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Rahayel S, Postuma R, Baril AA, Misic B, Pelletier A, Soucy JP, Montplaisir J, Dagher A, Gagnon JF. 99mTc-HMPAO SPECT Perfusion Signatures Associated With Clinical Progression in Patients With Isolated REM Sleep Behavior Disorder. Neurology 2024; 102:e208015. [PMID: 38315966 PMCID: PMC10890831 DOI: 10.1212/wnl.0000000000208015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 10/03/2023] [Indexed: 02/07/2024] Open
Abstract
BACKGROUND AND OBJECTIVES Idiopathic/isolated REM sleep behavior disorder (iRBD) is associated with dementia with Lewy bodies and Parkinson disease. Despite evidence of abnormal cerebral perfusion in iRBD, there is currently no pattern that can predict whether an individual will develop dementia with Lewy bodies or Parkinson disease. The objective was to identify a perfusion signature associated with conversion to dementia with Lewy bodies in iRBD. METHODS Patients with iRBD underwent video-polysomnography, neurologic and neuropsychological assessments, and baseline 99mTc-HMPAO SPECT to assess relative cerebral blood flow. Partial least squares correlation was used to identify latent variables that maximized covariance between 27 clinical features and relative gray matter perfusion. Patient-specific scores on the latent variables were used to test the association with conversion to dementia with Lewy bodies compared with that with Parkinson disease. The signature's expression was also assessed in 24 patients with iRBD who underwent a second perfusion scan, 22 healthy controls, and 19 individuals with Parkinson disease. RESULTS Of the 137 participants, 93 underwent SPECT processing, namely 52 patients with iRBD (67.9 years, 73% men), 19 patients with Parkinson disease (67.3 years, 37% men), and 22 controls (67.0 years, 73% men). Of the 47 patients with iRBD followed up longitudinally (4.5 years), 12 (26%) developed a manifest synucleinopathy (4 dementia with Lewy bodies and 8 Parkinson disease). Analysis revealed 2 latent variables between relative blood flow and clinical features: the first was associated with a broad set of features that included motor, cognitive, and perceptual variables, age, and sex; the second was mostly associated with cognitive features and RBD duration. When brought back into the patient's space, the expression of the first variable was associated with conversion to a manifest synucleinopathy, whereas the second was associated with conversion to dementia with Lewy bodies. The expression of the patterns changed over time and was associated with worse motor features. DISCUSSION This study identified a brain perfusion signature associated with cognitive impairment in iRBD and transition to dementia with Lewy bodies. This signature, which can be derived from individual scans, has the potential to be developed into a biomarker that predicts dementia with Lewy bodies in at-risk individuals.
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Affiliation(s)
- Shady Rahayel
- From the Department of Medicine (S.R., A.-A.B.), University of Montreal; Centre for Advanced Research in Sleep Medicine (S.R., R.P., A.-A.B., A.P., J.M., J.-F.G.), CIUSSS-NÎM - Hôpital du Sacré-Cœur de Montréal; Department of Neurology (R.P., A.P.), Montreal General Hospital; The Neuro (Montreal Neurological Institute-Hospital) (B.M., J.-P.S., A.D.), McGill University; Department of Psychiatry (J.M.), University of Montreal; and Department of Psychology (J.-F.G.), Université du Québec à Montréal, Canada
| | - Ronald Postuma
- From the Department of Medicine (S.R., A.-A.B.), University of Montreal; Centre for Advanced Research in Sleep Medicine (S.R., R.P., A.-A.B., A.P., J.M., J.-F.G.), CIUSSS-NÎM - Hôpital du Sacré-Cœur de Montréal; Department of Neurology (R.P., A.P.), Montreal General Hospital; The Neuro (Montreal Neurological Institute-Hospital) (B.M., J.-P.S., A.D.), McGill University; Department of Psychiatry (J.M.), University of Montreal; and Department of Psychology (J.-F.G.), Université du Québec à Montréal, Canada
| | - Andrée-Ann Baril
- From the Department of Medicine (S.R., A.-A.B.), University of Montreal; Centre for Advanced Research in Sleep Medicine (S.R., R.P., A.-A.B., A.P., J.M., J.-F.G.), CIUSSS-NÎM - Hôpital du Sacré-Cœur de Montréal; Department of Neurology (R.P., A.P.), Montreal General Hospital; The Neuro (Montreal Neurological Institute-Hospital) (B.M., J.-P.S., A.D.), McGill University; Department of Psychiatry (J.M.), University of Montreal; and Department of Psychology (J.-F.G.), Université du Québec à Montréal, Canada
| | - Bratislav Misic
- From the Department of Medicine (S.R., A.-A.B.), University of Montreal; Centre for Advanced Research in Sleep Medicine (S.R., R.P., A.-A.B., A.P., J.M., J.-F.G.), CIUSSS-NÎM - Hôpital du Sacré-Cœur de Montréal; Department of Neurology (R.P., A.P.), Montreal General Hospital; The Neuro (Montreal Neurological Institute-Hospital) (B.M., J.-P.S., A.D.), McGill University; Department of Psychiatry (J.M.), University of Montreal; and Department of Psychology (J.-F.G.), Université du Québec à Montréal, Canada
| | - Amélie Pelletier
- From the Department of Medicine (S.R., A.-A.B.), University of Montreal; Centre for Advanced Research in Sleep Medicine (S.R., R.P., A.-A.B., A.P., J.M., J.-F.G.), CIUSSS-NÎM - Hôpital du Sacré-Cœur de Montréal; Department of Neurology (R.P., A.P.), Montreal General Hospital; The Neuro (Montreal Neurological Institute-Hospital) (B.M., J.-P.S., A.D.), McGill University; Department of Psychiatry (J.M.), University of Montreal; and Department of Psychology (J.-F.G.), Université du Québec à Montréal, Canada
| | - Jean-Paul Soucy
- From the Department of Medicine (S.R., A.-A.B.), University of Montreal; Centre for Advanced Research in Sleep Medicine (S.R., R.P., A.-A.B., A.P., J.M., J.-F.G.), CIUSSS-NÎM - Hôpital du Sacré-Cœur de Montréal; Department of Neurology (R.P., A.P.), Montreal General Hospital; The Neuro (Montreal Neurological Institute-Hospital) (B.M., J.-P.S., A.D.), McGill University; Department of Psychiatry (J.M.), University of Montreal; and Department of Psychology (J.-F.G.), Université du Québec à Montréal, Canada
| | - Jacques Montplaisir
- From the Department of Medicine (S.R., A.-A.B.), University of Montreal; Centre for Advanced Research in Sleep Medicine (S.R., R.P., A.-A.B., A.P., J.M., J.-F.G.), CIUSSS-NÎM - Hôpital du Sacré-Cœur de Montréal; Department of Neurology (R.P., A.P.), Montreal General Hospital; The Neuro (Montreal Neurological Institute-Hospital) (B.M., J.-P.S., A.D.), McGill University; Department of Psychiatry (J.M.), University of Montreal; and Department of Psychology (J.-F.G.), Université du Québec à Montréal, Canada
| | - Alain Dagher
- From the Department of Medicine (S.R., A.-A.B.), University of Montreal; Centre for Advanced Research in Sleep Medicine (S.R., R.P., A.-A.B., A.P., J.M., J.-F.G.), CIUSSS-NÎM - Hôpital du Sacré-Cœur de Montréal; Department of Neurology (R.P., A.P.), Montreal General Hospital; The Neuro (Montreal Neurological Institute-Hospital) (B.M., J.-P.S., A.D.), McGill University; Department of Psychiatry (J.M.), University of Montreal; and Department of Psychology (J.-F.G.), Université du Québec à Montréal, Canada
| | - Jean-François Gagnon
- From the Department of Medicine (S.R., A.-A.B.), University of Montreal; Centre for Advanced Research in Sleep Medicine (S.R., R.P., A.-A.B., A.P., J.M., J.-F.G.), CIUSSS-NÎM - Hôpital du Sacré-Cœur de Montréal; Department of Neurology (R.P., A.P.), Montreal General Hospital; The Neuro (Montreal Neurological Institute-Hospital) (B.M., J.-P.S., A.D.), McGill University; Department of Psychiatry (J.M.), University of Montreal; and Department of Psychology (J.-F.G.), Université du Québec à Montréal, Canada
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Gonçalves de Oliveira CE, de Araújo WM, de Jesus Teixeira ABM, Gonçalves GL, Itikawa EN. PCA and logistic regression in 2-[ 18F]FDG PET neuroimaging as an interpretable and diagnostic tool for Alzheimer's disease. Phys Med Biol 2024; 69:025003. [PMID: 37976549 DOI: 10.1088/1361-6560/ad0ddd] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 11/17/2023] [Indexed: 11/19/2023]
Abstract
Objective.to develop an optimization and training pipeline for a classification model based on principal component analysis and logistic regression using neuroimages from PET with 2-[18F]fluoro-2-deoxy-D-glucose (FDG PET) for the diagnosis of Alzheimer's disease (AD).Approach.as training data, 200 FDG PET neuroimages were used, 100 from the group of patients with AD and 100 from the group of cognitively normal subjects (CN), downloaded from the repository of the Alzheimer's Disease Neuroimaging Initiative (ADNI). Regularization methods L1 and L2 were tested and their respective strength varied by the hyperparameter C. Once the best combination of hyperparameters was determined, it was used to train the final classification model, which was then applied to test data, consisting of 192 FDG PET neuroimages, 100 from subjects with no evidence of AD (nAD) and 92 from the AD group, obtained at the Centro de Diagnóstico por Imagem (CDI).Main results.the best combination of hyperparameters was L1 regularization andC≈ 0.316. The final results on test data were accuracy = 88.54%, recall = 90.22%, precision = 86.46% and AUC = 94.75%, indicating that there was a good generalization to neuroimages outside the training set. Adjusting each principal component by its respective weight, an interpretable image was obtained that represents the regions of greater or lesser probability for AD given high voxel intensities. The resulting image matches what is expected by the pathophysiology of AD.Significance.our classification model was trained on publicly available and robust data and tested, with good results, on clinical routine data. Our study shows that it serves as a powerful and interpretable tool capable of assisting in the diagnosis of AD in the possession of FDG PET neuroimages. The relationship between classification model output scores and AD progression can and should be explored in future studies.
