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Khalil RM, Shulman LM, Gruber-Baldini AL, Shakya S, Fenderson R, Van Hoven M, Hausdorff JM, von Coelln R, Cummings MP. Simplification of Mobility Tests and Data Processing to Increase Applicability of Wearable Sensors as Diagnostic Tools for Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2024; 24:4983. [PMID: 39124030 PMCID: PMC11314738 DOI: 10.3390/s24154983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Revised: 07/21/2024] [Accepted: 07/29/2024] [Indexed: 08/12/2024]
Abstract
Quantitative mobility analysis using wearable sensors, while promising as a diagnostic tool for Parkinson's disease (PD), is not commonly applied in clinical settings. Major obstacles include uncertainty regarding the best protocol for instrumented mobility testing and subsequent data processing, as well as the added workload and complexity of this multi-step process. To simplify sensor-based mobility testing in diagnosing PD, we analyzed data from 262 PD participants and 50 controls performing several motor tasks wearing a sensor on their lower back containing a triaxial accelerometer and a triaxial gyroscope. Using ensembles of heterogeneous machine learning models incorporating a range of classifiers trained on a set of sensor features, we show that our models effectively differentiate between participants with PD and controls, both for mixed-stage PD (92.6% accuracy) and a group selected for mild PD only (89.4% accuracy). Omitting algorithmic segmentation of complex mobility tasks decreased the diagnostic accuracy of our models, as did the inclusion of kinesiological features. Feature importance analysis revealed that Timed Up and Go (TUG) tasks to contribute the highest-yield predictive features, with only minor decreases in accuracy for models based on cognitive TUG as a single mobility task. Our machine learning approach facilitates major simplification of instrumented mobility testing without compromising predictive performance.
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Affiliation(s)
- Rana M. Khalil
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA;
| | - Lisa M. Shulman
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Ann L. Gruber-Baldini
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (A.L.G.-B.); (S.S.)
| | - Sunita Shakya
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (A.L.G.-B.); (S.S.)
| | - Rebecca Fenderson
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Maxwell Van Hoven
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Jeffrey M. Hausdorff
- Center for the Study of Movement, Cognition, and Mobility, Neurological Institute, Tel Aviv Medical Center, Tel Aviv 6492416, Israel;
- Department of Physical Therapy, Faculty of Medicine & Health Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv 6997801, Israel
- Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL 60612, USA
- Department of Orthopedic Surgery, Rush University Medical Center, Chicago, IL 60612, USA
| | - Rainer von Coelln
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD 21201, USA; (L.M.S.); (R.F.); (M.V.H.)
| | - Michael P. Cummings
- Center for Bioinformatics and Computational Biology, University of Maryland, College Park, MD 20742, USA;
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Reddy A, Reddy RP, Roghani AK, Garcia RI, Khemka S, Pattoor V, Jacob M, Reddy PH, Sehar U. Artificial intelligence in Parkinson's disease: Early detection and diagnostic advancements. Ageing Res Rev 2024; 99:102410. [PMID: 38972602 DOI: 10.1016/j.arr.2024.102410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 07/04/2024] [Indexed: 07/09/2024]
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disorder, globally affecting men and women at an exponentially growing rate, with currently no cure. Disease progression starts when dopaminergic neurons begin to die. In PD, the loss of neurotransmitter, dopamine is responsible for the overall communication of neural cells throughout the body. Clinical symptoms of PD are slowness of movement, involuntary muscular contractions, speech & writing changes, lessened automatic movement, and chronic tremors in the body. PD occurs in both familial and sporadic forms and modifiable and non-modifiable risk factors and socioeconomic conditions cause PD. Early detectable diagnostics and treatments have been developed in the last several decades. However, we still do not have precise early detectable biomarkers and therapeutic agents/drugs that prevent and/or delay the disease process. Recently, artificial intelligence (AI) science and machine learning tools have been promising in identifying early detectable markers with a greater rate of accuracy compared to past forms of treatment and diagnostic processes. Artificial intelligence refers to the intelligence exhibited by machines or software, distinct from the intelligence observed in humans that is based on neural networks in a form and can be used to diagnose the longevity and disease severity of disease. The term Machine Learning or Neural Networks is a blanket term used to identify an emerging technology that is created to work in the way of a "human brain" using many intertwined neurons to achieve the same level of raw intelligence as that of a brain. These processes have been used for neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease, to assess the severity of the patient's condition. In the current article, we discuss the prevalence and incidence of PD, and currently available diagnostic biomarkers and therapeutic strategies. We also highlighted currently available artificial intelligence science and machine learning tools and their applications to detect disease and develop therapeutic interventions.
