman-Farah J, Macerollo A. A structural magnetic resonance imaging review of clinical motor outcomes from deep brain stimulation in movement disorders. Brain Commun 2023; 5:fcad171. [PMID: 37304793 PMCID: PMC10257440 DOI: 10.1093/braincomms/fcad171] [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: 11/13/2022] [Revised: 04/05/2023] [Accepted: 05/30/2023] [Indexed: 06/13/2023] Open
Abstract
Patients with movement disorders treated by deep brain stimulation do not always achieve successful therapeutic alleviation of motor symptoms, even in cases where surgery is without complications. Magnetic resonance imaging (MRI) offers methods to investigate structural brain-related factors that may be predictive of clinical motor outcomes. This review aimed to identify features which have been associated with variability in clinical post-operative motor outcomes in patients with Parkinson's disease, dystonia, and essential tremor from structural MRI modalities. We performed a literature search for articles published between 1 January 2000 and 1 April 2022 and identified 5197 articles. Following screening through our inclusion criteria, we identified 60 total studies (39 = Parkinson's disease, 11 = dystonia syndromes and 10 = essential tremor). The review captured a range of structural MRI methods and analysis techniques used to identify factors related to clinical post-operative motor outcomes from deep brain stimulation. Morphometric markers, including volume and cortical thickness were commonly identified in studies focused on patients with Parkinson's disease and dystonia syndromes. Reduced metrics in basal ganglia, sensorimotor and frontal regions showed frequent associations with reduced motor outcomes. Increased structural connectivity to subcortical nuclei, sensorimotor and frontal regions was also associated with greater motor outcomes. In patients with tremor, increased structural connectivity to the cerebellum and cortical motor regions showed high prevalence across studies for greater clinical motor outcomes. In addition, we highlight conceptual issues for studies assessing clinical response with structural MRI and discuss future approaches towards optimizing individualized therapeutic benefits. Although quantitative MRI markers are in their infancy for clinical purposes in movement disorder treatments, structural features obtained from MRI offer the powerful potential to identify candidates who are more likely to benefit from deep brain stimulation and provide insight into the complexity of disorder pathophysiology.
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Affiliation(s)
- Luke Andrews
- Correspondence to: Luke Andrews The BRAIN Lab, University of Liverpool Cancer Research Centre 200 London Rd, Liverpool L3 9TA, United Kingdom E-mail:
| | - Simon S Keller
- The Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L3 9TA, UK
| | - Jibril Osman-Farah
- Department of Neurology and Neurosurgery, The Walton Centre NHS Foundation Trust, Liverpool L97LJ, UK
| | - Antonella Macerollo
- Correspondence may also be sent to: Antonella Macerollo. The Walton Centre NHS Trust, Lower Lane Liverpool L9 7LJ, United Kingdom E-mail:
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6
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Diffusion Measures of Subcortical Structures Using High-Field MRI. Brain Sci 2023; 13:brainsci13030391. [PMID: 36979201 PMCID: PMC10046555 DOI: 10.3390/brainsci13030391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/11/2023] [Accepted: 02/15/2023] [Indexed: 02/26/2023] Open
Abstract
The pathology of Parkinson’s disease (PD) involves the death of dopaminergic neurons in the substantia nigra (SN), which slowly influences downstream basal ganglia pathways as dopamine transport diminishes. Diffusion magnetic resonance imaging (MRI) has been used to diagnose PD by assessing white matter connectivity in some brain areas. For this study, we applied Lead-DBS to human connectome project data to automatically segment 11 subcortical structures of 49 human connectome project subjects, reducing the reliance on manual segmentation for more consistency. The Lead-connectome pipeline, which utilizes DSI Studio to generate structural connectomes from each 3T and 7T diffusion image, was applied to 3T and 7T data to investigate possible differences in diffusion measures due to different acquisition protocols. Significantly higher fractional anisotropy (FA) values were found in the 3T left SN; significantly higher MD values were found in the 3T left SN and the right amygdala, SN, and subthalamic nucleus (STN); significantly higher AD values were found in the right RN and STN; and significantly higher RD values were found in the left RN and right amygdala. We illustrate a methodology for obtaining diffusion measures of basal ganglia and basal ganglia connectivity using diffusion images, as well as show possible differences in diffusion measures that can arise due to the differences in MRI acquisitions.