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Rau A, Schröter N, Blazhenets G, Maurer C, Urbach H, Meyer PT, Frings L. The metabolic spatial covariance pattern of definite idiopathic normal pressure hydrocephalus: an FDG PET study with principal components analysis. Alzheimers Res Ther 2023; 15:202. [PMID: 37980531 PMCID: PMC10657637 DOI: 10.1186/s13195-023-01339-x] [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: 06/28/2023] [Accepted: 10/24/2023] [Indexed: 11/20/2023]
Abstract
Identification of patients with idiopathic normal pressure hydrocephalus (iNPH) in a collective with suspected neurodegenerative disease is essential. This study aimed to determine the metabolic spatial covariance pattern of iNPH on FDG PET using an established technique based on scaled subprofile model principal components analysis (SSM-PCA).We identified 11 patients with definite iNPH. By applying SSM-PCA to the FDG PET data, they were compared to 48 age-matched healthy controls to determine the whole-brain voxel-wise metabolic spatial covariance pattern of definite iNPH (iNPH-related pattern, iNPHRP). The iNPHRP score was compared between groups of patients with definite iNPH, possible iNPH (N = 34), Alzheimer's (AD, N = 38), and Parkinson's disease (PD, N = 35) applying pairwise Mann-Whitney U tests and correction for multiple comparisons.SSM-PCA of FDG PET revealed an iNPHRP that is characterized by relative negative voxel weights at the vicinity of the lateral ventricles and relative positive weights in the paracentral midline region. The iNPHRP scores of patients with definite iNPH were substantially higher than in patients with AD and PD (both p < 0.05) and non-significantly higher than those of patients with possible iNPH. Subject scores of the iNPHRP discriminated definite iNPH from AD and PD with 96% and 100% accuracy and possible iNPH from AD and PD with 83% and 86% accuracy.We defined a novel metabolic spatial covariance pattern of iNPH that might facilitate the differential diagnosis of iNPH versus other neurodegenerative disorders. The knowledge of iNPH-associated alterations in the cerebral glucose metabolism is of high relevance as iNPH constitutes an important differential diagnosis to dementia and movement disorders.
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Affiliation(s)
- Alexander Rau
- Department of Neuroradiology, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany
- Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Nils Schröter
- Department of Neurology, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ganna Blazhenets
- Department of Nuclear Medicine, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Christoph Maurer
- Center for Geriatrics and Gerontology, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Horst Urbach
- Department of Neuroradiology, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Philipp T Meyer
- Department of Nuclear Medicine, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Lars Frings
- Department of Nuclear Medicine, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany.
- Center for Geriatrics and Gerontology, Medical Center - University of Freiburg and Faculty of Medicine, University of Freiburg, Freiburg, Germany.
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Yoshida Y, Yokoi T, Hara K, Watanabe H, Yamaguchi H, Bagarinao E, Masuda M, Kato T, Ogura A, Ohdake R, Kawabata K, Katsuno M, Kato K, Naganawa S, Okamura N, Yanai K, Sobue G. <Editors' Choice> Pattern of THK 5351 retention in normal aging involves core regions of resting state networks associated with higher cognitive function. NAGOYA JOURNAL OF MEDICAL SCIENCE 2023; 85:758-771. [PMID: 38155624 PMCID: PMC10751491 DOI: 10.18999/nagjms.85.4.758] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 12/22/2022] [Indexed: 12/30/2023]
Abstract
We aimed to elucidate the distribution pattern of the positron emission tomography probe [18F]THK 5351, a marker for astrogliosis and tau accumulation, in healthy aging. We also assessed the relationship between THK5351 retention and resting state networks. We enrolled 62 healthy participants in this study. All participants underwent magnetic resonance imaging/positron emission tomography scanning consisting of T1-weighted images, resting state functional magnetic resonance imaging, Pittsburgh Compound-B and THK positron emission tomography. The preprocessed THK images were entered into a scaled subprofile modeling/principal component analysis to extract THK distribution patterns. Using the most significant THK pattern, we generated regions of interest, and performed seed-based functional connectivity analyses. We also evaluated the functional connectivity overlap ratio to identify regions with high between-network connectivity. The most significant THK distributions were observed in the medial prefrontal cortex and bilateral putamen. The seed regions of interest in the medial prefrontal cortex had a functional connectivity map that significantly overlapped with regions of the dorsal default mode network. The seed regions of interest in the putamen showed strong overlap with the basal ganglia and anterior salience networks. The functional connectivity overlap ratio also showed that three peak regions had the characteristics of connector hubs. We have identified an age-related spatial distribution of THK in the medial prefrontal cortex and basal ganglia in normal aging. Interestingly, the distribution's peaks are located in regions of connector hubs that are strongly connected to large-scale resting state networks associated with higher cognitive function.
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Affiliation(s)
- Yusuke Yoshida
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Takamasa Yokoi
- Department of Neurology, Toyohashi Municipal Hospital, Toyohashi, Japan
| | - Kazuhiro Hara
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Hirohisa Watanabe
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
- Department of Neurology, School of Medicine, Fujita Health University, Toyoake, Japan
| | - Hiroshi Yamaguchi
- Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | | | - Michihito Masuda
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Toshiyasu Kato
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Aya Ogura
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Reiko Ohdake
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
| | - Kazuya Kawabata
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Masahisa Katsuno
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Katsuhiko Kato
- Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Nobuyuki Okamura
- Division of Pharmacology, Faculty of Medicine, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Kazuhiko Yanai
- Department of Pharmacology, Tohoku University School of Medicine, Sendai, Japan
| | - Gen Sobue
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
- Aichi Medical University, Nagakute, Japan
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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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Parkinson's Disease-Related Brain Metabolic Pattern Is Expressed in Schizophrenia Patients during Neuroleptic Drug-Induced Parkinsonism. Diagnostics (Basel) 2022; 13:diagnostics13010074. [PMID: 36611366 PMCID: PMC9818349 DOI: 10.3390/diagnostics13010074] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 12/18/2022] [Accepted: 12/20/2022] [Indexed: 12/29/2022] Open
Abstract
Drug-induced parkinsonism (DIP) is a frequent parkinsonian syndrome that appears as a result of pharmacotherapy for the management of psychosis. It could substantially hamper treatment and therefore its diagnosis has a direct influence on treatment effectiveness. Although of such high importance, there is a lack of systematic research for developing neuroimaging-based criteria for DIP diagnostics for such patients. Therefore, the current study was aimed at applying a metabolic brain imaging approach using the 18F-FDG positron emission tomography and spatial covariance analysis to reveal possible candidates for DIP markers. As a result, we demonstrated, to our knowledge, the first attempt at the application of the Parkinson's Disease-Related Pattern (PDRP) as a metabolic signature of parkinsonism for the assessment of PDRP expression for schizophrenia patients with DIP. As a result, we observed significant differences in PDRP expression between the control group and the groups with PD and DIP patients. Similar differences in PDRP expression were also found when the non-DIP schizophrenia patients were compared with the PD group. Therefore, our findings made it possible to conclude that PDRP is a promising tool for the development of clinically relevant criteria for the estimation of the risk of developing DIP.
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Abstract
Positron emission tomography greatly advanced our understanding on the underlying neural mechanisms of movement disorders. PET with flurodeoxyglucose (FDG) is especially useful as it depicts regional metabolic activity level that can predict patients' symptoms. Multivariate pattern analysis has been used to determine and quantify the co-varying brain networks associated with specific clinical traits of neurodegenerative disease. The result is a biomarker, useful for diagnosis, treatments, and follow up studies. Parkinsonian traits and parkinsonisms are associated with specific spatial pattern of metabolic abnormality useful for differential diagnosis. This approach has also been used for monitoring disease progression and novel treatment responses mostly in Parkinson's disease. In this book chapter, we, illustrate and discuss the significance of the brain networks associated with disease and their modification with neuroplastic changes.
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Wright, N, Patel, R, Chaulk, SJ, Alcolado, G, Podnar, D, Mota, N, Monson, CM, Girard, TA, Ko, JH. Novel Analysis Identifying Functional Connectivity Patterns Associated with Posttraumatic Stress Disorder. CHRONIC STRESS (THOUSAND OAKS, CALIF.) 2022; 6:24705470221092428. [PMID: 35465401 PMCID: PMC9019376 DOI: 10.1177/24705470221092428] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 03/17/2022] [Accepted: 03/21/2022] [Indexed: 06/14/2023]
Abstract
Posttraumatic stress disorder (PTSD) is a prevalent psychiatric disorder that can result from experiencing traumatic events. Accurate diagnosis and optimal treatment strategies can be difficult to achieve, due to the heterogeneous etiology and symptomology of PTSD, and overlap with other psychiatric disorders. Advancing our understanding of PTSD pathophysiology is therefore critical. While functional connectivity alterations have shown promise for elucidating the neurobiological mechanisms of PTSD, previous findings have been inconsistent. Eleven patients with PTSD in our first cohort (PTSD-A) and 11 trauma-exposed controls (TEC) underwent functional magnetic resonance imaging. First, we investigated the intrinsic connectivity within known resting state networks (eg, default mode, salience, and central executive networks) previously implicated in functional abnormalities with PTSD symptoms. Second, the overall topology of network structure was compared between PTSD-A and TEC using graph theory. Finally, we used a novel combination of graph theory analysis and scaled subprofile modeling (SSM) to identify a disease-related, covarying pattern of brain network organization. No significant group differences were found in intrinsic connectivity of known resting state networks and graph theory metrics (clustering coefficients, characteristic path length, smallworldness, global and local efficiencies, and degree centrality). The graph theory/SSM analysis revealed a topographical pattern of altered degree centrality differentiating PTSD-A from TEC. This PTSD-related network pattern expression was additionally investigated in a separate cohort of 33 subjects who were scanned with a different MRI scanner (22 patients with PTSD or PTSD-B, and 11 healthy trauma-naïve controls or TNC). Across all participant groups, pattern expression scores were significantly lower in the TEC group, while PTSD-A, PTSD-B, and TNC subject profiles did not differ from each other. Expression level of the pattern was correlated with symptom severity in the PTSD-B group. This method offers potential in developing objective biomarkers associated with PTSD. Possible interpretations and clinical implications will be discussed.
