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Buchert R, Huppertz HJ, Wegner F, Berding G, Brendel M, Apostolova I, Buhmann C, Poetter-Nerger M, Dierks A, Katzdobler S, Klietz M, Levin J, Mahmoudi N, Rinscheid A, Quattrone A, Rogozinski S, Rumpf JJ, Schneider C, Stoecklein S, Spetsieris PG, Eidelberg D, Sabri O, Barthel H, Wattjes MP, Höglinger G. Added value of FDG-PET for detection of progressive supranuclear palsy. J Neurol Neurosurg Psychiatry 2024:jnnp-2024-333590. [PMID: 39107038 DOI: 10.1136/jnnp-2024-333590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2024] [Accepted: 07/17/2024] [Indexed: 08/09/2024]
Abstract
BACKGROUND Diagnostic criteria for progressive supranuclear palsy (PSP) include midbrain atrophy in MRI and hypometabolism in [18F]fluorodeoxyglucose (FDG)-positron emission tomography (PET) as supportive features. Due to limited data regarding their relative and sequential value, there is no recommendation for an algorithm to combine both modalities to increase diagnostic accuracy. This study evaluated the added value of sequential imaging using state-of-the-art methods to analyse the images regarding PSP features. METHODS The retrospective study included 41 PSP patients, 21 with Richardson's syndrome (PSP-RS), 20 with variant PSP phenotypes (vPSP) and 46 sex- and age-matched healthy controls. A pretrained support vector machine (SVM) for the classification of atrophy profiles from automatic MRI volumetry was used to analyse T1w-MRI (output: MRI-SVM-PSP score). Covariance pattern analysis was applied to compute the expression of a predefined PSP-related pattern in FDG-PET (output: PET-PSPRP expression score). RESULTS The area under the receiver operating characteristic curve for the detection of PSP did not differ between MRI-SVM-PSP and PET-PSPRP expression score (p≥0.63): about 0.90, 0.95 and 0.85 for detection of all PSP, PSP-RS and vPSP. The MRI-SVM-PSP score achieved about 13% higher specificity and about 15% lower sensitivity than the PET-PSPRP expression score. Decision tree models selected the MRI-SVM-PSP score for the first branching and the PET-PSPRP expression score for a second split of the subgroup with normal MRI-SVM-PSP score, both in the whole sample and when restricted to PSP-RS or vPSP. CONCLUSIONS FDG-PET provides added value for PSP-suspected patients with normal/inconclusive T1w-MRI, regardless of PSP phenotype and the methods to analyse the images for PSP-typical features.
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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, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), 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 Eppendorf, Hamburg, Germany
| | | | - Alexander Dierks
- Department of Nuclear Medicine, University Hospital Augsburg, Augsburg, Germany
| | - Sabrina Katzdobler
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Martin Klietz
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Johannes Levin
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU Munich, 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
| | - Andrea Quattrone
- Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany
- Institute of Neurology, Department of Medical and Surgical Sciences, University "Magna Graecia" of Catanzaro, Catanzaro, Italy
| | | | | | - Christine Schneider
- Department of Neurology and Clinical Neurophysiology, University Hospital Augsburg, Augsburg, Germany
| | - Sophia Stoecklein
- Department of Radiology, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Phoebe G Spetsieris
- Feinstein Institutes for Medical Research Manhasset, Manhasset, New York, USA
| | - David Eidelberg
- Feinstein Institutes for Medical Research Manhasset, Manhasset, New York, USA
| | - Osama Sabri
- Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Henryk Barthel
- Department of Nuclear Medicine, University Hospital of Leipzig, Leipzig, Germany
| | - Mike P Wattjes
- Department of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Hannover, Germany
- Department of Neuroradiology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Günter Höglinger
- Department of Neurology, Hannover Medical School, Hannover, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Department of Neurology, University Hospital of Munich, LMU Munich, Munich, Germany
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2