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7
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Kähkölä J, Lahtinen M, Keinänen T, Katisko J. Stimulation of the Presupplementary Motor Area Cluster of the Subthalamic Nucleus Predicts More Consistent Clinical Outcomes. Neurosurgery 2022; 92:1058-1065. [PMID: 36700693 DOI: 10.1227/neu.0000000000002292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Accepted: 10/05/2022] [Indexed: 01/27/2023] Open
Abstract
BACKGROUND The development of diffusion tensor imaging and tractography has raised increasing interest in the functional targeting of deep brain stimulation of the subthalamic nucleus (STN) in Parkinson disease. OBJECTIVE To study, using deterministic tractography, the functional subdivisions of the STN and hyperdirect white matter connections located between the STN and the medial frontal cortex, especially the presupplementary motor area (preSMA), SMA, primary motor area (M1), and dorsolateral premotor cortex, and to study retrospectively whether this information correlates with clinical outcome. METHODS Twenty-two patients with Parkinson disease who underwent STN deep brain stimulation were analyzed. Using 3 T MR images, the medial frontal cortex was manually segmented into preSMA, SMA, M1, and dorsolateral premotor cortex, which were then used to determine the functional subdivisions of the lateral border of the STN. The intersectional quantities of the volume of activated tissue (VAT) and the hyperdirect white matter connections were calculated. The results were combined with clinical data including unilateral 12-month postoperative motor outcome and levodopa equivalent daily dose. RESULTS Stimulated clusters of the STN were connected mostly to the cortical SMA and preSMA regions. Patients with primarily preSMA cluster stimulation (presmaVAT% ≥ 50%) had good responses to the treatment with unilateral motor improvement over 40% and levodopa equivalent daily dose reduction over 60%. Larger VAT was not found to correlate with better patient outcomes. CONCLUSION Our study is the first to suggest that stimulating, predominantly, the STN cluster where preSMA hyperdirect pathways are located, could be predictive of more consistent treatment results.
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Affiliation(s)
- Johannes Kähkölä
- Oulu Research Group of Advanced Surgical Technologies and Physics - ORGASTP, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland
| | - Maija Lahtinen
- Oulu Research Group of Advanced Surgical Technologies and Physics - ORGASTP, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland.,Neurocenter, Oulu University Hospital, Oulu, Finland
| | - Tuija Keinänen
- Oulu Research Group of Advanced Surgical Technologies and Physics - ORGASTP, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland.,Neurocenter, Oulu University Hospital, Oulu, Finland
| | - Jani Katisko
- Oulu Research Group of Advanced Surgical Technologies and Physics - ORGASTP, Medical Research Center, Oulu University Hospital, University of Oulu, Oulu, Finland.,Neurocenter, Oulu University Hospital, Oulu, Finland
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8
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Deng JH, Zhang HW, Liu XL, Deng HZ, Lin F. Morphological changes in Parkinson's disease based on magnetic resonance imaging: A mini-review of subcortical structures segmentation and shape analysis. World J Psychiatry 2022; 12:1356-1366. [PMID: 36579355 PMCID: PMC9791612 DOI: 10.5498/wjp.v12.i12.1356] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2022] [Revised: 11/02/2022] [Accepted: 11/22/2022] [Indexed: 12/16/2022] Open
Abstract
Parkinson's disease (PD) is a neurodegenerative disorder caused by the loss of dopaminergic neurons in the substantia nigra, resulting in clinical symptoms, including bradykinesia, resting tremor, rigidity, and postural instability. The pathophysiological changes in PD are inextricably linked to the subcortical structures. Shape analysis is a method for quantifying the volume or surface morphology of structures using magnetic resonance imaging. In this review, we discuss the recent advances in morphological analysis techniques for studying the subcortical structures in PD in vivo. This approach includes available pipelines for volume and shape analysis, focusing on the morphological features of volume and surface area.