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Affiliation(s)
- Natalie Wright,
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
| | - Ronak Patel,
- Department of Clinical Health Psychology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Sarah J. Chaulk,
- Department of Clinical Health Psychology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Gillian Alcolado,
- Department of Clinical Health Psychology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - David Podnar,
- Department of Clinical Health Psychology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | - Natalie Mota,
- Department of Clinical Health Psychology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
| | | | - Todd A. Girard,
- Department of Psychology, Ryerson University, Toronto, ON, Canada
| | - Ji Hyun Ko,
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
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Janzen A, Kogan RV, Meles SK, Sittig E, Renken RJ, Geibl FF, Booij J, Stormezand G, Luster M, Mayer G, Leenders KL, Oertel WH. Rapid Eye Movement Sleep Behavior Disorder: Abnormal Cardiac Image and Progressive Abnormal Metabolic Brain Pattern. Mov Disord 2021; 37:624-629. [PMID: 34796976 DOI: 10.1002/mds.28859] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 10/29/2021] [Accepted: 10/29/2021] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND Isolated rapid eye movement sleep behavior disorder (iRBD) is prodromal for α-synucleinopathies. OBJECTIVE The aim of this study was to determine whether pathological cardiac [123 I]meta-iodobenzylguanidine scintigraphy ([123 I]MIBG) is associated with progression of [18 F]fluorodeoxyglucose-positron emission tomography-based Parkinson's disease (PD)-related brain pattern (PDRP) expression in iRBD. METHODS Seventeen subjects with iRBD underwent [18 F]fluorodeoxyglucose-positron emission tomography brain imaging twice ~3.6 years apart. In addition, [123 I]MIBG and [123 I]N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane single-photon emission computed tomography ([123 I]FP-CIT-SPECT) at baseline were performed. Olfactory, cognitive, and motor functions were tested annually. RESULTS Twelve of 17 subjects had pathological [123 I]MIBG. At baseline, 6 of 12 of these expressed the PDRP (suprathreshold PDRP z score). At follow-up, 12 of 17 subjects had suprathreshold PDRP z scores, associated with pathological [123 I]MIBG in 92% and with pathological [123 I]FP-CIT-SPECT in 75%. Subjects with pathological [123 I]MIBG had higher PDRP z score change per year (P = 0.027). Three subjects phenoconverted to PD; all had pathological [123 I]MIBG and [123 I]FP-CIT-SPECT, suprathreshold baseline PDRP z scores, and hyposmia. CONCLUSIONS Pathological [123 I]MIBG was associated with progressive and suprathreshold PDRP z scores at follow-up. Abnormal [123 I]MIBG likely identifies iRBD as prodromal PD earlier than pathological [123 I]FP-CIT-SPECT. © 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)
- Annette Janzen
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | - Rosalie V Kogan
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Sanne K Meles
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Elisabeth Sittig
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | - Remco J Renken
- Cognitive Neuroscience Center, Department of Biomedical Sciences of Cells & Systems, University of Groningen, Groningen, the Netherlands
| | - Fanni F Geibl
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | - Jan Booij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, Academic Medical Center Amsterdam, Amsterdam, the Netherlands
| | - Gilles Stormezand
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Markus Luster
- Department of Nuclear Medicine, Philipps-University Marburg, Marburg, Germany
| | - Geert Mayer
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | - Klaus L Leenders
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.,Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Wolfgang H Oertel
- Department of Neurology, Philipps-University Marburg, Marburg, Germany.,Institute for Neurogenomics, Helmholtz Center for Health and Environment, Munich, Germany
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10
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Beheshti I, Ko JH. Modulating brain networks associated with cognitive deficits in Parkinson's disease. Mol Med 2021; 27:24. [PMID: 33691622 PMCID: PMC7945662 DOI: 10.1186/s10020-021-00284-5] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/02/2021] [Indexed: 12/12/2022] Open
Abstract
Parkinson's disease (PD) is a relatively well characterised neurological disorder that primarily affects motor and cognitive functions. This paper reviews on how transcranial direct current stimulation (tDCS) can be used to modulate brain networks associated with cognitive deficits in PD. We first provide an overview of brain network abnormalities in PD, by introducing the brain network modulation approaches such as pharmacological interventions and brain stimulation techniques. We then present the potential underlying mechanisms of tDCS technique, and specifically highlight how tDCS can be applied to modulate brain network abnormality associated with cognitive dysfunction among PD patients. More importantly, we address the limitations of existing studies and suggest possible future directions, with the aim of helping researchers to further develop the use of tDCS technique in clinical settings.
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Affiliation(s)
- Iman Beheshti
- Department of Human Anatomy and Cell Science, University of Manitoba, 130-745 Bannatyne Ave., Winnipeg, MB R3E 0J9 Canada
- Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB Canada
| | - Ji Hyun Ko
- Department of Human Anatomy and Cell Science, University of Manitoba, 130-745 Bannatyne Ave., Winnipeg, MB R3E 0J9 Canada
- Kleysen Institute for Advanced Medicine, Health Science Centre, Winnipeg, MB Canada
- Graduate Program in Biomedical Engineering, University of Manitoba, Winnipeg, MB Canada
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11
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Spetsieris PG, Eidelberg D. Spectral guided sparse inverse covariance estimation of metabolic networks in Parkinson's disease. Neuroimage 2020; 226:117568. [PMID: 33246128 PMCID: PMC8409106 DOI: 10.1016/j.neuroimage.2020.117568] [Citation(s) in RCA: 17] [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: 07/10/2020] [Revised: 10/23/2020] [Accepted: 11/12/2020] [Indexed: 01/21/2023] Open
Abstract
In neurodegenerative disorders, a clearer understanding of the underlying aberrant networks facilitates the search for effective therapeutic targets and potential cures. [18F]-fluorodeoxyglucose (FDG) positron emission tomography (PET) imaging data of brain metabolism reflects the distribution of glucose consumption known to be directly related to neural activity. In FDG PET resting-state metabolic data, characteristic disease-related patterns have been identified in group analysis of various neurodegenerative conditions using principal component analysis of multivariate spatial covariance. Notably, among several parkinsonian syndromes, the identified Parkinson’s disease-related pattern (PDRP) has been repeatedly validated as an imaging biomarker of PD in independent groups worldwide. Although the primary nodal associations of this network are known, its connectivity is not fully understood. Here, we describe a novel approach to elucidate functional principal component (PC) network connections by performing graph theoretical sparse network derivation directly within the disease relevant PC partition layer of the whole brain data rather than by searching for associations retrospectively in whole brain sparse representations. Using sparse inverse covariance estimation of each overlapping PC partition layer separately, a single coherent network is detected for each layer in contrast to more spatially modular segmentation in whole brain data analysis. Using this approach, the major nodal hubs of the PD disease network are identified and their characteristic functional pathways are clearly distinguished within the basal ganglia, midbrain and parietal areas. Network associations are further clarified using Laplacian spectral analysis of the adjacency matrices. In addition, the innate discriminative capacity of the eigenvector centrality of the graph derived networks in differentiating PD versus healthy external data provides evidence of their validity.
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Affiliation(s)
- Phoebe G Spetsieris
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA.
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12
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Sörensen A, Blazhenets G, Schiller F, Meyer PT, Frings L. Amyloid biomarkers as predictors of conversion from mild cognitive impairment to Alzheimer's dementia: a comparison of methods. ALZHEIMERS RESEARCH & THERAPY 2020; 12:155. [PMID: 33213489 PMCID: PMC7678323 DOI: 10.1186/s13195-020-00721-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 11/05/2020] [Indexed: 11/30/2022]
Abstract
Background Amyloid-β (Aβ) PET is an established predictor of conversion from mild cognitive impairment (MCI) to Alzheimer’s dementia (AD). We compared three PET (including an approach based on voxel-wise Cox regression) and one cerebrospinal fluid (CSF) outcome measures in their predictive power. Methods Datasets were retrieved from the ADNI database. In a training dataset (N = 159), voxel-wise Cox regression and principal component analyses were used to identify conversion-related regions (Cox-VOI and AD conversion-related pattern (ADCRP), respectively). In a test dataset (N = 129), the predictive value of mean normalized 18F-florbetapir uptake (SUVR) in AD-typical brain regions (composite SUVR) or the Cox-VOI and the pattern expression score (PES) of ADCRP and CSF Aβ42/Aβ40 as predictors were compared by Cox models (corrected for age and sex). Results All four Aβ measures were significant predictors (p < 0.001). Prediction accuracies (Harrell’s c) showed step-wise significant increases from Cox-SUVR (c = 0.71; HR = 1.84 per Z-score increase), composite SUVR (c = 0.73; HR = 2.18), CSF Aβ42/Aβ40 (c = 0.75; HR = 3.89) to PES (c = 0.77; HR = 2.71). Conclusion The PES of ADCRP is the most predictive Aβ PET outcome measure, comparable to CSF Aβ42/Aβ40, with a slight but statistically significant advantage.
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Affiliation(s)
- Arnd Sörensen
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany.
| | - Ganna Blazhenets
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Florian Schiller
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Philipp Tobias Meyer
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
| | - Lars Frings
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106, Freiburg, Germany
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13
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Ge J, Wang M, Lin W, Wu P, Guan Y, Zhang H, Huang Z, Yang L, Zuo C, Jiang J, Rominger A, Shi K. Metabolic network as an objective biomarker in monitoring deep brain stimulation for Parkinson's disease: a longitudinal study. EJNMMI Res 2020; 10:131. [PMID: 33119814 PMCID: PMC7596139 DOI: 10.1186/s13550-020-00722-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 10/21/2020] [Indexed: 12/28/2022] Open
Abstract
Background With the advance of subthalamic nucleus (STN) deep brain stimulation (DBS) in the treatment of Parkinson’s disease (PD), it is desired to identify objective criteria for the monitoring of the therapy outcome.
This paper explores the feasibility of metabolic network derived from positron emission tomography (PET) with 18F-fluorodeoxyglucose in monitoring the STN DBS treatment for PD.
Methods Age-matched 33 PD patients, 33 healthy controls (HCs), 9 PD patients with bilateral DBS surgery and 9 controls underwent 18F-FDG PET scans. The DBS patients were followed longitudinally to investigate the alternations of the PD-related metabolic covariance pattern (PDRP) expressions. Results The PDRP expression was abnormally elevated in PD patients compared with HCs (P < 0.001). For DBS patients, a significant decrease in the Unified Parkinson’s Disease Rating Scale (UPDRS, P = 0.001) and PDRP expression (P = 0.004) was observed 3 months after STN DBS treatment, while a rollback was observed in both UPDRS and PDRP expressions (both P < 0.01) 12 months after treatment. The changes in PDRP expression mediated by STN DBS were generally in line with UPDRS improvement. The graphical network analysis shows increased connections at 3 months and a return at 12 months confirmed by small-worldness coefficient. Conclusions The preliminary results demonstrate the potential of metabolic network expression as complimentary objective biomarker for the assessment and monitoring of STN DBS treatment in PD patients. Clinical Trial Registration ChiCTR-DOC-16008645. http://www.chictr.org.cn/showproj.aspx?proj=13865.
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Affiliation(s)
- Jingjie Ge
- PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235, China
| | - Min Wang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China
| | - Wei Lin
- Department of Neurosurgery, 904 Hospital of PLA, Wuxi, China
| | - Ping Wu
- PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235, China
| | - Yihui Guan
- PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235, China
| | - Huiwei Zhang
- PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235, China
| | - Zhemin Huang
- PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235, China
| | - Likun Yang
- Department of Neurosurgery, 904 Hospital of PLA, Wuxi, China
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, 518 East Wuzhong Road, Shanghai, 200235, China. .,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, 99 Shangda Road, Shanghai, 200444, China. .,Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai, China.
