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Tian M, Zuo C, Cahid Civelek A, Carrio I, Watanabe Y, Kang KW, Murakami K, Prior JO, Zhong Y, Dou X, Yu C, Jin C, Zhou R, Liu F, Li X, Lu J, Zhang H, Wang J. International consensus on clinical use of presynaptic dopaminergic positron emission tomography imaging in parkinsonism. Eur J Nucl Med Mol Imaging 2024; 51:434-442. [PMID: 37789188 DOI: 10.1007/s00259-023-06403-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 08/15/2023] [Indexed: 10/05/2023]
Abstract
PURPOSE Presynaptic dopaminergic positron emission tomography (PET) imaging serves as an essential tool in diagnosing and differentiating patients with suspected parkinsonism, including idiopathic Parkinson's disease (PD) and other neurodegenerative and non-neurodegenerative diseases. The PET tracers most commonly used at the present time mainly target dopamine transporters (DAT), aromatic amino acid decarboxylase (AADC), and vesicular monoamine type 2 (VMAT2). However, established standards for the imaging procedure and interpretation of presynaptic dopaminergic PET imaging are still lacking. The goal of this international consensus is to help nuclear medicine practitioners procedurally perform presynaptic dopaminergic PET imaging. METHOD A multidisciplinary task group formed by experts from various countries discussed and approved the consensus for presynaptic dopaminergic PET imaging in parkinsonism, focusing on standardized recommendations, procedures, interpretation, and reporting. CONCLUSION This international consensus and practice guideline will help to promote the standardized use of presynaptic dopaminergic PET imaging in parkinsonism. It will become an international standard for this purpose in clinical practice.
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Affiliation(s)
- Mei Tian
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, 200235, China.
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, China.
- Human Phenome Institute, Fudan University, Shanghai, 201203, China.
| | - Chuantao Zuo
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, 200235, China.
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China.
| | - A Cahid Civelek
- Department of Radiology and Radiological Science, Division of Nuclear Medicine and Molecular Imaging, Johns Hopkins Medicine, Baltimore, MD, 21287, USA.
| | - Ignasi Carrio
- Research Institute and Department of Nuclear Medicine, Hospital Sant Pau, Autonomous University of Barcelona, 08025, Barcelona, Spain
| | - Yasuyoshi Watanabe
- Laboratory for Pathophysiological and Health Science, RIKEN Center for Biosystems Dynamics Research, Kobe, Hyogo, 650-0047, Japan
| | - Keon Wook Kang
- Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, 03080, Korea
| | - Koji Murakami
- Department of Radiology, Juntendo University Hospital, Tokyo, 113-8431, Japan
| | - John O Prior
- Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital, Lausanne, Switzerland
| | - Yan Zhong
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, China
| | - Xiaofeng Dou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, China
| | - Congcong Yu
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, China
| | - Chentao Jin
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, China
| | - Fengtao Liu
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, 200235, China
| | - Xinyi Li
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, 200235, China
| | - Jiaying Lu
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, Shanghai, 200235, China
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China
| | - Hong Zhang
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, 88 Jiefang Road, Hangzhou, 310009, China.
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, 310009, China.
- The College of Biomedical Engineering and Instrument Science of Zhejiang University, Hangzhou, 310007, China.
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, 310007, China.
| | - Jian Wang
- National Center for Neurological Disorders & National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, 200040, China.
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, 200235, China.
