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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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Lynch DG, Shah KA, Powell K, Wadolowski S, Tambo W, Strohl JJ, Unadkat P, Eidelberg D, Huerta PT, Li C. Neurobehavioral Impairments Predict Specific Cerebral Damage in Rat Model of Subarachnoid Hemorrhage. Transl Stroke Res 2023:10.1007/s12975-023-01180-2. [PMID: 37493939 DOI: 10.1007/s12975-023-01180-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 06/09/2023] [Accepted: 07/18/2023] [Indexed: 07/27/2023]
Abstract
Subarachnoid hemorrhage (SAH) is a severe form of stroke that can cause unpredictable and diffuse cerebral damage, which is difficult to detect until it becomes irreversible. Therefore, there is a need for a reliable method to identify dysfunctional regions and initiate treatment before permanent damage occurs. Neurobehavioral assessments have been suggested as a possible tool to detect and approximately localize dysfunctional cerebral regions. In this study, we hypothesized that a neurobehavioral assessment battery could be a sensitive and specific method for detecting damage in discrete cerebral regions following SAH. To test this hypothesis, a behavioral battery was employed at multiple time points after SAH induced via an endovascular perforation, and brain damage was confirmed via postmortem histopathological analysis. Our results demonstrate that impairment of sensorimotor function accurately predict damage in the cerebral cortex (AUC 0.905; sensitivity 81.8%; specificity 90.9%) and striatum (AUC 0.913; sensitivity 90.1%; specificity 100%), while impaired novel object recognition is a more accurate indicator of damage to the hippocampus (AUC 0.902; sensitivity 74.1%; specificity 83.3%) than impaired reference memory (AUC 0.746; sensitivity 72.2%; specificity 58.0%). Tests for anxiety-like and depression-like behaviors predict damage to the amygdala (AUC 0.900; sensitivity 77.0%; specificity 81.7%) and thalamus (AUC 0.963; sensitivity 86.3%; specificity 87.8%), respectively. This study suggests that recurring behavioral testing can accurately predict damage in specific brain regions, which could be developed into a clinical battery for early detection of SAH damage in humans, potentially improving early treatment and outcomes.
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Affiliation(s)
- Daniel G Lynch
- Translational Brain Research Laboratory, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Kevin A Shah
- Translational Brain Research Laboratory, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Department of Neurosurgery, North Shore University Hospital, Manhasset, NY, USA
| | - Keren Powell
- Translational Brain Research Laboratory, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Steven Wadolowski
- Translational Brain Research Laboratory, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Willians Tambo
- Translational Brain Research Laboratory, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY, USA
| | - Joshua J Strohl
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Laboratory of Immune and Neural Networks, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Prashin Unadkat
- Department of Neurosurgery, North Shore University Hospital, Manhasset, NY, USA
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY, USA
- Center for Neurosciences, Lab for Behavioral and Molecular Neuroimaging, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - David Eidelberg
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY, USA
- Center for Neurosciences, Lab for Behavioral and Molecular Neuroimaging, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Patricio T Huerta
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY, USA
- Laboratory of Immune and Neural Networks, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Chunyan Li
- Translational Brain Research Laboratory, The Feinstein Institutes for Medical Research, Manhasset, NY, USA.
- Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA.
- Department of Neurosurgery, North Shore University Hospital, Manhasset, NY, USA.
- Elmezzi Graduate School of Molecular Medicine, Manhasset, NY, USA.
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Vo A, Nguyen N, Fujita K, Schindlbeck KA, Rommal A, Bressman SB, Niethammer M, Eidelberg D. Disordered network structure and function in dystonia: pathological connectivity vs. adaptive responses. Cereb Cortex 2023; 33:6943-6958. [PMID: 36749014 PMCID: PMC10233302 DOI: 10.1093/cercor/bhad012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Revised: 12/21/2022] [Accepted: 01/10/2023] [Indexed: 02/08/2023] Open
Abstract
Primary dystonia is thought to emerge through abnormal functional relationships between basal ganglia and cerebellar motor circuits. These interactions may differ across disease subtypes and provide a novel biomarker for diagnosis and treatment. Using a network mapping algorithm based on resting-state functional MRI (rs-fMRI), a method that is readily implemented on conventional MRI scanners, we identified similar disease topographies in hereditary dystonia associated with the DYT1 or DYT6 mutations and in sporadic patients lacking these mutations. Both networks were characterized by contributions from the basal ganglia, cerebellum, thalamus, sensorimotor areas, as well as cortical association regions. Expression levels for the two networks were elevated in hereditary and sporadic dystonia, and in non-manifesting carriers of dystonia mutations. Nonetheless, the distribution of abnormal functional connections differed across groups, as did metrics of network organization and efficiency in key modules. Despite these differences, network expression correlated with dystonia motor ratings, significantly improving the accuracy of predictions based on thalamocortical tract integrity obtained with diffusion tensor MRI (DTI). Thus, in addition to providing unique information regarding the anatomy of abnormal brain circuits, rs-fMRI functional networks may provide a widely accessible method to help in the objective evaluation of new treatments for this disorder.
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Affiliation(s)
- An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Koji Fujita
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - Katharina A Schindlbeck
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
- Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
| | - Andrea Rommal
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - Susan B Bressman
- Department of Neurology, Mount Sinai Beth Israel, New York, NY 10003, USA
| | - Martin Niethammer
- 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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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Perovnik M, Rus T, Schindlbeck KA, Eidelberg D. Publisher Correction: Functional brain networks in the evaluation of patients with neurodegenerative disorders. Nat Rev Neurol 2023:10.1038/s41582-023-00824-z. [PMID: 37208497 DOI: 10.1038/s41582-023-00824-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
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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Lynch DG, Shah KA, Powell K, Wadolowski S, Ayol WT, Strohl JJ, Unadkat P, Eidelberg D, Huerta PT, Li C. Neurobehavioral impairments predict specific cerebral damage in rat model of subarachnoid hemorrhage. Res Sq 2023:rs.3.rs-2943917. [PMID: 37292945 PMCID: PMC10246236 DOI: 10.21203/rs.3.rs-2943917/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Subarachnoid hemorrhage (SAH) is a severe form of stroke that can cause unpredictable and diffuse cerebral damage, which is difficult to detect until it becomes irreversible. Therefore, there is a need for a reliable method to identify dysfunctional regions and initiate treatment before permanent damage occurs. Neurobehavioral assessments have been suggested as a possible tool to detect and approximately localize dysfunctional cerebral regions. In this study, we hypothesized that a neurobehavioral assessment battery could be a sensitive and specific early warning for damage in discrete cerebral regions following SAH. To test this hypothesis, a behavioral battery was employed at multiple time points after SAH induced via an endovascular perforation, and brain damage was confirmed via postmortem histopathological analysis. Our results demonstrate that impairment of sensorimotor function accurately predict damage in the cerebral cortex (AUC: 0.905; sensitivity: 81.8%; specificity: 90.9%) and striatum (AUC: 0.913; sensitivity: 90.1%; specificity: 100%), while impaired novel object recognition is a more accurate indicator of damage to the hippocampus (AUC: 0.902; sensitivity: 74.1%; specificity: 83.3%) than impaired reference memory (AUC: 0.746; sensitivity: 72.2%; specificity: 58.0%). Tests for anxiety-like and depression-like behaviors predict damage to the amygdala (AUC: 0.900; sensitivity: 77.0%; specificity: 81.7%) and thalamus (AUC: 0.963; sensitivity: 86.3%; specificity: 87.8%), respectively. This study suggests that recurring behavioral testing can accurately predict damage in specific brain regions, which could be developed into a clinical battery for early detection of SAH damage in humans, potentially improving early treatment and outcomes.
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11
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Rus T, Mlakar J, Ležaić L, Vo A, Nguyen N, Tang C, Fiorini M, Prieto E, Marti-Andres G, Arbizu J, Eidelberg D, Trošt M. Sporadic Creutzfeldt-Jakob disease is associated with reorganization of metabolic connectivity in a pathological brain network. Eur J Neurol 2023; 30:1035-1047. [PMID: 36583625 DOI: 10.1111/ene.15669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 12/23/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022]
Abstract
BACKGROUND AND PURPOSE Although sporadic Creutzfeldt-Jakob disease (sCJD) is a rare cause of dementia, it is critical to understand its functional networks as the prion protein spread throughout the brain may share similar mechanisms with other more common neurodegenerative disorders. In this study, the metabolic brain network associated with sCJD was investigated and its internal network organization was explored. METHODS We explored 2-[18 F]fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) brain scans of 29 sCJD patients, 56 normal controls (NCs) and 46 other dementia patients from two independent centers. sCJD-related pattern (CJDRP) was identified in a cohort of 16 pathologically proven sCJD patients and 16 age-matched NCs using scaled subprofile modeling/principal component analysis and was prospectively validated in an independent cohort of 13 sCJD patients and 20 NCs. The pattern's specificity was tested on other dementia patients and its clinical relevance by clinical correlations. The pattern's internal organization was further studied using graph theory methods. RESULTS The CJDRP was characterized by relative hypometabolism in the bilateral caudate, thalami, middle and superior frontal gyri, parietal lobe and posterior cingulum in association with relative hypermetabolism in the hippocampi, parahippocampal gyri and cerebellum. The pattern's expression significantly discriminated sCJD from NCs and other dementia patients (p < 0.005; receiver operating characteristic analysis CJD vs. NCs area under the curve [AUC] 0.90-0.96, sCJD vs. Alzheimer's disease AUC 0.78, sCJD vs. behavioral variant of frontotemporal dementia AUC 0.84). The pattern's expression significantly correlated with cognitive, functional decline and disease duration. The metabolic connectivity analysis revealed inefficient information transfer with specific network reorganization. CONCLUSIONS The CJDRP is a robust metabolic biomarker of sCJD. Due to its excellent clinical correlations it has the potential to monitor disease in emerging disease-modifying trials.