| | - Axel Rominger
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Kuangyu Shi
- Department of Nuclear Medicine, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland.,Department of Informatics, Technical University of Munich, Munich, Germany
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14
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Kogan RV, Janzen A, Meles SK, Sittig E, Renken RJ, Gurvits V, Mayer G, Leenders KL, Oertel WH. Four-Year Follow-up of [ 18 F]Fluorodeoxyglucose Positron Emission Tomography-Based Parkinson's Disease-Related Pattern Expression in 20 Patients with Isolated Rapid Eye Movement Sleep Behavior Disorder Shows Prodromal Progression. Mov Disord 2020; 36:230-235. [PMID: 32909650 PMCID: PMC7891341 DOI: 10.1002/mds.28260] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2020] [Revised: 07/17/2020] [Accepted: 08/06/2020] [Indexed: 01/08/2023] Open
Abstract
Background Isolated rapid eye movement sleep behavior disorder is known to be prodromal for alpha‐synucleinopathies, such as Parkinson's disease (PD) and dementia with Lewy bodies. The [18F]fluorodeoxyglucose‐positron emission tomography (PET)–based PD‐related brain pattern can be used to monitor disease progression. Objective We longitudinally investigated PD‐related brain pattern expression changes in 20 subjects with isolated rapid eye movement sleep behavior disorder to investigate whether this may be a suitable technique to study prodromal PD progression in these patients and to identify potential phenoconverters. Methods Subjects underwent two [18F]fluorodeoxyglucose‐PET brain scans ~3.7 years apart, along with baseline and repeated motor, cognitive, and olfactory testing within roughly the same time frame. Results At baseline, 8 of 20 (40%) subjects significantly expressed the PD‐related brain pattern (with z scores above the receiver operating characteristic–determined threshold). At follow‐up, six additional subjects exhibited significant PD‐related brain pattern expression (70% in total). PD‐related brain pattern expression increased in all subjects (P = 0.00008). Four subjects (20%), all with significant baseline PD‐related brain pattern expression, phenoconverted to clinical PD. Conclusions Suprathreshold PD‐related brain pattern expression and greater score rate of change may signify greater shorter‐term risk for phenoconversion. Our results support the use of serial PD‐related brain pattern expression measurements as a prodromal PD progression biomarker in patients with isolated rapid eye movement sleep behavior disorder. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society
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Affiliation(s)
- Rosalie V Kogan
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Annette Janzen
- Department of Neurology, Philipps-Universität Marburg, Marburg, Germany
| | - Sanne K Meles
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Elisabeth Sittig
- Department of Neurology, Philipps-Universität Marburg, Marburg, Germany
| | - Remco J Renken
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, University of Groningen, Groningen, the Netherlands
| | - Vita Gurvits
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Geert Mayer
- Department of Neurology, Philipps-Universität Marburg, Marburg, Germany
| | - Klaus L Leenders
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.,Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Wolfgang H Oertel
- Department of Neurology, Philipps-Universität Marburg, Marburg, Germany.,Institute for Neurogenomics, Helmholtz Center for Health and Environment, Munich, Germany
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15
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Peng S, Spetsieris PG, Eidelberg D, Ma Y. Radiomics and supervised machine learning in the diagnosis of parkinsonism with FDG PET: promises and challenges. ANNALS OF TRANSLATIONAL MEDICINE 2020; 8:808. [PMID: 32793653 PMCID: PMC7396243 DOI: 10.21037/atm.2020.04.33] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Phoebe G Spetsieris
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - David Eidelberg
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
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16
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Liu C, Jiang J, Zhou H, Zhang H, Wang M, Jiang J, Wu P, Ge J, Wang J, Ma Y, Zuo C. Brain Functional and Structural Signatures in Parkinson's Disease. Front Aging Neurosci 2020; 12:125. [PMID: 32528272 PMCID: PMC7264099 DOI: 10.3389/fnagi.2020.00125] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2019] [Accepted: 04/14/2020] [Indexed: 11/13/2022] Open
Abstract
The aim of this study is to explore functional and structural properties of abnormal brain networks associated with Parkinson’s disease (PD). 18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) and T1-weighted magnetic resonance imaging from 20 patients with moderate-stage PD and 20 age-matched healthy controls were acquired to identify disease-related patterns in functional and structural networks. Dual-modal images from another prospective subject of 15 PD patients were used as the validation group. Scaled Subprofile Modeling based on principal component analysis method was applied to determine disease-related patterns in both modalities, and brain connectome analysis based on graph theory was applied to verify these patterns. The results showed that the expressions of the metabolic and structural patterns in PD patients were significantly higher than healthy controls (PD1-HC, p = 0.0039, p = 0.0058; PD2-HC, p < 0.001, p = 0.044). The metabolic pattern was characterized by relative increased metabolic activity in pallidothalamic, pons, putamen, and cerebellum, associated with metabolic decreased in parietal–occipital areas. The structural pattern was characterized by relative decreased gray matter (GM) volume in pons, transverse temporal gyrus, left cuneus, right superior occipital gyrus, and right superior parietal lobule, associated with preservation in GM volume in pallidum and putamen. In addition, both patterns were verified in the connectome analysis. The findings suggest that significant overlaps between metabolic and structural patterns provide new evidence for elucidating the neuropathological mechanisms of PD.
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Affiliation(s)
- Chunhua Liu
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Jiehui Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.,Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication, Shanghai University, Shanghai, China
| | - Hucheng Zhou
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Huiwei Zhang
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Min Wang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Juanjuan Jiang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China
| | - Ping Wu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jingjie Ge
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Jian Wang
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yilong Ma
- Center for Neurosciences, Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, NY, United States
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China.,Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China
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17
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Schindlbeck KA, Vo A, Nguyen N, Tang CC, Niethammer M, Dhawan V, Brandt V, Saunders-Pullman R, Bressman SB, Eidelberg D. LRRK2 and GBA Variants Exert Distinct Influences on Parkinson's Disease-Specific Metabolic Networks. Cereb Cortex 2019; 30:2867-2878. [PMID: 31813991 DOI: 10.1093/cercor/bhz280] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 08/23/2019] [Accepted: 09/17/2019] [Indexed: 12/11/2022] Open
Abstract
The natural history of idiopathic Parkinson's disease (PD) varies considerably across patients. While PD is generally sporadic, there are known genetic influences: the two most common, mutations in the LRRK2 or GBA1 gene, are associated with slower and more aggressive progression, respectively. Here, we applied graph theory to metabolic brain imaging to understand the effects of genotype on the organization of previously established PD-specific networks. We found that closely matched PD patient groups with the LRRK2-G2019S mutation (PD-LRRK2) or GBA1 variants (PD-GBA) expressed the same disease networks as sporadic disease (sPD), but PD-LRRK2 and PD-GBA patients exhibited abnormal increases in network connectivity that were not present in sPD. Using a community detection strategy, we found that the location and modular distribution of these connections differed strikingly across genotypes. In PD-LRRK2, connections were gained within the network core, with the formation of distinct functional pathways linking the cerebellum and putamen. In PD-GBA, by contrast, the majority of functional connections were formed outside the core, involving corticocortical pathways at the network periphery. Strategically localized connections within the core in PD-LRRK2 may maintain PD network activity at lower levels than in PD-GBA, resulting in a less aggressive clinical course.
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Affiliation(s)
- Katharina A Schindlbeck
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Martin Niethammer
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Vicky Brandt
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Rachel Saunders-Pullman
- Department of Neurology, Mount Sinai Beth Israel, Mount Sinai Hospital, New York, NY 10003, USA
| | - Susan B Bressman
- Department of Neurology, Mount Sinai Beth Israel, Mount Sinai Hospital, New York, NY 10003, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
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18
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Ko JH, Spetsieris PG, Eidelberg D. Network Structure and Function in Parkinson's Disease. Cereb Cortex 2019; 28:4121-4135. [PMID: 29088324 DOI: 10.1093/cercor/bhx267] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023] Open
Abstract
Little is known of the structural and functional properties of abnormal brain networks associated with neurological disorders. We used a social network approach to characterize the properties of the Parkinson's disease (PD) metabolic topography in 4 independent patient samples and in an experimental non-human primate model. The PD network exhibited distinct features. Dense, mutually facilitating functional connections linked the putamen, globus pallidus, and thalamus to form a metabolically active core. The periphery was formed by weaker connections linking less active cortical regions. Notably, the network contained a separate module defined by interconnected, metabolically active nodes in the cerebellum, pons, frontal cortex, and limbic regions. Exaggeration of the small-world property was a consistent feature of disease networks in parkinsonian humans and in the non-human primate model; this abnormality was only partly corrected by dopaminergic treatment. The findings point to disease-related alterations in network structure and function as the basis for faulty information processing in this disorder.
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Affiliation(s)
- Ji Hyun Ko
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Phoebe G Spetsieris
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA.,Department of Neurology, Northwell Health, Manhasset, NY, USA
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19
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Blazhenets G, Ma Y, Sörensen A, Schiller F, Rücker G, Eidelberg D, Frings L, Meyer PT. Predictive Value of 18F-Florbetapir and 18F-FDG PET for Conversion from Mild Cognitive Impairment to Alzheimer Dementia. J Nucl Med 2019; 61:597-603. [PMID: 31628215 DOI: 10.2967/jnumed.119.230797] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 08/30/2019] [Indexed: 11/16/2022] Open
Abstract
The present study examined the predictive values of amyloid PET, 18F-FDG PET, and nonimaging predictors (alone and in combination) for development of Alzheimer dementia (AD) in a large population of patients with mild cognitive impairment (MCI). Methods: The study included 319 patients with MCI from the Alzheimer Disease Neuroimaging Initiative database. In a derivation dataset (n = 159), the following Cox proportional-hazards models were constructed, each adjusted for age and sex: amyloid PET using 18F-florbetapir (pattern expression score of an amyloid-β AD conversion-related pattern, constructed by principle-components analysis); 18F-FDG PET (pattern expression score of a previously defined 18F-FDG-based AD conversion-related pattern, constructed by principle-components analysis); nonimaging (functional activities questionnaire, apolipoprotein E, and mini-mental state examination score); 18F-FDG PET + amyloid PET; amyloid PET + nonimaging; 18F-FDG PET + nonimaging; and amyloid PET + 18F-FDG PET + nonimaging. In a second step, the results of Cox regressions were applied to a validation dataset (n = 160) to stratify subjects according to the predicted conversion risk. Results: On the basis of the independent validation dataset, the 18F-FDG PET model yielded a significantly higher predictive value than the amyloid PET model. However, both were inferior to the nonimaging model and were significantly improved by the addition of nonimaging variables. The best prediction accuracy was reached by combining 18F-FDG PET, amyloid PET, and nonimaging variables. The combined model yielded 5-y free-of-conversion rates of 100%, 64%, and 24% for the low-, medium- and high-risk groups, respectively. Conclusion: 18F-FDG PET, amyloid PET, and nonimaging variables represent complementary predictors of conversion from MCI to AD. Especially in combination, they enable an accurate stratification of patients according to their conversion risks, which is of great interest for patient care and clinical trials.
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Affiliation(s)
- Ganna Blazhenets
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York
| | - Arnd Sörensen
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Florian Schiller
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Gerta Rücker
- Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; and
| | - David Eidelberg
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York
| | - Lars Frings
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Center for Geriatrics and Gerontology Freiburg, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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20
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Wang M, Yan Z, Jiang J. Brain metabolic connectome classify mild cognitive impairment into Alzheimer's dementia . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2019; 2019:32-35. [PMID: 31945838 DOI: 10.1109/embc.2019.8857104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Identifying whether patients with mild cognitive impairment (MCI) are converting to Alzheimer's disease (AD) is clinically important, but there are still controversies and doubts. We aimed to develop a novel connectome approach which could accurately and precisely predict whether MCI patients are converted to AD using 18F-fluorodeoxyglucose positron emission tomography (FDG-PET). In our study, FDG-PET images were acquired from 84 patients with MCI who converted to AD within 48 months and 109 patients with MCI without conversion within 48 months from the Alzheimer's Disease Neuroimaging Initiative database. The experimental results showed that the classification performance about whether an MCI patient would convert to AD were 92.1%, 87.1%, 94.4% and 0.95 (Accuracy, Sensitivity, Specificity and AUC). The abnormality of functional connection was located at Middle frontal gyrus, Posterior cingulate gyrus, Precentral gyrus, Precuneus and Temporal lobe. These finding showed the brain connectome as a practical approach for developing predictive neuroimaging biomarker.