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Affiliation(s)
- Aananya Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Lubbock High School, Lubbock, TX 79401, USA.
| | - Ruhananhad P Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Lubbock High School, Lubbock, TX 79401, USA.
| | - Aryan Kia Roghani
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Frenship High School, Lubbock, TX 79382, USA.
| | - Ricardo Isaiah Garcia
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
| | - Sachi Khemka
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
| | - Vasanthkumar Pattoor
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; University of South Florida, Tampa, FL 33620, USA.
| | - Michael Jacob
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Biology, The University of Texas at San Antonio, San Antonio, TX 78249, USA.
| | - P Hemachandra Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Nutritional Sciences Department, College of Human Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Public Health Department of Graduate School of Biomedical Sciences, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department pf Speech, Language and Hearing Services, School Health Professions, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
| | - Ujala Sehar
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
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3
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Westenberger A, Skrahina V, Usnich T, Beetz C, Vollstedt EJ, Laabs BH, Paul JJ, Curado F, Skobalj S, Gaber H, Olmedillas M, Bogdanovic X, Ameziane N, Schell N, Aasly JO, Afshari M, Agarwal P, Aldred J, Alonso-Frech F, Anderson R, Araújo R, Arkadir D, Avenali M, Balal M, Benizri S, Bette S, Bhatia P, Bonello M, Braga-Neto P, Brauneis S, Cardoso FEC, Cavallieri F, Classen J, Cohen L, Coletta D, Crosiers D, Cullufi P, Dashtipour K, Demirkiran M, de Carvalho Aguiar P, De Rosa A, Djaldetti R, Dogu O, dos Santos Ghilardi MG, Eggers C, Elibol B, Ellenbogen A, Ertan S, Fabiani G, Falkenburger BH, Farrow S, Fay-Karmon T, Ferencz GJ, Fonoff ET, Fragoso YD, Genç G, Gorospe A, Grandas F, Gruber D, Gudesblatt M, Gurevich T, Hagenah J, Hanagasi HA, Hassin-Baer S, Hauser RA, Hernández-Vara J, Herting B, Hinson VK, Hogg E, Hu MT, Hummelgen E, Hussey K, Infante J, Isaacson SH, Jauma S, Koleva-Alazeh N, Kuhlenbäumer G, Kühn A, Litvan I, López-Manzanares L, Luxmore M, Manandhar S, Marcaud V, Markopoulou K, Marras C, McKenzie M, Matarazzo M, Merello M, Mollenhauer B, Morgan JC, Mullin S, Musacchio T, Myers B, Negrotti A, Nieves A, Nitsan Z, Oskooilar N, Öztop-Çakmak Ö, Pal G, Pavese N, Percesepe A, Piccoli T, Pinto de Souza C, Prell T, Pulera M, Raw J, Reetz K, Reiner J, Rosenberg D, Ruiz-Lopez M, Ruiz Martinez J, Sammler E, Santos-Lobato BL, Saunders-Pullman R, Schlesinger I, Schofield CM, Schumacher-Schuh AF, Scott B, Sesar Á, Shafer SJ, Sheridan R, Silverdale M, Sophia R, Spitz M, Stathis P, Stocchi F, Tagliati M, Tai YF, Terwecoren A, Thonke S, Tönges L, Toschi G, Tumas V, Urban PP, Vacca L, Vandenberghe W, Valente EM, Valzania F, Vela-Desojo L, Weill C, Weise D, Wojcieszek J, Wolz M, Yahalom G, Yalcin-Cakmakli G, Zittel S, Zlotnik Y, Kandaswamy KK, Balck A, Hanssen H, Borsche M, Lange LM, Csoti I, Lohmann K, Kasten M, Brüggemann N, Rolfs A, Klein C, Bauer P. Relevance of genetic testing in the gene-targeted trial era: the Rostock Parkinson's disease study. Brain 2024; 147:2652-2667. [PMID: 39087914 PMCID: PMC11292909 DOI: 10.1093/brain/awae188] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 02/27/2024] [Accepted: 03/24/2024] [Indexed: 08/02/2024] Open