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Affiliation(s)
- Tomaž Rus
- Department of Neurology, University Medical Centre, Ljubljana, Slovenia
- Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Jernej Mlakar
- Institute of Pathology, Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
| | - Luka Ležaić
- Department of Nuclear Medicine, University Medical Centre, Ljubljana, Slovenia
| | - An Vo
- Center for Neurosciences, Feinstein Institutes for Medical Research, Manhasset, New York City, USA
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, Bronx, New York City, USA
| | - Chris Tang
- Center for Neurosciences, Feinstein Institutes for Medical Research, Manhasset, New York City, USA
| | - Michele Fiorini
- Section of Neuropathology, Department of Neuroscience, Biomedicine and Movement, University of Verona, Verona, Italy
| | - Elena Prieto
- Department of Nuclear Medicine and Molecular Imaging, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Gloria Marti-Andres
- Department of Neurology, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - Javier Arbizu
- Department of Nuclear Medicine and Molecular Imaging, Clínica Universidad de Navarra, University of Navarra, Pamplona, Spain
| | - David Eidelberg
- Center for Neurosciences, Feinstein Institutes for Medical Research, Manhasset, New York City, USA
| | - Maja Trošt
- Department of Neurology, University Medical Centre, Ljubljana, Slovenia
- Medical Faculty, University of Ljubljana, Ljubljana, Slovenia
- Department of Nuclear Medicine, University Medical Centre, Ljubljana, Slovenia
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12
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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13
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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: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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14
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Perovnik M, Tang CC, Šalamon S, Eidelberg D. Longitudinal changes in the expression of Alzheimer’s disease‐related pattern and default mode network. Alzheimers Dement 2022. [DOI: 10.1002/alz.067976] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Matej Perovnik
- Department for Neurology, UMC Ljubljana Ljubljana Slovenia
| | - Chris C Tang
- The Feinstein Institute for Medical Research Manhasset NY USA
| | - Sandi Šalamon
- Faculty of Mathematics and Physics Ljubljana Slovenia
| | - David Eidelberg
- The Feinstein Institute for Medical Research Manhasset NY USA
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15
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Perovnik M, Tang CC, Šalamon S, Eidelberg D. Longitudinal changes in the expression of Alzheimer’s disease‐related pattern and default mode network. Alzheimers Dement 2022. [DOI: 10.1002/alz.067928] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Matej Perovnik
- Department for Neurology, UMC Ljubljana Ljubljana Slovenia
| | - Chris C Tang
- The Feinstein Institute for Medical Research Manhasset NY USA
| | - Sandi Šalamon
- Faculty of Mathematics and Physics Ljubljana Slovenia
| | - David Eidelberg
- The Feinstein Institute for Medical Research Manhasset NY USA
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16
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Rus T, Perovnik M, Vo A, Nguyen N, Tang C, Jamšek J, Šurlan Popović K, Grimmer T, Yakushev I, Diehl‐Schmid J, Eidelberg D, Trošt M. Disease specific and nonspecific metabolic brain networks in behavioral variant of frontotemporal dementia. Hum Brain Mapp 2022; 44:1079-1093. [PMID: 36334269 PMCID: PMC9875921 DOI: 10.1002/hbm.26140] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 10/10/2022] [Accepted: 10/24/2022] [Indexed: 11/08/2022] Open
Abstract
Behavioral variant of frontotemporal dementia (bvFTD) is common among young-onset dementia patients. While bvFTD-specific multivariate metabolic brain pattern (bFDRP) has been identified previously, little is known about its temporal evolution, internal structure, effect of atrophy, and its relationship with nonspecific resting-state networks such as default mode network (DMN). In this multicenter study, we explored FDG-PET brain scans of 111 bvFTD, 26 Alzheimer's disease, 16 Creutzfeldt-Jakob's disease, 24 semantic variant primary progressive aphasia (PPA), 18 nonfluent variant PPA and 77 healthy control subjects (HC) from Slovenia, USA, and Germany. bFDRP was identified in a cohort of 20 bvFTD patients and age-matched HC using scaled subprofile model/principle component analysis and validated in three independent cohorts. It was characterized by hypometabolism in frontal cortex, insula, anterior/middle cingulate, caudate, thalamus, and temporal poles. Its expression in bvFTD patients was significantly higher compared to HC and other dementia syndromes (p < .0004), correlated with cognitive decline (p = .0001), and increased over time in longitudinal cohort (p = .0007). Analysis of internal network organization by graph-theory methods revealed prominent network disruption in bvFTD patients. We have further found a specific atrophy-related pattern grossly corresponding to bFDRP; however, its contribution to the metabolic pattern was minimal. Finally, despite the overlap between bFDRP and FDG-PET-derived DMN, we demonstrated a predominant role of the specific bFDRP. Taken together, we validated the bFDRP network as a diagnostic/prognostic biomarker specific for bvFTD, provided a unique insight into its highly reproducible internal structure, and proved that bFDRP is unaffected by structural atrophy and independent of normal resting state networks loss.
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Affiliation(s)
- Tomaž Rus
- Department of NeurologyUMC LjubljanaLjubljanaSlovenia,Medical FacultyUniversity of LjubljanaLjubljanaSlovenia
| | | | - An Vo
- Center for NeurosciencesFeinstein Institutes for Medical ResearchManhassetNew YorkUSA
| | - Nha Nguyen
- Department of GeneticsAlbert Einstein College of MedicineBronxNew YorkUSA
| | - Chris Tang
- Center for NeurosciencesFeinstein Institutes for Medical ResearchManhassetNew YorkUSA
| | - Jan Jamšek
- Department of Nuclear MedicineUMC LjubljanaLjubljanaSlovenia
| | | | - Timo Grimmer
- Department of Psychiatry and Psychotherapy, Klinikum rechts der IsarTechnical University of Munich, School of MedicineMunichGermany
| | - Igor Yakushev
- Department of Nuclear Medicine, Klinikum rechts der IsarTechnical University of MunichMunichGermany,TUM Neuroimaging Center, Klinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Janine Diehl‐Schmid
- Department of Psychiatry and Psychotherapy, Klinikum rechts der IsarTechnical University of Munich, School of MedicineMunichGermany
| | - David Eidelberg
- Center for NeurosciencesFeinstein Institutes for Medical ResearchManhassetNew YorkUSA
| | - Maja Trošt
- Department of NeurologyUMC LjubljanaLjubljanaSlovenia,Medical FacultyUniversity of LjubljanaLjubljanaSlovenia,Department of Nuclear MedicineUMC LjubljanaLjubljanaSlovenia
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17
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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: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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18
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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] [What about the content of this article? (0)] [Affiliation(s)] [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] [What about the content of this article? (0)] [Affiliation(s)] [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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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] [What about the content of this article? (0)] [Affiliation(s)] [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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21
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Mader S, Brimberg L, Vo A, Strohl JJ, Crawford JM, Bonnin A, Carrión J, Campbell D, Huerta TS, La Bella A, Berlin R, Dewey SL, Hellman M, Eidelberg D, Dujmovic I, Drulovic J, Bennett JL, Volpe BT, Huerta PT, Diamond B. In utero exposure to maternal anti-aquaporin-4 antibodies alters brain vasculature and neural dynamics in male mouse offspring. Sci Transl Med 2022; 14:eabe9726. [PMID: 35442708 PMCID: PMC9973562 DOI: 10.1126/scitranslmed.abe9726] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The fetal brain is constantly exposed to maternal IgG before the formation of an effective blood-brain barrier (BBB). Here, we studied the consequences of fetal brain exposure to an antibody to the astrocytic protein aquaporin-4 (AQP4-IgG) in mice. AQP4-IgG was cloned from a patient with neuromyelitis optica spectrum disorder (NMOSD), an autoimmune disease that can affect women of childbearing age. We found that embryonic radial glia cells in neocortex express AQP4. These cells are critical for blood vessel and BBB formation through modulation of the WNT signaling pathway. Male fetuses exposed to AQP4-IgG had abnormal cortical vasculature and lower expression of WNT signaling molecules Wnt5a and Wnt7a. Positron emission tomography of adult male mice exposed in utero to AQP4-IgG revealed increased blood flow and BBB leakiness in the entorhinal cortex. Adult male mice exposed in utero to AQP4-IgG had abnormal cortical vessels, fewer dendritic spines in pyramidal and stellate neurons, and more S100β+ astrocytes in the entorhinal cortex. Behaviorally, they showed impairments in the object-place memory task. Neural recordings indicated that their grid cell system, within the medial entorhinal cortex, did not map the local environment appropriately. Collectively, these data implicate in utero binding of AQP4-IgG to radial glia cells as a mechanism for alterations of the developing male brain and adds NMOSD to the conditions in which maternal IgG may cause persistent brain dysfunction in offspring.
