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Eidelberg D, Tang C, Nakano Y, Vo A, Nguyen N, Schindlbeck K, Poston K, Gagnon JF, Postuma R, Niethammer M, Ma Y, Peng S, Dhawan V. Longitudinal Network Changes and Phenoconversion Risk in Isolated REM Sleep Behavior Disorder. RESEARCH SQUARE 2024:rs.3.rs-4427198. [PMID: 38853923 PMCID: PMC11160876 DOI: 10.21203/rs.3.rs-4427198/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Isolated rapid eye movement sleep behavior disorder (iRBD) is a prodromal syndrome for Parkinson's disease (PD) and related α-synucleinopathies. We conducted a longitudinal imaging study of network changes in iRBD and their relationship to phenoconversion. Expression levels for the PD-related motor and cognitive networks (PDRP and PDCP) were measured at baseline, 2 and 4 years, along with dopamine transporter (DAT) binding. PDRP and PDCP expression increased over time, with higher values in the former network. While abnormal functional connections were identified initially within the PDRP, others bridging the two networks appeared later. A model based on the rates of PDRP progression and putamen dopamine loss predicted phenoconversion within 1.2 years in individuals with iRBD. In aggregate, the data suggest that maladaptive reorganization of brain networks takes place in iRBD years before phenoconversion. Network expression and DAT binding measures can be used together to assess phenoconversion risk in these individuals.
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Affiliation(s)
| | - Chris Tang
- The Feinstein Institutes for Medical Research
| | | | - An Vo
- The Feinstein Institutes for Medical Research
| | | | | | | | | | | | | | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
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Unadkat P, Vo A, Ma Y, Peng S, Nguyen N, Niethammer M, Tang CC, Dhawan V, Ramdhani R, Fenoy A, Caminiti SP, Perani D, Eidelberg D. Deep brain stimulation of the subthalamic nucleus for Parkinson's disease: A network imaging marker of the treatment response. RESEARCH SQUARE 2024:rs.3.rs-4178280. [PMID: 38766007 PMCID: PMC11100869 DOI: 10.21203/rs.3.rs-4178280/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Subthalamic nucleus deep brain stimulation (STN-DBS) alleviates motor symptoms of Parkinson's disease (PD), thereby improving quality of life. However, quantitative brain markers to evaluate DBS responses and select suitable patients for surgery are lacking. Here, we used metabolic brain imaging to identify a reproducible STN-DBS network for which individual expression levels increased with stimulation in proportion to motor benefit. Of note, measurements of network expression from metabolic and BOLD imaging obtained preoperatively predicted motor outcomes determined after DBS surgery. Based on these findings, we computed network expression in 175 PD patients, with time from diagnosis ranging from 0 to 21 years, and used the resulting data to predict the outcome of a potential STN-DBS procedure. While minimal benefit was predicted for patients with early disease, the proportion of potential responders increased after 4 years. Clinically meaningful improvement with stimulation was predicted in 18.9 - 27.3% of patients depending on disease duration.
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Affiliation(s)
| | - An Vo
- The Feinstein Institutes for Medical Research
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | | | | | | | | | - Ritesh Ramdhani
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell
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van der Horn HJ, Vakhtin AA, Julio K, Nitschke S, Shaff N, Dodd AB, Erhardt E, Phillips JP, Pirio Richardson S, Deligtisch A, Stewart M, Suarez Cedeno G, Meles SK, Mayer AR, Ryman SG. Parkinson's disease cerebrovascular reactivity pattern: A feasibility study. J Cereb Blood Flow Metab 2024:271678X241241895. [PMID: 38578669 DOI: 10.1177/0271678x241241895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
A mounting body of research points to cerebrovascular dysfunction as a fundamental element in the pathophysiology of Parkinson's disease (PD). In the current feasibility study, blood-oxygen-level-dependent (BOLD) MRI was used to measure cerebrovascular reactivity (CVR) in response to hypercapnia in 26 PD patients and 16 healthy controls (HC), and aimed to find a multivariate pattern specific to PD. Whole-brain maps of CVR amplitude (i.e., magnitude of response to CO2) and latency (i.e., time to reach maximum amplitude) were computed, which were further analyzed using scaled sub-profile model principal component analysis (SSM-PCA) with leave-one-out cross-validation. A meaningful pattern based on CVR latency was identified, which was named the PD CVR pattern (PD-CVRP). This pattern was characterized by relatively increased latency in basal ganglia, sensorimotor cortex, supplementary motor area, thalamus and visual cortex, as well as decreased latency in the cerebral white matter, relative to HC. There were no significant associations with clinical measures, though sample size may have limited our ability to detect significant associations. In summary, the PD-CVRP highlights the importance of cerebrovascular dysfunction in PD, and may be a potential biomarker for future clinical research and practice.
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Affiliation(s)
- Harm Jan van der Horn
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Andrei A Vakhtin
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Kayla Julio
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Stephanie Nitschke
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Nicholas Shaff
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Andrew B Dodd
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Erik Erhardt
- Department of Mathematics and Statistics, University of New Mexico, Albuquerque, NM, USA
| | - John P Phillips
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Sarah Pirio Richardson
- Nene and Jamie Koch Comprehensive Movement Disorder Center, Department of Neurology, University of New Mexico, Albuquerque, NM, USA
- New Mexico VA Health Care System, Albuquerque, NM, USA
| | - Amanda Deligtisch
- Nene and Jamie Koch Comprehensive Movement Disorder Center, Department of Neurology, University of New Mexico, Albuquerque, NM, USA
| | - Melanie Stewart
- Nene and Jamie Koch Comprehensive Movement Disorder Center, Department of Neurology, University of New Mexico, Albuquerque, NM, USA
| | - Gerson Suarez Cedeno
- Nene and Jamie Koch Comprehensive Movement Disorder Center, Department of Neurology, University of New Mexico, Albuquerque, NM, USA
| | - Sanne K Meles
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Andrew R Mayer
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
| | - Sephira G Ryman
- Department of Translational Neuroscience, The Mind Research Network, Albuquerque, NM, USA
- Nene and Jamie Koch Comprehensive Movement Disorder Center, Department of Neurology, University of New Mexico, Albuquerque, NM, USA
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Lövdal SS, Carli G, Orso B, Biehl M, Arnaldi D, Mattioli P, Janzen A, Sittig E, Morbelli S, Booij J, Oertel WH, Leenders KL, Meles SK. Investigating the aspect of asymmetry in brain-first versus body-first Parkinson's disease. NPJ Parkinsons Dis 2024; 10:74. [PMID: 38555343 PMCID: PMC10981719 DOI: 10.1038/s41531-024-00685-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Accepted: 03/13/2024] [Indexed: 04/02/2024] Open
Abstract
Parkinson's disease (PD) is characterized by a progressive loss of dopaminergic neurons in the substantia nigra. Recent literature has proposed two subgroups of PD. The "body-first subtype" is associated with a prodrome of isolated REM-sleep Behavior Disorder (iRBD) and a relatively symmetric brain degeneration. The "brain-first subtype" is suggested to have a more asymmetric degeneration and a prodromal stage without RBD. This study aims to investigate the proposed difference in symmetry of the degeneration pattern in the presumed body and brain-first PD subtypes. We analyzed 123I-FP-CIT (DAT SPECT) and 18F-FDG PET brain imaging in three groups of patients (iRBD, n = 20, de novo PD with prodromal RBD, n = 22, and de novo PD without RBD, n = 16) and evaluated dopaminergic and glucose metabolic symmetry. The RBD status of all patients was confirmed with video-polysomnography. The PD groups did not differ from each other with regard to the relative or absolute asymmetry of DAT uptake in the putamen (p = 1.0 and p = 0.4, respectively). The patient groups also did not differ from each other with regard to the symmetry of expression of the PD-related metabolic pattern (PDRP) in each hemisphere. The PD groups had no difference in symmetry considering mean FDG uptake in left and right regions of interest and generally had the same degree of symmetry as controls, while the iRBD patients had nine regions with abnormal left-right differences (p < 0.001). Our findings do not support the asymmetry aspect of the "body-first" versus "brain-first" hypothesis.
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Affiliation(s)
- S S Lövdal
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, Netherlands.
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands.
| | - G Carli
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, Netherlands
| | - B Orso
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - M Biehl
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands
- SMQB, Institute of Metabolism and Systems Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK
| | - D Arnaldi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Neurophysiopathology Unit, IRCCS Ospedale Policlinico S. Martino, Genoa, Italy
| | - P Mattioli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- Neurophysiopathology Unit, IRCCS Ospedale Policlinico S. Martino, Genoa, Italy
| | - A Janzen
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | - E Sittig
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | - S Morbelli
- Department of Health Sciences, University of Genoa, Genoa, Italy
- Nuclear Medicine Unit, IRCCS Ospedale Policlinico S. Martino, Genoa, Italy
| | - J Booij
- Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - W H Oertel
- Department of Neurology, Philipps-University Marburg, Marburg, Germany
| | - K L Leenders
- Department of Nuclear Medicine and Molecular Imaging, University Medical Center Groningen, Groningen, Netherlands
| | - S K Meles
- Department of Neurology, University Medical Center Groningen, Groningen, Netherlands
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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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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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Niethammer M, Tang CC, Jamora RDG, Vo A, Nguyen N, Ma Y, Peng S, Waugh JL, Westenberger A, Eidelberg D. A Network Imaging Biomarker of X-Linked Dystonia-Parkinsonism. Ann Neurol 2023; 94:684-695. [PMID: 37376770 DOI: 10.1002/ana.26732] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 06/26/2023] [Accepted: 06/26/2023] [Indexed: 06/29/2023]
Abstract
OBJECTIVE The purpose of this study was to characterize a metabolic brain network associated with X-linked dystonia-parkinsonism (XDP). METHODS Thirty right-handed Filipino men with XDP (age = 44.4 ± 8.5 years) and 30 XDP-causing mutation negative healthy men from the same population (age = 37.4 ± 10.5 years) underwent [18 F]-fluorodeoxyglucose positron emission tomography. Scans were analyzed using spatial covariance mapping to identify a significant XDP-related metabolic pattern (XDPRP). Patients were rated clinically at the time of imaging according to the XDP-Movement Disorder Society of the Philippines (MDSP) scale. RESULTS We identified a significant XDPRP topography from 15 randomly selected subjects with XDP and 15 control subjects. This pattern was characterized by bilateral metabolic reductions in caudate/putamen, frontal operculum, and cingulate cortex, with relative increases in the bilateral somatosensory cortex and cerebellar vermis. Age-corrected expression of XDPRP was significantly elevated (p < 0.0001) in XDP compared to controls in the derivation set and in the remaining 15 patients (testing set). We validated the XDPRP topography by identifying a similar pattern in the original testing set (r = 0.90, p < 0.0001; voxel-wise correlation between both patterns). Significant correlations between XDPRP expression and clinical ratings for parkinsonism-but not dystonia-were observed in both XDP groups. Further network analysis revealed abnormalities of information transfer through the XDPRP space, with loss of normal connectivity and gain of abnormal functional connections linking network nodes with outside brain regions. INTERPRETATION XDP is associated with a characteristic metabolic network associated with abnormal functional connectivity among the basal ganglia, thalamus, motor regions, and cerebellum. Clinical signs may relate to faulty information transfer through the network to outside brain regions. ANN NEUROL 2023;94:684-695.
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Affiliation(s)
- Martin Niethammer
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York
| | - Roland Dominic G Jamora
- Institute for Neurosciences, St. Luke's Medical Center, Quezon City, Philippines
- Department of Neurosciences, College of Medicine and Philippine General Hospital, University of the Philippines Manila, Manila, Philippines
| | - An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York
- Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, Bronx, New York
| | - Yilong Ma
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York
- Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
| | - Shichun Peng
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York
| | - Jeff L Waugh
- Division of Pediatric Neurology, Department of Pediatrics, University of Texas Southwestern, Dallas, Texas
| | - Ana Westenberger
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York
- Department of Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
- Molecular Medicine, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York
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8
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Barbero JA, Unadkat P, Choi YY, Eidelberg D. Functional Brain Networks to Evaluate Treatment Responses in Parkinson's Disease. Neurotherapeutics 2023; 20:1653-1668. [PMID: 37684533 PMCID: PMC10684458 DOI: 10.1007/s13311-023-01433-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
Network analysis of functional brain scans acquired with [18F]-fluorodeoxyglucose positron emission tomography (FDG PET, to map cerebral glucose metabolism), or resting-state functional magnetic resonance imaging (rs-fMRI, to map blood oxygen level-dependent brain activity) has increasingly been used to identify and validate reproducible circuit abnormalities associated with neurodegenerative disorders such as Parkinson's disease (PD). In addition to serving as imaging markers of the underlying disease process, these networks can be used singly or in combination as an adjunct to clinical diagnosis and as a screening tool for therapeutics trials. Disease networks can also be used to measure rates of progression in natural history studies and to assess treatment responses in individual subjects. Recent imaging studies in PD subjects scanned before and after treatment have revealed therapeutic effects beyond the modulation of established disease networks. Rather, other mechanisms of action may be at play, such as the induction of novel functional brain networks directly by treatment. To date, specific treatment-induced networks have been described in association with novel interventions for PD such as subthalamic adeno-associated virus glutamic acid decarboxylase (AAV2-GAD) gene therapy, as well as sham surgery or oral placebo under blinded conditions. Indeed, changes in the expression of these networks with treatment have been found to correlate consistently with clinical outcome. In aggregate, these attributes suggest a role for functional brain networks as biomarkers in future clinical trials.
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Affiliation(s)
- János A Barbero
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
| | - Prashin Unadkat
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
- Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY, 11030, USA
| | - Yoon Young Choi
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA.
- Molecular Medicine and Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, 11549, USA.