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2
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Barbero JA, Unadkat P, Choi YY, Eidelberg D. Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease. Neurotherapeutics 2023; 20:1653-1668. [PMID: 37684533 PMCID: PMC10684458 DOI: 10.1007/s13311-023-01433-w] [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] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
Network analysis of functional brain scans acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET, to map cerebral glucose metabolism), or resting-state functional magnetic resonance imaging (rs-fMRI, to map blood oxygen level-dependent brain activity) has increasingly been used to identify and validate reproducible circuit abnormalities associated with neurodegenerative disorders such as Parkinson's disease (PD). In addition to serving as imaging markers of the underlying disease process, these networks can be used singly or in combination as an adjunct to clinical diagnosis and as a screening tool for therapeutics trials. Disease networks can also be used to measure rates of progression in natural history studies and to assess treatment responses in individual subjects. Recent imaging studies in PD subjects scanned before and after treatment have revealed therapeutic effects beyond the modulation of established disease networks. Rather, other mechanisms of action may be at play, such as the induction of novel functional brain networks directly by treatment. To date, specific treatment-induced networks have been described in association with novel interventions for PD such as subthalamic adeno-associated virus glutamic acid decarboxylase (AAV2-GAD) gene therapy, as well as sham surgery or oral placebo under blinded conditions. Indeed, changes in the expression of these networks with treatment have been found to correlate consistently with clinical outcome. In aggregate, these attributes suggest a role for functional brain networks as biomarkers in future clinical trials.
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Affiliation(s)
- János A Barbero
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
| | - Prashin Unadkat
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY, 11030, USA
| | - Yoon Young Choi
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA.
- Molecular Medicine and Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA.
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3
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Buchert R, Wegner F, Huppertz HJ, Berding G, Brendel M, Apostolova I, Buhmann C, Dierks A, Katzdobler S, Klietz M, Levin J, Mahmoudi N, Rinscheid A, Rogozinski S, Rumpf JJ, Schneider C, Stöcklein S, Spetsieris PG, Eidelberg D, Wattjes MP, Sabri O, Barthel H, Höglinger G. Automatic covariance pattern analysis outperforms visual reading of 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) in variant progressive supranuclear palsy. Mov Disord 2023; 38:1901-1913. [PMID: 37655363 DOI: 10.1002/mds.29581] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 07/19/2023] [Accepted: 07/31/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND To date, studies on positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) in progressive supranuclear palsy (PSP) usually included PSP cohorts overrepresenting patients with Richardson's syndrome (PSP-RS). OBJECTIVES To evaluate FDG-PET in a patient sample representing the broad phenotypic PSP spectrum typically encountered in routine clinical practice. METHODS This retrospective, multicenter study included 41 PSP patients, 21 (51%) with RS and 20 (49%) with non-RS variants of PSP (vPSP), and 46 age-matched healthy controls. Two state-of-the art methods for the interpretation of FDG-PET were compared: visual analysis supported by voxel-based statistical testing (five readers) and automatic covariance pattern analysis using a predefined PSP-related pattern. RESULTS Sensitivity and specificity of the majority visual read for the detection of PSP in the whole cohort were 74% and 72%, respectively. The percentage of false-negative cases was 10% in the PSP-RS subsample and 43% in the vPSP subsample. Automatic covariance pattern analysis provided sensitivity and specificity of 93% and 83% in the whole cohort. The percentage of false-negative cases was 0% in the PSP-RS subsample and 15% in the vPSP subsample. CONCLUSIONS Visual interpretation of FDG-PET supported by voxel-based testing provides good accuracy for the detection of PSP-RS, but only fair sensitivity for vPSP. Automatic covariance pattern analysis outperforms visual interpretation in the detection of PSP-RS, provides clinically useful sensitivity for vPSP, and reduces the rate of false-positive findings. Thus, pattern expression analysis is clinically useful to complement visual reading and voxel-based testing of FDG-PET in suspected PSP. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Florian Wegner