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Affiliation(s)
- Simone Mader
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA.,Institute of Clinical Neuroimmunology, Biomedical Center of the Ludwig Maximilian University of Munich, Munich 82152, Germany.,Corresponding author
| | - Lior Brimberg
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA
| | - An Vo
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA
| | - Joshua J. Strohl
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA.,Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY 11030, USA
| | - James M. Crawford
- Department of Pathology and Laboratory Medicine, Northwell Health, Manhasset, NY 11030, USA
| | - Alexandre Bonnin
- Department of Physiology and Neurosciences, Zilkha Neurogenetic Institute, University of Southern California, Keck School of Medicine, Los Angeles, CA 90033, USA
| | - Joseph Carrión
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA
| | - Delcora Campbell
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA
| | - Tomás S. Huerta
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA.,Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY 11030, USA
| | - Andrea La Bella
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA
| | - Roseann Berlin
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA
| | - Stephen L. Dewey
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA
| | - Matthew Hellman
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA
| | - David Eidelberg
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA
| | - Irena Dujmovic
- Clinical Center of Serbia University School of Medicine, Belgrade, 11000, Serbia.,Department of Neurology, University of North Carolina, School of Medicine, Chapel Hill, NC 27517, USA
| | - Jelena Drulovic
- Clinical Center of Serbia University School of Medicine, Belgrade, 11000, Serbia
| | - Jeffrey L. Bennett
- Department of Neurology and Ophthalmology, Programs in Neuroscience and Immunology, University of Colorado Denver, School of Medicine, Denver, CO 80045, USA
| | - Bruce T. Volpe
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA
| | - Patricio T. Huerta
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA.,Department of Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Manhasset, NY 11030, USA
| | - Betty Diamond
- The Feinstein Institutes for Medical Research, Northwell Health, Manhasset NY 11030, USA
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22
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Dhawan V, Niethammer MH, Lesser ML, Pappas KN, Hellman M, Fitzpatrick TM, Bjelke D, Singh J, Quatarolo LM, Choi YY, Oh A, Eidelberg D, Chaly T. Prospective F-18 FDOPA PET Imaging Study in Human PD. Nucl Med Mol Imaging 2022; 56:147-157. [DOI: 10.1007/s13139-022-00748-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 03/18/2022] [Accepted: 03/21/2022] [Indexed: 11/28/2022] Open
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23
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Brakedal B, Dölle C, Riemer F, Ma Y, Nido GS, Skeie GO, Craven AR, Schwarzlmüller T, Brekke N, Diab J, Sverkeli L, Skjeie V, Varhaug K, Tysnes OB, Peng S, Haugarvoll K, Ziegler M, Grüner R, Eidelberg D, Tzoulis C. The NADPARK study: A randomized phase I trial of nicotinamide riboside supplementation in Parkinson's disease. Cell Metab 2022; 34:396-407.e6. [PMID: 35235774 DOI: 10.1016/j.cmet.2022.02.001] [Citation(s) in RCA: 96] [Impact Index Per Article: 48.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 11/17/2021] [Accepted: 01/31/2022] [Indexed: 02/07/2023]
Abstract
We conducted a double-blinded phase I clinical trial to establish whether nicotinamide adenine dinucleotide (NAD) replenishment therapy, via oral intake of nicotinamide riboside (NR), is safe, augments cerebral NAD levels, and impacts cerebral metabolism in Parkinson's disease (PD). Thirty newly diagnosed, treatment-naive patients received 1,000 mg NR or placebo for 30 days. NR treatment was well tolerated and led to a significant, but variable, increase in cerebral NAD levels-measured by 31phosphorous magnetic resonance spectroscopy-and related metabolites in the cerebrospinal fluid. NR recipients showing increased brain NAD levels exhibited altered cerebral metabolism, measured by 18fluoro-deoxyglucose positron emission tomography, and this was associated with mild clinical improvement. NR augmented the NAD metabolome and induced transcriptional upregulation of processes related to mitochondrial, lysosomal, and proteasomal function in blood cells and/or skeletal muscle. Furthermore, NR decreased the levels of inflammatory cytokines in serum and cerebrospinal fluid. Our findings nominate NR as a potential neuroprotective therapy for PD, warranting further investigation in larger trials.
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Affiliation(s)
- Brage Brakedal
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Christian Dölle
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Frank Riemer
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Gonzalo S Nido
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Geir Olve Skeie
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Alexander R Craven
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Biological and Medical Psychology, University of Bergen, Bergen, Norway; Department of Clinical Engineering, Haukeland University Hospital, Bergen, Norway
| | - Thomas Schwarzlmüller
- Department of Clinical Medicine, University of Bergen, Bergen, Norway; Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Njål Brekke
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Joseph Diab
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Lars Sverkeli
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Vivian Skjeie
- Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - Kristin Varhaug
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Ole-Bjørn Tysnes
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Kristoffer Haugarvoll
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway
| | - Mathias Ziegler
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Renate Grüner
- Mohn Medical Imaging and Visualization Centre (MMIV), Department of Radiology, Haukeland University Hospital, Bergen, Norway
| | - David Eidelberg
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Charalampos Tzoulis
- Neuro-SysMed, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway.
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24
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Rus T, Ležaić L, Emeršič A, Kramberger MG, Pirtošek Z, Eidelberg D, Trost M. Abnormal metabolic brain network in behavioral variant of frontotemporal dementia. Alzheimers Dement 2021. [DOI: 10.1002/alz.056357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Tomaž Rus
- Department for Neurology UMC Ljubljana Ljubljana Slovenia
| | - Luka Ležaić
- Department for Nuclear Medicine UMC Ljubljana Ljubljana Slovenia
| | | | | | | | - David Eidelberg
- The Feinstein Institute for Medical Research Manhasset NY USA
| | - Maja Trost
- Department for Neurology UMC Ljubljana Ljubljana Slovenia
- Department for Nuclear Medicine UMC Ljubljana Ljubljana Slovenia
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25
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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] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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26
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Rothman JE, Eidelberg D, Rothman SL, Holford TR, Rothman DL. Analysis of the time course of COVID-19 cases and deaths from countries with extensive testing allows accurate early estimates of the age specific symptomatic CFR values. PLoS One 2021; 16:e0253843. [PMID: 34407073 PMCID: PMC8372929 DOI: 10.1371/journal.pone.0253843] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 06/14/2021] [Indexed: 01/10/2023] Open
Abstract
BACKGROUND Knowing the true infected and symptomatic case fatality ratios (IFR and CFR) for COVID-19 is of high importance for epidemiological model projections. Early in the pandemic many locations had limited testing and reporting, so that standard methods for determining IFR and CFR required large adjustments for missed cases. We present an alternate approach, based on results from the countries at the time that had a high test to positive case ratio to estimate symptomatic CFR. METHODS We calculated age specific (0-69, 70-79, 80+ years old) time corrected crude symptomatic CFR values from 7 countries using two independent time to fatality correction methods. Data was obtained through May 7, 2020. We applied linear regression to determine whether the mean of these coefficients had converged to the true symptomatic CFR values. We then tested these coefficients against values derived in later studies as well as a large random serological study in NYC at that time. RESULTS The age dependent symptomatic CFR values accurately predicted the percentage of the population infected as reported by two random testing studies in NYC. They also were in good agreement with later studies that estimated age specific IFR and CFR values from serological studies and more extensive data sets available later in the pandemic. CONCLUSIONS We found that for regions with extensive testing it is possible to get early accurate symptomatic CFR coefficients. These values, in combination with an estimate of the age dependence of infection, allows symptomatic CFR values and percentage of the population that is infected to be determined in similar regions with limited testing.
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Affiliation(s)
- Jessica E. Rothman
- Department of Epidemiology of Microbial Diseases, Yale University School of Public Health, New Haven, CT, United States of America
| | - David Eidelberg
- Center for Neurosciences, Institute of Molecular Medicine, Northwell Health, Manhasset, New York, United States of America
| | - Samantha L. Rothman
- Departments of Mathematics and Computer Science, Tulane University, New Orleans, LA, United States of America
| | - Theodore R. Holford
- Departments of Biostatistics, and Statistics and Data Science, Yale University School of Public Health and Yale University Graduate School of Arts and Sciences, New Haven, CT, United States of America
| | - Douglas L. Rothman
- Departments of Radiology and Biomedical Engineering, Yale University School of Medicine, New Haven, CT, United States of America
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27
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Schindlbeck KA, Vo A, Mattis PJ, Villringer K, Marzinzik F, Fiebach JB, Eidelberg D. Cognition-Related Functional Topographies in Parkinson's Disease: Localized Loss of the Ventral Default Mode Network. Cereb Cortex 2021; 31:5139-5150. [PMID: 34148072 DOI: 10.1093/cercor/bhab148] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 05/11/2021] [Accepted: 05/12/2021] [Indexed: 02/07/2023] Open
Abstract
Cognitive dysfunction in Parkinson's disease (PD) is associated with increased expression of the PD cognition-related pattern (PDCP), which overlaps with the normal default mode network (DMN). Here, we sought to determine the degree to which the former network represents loss of the latter as a manifestation of the disease process. To address this, we first analyzed metabolic images (fluorodeoxyglucose positron emission tomography [PET]) from a large PD sample with varying cognitive performance. Cognitive impairment in these patients correlated with increased PDCP expression as well as DMN loss. We next determined the spatial relationship of the 2 topographies at the subnetwork level. To this end, we analyzed resting-state functional magnetic resonance imaging (rs-fMRI) data from an independent population. This approach uncovered a significant PD cognition-related network that resembled previously identified PET- and rs-fMRI-based PDCP topographies. Further analysis revealed selective loss of the ventral DMN subnetwork (precuneus and posterior cingulate cortex) in PD, whereas the anterior and posterior components were not affected by the disease. Importantly, the PDCP also included a number of non-DMN regions such as the dorsolateral prefrontal and medial temporal cortex. The findings show that the PDCP is a reproducible cognition-related network that is topographically distinct from the normal DMN.
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Affiliation(s)
- Katharina A Schindlbeck
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
| | - Paul J Mattis
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA.,Department of Neurology, Northwell Health, Manhasset, NY 11030, USA
| | - Kersten Villringer
- Center for Stroke Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, Berlin 12200, Germany
| | - Frank Marzinzik
- Department of Neurology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, Berlin 12200, Germany
| | - Jochen B Fiebach
- Center for Stroke Research, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Hindenburgdamm 30, Berlin 12200, Germany
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 11030, USA
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28
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Sidtis JJ, Sidtis DVL, Dhawan V, Tagliati M, Eidelberg D. Stimulation of the Subthalamic Nucleus Changes Cortical-Subcortical Blood Flow Patterns During Speech: A Positron Emission Tomography Study. Front Neurol 2021; 12:684596. [PMID: 34122323 PMCID: PMC8187801 DOI: 10.3389/fneur.2021.684596] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2021] [Accepted: 05/05/2021] [Indexed: 12/18/2022] Open
Abstract
Background: Deep brain stimulation of the subthalamic nucleus (STN-DBS) is an effective treatment for Parkinson's disease (PD) but can have an adverse effect on speech. In normal speakers and in those with spinocerebellar ataxia, an inverse relationship between regional cerebral blood flow (rCBF) in the left inferior frontal (IFG) region and the right caudate (CAU) is associated with speech rate. This pattern was examined to determine if it was present in PD, and if so, whether it was altered by STN-DBS. Methods: Positron Emission Tomography (PET) measured rCBF during speech in individuals with PD not treated with STN-DBS (n = 7), and those treated with bilateral STN-DBS (n = 7). Previously reported results from non-PD control subjects (n = 16) were reported for comparison. The possible relationships between speech rate during scanning and data from the left and right IFG and CAU head regions were investigated using a step-wise multiple linear regression to identify brain regions that interacted to predict speech rate. Results: The multiple linear regression analysis replicated previously reported predictive coefficients for speech rate involving the left IFG and right CAU regions. However, the relationships between these predictive coefficients and speech rates were abnormal in both PD groups. In PD who had not received STN-DBS, the right CAU coefficient decreased normally with increasing speech rate but the left IFG coefficient abnormally decreased. With STN-DBS, this pattern was partially normalized with the addition of a left IFG coefficient that increased with speech rate, as in normal controls, but the abnormal left IFG decreasing coefficient observed in PD remained. The magnitudes of both cortical predictive coefficients but not the CAU coefficient were exaggerated with STN-DBS. Conclusions: STN-DBS partially corrects the abnormal relationships between rCBF and speech rate found in PD by introducing a left IFG subregion that increases with speech rate, but the conflicting left IFG subregion response remained. Conflicting IFG responses may account for some of the speech problems observed after STN-DBS. Cortical and subcortical regions may be differentially affected by STN-DBS.