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9
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Buchert R, Wegner F, Huppertz HJ, Berding G, Brendel M, Apostolova I, Buhmann C, Dierks A, Katzdobler S, Klietz M, Levin J, Mahmoudi N, Rinscheid A, Rogozinski S, Rumpf JJ, Schneider C, Stöcklein S, Spetsieris PG, Eidelberg D, Wattjes MP, Sabri O, Barthel H, Höglinger G. Automatic covariance pattern analysis outperforms visual reading of 18 F-fluorodeoxyglucose-positron emission tomography (FDG-PET) in variant progressive supranuclear palsy. Mov Disord 2023; 38:1901-1913. [PMID: 37655363 DOI: 10.1002/mds.29581] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 07/19/2023] [Accepted: 07/31/2023] [Indexed: 09/02/2023] Open
Abstract
BACKGROUND To date, studies on positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) in progressive supranuclear palsy (PSP) usually included PSP cohorts overrepresenting patients with Richardson's syndrome (PSP-RS). OBJECTIVES To evaluate FDG-PET in a patient sample representing the broad phenotypic PSP spectrum typically encountered in routine clinical practice. METHODS This retrospective, multicenter study included 41 PSP patients, 21 (51%) with RS and 20 (49%) with non-RS variants of PSP (vPSP), and 46 age-matched healthy controls. Two state-of-the art methods for the interpretation of FDG-PET were compared: visual analysis supported by voxel-based statistical testing (five readers) and automatic covariance pattern analysis using a predefined PSP-related pattern. RESULTS Sensitivity and specificity of the majority visual read for the detection of PSP in the whole cohort were 74% and 72%, respectively. The percentage of false-negative cases was 10% in the PSP-RS subsample and 43% in the vPSP subsample. Automatic covariance pattern analysis provided sensitivity and specificity of 93% and 83% in the whole cohort. The percentage of false-negative cases was 0% in the PSP-RS subsample and 15% in the vPSP subsample. CONCLUSIONS Visual interpretation of FDG-PET supported by voxel-based testing provides good accuracy for the detection of PSP-RS, but only fair sensitivity for vPSP. Automatic covariance pattern analysis outperforms visual interpretation in the detection of PSP-RS, provides clinically useful sensitivity for vPSP, and reduces the rate of false-positive findings. Thus, pattern expression analysis is clinically useful to complement visual reading and voxel-based testing of FDG-PET in suspected PSP. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Ralph Buchert
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Florian Wegner
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | | | - Georg Berding
- Department of Nuclear Medicine, Hannover Medical School, Hannover, Germany
| | - Matthias Brendel
- Department of Nuclear Medicine, University Hospital of Munich, LMU, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Ivayla Apostolova
- Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Carsten Buhmann
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Alexander Dierks
- Department of Nuclear Medicine, University Hospital Augsburg, Augsburg, Germany
| | - Sabrina Katzdobler
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU, Munich, Germany
| | - Martin Klietz
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU, Munich, Germany
| | - Nima Mahmoudi
- Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Andreas Rinscheid
- Medical Physics and Radiation Protection, University Hospital Augsburg, Augsburg, Germany
| | | | | | - Christine Schneider
- Department of Neurology and Clinical Neurophysiology, University Hospital Augsburg, Augsburg, Germany
| | - Sophia Stöcklein
- Department of Radiology, University Hospital of Munich, LMU, Munich, Germany
| | - Phoebe G Spetsieris
- The Feinstein Institutes for Medical Research Manhasset, Manhasset, New York, USA
| | - David Eidelberg
- The Feinstein Institutes for Medical Research Manhasset, Manhasset, New York, USA
| | - Mike P Wattjes
- Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Osama Sabri
- Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Henryk Barthel
- Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Günter Höglinger
- Department of Neurology, Hannover Medical School, Hannover, Germany
- German Center for Neurodegenerative Diseases (DZNE) Munich, Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU, Munich, Germany
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10
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Perovnik M, Tang CC, Namías M, Eidelberg D. Longitudinal changes in metabolic network activity in early Alzheimer's disease. Alzheimers Dement 2023; 19:4061-4072. [PMID: 37204815 DOI: 10.1002/alz.13137] [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: 02/23/2023] [Revised: 04/19/2023] [Accepted: 04/20/2023] [Indexed: 05/20/2023]
Abstract
INTRODUCTION The progression of Alzheimer's disease (AD) has been linked to two metabolic networks, the AD-related pattern (ADRP) and the default mode network (DMN). METHODS Converting and clinically stable cognitively normal subjects (n = 47) and individuals with mild cognitive impairment (n = 96) underwent 2-[18 F]fluoro-2-deoxy-D-glucose (FDG) positron emission tomography (PET) three or more times over 6 years (nscans = 705). Expression levels for ADRP and DMN were measured in each subject and time point, and the resulting changes were correlated with cognitive performance. The role of network expression in predicting conversion to dementia was also evaluated. RESULTS Longitudinal increases in ADRP expression were observed in converters, while age-related DMN loss was seen in converters and nonconverters. Cognitive decline correlated with increases in ADRP and declines in DMN, but conversion to dementia was predicted only by baseline ADRP levels. DISCUSSION The results point to the potential utility of ADRP as an imaging biomarker of AD progression.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Mauro Namías
- Fundación Centro Diagnóstico Nuclear, Buenos Aires, Argentina
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
- Molecular Medicine and Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, New York, USA
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11
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Rus T, Mlakar J, Jamšek J, Trošt M. Metabolic Brain Changes Can Predict the Underlying Pathology in Neurodegenerative Brain Disorders: A Case Report of Sporadic Creutzfeldt-Jakob Disease with Concomitant Parkinson's Disease. Int J Mol Sci 2023; 24:13081. [PMID: 37685887 PMCID: PMC10488131 DOI: 10.3390/ijms241713081] [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: 08/08/2023] [Revised: 08/21/2023] [Accepted: 08/22/2023] [Indexed: 09/10/2023] Open
Abstract
The co-occurrence of multiple proteinopathies is being increasingly recognized in neurodegenerative disorders and poses a challenge in differential diagnosis and patient selection for clinical trials. Changes in brain metabolism captured by positron emission tomography (PET) with 18 F-fluorodeoxyglucose (FDG) allow us to differentiate between different neurodegenerative disorders either by visual exploration or by studying disease-specific metabolic networks in individual patients. However, the impact of multiple proteinopathies on brain metabolism and metabolic networks remains unknown due to the absence of pathological studies. In this case study, we present a 67-year-old patient with rapidly progressing dementia clinically diagnosed with probable sporadic Creutzfeldt-Jakob disease (sCJD). However, in addition to the expected pronounced cortical and subcortical hypometabolism characteristic of sCJD, the brain FDG PET revealed an intriguing finding of unexpected relative hypermetabolism in the bilateral putamina, raising suspicions of coexisting Parkinson's disease (PD). Additional investigation of disease-specific metabolic brain networks revealed elevated expression of both CJD-related pattern (CJDRP) and PD-related pattern (PDRP) networks. The patient eventually developed akinetic mutism and passed away seven weeks after symptom onset. Neuropathological examination confirmed neuropathological changes consistent with sCJD and the presence of Lewy bodies confirming PD pathology. Additionally, hyperphosphorylated tau and TDP-43 pathology were observed, a combination of four proteinopathies that had not been previously reported. Overall, this case provides valuable insights into the complex interplay of neurodegenerative pathologies and their impact on metabolic brain changes, emphasizing the role of metabolic brain imaging in evaluating potential presence of multiple proteinopathies.
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Affiliation(s)
- Tomaž Rus
- Department of Neurology, University Medical Center Ljubljana, Zaloška cesta 2a, 1000 Ljubljana, Slovenia;
| | - Jernej Mlakar
- Institute of Pathology, Medical Faculty, University of Ljubljana, Korytkova ulica 2, 1000 Ljubljana, Slovenia;
| | - Jan Jamšek
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloška cesta 7, 1000 Ljubljana, Slovenia;
| | - Maja Trošt
- Department of Neurology, University Medical Center Ljubljana, Zaloška cesta 2a, 1000 Ljubljana, Slovenia;
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloška cesta 7, 1000 Ljubljana, Slovenia;
- Medical Faculty, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
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12
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Spetsieris PG, Eidelberg D. Parkinson's disease progression: Increasing expression of an invariant common core subnetwork. Neuroimage Clin 2023; 39:103488. [PMID: 37660556 PMCID: PMC10491857 DOI: 10.1016/j.nicl.2023.103488] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/31/2023] [Accepted: 08/02/2023] [Indexed: 09/05/2023]
Abstract
Notable success has been achieved in the study of neurodegenerative conditions using reduction techniques such as principal component analysis (PCA) and sparse inverse covariance estimation (SICE) in positron emission tomography (PET) data despite their widely differing approach. In a recent study of SICE applied to metabolic scans from Parkinson's disease (PD) patients, we showed that by using PCA to prespecify disease-related partition layers, we were able to optimize maps of functional metabolic connectivity within the relevant networks. Here, we show the potential of SICE, enhanced by disease-specific subnetwork partitions, to identify key regional hubs and their connections, and track their associations in PD patients with increasing disease duration. This approach enabled the identification of a core zone that included elements of the striatum, pons, cerebellar vermis, and parietal cortex and provided a deeper understanding of progressive changes in their connectivity. This subnetwork constituted a robust invariant disease feature that was unrelated to phenotype. Mean expression levels for this subnetwork increased steadily in a group of 70 PD patients spanning a range of symptom durations between 1 and 21 years. The findings were confirmed in a validation sample of 69 patients with up to 32 years of symptoms. The common core elements represent possible targets for disease modification, while their connections to external regions may be better suited for symptomatic treatment.
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Affiliation(s)
- Phoebe G Spetsieris
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, United States
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, United States; Molecular Medicine and Neurology, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, United States.
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13
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Luciw NJ, Grigorian A, Dimick MK, Jiang G, Chen JJ, Graham SJ, Goldstein BI, MacIntosh BJ. Classifying youth with bipolar disorder versus healthy youth using cerebral blood flow patterns. J Psychiatry Neurosci 2023; 48:E305-E314. [PMID: 37643801 PMCID: PMC10473037 DOI: 10.1503/jpn.230012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 04/14/2023] [Accepted: 05/27/2023] [Indexed: 08/31/2023] Open
Abstract
BACKGROUND Clinical neuroimaging studies often investigate group differences between patients and controls, yet multivariate imaging features may enable individual-level classification. This study aims to classify youth with bipolar disorder (BD) versus healthy youth using grey matter cerebral blood flow (CBF) data analyzed with logistic regressions. METHODS Using a 3 Tesla magnetic resonance imaging (MRI) system, we collected pseudo-continuous, arterial spin-labelling, resting-state functional MRI (rfMRI) and T 1-weighted images from youth with BD and healthy controls. We used 3 logistic regression models to classify youth with BD versus controls, controlling for age and sex, using mean grey matter CBF as a single explanatory variable, quantitative CBF features based on principal component analysis (PCA) or relative (intensity-normalized) CBF features based on PCA. We also carried out a comparison analysis using rfMRI data. RESULTS The study included 46 patients with BD (mean age 17 yr, standard deviation [SD] 1 yr; 25 females) and 49 healthy controls (mean age 16 yr, SD 2 yr; 24 females). Global mean CBF and multivariate quantitative CBF offered similar classification performance that was above chance. The association between CBF images and the feature map was not significantly different between groups (p = 0.13); however, the multivariate classifier identified regions with lower CBF among patients with BD (ΔCBF = -2.94 mL/100 g/min; permutation test p = 0047). Classification performance decreased when considering rfMRI data. LIMITATIONS We cannot comment on which CBF principal component is most relevant to the classification. Participants may have had various mood states, comorbidities, demographics and medication records. CONCLUSION Brain CBF features can classify youth with BD versus healthy controls with above-chance accuracy using logistic regression. A global CBF feature may offer similar classification performance to distinct multivariate CBF features.