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | | | - Georg Berding
- Department of Nuclear Medicine, Hannover Medical School, Hannover, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital of Munich, LMU, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Ivayla Apostolova
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carsten Buhmann
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexander Dierks
- Department of Nuclear Medicine, University Hospital Augsburg, Augsburg, Germany
| | - Sabrina Katzdobler
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU, Munich, Germany
| | - Martin Klietz
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU, Munich, Germany
| | - Nima Mahmoudi
- Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Andreas Rinscheid
- Medical Physics and Radiation Protection, University Hospital Augsburg, Augsburg, Germany
| | | | | | - Christine Schneider
- Department of Neurology and Clinical Neurophysiology, University Hospital Augsburg, Augsburg, Germany
| | - Sophia Stöcklein
- Department of Radiology, University Hospital of Munich, LMU, Munich, Germany
| | - Phoebe G Spetsieris
- The Feinstein Institutes for Medical Research Manhasset, Manhasset, New York, USA
| | - David Eidelberg
- The Feinstein Institutes for Medical Research Manhasset, Manhasset, New York, USA
| | - Mike P Wattjes
- Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Osama Sabri
- Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Henryk Barthel
- Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Günter Höglinger
- Department of Neurology, Hannover Medical School, Hannover, Germany
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU, Munich, Germany
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4
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Ellis EG, Joutsa J, Morrison-Ham J, Younger EFP, Saward JB, Caeyenberghs K, Corp DT. Large-scale activation likelihood estimation meta-analysis of parkinsonian disorders. Brain Commun 2023; 5:fcad172. [PMID: 37324240 PMCID: PMC10265724 DOI: 10.1093/braincomms/fcad172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/31/2023] [Accepted: 05/29/2023] [Indexed: 06/17/2023] Open
Abstract
Parkinsonism is a feature of several neurodegenerative disorders, including Parkinson's disease, progressive supranuclear palsy, corticobasal syndrome and multiple system atrophy. Neuroimaging studies have yielded insights into parkinsonian disorders; however, due to variability in results, the brain regions consistently implicated in these disorders remain to be characterized. The aim of this meta-analysis was to identify consistent brain abnormalities in individual parkinsonian disorders (Parkinson's disease, progressive supranuclear palsy, corticobasal syndrome and multiple system atrophy) and to investigate any shared abnormalities across disorders. A total of 44 591 studies were systematically screened following searches of two databases. A series of whole-brain activation likelihood estimation meta-analyses were performed on 132 neuroimaging studies (69 Parkinson's disease; 23 progressive supranuclear palsy; 17 corticobasal syndrome; and 23 multiple system atrophy) utilizing anatomical MRI, perfusion or metabolism PET and single-photon emission computed tomography. Meta-analyses were performed in each parkinsonian disorder within each imaging modality, as well as across all included disorders. Results in progressive supranuclear palsy and multiple system atrophy aligned with current imaging markers for diagnosis, encompassing the midbrain, and brainstem and putamen, respectively. PET imaging studies of patients with Parkinson's disease most consistently reported abnormality of the middle temporal gyrus. No significant clusters were identified in corticobasal syndrome. When examining abnormalities shared across all four disorders, the caudate was consistently reported in MRI studies, whilst the thalamus, inferior frontal gyrus and middle temporal gyri were commonly implicated by PET. To our knowledge, this is the largest meta-analysis of neuroimaging studies in parkinsonian disorders and the first to characterize brain regions implicated across parkinsonian disorders.