Abstract
Estimates of the spectrum and frequency of pathogenic variants in Parkinson's disease (PD) in different populations are currently limited and biased. Furthermore, although therapeutic modification of several genetic targets has reached the clinical trial stage, a major obstacle in conducting these trials is that PD patients are largely unaware of their genetic status and, therefore, cannot be recruited. Expanding the number of investigated PD-related genes and including genes related to disorders with overlapping clinical features in large, well-phenotyped PD patient groups is a prerequisite for capturing the full variant spectrum underlying PD and for stratifying and prioritizing patients for gene-targeted clinical trials. The Rostock Parkinson's disease (ROPAD) study is an observational clinical study aiming to determine the frequency and spectrum of genetic variants contributing to PD in a large international cohort. We investigated variants in 50 genes with either an established relevance for PD or possible phenotypic overlap in a group of 12 580 PD patients from 16 countries [62.3% male; 92.0% White; 27.0% positive family history (FH+), median age at onset (AAO) 59 years] using a next-generation sequencing panel. Altogether, in 1864 (14.8%) ROPAD participants (58.1% male; 91.0% White, 35.5% FH+, median AAO 55 years), a PD-relevant genetic test (PDGT) was positive based on GBA1 risk variants (10.4%) or pathogenic/likely pathogenic variants in LRRK2 (2.9%), PRKN (0.9%), SNCA (0.2%) or PINK1 (0.1%) or a combination of two genetic findings in two genes (∼0.2%). Of note, the adjusted positive PDGT fraction, i.e. the fraction of positive PDGTs per country weighted by the fraction of the population of the world that they represent, was 14.5%. Positive PDGTs were identified in 19.9% of patients with an AAO ≤ 50 years, in 19.5% of patients with FH+ and in 26.9% with an AAO ≤ 50 years and FH+. In comparison to the idiopathic PD group (6846 patients with benign variants), the positive PDGT group had a significantly lower AAO (4 years, P = 9 × 10-34). The probability of a positive PDGT decreased by 3% with every additional AAO year (P = 1 × 10-35). Female patients were 22% more likely to have a positive PDGT (P = 3 × 10-4), and for individuals with FH+ this likelihood was 55% higher (P = 1 × 10-14). About 0.8% of the ROPAD participants had positive genetic testing findings in parkinsonism-, dystonia/dyskinesia- or dementia-related genes. In the emerging era of gene-targeted PD clinical trials, our finding that ∼15% of patients harbour potentially actionable genetic variants offers an important prospect to affected individuals and their families and underlines the need for genetic testing in PD patients. Thus, the insights from the ROPAD study allow for data-driven, differential genetic counselling across the spectrum of different AAOs and family histories and promote a possible policy change in the application of genetic testing as a routine part of patient evaluation and care in PD.