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Affiliation(s)
- Jin-Huan Deng
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518035, Guangdong Province, China
| | - Han-Wen Zhang
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518035, Guangdong Province, China
| | - Xiao-Lei Liu
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518035, Guangdong Province, China
| | - Hua-Zhen Deng
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518035, Guangdong Province, China
| | - Fan Lin
- Department of Radiology, The First Affiliated Hospital of Shenzhen University, Health Science Center, Shenzhen Second People’s Hospital, Shenzhen 518035, Guangdong Province, China
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9
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Sammartino F, Marsh R, Yeh FC, Sondergaard A, Changizi BK, Krishna V. Radiological identification of the globus pallidus motor subregion in Parkinson's disease. J Neurosurg 2022; 137:175-183. [PMID: 34740190 DOI: 10.3171/2021.7.jns21858] [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: 03/31/2021] [Accepted: 07/01/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Globus pallidus (GP) lesioning improves motor symptoms of Parkinson's disease (PD) and is occasionally associated with nonmotor side effects. Although these variable clinical effects were shown to be site-specific within the GP, the motor and nonmotor subregions have not been distinguished radiologically in patients with PD. The GP was recently found to have a distinct radiological signature on diffusion MRI (dMRI), potentially related to its unique cellular content and organization (or tissue architecture). In this study, the authors hypothesize that the magnitude of water diffusivity, a surrogate for tissue architecture, will radiologically distinguish motor from nonmotor GP subregions in patients with PD. They also hypothesize that the therapeutic focused ultrasound pallidotomy lesions will preferentially overlap the motor subregion. METHODS Diffusion MRI from healthy subjects (n = 45, test-retest S1200 cohort) and PD patients (n = 33) was parcellated based on the magnitude of water diffusivity in the GP, as measured orientation distribution function (ODF). A clustering algorithm was used to identify GP parcels with distinct ODF magnitude. The individual parcels were used as seeds for tractography to distinguish motor from nonmotor subregions. The locations of focused ultrasound lesions relative to the GP parcels were also analyzed in 11 patients with PD. RESULTS Radiologically, three distinct parcels were identified within the GP in healthy controls and PD patients: posterior, central, and anterior. The posterior and central parcels comprised the motor subregion and the anterior parcel was classified as a nonmotor subregion based on their tractography connections. The focused ultrasound lesions preferentially overlapped with the motor subregion (posterior more than central). The hotspots for motor improvement were localized in the posterior GP parcel. CONCLUSIONS Using a data-driven approach of ODF-based parcellation, the authors radiologically distinguished GP motor subregions in patients with PD. This method can aid stereotactic targeting in patients with PD undergoing surgical treatments, especially focused ultrasound ablation.
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Affiliation(s)
| | | | - Fang-Cheng Yeh
- 2Department of Neurological Surgery, University of Pittsburgh, Pennsylvania
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10
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Rickert CA, Lieleg O. Machine learning approaches for biomolecular, biophysical, and biomaterials research. BIOPHYSICS REVIEWS 2022; 3:021306. [PMID: 38505413 PMCID: PMC10914139 DOI: 10.1063/5.0082179] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/12/2022] [Indexed: 03/21/2024]
Abstract
A fluent conversation with a virtual assistant, person-tailored news feeds, and deep-fake images created within seconds-all those things that have been unthinkable for a long time are now a part of our everyday lives. What these examples have in common is that they are realized by different means of machine learning (ML), a technology that has fundamentally changed many aspects of the modern world. The possibility to process enormous amount of data in multi-hierarchical, digital constructs has paved the way not only for creating intelligent systems but also for obtaining surprising new insight into many scientific problems. However, in the different areas of biosciences, which typically rely heavily on the collection of time-consuming experimental data, applying ML methods is a bit more challenging: Here, difficulties can arise from small datasets and the inherent, broad variability, and complexity associated with studying biological objects and phenomena. In this Review, we give an overview of commonly used ML algorithms (which are often referred to as "machines") and learning strategies as well as their applications in different bio-disciplines such as molecular biology, drug development, biophysics, and biomaterials science. We highlight how selected research questions from those fields were successfully translated into machine readable formats, discuss typical problems that can arise in this context, and provide an overview of how to resolve those encountered difficulties.