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Affiliation(s)
- John J Sidtis
- Brain and Behavior Laboratory, Geriatrics Department, Nathan Kline Institute, Orangeburg, NY, United States.,Department of Psychiatry, School of Medicine, New York University Langone, New York, NY, United States
| | - Diana Van Lancker Sidtis
- Brain and Behavior Laboratory, Geriatrics Department, Nathan Kline Institute, Orangeburg, NY, United States.,Department of Communicative Disorders and Sciences, New York University Steinhardt School, New York, NY, United States
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, United States
| | - Michele Tagliati
- Department of Neurology, Cedars-Sinai Medical Center, Los Angeles, CA, United States
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, United States
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29
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Peng S, Dhawan V, Eidelberg D, Ma Y. Neuroimaging evaluation of deep brain stimulation in the treatment of representative neurodegenerative and neuropsychiatric disorders. Bioelectron Med 2021; 7:4. [PMID: 33781350 PMCID: PMC8008578 DOI: 10.1186/s42234-021-00065-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 03/02/2021] [Indexed: 01/16/2023] Open
Abstract
Brain stimulation technology has become a viable modality of reversible interventions in the effective treatment of many neurological and psychiatric disorders. It is aimed to restore brain dysfunction by the targeted delivery of specific electronic signal within or outside the brain to modulate neural activity on local and circuit levels. Development of therapeutic approaches with brain stimulation goes in tandem with the use of neuroimaging methodology in every step of the way. Indeed, multimodality neuroimaging tools have played important roles in target identification, neurosurgical planning, placement of stimulators and post-operative confirmation. They have also been indispensable in pre-treatment screen to identify potential responders and in post-treatment to assess the modulation of brain circuitry in relation to clinical outcome measures. Studies in patients to date have elucidated novel neurobiological mechanisms underlying the neuropathogenesis, action of stimulations, brain responses and therapeutic efficacy. In this article, we review some applications of deep brain stimulation for the treatment of several diseases in the field of neurology and psychiatry. We highlight how the synergistic combination of brain stimulation and neuroimaging technology is posed to accelerate the development of symptomatic therapies and bring revolutionary advances in the domain of bioelectronic medicine.
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Affiliation(s)
- Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York, 11030, USA
| | - Vijay Dhawan
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York, 11030, USA
| | - David Eidelberg
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York, 11030, USA
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, New York, 11030, USA.
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30
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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: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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31
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Fujita K, Peng S, Ma Y, Tang CC, Hellman M, Feigin A, Eidelberg D, Dhawan V. Blood-brain barrier permeability in Parkinson's disease patients with and without dyskinesia. J Neurol 2021; 268:2246-2255. [PMID: 33502551 DOI: 10.1007/s00415-021-10411-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 12/09/2020] [Accepted: 01/15/2021] [Indexed: 11/28/2022]
Abstract
OBJECTIVE Recent studies on a rodent model of Parkinson's disease (PD) have raised the possibility of increased blood-brain barrier (BBB) permeability, demonstrated by histology, autoradiography, and positron emission tomography (PET). However, in human PD patients, in vivo evidence of increased BBB permeability is lacking. We examined the hypothesis that levodopa treatment increases BBB permeability in human subjects with PD, particularly in those with levodopa-induced dyskinesia (LID). METHODS We used rubidium-82 (82Rb) and PET to quantify BBB influx in vivo in 19 PD patients, including eight with LID, and 12 age- and sex-matched healthy subjects. All subjects underwent baseline 82Rb scans. Seventeen chronically levodopa-treated patients were additionally rescanned during intravenous levodopa infusion. Influx rate constant, K1, by compartmental modeling or net influx transport, Ki, by graphical approach could not be estimated reliably. However, Vd, the "apparent volume of distribution" based on the 82Rb concentration in brain tissue and blood, was estimated with good stability as a local measure of the volume of distribution. RESULTS Rubidium influx into brain tissue was undetectable in PD patients with or without LID, scanned on and off drug. No significant differences in regional Vd were observed for PD patients with or without LID relative to healthy subjects, except in left thalamus. Moreover, changes in Vd measured off- and on-levodopa infusion were also not significant for dyskinetic and non-dyskinetic subjects. CONCLUSION 82Rb PET did not reveal significant changes in BBB permeability in PD patients.
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Affiliation(s)
- Koji Fujita
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - Shichun Peng
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - Yilong Ma
- 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
| | - Matthew Hellman
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - Andrew Feigin
- 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
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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34
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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. J Parkinsons Dis 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] [What about the content of this article? (0)] [Affiliation(s)] [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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Hirano S, Sugiyama A, Ma Y, Tang C, Shimada H, Eidelberg D, Kuwabara S. Differences of cerebral perfusion between subtype of multiple system atrophy with predominant cerebellar ataxia and with predominant parkinsonism. Parkinsonism Relat Disord 2020. [DOI: 10.1016/j.parkreldis.2020.06.068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Shen B, Wei S, Ge J, Peng S, Liu F, Li L, Guo S, Wu P, Zuo C, Eidelberg D, Wang J, Ma Y. Reproducible metabolic topographies associated with multiple system atrophy: Network and regional analyses in Chinese and American patient cohorts. Neuroimage Clin 2020; 28:102416. [PMID: 32987300 PMCID: PMC7520431 DOI: 10.1016/j.nicl.2020.102416] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 08/29/2020] [Accepted: 09/03/2020] [Indexed: 11/18/2022]
Abstract
This study produced reliable metabolic brain networks for multiple system atrophy. Network scores discriminated this disorder from other major forms of Parkinsonism. Network scores correlated with clinical stages and motor symptoms in this disorder. The network was highly reproducible across Chinese and American patient cohorts. Network scores provided a clinically useful biomarker in a multi-center setting.
Purpose Multiple system atrophy (MSA) is an atypical parkinsonian syndrome and often difficult to discriminate clinically from progressive supranuclear palsy (PSP) and Parkinson's disease (PD) in early stages. Although a characteristic metabolic brain network has been reported for MSA, it is unknown whether this network can provide a clinically useful biomarker in different centers. This study was aimed to identify and cross-validate MSA-related brain network and assess its ability for differential diagnosis and clinical correlations in Chinese and American patient cohorts. Methods We included 18F-FDG PET scans retrospectively from 128 clinically diagnosed parkinsonian patients (34 MSA, 34 PSP and 60 PD) and 40 normal subjects in China and in the USA. Using PET images from 20 moderate-stage MSA patients of parkinsonian subtype and 20 normal subjects in both centers, we reproduced MSA-related pattern (MSAPRP) of spatial covariance and estimated its reliability. MSAPRP scores were evaluated in assessing differential diagnosis among moderate- and early-stage MSA, PSP or PD patients and clinical correlations with disease severity. Regional metabolic differences were detected using statistical parameter mapping analysis. MSA-related network and regional topographies of metabolic abnormality were cross-validated between the Chinese and American cohorts. Results We generated a highly reliable MSAPRP characterized by decreased loading in inferior frontal cortex, striatum and cerebellum, and increased loading in sensorimotor, parietal and occipital cortices. MSAPRP scores discriminated between normal, MSA, PSP and PD subjects and correlated with standardized ratings of clinical stages and motor symptoms in MSA. High similarities in MSAPRPs, network scores and corresponding maps of metabolic abnormality were observed between two different cohorts. Conclusion We have demonstrated reproducible metabolic topographies associated with MSA at both network and regional levels in two independent patient cohorts. Moreover, MSAPRP scores are sensitive for evaluating disease discrimination and clinical correlates. This study supports differential diagnosis of MSA regardless of different patient populations, PET scanners and imaging protocols.
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Affiliation(s)
- Bo Shen
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Sidi Wei
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Jingjie Ge
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Fengtao Liu
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Ling Li
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Sisi Guo
- Department of Neurology, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Ping Wu
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - Chuantao Zuo
- PET Center, Huashan Hospital, Fudan University, Shanghai, China
| | - David Eidelberg
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Jian Wang
- Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China.
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA.