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Affiliation(s)
- Ana Westenberger
- Institute of Neurogenetics, University of Lübeck, University Medical Center Schleswig-Holstein, 23538 Lübeck, Schleswig-Holstein, Germany
| | - Volha Skrahina
- CENTOGENE GmbH, 18055 Rostock, Mecklenburg-Vorpommern, Germany
| | - Tatiana Usnich
- Institute of Neurogenetics, University of Lübeck, University Medical Center Schleswig-Holstein, 23538 Lübeck, Schleswig-Holstein, Germany
| | - Christian Beetz
- CENTOGENE GmbH, 18055 Rostock, Mecklenburg-Vorpommern, Germany
| | - Eva-Juliane Vollstedt
- Institute of Neurogenetics, University of Lübeck, University Medical Center Schleswig-Holstein, 23538 Lübeck, Schleswig-Holstein, Germany
| | - Björn-Hergen Laabs
- Institute of Medical Biometry and Statistics, University of Lübeck, University Medical Center Schleswig-Holstein, 23562 Lübeck, Schleswig-Holstein, Germany
| | - Jefri J Paul
- CENTOGENE GmbH, 18055 Rostock, Mecklenburg-Vorpommern, Germany
| | - Filipa Curado
- CENTOGENE GmbH, 18055 Rostock, Mecklenburg-Vorpommern, Germany
| | - Snezana Skobalj
- CENTOGENE GmbH, 18055 Rostock, Mecklenburg-Vorpommern, Germany
| | - Hanaa Gaber
- CENTOGENE GmbH, 18055 Rostock, Mecklenburg-Vorpommern, Germany
- Department of Clinical Project Management, IQVIA, 60549 Frankfurt am Main, Hessen, Germany
| | | | | | - Najim Ameziane
- CENTOGENE GmbH, 18055 Rostock, Mecklenburg-Vorpommern, Germany
| | - Nathalie Schell
- Institute of Neurogenetics, University of Lübeck, University Medical Center Schleswig-Holstein, 23538 Lübeck, Schleswig-Holstein, Germany
| | - Jan Olav Aasly
- Department of Neurology, St. Olavs Hospital, 7006 Trondheim, Trøndelag, Norway
- Department of Neuroscience, Norwegian University of Science and Technology, 7034 Trondheim, Norway
| | - Mitra Afshari
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL 60612, USA
| | - Pinky Agarwal
- Evergreen Health Neuroscience Institute, Kirkland, WA 98034, USA
| | - Jason Aldred
- Inland Northwest Research, Spokane, WA 99202, USA
| | - Fernando Alonso-Frech
- Department of Neurology, Movement Disorders Unit, Hospital Clínico San Carlos, 28040 Madrid, Madrid, Spain
| | | | - Rui Araújo
- Department of Neurology, Centro Hospitalar Universitário de São João, 4200-319 Porto, Porto District, Portugal
- Department of Clinical Neurosciences and Mental Health, Faculty of Medicine, University of Porto, 4200-319 Porto, Porto District, Portugal
| | - David Arkadir
- Department of Neurology, Faculty of Medicine, Hadassah Medical Organization, Hebrew University, 91120 Jerusalem, Jerusalem District, Israel
| | - Micol Avenali
- Neurogenetics Research Center, IRCCS Mondino Foundation, 27100 Pavia, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Lombardy, Italy
| | - Mehmet Balal
- Department of Neurology, School of Medicine, Çukurova University, 01330 Adana, Adana, Turkey
| | - Sandra Benizri
- Movement Disorders Unit, Assuta Ramat Ha Hayal Hospital, 69710 Tel Aviv, Tel Aviv District, Israel
| | - Sagari Bette
- Parkinson’s Disease and Movement Disorders Center of Boca Raton, Boca Raton, FL 33486, USA
| | | | - Michael Bonello
- Department of Neurology, The Walton Centre NHS Foundation Trust, Liverpool, Merseyside L9 7LJ, UK
| | - Pedro Braga-Neto
- Division of Neurology, Department of Clinical Medicine, Federal University of Ceará, 60430-140 Fortaleza, Brazil
- Center of Health Science, Universidade Estadual do Ceará, 60714-903 Fortaleza, Ceará, Brazil
| | | | - Francisco Eduardo Costa Cardoso