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11
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Sanmartino F, Cruz-Gómez ÁJ, Rashid-López R, Lozano-Soto E, López-Sosa F, Zuazo A, Riqué-Dormido J, Espinosa-Rosso R, González-Rosa JJ. Subthalamic Beta Activity in Parkinson's Disease May Be Linked to Dorsal Striatum Gray Matter Volume and Prefrontal Cortical Thickness: A Pilot Study. Front Neurol 2022; 13:799696. [PMID: 35401426 PMCID: PMC8985754 DOI: 10.3389/fneur.2022.799696] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 02/24/2022] [Indexed: 11/30/2022] Open
Abstract
Background Excessive oscillations at beta frequencies (13–35 Hz) in the subthalamic nucleus (STN) represent a pathophysiological hallmark of Parkinson's disease (PD), which correlates well with parkinsonian symptoms and is reduced in response to standard disease treatments. However, the association of disease-specific regional gray matter (GM) atrophy or cortical thickness (CT) with the presence of STN beta oscillatory activity has been poorly investigated but is of relevance given the potential of these variables for extracting information about PD pathophysiology. This exploratory study investigated the involvement of regional GM volume and CT in the basal ganglia-cortical network and its potential association with the presence of STN beta oscillatory activity in PD. Methods We acquired preoperative GM densities on T1-weighted magnetic resonance imaging scans and we carried out regional estimation of GM volume and CT. LFP activities from the STN were recorded post-operatively in 7 cognitively preserved PD patients off dopaminergic medication undergoing deep-brain stimulation surgery. Oscillatory beta power was determined by power spectral density of 4-min resting state STN LFP activity. Spearman partial correlations and regression analysis were used to screen the presence of STN beta power for their relationship with GM volume and CT measurements. Results After controlling for the effects of age, educational level, and disease duration, and after correcting for multiple testing, enhanced STN beta power showed significant and negative correlations between, first, volume of the right putamen and left caudate nucleus, and second, smaller CT in frontal regions involving the left rostral middle frontal gyrus (MFG) and left medial orbitofrontal gyrus. A lower volume in the right putamen and a lower CT in the left MFG demonstrated the strongest associations with increased STN beta power. Conclusions These tentative results seem to suggest that STN LFP beta frequencies may be mainly linked to different but ongoing parallel neurodegenerative processes, on the one hand, to GM volume reduction in dorsal striatum, and on the other hand, to CT reduction of prefrontal-“associative” regions. These findings could further delineate the brain structural interactions underpinning the exaggerated STN beta activity commonly observed in PD patients.
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Affiliation(s)
- Florencia Sanmartino
- Department of Psychology, University of Cadiz, Cádiz, Spain.,Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cádiz, Spain
| | - Álvaro J Cruz-Gómez
- Department of Psychology, University of Cadiz, Cádiz, Spain.,Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cádiz, Spain
| | - Raúl Rashid-López
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cádiz, Spain.,Department of Neurology, Puerta del Mar University Hospital, Cádiz, Spain
| | - Elena Lozano-Soto
- Department of Psychology, University of Cadiz, Cádiz, Spain.,Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cádiz, Spain
| | - Fernando López-Sosa
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cádiz, Spain
| | - Amaya Zuazo
- Department of Radiodiagnostic and Medical Imaging, Puerta del Mar University Hospital, Cádiz, Spain
| | - Jesús Riqué-Dormido
- Department of Neurosurgery, Puerta del Mar University Hospital, Cádiz, Spain
| | - Raúl Espinosa-Rosso
- Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cádiz, Spain.,Department of Neurology, Puerta del Mar University Hospital, Cádiz, Spain.,Department of Neurology, Jerez de la Frontera University Hospital, Jerez de la Frontera, Spain
| | - Javier J González-Rosa
- Department of Psychology, University of Cadiz, Cádiz, Spain.,Psychophysiology and Neuroimaging Group, Institute of Biomedical Research Cadiz (INiBICA), Cádiz, Spain
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12
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Milardi D, Antonio Basile G, Faskowitz J, Bertino S, Quartarone A, Anastasi G, Bramanti A, Ciurleo R, Cacciola A. Effects of diffusion signal modeling and segmentation approaches on subthalamic nucleus parcellation. Neuroimage 2022; 250:118959. [PMID: 35122971 DOI: 10.1016/j.neuroimage.2022.118959] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 11/24/2021] [Accepted: 01/31/2022] [Indexed: 12/14/2022] Open