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Aracri F, Quattrone A, Bianco MG, Sarica A, De Maria M, Calomino C, Crasà M, Nisticò R, Buonocore J, Vescio B, Vaccaro MG, Quattrone A. Multimodal imaging and electrophysiological study in the differential diagnosis of rest tremor. Front Neurol 2024; 15:1399124. [PMID: 38854965 PMCID: PMC11160119 DOI: 10.3389/fneur.2024.1399124] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Accepted: 05/08/2024] [Indexed: 06/11/2024] Open
Abstract
Introduction Distinguishing tremor-dominant Parkinson's disease (tPD) from essential tremor with rest tremor (rET) can be challenging and often requires dopamine imaging. This study aimed to differentiate between these two diseases through a machine learning (ML) approach based on rest tremor (RT) electrophysiological features and structural MRI data. Methods We enrolled 72 patients including 40 tPD patients and 32 rET patients, and 45 control subjects (HC). RT electrophysiological features (frequency, amplitude, and phase) were calculated using surface electromyography (sEMG). Several MRI morphometric variables (cortical thickness, surface area, cortical/subcortical volumes, roughness, and mean curvature) were extracted using Freesurfer. ML models based on a tree-based classification algorithm termed XGBoost using MRI and/or electrophysiological data were tested in distinguishing tPD from rET patients. Results Both structural MRI and sEMG data showed acceptable performance in distinguishing the two patient groups. Models based on electrophysiological data performed slightly better than those based on MRI data only (mean AUC: 0.92 and 0.87, respectively; p = 0.0071). The top-performing model used a combination of sEMG features (amplitude and phase) and MRI data (cortical volumes, surface area, and mean curvature), reaching AUC: 0.97 ± 0.03 and outperforming models using separately either MRI (p = 0.0001) or EMG data (p = 0.0231). In the best model, the most important feature was the RT phase. Conclusion Machine learning models combining electrophysiological and MRI data showed great potential in distinguishing between tPD and rET patients and may serve as biomarkers to support clinicians in the differential diagnosis of rest tremor syndromes in the absence of expensive and invasive diagnostic procedures such as dopamine imaging.
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Affiliation(s)
- Federica Aracri
- Neuroscience Research Center, University “Magna Graecia”, Catanzaro, Italy
| | - Andrea Quattrone
- Neuroscience Research Center, University “Magna Graecia”, Catanzaro, Italy
- Institute of Neurology, University “Magna Graecia”, Catanzaro, Italy
| | | | - Alessia Sarica
- Neuroscience Research Center, University “Magna Graecia”, Catanzaro, Italy
| | - Marida De Maria
- Neuroscience Research Center, University “Magna Graecia”, Catanzaro, Italy
| | - Camilla Calomino
- Neuroscience Research Center, University “Magna Graecia”, Catanzaro, Italy
| | - Marianna Crasà
- Neuroscience Research Center, University “Magna Graecia”, Catanzaro, Italy
| | - Rita Nisticò
- Neuroscience Research Center, University “Magna Graecia”, Catanzaro, Italy
| | - Jolanda Buonocore
- Institute of Neurology, University “Magna Graecia”, Catanzaro, Italy
| | | | | | - Aldo Quattrone
- Neuroscience Research Center, University “Magna Graecia”, Catanzaro, Italy
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Mohajer B. Faster, More Practical, but Still Accurate: Deep Learning for Diagnosis of Progressive Supranuclear Palsy. Radiol Artif Intell 2024; 6:e240181. [PMID: 38691010 PMCID: PMC11140513 DOI: 10.1148/ryai.240181] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/01/2024] [Accepted: 04/08/2024] [Indexed: 05/03/2024]
Affiliation(s)
- Bahram Mohajer
- From the Department of Radiology, Hospital of the University of Pennsylvania, 3400 Spruce St, Philadelphia, PA 19104-4283
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Calomino C, Quattrone A, Bianco MG, Nisticò R, Buonocore J, Crasà M, Vaccaro MG, Sarica A, Quattrone A. Combined cortical thickness and blink reflex recovery cycle to differentiate essential tremor with and without resting tremor. Front Neurol 2024; 15:1372262. [PMID: 38585347 PMCID: PMC10995929 DOI: 10.3389/fneur.2024.1372262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2024] [Accepted: 02/14/2024] [Indexed: 04/09/2024] Open
Abstract