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Greuel A, Trezzi JP, Glaab E, Ruppert MC, Maier F, Jäger C, Hodak Z, Lohmann K, Ma Y, Eidelberg D, Timmermann L, Hiller K, Tittgemeyer M, Drzezga A, Diederich N, Eggers C. GBA Variants in Parkinson's Disease: Clinical, Metabolomic, and Multimodal Neuroimaging Phenotypes. Mov Disord 2020; 35:2201-2210. [PMID: 32853481 DOI: 10.1002/mds.28225] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2020] [Revised: 06/23/2020] [Accepted: 07/06/2020] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Alterations in the GBA gene (NM_000157.3) are the most important genetic risk factor for Parkinson's disease (PD). Biallelic GBA mutations cause the lysosomal storage disorder Gaucher's disease. The GBA variants p.E365K and p.T408M are associated with PD but not with Gaucher's disease. The pathophysiological role of these variants needs to be further explored. OBJECTIVE This study analyzed clinical, neuropsychological, metabolic, and neuroimaging phenotypes of patients with PD carrying the GBA variants p.E365K and p.T408M. METHODS GBA was sequenced in 56 patients with mid-stage PD. Carriers of GBA variants were compared with noncarriers regarding clinical history and symptoms, neuropsychological features, metabolomics, and multimodal neuroimaging. Blood plasma gas chromatography coupled to mass spectrometry, 6-[18 F]fluoro-L-Dopa positron emission tomography (PET), [18 F]fluorodeoxyglucose PET, and resting-state functional magnetic resonance imaging were performed. RESULTS Sequence analysis detected 13 heterozygous GBA variant carriers (7 with p.E365K, 6 with p.T408M). One patient carried a GBA mutation (p.N409S) and was excluded. Clinical history and symptoms were not significantly different between groups. Global cognitive performance was lower in variant carriers. Metabolomic group differences were suggestive of more severe PD-related alterations in carriers versus noncarriers. Both PET scans showed signs of a more advanced disease; [18 F]fluorodeoxyglucose PET and functional magnetic resonance imaging showed similarities with Lewy body dementia and PD dementia in carriers. CONCLUSIONS This is the first study to comprehensively assess (neuro-)biological phenotypes of GBA variants in PD. Metabolomics and neuroimaging detected more significant group differences than clinical and behavioral evaluation. These alterations could be promising to monitor effects of disease-modifying treatments targeting glucocerebrosidase metabolism. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Andrea Greuel
- Department of Neurology, University Hospital Giessen and Marburg, Marburg, Germany
| | - Jean-Pierre Trezzi
- Integrated Biobank of Luxembourg, Luxembourg Institute of Health, Dudelange, Luxembourg.,Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Enrico Glaab
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Marina C Ruppert
- Department of Neurology, University Hospital Giessen and Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, Universities of Marburg and Giessen, Marburg, Germany
| | - Franziska Maier
- Department of Psychiatry and Psychotherapy, Medical Faculty, University Hospital of Cologne, Cologne, Germany
| | - Christian Jäger
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Zdenka Hodak
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Katja Lohmann
- Institute of Neurogenetics, University of Lübeck, Lübeck, Germany
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - David Eidelberg
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Lars Timmermann
- Department of Neurology, University Hospital Giessen and Marburg, Marburg, Germany
| | - Karsten Hiller
- Institute for Biochemistry, Biotechnology and Bioinformatics, University of Braunschweig, Braunschweig, Germany
| | - Marc Tittgemeyer
- Max Planck Institute for Metabolism Research, Cologne, Germany.,Cologne Cluster of Excellence in Cellular Stress and Aging-Associated Disease, Cologne, Germany
| | - Alexander Drzezga
- Department of Nuclear Medicine, Medical Faculty and University Hospital Cologne, University of Cologne, Cologne, Germany.,German Center for Neurodegenerative Diseases, Bonn, Germany.,Cognitive Neuroscience, Institute of Neuroscience and Medicine, Research Center Jülich, Jülich, Germany
| | - Nico Diederich
- Department of Neurology, Centre Hospitalier de Luxembourg, Luxembourg City, Luxembourg
| | - Carsten Eggers
- Department of Neurology, University Hospital Giessen and Marburg, Marburg, Germany.,Center for Mind, Brain and Behavior, Universities of Marburg and Giessen, Marburg, Germany
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Peng S, Spetsieris PG, Eidelberg D, Ma Y. Radiomics and supervised machine learning in the diagnosis of parkinsonism with FDG PET: promises and challenges. Ann Transl Med 2020; 8:808. [PMID: 32793653 PMCID: PMC7396243 DOI: 10.21037/atm.2020.04.33] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Affiliation(s)
- Shichun Peng
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Phoebe G Spetsieris
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - David Eidelberg
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
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Rus T, Tomše P, Jensterle L, Ležaić L, Stokin CL, Popović M, Tang CC, Eidelberg D, Pirtošek Z, Trošt M. Atypical clinical presentation of pathologically proven Parkinson's disease: The role of Parkinson's disease related metabolic pattern. Parkinsonism Relat Disord 2020; 78:1-3. [PMID: 32659618 DOI: 10.1016/j.parkreldis.2020.06.035] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Revised: 06/10/2020] [Accepted: 06/29/2020] [Indexed: 10/23/2022]
Abstract
Regional changes in brain metabolism upgraded with measurements of specific metabolic brain patterns and automated diagnostic algorithms can help to differentiate among neurodegenerative parkinsonisms, but with few reports on pathological confirmation. Here we describe a parkinsonian patient with atypical presentation and 18F-FDG-PET imaging consistent with idiopathic Parkinson's disease. The latter was confirmed at the pathohistological examination.
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Affiliation(s)
- Tomaž Rus
- Department of Neurology, UMC Ljubljana, Zaloška Cesta 2, 1000, Ljubljana, Slovenia.
| | - Petra Tomše
- Department of Nuclear Medicine, UMC Ljubljana, Zaloška Cesta 7, 1000, Ljubljana, Slovenia
| | - Luka Jensterle
- Department of Nuclear Medicine, UMC Ljubljana, Zaloška Cesta 7, 1000, Ljubljana, Slovenia
| | - Luka Ležaić
- Department of Nuclear Medicine, UMC Ljubljana, Zaloška Cesta 7, 1000, Ljubljana, Slovenia
| | - Clara Limbäck Stokin
- Institute of Pathology, Medical Faculty, University of Ljubljana, Korytkova 2, 1000, Ljubljana, Slovenia; Department of Histopathology, Imperial College Healthcare NHS Trust, London, UK; Department of Brain Sciences, Imperial College London, Du Cane Road, London, UK
| | - Mara Popović
- Institute of Pathology, Medical Faculty, University of Ljubljana, Korytkova 2, 1000, Ljubljana, Slovenia
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - Zvezdan Pirtošek
- Department of Neurology, UMC Ljubljana, Zaloška Cesta 2, 1000, Ljubljana, Slovenia; Chair of Neurology, Medical Faculty, University of Ljubljana, Vrazov Trg 2, 1000, Ljubljana, Slovenia
| | - Maja Trošt
- Department of Neurology, UMC Ljubljana, Zaloška Cesta 2, 1000, Ljubljana, Slovenia; Chair of Neurology, Medical Faculty, University of Ljubljana, Vrazov Trg 2, 1000, Ljubljana, Slovenia
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Hauser RA, Eidelberg D, Pahwa R, Riggare S, Cenci MA. Reply to: Letter to Editor by Chaudhuri, Jenner, Antonini. Mov Disord 2020; 35:901. [PMID: 32415716 DOI: 10.1002/mds.28043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Accepted: 03/16/2020] [Indexed: 11/11/2022] Open
Affiliation(s)
- Robert A Hauser
- Department of Neurology, University of South Florida, Tampa, Florida, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York, USA
| | - Rajesh Pahwa
- Movement Disorders Division, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Sara Riggare
- Department for Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden
| | - M Angela Cenci
- Basal Ganglia Pathophysiology Unit, Department of Experimental Medical Science, Lund University, Lund, Sweden
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Rus T, Tomše P, Jensterle L, Grmek M, Pirtošek Z, Eidelberg D, Tang C, Trošt M. Differential diagnosis of parkinsonian syndromes: a comparison of clinical and automated - metabolic brain patterns' based approach. Eur J Nucl Med Mol Imaging 2020; 47:2901-2910. [PMID: 32337633 DOI: 10.1007/s00259-020-04785-z] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2019] [Accepted: 03/20/2020] [Indexed: 12/30/2022]
Abstract
PURPOSE Differentiation among parkinsonian syndromes may be clinically challenging, especially at early disease stages. In this study, we used 18F-FDG-PET brain imaging combined with an automated image classification algorithm to classify parkinsonian patients as Parkinson's disease (PD) or as an atypical parkinsonian syndrome (APS) at the time when the clinical diagnosis was still uncertain. In addition to validating the algorithm, we assessed its utility in a "real-life" clinical setting. METHODS One hundred thirty-seven parkinsonian patients with uncertain clinical diagnosis underwent 18F-FDG-PET and were classified using an automated image-based algorithm. For 66 patients in cohort A, the algorithm-based diagnoses were compared with their final clinical diagnoses, which were the gold standard for cohort A and were made 2.2 ± 1.1 years (mean ± SD) later by a movement disorder specialist. Seventy-one patients in cohort B were diagnosed by general neurologists, not strictly following diagnostic criteria, 2.5 ± 1.6 years after imaging. The clinical diagnoses were compared with the algorithm-based ones, which were considered the gold standard for cohort B. RESULTS Image-based automated classification of cohort A resulted in 86.0% sensitivity, 92.3% specificity, 97.4% positive predictive value (PPV), and 66.7% negative predictive value (NPV) for PD, and 84.6% sensitivity, 97.7% specificity, 91.7% PPV, and 95.5% NPV for APS. In cohort B, general neurologists achieved 94.7% sensitivity, 83.3% specificity, 81.8% PPV, and 95.2% NPV for PD, while 88.2%, 76.9%, 71.4%, and 90.9% for APS. CONCLUSION The image-based algorithm had a high specificity and the predictive values in classifying patients before a final clinical diagnosis was reached by a specialist. Our data suggest that it may improve the diagnostic accuracy by 10-15% in PD and 20% in APS when a movement disorder specialist is not easily available.
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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.
| | - Petra Tomše
- Department of Nuclear Medicine, UMC Ljubljana, Zaloška cesta 7, 1000, Ljubljana, Slovenia
| | - Luka Jensterle
- Department of Nuclear Medicine, UMC Ljubljana, Zaloška cesta 7, 1000, Ljubljana, Slovenia
| | - Marko Grmek
- Department of Nuclear Medicine, UMC Ljubljana, Zaloška cesta 7, 1000, Ljubljana, Slovenia
| | - Zvezdan Pirtošek
- Department of Neurology, UMC Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia.,Medical Faculty, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA
| | - Chris Tang
- Center for Neurosciences, The Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, NY, 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
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Barker RA, Björklund A, Gash DM, Whone A, Van Laar A, Kordower JH, Bankiewicz K, Kieburtz K, Saarma M, Booms S, Huttunen HJ, Kells AP, Fiandaca MS, Stoessl AJ, Eidelberg D, Federoff H, Voutilainen MH, Dexter DT, Eberling J, Brundin P, Isaacs L, Mursaleen L, Bresolin E, Carroll C, Coles A, Fiske B, Matthews H, Lungu C, Wyse RK, Stott S, Lang AE. GDNF and Parkinson's Disease: Where Next? A Summary from a Recent Workshop. J Parkinsons Dis 2020; 10:875-891. [PMID: 32508331 PMCID: PMC7458523 DOI: 10.3233/jpd-202004] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 05/18/2020] [Indexed: 12/22/2022]
Abstract
The concept of repairing the brain with growth factors has been pursued for many years in a variety of neurodegenerative diseases including primarily Parkinson's disease (PD) using glial cell line-derived neurotrophic factor (GDNF). This neurotrophic factor was discovered in 1993 and shown to have selective effects on promoting survival and regeneration of certain populations of neurons including the dopaminergic nigrostriatal pathway. These observations led to a series of clinical trials in PD patients including using infusions or gene delivery of GDNF or the related growth factor, neurturin (NRTN). Initial studies, some of which were open label, suggested that this approach could be of value in PD when the agent was injected into the putamen rather than the cerebral ventricles. In subsequent double-blind, placebo-controlled trials, the most recent reporting in 2019, treatment with GDNF did not achieve its primary end point. As a result, there has been uncertainty as to whether GDNF (and by extrapolation, related GDNF family neurotrophic factors) has merit in the future treatment of PD. To critically appraise the existing work and its future, a special workshop was held to discuss and debate this issue. This paper is a summary of that meeting with recommendations on whether there is a future for this therapeutic approach and also what any future PD trial involving GDNF and other GDNF family neurotrophic factors should consider in its design.