Abstract
The subthalamic nucleus (STN) is commonly used as a surgical target for deep brain stimulation in movement disorders such as Parkinson's Disease. Tractography-derived connectivity-based parcellation (CBP) has been recently proposed as a suitable tool for non-invasive in vivo identification and pre-operative targeting of specific functional territories within the human STN. However, a well-established, accurate and reproducible protocol for STN parcellation is still lacking. The present work aims at testing the effects of different tractography-based approaches for the reconstruction of STN functional territories. We reconstructed functional territories of the STN on the high-quality dataset of 100 unrelated healthy subjects and on the test-retest dataset of the Human Connectome Project (HCP) repository. Connectivity-based parcellation was performed with a hypothesis-driven approach according to cortico-subthalamic connectivity, after dividing cortical areas into three groups: associative, limbic and sensorimotor. Four parcellation pipelines were compared, combining different signal modeling techniques (single-fiber vs multi-fiber) and different parcellation approaches (winner takes all parcellation vs fiber density thresholding). We tested these procedures on STN regions of interest obtained from three different, commonly employed, subcortical atlases. We evaluated the pipelines both in terms of between-subject similarity, assessed on the cohort of 100 unrelated healthy subjects, and of within-subject similarity, using a second cohort of 44 subjects with available test-retest data. We found that each parcellation provides converging results in terms of location of the identified parcels, but with significative variations in size and shape. All pipelines obtained very high within-subject similarity, with tensor-based approaches outperforming multi-fiber pipelines. On the other hand, higher between-subject similarity was found with multi-fiber signal modeling techniques combined with fiber density thresholding. We suggest that a fine-tuning of tractography-based parcellation may lead to higher reproducibility and aid the development of an optimized surgical targeting protocol.
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Affiliation(s)
- Demetrio Milardi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy.
| | - Gianpaolo Antonio Basile
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Joshua Faskowitz
- Program in Neuroscience, Indiana University, Bloomington, IN, USA; Department of Psychological and Brain Sciences, Indiana University, 1101 E. 10th Street, Bloomington, IN, 47405, USA
| | - Salvatore Bertino
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Angelo Quartarone
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Giuseppe Anastasi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Alessia Bramanti
- Department of Medicine, Surgery and Dentistry "Medical School of Salerno"- University of Salerno, Italy
| | | | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy.
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13
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Shao Y, Bao X, Song C, Cui D. Application of Dynamic Enhanced Magnetic Resonance Imaging in the Diagnosis of Hematological Malignancies. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:4623850. [PMID: 34976327 PMCID: PMC8718298 DOI: 10.1155/2021/4623850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/26/2021] [Accepted: 10/09/2021] [Indexed: 11/17/2022]
Abstract
The use of dynamic enhanced magnetic resonance imaging technology can effectively explore the diagnosis and clinical application of hematological malignancies. This paper selected 60 patients with hematological malignancies from 2015 to 2019; all of whom were diagnosed with hematological malignancies, including 40 men and 20 women, aged between 40 and 77 years. The main clinical manifestations of the patient are hematological malignancies, fever, and other symptoms. We used Siemens 3.0T to perform MRI and dynamic enhanced MRI examinations on 30 patients with hematological tumors. The PACS system was used to collect and organize clinical data. All patients were pathologically confirmed and clinically diagnosed with hematological malignancies. Based on the clinical data of the patients, retrospective analysis and summary were conducted and the clinical manifestations of hematological malignancies were discussed. The results showed that the diagnostic accuracy of 30 cases of dynamic enhanced MRI was 100%, while the diagnostic accuracy of ordinary MRI was lower than that of dynamic enhanced MRI, P < 0.05, and the difference was statistically significant. In addition, compared with dynamic enhanced MRI and MRI, P > 0.05, the difference was not statistically significant. Therefore, the application of dynamic enhanced MRI in the diagnosis of hematological malignancies is valuable.