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Affiliation(s)
- Nicholas J Luciw
- From Hurvitz Brain Sciences, Sunnybrook Research Institute, Toronto, Ont. (Luciw, Jiang, Graham, MacIntosh); the Department of Medical Biophysics, University of Toronto, Toronto, Ont. (Luciw, Jiang, Chen, Graham, MacIntosh); the Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Ont. (Grigorian, Dimick, Goldstein); the Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ont. (Dimick, Goldstein); the Rotman Research Institute, Baycrest Health Sciences, Toronto, Ont. (Chen); the Institute of Biomedical Engineering, University of Toronto, Toronto, Ont. (Chen); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Goldstein); the Sandra Black Centre for Brain Resilience & Recovery, Toronto, Ont. (MacIntosh); the Computational Radiology & Artificial Intelligence Unit, Oslo University Hospital, Norway (MacIntosh)
| | - Anahit Grigorian
- From Hurvitz Brain Sciences, Sunnybrook Research Institute, Toronto, Ont. (Luciw, Jiang, Graham, MacIntosh); the Department of Medical Biophysics, University of Toronto, Toronto, Ont. (Luciw, Jiang, Chen, Graham, MacIntosh); the Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Ont. (Grigorian, Dimick, Goldstein); the Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ont. (Dimick, Goldstein); the Rotman Research Institute, Baycrest Health Sciences, Toronto, Ont. (Chen); the Institute of Biomedical Engineering, University of Toronto, Toronto, Ont. (Chen); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Goldstein); the Sandra Black Centre for Brain Resilience & Recovery, Toronto, Ont. (MacIntosh); the Computational Radiology & Artificial Intelligence Unit, Oslo University Hospital, Norway (MacIntosh)
| | - Mikaela K Dimick
- From Hurvitz Brain Sciences, Sunnybrook Research Institute, Toronto, Ont. (Luciw, Jiang, Graham, MacIntosh); the Department of Medical Biophysics, University of Toronto, Toronto, Ont. (Luciw, Jiang, Chen, Graham, MacIntosh); the Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Ont. (Grigorian, Dimick, Goldstein); the Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ont. (Dimick, Goldstein); the Rotman Research Institute, Baycrest Health Sciences, Toronto, Ont. (Chen); the Institute of Biomedical Engineering, University of Toronto, Toronto, Ont. (Chen); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Goldstein); the Sandra Black Centre for Brain Resilience & Recovery, Toronto, Ont. (MacIntosh); the Computational Radiology & Artificial Intelligence Unit, Oslo University Hospital, Norway (MacIntosh)
| | - Guocheng Jiang
- From Hurvitz Brain Sciences, Sunnybrook Research Institute, Toronto, Ont. (Luciw, Jiang, Graham, MacIntosh); the Department of Medical Biophysics, University of Toronto, Toronto, Ont. (Luciw, Jiang, Chen, Graham, MacIntosh); the Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Ont. (Grigorian, Dimick, Goldstein); the Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ont. (Dimick, Goldstein); the Rotman Research Institute, Baycrest Health Sciences, Toronto, Ont. (Chen); the Institute of Biomedical Engineering, University of Toronto, Toronto, Ont. (Chen); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Goldstein); the Sandra Black Centre for Brain Resilience & Recovery, Toronto, Ont. (MacIntosh); the Computational Radiology & Artificial Intelligence Unit, Oslo University Hospital, Norway (MacIntosh)
| | - J Jean Chen
- From Hurvitz Brain Sciences, Sunnybrook Research Institute, Toronto, Ont. (Luciw, Jiang, Graham, MacIntosh); the Department of Medical Biophysics, University of Toronto, Toronto, Ont. (Luciw, Jiang, Chen, Graham, MacIntosh); the Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Ont. (Grigorian, Dimick, Goldstein); the Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ont. (Dimick, Goldstein); the Rotman Research Institute, Baycrest Health Sciences, Toronto, Ont. (Chen); the Institute of Biomedical Engineering, University of Toronto, Toronto, Ont. (Chen); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Goldstein); the Sandra Black Centre for Brain Resilience & Recovery, Toronto, Ont. (MacIntosh); the Computational Radiology & Artificial Intelligence Unit, Oslo University Hospital, Norway (MacIntosh)
| | - Simon J Graham
- From Hurvitz Brain Sciences, Sunnybrook Research Institute, Toronto, Ont. (Luciw, Jiang, Graham, MacIntosh); the Department of Medical Biophysics, University of Toronto, Toronto, Ont. (Luciw, Jiang, Chen, Graham, MacIntosh); the Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Ont. (Grigorian, Dimick, Goldstein); the Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ont. (Dimick, Goldstein); the Rotman Research Institute, Baycrest Health Sciences, Toronto, Ont. (Chen); the Institute of Biomedical Engineering, University of Toronto, Toronto, Ont. (Chen); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Goldstein); the Sandra Black Centre for Brain Resilience & Recovery, Toronto, Ont. (MacIntosh); the Computational Radiology & Artificial Intelligence Unit, Oslo University Hospital, Norway (MacIntosh)
| | - Benjamin I Goldstein
- From Hurvitz Brain Sciences, Sunnybrook Research Institute, Toronto, Ont. (Luciw, Jiang, Graham, MacIntosh); the Department of Medical Biophysics, University of Toronto, Toronto, Ont. (Luciw, Jiang, Chen, Graham, MacIntosh); the Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Ont. (Grigorian, Dimick, Goldstein); the Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ont. (Dimick, Goldstein); the Rotman Research Institute, Baycrest Health Sciences, Toronto, Ont. (Chen); the Institute of Biomedical Engineering, University of Toronto, Toronto, Ont. (Chen); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Goldstein); the Sandra Black Centre for Brain Resilience & Recovery, Toronto, Ont. (MacIntosh); the Computational Radiology & Artificial Intelligence Unit, Oslo University Hospital, Norway (MacIntosh)
| | - Bradley J MacIntosh
- From Hurvitz Brain Sciences, Sunnybrook Research Institute, Toronto, Ont. (Luciw, Jiang, Graham, MacIntosh); the Department of Medical Biophysics, University of Toronto, Toronto, Ont. (Luciw, Jiang, Chen, Graham, MacIntosh); the Centre for Youth Bipolar Disorder, Centre for Addiction and Mental Health, Toronto, Ont. (Grigorian, Dimick, Goldstein); the Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ont. (Dimick, Goldstein); the Rotman Research Institute, Baycrest Health Sciences, Toronto, Ont. (Chen); the Institute of Biomedical Engineering, University of Toronto, Toronto, Ont. (Chen); the Department of Psychiatry, University of Toronto, Toronto, Ont. (Goldstein); the Sandra Black Centre for Brain Resilience & Recovery, Toronto, Ont. (MacIntosh); the Computational Radiology & Artificial Intelligence Unit, Oslo University Hospital, Norway (MacIntosh)
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Schröter N, Bormann T, Rijntjes M, Blazhenets G, Berti R, Sajonz BE, Urbach H, Weiller C, Meyer PT, Rau A, Frings L. Cognitive Deficits in Parkinson's Disease Are Associated with Neuronal Dysfunction and Not White Matter Lesions. Mov Disord Clin Pract 2023; 10:1066-1073. [PMID: 37476309 PMCID: PMC10354622 DOI: 10.1002/mdc3.13792] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 04/25/2023] [Accepted: 05/04/2023] [Indexed: 07/22/2023] Open
Abstract
Background Cognitive deficits considerably contribute to the patient's burden in Parkinson's disease (PD). While cognitive decline is linked to neuronal dysfunction, the additional role of white matter lesions (WML) is discussed controversially. Objective To investigate the influence of WML, in comparison to neuronal dysfunction, on cognitive deficits in PD. Methods We prospectively recruited patients with PD who underwent neuropsychological assessment using the Mattis Dementia Rating Scale 2 (DRS-2) or Parkinson Neuropsychometric Dementia Assessment (PANDA) and both MRI and PET with [18F]fluorodeoxyglucose (FDG). WML-load and PD cognition-related covariance pattern (PDCP) as a measure of neuronal dysfunction were read out. Relationship between cognitive performance and rank-transformed WML was analyzed with linear regression, controlling for the patients' age. PDCP subject scores were investigated likewise and in a second step adjusting for age and WML load. Results Inclusion criteria were met by 76 patients with a mean (± SD) age of 63.5 ± 9.0 years and disease duration of 10.7 ± 5.4 years. Neuropsychological testing revealed front executive and parietal deficits and a median DRS-2 score of 137 (range 119-144)/144 and PANDA score of 22 (range 3-30)/30. No association between WML and cognition was observed, whereas PDCP subject scores showed a trend-level negative correlation with the DRS-2 (P = 0.060) as well as a negative correlation with PANDA (P = 0.049) which persisted also after additional correction for WML (P = 0.039). Conclusion The present study indicates that microangiopathic WML do not have a relevant impact on neurocognitive performance in PD whereas neuronal dysfunction does.
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Affiliation(s)
- Nils Schröter
- Department of Neurology and Clinical Neuroscience, Medical Center, Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Tobias Bormann
- Department of Neurology and Clinical Neuroscience, Medical Center, Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Michel Rijntjes
- Department of Neurology and Clinical Neuroscience, Medical Center, Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Ganna Blazhenets
- Department of Nuclear Medicine, Medical Center, Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Raissa Berti
- Department of Nuclear Medicine, Medical Center, Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Bastian E.A. Sajonz
- Department of Stereotactic and Functional Neurosurgery, Medical Center, Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Horst Urbach
- Department of Neuroradiology, Medical Center, Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Cornelius Weiller
- Department of Neurology and Clinical Neuroscience, Medical Center, Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Philipp T. Meyer
- Department of Nuclear Medicine, Medical Center, Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Alexander Rau
- Department of Neuroradiology, Medical Center, Faculty of MedicineUniversity of FreiburgFreiburgGermany
- Department of Diagnostic and Interventional Radiology, Medical Center, Faculty of MedicineUniversity of FreiburgFreiburgGermany
| | - Lars Frings
- Department of Nuclear Medicine, Medical Center, Faculty of MedicineUniversity of FreiburgFreiburgGermany
- Center for Geriatrics and Gerontology Freiburg, Medical Center, Faculty of MedicineUniversity of FreiburgFreiburgGermany
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15
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Štokelj E, Tomše P, Tomanič T, Dhawan V, Eidelberg D, Trošt M, Simončič U. Effect of the identification group size and image resolution on the diagnostic performance of metabolic Alzheimer's disease-related pattern. EJNMMI Res 2023; 13:47. [PMID: 37222957 DOI: 10.1186/s13550-023-01001-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 05/16/2023] [Indexed: 05/25/2023] Open
Abstract
BACKGROUND Alzheimer's disease-related pattern (ADRP) is a metabolic brain biomarker of Alzheimer's disease (AD). While ADRP is being introduced into research, the effect of the size of the identification cohort and the effect of the resolution of identification and validation images on ADRP's performance need to be clarified. METHODS 240 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography images [120 AD/120 cognitive normals (CN)] were selected from the Alzheimer's disease neuroimaging initiative database. A total of 200 images (100 AD/100 CN) were used to identify different versions of ADRP using a scaled subprofile model/principal component analysis. For this purpose, five identification groups were randomly selected 25 times. The identification groups differed in the number of images (20 AD/20 CN, 30 AD/30 CN, 40 AD/40 CN, 60 AD/60 CN, and 80 AD/80 CN) and image resolutions (6, 8, 10, 12, 15 and 20 mm). A total of 750 ADRPs were identified and validated through the area under the curve (AUC) values on the remaining 20 AD/20 CN with six different image resolutions. RESULTS ADRP's performance for the differentiation between AD patients and CN demonstrated only a marginal average AUC increase, when the number of subjects in the identification group increases (AUC increase for about 0.03 from 20 AD/20 CN to 80 AD/80 CN). However, the average of the lowest five AUC values increased with the increasing number of participants (AUC increase for about 0.07 from 20 AD/20 CN to 30 AD/30 CN and for an additional 0.02 from 30 AD/30 CN to 40 AD/40 CN). The resolution of the identification images affects ADRP's diagnostic performance only marginally in the range from 8 to 15 mm. ADRP's performance stayed optimal even when applied to validation images of resolution differing from the identification images. CONCLUSIONS While small (20 AD/20 CN images) identification cohorts may be adequate in a favorable selection of cases, larger cohorts (at least 30 AD/30 CN images) shall be preferred to overcome possible/random biological differences and improve ADRP's diagnostic performance. ADRP's performance stays stable even when applied to the validation images with a resolution different than the resolution of the identification ones.
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Affiliation(s)
- Eva Štokelj
- Faculty of Mathematics and Physics, University of Ljubljana, Jadranska ulica 19, 1000, Ljubljana, Slovenia.
| | - Petra Tomše
- Department of Nuclear Medicine, University Medical Centre Ljubljana, Zaloška cesta 7, 1000, Ljubljana, Slovenia
| | - Tadej Tomanič
- Faculty of Mathematics and Physics, University of Ljubljana, Jadranska ulica 19, 1000, Ljubljana, Slovenia
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institute for Medical Research, 350 Community Dr, Manhasset, NY, 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, 350 Community Dr, Manhasset, NY, 11030, USA
| | - Maja Trošt
- Department of Nuclear Medicine, University Medical Centre Ljubljana, Zaloška cesta 7, 1000, Ljubljana, Slovenia
- Department of Neurology, University Medical Centre Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia
| | - Urban Simončič
- Faculty of Mathematics and Physics, University of Ljubljana, Jadranska ulica 19, 1000, Ljubljana, Slovenia
- Jožef Stefan Institute, Jamova cesta 39, 1000, Ljubljana, Slovenia
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16
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Vo A, Schindlbeck KA, Nguyen N, Rommal A, Spetsieris PG, Tang CC, Choi YY, Niethammer M, Dhawan V, Eidelberg D. Adaptive and pathological connectivity responses in Parkinson's disease brain networks. Cereb Cortex 2023; 33:917-932. [PMID: 35325051 PMCID: PMC9930629 DOI: 10.1093/cercor/bhac110] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2021] [Revised: 02/23/2022] [Accepted: 02/24/2022] [Indexed: 11/12/2022] Open
Abstract
Functional imaging has been used extensively to identify and validate disease-specific networks as biomarkers in neurodegenerative disorders. It is not known, however, whether the connectivity patterns in these networks differ with disease progression compared to the beneficial adaptations that may also occur over time. To distinguish the 2 responses, we focused on assortativity, the tendency for network connections to link nodes with similar properties. High assortativity is associated with unstable, inefficient flow through the network. Low assortativity, by contrast, involves more diverse connections that are also more robust and efficient. We found that in Parkinson's disease (PD), network assortativity increased over time. Assoratitivty was high in clinically aggressive genetic variants but was low for genes associated with slow progression. Dopaminergic treatment increased assortativity despite improving motor symptoms, but subthalamic gene therapy, which remodels PD networks, reduced this measure compared to sham surgery. Stereotyped changes in connectivity patterns underlie disease progression and treatment responses in PD networks.
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Affiliation(s)
| | | | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA
| | - Andrea Rommal
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - Phoebe G Spetsieris
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - Yoon Young Choi
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - Martin Niethammer
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - David Eidelberg
- Corresponding author: Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA.