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21
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Kogan RV, de Jong BA, Renken RJ, Meles SK, van Snick PJ, Golla S, Rijnsdorp S, Perani D, Leenders KL, Boellaard R. Factors affecting the harmonization of disease-related metabolic brain pattern expression quantification in [ 18F]FDG-PET (PETMETPAT). ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2019; 11:472-482. [PMID: 31294076 PMCID: PMC6595051 DOI: 10.1016/j.dadm.2019.04.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Introduction The implementation of spatial-covariance [18F]fluorodeoxyglucose positron emission tomography–based disease-related metabolic brain patterns as biomarkers has been hampered by intercenter imaging differences. Within the scope of the JPND-PETMETPAT working group, we illustrate the impact of these differences on Parkinson's disease–related pattern (PDRP) expression scores. Methods Five healthy controls, 5 patients with idiopathic rapid eye movement sleep behavior disorder, and 5 patients with Parkinson's disease were scanned on one positron emission tomography/computed tomography system with multiple image reconstructions. In addition, one Hoffman 3D Brain Phantom was scanned on several positron emission tomography/computed tomography systems using various reconstructions. Effects of image contrast on PDRP scores were also examined. Results Human and phantom raw PDRP scores were systematically influenced by scanner and reconstruction effects. PDRP scores correlated inversely to image contrast. A Gaussian spatial filter reduced contrast while decreasing intercenter score differences. Discussion Image contrast should be considered in harmonization efforts. A Gaussian filter may reduce noise and intercenter effects without sacrificing sensitivity. Phantom measurements will be important for correcting PDRP score offsets.
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Affiliation(s)
- Rosalie V. Kogan
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
- Corresponding author. Tel.: +31-50-3613541; Fax: +31-50-3611687.
| | - Bas A. de Jong
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Remco J. Renken
- Neuroimaging Center, Department of Neuroscience, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Sanne K. Meles
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Paul J.H. van Snick
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Sandeep Golla
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, The Netherlands
| | - Sjoerd Rijnsdorp
- Department of Medical Physics, Catharina Hospital, Eindhoven, The Netherlands
| | - Daniela Perani
- San Raffaele University and Division of Neuroscience, San Raffaele Scientific Institute, Milan, Italy
| | - Klaus L. Leenders
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Ronald Boellaard
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Yokoi T, Watanabe H, Yamaguchi H, Bagarinao E, Masuda M, Imai K, Ogura A, Ohdake R, Kawabata K, Hara K, Riku Y, Ishigaki S, Katsuno M, Miyao S, Kato K, Naganawa S, Harada R, Okamura N, Yanai K, Yoshida M, Sobue G. Involvement of the Precuneus/Posterior Cingulate Cortex Is Significant for the Development of Alzheimer's Disease: A PET (THK5351, PiB) and Resting fMRI Study. Front Aging Neurosci 2018; 10:304. [PMID: 30344488 PMCID: PMC6182068 DOI: 10.3389/fnagi.2018.00304] [Citation(s) in RCA: 55] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2018] [Accepted: 09/13/2018] [Indexed: 01/02/2023] Open
Abstract
Background: Imaging studies in Alzheimer’s disease (AD) have yet to answer the underlying questions concerning the relationship among tau retention, neuroinflammation, network disruption and cognitive decline. We compared the spatial retention patterns of 18F-THK5351 and resting state network (RSN) disruption in patients with early AD and healthy controls. Methods: We enrolled 23 11C-Pittsburgh compound B (PiB)-positive patients with early AD and 24 11C-PiB-negative participants as healthy controls. All participants underwent resting state functional MRI and 18F-THK5351 PET scans. We used scaled subprofile modeling/principal component analysis (SSM/PCA) to reduce the complexity of multivariate data and to identify patterns that exhibited the largest statistical effects (variances) in THK5351 concentration in AD and healthy controls. Findings: SSM/PCA identified a significant spatial THK5351 pattern composed by mainly three clusters including precuneus/posterior cingulate cortex (PCC), right and left dorsolateral prefrontal cortex (DLPFC) which accounted for 23.6% of the total subject voxel variance of the data and had 82.6% sensitivity and 79.1% specificity in discriminating AD from healthy controls. There was a significant relationship between the intensity of the 18F-THK5351 covariation pattern and cognitive scores in AD. The spatial patterns of 18F-THK5351 uptake showed significant similarity with intrinsic functional connectivity, especially in the PCC network. Seed-based connectivity analysis from the PCC showed significant decrease in connectivity over widespread brain regions in AD patients. An evaluation of an autopsied AD patient with Braak V showed that 18F-THK5351 retention corresponded to tau deposition, monoamine oxidase-B (MAO-B) and astrogliosis in the precuneus/PCC. Interpretation: We identified an AD-specific spatial pattern of 18F-THK5351 retention in the precuneus/PCC, an important connectivity hub region in the brain. Disruption of the functional connections of this important network hub may play an important role in developing dementia in AD.
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Affiliation(s)
- Takamasa Yokoi
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Hirohisa Watanabe
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Brain and Mind Research Center, Nagoya University, Nagoya, Japan
| | | | | | - Michihito Masuda
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Kazunori Imai
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Aya Ogura
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Reiko Ohdake
- Brain and Mind Research Center, Nagoya University, Nagoya, Japan
| | - Kazuya Kawabata
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Kazuhiro Hara
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Yuichi Riku
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shinsuke Ishigaki
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Masahisa Katsuno
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shinichi Miyao
- Department of Neurology, Meitetsu Hospital, Nagoya, Japan
| | - Katsuhiko Kato
- Department of Radiological and Medical Laboratory Sciences, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Japan
| | - Ryuichi Harada
- Department of Pharmacology, Tohoku University School of Medicine, Sendai, Japan
| | - Nobuyuki Okamura
- Division of Pharmacology, Faculty of Medicine, Tohoku Medical and Pharmaceutical University, Sendai, Japan
| | - Kazuhiko Yanai
- Department of Pharmacology, Tohoku University School of Medicine, Sendai, Japan
| | - Mari Yoshida
- Department of Neuropathology, Institute for Medical Science of Aging, Aichi Medical University, Nagakute, Japan
| | - Gen Sobue
- Department of Neurology, Nagoya University Graduate School of Medicine, Nagoya, Japan.,Brain and Mind Research Center, Nagoya University, Nagoya, Japan
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Katako A, Shelton P, Goertzen AL, Levin D, Bybel B, Aljuaid M, Yoon HJ, Kang DY, Kim SM, Lee CS, Ko JH. Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia. Sci Rep 2018; 8:13236. [PMID: 30185806 PMCID: PMC6125295 DOI: 10.1038/s41598-018-31653-6] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2018] [Accepted: 08/23/2018] [Indexed: 01/17/2023] Open
Abstract
Utilizing the publicly available neuroimaging database enabled by Alzheimer's disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/ ), we have compared the performance of automated classification algorithms that differentiate AD vs. normal subjects using Positron Emission Tomography (PET) with fluorodeoxyglucose (FDG). General linear model, scaled subprofile modeling and support vector machines were examined. Among the tested classification methods, support vector machine with Iterative Single Data Algorithm produced the best performance, i.e., sensitivity (0.84) × specificity (0.95), by 10-fold cross-validation. We have applied the same classification algorithm to four different datasets from ADNI, Health Science Centre (Winnipeg, Canada), Dong-A University Hospital (Busan, S. Korea) and Asan Medical Centre (Seoul, S. Korea). Our data analyses confirmed that the support vector machine with Iterative Single Data Algorithm showed the best performance in prediction of future development of AD from the prodromal stage (mild cognitive impairment), and that it was also sensitive to other types of dementia such as Parkinson's Disease Dementia and Dementia with Lewy Bodies, and that perfusion imaging using single photon emission computed tomography may achieve a similar accuracy to that of FDG-PET.
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Affiliation(s)
- Audrey Katako
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.,Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, Manitoba, Canada
| | - Paul Shelton
- Section of Neurology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Andrew L Goertzen
- Section of Nuclear Medicine, Department of Radiology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Daniel Levin
- Section of Nuclear Medicine, Department of Radiology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Bohdan Bybel
- Section of Nuclear Medicine, Department of Radiology, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Maram Aljuaid
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada.,Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, Manitoba, Canada
| | - Hyun Jin Yoon
- Department of Nuclear Medicine, College of Medicine, Dong-A University, Busan, South Korea
| | - Do Young Kang
- Department of Nuclear Medicine, College of Medicine, Dong-A University, Busan, South Korea
| | - Seok Min Kim
- Institute of Parkinson's Clinical Research, Ulsan University College of Medicine, Seoul, South Korea
| | - Chong Sik Lee
- Institute of Parkinson's Clinical Research, Ulsan University College of Medicine, Seoul, South Korea.,Department of Neurology, Asan Medical Center, Seoul, South Korea
| | - Ji Hyun Ko
- Department of Human Anatomy and Cell Science, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, Manitoba, Canada. .,Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Health Sciences Centre, Winnipeg, Manitoba, Canada.
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Nazem A, Tang CC, Spetsieris P, Dresel C, Gordon ML, Diehl-Schmid J, Grimmer T, Yakushev I, Mattis PJ, Ma Y, Dhawan V, Eidelberg D. A multivariate metabolic imaging marker for behavioral variant frontotemporal dementia. ALZHEIMER'S & DEMENTIA: DIAGNOSIS, ASSESSMENT & DISEASE MONITORING 2018; 10:583-594. [PMID: 30417069 PMCID: PMC6215979 DOI: 10.1016/j.dadm.2018.07.009] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Introduction The heterogeneity of behavioral variant frontotemporal dementia (bvFTD) calls for multivariate imaging biomarkers. Methods We studied a total of 148 dementia patients from the Feinstein Institute (Center-A: 25 bvFTD and 10 Alzheimer's disease), Technical University of Munich (Center-B: 44 bvFTD and 29 FTD language variants), and Alzheimer's Disease Neuroimaging Initiative (40 Alzheimer's disease subjects). To identify the covariance pattern of bvFTD (behavioral variant frontotemporal dementia–related pattern [bFDRP]), we applied principal component analysis to combined 18F-fluorodeoxyglucose–positron emission tomography scans from bvFTD and healthy subjects. The phenotypic specificity and clinical correlates of bFDRP expression were assessed in independent testing sets. Results The bFDRP was identified in Center-A data (24.1% of subject × voxel variance; P < .001), reproduced in Center-B data (P < .001), and independently validated using combined testing data (receiver operating characteristics–area under the curve = 0.97; P < .0001). The expression of bFDRP was specifically elevated in bvFTD patients (P < .001) and was significantly higher at more advanced disease stages (P = .035:duration; P < .01:severity). Discussion The bFDRP can be used as a quantitative imaging marker to gauge the underlying disease process and aid in the differential diagnosis of bvFTD.