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Affiliation(s)
- Elizabeth G Ellis
- Correspondence to: Elizabeth G. Ellis Cognitive Neuroscience Unit, School of Psychology Deakin University, 221 Burwood Highway Burwood, VIC 3125, Australia E-mail:
| | - Juho Joutsa
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
- Turku Brain and Mind Center, Clinical Neurosciences, University of Turku, Turku 20520, Finland
- Turku PET Centre, Neurocenter, Turku University Hospital, Turku 20520, Finland
| | - Jordan Morrison-Ham
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC 3220, Australia
| | - Ellen F P Younger
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC 3220, Australia
| | - Jacqueline B Saward
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC 3220, Australia
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, VIC 3220, Australia
| | - Daniel T Corp
- Correspondence may also be addressed to: Daniel T. Corp Cognitive Neuroscience Unit, School of Psychology Deakin University, 221 Burwood Highway Burwood, VIC 3125, Australia E-mail:
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5
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Meng M, Liu F, Ma Y, Qin W, Guo L, Peng S, Gordon ML, Wang Y, Zhang N. The identification and cognitive correlation of perfusion patterns measured with arterial spin labeling MRI in Alzheimer's disease. Alzheimers Res Ther 2023; 15:75. [PMID: 37038198 PMCID: PMC10088108 DOI: 10.1186/s13195-023-01222-9] [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: 11/24/2021] [Accepted: 03/28/2023] [Indexed: 04/12/2023]
Abstract
BACKGROUND Vascular dysfunction, including cerebral hypoperfusion, plays an important role in the pathogenesis and progression of Alzheimer's disease (AD), independent of amyloid and tau pathology. We established an AD-related perfusion pattern (ADRP) measured with arterial spin labeling (ASL) MRI using multivariate spatial covariance analysis. METHODS We obtained multimodal MRI including pseudo-continuous ASL and neurocognitive testing in a total of 55 patients with a diagnosis of mild to moderate AD supported by amyloid PET and 46 normal controls (NCs). An ADRP was established from an identification cohort of 32 patients with AD and 32 NCs using a multivariate analysis method based on scaled subprofile model/principal component analysis, and pattern expression in individual subjects was quantified for both the identification cohort and a validation cohort (23 patients with AD and 14 NCs). Subject expression score of the ADRP was then used to assess diagnostic accuracy and cognitive correlations in AD patients and compared with global and regional cerebral blood flow (CBF) in specific areas identified from voxel-based univariate analysis. RESULTS The ADRP featured negative loading in the bilateral middle and posterior cingulate and precuneus, inferior parietal lobule, and frontal areas, and positive loading in the right cerebellum and bilateral basal areas. Subject expression score of the ADRP was significantly elevated in AD patients compared with NCs (P < 0.001) and showed good diagnostic accuracy for AD with area under receiver-operator curve of 0.87 [95% CI (0.78-0.96)] in the identification cohort and 0.85 in the validation cohort. Moreover, there were negative correlations between subject expression score and global cognitive function and performance in various cognitive domains in patients with AD. The characteristics of the ADRP topography and subject expression scores were supported by analogous findings obtained with regional CBF. CONCLUSIONS We have reported a characteristic perfusion pattern associated with AD using ASL MRI. Subject expression score of this spatial covariance pattern is a promising MRI biomarker for the identification and monitoring of AD.
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Affiliation(s)
- Meng Meng
- Department of Neurology, Tianjin Medical University General Hospital Airport Site, Tianjin, China
| | - Fang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, 154, Anshan Road, Tianjin, 300052, China
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Department of Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra-Northwell, Hofstra University, Hempstead, NY, USA
| | - Wen Qin
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Lining Guo
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Marc L Gordon
- The Litwin-Zucker Research Center, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
- Departments of Neurology and Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra-Northwell, Hofstra University, Hempstead, NY, USA
| | - Yue Wang
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, 154, Anshan Road, Tianjin, 300052, China
| | - Nan Zhang
- Department of Neurology, Tianjin Medical University General Hospital Airport Site, Tianjin, China.
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, 154, Anshan Road, Tianjin, 300052, China.