Objective To investigate the performance of structural MRI cortical and subcortical morphometric data combined with blink-reflex recovery cycle (BRrc) values using machine learning (ML) models in distinguishing between essential tremor (ET) with resting tremor (rET) and classic ET. Methods We enrolled 47 ET, 43 rET patients and 45 healthy controls (HC). All participants underwent brain 3 T-MRI and BRrc examination at different interstimulus intervals (ISIs, 100-300 msec). MRI data (cortical thickness, volumes, surface area, roughness, mean curvature and subcortical volumes) were extracted using Freesurfer on T1-weighted images. We employed two decision tree-based ML classification algorithms (eXtreme Gradient Boosting [XGBoost] and Random Forest) combining MRI data and BRrc values to differentiate between rET and ET patients. Results ML models based exclusively on MRI features reached acceptable performance (AUC: 0.85-0.86) in differentiating rET from ET patients and from HC. Similar performances were obtained by ML models based on BRrc data (AUC: 0.81-0.82 in rET vs. ET and AUC: 0.88-0.89 in rET vs. HC). ML models combining imaging data (cortical thickness, surface, roughness, and mean curvature) together with BRrc values showed the highest classification performance in distinguishing between rET and ET patients, reaching AUC of 0.94 ± 0.05. The improvement in classification performances when BRrc data were added to imaging features was confirmed by both ML algorithms. Conclusion This study highlights the usefulness of adding a simple electrophysiological assessment such as BRrc to MRI cortical morphometric features for accurately distinguishing rET from ET patients, paving the way for a better classification of these ET syndromes.
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Affiliation(s)
- Camilla Calomino
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Andrea Quattrone
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
- Department of Medical and Surgical Sciences, Institute of Neurology, Magna Graecia University, Catanzaro, Italy
| | - Maria Giovanna Bianco
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Rita Nisticò
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Jolanda Buonocore
- Department of Medical and Surgical Sciences, Institute of Neurology, Magna Graecia University, Catanzaro, Italy
| | - Marianna Crasà
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Maria Grazia Vaccaro
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Alessia Sarica
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
| | - Aldo Quattrone
- Neuroscience Research Center, Department of Medical and Surgical Sciences, Magna Graecia University, Catanzaro, Italy
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Salsone M, Vescio B, Quattrone A, Marelli S, Castelnuovo A, Casoni F, Quattrone A, Ferini-Strambi L. Periodic Leg Movements during Sleep Associated with REM Sleep Behavior Disorder: A Machine Learning Study. Diagnostics (Basel) 2024; 14:363. [PMID: 38396401 PMCID: PMC10888394 DOI: 10.3390/diagnostics14040363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2023] [Revised: 01/20/2024] [Accepted: 02/01/2024] [Indexed: 02/25/2024] Open
Abstract
Most patients with idiopathic REM sleep behavior disorder (iRBD) present peculiar repetitive leg jerks during sleep in their clinical spectrum, called periodic leg movements (PLMS). The clinical differentiation of iRBD patients with and without PLMS is challenging, without polysomnographic confirmation. The aim of this study is to develop a new Machine Learning (ML) approach to distinguish between iRBD phenotypes. Heart rate variability (HRV) data were acquired from forty-two consecutive iRBD patients (23 with PLMS and 19 without PLMS). All participants underwent video-polysomnography to confirm the clinical diagnosis. ML models based on Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) were trained on HRV data, and classification performances were assessed using Leave-One-Out cross-validation. No significant clinical differences emerged between the two groups. The RF model showed the best performance in differentiating between iRBD phenotypes with excellent accuracy (86%), sensitivity (96%), and specificity (74%); SVM and XGBoost had good accuracy (81% and 78%, respectively), sensitivity (83% for both), and specificity (79% and 72%, respectively). In contrast, LR had low performances (accuracy 71%). Our results demonstrate that ML algorithms accurately differentiate iRBD patients from those without PLMS, encouraging the use of Artificial Intelligence to support the diagnosis of clinically indistinguishable iRBD phenotypes.