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Affiliation(s)
- Roger A. Barker
- Cambridge Centre for Brain Repair, Department of Clinical Neuroscience and WT-MRC Cambridge Stem Cell Institute, Cambridge, UK
| | | | - Don M. Gash
- Professor Emeritus of Neuroscience, University of Kentucky, Lexington, KY, USA
| | - Alan Whone
- Translational Health Sciences, Bristol Medical School, University of Bristol and Neurological and Musculoskeletal Sciences Division, North Bristol NHS Trust, Bristol, UK
| | | | - Jeffrey H. Kordower
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, USA
| | - Krystof Bankiewicz
- Neurological Surgery, Gilbert and Kathryn Mitchell Endowed Chair, Director, Brain Health and Performance Center, The Ohio State University, Department of Neurological Surgery, Columbus, OH, USA
| | - Karl Kieburtz
- Center for Health & Technology, and the Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA
| | - Mart Saarma
- Institute of Biotechnology, HiLIFE, University of Helsinki, Helsinki, Finland
| | | | - Henri J. Huttunen
- Herantis Pharma Plc, Finland
- Neuroscience Center, HiLIFE, University of Helsinki, Finland
| | | | | | - A. Jon Stoessl
- Pacific Parkinson’s Research Centre & Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Canada
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY, USA
| | - Howard Federoff
- School of Medicine, Susan and Henry College of Health Sciences, University of California, Irvine and CEO, Aspen Neuroscience, San Diego, CA, USA
| | | | | | - Jamie Eberling
- The Michael J. Fox Foundation for Parkinson’s Research, New York, NY, USA
| | - Patrik Brundin
- Center for Neurodegenerative Science, Van Andel Institute, Grand Rapids, MI, USA
| | | | - Leah Mursaleen
- The Cure Parkinson’s Trust, London, UK
- School of Life Sciences, University of Westminster, UK and School of Pharmacy, University College London, UK
| | | | | | - Alasdair Coles
- Department of Clinical Neuroscience, University of Cambridge, Addenbrooke’s Hospital, Cambridge, UK
| | - Brian Fiske
- The Michael J. Fox Foundation for Parkinson’s Research, New York, NY, USA
| | | | - Codrin Lungu
- Division of Clinical Research, National Institute of Neurological Disorders and Stroke, Rockville, MD, USA
| | | | | | - Anthony E. Lang
- The Edmond J Safra Program in Parkinson’s Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, and the Department of Medicine, University of Toronto, Toronto, Ontario, Canada
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Mishra VR, Sreenivasan KR, Yang Z, Zhuang X, Cordes D, Mari Z, Litvan I, Fernandez HH, Eidelberg D, Ritter A, Cummings JL, Walsh RR. Unique white matter structural connectivity in early-stage drug-naive Parkinson disease. Neurology 2019; 94:e774-e784. [PMID: 31882528 DOI: 10.1212/wnl.0000000000008867] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 08/28/2019] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To investigate the topographic arrangement and strength of whole-brain white matter (WM) structural connectivity in patients with early-stage drug-naive Parkinson disease (PD). METHODS We employed a model-free data-driven approach for computing whole-brain WM topologic arrangement and connectivity strength between brain regions by utilizing diffusion MRI of 70 participants with early-stage drug-naive PD and 41 healthy controls. Subsequently, we generated a novel group-specific WM anatomical network by minimizing variance in anatomical connectivity of each group. Global WM connectivity strength and network measures were computed on this group-specific WM anatomical network and were compared between the groups. We tested correlations of these network measures with clinical measures in PD to assess their pathophysiologic relevance. RESULTS PD-relevant cortical and subcortical regions were identified in the novel PD-specific WM anatomical network. Impaired modular organization accompanied by a correlation of network measures with multiple clinical variables in early PD were revealed. Furthermore, disease duration was negatively correlated with global connectivity strength of the PD-specific WM anatomical network. CONCLUSION By minimizing variance in anatomical connectivity, this study found the presence of a novel WM structural connectome in early PD that correlated with clinical symptoms, despite the lack of a priori analytic assumptions. This included the novel finding of increased structural connectivity between known PD-relevant brain regions. The current study provides a framework for further investigation of WM structural changes underlying the clinical and pathologic heterogeneity of PD.
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Affiliation(s)
- Virendra R Mishra
- From Imaging Research (V.R.M., K.R.S., Z.Y., X.Z., D.C.), Lou Ruvo Center for Brain Health (Z.M., A.R., J.L.C.), Cleveland Clinic Foundation, Las Vegas, NV; Departments of Psychology and Neuroscience (D.C.), University of Colorado at Boulder; Department of Neurosciences (I.L.), University of California San Diego, La Jolla; Center for Neurological Restoration (H.H.F.), Cleveland Clinic, OH; Center for Neurosciences (D.E.), Feinstein Institute for Medical Research, Manhasset, NY; UNLV Department of Brain Health (J.L.C.), School of Integrated Health Sciences, Las Vegas, NV; and Muhammad Ali Parkinson Center (R.R.W.), Barrow Neurological Institute, Phoenix, AZ.
| | - Karthik R Sreenivasan
- From Imaging Research (V.R.M., K.R.S., Z.Y., X.Z., D.C.), Lou Ruvo Center for Brain Health (Z.M., A.R., J.L.C.), Cleveland Clinic Foundation, Las Vegas, NV; Departments of Psychology and Neuroscience (D.C.), University of Colorado at Boulder; Department of Neurosciences (I.L.), University of California San Diego, La Jolla; Center for Neurological Restoration (H.H.F.), Cleveland Clinic, OH; Center for Neurosciences (D.E.), Feinstein Institute for Medical Research, Manhasset, NY; UNLV Department of Brain Health (J.L.C.), School of Integrated Health Sciences, Las Vegas, NV; and Muhammad Ali Parkinson Center (R.R.W.), Barrow Neurological Institute, Phoenix, AZ
| | - Zhengshi Yang
- From Imaging Research (V.R.M., K.R.S., Z.Y., X.Z., D.C.), Lou Ruvo Center for Brain Health (Z.M., A.R., J.L.C.), Cleveland Clinic Foundation, Las Vegas, NV; Departments of Psychology and Neuroscience (D.C.), University of Colorado at Boulder; Department of Neurosciences (I.L.), University of California San Diego, La Jolla; Center for Neurological Restoration (H.H.F.), Cleveland Clinic, OH; Center for Neurosciences (D.E.), Feinstein Institute for Medical Research, Manhasset, NY; UNLV Department of Brain Health (J.L.C.), School of Integrated Health Sciences, Las Vegas, NV; and Muhammad Ali Parkinson Center (R.R.W.), Barrow Neurological Institute, Phoenix, AZ
| | - Xiaowei Zhuang
- From Imaging Research (V.R.M., K.R.S., Z.Y., X.Z., D.C.), Lou Ruvo Center for Brain Health (Z.M., A.R., J.L.C.), Cleveland Clinic Foundation, Las Vegas, NV; Departments of Psychology and Neuroscience (D.C.), University of Colorado at Boulder; Department of Neurosciences (I.L.), University of California San Diego, La Jolla; Center for Neurological Restoration (H.H.F.), Cleveland Clinic, OH; Center for Neurosciences (D.E.), Feinstein Institute for Medical Research, Manhasset, NY; UNLV Department of Brain Health (J.L.C.), School of Integrated Health Sciences, Las Vegas, NV; and Muhammad Ali Parkinson Center (R.R.W.), Barrow Neurological Institute, Phoenix, AZ
| | - Dietmar Cordes
- From Imaging Research (V.R.M., K.R.S., Z.Y., X.Z., D.C.), Lou Ruvo Center for Brain Health (Z.M., A.R., J.L.C.), Cleveland Clinic Foundation, Las Vegas, NV; Departments of Psychology and Neuroscience (D.C.), University of Colorado at Boulder; Department of Neurosciences (I.L.), University of California San Diego, La Jolla; Center for Neurological Restoration (H.H.F.), Cleveland Clinic, OH; Center for Neurosciences (D.E.), Feinstein Institute for Medical Research, Manhasset, NY; UNLV Department of Brain Health (J.L.C.), School of Integrated Health Sciences, Las Vegas, NV; and Muhammad Ali Parkinson Center (R.R.W.), Barrow Neurological Institute, Phoenix, AZ
| | - Zoltan Mari
- From Imaging Research (V.R.M., K.R.S., Z.Y., X.Z., D.C.), Lou Ruvo Center for Brain Health (Z.M., A.R., J.L.C.), Cleveland Clinic Foundation, Las Vegas, NV; Departments of Psychology and Neuroscience (D.C.), University of Colorado at Boulder; Department of Neurosciences (I.L.), University of California San Diego, La Jolla; Center for Neurological Restoration (H.H.F.), Cleveland Clinic, OH; Center for Neurosciences (D.E.), Feinstein Institute for Medical Research, Manhasset, NY; UNLV Department of Brain Health (J.L.C.), School of Integrated Health Sciences, Las Vegas, NV; and Muhammad Ali Parkinson Center (R.R.W.), Barrow Neurological Institute, Phoenix, AZ
| | - Irene Litvan
- From Imaging Research (V.R.M., K.R.S., Z.Y., X.Z., D.C.), Lou Ruvo Center for Brain Health (Z.M., A.R., J.L.C.), Cleveland Clinic Foundation, Las Vegas, NV; Departments of Psychology and Neuroscience (D.C.), University of Colorado at Boulder; Department of Neurosciences (I.L.), University of California San Diego, La Jolla; Center for Neurological Restoration (H.H.F.), Cleveland Clinic, OH; Center for Neurosciences (D.E.), Feinstein Institute for Medical Research, Manhasset, NY; UNLV Department of Brain Health (J.L.C.), School of Integrated Health Sciences, Las Vegas, NV; and Muhammad Ali Parkinson Center (R.R.W.), Barrow Neurological Institute, Phoenix, AZ
| | - Hubert H Fernandez
- From Imaging Research (V.R.M., K.R.S., Z.Y., X.Z., D.C.), Lou Ruvo Center for Brain Health (Z.M., A.R., J.L.C.), Cleveland Clinic Foundation, Las Vegas, NV; Departments of Psychology and Neuroscience (D.C.), University of Colorado at Boulder; Department of Neurosciences (I.L.), University of California San Diego, La Jolla; Center for Neurological Restoration (H.H.F.), Cleveland Clinic, OH; Center for Neurosciences (D.E.), Feinstein Institute for Medical Research, Manhasset, NY; UNLV Department of Brain Health (J.L.C.), School of Integrated Health Sciences, Las Vegas, NV; and Muhammad Ali Parkinson Center (R.R.W.), Barrow Neurological Institute, Phoenix, AZ