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Lu J, Wang M, Wu P, Yakushev I, Zhang H, Ziegler S, Jiang J, Förster S, Wang J, Schwaiger M, Rominger A, Huang SC, Liu F, Zuo C, Shi K. Adjustment for the Age- and Gender-Related Metabolic Changes Improves the Differential Diagnosis of Parkinsonism. PHENOMICS (CHAM, SWITZERLAND) 2023; 3:50-63. [PMID: 36939769 PMCID: PMC9883378 DOI: 10.1007/s43657-022-00079-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 09/21/2022] [Accepted: 09/23/2022] [Indexed: 06/18/2023]
Abstract
Age and gender are the important factors for brain metabolic declines in both normal aging and neurodegeneration, and the confounding effects may influence early and differential diagnosis of neurodegenerative diseases based on the [18F]fluorodeoxyglucose positron emission tomography ([18F]FDG PET). We aimed to explore the potential of the adjustment of age- and gender-related confounding factors on [18F]FDG PET images in differentiation of Parkinson's disease (PD), multiple system atrophy (MSA) and progressive supra-nuclear palsy (PSP). Eight hundred and seventy-seven clinically definitely diagnosed Parkinsonian patients from a benchmark Huashan Parkinsonian PET imaging database were included. An age- and gender-adjusted Z (AGAZ) score was established based on the gender-specific longitudinal metabolic changes on healthy subjects. AGAZ scores and standardized uptake value ratio (SUVR) values were quantified at regional-level and support vector machine-based error-correcting output codes method was applied for classification. Additional references of the classifications based on metabolic pattern scores were included. The feature-based AGAZ score showed the best performance in classification (accuracy for PD, MSA, PSP: 93.1%, 96.3%, 94.8%). In both genders, the AGAZ score consistently achieved the best efficiency, and the improvements compared to the conventional SUVR value for PD, MSA, and PSP mainly laid in specificity (Male: 5.7%; Female: 11.1%), sensitivity (Male: 7.2%; Female: 7.3%), and sensitivity (Male: 7.3%; Female: 17.2%). Female patients benefited more from the adjustment on [18F]FDG PET in MSA and PSP groups (absolute net reclassification index, p < 0.001). Collectively, the adjustment of age- and gender-related confounding factors may improve the differential diagnosis of Parkinsonism. Particularly, the diagnosis of female Parkinsonian population has the best improvement from this correction. Supplementary Information The online version contains supplementary material available at 10.1007/s43657-022-00079-6.
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Affiliation(s)
- Jiaying Lu
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235 China
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Min Wang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444 China
- Department of Informatics, Technische Universität München, 80333 Munich, Germany
| | - Ping Wu
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235 China
- National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200040 China
| | - Igor Yakushev
- Department of Nuclear Medicine, Technische Universität München, 80333 Munich, Germany
| | - Huiwei Zhang
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235 China
| | - Sibylle Ziegler
- Department of Nuclear Medicine, University Hospital LMU Munich, 80539 Munich, Germany
| | - Jiehui Jiang
- Institute of Biomedical Engineering, School of Life Sciences, Shanghai University, Shanghai, 200444 China
| | - Stefan Förster
- Department of Nuclear Medicine, Technische Universität München, 80333 Munich, Germany
- Department of Nuclear Medicine, Klinikum Bayreuth, 95445, Bayreuth, Germany
| | - Jian Wang
- National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200040 China
- Department of Neurology, Huashan Hospital, Fudan University, 12 Middle Wulumuqi Road, Shanghai, 200040 China
| | - Markus Schwaiger
- Klinikum r. d. Isar, Technische Universität München, 95445 Munich, Germany
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
| | - Sung-Cheng Huang
- Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, 90095 USA
| | - Fengtao Liu
- National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200040 China
- Department of Neurology, Huashan Hospital, Fudan University, 12 Middle Wulumuqi Road, Shanghai, 200040 China
| | - Chuantao Zuo
- Department of Nuclear Medicine and PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235 China
- National Research Center for Aging and Medicine and National Center for Neurological Disorders, Huashan Hospital, Fudan University, Shanghai, 200040 China
- Human Phenome Institute, Fudan University, Shanghai, 200433 China
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, 3010 Bern, Switzerland
- Department of Informatics, Technische Universität München, 80333 Munich, Germany
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7
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Perovnik M, Rus T, Schindlbeck KA, Eidelberg D. Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat Rev Neurol 2023; 19:73-90. [PMID: 36539533 DOI: 10.1038/s41582-022-00753-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2022] [Indexed: 12/24/2022]
Abstract
Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Tomaž Rus
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | | | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA.