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Affiliation(s)
- Maria Salsone
- Institute of Molecular Bioimaging and Physiology, National Research Council, 20054 Segrate, Italy
- Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, 20132 Milan, Italy; (S.M.); (F.C.); (L.F.-S.)
| | - Basilio Vescio
- Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology (IBFM), National Research Council (CNR), 88100 Catanzaro, Italy;
- Biotecnomed S.C.aR.L., c/o Magna Graecia University, G Building, lev.1, 88100 Catanzaro, Italy
| | - Andrea Quattrone
- Institute of Neurology, Magna Graecia University, 88100 Catanzaro, Italy;
| | - Sara Marelli
- Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, 20132 Milan, Italy; (S.M.); (F.C.); (L.F.-S.)
| | - Alessandra Castelnuovo
- Sleep Disorders Center, Division of Neuroscience, Vita-Salute San Raffaele University, 20132 Milan, Italy;
| | - Francesca Casoni
- Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, 20132 Milan, Italy; (S.M.); (F.C.); (L.F.-S.)
| | - Aldo Quattrone
- Neuroscience Research Center, Magna Graecia University, 88100 Catanzaro, Italy
| | - Luigi Ferini-Strambi
- Sleep Disorders Center, Division of Neuroscience, San Raffaele Scientific Institute, 20132 Milan, Italy; (S.M.); (F.C.); (L.F.-S.)
- Sleep Disorders Center, Division of Neuroscience, Vita-Salute San Raffaele University, 20132 Milan, Italy;
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Brinia ME, Kapsali I, Giagkou N, Constantinides VC. Planimetric and Volumetric Brainstem MRI Markers in Progressive Supranuclear Palsy, Multiple System Atrophy, and Corticobasal Syndrome. A Systematic Review and Meta-Analysis. Neurol Int 2023; 16:1-19. [PMID: 38392951 PMCID: PMC10892270 DOI: 10.3390/neurolint16010001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Revised: 12/13/2023] [Accepted: 12/13/2023] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Various MRI markers-including midbrain and pons areas (Marea, Parea) and volumes (Mvol, Pvol), ratios (M/Parea, M/Pvol), and composite markers (magnetic resonance imaging Parkinsonism Indices 1,2; MRPI 1,2)-have been proposed as imaging markers of Richardson's syndrome (RS) and multiple system atrophy-Parkinsonism (MSA-P). A systematic review/meta-analysis of relevant studies aiming to compare the diagnostic accuracy of these imaging markers is lacking. METHODS Pubmed and Scopus were searched for studies with >10 patients (RS, MSA-P or CBS) and >10 controls with data on Marea, Parea, Mvol, Pvol, M/Parea, M/Pvol, MRPI 1, and MRPI 2. Cohen's d, as a measure of effect size, was calculated for all markers in RS, MSA-P, and CBS. RESULTS Twenty-five studies on RS, five studies on MSA-P, and four studies on CBS were included. Midbrain area provided the greatest effect size for differentiating RS from controls (Cohen's d = -3.10; p < 0.001), followed by M/Parea and MRPI 1. MSA-P had decreased midbrain and pontine areas. Included studies exhibited high heterogeneity, whereas publication bias was low. CONCLUSIONS Midbrain area is the optimal MRI marker for RS, and pons area is optimal for MSA-P. M/Parea and MRPIs produce smaller effect sizes for differentiating RS from controls.
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Affiliation(s)
| | | | | | - Vasilios C. Constantinides
- First Department of Neurology, School of Medicine, National and Kapodistrian University of Athens, Eginition Hospital, 11528 Athens, Greece; (M.-E.B.); (I.K.)
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