| | - David Eidelberg
- From Imaging Research (V.R.M., K.R.S., Z.Y., X.Z., D.C.), Lou Ruvo Center for Brain Health (Z.M., A.R., J.L.C.), Cleveland Clinic Foundation, Las Vegas, NV; Departments of Psychology and Neuroscience (D.C.), University of Colorado at Boulder; Department of Neurosciences (I.L.), University of California San Diego, La Jolla; Center for Neurological Restoration (H.H.F.), Cleveland Clinic, OH; Center for Neurosciences (D.E.), Feinstein Institute for Medical Research, Manhasset, NY; UNLV Department of Brain Health (J.L.C.), School of Integrated Health Sciences, Las Vegas, NV; and Muhammad Ali Parkinson Center (R.R.W.), Barrow Neurological Institute, Phoenix, AZ
| | - Aaron Ritter
- From Imaging Research (V.R.M., K.R.S., Z.Y., X.Z., D.C.), Lou Ruvo Center for Brain Health (Z.M., A.R., J.L.C.), Cleveland Clinic Foundation, Las Vegas, NV; Departments of Psychology and Neuroscience (D.C.), University of Colorado at Boulder; Department of Neurosciences (I.L.), University of California San Diego, La Jolla; Center for Neurological Restoration (H.H.F.), Cleveland Clinic, OH; Center for Neurosciences (D.E.), Feinstein Institute for Medical Research, Manhasset, NY; UNLV Department of Brain Health (J.L.C.), School of Integrated Health Sciences, Las Vegas, NV; and Muhammad Ali Parkinson Center (R.R.W.), Barrow Neurological Institute, Phoenix, AZ
| | - Jeffrey L Cummings
- From Imaging Research (V.R.M., K.R.S., Z.Y., X.Z., D.C.), Lou Ruvo Center for Brain Health (Z.M., A.R., J.L.C.), Cleveland Clinic Foundation, Las Vegas, NV; Departments of Psychology and Neuroscience (D.C.), University of Colorado at Boulder; Department of Neurosciences (I.L.), University of California San Diego, La Jolla; Center for Neurological Restoration (H.H.F.), Cleveland Clinic, OH; Center for Neurosciences (D.E.), Feinstein Institute for Medical Research, Manhasset, NY; UNLV Department of Brain Health (J.L.C.), School of Integrated Health Sciences, Las Vegas, NV; and Muhammad Ali Parkinson Center (R.R.W.), Barrow Neurological Institute, Phoenix, AZ
| | - Ryan R Walsh
- From Imaging Research (V.R.M., K.R.S., Z.Y., X.Z., D.C.), Lou Ruvo Center for Brain Health (Z.M., A.R., J.L.C.), Cleveland Clinic Foundation, Las Vegas, NV; Departments of Psychology and Neuroscience (D.C.), University of Colorado at Boulder; Department of Neurosciences (I.L.), University of California San Diego, La Jolla; Center for Neurological Restoration (H.H.F.), Cleveland Clinic, OH; Center for Neurosciences (D.E.), Feinstein Institute for Medical Research, Manhasset, NY; UNLV Department of Brain Health (J.L.C.), School of Integrated Health Sciences, Las Vegas, NV; and Muhammad Ali Parkinson Center (R.R.W.), Barrow Neurological Institute, Phoenix, AZ.
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Cenci MA, Riggare S, Pahwa R, Eidelberg D, Hauser RA. Dyskinesia matters. Mov Disord 2019; 35:392-396. [PMID: 31872501 DOI: 10.1002/mds.27959] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 11/18/2019] [Accepted: 12/02/2019] [Indexed: 12/12/2022] Open
Abstract
Levodopa-induced dyskinesia (LID) represents a significant source of discomfort for people with Parkinson's disease (PD). It negatively affects quality of life, it is associated with both motor and nonmotor fluctuations, and it brings an increased risk of disability, balance problems, and falls. Although the prevalence of severe LID appears to be lower than in previous eras (likely owing to a more conservative use of oral levodopa), we have not yet found a way to prevent the development of this complication. Advanced surgical therapies, such as deep brain stimulation, ameliorate LID, but only a minority of PD patients qualify for these interventions. Although some have argued that PD patients would rather be ON with dyskinesia than OFF, the deeper truth is that patients would very much prefer to be ON without dyskinesia. As researchers and clinicians, we should aspire to make that goal a reality. To this end, translational research on LID is to be encouraged and persistently pursued. © 2019 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- M Angela Cenci
- Basal Ganglia Pathophysiology Unit, Dept. of Experimental Medical Science, Lund University, Lund, Sweden
| | - Sara Riggare
- Department for Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden
| | - Rajesh Pahwa
- University of Kansas Medical Center, Movement Disorders Division, Kansas City, Kansas, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, New York, USA
| | - Robert A Hauser
- University of South Florida, Department of Neurology, Tampa, Florida, USA
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Schindlbeck KA, Lucas-Jiménez O, Tang CC, Morbelli S, Arnaldi D, Pardini M, Pagani M, Ibarretxe-Bilbao N, Ojeda N, Nobili F, Eidelberg D. Metabolic Network Abnormalities in Drug-Naïve Parkinson's Disease. Mov Disord 2019; 35:587-594. [PMID: 31872507 DOI: 10.1002/mds.27960] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Revised: 11/26/2019] [Accepted: 12/02/2019] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND An ideal imaging biomarker for a neurodegenerative disorder should be able to measure abnormalities in the earliest stages of the disease. OBJECTIVE We investigated metabolic network changes in two independent cohorts of drug-naïve Parkinson's disease (PD) patients who have not been exposed to dopaminergic medication. METHODS We scanned 85 de novo, drug-naïve PD patients and 85 age-matched healthy control subjects from Italy (n = 96) and the United States (n = 74) with [18 F]-fluorodeoxyglucose PET. All patients had clinical follow-ups to verify the diagnosis of idiopathic PD. Spatial covariance analysis was used to identify and validate de novo PD-related metabolic patterns in the Italian and U.S. cohorts. We compared the de novo PD-related metabolic patterns to the original PD-related pattern that was identified in more advanced patients who had been on chronic dopaminergic treatment. RESULTS De novo PD-related metabolic patterns were identified in each of the two independent cohorts of drug-naïve PD patients, and each differentiated PD patients from healthy control subjects. Expression values for these disease patterns were elevated in drug-naïve PD patients relative to healthy controls in the identification as well as in each of the validation subgroups. The two de novo PD-related metabolic patterns were topographically very similar to each other and to the original PD-related pattern. CONCLUSIONS Reproducible PD-related patterns are expressed in de novo, drug-naïve PD patients. In PD, disease-related metabolic patterns have stereotyped topographies that develop independently of chronic levodopa treatment. © 2019 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Katharina A Schindlbeck
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Olaia Lucas-Jiménez
- Department of Methods and Experimental Psychology, Faculty of Psychology and Education, University of Deusto, Bilbao, Spain
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
| | - Silvia Morbelli
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Department of Health Science (DISSAL), University of Genoa, Genoa, Italy
| | - Dario Arnaldi
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother-Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Matteo Pardini
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother-Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Marco Pagani
- Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy.,Department of Nuclear Medicine, Karolinska Hospital, Stockholm, Sweden
| | - Naroa Ibarretxe-Bilbao
- Department of Methods and Experimental Psychology, Faculty of Psychology and Education, University of Deusto, Bilbao, Spain
| | - Natalia Ojeda
- Department of Methods and Experimental Psychology, Faculty of Psychology and Education, University of Deusto, Bilbao, Spain
| | - Flavio Nobili
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy.,Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother-Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, New York, USA
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Schindlbeck KA, Vo A, Nguyen N, Tang CC, Niethammer M, Dhawan V, Brandt V, Saunders-Pullman R, Bressman SB, Eidelberg D. LRRK2 and GBA Variants Exert Distinct Influences on Parkinson's Disease-Specific Metabolic Networks. Cereb Cortex 2019; 30:2867-2878. [PMID: 31813991 DOI: 10.1093/cercor/bhz280] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 08/23/2019] [Accepted: 09/17/2019] [Indexed: 12/11/2022] Open
Abstract
The natural history of idiopathic Parkinson's disease (PD) varies considerably across patients. While PD is generally sporadic, there are known genetic influences: the two most common, mutations in the LRRK2 or GBA1 gene, are associated with slower and more aggressive progression, respectively. Here, we applied graph theory to metabolic brain imaging to understand the effects of genotype on the organization of previously established PD-specific networks. We found that closely matched PD patient groups with the LRRK2-G2019S mutation (PD-LRRK2) or GBA1 variants (PD-GBA) expressed the same disease networks as sporadic disease (sPD), but PD-LRRK2 and PD-GBA patients exhibited abnormal increases in network connectivity that were not present in sPD. Using a community detection strategy, we found that the location and modular distribution of these connections differed strikingly across genotypes. In PD-LRRK2, connections were gained within the network core, with the formation of distinct functional pathways linking the cerebellum and putamen. In PD-GBA, by contrast, the majority of functional connections were formed outside the core, involving corticocortical pathways at the network periphery. Strategically localized connections within the core in PD-LRRK2 may maintain PD network activity at lower levels than in PD-GBA, resulting in a less aggressive clinical course.