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8
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Rus T, Perovnik M, Vo A, Nguyen N, Tang C, Jamšek J, Šurlan Popović K, Grimmer T, Yakushev I, Diehl‐Schmid J, Eidelberg D, Trošt M. Disease specific and nonspecific metabolic brain networks in behavioral variant of frontotemporal dementia. Hum Brain Mapp 2022; 44:1079-1093. [PMID: 36334269 PMCID: PMC9875921 DOI: 10.1002/hbm.26140] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 10/10/2022] [Accepted: 10/24/2022] [Indexed: 11/08/2022] Open
Abstract
Behavioral variant of frontotemporal dementia (bvFTD) is common among young-onset dementia patients. While bvFTD-specific multivariate metabolic brain pattern (bFDRP) has been identified previously, little is known about its temporal evolution, internal structure, effect of atrophy, and its relationship with nonspecific resting-state networks such as default mode network (DMN). In this multicenter study, we explored FDG-PET brain scans of 111 bvFTD, 26 Alzheimer's disease, 16 Creutzfeldt-Jakob's disease, 24 semantic variant primary progressive aphasia (PPA), 18 nonfluent variant PPA and 77 healthy control subjects (HC) from Slovenia, USA, and Germany. bFDRP was identified in a cohort of 20 bvFTD patients and age-matched HC using scaled subprofile model/principle component analysis and validated in three independent cohorts. It was characterized by hypometabolism in frontal cortex, insula, anterior/middle cingulate, caudate, thalamus, and temporal poles. Its expression in bvFTD patients was significantly higher compared to HC and other dementia syndromes (p < .0004), correlated with cognitive decline (p = .0001), and increased over time in longitudinal cohort (p = .0007). Analysis of internal network organization by graph-theory methods revealed prominent network disruption in bvFTD patients. We have further found a specific atrophy-related pattern grossly corresponding to bFDRP; however, its contribution to the metabolic pattern was minimal. Finally, despite the overlap between bFDRP and FDG-PET-derived DMN, we demonstrated a predominant role of the specific bFDRP. Taken together, we validated the bFDRP network as a diagnostic/prognostic biomarker specific for bvFTD, provided a unique insight into its highly reproducible internal structure, and proved that bFDRP is unaffected by structural atrophy and independent of normal resting state networks loss.
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Affiliation(s)
- Tomaž Rus
- Department of NeurologyUMC LjubljanaLjubljanaSlovenia,Medical FacultyUniversity of LjubljanaLjubljanaSlovenia
| | | | - An Vo
- Center for NeurosciencesFeinstein Institutes for Medical ResearchManhassetNew YorkUSA
| | - Nha Nguyen
- Department of GeneticsAlbert Einstein College of MedicineBronxNew YorkUSA
| | - Chris Tang
- Center for NeurosciencesFeinstein Institutes for Medical ResearchManhassetNew YorkUSA
| | - Jan Jamšek
- Department of Nuclear MedicineUMC LjubljanaLjubljanaSlovenia
| | | | - Timo Grimmer
- Department of Psychiatry and Psychotherapy, Klinikum rechts der IsarTechnical University of Munich, School of MedicineMunichGermany
| | - Igor Yakushev
- Department of Nuclear Medicine, Klinikum rechts der IsarTechnical University of MunichMunichGermany,TUM Neuroimaging Center, Klinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Janine Diehl‐Schmid
- Department of Psychiatry and Psychotherapy, Klinikum rechts der IsarTechnical University of Munich, School of MedicineMunichGermany
| | - David Eidelberg
- Center for NeurosciencesFeinstein Institutes for Medical ResearchManhassetNew YorkUSA
| | - Maja Trošt
- Department of NeurologyUMC LjubljanaLjubljanaSlovenia,Medical FacultyUniversity of LjubljanaLjubljanaSlovenia,Department of Nuclear MedicineUMC LjubljanaLjubljanaSlovenia
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9
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Jellinger K. Evaluation of Multiple System Atrophy Subtypes with FDG-PET. Ann Indian Acad Neurol 2021; 24:468. [PMID: 34728935 PMCID: PMC8513978 DOI: 10.4103/aian.aian_54_21] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Accepted: 01/20/2021] [Indexed: 11/04/2022] Open
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10
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Shen C, Chen L, Ge JJ, Lu JY, Chen QS, He SJ, Li XY, Zhao J, Sun YM, Wu P, Wu JJ, Liu FT, Wang J. Cerebral Metabolism Related to Cognitive Impairments in Multiple System Atrophy. Front Neurol 2021; 12:652059. [PMID: 33868154 PMCID: PMC8047308 DOI: 10.3389/fneur.2021.652059] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Accepted: 03/10/2021] [Indexed: 11/17/2022] Open