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Affiliation(s)
- Amir Nazem
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY, USA.,Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Phoebe Spetsieris
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Christian Dresel
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Marc L Gordon
- Department of Neurology, Northwell Health, Manhasset, NY, USA.,Litwin-Zucker Research Center for the Study of Alzheimer's Disease, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Janine Diehl-Schmid
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Timo Grimmer
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Igor Yakushev
- Department of Nuclear Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Paul J Mattis
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Yilong Ma
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
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Fujita K, Sako W, Vo A, Bressman SB, Eidelberg D. Disruption of network for visual perception of natural motion in primary dystonia. Hum Brain Mapp 2017; 39:1163-1174. [PMID: 29214728 DOI: 10.1002/hbm.23907] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2017] [Revised: 10/30/2017] [Accepted: 11/29/2017] [Indexed: 11/07/2022] Open
Abstract
In healthy subjects, brain activation in motor regions is greater during the visual perception of "natural" target motion, which complies with the two-thirds power law, than of "unnatural" motion, which does not. It is unknown whether motion perception is normally mediated by a specific network that can be altered in the setting of disease. We used block-design functional magnetic resonance imaging and covariance analysis to identify normal network topographies activated in response to "natural" versus "unnatural" motion. A visual motion perception-related pattern (VPRP) was identified in 12 healthy subjects, characterized by covarying activation responses in the inferior parietal lobule, frontal operculum, lateral occipitotemporal cortex, amygdala, and cerebellum (Crus I). Selective VPRP activation during "natural" motion was confirmed in 12 testing scans from healthy subjects. Consistent network activation was not seen, however, in 29 patients with dystonia, a neurodevelopmental disorder in which motion perception pathways may be involved. Using diffusion tractography, we evaluated the integrity of anatomical connections between the major VPRP nodes. Indeed, fiber counts in these pathways were substantially reduced in the dystonia subjects. In aggregate, the findings associate normal motion perception with a discrete brain network which can be disrupted under pathological conditions.
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Affiliation(s)
- Koji Fujita
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, 11030
| | - Wataru Sako
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, 11030
| | - An Vo
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, 11030
| | - Susan B Bressman
- Mirken Department of Neurology, Mount Sinai Beth Israel, New York, NY, 10003
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, 11030
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Distinct brain metabolic patterns separately associated with cognition, motor function, and aging in Parkinson's disease dementia. Neurobiol Aging 2017; 60:81-91. [DOI: 10.1016/j.neurobiolaging.2017.08.020] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2017] [Revised: 08/16/2017] [Accepted: 08/19/2017] [Indexed: 11/20/2022]
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Ko JH, Lee CS, Eidelberg D. Metabolic network expression in parkinsonism: Clinical and dopaminergic correlations. J Cereb Blood Flow Metab 2017; 37:683-693. [PMID: 26980757 PMCID: PMC5381458 DOI: 10.1177/0271678x16637880] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Little is known of the precise relationship between the expression of disease-related metabolic patterns and nigrostriatal dopaminergic dysfunction in parkinsonism. We studied 51 subjects with Parkinson's disease (PD) (18 non-demented, 24 demented, and 9 dementia with Lewy bodies) and 127 with atypical parkinsonian syndromes (47 multiple system atrophy (MSA), 38 progressive supranuclear palsy (PSP), and 42 corticobasal syndrome (CBS)) with 18F-fluorodeoxyglucose PET to quantify the expression of previously validated disease-related patterns for PD, MSA, PSP, and CBS and 18F-fluoropropyl-β-CIT PET to quantify caudate and putamen dopamine transporter (DAT) binding. The patients in each group exhibited significant elevations in the expression of the corresponding disease-related pattern ( p < 0.001), relative to 16 healthy subjects. With the exception of cerebellar MSA (MSA-C), all groups displayed significant reductions in putamen DAT binding relative to healthy subjects ( p < 0.05). Correlations between the dopaminergic and metabolic measures were significant in PD and CBS but not in MSA and PSP. In all patient groups with the exception of MSA-C and CBS, pattern expression values and DAT binding correlated with disease duration and severity measures. The findings suggest that in these parkinsonian disorders, metabolic network expression and DAT binding provide complementary information regarding the underlying disease process.
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Affiliation(s)
- Ji Hyun Ko
- 1 Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Chong Sik Lee
- 2 Department of Neurology, Asan Medical Center, Seoul, Republic of Korea
| | - David Eidelberg
- 1 Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
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Mattis PJ, Niethammer M, Sako W, Tang CC, Nazem A, Gordon ML, Brandt V, Dhawan V, Eidelberg D. Distinct brain networks underlie cognitive dysfunction in Parkinson and Alzheimer diseases. Neurology 2016; 87:1925-1933. [PMID: 27708130 PMCID: PMC5100716 DOI: 10.1212/wnl.0000000000003285] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2016] [Accepted: 07/18/2016] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE To determine whether cognitive impairment in Parkinson disease (PD) and Alzheimer disease (AD) derives from the same network pathology. METHODS We analyzed 18F-fluorodeoxyglucose PET scans from 40 patients with AD and 40 age-matched healthy controls from the Alzheimer's Disease Neuroimaging Initiative and scanned an additional 10 patients with AD and 10 healthy controls at The Feinstein Institute for Medical Research to derive an AD-related metabolic pattern (ADRP) analogous to our previously established PD cognition-related pattern (PDCP) and PD motor-related pattern (PDRP). We computed individual subject expression values for ADRP and PDCP in 89 patients with PD and correlated summary scores for cognitive functioning with network expression. We also evaluated changes in ADRP and PDCP expression in a separate group of 15 patients with PD scanned serially over a 4-year period. RESULTS Analysis revealed a significant AD-related metabolic topography characterized by covarying metabolic reductions in the hippocampus, parahippocampal gyrus, and parietal and temporal association regions. Expression of ADRP, but not PDCP, was elevated in both AD groups and correlated with worse cognitive summary scores. Patients with PD showed slight ADRP expression, due to topographic overlap with the network underlying PD motor-related pattern degeneration, but only their PDCP expression values increased as cognitive function and executive performance declined. Longitudinal data in PD disclosed an analogous dissociation of network expression. CONCLUSIONS Cognitive dysfunction in PD is associated with a specific brain network that is largely spatially and functionally distinct from that seen in relation to AD.
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Affiliation(s)
- Paul J Mattis
- From the Center for Neurosciences (P.J.M., M.N., W.S., C.C.T., A.N., V.B., V.D., D.E.) and Litwin-Zucker Research Center for the Study of Alzheimer's Disease (M.L.G.), The Feinstein Institute for Medical Research, Manhasset; and Department of Neurology (P.J.M., M.N., M.L.G., D.E.), Northwell Health, Manhasset, NY
| | - Martin Niethammer
- From the Center for Neurosciences (P.J.M., M.N., W.S., C.C.T., A.N., V.B., V.D., D.E.) and Litwin-Zucker Research Center for the Study of Alzheimer's Disease (M.L.G.), The Feinstein Institute for Medical Research, Manhasset; and Department of Neurology (P.J.M., M.N., M.L.G., D.E.), Northwell Health, Manhasset, NY
| | - Wataru Sako
- From the Center for Neurosciences (P.J.M., M.N., W.S., C.C.T., A.N., V.B., V.D., D.E.) and Litwin-Zucker Research Center for the Study of Alzheimer's Disease (M.L.G.), The Feinstein Institute for Medical Research, Manhasset; and Department of Neurology (P.J.M., M.N., M.L.G., D.E.), Northwell Health, Manhasset, NY
| | - Chris C Tang
- From the Center for Neurosciences (P.J.M., M.N., W.S., C.C.T., A.N., V.B., V.D., D.E.) and Litwin-Zucker Research Center for the Study of Alzheimer's Disease (M.L.G.), The Feinstein Institute for Medical Research, Manhasset; and Department of Neurology (P.J.M., M.N., M.L.G., D.E.), Northwell Health, Manhasset, NY
| | - Amir Nazem
- From the Center for Neurosciences (P.J.M., M.N., W.S., C.C.T., A.N., V.B., V.D., D.E.) and Litwin-Zucker Research Center for the Study of Alzheimer's Disease (M.L.G.), The Feinstein Institute for Medical Research, Manhasset; and Department of Neurology (P.J.M., M.N., M.L.G., D.E.), Northwell Health, Manhasset, NY
| | - Marc L Gordon
- From the Center for Neurosciences (P.J.M., M.N., W.S., C.C.T., A.N., V.B., V.D., D.E.) and Litwin-Zucker Research Center for the Study of Alzheimer's Disease (M.L.G.), The Feinstein Institute for Medical Research, Manhasset; and Department of Neurology (P.J.M., M.N., M.L.G., D.E.), Northwell Health, Manhasset, NY
| | - Vicky Brandt
- From the Center for Neurosciences (P.J.M., M.N., W.S., C.C.T., A.N., V.B., V.D., D.E.) and Litwin-Zucker Research Center for the Study of Alzheimer's Disease (M.L.G.), The Feinstein Institute for Medical Research, Manhasset; and Department of Neurology (P.J.M., M.N., M.L.G., D.E.), Northwell Health, Manhasset, NY
| | - Vijay Dhawan
- From the Center for Neurosciences (P.J.M., M.N., W.S., C.C.T., A.N., V.B., V.D., D.E.) and Litwin-Zucker Research Center for the Study of Alzheimer's Disease (M.L.G.), The Feinstein Institute for Medical Research, Manhasset; and Department of Neurology (P.J.M., M.N., M.L.G., D.E.), Northwell Health, Manhasset, NY
| | - David Eidelberg
- From the Center for Neurosciences (P.J.M., M.N., W.S., C.C.T., A.N., V.B., V.D., D.E.) and Litwin-Zucker Research Center for the Study of Alzheimer's Disease (M.L.G.), The Feinstein Institute for Medical Research, Manhasset; and Department of Neurology (P.J.M., M.N., M.L.G., D.E.), Northwell Health, Manhasset, NY.
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29
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Jourdain VA, Tang CC, Holtbernd F, Dresel C, Choi YY, Ma Y, Dhawan V, Eidelberg D. Flow-metabolism dissociation in the pathogenesis of levodopa-induced dyskinesia. JCI Insight 2016; 1:e86615. [PMID: 27699242 DOI: 10.1172/jci.insight.86615] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023] Open
Abstract
Levodopa-induced dyskinesia (LID) is the most common, disruptive complication of Parkinson's disease (PD) pharmacotherapy, yet despite decades of research, the changes in regional brain function underlying LID remain largely unknown. We previously found that the cerebral vasomotor and metabolic responses to levodopa are dissociated in PD subjects. Nonetheless, it is unclear whether levodopa-mediated dissociation is exaggerated in LID or distinguishes LID from non-LID subjects. To explore this possibility, we used dual-tracer positron emission tomography to quantify regional cerebral blood flow and metabolic activity in 28 PD subjects (14 LID, 14 non-LID), scanned before and during intravenous levodopa infusion. Levodopa-mediated dissociation was most prominent in the posterior putamen (P < 0.0001) and greater in LID than in non-LID and test-retest subjects. Strikingly, LID subjects also showed increased sensorimotor cortex (SMC) activity in the baseline, unmedicated state. Imaging data from an independent PD sample (106 subjects) linked these differences to loss of mesocortical dopamine terminals in advanced patients. In aggregate, the data suggest that LID results from an overactive vasomotor response to levodopa in the putamen on a background of disease-related increases in SMC activity. LID may thus be amenable to treatment that modulates the function of these 2 regions.