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Affiliation(s)
- Katharina A Schindlbeck
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - An Vo
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Nha Nguyen
- Department of Genetics, Albert Einstein College of Medicine, Bronx, NY 10461, USA
| | - Chris C Tang
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Martin Niethammer
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Vijay Dhawan
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Vicky Brandt
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
| | - Rachel Saunders-Pullman
- Department of Neurology, Mount Sinai Beth Israel, Mount Sinai Hospital, New York, NY 10003, USA
| | - Susan B Bressman
- Department of Neurology, Mount Sinai Beth Israel, Mount Sinai Hospital, New York, NY 10003, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institutes for Medical Research, Manhasset, NY 10030, USA
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Abstract
Little is known of the structural and functional properties of abnormal brain networks associated with neurological disorders. We used a social network approach to characterize the properties of the Parkinson's disease (PD) metabolic topography in 4 independent patient samples and in an experimental non-human primate model. The PD network exhibited distinct features. Dense, mutually facilitating functional connections linked the putamen, globus pallidus, and thalamus to form a metabolically active core. The periphery was formed by weaker connections linking less active cortical regions. Notably, the network contained a separate module defined by interconnected, metabolically active nodes in the cerebellum, pons, frontal cortex, and limbic regions. Exaggeration of the small-world property was a consistent feature of disease networks in parkinsonian humans and in the non-human primate model; this abnormality was only partly corrected by dopaminergic treatment. The findings point to disease-related alterations in network structure and function as the basis for faulty information processing in this disorder.
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Affiliation(s)
- Ji Hyun Ko
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - Phoebe G Spetsieris
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA
| | - David Eidelberg
- Center for Neurosciences, The Feinstein Institute for Medical Research, Manhasset, NY, USA.,Department of Neurology, Northwell Health, Manhasset, NY, USA
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48
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Blazhenets G, Ma Y, Sörensen A, Schiller F, Rücker G, Eidelberg D, Frings L, Meyer PT. Predictive Value of 18F-Florbetapir and 18F-FDG PET for Conversion from Mild Cognitive Impairment to Alzheimer Dementia. J Nucl Med 2019; 61:597-603. [PMID: 31628215 DOI: 10.2967/jnumed.119.230797] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2019] [Accepted: 08/30/2019] [Indexed: 11/16/2022] Open
Abstract
The present study examined the predictive values of amyloid PET, 18F-FDG PET, and nonimaging predictors (alone and in combination) for development of Alzheimer dementia (AD) in a large population of patients with mild cognitive impairment (MCI). Methods: The study included 319 patients with MCI from the Alzheimer Disease Neuroimaging Initiative database. In a derivation dataset (n = 159), the following Cox proportional-hazards models were constructed, each adjusted for age and sex: amyloid PET using 18F-florbetapir (pattern expression score of an amyloid-β AD conversion-related pattern, constructed by principle-components analysis); 18F-FDG PET (pattern expression score of a previously defined 18F-FDG-based AD conversion-related pattern, constructed by principle-components analysis); nonimaging (functional activities questionnaire, apolipoprotein E, and mini-mental state examination score); 18F-FDG PET + amyloid PET; amyloid PET + nonimaging; 18F-FDG PET + nonimaging; and amyloid PET + 18F-FDG PET + nonimaging. In a second step, the results of Cox regressions were applied to a validation dataset (n = 160) to stratify subjects according to the predicted conversion risk. Results: On the basis of the independent validation dataset, the 18F-FDG PET model yielded a significantly higher predictive value than the amyloid PET model. However, both were inferior to the nonimaging model and were significantly improved by the addition of nonimaging variables. The best prediction accuracy was reached by combining 18F-FDG PET, amyloid PET, and nonimaging variables. The combined model yielded 5-y free-of-conversion rates of 100%, 64%, and 24% for the low-, medium- and high-risk groups, respectively. Conclusion: 18F-FDG PET, amyloid PET, and nonimaging variables represent complementary predictors of conversion from MCI to AD. Especially in combination, they enable an accurate stratification of patients according to their conversion risks, which is of great interest for patient care and clinical trials.
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Affiliation(s)
- Ganna Blazhenets
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Yilong Ma
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York
| | - Arnd Sörensen
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Florian Schiller
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Gerta Rücker
- Institute of Medical Biometry and Statistics, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; and
| | - David Eidelberg
- Center for Neurosciences, Institute of Molecular Medicine, The Feinstein Institutes for Medical Research, Northwell Health, Manhasset, New York
| | - Lars Frings
- Department of Nuclear Medicine, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.,Center for Geriatrics and Gerontology Freiburg, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
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49
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Ploran E, Tang C, Mackay M, Small M, Anderson E, Storbeck J, Bascetta B, Kang S, Aranow C, Sartori C, Watson P, Volpe B, Diamond B, Eidelberg D. Assessing cognitive impairment in SLE: examining relationships between resting glucose metabolism and anti-NMDAR antibodies with navigational performance. Lupus Sci Med 2019; 6:e000327. [PMID: 31413849 PMCID: PMC6667777 DOI: 10.1136/lupus-2019-000327] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Revised: 05/17/2019] [Accepted: 06/10/2019] [Indexed: 11/22/2022]
Abstract
OBJECTIVE Resting Fluorine-18 fluorodeoxyglucose positron emission tomography (FDG-PET) brain imaging and neuropsychological testing were used to investigate the usefulness of a spatial navigation task (SNT) as a performance benchmark for cognitive impairment related to anti-N-methyl D-aspartate (anti-NMDA) receptor antibodies (DNRAb) in SLE. METHODS Neuropsychological assessments, including a desktop 3-D virtual SNT, were performed on 19 SLE participants and 9 healthy control (HC) subjects. SLE participants had stable disease activity and medication doses and no history of neuropsychiatric illness or current use of mind-altering medications. Resting FDG-PET scans were obtained on all SLE participants and compared with a historical set from 25 age-matched and sex-matched HCs. Serum DNRAb titres were measured by ELISA. RESULTS 11/19 (58%) of SLE participants failed to complete the SNT (SNT-) compared with 2/9 (22%) of HCs. Compared with 7/9 (78%) in HCs, only 2/9 (22%; p=0.037) of SLE participants with high serum DNRAb titres completed the SNT, in contrast to 6/10 (60%; p=0.810) in SLE participants with low DNRAb titres. Voxel-wise comparison of FDG-PET scans between the 8 SLE participants successfully completing the SNT task (SNT+) and the 11 SNT- SLE participants revealed increased metabolism in the SNT+ participants (p<0.001) in the left anterior putamen/caudate, right anterior putamen, left prefrontal cortex (BA 9), right prefrontal cortex (BA 9/10) and left lateral and medial frontal cortex (BA 8). Compared with HCs, the SNT+ group demonstrated increased metabolism in all regions (p<0.02) except for the right prefrontal cortex (BA 9), whereas the SNT- group demonstrated either significantly decreased or similar metabolism in these seven regions. CONCLUSIONS SNT performance is associated with serum DNRAb titres and resting glucose metabolism in the anterior putamen/caudate and frontal cortex, suggesting compensatory neural recruitment in SNT-associated regions is necessary for successful completion of the task. The SNT therefore has potential for use as a marker for SLE-mediated cognitive impairment.
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Affiliation(s)
- Elisabeth Ploran
- Department of Psychology, Hofstra University, Hempstead, New York, USA
| | - Chris Tang
- Center for Neurosciences, Feinstein Institute for Medical Research, Manhasset, New York, USA
| | - Meggan Mackay
- The Center for Autoimmune, Musculoskeletal, and Hematopoietic Diseases, Feinstein Institute for Medical Research, Manhasset, New York, USA
| | - Michael Small
- Center for Neurosciences, Feinstein Institute for Medical Research, Manhasset, New York, USA
| | - Erik Anderson
- The Center for Autoimmune, Musculoskeletal, and Hematopoietic Diseases, Feinstein Institute for Medical Research, Manhasset, New York, USA
| | - Justin Storbeck
- Department of Psychology, Queens College, Flushing, New York, USA
| | | | - Simran Kang
- Department of Psychology, Queens College, Flushing, New York, USA
| | - Cynthia Aranow
- The Center for Autoimmune, Musculoskeletal, and Hematopoietic Diseases, Feinstein Institute for Medical Research, Manhasset, New York, USA
| | - Carl Sartori
- The Center for Autoimmune, Musculoskeletal, and Hematopoietic Diseases, Feinstein Institute for Medical Research, Manhasset, New York, USA
| | - Philip Watson
- Department of Psychiatry, Zucker Hillside Hospital, Glen Oaks, New York, USA
| | - Bruce Volpe
- Center for Biomedical Science, Feinstein Institute for Medical Research, Manhasset, New York, USA
| | - Betty Diamond
- The Center for Autoimmune, Musculoskeletal, and Hematopoietic Diseases, Feinstein Institute for Medical Research, Manhasset, New York, USA
| | - David Eidelberg
- Center for Neurosciences, Feinstein Institute for Medical Research, Manhasset, New York, USA
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50
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Rus T, Perovnik M, Grmek M, Popovic M, Kramberger MG, Tang CC, Eidelberg D, Trost M. O1‐04‐04: METABOLIC BRAIN PATTERN OF CREUTZFELDT‐JAKOB DISEASE AND ITS BIOLOGICAL SIGNIFICANCE. Alzheimers Dement 2019. [DOI: 10.1016/j.jalz.2019.06.4538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
Affiliation(s)
- Tomaž Rus
- Department for Neurology UMC Ljubljana Ljubljana Slovenia
| | - Matej Perovnik
- Department for Neurology UMC Ljubljana Ljubljana Slovenia
| | - Marko Grmek
- Department of Nuclear Medicine UMC Ljubljana Ljubljana Slovenia
| | - Mara Popovic
- Institute of Pathology, Medical Faculty University of Ljubljana Ljubljana Slovenia
| | | | - Chris C. Tang
- The Feinstein Institute for Medical Research Manhasset NY USA
| | - David Eidelberg
- The Feinstein Institute for Medical Research Manhasset NY USA
| | - Maja Trost
- Department for Neurology UMC Ljubljana Ljubljana Slovenia
- Medical Faculty University of Ljubljana Ljubljana Slovenia
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