Abstract
Objective: We aimed to characterize the cognitive profiles in multiple system atrophy (MSA) and explore the cerebral metabolism related to the cognitive decline in MSA using 18F-fluorodeoxyglucose (18F-FDG) Positron Emission Tomography (PET). Methods: In this study, 105 MSA patients were included for cognitive assessment and 84 of them were enrolled for 18F-FDG PET analysis. The comprehensive neuropsychological tests covered five main domains including execution, attention, memory, language, and visuospatial function. The cognitive statuses were classified to MSA with normal cognition (MSA-NC) and MSA with cognitive impairment (MSA-CI), including dementia (MSA-D), and mild cognitive impairment (MSA-MCI). With 18F-FDG PET imaging, the cerebral metabolism differences among different cognitive statuses were analyzed using statistical parametric mapping and post-hoc analysis. Results: Among 84 MSA patients, 52 patients were found with MSA-CI, including 36 patients as MSA-MCI and 16 patients as MSA-D. In detail, the cognitive impairments were observed in all the five domains, primarily in attention, executive function and memory. In 18F-FDG PET imaging, MSA-D and MSA-MCI patients exhibited hypometabolism in left middle and superior frontal lobe compared with MSA-NC (p < 0.001). The normalized regional cerebral metabolic rate of glucose (rCMRglc) in left middle frontal lobe showed relative accuracy in discriminating MSA-CI and MSA-NC [areas under the curve (AUC) = 0.750; 95%CI = 0.6391–0.8609]. Conclusions: Cognitive impairments were not rare in MSA, and the hypometabolism in frontal lobe may contribute to such impairments.
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Affiliation(s)
- Cong Shen
- Department of Neurology and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Li Chen
- Department of Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
| | - Jing-Jie Ge
- Positron emission tomography (PET) Center at Huashan Hospital, Institute of Functional and Molecular Medical Imaging, Human Phenome Institute, Fudan University, Shanghai, China
| | - Jia-Ying Lu
- Positron emission tomography (PET) Center at Huashan Hospital, Institute of Functional and Molecular Medical Imaging, Human Phenome Institute, Fudan University, Shanghai, China
| | - Qi-Si Chen
- Department of Neurology and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Shu-Jin He
- Department of Neurology and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Xin-Yi Li
- Department of Neurology and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jue Zhao
- Department of Neurology and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Yi-Min Sun
- Department of Neurology and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Ping Wu
- Positron emission tomography (PET) Center at Huashan Hospital, Institute of Functional and Molecular Medical Imaging, Human Phenome Institute, Fudan University, Shanghai, China
| | - Jian-Jun Wu
- Department of Neurology and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Feng-Tao Liu
- Department of Neurology and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Jian Wang
- Department of Neurology and National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China
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11
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Kim R, Lee JY, Kim YK, Kim H, Yoon EJ, Shin JH, Yoo D, Nam H, Jeon B. Longitudinal Changes in Isolated Rapid Eye Movement Sleep Behavior Disorder-Related Metabolic Pattern Expression. Mov Disord 2021; 36:1889-1898. [PMID: 33788284 PMCID: PMC8451853 DOI: 10.1002/mds.28592] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/17/2021] [Accepted: 03/08/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND It remains unclear whether and how the isolated rapid eye movement (REM) sleep behavior disorder (iRBD)-related metabolic pattern (RBDRP) changes with disease progression in iRBD. OBJECTIVE To examine longitudinal changes in RBDRP expression in iRBD patients and to explore trajectories of relative metabolic activities of individual brain regions constituting RBDRP. METHODS In this cohort study, 25 iRBD patients (mean age [±standard deviation], 69.2 ± 5.3 years; 12 [48%] patients were men) and 24 age-matched healthy controls were included. The patients underwent at least two 18 F-fluorodeoxyglucose positron emission tomography scans at baseline and at the 2-year and/or 4-year follow-ups. We measured