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Vo A, Sako W, Fujita K, Peng S, Mattis PJ, Skidmore FM, Ma Y, Uluğ AM, Eidelberg D. Parkinson's disease-related network topographies characterized with resting state functional MRI. Hum Brain Mapp 2016; 38:617-630. [PMID: 27207613 DOI: 10.1002/hbm.23260] [Citation(s) in RCA: 48] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2015] [Revised: 03/24/2016] [Accepted: 05/03/2016] [Indexed: 11/10/2022] Open
Abstract
Spatial covariance mapping can be used to identify and measure the activity of disease-related functional brain networks. While this approach has been widely used in the analysis of cerebral blood flow and metabolic PET scans, it is not clear whether it can be reliably applied to resting state functional MRI (rs-fMRI) data. In this study, we present a novel method based on independent component analysis (ICA) to characterize specific network topographies associated with Parkinson's disease (PD). Using rs-fMRI data from PD and healthy subjects, we used ICA with bootstrap resampling to identify a PD-related pattern that reliably discriminated the two groups. This topography, termed rs-MRI PD-related pattern (fPDRP), was similar to previously characterized disease-related patterns identified using metabolic PET imaging. Following pattern identification, we validated the fPDRP by computing its expression in rs-fMRI testing data on a prospective case basis. Indeed, significant increases in fPDRP expression were found in separate sets of PD and control subjects. In addition to providing a similar degree of group separation as PET, fPDRP values correlated with motor disability and declined toward normal with levodopa administration. Finally, we used this approach in conjunction with neuropsychological performance measures to identify a separate PD cognition-related pattern in the patients. This pattern, termed rs-fMRI PD cognition-related pattern (fPDCP), was topographically similar to its PET-derived counterpart. Subject scores for the fPDCP correlated with executive function in both training and testing data. These findings suggest that ICA can be used in conjunction with bootstrap resampling to identify and validate stable disease-related network topographies in rs-fMRI. Hum Brain Mapp 38:617-630, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- An Vo
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
| | - Wataru Sako
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
| | - Koji Fujita
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
| | - Shichun Peng
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
| | - Paul J Mattis
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York.,Department of Neurology, Northwell Health, Manhasset, New York
| | - Frank M Skidmore
- Department of Neurology, University of Alabama School of Medicine, Birmingham, Alabama
| | - Yilong Ma
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
| | - Aziz M Uluğ
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York
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31
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Holtbernd F, Tang CC, Feigin A, Dhawan V, Ghilardi MF, Paulsen JS, Guttman M, Eidelberg D. Longitudinal Changes in the Motor Learning-Related Brain Activation Response in Presymptomatic Huntington's Disease. PLoS One 2016; 11:e0154742. [PMID: 27192167 PMCID: PMC4871440 DOI: 10.1371/journal.pone.0154742] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2015] [Accepted: 04/18/2016] [Indexed: 11/19/2022] Open
Abstract
Neurocognitive decline, including deficits in motor learning, occurs in the presymptomatic phase of Huntington's disease (HD) and precedes the onset of motor symptoms. Findings from recent neuroimaging studies have linked these deficits to alterations in fronto-striatal and fronto-parietal brain networks. However, little is known about the temporal dynamics of these networks when subjects approach phenoconversion. Here, 10 subjects with presymptomatic HD were scanned with 15O-labeled water at baseline and again 1.5 years later while performing a motor sequence learning task and a kinematically matched control task. Spatial covariance analysis was utilized to characterize patterns of change in learning-related neural activation occurring over time in these individuals. Pattern expression was compared to corresponding values in 10 age-matched healthy control subjects. Spatial covariance analysis revealed significant longitudinal changes in the expression of a specific learning-related activation pattern characterized by increasing activity in the right orbitofrontal cortex, with concurrent reductions in the right medial prefrontal and posterior cingulate regions, the left insula, left precuneus, and left cerebellum. Changes in the expression of this pattern over time correlated with baseline measurements of disease burden and learning performance. The network changes were accompanied by modest improvement in learning performance that took place concurrently in the gene carriers. The presence of increased network activity in the setting of stable task performance is consistent with a discrete compensatory mechanism. The findings suggest that this effect is most pronounced in the late presymptomatic phase of HD, as subjects approach clinical onset.
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Affiliation(s)
- Florian Holtbernd
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York, United States of America
| | - Chris C. Tang
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York, United States of America
| | - Andrew Feigin
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York, United States of America
- Department of Neurology, Northwell Health, Manhasset, New York, United States of America
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York, United States of America
| | - Maria Felice Ghilardi
- Department of Physiology, Pharmacology, and Neuroscience, City University of New York Medical School, New York, New York, United States of America
| | - Jane S. Paulsen
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, United States of America
| | - Mark Guttman
- Department of Neurology, University of Toronto, Toronto, Ontario, Canada
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York, United States of America
- Department of Neurology, Northwell Health, Manhasset, New York, United States of America
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32
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Meles SK, Tang CC, Teune LK, Dierckx RA, Dhawan V, Mattis PJ, Leenders KL, Eidelberg D. Abnormal metabolic pattern associated with cognitive impairment in Parkinson's disease: a validation study. J Cereb Blood Flow Metab 2015; 35:1478-84. [PMID: 26058693 PMCID: PMC4640325 DOI: 10.1038/jcbfm.2015.112] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 04/23/2015] [Accepted: 04/27/2015] [Indexed: 11/09/2022]
Abstract
Cognitive deficits in Parkinson's disease (PD) have been associated with a specific metabolic covariance pattern. Although the expression of this PD cognition-related pattern (PDCP) correlates with neuropsychological performance, it is not known whether the PDCP topography is reproducible across PD populations. We therefore sought to identify a PDCP topography in a new sample comprised of 19 Dutch PD subjects. Network analysis of metabolic scans from these individuals revealed a significant PDCP that resembled the original network topography. Expression values for the new PDCP correlated (P=0.001) with executive dysfunction on the Frontal Assessment Battery (FAB). Subject scores for the new PDCP correlated (P<0.001) with corresponding values for the original pattern, which also correlated (P<0.005) with FAB scores in this patient group. For further validation, subject scores for the new PDCP were computed in an independent group of 86 American PD patients. In this cohort, subject scores for the new and original PDCP topographies were closely correlated (P<0.001); significant correlations between pattern expression and cognitive performance (P<0.05) were observed for both PDCP topographies. These findings suggest that the PDCP is a replicable imaging marker of PD cognitive dysfunction.
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Affiliation(s)
- Sanne K Meles
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Laura K Teune
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Rudi A Dierckx
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Paul J Mattis
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Klaus L Leenders
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
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Holtbernd F, Ma Y, Peng S, Schwartz F, Timmermann L, Kracht L, Fink GR, Tang CC, Eidelberg D, Eggers C. Dopaminergic correlates of metabolic network activity in Parkinson's disease. Hum Brain Mapp 2015; 36:3575-85. [PMID: 26037537 DOI: 10.1002/hbm.22863] [Citation(s) in RCA: 64] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2014] [Revised: 04/27/2015] [Accepted: 05/18/2015] [Indexed: 11/06/2022] Open
Abstract
Parkinson's disease (PD) is associated with distinct metabolic covariance patterns that relate to the motor and cognitive manifestations of the disorder. It is not known, however, how the expression of these patterns relates to measurements of nigrostriatal dopaminergic activity from the same individuals. To explore these associations, we studied 106 PD subjects who underwent cerebral PET with both (18) F-fluorodeoxyglucose (FDG) and (18) F-fluoro-L-dopa (FDOPA). Expression values for the PD motor- and cognition-related metabolic patterns (PDRP and PDCP, respectively) were computed for each subject; these measures were correlated with FDOPA uptake on a voxel-by-voxel basis. To explore the relationship between dopaminergic function and local metabolic activity, caudate and putamen FDOPA PET signal was correlated voxel-wise with FDG uptake over the entire brain. PDRP expression correlated with FDOPA uptake in caudate and putamen (P < 0.001), while PDCP expression correlated with uptake in the anterior striatum (P < 0.001). While statistically significant, the correlations were only of modest size, accounting for less than 20% of the overall variation in these measures. After controlling for PDCP expression, PDRP correlations were significant only in the posterior putamen. Of note, voxel-wise correlations between caudate/putamen FDOPA uptake and whole-brain FDG uptake were significant almost exclusively in PDRP regions. Overall, the data indicate that PDRP and PDCP expression correlates significantly with PET indices of presynaptic dopaminergic functioning obtained in the same individuals. Even so, the modest size of these correlations suggests that in PD patients, individual differences in network activity cannot be explained solely by nigrostriatal dopamine loss.
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Affiliation(s)
- Florian Holtbernd
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York, 11030
| | - Yilong Ma
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York, 11030
| | - Shichun Peng
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York, 11030
| | - Frank Schwartz
- Department of Neurology, University of Cologne, Cologne, Germany
| | - Lars Timmermann
- Department of Neurology, University of Cologne, Cologne, Germany
| | - Lutz Kracht
- Max-Planck Institute for Neurological Research, Cologne, Germany
| | - Gereon R Fink
- Department of Neurology, University of Cologne, Cologne, Germany.,Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Center, Jülich, Germany
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York, 11030
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York, 11030
| | - Carsten Eggers
- Department of Neurology, University of Cologne, Cologne, Germany.,Max-Planck Institute for Neurological Research, Cologne, Germany
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Abstract
The delineation of resting state networks (RSNs) in the human brain relies on the analysis of temporal fluctuations in functional MRI signal, representing a small fraction of total neuronal activity. Here, we used metabolic PET, which maps nonfluctuating signals related to total activity, to identify and validate reproducible RSN topographies in healthy and disease populations. In healthy subjects, the dominant (first component) metabolic RSN was topographically similar to the default mode network (DMN). In contrast, in Parkinson's disease (PD), this RSN was subordinated to an independent disease-related pattern. Network functionality was assessed by quantifying metabolic RSN expression in cerebral blood flow PET scans acquired at rest and during task performance. Consistent task-related deactivation of the "DMN-like" dominant metabolic RSN was observed in healthy subjects and early PD patients; in contrast, the subordinate RSNs were activated during task performance. Network deactivation was reduced in advanced PD; this abnormality was partially corrected by dopaminergic therapy. Time-course comparisons of DMN loss in longitudinal resting metabolic scans from PD and Alzheimer's disease subjects illustrated that significant reductions appeared later for PD, in parallel with the development of cognitive dysfunction. In contrast, in Alzheimer's disease significant reductions in network expression were already present at diagnosis, progressing over time. Metabolic imaging can directly provide useful information regarding the resting organization of the brain in health and disease.
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35
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Ko JH, Choi YY, Eidelberg D. Graph Theory-Guided Transcranial Magnetic Stimulation in Neurodegenerative Disorders. Bioelectron Med 2014. [DOI: 10.15424/bioelectronmed.2014.00004] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
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36
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Wu P, Yu H, Peng S, Dauvilliers Y, Wang J, Ge J, Zhang H, Eidelberg D, Ma Y, Zuo C. Consistent abnormalities in metabolic network activity in idiopathic rapid eye movement sleep behaviour disorder. ACTA ACUST UNITED AC 2014; 137:3122-8. [PMID: 25338949 DOI: 10.1093/brain/awu290] [Citation(s) in RCA: 117] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Rapid eye movement sleep behaviour disorder has been evaluated using Parkinson's disease-related metabolic network. It is unknown whether this disorder is itself associated with a unique metabolic network. 18F-fluorodeoxyglucose positron emission tomography was performed in 21 patients (age 65.0±5.6 years) with idiopathic rapid eye movement sleep behaviour disorder and 21 age/gender-matched healthy control subjects (age 62.5±7.5 years) to identify a disease-related pattern and examine its evolution in 21 hemi-parkinsonian patients (age 62.6±5.0 years) and 16 moderate parkinsonian patients (age 56.9±12.2 years). We identified a rapid eye movement sleep behaviour disorder-related metabolic network characterized by increased activity in pons, thalamus, medial frontal and sensorimotor areas, hippocampus, supramarginal and inferior temporal gyri, and posterior cerebellum, with decreased activity in occipital and superior temporal regions. Compared to the healthy control subjects, network expressions were elevated (P<0.0001) in the patients with this disorder and in the parkinsonian cohorts but decreased with disease progression. Parkinson's disease-related network activity was also elevated (P<0.0001) in the patients with rapid eye movement sleep behaviour disorder but lower than in the hemi-parkinsonian cohort. Abnormal metabolic networks may provide markers of idiopathic rapid eye movement sleep behaviour disorder to identify those at higher risk to develop neurodegenerative parkinsonism.