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Perovnik M, Rus T, Schindlbeck KA, Eidelberg D. Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat Rev Neurol 2023; 19:73-90. [PMID: 36539533 DOI: 10.1038/s41582-022-00753-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/21/2022] [Indexed: 12/24/2022]
Abstract
Network analytical tools are increasingly being applied to brain imaging maps of resting metabolic activity (PET) or blood oxygenation-dependent signals (functional MRI) to characterize the abnormal neural circuitry that underlies brain diseases. This approach is particularly valuable for the study of neurodegenerative disorders, which are characterized by stereotyped spread of pathology along discrete neural pathways. Identification and validation of disease-specific brain networks facilitate the quantitative assessment of pathway changes over time and during the course of treatment. Network abnormalities can often be identified before symptom onset and can be used to track disease progression even in the preclinical period. Likewise, network activity can be modulated by treatment and might therefore be used as a marker of efficacy in clinical trials. Finally, early differential diagnosis can be achieved by simultaneously measuring the activity levels of multiple disease networks in an individual patient's scans. Although these techniques were originally developed for PET, over the past several years analogous methods have been introduced for functional MRI, a more accessible non-invasive imaging modality. This advance is expected to broaden the application of network tools to large and diverse patient populations.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Tomaž Rus
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | | | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA.
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Abstract
Imaging of mild traumatic brain injury (TBI) using conventional techniques such as CT or MRI often results in no specific imaging correlation that would explain cognitive and clinical symptoms. Molecular imaging of mild TBI suggests that secondary events after injury can be detected using PET. However, no single specific pattern emerges that can aid in diagnosing the injury or determining the prognosis of the long-term behavioral profiles, indicating the heterogeneous and diffuse nature of TBI. Chronic traumatic encephalopathy, a primary tauopathy, has been shown to be strongly associated with repetitive TBI. In vivo data on the available tau PET tracers, however, have produced mixed results and overall low retention profiles in athletes with a history of repetitive mild TBI. Here, we emphasize that the lack of a mechanistic understanding of chronic TBI has posed a challenge when interpreting the results of molecular imaging biomarkers. We advocate for better target identification, improved analysis techniques such as machine learning or artificial intelligence, and novel tracer development.
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Affiliation(s)
- Gérard N. Bischof
- Department of Nuclear Medicine, University of Cologne, Cologne, Germany;,Institute for Neuroscience and Medicine II–Molecular Organization of the Brain, Research Center Juelich, Juelich, Germany; and
| | - Donna J. Cross
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah
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19
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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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20
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Tripathi S, Verghese J, Callisaya M, Mahoney JR, Srikanth V, Blumen HM. Brain patterns of pace - but not rhythm - are associated with vascular disease in older adults. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2022; 3:100154. [PMID: 36389342 PMCID: PMC9646823 DOI: 10.1016/j.cccb.2022.100154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/15/2022] [Revised: 11/01/2022] [Accepted: 11/01/2022] [Indexed: 11/06/2022]
Abstract
Background Distinct domains of gait such as pace and rhythm are linked to an increased risk for cognitive decline, falls, and dementia in aging. The brain substrates supporting these domains and underlying diseases, however, remain relatively unknown. The current study aimed to identify patterns of gray matter volume (GMV) associated with pace and rhythm, and whether these patterns vary as a function of vascular and non-vascular comorbidities. Methods A cross-sectional sample of 297 older adults (M Age = 72.5 years ± 7.2 years, 43% women) without dementia was drawn from the Tasmanian Study of Cognition and Gait (TASCOG). Factor analyses were used to reduce eight quantitative gait variables into two domains. The "pace" domain was primarily composed of gait speed, stride length, and double support time. The "rhythm" domain was composed of swing time, stance time, and cadence. Multivariate covariance-based analyses adjusted for age, sex, education, total intracranial volume, and presence of mild cognitive impairment identified gray matter volume (GMV) patterns associated with pace and rhythm, as well as participant-specific expression (or factor) scores for each pattern. Results Pace was positively associated with GMV in the right superior temporal sulcus, bilateral supplementary motor areas (SMA), and bilateral cerebellar regions. Rhythm was positively associated with GMV in bilateral SMA, prefrontal, cingulate, and paracingulate cortices. The GMV pattern associated with pace was less expressed in participants with any vascular disease; this association was also found independently with hypertension, diabetes, and myocardial infarction. Conclusion Both pace and rhythm domains of gait were associated with the volume of brain structures that have been linked to controlled and automatic aspects of gait control, as well as with structures involved in multisensory integration. Only the brain structures associated with pace, however, were associated with vascular disease.
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Affiliation(s)
- Susmit Tripathi
- Department of Neurology, Weill Cornell Medicine, New York, NY 10065, USA
| | - Joe Verghese
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY 10461, USA,Department of Neurology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Michele Callisaya
- Peninsula Clinical School, Central Clinical School, Monash University, Victoria, Australia,Menzies Institute for Medical Research, University of Tasmania, Tasmania, Australia
| | - Jeannette R. Mahoney
- Department of Neurology, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Velandai Srikanth
- Peninsula Clinical School, Central Clinical School, Monash University, Victoria, Australia,Menzies Institute for Medical Research, University of Tasmania, Tasmania, Australia
| | - Helena M. Blumen
- Department of Medicine, Albert Einstein College of Medicine, Bronx, NY 10461, USA,Department of Neurology, Albert Einstein College of Medicine, Bronx, NY 10461, USA,Corresponding author at: Department of Medicine (Geriatrics), Department of Neurology (Cognitive & Motor Aging), Albert Einstein College of Medicine, 1225 Morris Park Avenue, Van Etten Building, Room 319 Bronx, NY 10461, USA.
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21
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Perovnik M, Vo A, Nguyen N, Jamšek J, Rus T, Tang CC, Trošt M, Eidelberg D. Automated differential diagnosis of dementia syndromes using FDG PET and machine learning. Front Aging Neurosci 2022; 14:1005731. [PMID: 36408106 PMCID: PMC9667048 DOI: 10.3389/fnagi.2022.1005731] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Accepted: 10/10/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND Metabolic brain imaging with 2-[18F]fluoro-2-deoxy-D-glucose positron emission tomography (FDG PET) is a supportive diagnostic and differential diagnostic tool for neurodegenerative dementias. In the clinic, scans are usually visually interpreted. However, computer-aided approaches can improve diagnostic accuracy. We aimed to build two machine learning classifiers, based on two sets of FDG PET-derived features, for differential diagnosis of common dementia syndromes. METHODS We analyzed FDG PET scans from three dementia cohorts [63 dementia due to Alzheimer's disease (AD), 79 dementia with Lewy bodies (DLB) and 23 frontotemporal dementia (FTD)], and 41 normal controls (NCs). Patients' clinical diagnosis at follow-up (25 ± 20 months after scanning) or cerebrospinal fluid biomarkers for Alzheimer's disease was considered a gold standard. FDG PET scans were first visually evaluated. Scans were pre-processed, and two sets of features extracted: (1) the expressions of previously identified metabolic brain patterns, and (2) the mean uptake value in 95 regions of interest (ROIs). Two multi-class support vector machine (SVM) classifiers were tested and their diagnostic performance assessed and compared to visual reading. Class-specific regional feature importance was assessed with Shapley Additive Explanations. RESULTS Pattern- and ROI-based classifier achieved higher overall accuracy than expert readers (78% and 80% respectively, vs. 71%). Both SVM classifiers performed similarly to one another and to expert readers in AD (F1 = 0.74, 0.78, and 0.78) and DLB (F1 = 0.81, 0.81, and 0.78). SVM classifiers outperformed expert readers in FTD (F1 = 0.87, 0.83, and 0.63), but not in NC (F1 = 0.71, 0.75, and 0.92). Visualization of the SVM model showed bilateral temporal cortices and cerebellum to be the most important features for AD; occipital cortices, hippocampi and parahippocampi, amygdala, and middle temporal lobes for DLB; bilateral frontal cortices, middle and anterior cingulum for FTD; and bilateral angular gyri, pons, and vermis for NC. CONCLUSION Multi-class SVM classifiers based on the expression of characteristic metabolic brain patterns or ROI glucose uptake, performed better than experts in the differential diagnosis of common dementias using FDG PET scans. Experts performed better in the recognition of normal scans and a combined approach may yield optimal results in the clinical setting.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia,Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia,Center for Neurosciences, The Feinstein Institutes for Medical Research, New York, NY, United States,*Correspondence: Matej Perovnik,
| | - An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, New York, NY, United States
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, New York, NY, United States
| | - Jan Jamšek
- Department of Nuclear Medicine, University Medical Center Ljubljana, Ljubljana, Slovenia
| | - Tomaž Rus
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia
| | - Chris C. Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, New York, NY, United States
| | - Maja Trošt
- Department of Neurology, University Medical Center Ljubljana, Ljubljana, Slovenia,Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia,Department of Nuclear Medicine, University Medical Center Ljubljana, Ljubljana, Slovenia
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, New York, NY, United States
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22
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Rus T, Schindlbeck KA, Tang CC, Vo A, Dhawan V, Trošt M, Eidelberg D. Stereotyped Relationship Between Motor and Cognitive Metabolic Networks in Parkinson's Disease. Mov Disord 2022; 37:2247-2256. [PMID: 36054380 PMCID: PMC9669200 DOI: 10.1002/mds.29188] [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: 04/19/2022] [Revised: 06/29/2022] [Accepted: 07/20/2022] [Indexed: 11/11/2022] Open
Abstract
BACKGROUND Idiopathic Parkinson's disease (iPD) is associated with two distinct brain networks, PD-related pattern (PDRP) and PD-related cognitive pattern (PDCP), which correlate respectively with motor and cognitive symptoms. The relationship between the two networks in individual patients is unclear. OBJECTIVE To determine whether a consistent relationship exists between these networks, we measured the difference between PDRP and PDCP expression, termed delta, on an individual basis in independent populations of patients with iPD (n = 356), patients with idiopathic REM sleep behavioral disorder (iRBD) (n = 21), patients with genotypic PD (gPD) carrying GBA1 variants (n = 12) or the LRRK2-G2019S mutation (n = 14), patients with atypical parkinsonian syndromes (n = 238), and healthy control subjects (n = 95) from the United States, Slovenia, India, and South Korea. METHODS We used [18 F]-fluorodeoxyglucose positron emission tomography and resting-state fMRI to quantify delta and to compare the measure across samples; changes in delta over time were likewise assessed in longitudinal patient samples. Lastly, we evaluated delta in prodromal individuals with iRBD and subjects with gPD. RESULTS Delta was abnormally elevated in each of the four iPD samples (P < 0.05), as well as in the at-risk iRBD group (P < 0.05), with increasing values over time (P < 0.001). PDRP predominance was also present in gPD, with higher values in patients with GBA1 variants compared with the less aggressive LRRK2-G2019S mutation (P = 0.005). This trend was not observed in patients with atypical parkinsonian syndromes, who were accurately discriminated from iPD based on PDRP expression and delta (area under the curve = 0.85; P < 0.0001). CONCLUSIONS PDRP predominance, quantified by delta, assays the spread of dysfunction from motor to cognitive networks in patients with PD. Delta may therefore aid in differential diagnosis and in tracking disease progression in individual patients. © 2022 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Tomaž Rus
- Department of Neurology, UMC Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
- Medical Faculty, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
| | - Katharina A. Schindlbeck
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York 11030, USA
| | - Chris C. Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York 11030, USA
| | - An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York 11030, USA
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York 11030, USA
| | - Maja Trošt
- Department of Neurology, UMC Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
- Medical Faculty, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia
- Department of Nuclear Medicine, UMC Ljubljana, Zaloška cesta 7, 1000 Ljubljana, Slovenia
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York 11030, USA
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Unadkat P, Eidelberg D. Commentary on: A Network Approach to Understanding the Effects of Focused Ultrasound for Essential Tremor: Insights into Pathophysiology, Treatment, and Imaging Biomarkers. Neurotherapeutics 2022; 19:1883-1885. [PMID: 36303100 PMCID: PMC9723042 DOI: 10.1007/s13311-022-01321-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/18/2022] [Indexed: 12/13/2022] Open
Affiliation(s)
- Prashin Unadkat
- Elmezzi Graduate School of Molecular Medicine, Northwell Health, Manhasset, USA
- Center for Neurosciences, Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
- Department of Neurosurgery, Zucker School of Medicine at Hofstra/Northwell Health, Manhasset, USA
| | - David Eidelberg
- Center for Neurosciences, Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA.
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Identification and validation of a gray matter volume network in Alzheimer's disease. J Neurol Sci 2022; 440:120344. [PMID: 35908305 DOI: 10.1016/j.jns.2022.120344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2022] [Revised: 07/12/2022] [Accepted: 07/14/2022] [Indexed: 11/20/2022]
Abstract
OBJECTIVE This study aims to identify and validate a gray matter volume network in patients with Alzheimer's disease (AD). METHODS To identify a disease-related network, a principal component analysis-based algorithm, Scaled Subprofile Model, was applied to gray matter volume data derived from structural T1-weighted magnetic resonance imaging of the training sample that consisted of nine patients with AD (women, four; dementia, seven; mild cognitive impairment, two; age, 66.7 ± 8.8 [mean ± SD] years) with positive 18F-flutemetamol amyloid positron emission tomography and eight age-matched healthy controls obtained on-site. The network expression scores were calculated by topographic profile rating in the validation sample obtained via the Open Access Series of Imaging Studies and comprised 12 patients with AD dementia (women, four; age, 70.0 ± 3.7 years) and 12 age-matched healthy controls. RESULTS A significant network from the training sample, for which subject expression differed between the groups (permutation test, P = 0.006; sensitivity and specificity, 100%; area under the curve, 1), was identified. This network was represented by the principal components 1, 2, and 3 and showed a relative decrease in the inferior parietal lobule including angular gyrus, inferior temporal gyrus, premotor cortex, amygdala, hippocampus, and precuneus. It significantly differed between the groups with a sensitivity, specificity, and area under the curve of 83%, 91%, and 0.85, respectively, in the validation sample (P = 0.003). CONCLUSIONS An AD-related gray matter volume network that captured relevant regions was identified in amyloid positron emission tomography-positive patients and validated in an independent sample.