the RBDRP expression of the patients and controls which was validated by reproduction in a separate iRBD cohort (n = 13). RESULTS At baseline, the RBDRP expression discriminated iRBD patients from healthy controls. However, the RBDRP expression z scores tended to decrease over time in the patients, especially with longer follow-ups, and this tendency was observed even in patients with high-risk of phenoconversion. Furthermore, the degree of RBDRP expression at baseline did not predict the disease conversion. The RBDRP breakdown was mainly provoked by the attenuation of relative hypermetabolism in the frontal cortex including premotor areas and relative hypometabolism in the occipital cortex. The putaminal metabolic activity increased steadily with the disease progression. CONCLUSIONS The RBDRP expression in iRBD patients was altered significantly over time. Some of the brain metabolic changes seem to represent attempted functional compensation against ongoing neurodegeneration. The RBDRP expression measurement at one time point may not be a reliable biomarker for predicting disease conversion. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Ryul Kim
- Department of Neurology, Inha University Hospital, Incheon, South Korea.,Department of Neurology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul National University College of Medicine, Seoul, South Korea
| | - Jee-Young Lee
- Department of Neurology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul National University College of Medicine, Seoul, South Korea
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Heejung Kim
- Department of Nuclear Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea.,Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, South Korea
| | - Eun Jin Yoon
- Department of Nuclear Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Jung Hwan Shin
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
| | - Dallah Yoo
- Department of Neurology, Kyung Hee University Hospital, Seoul, South Korea
| | - Hyunwoo Nam
- Department of Neurology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul National University College of Medicine, Seoul, South Korea
| | - Beomseok Jeon
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
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12
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Peng S, Dhawan V, Eidelberg D, Ma Y. Neuroimaging evaluation of deep brain stimulation in the treatment of representative neurodegenerative and neuropsychiatric disorders. Bioelectron Med 2021; 7:4. [PMID: 33781350 PMCID: PMC8008578 DOI: 10.1186/s42234-021-00065-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 03/02/2021] [Indexed: 01/16/2023] Open
Abstract
Brain stimulation technology has become a viable modality of reversible interventions in the effective treatment of many neurological and psychiatric disorders. It is aimed to restore brain dysfunction by the targeted delivery of specific electronic signal within or outside the brain to modulate neural activity on local and circuit levels. Development of therapeutic approaches with brain stimulation goes in tandem with the use of neuroimaging methodology in every step of the way. Indeed, multimodality neuroimaging tools have played important roles in target identification, neurosurgical planning, placement of stimulators and post-operative confirmation. They have also been indispensable in pre-treatment screen to identify potential responders and in post-treatment to assess the modulation of brain circuitry in relation to clinical outcome measures. Studies in patients to date have elucidated novel neurobiological mechanisms underlying the neuropathogenesis, action of stimulations, brain responses and therapeutic efficacy. In this article, we review some applications of deep brain stimulation for the treatment of several diseases in the field of neurology and psychiatry. We highlight how the synergistic combination of brain stimulation and neuroimaging technology is posed to accelerate the development of symptomatic therapies and bring revolutionary advances in the domain of bioelectronic medicine.
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Affiliation(s)
- Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York, 11030, USA
| | - Vijay Dhawan
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York, 11030, USA
| | - David Eidelberg
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York, 11030, USA
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York, 11030, USA.
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