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Affiliation(s)
- Ping Wu
- 1 PET Centre, Department of Nuclear Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
| | - Huan Yu
- 2 Department of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
| | - Shichun Peng
- 3 Centre for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, NY 11030, USA
| | - Yves Dauvilliers
- 4 National Reference Network for Narcolepsy, Sleep Unit, Department of Neurology, Hôpital Gui-de-Chauliac, CHU Montpellier, Inserm U1061, University of Montpellier 1, Montpellier, France
| | - Jian Wang
- 2 Department of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
| | - Jingjie Ge
- 1 PET Centre, Department of Nuclear Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
| | - Huiwei Zhang
- 1 PET Centre, Department of Nuclear Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
| | - David Eidelberg
- 3 Centre for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, NY 11030, USA
| | - Yilong Ma
- 3 Centre for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, NY 11030, USA
| | - Chuantao Zuo
- 1 PET Centre, Department of Nuclear Medicine, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, China
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37
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Niethammer M, Tang CC, Feigin A, Allen PJ, Heinen L, Hellwig S, Amtage F, Hanspal E, Vonsattel JP, Poston KL, Meyer PT, Leenders KL, Eidelberg D. A disease-specific metabolic brain network associated with corticobasal degeneration. ACTA ACUST UNITED AC 2014; 137:3036-46. [PMID: 25208922 DOI: 10.1093/brain/awu256] [Citation(s) in RCA: 83] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Corticobasal degeneration is an uncommon parkinsonian variant condition that is diagnosed mainly on clinical examination. To facilitate the differential diagnosis of this disorder, we used metabolic brain imaging to characterize a specific network that can be used to discriminate corticobasal degeneration from other atypical parkinsonian syndromes. Ten non-demented patients (eight females/two males; age 73.9 ± 5.7 years) underwent metabolic brain imaging with (18)F-fluorodeoxyglucose positron emission tomography for atypical parkinsonism. These individuals were diagnosed clinically with probable corticobasal degeneration. This diagnosis was confirmed in the three subjects who additionally underwent post-mortem examination. Ten age-matched healthy subjects (five females/five males; age 71.7 ± 6.7 years) served as controls for the imaging studies. Spatial covariance analysis was applied to scan data from the combined group to identify a significant corticobasal degeneration-related metabolic pattern that discriminated (P < 0.001) the patients from the healthy control group. This pattern was characterized by bilateral, asymmetric metabolic reductions involving frontal and parietal cortex, thalamus, and caudate nucleus. These pattern-related changes were greater in magnitude in the cerebral hemisphere opposite the more clinically affected body side. The presence of this corticobasal degeneration-related metabolic topography was confirmed in two independent testing sets of patient and control scans, with elevated pattern expression (P < 0.001) in both disease groups relative to corresponding normal values. We next determined whether prospectively computed expression values for this pattern accurately discriminated corticobasal degeneration from multiple system atrophy and progressive supranuclear palsy (the two most common atypical parkinsonian syndromes) on a single case basis. Based upon this measure, corticobasal degeneration was successfully distinguished from multiple system atrophy (P < 0.001) but not progressive supranuclear palsy, presumably because of the overlap (∼ 24%) that existed between the corticobasal degeneration- and the progressive supranuclear palsy-related metabolic topographies. Nonetheless, excellent discrimination between these disease entities was achieved by computing hemispheric asymmetry scores for the corticobasal degeneration-related pattern on a prospective single scan basis. Indeed, a logistic algorithm based on the asymmetry scores combined with separately computed expression values for a previously validated progressive supranuclear palsy-related pattern provided excellent specificity (corticobasal degeneration: 92.7%; progressive supranuclear palsy: 94.1%) in classifying 58 testing subjects. In conclusion, corticobasal degeneration is associated with a reproducible disease-related metabolic covariance pattern that may help to distinguish this disorder from other atypical parkinsonian syndromes.
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Affiliation(s)
- Martin Niethammer
- 1 Centre for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY 11030, USA
| | - Chris C Tang
- 1 Centre for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY 11030, USA
| | - Andrew Feigin
- 1 Centre for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY 11030, USA
| | - Patricia J Allen
- 1 Centre for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY 11030, USA
| | - Lisette Heinen
- 2 Department of Neurology, University Medical Centre Groningen, Groningen, The Netherlands
| | - Sabine Hellwig
- 3 Department of Neurology, University Hospital Freiburg, Freiburg, Germany
| | - Florian Amtage
- 3 Department of Neurology, University Hospital Freiburg, Freiburg, Germany
| | - Era Hanspal
- 4 Parkinson's Disease and Movement Disorders Centre, Albany Medical Centre, Albany, NY 12208, USA
| | - Jean Paul Vonsattel
- 5 The New York Brain Bank, Columbia Presbyterian Medical Centre, New York, NY 10032, USA
| | - Kathleen L Poston
- 6 Department of Neurology and Neurological Sciences, Stanford University Medical Centre, Stanford, CA 94305, USA
| | - Philipp T Meyer
- 7 Department of Nuclear Medicine, University Hospital Freiburg, Freiburg, Germany
| | - Klaus L Leenders
- 2 Department of Neurology, University Medical Centre Groningen, Groningen, The Netherlands
| | - David Eidelberg
- 1 Centre for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY 11030, USA
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38
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Ko JH, Feigin A, Mattis PJ, Tang CC, Ma Y, Dhawan V, During MJ, Kaplitt MG, Eidelberg D. Network modulation following sham surgery in Parkinson's disease. J Clin Invest 2014; 124:3656-66. [PMID: 25036712 DOI: 10.1172/jci75073] [Citation(s) in RCA: 60] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2014] [Accepted: 05/08/2014] [Indexed: 02/01/2023] Open
Abstract
Patient responses to placebo and sham effects are a major obstacle to the development of therapies for brain disorders, including Parkinson's disease (PD). Here, we used functional brain imaging and network analysis to study the circuitry underlying placebo effects in PD subjects randomized to sham surgery as part of a double-blind gene therapy trial. Metabolic imaging was performed prior to randomization, then again at 6 and 12 months after sham surgery. In this cohort, the sham response was associated with the expression of a distinct cerebello-limbic circuit. The expression of this network increased consistently in patients blinded to treatment and correlated with independent clinical ratings. Once patients were unblinded, network expression declined toward baseline levels. Analogous network alterations were not seen with open-label levodopa treatment or during disease progression. Furthermore, sham outcomes in blinded patients correlated with baseline network expression, suggesting the potential use of this quantitative measure to identify "sham-susceptible" subjects before randomization. Indeed, Monte Carlo simulations revealed that a priori exclusion of such individuals substantially lowers the number of randomized participants needed to demonstrate treatment efficacy. Individualized subject selection based on a predetermined network criterion may therefore limit the need for sham interventions in future clinical trials.
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39
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Ko JH, Spetsieris P, Ma Y, Dhawan V, Eidelberg D. Quantifying significance of topographical similarities of disease-related brain metabolic patterns. PLoS One 2014; 9:e88119. [PMID: 24498250 PMCID: PMC3909315 DOI: 10.1371/journal.pone.0088119] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Accepted: 01/05/2014] [Indexed: 11/30/2022] Open
Abstract
Multivariate analytical routines have become increasingly popular in the study of cerebral function in health and in disease states. Spatial covariance analysis of functional neuroimaging data has been used to identify and validate characteristic topographies associated with specific brain disorders. Voxel-wise correlations can be used to assess similarities and differences that exist between covariance topographies. While the magnitude of the resulting topographical correlations is critical, statistical significance can be difficult to determine in the setting of large data vectors (comprised of over 100,000 voxel weights) and substantial autocorrelation effects. Here, we propose a novel method to determine the p-value of such correlations using pseudo-random network simulations.
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Affiliation(s)
- Ji Hyun Ko
- Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York, United States of America
- * E-mail:
| | - Phoebe Spetsieris
- Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York, United States of America
| | - Yilong Ma
- Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York, United States of America
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York, United States of America
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, North Shore-Long Island Jewish Health System, Manhasset, New York, United States of America
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Holtbernd F, Gagnon JF, Postuma RB, Ma Y, Tang CC, Feigin A, Dhawan V, Vendette M, Soucy JP, Eidelberg D, Montplaisir J. Abnormal metabolic network activity in REM sleep behavior disorder. Neurology 2014; 82:620-7. [PMID: 24453082 DOI: 10.1212/wnl.0000000000000130] [Citation(s) in RCA: 133] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVE To determine whether the Parkinson disease-related covariance pattern (PDRP) expression is abnormally increased in idiopathic REM sleep behavior disorder (RBD) and whether increased baseline activity is associated with greater individual risk of subsequent phenoconversion. METHODS For this cohort study, we recruited 2 groups of RBD and control subjects. Cohort 1 comprised 10 subjects with RBD (63.5 ± 9.4 years old) and 10 healthy volunteers (62.7 ± 8.6 years old) who underwent resting-state metabolic brain imaging with (18)F-fluorodeoxyglucose PET. Cohort 2 comprised 17 subjects with RBD (68.9 ± 4.8 years old) and 17 healthy volunteers (66.6 ± 6.0 years old) who underwent resting brain perfusion imaging with ethylcysteinate dimer SPECT. The latter group was followed clinically for 4.6 ± 2.5 years by investigators blinded to the imaging results. PDRP expression was measured in both RBD groups and compared with corresponding control values. RESULTS PDRP expression was elevated in both groups of subjects with RBD (cohort 1: p < 0.04; cohort 2: p < 0.005). Of the 17 subjects with long-term follow-up, 8 were diagnosed with Parkinson disease or dementia with Lewy bodies; the others did not phenoconvert. For individual subjects with RBD, final phenoconversion status was predicted using a logistical regression model based on PDRP expression and subject age at the time of imaging (r(2) = 0.64, p < 0.0001). CONCLUSIONS Latent network abnormalities in subjects with idiopathic RBD are associated with a greater likelihood of subsequent phenoconversion to a progressive neurodegenerative syndrome.
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Affiliation(s)
- Florian Holtbernd
- From the Center for Neurosciences (F.H., Y.M., C.C.T., A.F., V.D., D.E.), The Feinstein Institute for Medical Research, Manhasset, NY; Center for Advanced Research in Sleep Medicine (J.-F.G., R.B.P., M.V., J.M.), Hôpital du Sacré-Coeur de Montréal; Department of Psychology (J.-F.G., M.V.), Université du Québec à Montréal; Department of Neurology (R.B.P.), Montreal General Hospital; Montreal Neurological Institute (J.-P.S.), McGill University; and Department of Psychiatry (J.M.), University of Montreal, Montréal, Canada
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