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Perovnik M, Tomše P, Jamšek J, Emeršič A, Tang C, Eidelberg D, Trošt M. Identification and validation of Alzheimer's disease-related metabolic brain pattern in biomarker confirmed Alzheimer's dementia patients. Sci Rep 2022; 12:11752. [PMID: 35817836 PMCID: PMC9273623 DOI: 10.1038/s41598-022-15667-9] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Accepted: 06/28/2022] [Indexed: 12/12/2022] Open
Abstract
Metabolic brain biomarkers have been incorporated in various diagnostic guidelines of neurodegenerative diseases, recently. To improve their diagnostic accuracy a biologically and clinically homogeneous sample is needed for their identification. Alzheimer's disease-related pattern (ADRP) has been identified previously in cohorts of clinically diagnosed patients with dementia due to Alzheimer's disease (AD), meaning that its diagnostic accuracy might have been reduced due to common clinical misdiagnosis. In our study, we aimed to identify ADRP in a cohort of AD patients with CSF confirmed diagnosis, validate it in large out-of-sample cohorts and explore its relationship with patients' clinical status. For identification we analyzed 2-[18F]FDG PET brain scans of 20 AD patients and 20 normal controls (NCs). For validation, 2-[18F]FDG PET scans from 261 individuals with AD, behavioral variant of frontotemporal dementia, mild cognitive impairment and NC were analyzed. We identified an ADRP that is characterized by relatively reduced metabolic activity in temporoparietal cortices, posterior cingulate and precuneus which co-varied with relatively increased metabolic activity in the cerebellum. ADRP expression significantly differentiated AD from NC (AUC = 0.95) and other dementia types (AUC = 0.76-0.85) and its expression correlated with clinical measures of global cognition and neuropsychological indices in all cohorts.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Zaloska cesta 2, 1000, Ljubljana, Slovenia.
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia.
| | - Petra Tomše
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloska cesta 2, 1000, Ljubljana, Slovenia
| | - Jan Jamšek
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloska cesta 2, 1000, Ljubljana, Slovenia
| | - Andreja Emeršič
- Laboratory for CSF Diagnostics, Department of Neurology, University Medical Center Ljubljana, Zaloska cesta 2, 1000, Ljubljana, Slovenia
| | - Chris Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - Maja Trošt
- Department of Neurology, University Medical Center Ljubljana, Zaloska cesta 2, 1000, Ljubljana, Slovenia
- Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloska cesta 2, 1000, Ljubljana, Slovenia
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Perovnik M, Tomše P, Jamšek J, Tang C, Eidelberg D, Trošt M. Metabolic brain pattern in dementia with Lewy bodies: Relationship to Alzheimer's disease topography. Neuroimage Clin 2022; 35:103080. [PMID: 35709556 PMCID: PMC9207351 DOI: 10.1016/j.nicl.2022.103080] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Revised: 05/26/2022] [Accepted: 06/05/2022] [Indexed: 10/28/2022]
Abstract
PURPOSE Dementia with Lewy bodies (DLB) is the second most common neurodegenerative dementia, that shares clinical and metabolic similarities with both Alzheimer's and Parkinson's disease. In this study we aimed to identify a DLB-related pattern (DLBRP), study its relationship with other metabolic brain patterns and explore its diagnostic and prognostic value. METHODS A cohort of 79 participants with DLB, 63 with dementia due to Alzheimer's disease (AD) and 41 normal controls (NCs) and their 2-[18F]FDG PET scans were analysed for identification and validation of DLBRP. Voxel-wise correlation and multiple linear regression were used to study the relation between DLBRP and Alzheimer's disease-related pattern (ADRP), Parkinson's disease-related pattern (PDRP) and PD-related cognitive pattern (PDCP). Diagnostic and prognostic value of DLBRP and of modified DLBRP after accounting for ADRP overlap (DLBRP ⊥ ADRP), were explored. RESULTS The newly identified DLBRP shared topographic similarities with ADRP (R2 = 24%) and PDRP (R2 = 37%), but not with PDCP. We could accurately discriminate between DLB and NC (AUC = 0.99) based on DLBRP expression, and between DLB and AD (AUC = 0.87) based on DLBRP ⊥ ADRP expression. DLBRP expression correlated with cognitive impairment, but the correlation was lost after accounting for ADRP overlap. DLBRP and DLBRP ⊥ ADRP correlated with patients' survival time. CONCLUSION DLBRP has proven to be a specific metabolic brain biomarker of DLB, sharing similarities with ADRP and PDRP, but not PDCP. We observed a similar metabolic mechanism underlying cognitive impairment in DLB and AD. DLB-specific metabolic changes were more detrimental for overall survival.
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Affiliation(s)
- Matej Perovnik
- Department of Neurology, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia; Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia.
| | - Petra Tomše
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
| | - Jan Jamšek
- Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
| | - Chris Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY 11030, USA
| | - Maja Trošt
- Department of Neurology, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia; Faculty of Medicine, University of Ljubljana, Vrazov trg 2, 1000 Ljubljana, Slovenia; Department of Nuclear Medicine, University Medical Center Ljubljana, Zaloška cesta 2, 1000 Ljubljana, Slovenia
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Abnormal metabolic covariance patterns associated with multiple system atrophy and progressive supranuclear palsy. Phys Med 2022; 98:131-138. [DOI: 10.1016/j.ejmp.2022.04.016] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/15/2022] [Accepted: 04/27/2022] [Indexed: 01/09/2023] Open
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Cerebral metabolic pattern associated with progressive parkinsonism in non-human primates reveals early cortical hypometabolism. Neurobiol Dis 2022; 167:105669. [DOI: 10.1016/j.nbd.2022.105669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 02/07/2022] [Accepted: 02/21/2022] [Indexed: 11/17/2022] Open
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Steffener J, Habeck C, Franklin D, Lau M, Yakoub Y, Gad M. Subjective difficulty in a verbal recognition-based memory task: Exploring brain-behaviour relationships at the individual level in healthy young adults. Neuroimage 2022; 257:119301. [PMID: 35568348 DOI: 10.1016/j.neuroimage.2022.119301] [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: 10/12/2021] [Revised: 05/05/2022] [Accepted: 05/09/2022] [Indexed: 11/18/2022] Open
Abstract
The vast majority of fMRI studies of task-related brain activity utilize common levels of task demands and analyses that rely on the central tendencies of the data. This approach does not take into account perceived difficulty nor regional variations in brain activity between people. The results are findings of brain-behavior relationships that weaken as sample sizes increase. Participants of the current study included twenty-six healthy young adults evenly split between the sexes. The current work utilizes five parametrically modulated levels of memory load centered around each individual's predetermined working memory cognitive capacity. Principal components analyses (PCA) identified the group-level central tendency of the data. After removing the group effect from the data, PCA identified individual-level patterns of brain activity across the five levels of task demands. Expression of the group effect significantly differed between the sexes across all load levels. Expression of the individual level patterns demonstrated a significant load by sex interaction. Furthermore, expressions of the individual maps make better predictors of response time behavior than group-derived maps. We demonstrated that utilization of an individual's unique pattern of brain activity in response to increasing a task's perceived difficulty is a better predictor of brain-behavior relationships than study designs and analyses focused on identification of group effects. Furthermore, these methods facilitate exploration into how individual differences in patterns of brain activity relate to individual differences in behavior and cognition.
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Affiliation(s)
- Jason Steffener
- Interdisciplinary School of Health Science, University of Ottawa, 200 Lees, Lees Campus, Office # E250E, Ottawa, ON K1S 5S9, Canada.
| | - Chris Habeck
- Cognitive Neuroscience Division, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and The Aging Brain, Columbia University College of Physicians and Surgeons, New York, New York, United States
| | - Dylan Franklin
- School of Psychology, University of Ottawa, Ottawa, ON, Canada
| | - Meghan Lau
- Interdisciplinary School of Health Science, University of Ottawa, 200 Lees, Lees Campus, Office # E250E, Ottawa, ON K1S 5S9, Canada
| | - Yara Yakoub
- Interdisciplinary School of Health Science, University of Ottawa, 200 Lees, Lees Campus, Office # E250E, Ottawa, ON K1S 5S9, Canada
| | - Maryse Gad
- Interdisciplinary School of Health Science, University of Ottawa, 200 Lees, Lees Campus, Office # E250E, Ottawa, ON K1S 5S9, Canada
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Wang XK, Wang XQ, Yang X, Yuan LX. Gray Matter Network Associated With Attention in Children With Attention Deficit Hyperactivity Disorder. Front Psychiatry 2022; 13:922720. [PMID: 35859604 PMCID: PMC9289184 DOI: 10.3389/fpsyt.2022.922720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 06/07/2022] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND Attention deficit hyperactivity disorder (ADHD) is one of the most prevalent childhood-onset neurodevelopmental disorders; however, the underlying neural mechanisms for the inattention symptom remain elusive for children with ADHD. At present, the majority of studies have analyzed the structural MRI (sMRI) with the univariate method, which fails to demonstrate the interregional covarying relationship of gray matter (GM) volumes among brain regions. The scaled subprofile model of principal component analysis (SSM-PCA) is a multivariate method, which can detect more robust brain-behavioral phenotype association compared to the univariate analysis method. This study aims to identify the GM network associated with attention in children with ADHD by applying SSM-PCA to the sMRI. METHODS The sMRI of 209 children with ADHD and 209 typically developing controls (TDCs) aged 7-14 years from the ADHD-200 dataset was used for anatomical computation, and the GM volume in each brain region was acquired. Then, SSM-PCA was applied to the GM volumes of all the subjects to capture the GM network of children with ADHD (i.e., ADHD-related pattern). The relationship between the expression of ADHD-related pattern and inattention symptom was further investigated. Finally, the influence of sample size on the analysis of this study was explored. RESULTS The ADHD-related pattern mainly included putamen, pallium, caudate, thalamus, right accumbens, superior/middle/inferior frontal cortex, superior occipital cortex, superior parietal cortex, and left middle occipital cortex. In addition, the expression of the ADHD-related pattern was related to inattention scores measured by the Conners' Parent Rating Scale long version (CPRS-LV; r = 0.25, p = 0.0004) and the DuPaul ADHD Rating Scale IV (ADHD-RS; r = 0.18, p = 0.03). Finally, we found that when the sample size was 252, the results of ADHD-related pattern were relatively reliable. Similarly, the sample size needed to be 162 when exploring the relationship between ADHD-related pattern and behavioral indicator measured by CPRS-LV. CONCLUSION We captured a GM network associated with attention in children with ADHD, which is different from that in adolescents and adults with ADHD. Our findings may shed light on the diverse neural mechanisms of inattention and provide treatment targets for children with ADHD.
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Affiliation(s)
- Xing-Ke Wang
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China.,Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China
| | - Xiu-Qin Wang
- Jing Hengyi School of Education, Hangzhou Normal University, Hangzhou, China.,Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,TMS Center, Deqing Hospital of Hangzhou Normal University, Zhejiang, China
| | - Xue Yang
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,TMS Center, Deqing Hospital of Hangzhou Normal University, Zhejiang, China
| | - Li-Xia Yuan
- Center for Cognition and Brain Disorders, The Affiliated Hospital of Hangzhou Normal University, Hangzhou, China.,TMS Center, Deqing Hospital of Hangzhou Normal University, Zhejiang, China.,Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, China.,Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments, Hangzhou Normal University, Hangzhou, China
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Exploring brain glucose metabolic patterns in cognitively normal adults at risk of Alzheimer's disease: A cross-validation study with Chinese and ADNI cohorts. Neuroimage Clin 2021; 33:102900. [PMID: 34864286 PMCID: PMC8648808 DOI: 10.1016/j.nicl.2021.102900] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 11/17/2021] [Accepted: 11/24/2021] [Indexed: 12/13/2022]
Abstract
At-risk AD-related metabolic covariance patterns were proposed for cognitively NCs. Patterns were cross-validated in two independent cohorts of Chinese and Americans. Pattern expression scores were significantly higher in Aβ+ NCs than in Aβ- NCs. Pattern expression scores were stable over time based on follow-up data. Pattern expression scores correlated with CSF tau biomarkers, but not with brain Aβ deposition.
Objective Disease-related metabolic brain patterns have been verified for a variety of neurodegenerative diseases including Alzheimer’s disease (AD). This study aimed to explore and validate the pattern derived from cognitively normal controls (NCs) in the Alzheimer’s continuum. Methods This study was based on two cohorts; one from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the other from the Sino Longitudinal Study on Cognitive Decline (SILCODE). Each subject underwent [18F]fluoro-2-deoxyglucose positron emission tomography (PET) and [18F]florbetapir-PET imaging. Participants were binary-grouped based on β-amyloid (Aβ) status, and the positivity was defined as Aβ+. Voxel-based scaled subprofile model/principal component analysis (SSM/PCA) was used to generate the “at-risk AD-related metabolic pattern (ARADRP)” for NCs. The pattern expression score was obtained and compared between the groups, and receiver operating characteristic curves were drawn. Notably, we conducted cross-validation to verify the robustness and correlation analyses to explore the relationships between the score and AD-related pathological biomarkers. Results Forty-eight Aβ+ NCs and 48 Aβ- NCs were included in the ADNI cohort, and 25 Aβ+ NCs and 30 Aβ- NCs were included in the SILCODE cohort. The ARADRPs were identified from the combined cohorts and the two separate cohorts, characterized by relatively lower regional loadings in the posterior parts of the precuneus, posterior cingulate, and regions of the temporal gyrus, as well as relatively higher values in the superior/middle frontal gyrus and other areas. Patterns identified from the two separate cohorts showed some regional differences, including the temporal gyrus, basal ganglia regions, anterior parts of the precuneus, and middle cingulate. Cross-validation suggested that the pattern expression score was significantly higher in the Aβ+ group of both cohorts (p < 0.01), and contributed to the diagnosis of Aβ+ NCs (with area under the curve values of 0.696–0.815). The correlation analysis revealed that the score was related to tau pathology measured in cerebrospinal fluid (p-tau: p < 0.02; t-tau: p < 0.03), but not Aβ pathology assessed with [18F]florbetapir-PET (p > 0.23). Conclusions ARADRP exists for NCs, and the acquired pattern expression score shows a certain ability to discriminate Aβ+ NCs from Aβ- NCs. The SSM/PCA method is expected to be helpful in the ultra-early diagnosis of AD in clinical practice.