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Affiliation(s)
- Yanping Shao
- Department of Hematology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang 317000, China
| | - Xueping Bao
- Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang 317000, China
| | - Caifang Song
- Department of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang 317000, China
| | - Danping Cui
- Department of Geriatrics, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou, Zhejiang 317000, China
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14
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Rodriguez-Rojas R, Pineda-Pardo JA, Mañez-Miro J, Sanchez-Turel A, Martinez-Fernandez R, Del Alamo M, DeLong M, Obeso JA. Functional Topography of the Human Subthalamic Nucleus: Relevance for Subthalamotomy in Parkinson's Disease. Mov Disord 2021; 37:279-290. [PMID: 34859498 DOI: 10.1002/mds.28862] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 10/26/2021] [Accepted: 11/03/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The subthalamic nucleus (STN) is considered a key structure in motor, behavioral, and emotional control. Although identification of the functional topography of the STN has therapeutic implications in the treatment of the motor features of Parkinson's disease (PD), the details of its functional and somatotopic organization in humans are not well understood. OBJECTIVE The aim of this study was to characterize the functional organization of the STN and its correlation with the motor outcomes induced by subthalamotomy. METHODS We used diffusion-weighted imaging to assess STN connectivity patterns in 23 healthy control subjects and 86 patients with PD, of whom 39 received unilateral subthalamotomy. Analytical tractography was used to reconstruct structural cortico-subthalamic connectivity. A diffusion-weighted imaging/functional magnetic resonance imaging-driven somatotopic parcellation of the STN was defined to delineate the representation of the upper and lower limb in the STN. RESULTS We confirmed a connectional gradient to sensorimotor, supplementary-motor, associative, and limbic cortical regions, spanning from posterior-dorsal-lateral to anterior-ventral-medial portions of the STN, with intermediate overlapping zones. Functional magnetic resonance imaging-driven parcellation demonstrated dual segregation of motor cortico-subthalamic projections in humans. Moreover, the relationship between lesion topography and functional anatomy of the STN explains specific improvement in bradykinesia, rigidity, and tremor induced by subthalamotomy. CONCLUSIONS Our results support an interplay between segregation and integration of cortico-subthalamic projections, suggesting the coexistence of parallel and convergent information processing. Identifying the functional topography of the STN will facilitate better definition of the optimal location for functional neurosurgical approaches, that is, electrode placement and lesion location, and improve specific cardinal features in PD. © 2021 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Rafael Rodriguez-Rojas
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,Universidad CEU-San Pablo University, Madrid, Spain.,Network Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), Carlos III Institute, Madrid, Spain
| | - Jose A Pineda-Pardo
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,Universidad CEU-San Pablo University, Madrid, Spain.,Network Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), Carlos III Institute, Madrid, Spain
| | - Jorge Mañez-Miro
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
| | - Alicia Sanchez-Turel
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
| | - Raul Martinez-Fernandez
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,Universidad CEU-San Pablo University, Madrid, Spain.,Network Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), Carlos III Institute, Madrid, Spain
| | - Marta Del Alamo
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain
| | - Mahlon DeLong
- Department of Neurology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Jose A Obeso
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Hospital Universitario HM Puerta del Sur, HM Hospitales, Madrid, Spain.,Universidad CEU-San Pablo University, Madrid, Spain.,Network Center for Biomedical Research on Neurodegenerative Diseases (CIBERNED), Carlos III Institute, Madrid, Spain
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15
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Vachha B, Huang SY. MRI with ultrahigh field strength and high-performance gradients: challenges and opportunities for clinical neuroimaging at 7 T and beyond. Eur Radiol Exp 2021; 5:35. [PMID: 34435246 PMCID: PMC8387544 DOI: 10.1186/s41747-021-00216-2] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 03/30/2021] [Indexed: 12/12/2022] Open
Abstract
Research in ultrahigh magnetic field strength combined with ultrahigh and ultrafast gradient technology has provided enormous gains in sensitivity, resolution, and contrast for neuroimaging. This article provides an overview of the technical advantages and challenges of performing clinical neuroimaging studies at ultrahigh magnetic field strength combined with ultrahigh and ultrafast gradient technology. Emerging clinical applications of 7-T MRI and state-of-the-art gradient systems equipped with up to 300 mT/m gradient strength are reviewed, and the impact and benefits of such advances to anatomical, structural and functional MRI are discussed in a variety of neurological conditions. Finally, an outlook and future directions for ultrahigh field MRI combined with ultrahigh and ultrafast gradient technology in neuroimaging are examined.
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Affiliation(s)
- Behroze Vachha
- Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, 149 13th Street, Room 2301, Charlestown, MA, 02129, USA.