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Yakushev I, Ripp I, Wang M, Savio A, Schutte M, Lizarraga A, Bogdanovic B, Diehl-Schmid J, Hedderich DM, Grimmer T, Shi K. Mapping covariance in brain FDG uptake to structural connectivity. Eur J Nucl Med Mol Imaging 2021; 49:1288-1297. [PMID: 34677627 PMCID: PMC8921091 DOI: 10.1007/s00259-021-05590-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 10/11/2021] [Indexed: 11/25/2022]
Abstract
PURPOSE Inter-subject covariance of regional 18F-fluorodeoxyglucose (FDG) PET measures (FDGcov) as proxy of brain connectivity has been gaining an increasing acceptance in the community. Yet, it is still unclear to what extent FDGcov is underlied by actual structural connectivity via white matter fiber tracts. In this study, we quantified the degree of spatial overlap between FDGcov and structural connectivity networks. METHODS We retrospectively analyzed neuroimaging data from 303 subjects, both patients with suspected neurodegenerative disorders and healthy individuals. For each subject, structural magnetic resonance, diffusion tensor imaging, and FDG-PET data were available. The images were spatially normalized to a standard space and segmented into 62 anatomical regions using a probabilistic atlas. Sparse inverse covariance estimation was employed to estimate FDGcov. Structural connectivity was measured by streamline tractography through fiber assignment by continuous tracking. RESULTS For the whole brain, 55% of detected connections were found to be convergent, i.e., present in both FDGcov and structural networks. This metric for random networks was significantly lower, i.e., 12%. Convergent were 80% of intralobe connections and only 30% of interhemispheric interlobe connections. CONCLUSION Structural connectivity via white matter fiber tracts is a relevant substrate of FDGcov, underlying around a half of connections at the whole brain level. Short-range white matter tracts appear to be a major substrate of intralobe FDGcov connections.
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Affiliation(s)
- Igor Yakushev
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, Germany.
- Klinikum rechts der Isar, School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Munich, Germany.
| | - Isabelle Ripp
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, Germany
- Klinikum rechts der Isar, School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Munich, Germany
| | - Min Wang
- Shanghai Institute for Advanced Communication and Data Science, Shanghai, China
| | - Alex Savio
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, Germany
| | - Michael Schutte
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, Germany
- Department Biology II, Ludwig Maximilian University of Munich, Munich, Germany
| | - Aldana Lizarraga
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, Germany
- Klinikum rechts der Isar, School of Medicine, Neuroimaging Center (TUM-NIC), Technical University of Munich, Munich, Germany
| | - Borjana Bogdanovic
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, Germany
| | - Janine Diehl-Schmid
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Dennis M Hedderich
- Department of Neuroradiology, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Timo Grimmer
- Department of Psychiatry and Psychotherapy, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Munich, Germany
| | - Kuangyu Shi
- Department of Nuclear Medicine, Klinikum rechts der Isar, School of Medicine, Technical University of Munich, Ismaninger Str. 22, Munich, 81675, Germany
- Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland
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Taherpour J, Jaber M, Voges B, Apostolova I, Sauvigny T, House PM, Lanz M, Lindenau M, Klutmann S, Martens T, Stodieck S, Buchert R. Predicting the outcome of epilepsy surgery by covariance pattern analysis of ictal perfusion SPECT. J Nucl Med 2021; 63:925-930. [PMID: 34593599 DOI: 10.2967/jnumed.121.262702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 09/09/2021] [Indexed: 11/16/2022] Open
Abstract
Previous studies on the utility of specific perfusion patterns in ictal brain perfusion SPECT for predicting the outcome of temporal lobe epilepsy surgery used qualitative visual pattern classification, semi-quantitative region-of-interest analysis or conventional univariate voxel-based testing, which are limited by intra- and inter-rater variability and/or low sensitivity to capture functional interactions among brain regions. The present study performed covariance pattern analysis of ictal perfusion SPECT using the Scaled Subprofile Model for unbiased identification of predictive covariance patterns. Methods: The study retrospectively included 18 responders to temporal lobe epilepsy surgery (Engel I-A at 12 months follow-up) and 18 non-responders (≥ Engel I-B). Ictal SPECT images were analyzed with the Scaled Subprofile Model blinded to group membership for unbiased identification of the 16 covariance patterns explaining the highest proportion of variance in the whole data set. Individual expression scores of the covariance patterns were evaluated for predicting seizure freedom after temporal lobe surgery by ROC analysis. Kaplan-Meier analysis including all available follow-up data (up to 60 months after surgery) was also performed. Results: Amongst the 16 covariance patterns only one showed a different expression between responders and non-responders (P = 0.03). This 'favorable ictal perfusion pattern' resembled the typical ictal perfusion pattern in temporomesial epilepsy. The expression score of the pattern provided an area of 0.744 (95%-confidence interval 0.577-0.911, P = 0.004) under the ROC curve. Kaplan-Meier analysis revealed a statistical trend towards longer seizure freedom in patients with positive expression score (P = 0.06). The median estimated seizure free time was 48 months in patients with positive expression score versus 6 months in patients with negative expression score. Conclusion: The expression of the 'favorable ictal perfusion pattern' identified by covariance analysis of ictal brain perfusion SPECT provides independent (from demographical and clinical variables) information for the prediction of seizure freedom after temporal lobe epilepsy surgery. The expression of this pattern is easily computed for new ictal SPECT images and, therefore, might be used to support the decision for or against temporal lobe surgery in clinical patient care.
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Affiliation(s)
| | - Marian Jaber
- University Medical Center Hamburg-Eppendorf, Germany
| | | | | | | | | | | | | | | | | | | | - Ralph Buchert
- University Medical Center Hamburg-Eppendorf, Germany
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Rommal A, Vo A, Schindlbeck KA, Greuel A, Ruppert MC, Eggers C, Eidelberg D. Parkinson's disease-related pattern (PDRP) identified using resting-state functional MRI: Validation study. NEUROIMAGE: REPORTS 2021. [DOI: 10.1016/j.ynirp.2021.100026] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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35
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Prak RF, Marsman JBC, Renken R, van der Naalt J, Zijdewind I. Fatigue following mild traumatic brain injury relates to visual processing and effort perception in the context of motor performance. Neuroimage Clin 2021; 32:102783. [PMID: 34425550 PMCID: PMC8379650 DOI: 10.1016/j.nicl.2021.102783] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 08/02/2021] [Accepted: 08/06/2021] [Indexed: 01/10/2023]
Abstract
INTRODUCTION Following mild traumatic brain injury (mTBI), a substantial number of patients experience disabling fatigue for months after the initial injury. To date, the underlying mechanisms of fatigue remain unclear. Recently, it was shown that mTBI patients with persistent fatigue do not demonstrate increased performance fatigability (i.e., objective performance decline) during a sustained motor task. However, it is not known whether the neural activation required to sustain this performance is altered after mTBI. METHODS Blood oxygen level-dependent (BOLD) fMRI data were acquired from 19 mTBI patients (>3 months post-injury) and 19 control participants during two motor tasks. Force was recorded from the index finger abductors of both hands during submaximal contractions and a 2-minute maximal voluntary contraction (MVC) with the right hand. Voluntary muscle activation (i.e., CNS drive) was indexed during the sustained MVC using peripheral nerve stimulation. Fatigue was quantified using the Fatigue Severity Scale (FSS) and Modified Fatigue Impact Scale (MFIS). Questionnaire, task, and BOLD data were compared across groups, and linear regression was used to evaluate the relationship between BOLD-activity and fatigue in the mTBI group. RESULTS The mTBI patients reported significantly higher levels of fatigue (FSS: 5.3 vs. 2.6, p < 0.001). Both mTBI- and control groups demonstrated significant performance fatigability during the sustained MVC, but no significant differences in task performance or BOLD-activity were observed between groups. However, mTBI patients reporting higher FSS scores showed increased BOLD-activity in the bilateral visual cortices (mainly extrastriate) and the left midcingulate gyrus. Furthermore, across all participants mean voluntary muscle activation during the sustained MVC correlated with long lasting post-contraction BOLD-activation in the right insula and midcingulate cortex. CONCLUSION The fMRI findings suggest that self-reported fatigue in mTBI may relate to visual processing and effort perception. Long lasting activation associated with high levels of CNS drive might be related to changes in cortical homeostasis in the context of high effort.
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Affiliation(s)
- Roeland F Prak
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands.
| | - Jan-Bernard C Marsman
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Remco Renken
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Joukje van der Naalt
- Department of Neurology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
| | - Inge Zijdewind
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands
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Guedj E, Morbelli S, Kaphan E, Campion JY, Dudouet P, Ceccaldi M, Cammilleri S, Nobili F, Eldin C. From early limbic inflammation to long COVID sequelae. Brain 2021; 144:e65. [PMID: 34142116 DOI: 10.1093/brain/awab215] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/25/2021] [Accepted: 05/26/2021] [Indexed: 01/08/2023] Open
Affiliation(s)
- Eric Guedj
- Aix Marseille Univ, APHM, CNRS, Centrale Marseille, Institut Fresnel, Timone Hospital, CERIMED, Nuclear Medicine Department, Marseille, France
| | - Silvia Morbelli
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.,IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Elsa Kaphan
- APHM, Service de Neurologie, Hôpital de la Timone, Marseille, France
| | - Jacques-Yves Campion
- Aix Marseille Univ, APHM, CNRS, Centrale Marseille, Institut Fresnel, Timone Hospital, CERIMED, Nuclear Medicine Department, Marseille, France
| | - Pierre Dudouet
- Aix Marseille Univ, IRD, AP-HM, MEPHI, Marseille, France
| | - Mathieu Ceccaldi
- Aix Marseille Univ, INSERM, Inst Neurosci Syst, & APHM, Service de Neurologie et de Neuropsychologie, CHU Timone, Marseille, France
| | - Serge Cammilleri
- Aix Marseille Univ, APHM, CNRS, Centrale Marseille, Institut Fresnel, Timone Hospital, CERIMED, Nuclear Medicine Department, Marseille, France
| | - Flavio Nobili
- Department of Health Sciences (DISSAL), University of Genoa, Genoa, Italy.,Department of Neuroscience (DINOGMI), University of Genoa, Genoa, Italy
| | - Carole Eldin
- Aix Marseille Univ, IRD, AP-HM, SSA, VITROME, IHU-Méditerranée Infection, Marseille, France
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The Cerebellum Is a Common Key for Visuospatial Execution and Attention in Parkinson's Disease. Diagnostics (Basel) 2021; 11:diagnostics11061042. [PMID: 34204073 PMCID: PMC8229154 DOI: 10.3390/diagnostics11061042] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 04/18/2021] [Accepted: 06/02/2021] [Indexed: 11/17/2022] Open
Abstract
Cognitive decline affects the clinical course in patients with Parkinson's disease (PD) and contributes to a poor prognosis. However, little is known about the underlying network-level abnormalities associated with each cognitive domain. We aimed to identify the networks related to each cognitive domain in PD using resting-state functional magnetic resonance imaging (MRI). Forty patients with PD and 15 normal controls were enrolled. All subjects underwent MRI and the Mini-Mental State Examination. Furthermore, the cognitive function of patients with PD was assessed using the Montreal Cognitive Assessment (MoCA). We used independent component analysis of the resting-state functional MRI for functional segmentation, followed by reconstruction to identify each domain-related network, to predict scores in PD using multiple regression models. Six networks were identified, as follows: the visuospatial-executive-domain-related network (R2 = 0.54, p < 0.001), naming-domain-related network (R2 = 0.39, p < 0.001), attention-domain-related network (R2 = 0.86, p < 0.001), language-domain-related network (R2 = 0.64, p < 0.001), abstraction-related network (R2 = 0.10, p < 0.05), and orientation-domain-related network (R2 = 0.64, p < 0.001). Cerebellar lobule VII was involved in the visuospatial-executive-domain-related and attention-domain-related networks. These two domains are involved in the first three listed nonamnestic cognitive impairment in the diagnostic criteria for PD with dementia (PDD). Furthermore, Brodmann area 10 contributed most frequently to each domain-related network. Collectively, these findings suggest that cerebellar lobule VII may play a key role in cognitive impairment in nonamnestic types of PDD.
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Brain metabolic characteristics distinguishing typical and atypical benign epilepsy with centro-temporal spikes. Eur Radiol 2021; 31:9335-9345. [PMID: 34050803 DOI: 10.1007/s00330-021-08051-0] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2020] [Revised: 03/24/2021] [Accepted: 05/05/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVES Atypical benign epilepsy with centro-temporal spikes (BECTS) have less favorable outcomes than typical BECTS, and thus should be accurately identified for adequate treatment. We aimed to investigate the glucose metabolic differences between typical and atypical BECTS using 18F-fluorodeoxyglucose positron emission tomography ([18F]FDG PET) imaging, and explore whether these differences can help distinguish. METHODS Forty-six patients with typical BECTS, 31 patients with atypical BECTS and 23 controls who underwent [18F]FDG PET examination were retrospectively involved. Absolute asymmetry index (|AI|) was applied to evaluate the severity of metabolic abnormality. Glucose metabolic differences were investigated among typical BECTS, atypical BECTS, and controls by using statistical parametric mapping (SPM). Logistic regression analyses were performed based on clinical, PET, and hybrid features. RESULTS The |AI| was found significantly higher in atypical BECTS than in typical BECTS (p = 0.040). Atypical BECTS showed more hypo-metabolism regions than typical BECTS, mainly located in the fronto-temporo-parietal cortex. The PET model had significantly higher area under the curve (AUC) than the clinical model (0.91 vs. 0.70, p = 0.006). The hybrid model had the highest sensitivity (0.90), specificity (0.85), and accuracy (0.87) of all three models. CONCLUSIONS Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, depending on which the two groups can be well distinguished. The combination of metabolic characteristics and clinical variables has the potential to be used clinically to distinguish between typical and atypical BECTS. KEY POINTS • Distinguishing between typical and atypical BECTS is very important for the formulation of treatment regimens in clinical practice. • Atypical BECTS showed more widespread and severe hypo-metabolism than typical BECTS, mainly located in the fronto-temporo-parietal cortex. • The logistic regression model based on PET outperformed that based on clinical characteristics in classification of typical and atypical BECTS, and the hybrid model achieved the best classification performance.
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Hosp JA, Dressing A, Blazhenets G, Bormann T, Rau A, Schwabenland M, Thurow J, Wagner D, Waller C, Niesen WD, Frings L, Urbach H, Prinz M, Weiller C, Schroeter N, Meyer PT. Cognitive impairment and altered cerebral glucose metabolism in the subacute stage of COVID-19. Brain 2021; 144:1263-1276. [PMID: 33822001 PMCID: PMC8083602 DOI: 10.1093/brain/awab009] [Citation(s) in RCA: 220] [Impact Index Per Article: 73.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/25/2020] [Accepted: 10/29/2020] [Indexed: 12/11/2022] Open
Abstract
During the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, neurological symptoms increasingly moved into the focus of interest. In this prospective cohort study, we assessed neurological and cognitive symptoms in hospitalized coronavirus disease-19 (COVID-19) patients and aimed to determine their neuronal correlates. Patients with reverse transcription-PCR-confirmed COVID-19 infection who required inpatient treatment primarily because of non-neurological complications were screened between 20 April 2020 and 12 May 2020. Patients (age > 18 years) were included in our cohort when presenting with at least one new neurological symptom (defined as impaired gustation and/or olfaction, performance < 26 points on a Montreal Cognitive Assessment and/or pathological findings on clinical neurological examination). Patients with ≥2 new symptoms were eligible for further diagnostics using comprehensive neuropsychological tests, cerebral MRI and 18fluorodeoxyglucose (FDG) PET as soon as infectivity was no longer present. Exclusion criteria were: premorbid diagnosis of cognitive impairment, neurodegenerative diseases or intensive care unit treatment. Of 41 COVID-19 inpatients screened, 29 patients (65.2 ± 14.4 years; 38% female) in the subacute stage of disease were included in the register. Most frequently, gustation and olfaction were disturbed in 29/29 and 25/29 patients, respectively. Montreal Cognitive Assessment performance was impaired in 18/26 patients (mean score 21.8/30) with emphasis on frontoparietal cognitive functions. This was confirmed by detailed neuropsychological testing in 15 patients. 18FDG PET revealed pathological results in 10/15 patients with predominant frontoparietal hypometabolism. This pattern was confirmed by comparison with a control sample using voxel-wise principal components analysis, which showed a high correlation (R2 = 0.62) with the Montreal Cognitive Assessment performance. Post-mortem examination of one patient revealed white matter microglia activation but no signs of neuroinflammation. Neocortical dysfunction accompanied by cognitive decline was detected in a relevant fraction of patients with subacute COVID-19 initially requiring inpatient treatment. This is of major rehabilitative and socioeconomic relevance.
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Affiliation(s)
- Jonas A Hosp
- Department of Neurology and Clinical Neuroscience, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Andrea Dressing
- Department of Neurology and Clinical Neuroscience, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Freiburg Brain Imaging Center, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ganna Blazhenets
- Department of Nuclear Medicine, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Tobias Bormann
- Department of Neurology and Clinical Neuroscience, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Alexander Rau
- Department of Neuroradiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marius Schwabenland
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Johannes Thurow
- Department of Nuclear Medicine, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Dirk Wagner
- Division of Infectious Diseases, Department of Internal Medicine II, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Cornelius Waller
- Department of Internal Medicine I, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Wolf D Niesen
- Department of Neurology and Clinical Neuroscience, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Lars Frings
- Department of Nuclear Medicine, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Horst Urbach
- Department of Neuroradiology, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marco Prinz
- Institute of Neuropathology, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Center for Basics in NeuroModulation (NeuroModulBasics), Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Signalling Research Centres BIOSS and CIBSS, University of Freiburg, Freiburg, Germany
| | - Cornelius Weiller
- Department of Neurology and Clinical Neuroscience, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Freiburg Brain Imaging Center, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Nils Schroeter
- Department of Neurology and Clinical Neuroscience, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Philipp T Meyer
- Department of Nuclear Medicine, Medical Center, University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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Neuropathological correlation supports automated image-based differential diagnosis in parkinsonism. Eur J Nucl Med Mol Imaging 2021; 48:3522-3529. [PMID: 33839891 DOI: 10.1007/s00259-021-05302-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 03/07/2021] [Indexed: 10/21/2022]
Abstract
PURPOSE Up to 25% of patients diagnosed as idiopathic Parkinson's disease (IPD) have an atypical parkinsonian syndrome (APS). We had previously validated an automated image-based algorithm to discriminate between IPD, multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). While the algorithm was accurate with respect to the final clinical diagnosis after long-term expert follow-up, its relationship to the initial referral diagnosis and to the neuropathological gold standard is not known. METHODS Patients with an uncertain diagnosis of parkinsonism were referred for 18F-fluorodeoxyglucose (FDG) PET to classify patients as IPD or as APS based on the automated algorithm. Patients were followed by a movement disorder specialist and subsequently underwent neuropathological examination. The image-based classification was compared to the neuropathological diagnosis in 15 patients with parkinsonism. RESULTS At the time of referral to PET, the clinical impression was only 66.7% accurate. The algorithm correctly identified 80% of the cases as IPD or APS (p = 0.02) and 87.5% of the APS cases as MSA or PSP (p = 0.03). The final clinical diagnosis was 93.3% accurate (p < 0.001), but needed several years of expert follow-up. CONCLUSION The image-based classifications agreed well with autopsy and can help to improve diagnostic accuracy during the period of clinical uncertainty.
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Kim R, Lee JY, Kim YK, Kim H, Yoon EJ, Shin JH, Yoo D, Nam H, Jeon B. Longitudinal Changes in Isolated Rapid Eye Movement Sleep Behavior Disorder-Related Metabolic Pattern Expression. Mov Disord 2021; 36:1889-1898. [PMID: 33788284 PMCID: PMC8451853 DOI: 10.1002/mds.28592] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 02/17/2021] [Accepted: 03/08/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND It remains unclear whether and how the isolated rapid eye movement (REM) sleep behavior disorder (iRBD)-related metabolic pattern (RBDRP) changes with disease progression in iRBD. OBJECTIVE To examine longitudinal changes in RBDRP expression in iRBD patients and to explore trajectories of relative metabolic activities of individual brain regions constituting RBDRP. METHODS In this cohort study, 25 iRBD patients (mean age [±standard deviation], 69.2 ± 5.3 years; 12 [48%] patients were men) and 24 age-matched healthy controls were included. The patients underwent at least two 18 F-fluorodeoxyglucose positron emission tomography scans at baseline and at the 2-year and/or 4-year follow-ups. We measured the RBDRP expression of the patients and controls which was validated by reproduction in a separate iRBD cohort (n = 13). RESULTS At baseline, the RBDRP expression discriminated iRBD patients from healthy controls. However, the RBDRP expression z scores tended to decrease over time in the patients, especially with longer follow-ups, and this tendency was observed even in patients with high-risk of phenoconversion. Furthermore, the degree of RBDRP expression at baseline did not predict the disease conversion. The RBDRP breakdown was mainly provoked by the attenuation of relative hypermetabolism in the frontal cortex including premotor areas and relative hypometabolism in the occipital cortex. The putaminal metabolic activity increased steadily with the disease progression. CONCLUSIONS The RBDRP expression in iRBD patients was altered significantly over time. Some of the brain metabolic changes seem to represent attempted functional compensation against ongoing neurodegeneration. The RBDRP expression measurement at one time point may not be a reliable biomarker for predicting disease conversion. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Ryul Kim
- Department of Neurology, Inha University Hospital, Incheon, South Korea.,Department of Neurology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul National University College of Medicine, Seoul, South Korea
| | - Jee-Young Lee
- Department of Neurology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul National University College of Medicine, Seoul, South Korea
| | - Yu Kyeong Kim
- Department of Nuclear Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Heejung Kim
- Department of Nuclear Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea.,Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, South Korea
| | - Eun Jin Yoon
- Department of Nuclear Medicine, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea
| | - Jung Hwan Shin
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
| | - Dallah Yoo
- Department of Neurology, Kyung Hee University Hospital, Seoul, South Korea
| | - Hyunwoo Nam
- Department of Neurology, Seoul National University-Seoul Metropolitan Government Boramae Medical Center, Seoul National University College of Medicine, Seoul, South Korea
| | - Beomseok Jeon
- Department of Neurology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, South Korea
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Peng S, Tang C, Schindlbeck K, Rydzinski Y, Dhawan V, Spetsieris PG, Ma Y, Eidelberg D. Dynamic 18F-FPCIT PET: Quantification of Parkinson's disease metabolic networks and nigrostriatal dopaminergic dysfunction in a single imaging session. J Nucl Med 2021; 62:jnumed.120.257345. [PMID: 33741649 PMCID: PMC8612203 DOI: 10.2967/jnumed.120.257345] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2020] [Revised: 03/09/2021] [Accepted: 03/09/2021] [Indexed: 11/16/2022] Open
Abstract
Previous multi-center imaging studies with 18F-FDG PET have established the presence of Parkinson's disease motor- and cognition-related metabolic patterns termed PDRP and PDCP in patients with this disorder. Given that in PD cerebral perfusion and glucose metabolism are typically coupled in the absence of medication, we determined whether subject expression of these disease networks can be quantified in early-phase images from dynamic 18F-FPCIT PET scans acquired to assess striatal dopamine transporter (DAT) binding. Methods: We studied a cohort of early-stage PD patients and age-matched healthy control subjects who underwent 18F-FPCIT at baseline; scans were repeated 4 years later in a smaller subset of patients. The early 18F-FPCIT frames, which reflect cerebral perfusion, were used to compute PDRP and PDCP expression (subject scores) in each subject, and compared to analogous measures computed based on 18F-FDG PET scan when additionally available. The late 18F-FPCIT frames were used to measure caudate and putamen DAT binding in the same individuals. Results: PDRP subject scores from early-phase 18F-FPCIT and 18F-FDG scans were elevated and striatal DAT binding reduced in PD versus healthy subjects. The PDRP scores from 18F-FPCIT correlated with clinical motor ratings, disease duration, and with corresponding measures from 18F-FDG PET. In addition to correlating with disease duration and analogous 18F-FDG PET values, PDCP scores correlated with DAT binding in the caudate/anterior putamen. PDRP and PDCP subject scores using either method rose over 4 years whereas striatal DAT binding declined over the same time period. Conclusion: Early-phase images obtained with 18F-FPCIT PET can provide an alternative to 18F-FDG PET for PD network quantification. This technique therefore allows PDRP/PDCP expression and caudate/putamen DAT binding to be evaluated with a single tracer in one scanning session.
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Affiliation(s)
- Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York; and
| | - Chris Tang
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York; and
| | - Katharina Schindlbeck
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York; and
| | - Yaacov Rydzinski
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Vijay Dhawan
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York; and
| | - Phoebe G. Spetsieris
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York; and
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York; and
| | - David Eidelberg
- Center for Neurosciences, Institute of Molecular Medicine, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York; and
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Peretti DE, Renken RJ, Reesink FE, de Jong BM, De Deyn PP, Dierckx RAJO, Doorduin J, Boellaard R, Vállez García D. Feasibility of pharmacokinetic parametric PET images in scaled subprofile modelling using principal component analysis. NEUROIMAGE-CLINICAL 2021; 30:102625. [PMID: 33756179 PMCID: PMC8020472 DOI: 10.1016/j.nicl.2021.102625] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 03/05/2021] [Indexed: 11/30/2022]
Abstract
Scaled subprofile model using principal component analysis (SSM/PCA) is a multivariate analysis technique used, mainly in [18F]-2-fluoro-2-deoxy-d-glucose (FDG) PET studies, for the generation of disease-specific metabolic patterns (DP) that may aid with the classification of subjects with neurological disorders, like Alzheimer’s disease (AD). The aim of this study was to explore the feasibility of using quantitative parametric images for this type of analysis, with dynamic [11C]-labelled Pittsburgh Compound B (PIB) PET data as an example. Therefore, 15 AD patients and 15 healthy control subjects were included in an SSM/PCA analysis to generate four AD-DPs using relative cerebral blood flow (R1), binding potential (BPND) and SUVR images derived from dynamic PIB and static FDG-PET studies. Furthermore, 49 new subjects with a variety of neurodegenerative cognitive disorders were tested against these DPs. The AD-DP was characterized by a reduction in the frontal, parietal, and temporal lobes voxel values for R1 and SUVR-FDG DPs; and by a general increase of values in cortical areas for BPND and SUVR-PIB DPs. In conclusion, the results suggest that the combination of parametric images derived from a single dynamic scan might be a good alternative for subject classification instead of using 2 independent PET studies.