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16
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Solomon O, Palnitkar T, Patriat R, Braun H, Aman J, Park MC, Vitek J, Sapiro G, Harel N. Deep-learning based fully automatic segmentation of the globus pallidus interna and externa using ultra-high 7 Tesla MRI. Hum Brain Mapp 2021; 42:2862-2879. [PMID: 33738898 PMCID: PMC8127160 DOI: 10.1002/hbm.25409] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2020] [Revised: 02/16/2021] [Accepted: 03/01/2021] [Indexed: 12/21/2022] Open
Abstract
Deep brain stimulation (DBS) surgery has been shown to dramatically improve the quality of life for patients with various motor dysfunctions, such as those afflicted with Parkinson's disease (PD), dystonia, and essential tremor (ET), by relieving motor symptoms associated with such pathologies. The success of DBS procedures is directly related to the proper placement of the electrodes, which requires the ability to accurately detect and identify relevant target structures within the subcortical basal ganglia region. In particular, accurate and reliable segmentation of the globus pallidus (GP) interna is of great interest for DBS surgery for PD and dystonia. In this study, we present a deep-learning based neural network, which we term GP-net, for the automatic segmentation of both the external and internal segments of the globus pallidus. High resolution 7 Tesla images from 101 subjects were used in this study; GP-net is trained on a cohort of 58 subjects, containing patients with movement disorders as well as healthy control subjects. GP-net performs 3D inference in a patient-specific manner, alleviating the need for atlas-based segmentation. GP-net was extensively validated, both quantitatively and qualitatively over 43 test subjects including patients with movement disorders and healthy control and is shown to consistently produce improved segmentation results compared with state-of-the-art atlas-based segmentations. We also demonstrate a postoperative lead location assessment with respect to a segmented globus pallidus obtained by GP-net.
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Affiliation(s)
- Oren Solomon
- Department of Radiology, Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Tara Palnitkar
- Department of Radiology, Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
- Department of NeurologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Re'mi Patriat
- Department of Radiology, Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Henry Braun
- Department of Radiology, Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Joshua Aman
- Department of NeurologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Michael C. Park
- Department of NeurologyUniversity of MinnesotaMinneapolisMinnesotaUSA
- Department of NeurosurgeryUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Jerrold Vitek
- Department of NeurologyUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Guillermo Sapiro
- Department of Electrical and Computer EngineeringDuke UniversityDurhamNorth CarolinaUSA
- Department of Biomedical EngineeringDuke UniversityDurhamNorth CarolinaUSA
- Department of Computer ScienceDuke UniversityDurhamNorth CarolinaUSA
- Department of MathematicsDuke UniversityDurhamNorth CarolinaUSA
| | - Noam Harel
- Department of Radiology, Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
- Department of NeurosurgeryUniversity of MinnesotaMinneapolisMinnesotaUSA
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17
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Preliminary Study on the Application of Ultrahigh Field Magnetic Resonance in Moyamoya Disease. OXIDATIVE MEDICINE AND CELLULAR LONGEVITY 2021; 2021:5653948. [PMID: 33520085 PMCID: PMC7817260 DOI: 10.1155/2021/5653948] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 11/08/2020] [Accepted: 11/30/2020] [Indexed: 12/20/2022]
Abstract
Magnetic resonance imaging (MRI) is widely used for the evaluation of moyamoya disease (MMD). In this paper, we describe the features of time-of-flight magnetic resonance angiography (TOF-MRA) and susceptibility-weighted imaging (SWI) at 7 T in a series of MMD patients. In this prospective pilot study, 7 patients (median age: 45.6 years; range: 30-52 years) with MMD and no contraindications for MRI underwent T2-weighted, SWI, and TOF-MRA sequences using a research 7 T head-only scanner. We show that such sequences at ultrahigh field (UHF) represent new and valuable approaches to unravel and characterize MMD. While SWI reveals more remarkable imaging signs related to an improved magnitude and phase contrast imaging, the collateral network pathways in MMD could be excellently delineated using 7 T TOF-MRA. In particular, using SWI and MRA fusion images in UHF MRI helps to improve the detection of bleeding points in hemorrhagic MMD. Our findings indicate that ultrahigh field MRI is very promising to access the severity of the disease and may facilitate revascularization surgery of MMD patients.
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