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Affiliation(s)
- Débora E Peretti
- University of Groningen, University Medical Center Groningen, Department of Nuclear Medicine and Molecular Imaging, The Netherlands.
| | - Remco J Renken
- University of Groningen, University Medical Center Groningen, Cognitive Neuroscience Centre, Department of Biomedical Sciences of Cell & Systems, The Netherlands
| | - Fransje E Reesink
- University of Groningen, University Medical Center Groningen, Department of Neurology, Alzheimer Research Centre, The Netherlands
| | - Bauke M de Jong
- University of Groningen, University Medical Center Groningen, Department of Neurology, Alzheimer Research Centre, The Netherlands
| | - Peter P De Deyn
- University of Groningen, University Medical Center Groningen, Department of Neurology, Alzheimer Research Centre, The Netherlands; University of Antwerp, Institute Born-Bunge, Laboratory of Neurochemistry and Behaviour, Belgium
| | - Rudi A J O Dierckx
- University of Groningen, University Medical Center Groningen, Department of Nuclear Medicine and Molecular Imaging, The Netherlands
| | - Janine Doorduin
- University of Groningen, University Medical Center Groningen, Department of Nuclear Medicine and Molecular Imaging, The Netherlands
| | - Ronald Boellaard
- University of Groningen, University Medical Center Groningen, Department of Nuclear Medicine and Molecular Imaging, The Netherlands
| | - David Vállez García
- University of Groningen, University Medical Center Groningen, Department of Nuclear Medicine and Molecular Imaging, The Netherlands
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Blazhenets G, Frings L, Ma Y, Sörensen A, Eidelberg D, Wiltfang J, Meyer PT. Validation of the Alzheimer Disease Dementia Conversion-Related Pattern as an ATN Biomarker of Neurodegeneration. Neurology 2021; 96:e1358-e1368. [PMID: 33408150 DOI: 10.1212/wnl.0000000000011521] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 11/09/2020] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To determine whether the Alzheimer disease (AD) dementia conversion-related pattern (ADCRP) on [18F]FDG PET can serve as a valid predictor for the development of AD dementia, the individual expression of the ADCRP (subject score) and its prognostic value were examined in patients with mild cognitive impairment (MCI) and biologically defined AD. METHODS A total of 269 patients with available [18F]FDG PET, [18F]AV-45 PET, phosphorylated and total tau in CSF, and neurofilament light chain in plasma were included. Following the AT(N) classification scheme, where AD is defined biologically by in vivo biomarkers of β-amyloid (Aβ) deposition ("A") and pathologic tau ("T"), patients were categorized to the A-T-, A+T-, A+T+ (AD), and A-T+ groups. RESULTS The mean subject score of the ADCRP was significantly higher in the A+T+ group compared to each of the other group (all p < 0.05) but was similar among the latter (all p > 0.1). Within the A+T+ group, the subject score of ADCRP was a significant predictor of conversion to dementia (hazard ratio, 2.02 per z score increase; p < 0.001), with higher predictive value than of alternative biomarkers of neurodegeneration (total tau and neurofilament light chain). Stratification of A+T+ patients by the subject score of ADCRP yielded well-separated groups of high, medium, and low conversion risks. CONCLUSIONS The ADCRP is a valuable biomarker of neurodegeneration in patients with MCI and biologically defined AD. It shows great potential for stratifying the risk and estimating the time to conversion to dementia in patients with MCI and underlying AD (A+T+). CLASSIFICATION OF EVIDENCE This study provides Class I evidence that [18F]FDG PET predicts the development of AD dementia in individuals with MCI and underlying AD as defined by the AT(N) framework.
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Affiliation(s)
- Ganna Blazhenets
- From the Department of Nuclear Medicine (G.B., L.F., A.S., P.T.M.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg; Center for Neurosciences (Y.M., D.E.), Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY; and Department of Psychiatry and Psychotherapy (J.W.), University Medical Center, Georg-August-University, Göttingen, Germany.
| | - Lars Frings
- From the Department of Nuclear Medicine (G.B., L.F., A.S., P.T.M.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg; Center for Neurosciences (Y.M., D.E.), Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY; and Department of Psychiatry and Psychotherapy (J.W.), University Medical Center, Georg-August-University, Göttingen, Germany
| | - Yilong Ma
- From the Department of Nuclear Medicine (G.B., L.F., A.S., P.T.M.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg; Center for Neurosciences (Y.M., D.E.), Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY; and Department of Psychiatry and Psychotherapy (J.W.), University Medical Center, Georg-August-University, Göttingen, Germany
| | - Arnd Sörensen
- From the Department of Nuclear Medicine (G.B., L.F., A.S., P.T.M.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg; Center for Neurosciences (Y.M., D.E.), Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY; and Department of Psychiatry and Psychotherapy (J.W.), University Medical Center, Georg-August-University, Göttingen, Germany
| | - David Eidelberg
- From the Department of Nuclear Medicine (G.B., L.F., A.S., P.T.M.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg; Center for Neurosciences (Y.M., D.E.), Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY; and Department of Psychiatry and Psychotherapy (J.W.), University Medical Center, Georg-August-University, Göttingen, Germany
| | - Jens Wiltfang
- From the Department of Nuclear Medicine (G.B., L.F., A.S., P.T.M.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg; Center for Neurosciences (Y.M., D.E.), Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY; and Department of Psychiatry and Psychotherapy (J.W.), University Medical Center, Georg-August-University, Göttingen, Germany
| | - Philipp T Meyer
- From the Department of Nuclear Medicine (G.B., L.F., A.S., P.T.M.), Medical Center University of Freiburg, Faculty of Medicine, University of Freiburg; Center for Neurosciences (Y.M., D.E.), Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY; and Department of Psychiatry and Psychotherapy (J.W.), University Medical Center, Georg-August-University, Göttingen, Germany
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van Veen R, Gurvits V, Kogan RV, Meles SK, de Vries GJ, Renken RJ, Rodriguez-Oroz MC, Rodriguez-Rojas R, Arnaldi D, Raffa S, de Jong BM, Leenders KL, Biehl M. An application of generalized matrix learning vector quantization in neuroimaging. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 197:105708. [PMID: 32977181 DOI: 10.1016/j.cmpb.2020.105708] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Accepted: 08/08/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND AND OBJECTIVE Neurodegenerative diseases like Parkinson's disease often take several years before they can be diagnosed reliably based on clinical grounds. Imaging techniques such as MRI are used to detect anatomical (structural) pathological changes. However, these kinds of changes are usually seen only late in the development. The measurement of functional brain activity by means of [18F]fluorodeoxyglucose positron emission tomography (FDG-PET) can provide useful information, but its interpretation is more difficult. The scaled sub-profile model principal component analysis (SSM/PCA) was shown to provide more useful information than other statistical techniques. Our objective is to improve the performance further by combining SSM/PCA and prototype-based generalized matrix learning vector quantization (GMLVQ). METHODS We apply a combination of SSM/PCA and GMLVQ as a classifier. In order to demonstrate the combination's validity, we analyze FDG-PET data of Parkinson's disease (PD) patients collected at three different neuroimaging centers in Europe. We determine the diagnostic performance by performing a ten times repeated ten fold cross validation. Additionally, discriminant visualizations of the data are included. The prototypes and relevance of GMLVQ are transformed back to the original voxel space by exploiting the linearity of SSM/PCA. The resulting prototypes and relevance profiles have then been assessed by three neurologists. RESULTS One important finding is that discriminative visualization can help to identify disease-related properties as well as differences which are due to center-specific factors. Secondly, the neurologist assessed the interpretability of the method and confirmed that prototypes are similar to known activity profiles of PD patients. CONCLUSION We have shown that the presented combination of SSM/PCA and GMLVQ can provide useful means to assess and better understand characteristic differences in FDG-PET data from PD patients and HCs. Based on the assessments by medical experts and the results of our computational analysis we conclude that the first steps towards a diagnostic support system have been taken successfully.
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Affiliation(s)
- Rick van Veen
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, the Netherlands.
| | - Vita Gurvits
- Department of Nuclear Medicine & Molecular Imaging, University Medical Center Groningen, the Netherlands
| | - Rosalie V Kogan
- Department of Nuclear Medicine & Molecular Imaging, University Medical Center Groningen, the Netherlands
| | - Sanne K Meles
- Department of Neurology, University Medical Centre Groningen, the Netherlands
| | | | - Remco J Renken
- Department of Biomedical Sciences of Cells & Systems, Cognitive Neuroscience Center, University Medical Center Groningen, the Netherlands
| | | | | | - Dario Arnaldi
- Department of Neuroscience, University of Genoa, Italy; Neurology Clinic, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Stefano Raffa
- Department of Health Sciences, University of Genoa, Italy; Nuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Bauke M de Jong
- Department of Neurology, University Medical Centre Groningen, the Netherlands
| | - Klaus L Leenders
- Department of Nuclear Medicine & Molecular Imaging, University Medical Center Groningen, the Netherlands
| | - Michael Biehl
- Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, the Netherlands
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46
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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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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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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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Tang CC, Holtbernd F, Ma Y, Spetsieris P, Oh A, Fink GR, Timmermann L, Eggers C, Eidelberg D. Hemispheric Network Expression in Parkinson's Disease: Relationship to Dopaminergic Asymmetries. JOURNAL OF PARKINSONS DISEASE 2020; 10:1737-1749. [PMID: 32925097 DOI: 10.3233/jpd-202117] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND Parkinson's disease (PD) is characterized by brain metabolic networks, specifically associated with motor and cognitive manifestations. Few studies have investigated network changes in cerebral hemispheres ipsilateral and contralateral to the clinically more affected body side. OBJECTIVE We examined hemispheric network abnormalities and their relationship to striatal dopaminergic deficits in PD patients at different stages. METHODS 45 PD patients underwent dual-tracer positron emission tomography (PET) with 18F-fluorodeoxyglucose (FDG) and 18F-fluorodopa (FDOPA) in a high-resolution PET scanner. In all patients, we computed expression levels for the PD-related motor/cognition metabolic patterns (PDRP/PDCP) as well as putamen/caudate FDOPA uptake values in both hemispheres. Resulting hemispheric measures in the PD group were compared with corresponding healthy control values and assessed across disease stages. RESULTS Hemispheric PDRP and PDCP expression was significantly elevated contralateral and ipsilateral to the more affected body side in patients with unilateral symptoms (H&Y 1: p < 0.01) and in patients with bilateral limb involvement (H&Y 2-3: p < 0.001; H&Y 4: p < 0.003). Elevations in pattern expression were symmetrical at all disease stages. By contrast, FDOPA uptake in the caudate and putamen was reduced bilaterally (p < 0.002), with lower values on both sides at more advanced disease stages. Hemispheric uptake was asymmetrical in both striatal regions, with lower contralateral values at all disease stages. The magnitude of hemispheric uptake asymmetry was smaller with more advanced disease, reflecting greater change ipsilaterally. CONCLUSION Symmetrical network expression in PD represents bilateral functional effects unrelated to nigrostriatal dopaminergic asymmetries.
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Affiliation(s)
- Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Florian Holtbernd
- RWTH Aachen University, Department of Neurology, Aachen, Germany.,JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Juelich Research Centre and RWTH Aachen University, Aachen, Germany.,Institute of Neuroscience and Medicine 4 (INM-4), Juelich Research Centre, Juelich, Germany
| | - Yilong Ma
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Phoebe Spetsieris
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Alice Oh
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Gereon R Fink
- Department of Neurology, University of Cologne, Cologne, Germany.,Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany
| | - Lars Timmermann
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Jülich Research Centre, Jülich, Germany.,Department of Neurology, University Hospital of Giessen and Marburg, Marburg, Germany
| | - Carsten Eggers
- Department of Neurology, University Hospital of Giessen and Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, Universities Marburg and Giessen, Marburg, Germany
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
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Watanabe H, Hattori T, Kume A, Misu K, Ito T, Koike Y, Johnson TA, Kamitsuji S, Kamatani N, Sobue G. Improved Parkinsons disease motor score in a single-arm open-label trial of febuxostat and inosine. Medicine (Baltimore) 2020; 99:e21576. [PMID: 32871874 PMCID: PMC7458241 DOI: 10.1097/md.0000000000021576] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
BACKGROUND Cellular energetics play an important role in Parkinsons disease etiology, but no treatments directly address this deficiency. Our past research showed that treatment with febuxostat and inosine increased blood hypoxanthine and ATP in healthy adults, and a preliminary trial in 3 Parkinson's disease patients suggested some symptomatic improvements with no adverse effects. METHODS To examine the efficacy on symptoms and safety in a larger group of Parkinsons disease patients, we conducted a single-arm, open-label trial at 5 Japanese neurology clinics and enrolled thirty patients (nmales = 11; nfemales = 19); 26 patients completed the study (nmales = 10; nfemales = 16). Each patient was administered febuxostat 20 mg and inosine 500 mg twice-per-day (after breakfast and dinner) for 8 weeks. The primary endpoint was the difference of MDS-UPDRS Part III score immediately before and after 57 days of treatment. RESULTS Serum hypoxanthine concentrations were raised significantly after treatment (Pre = 11.4 μM; Post = 38.1 μM; P < .0001). MDS-UPDRS Part III score was significantly lower after treatment (Pre = 28.1 ± 9.3; Post = 24.7 ± 10.8; mean ± SD; P = .0146). Sixteen adverse events occurred in 13/29 (44.8%) patients, including 1 serious adverse event (fracture of the second lumbar vertebra) that was considered not related to the treatment. CONCLUSIONS The results of this study suggest that co-administration of febuxostat and inosine is relatively safe and effective for improving symptoms of Parkinsons disease patients. Further controlled trials need to be performed to confirm the symptomatic improvement and to examine the disease-modifying effect in long-term trials.
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Affiliation(s)
- Hirohisa Watanabe
- Nagoya University Graduate School of Medicine, Brain and Mind Research Center, Nagoya
- Fujita Health University School of Medicine, Department of Neurology, Toyoake
| | | | | | | | | | | | | | | | | | - Gen Sobue
- Nagoya University Graduate School of Medicine, Brain and Mind Research Center, Nagoya
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