- Movement Disorders Unit, Neurology Service, Department of Internal Medicine, Federal University of Minas Gerais, 31270-901 Belo Horizonte, Minas Gerais, Brazil
| | - Francesco Cavallieri
- Neurology Unit, Neuromotor and Rehabilitation Department, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, 42122 Reggio Emilia, Emilia-Romagna, Italy
| | - Joseph Classen
- Department of Neurology, Leipzig University Medical Center, 04103 Leipzig, Saxony, Germany
| | | | - Della Coletta
- Department of Neurology, Universidade do Estado do Amazonas, 69050-010 Manaus AM, Amazonas, Brazil
| | - David Crosiers
- Department of Neurology, Antwerp University Hospital, 2650 Edegem, Flemish, Belgium
- Translational Neurosciences, Faculty of Medicine and Health Sciences, University of Antwerp, 2610 Antwerp, Flemish, Belgium
| | - Paskal Cullufi
- Pediatric Department, University Hospital ‘Mother Teresa’, 1001 Tirana, Tirana County, Albania
| | - Khashayar Dashtipour
- Department of Neurology, Division of Movement Disorders, Loma Linda University School of Medicine, Loma Linda, CA 92354, USA
| | - Meltem Demirkiran
- Department of Neurology, School of Medicine, Çukurova University, 01330 Adana, Adana, Turkey
| | - Patricia de Carvalho Aguiar
- Department of Neurology and Neurosurgery, Hospital Israelita Albert Einstein, 05651-901 Sao Paulo, Sao Paulo, Brazil
| | - Anna De Rosa
- Department of Neurosciences and Reproductive and Odontostomatological Sciences, Federico II University, 80131 Naples, Campania Region, Italy
| | - Ruth Djaldetti
- Department of Neurology, Movement Disorders Clinic, Rabin Medical Center-Beilinson Hospital, 49100 Petach Tikva, Central District, Israel
- Sackler Faculty of Medicine, Tel Aviv University, 6997801 Tel Aviv, Tel Aviv District, Israel
| | - Okan Dogu
- Department of Neurology, Mersin University, 33343 Mersin, Mersin Province, Turkey
| | - Maria Gabriela dos Santos Ghilardi
- Laboratory of Neuroscience, Hospital Sírio-Libanês, 01308-050 São Paulo, São Paulo, Brazil
- Department of Neurology, University of São Paulo Medical School, 01246-903 São Paulo, São Paulo, Brazil
| | - Carsten Eggers
- Department of Neurology, University Hospital Marburg, 35037 Marburg, Hesse, Germany
- Department of Neurology, Knappschaftskrankenhaus Bottrop, 46242 Bottrop, North Rhine-Westphalia, Germany
| | - Bulent Elibol
- Department of Neurology, Faculty of Medicine, Hacettepe University, 06100 Ankara, Ankara, Turkey
| | - Aaron Ellenbogen
- Michigan Institute for Neurological Disorders, Farmington Hills, MI 48334, USA
- Quest Research Institute, Farmington Hills, MI 48334, USA
| | - Sibel Ertan
- Department of Neurology, Koç University, 34450 Istanbul, Istanbul, Turkey
| | - Giorgio Fabiani
- Movement Disorders Unit, Hospital Angelina Caron, 83430-000 Curitiba, Paraná, Brazil
| | - Björn H Falkenburger
- Department of Neurology, University Hospital and Faculty of Medicine Carl Gustav Carus, 01307 Dresden, Saxony, Germany
| | - Simon Farrow
- Clinical Research Center of Nevada, Las Vegas, NV 89119, USA
| | - Tsviya Fay-Karmon
- Sackler Faculty of Medicine, Tel Aviv University, 6997801 Tel Aviv, Tel Aviv District, Israel
- Movement Disorders Institute and Department of Neurology, Chaim Sheba Medical Center, 52621 Ramat-Gan, Tel Aviv District, Israel
| | - Gerald J Ferencz
- Shore Neurology, RWJBarnabas Health Medical Group, Toms River, NJ 08755, USA
| | - Erich Talamoni Fonoff
- Laboratory of Neuroscience, Hospital Sírio-Libanês, 01308-050 São Paulo, São Paulo, Brazil
- Department of Neurology, University of São Paulo Medical School, 01246-903 São Paulo, São Paulo, Brazil
| | - Yara Dadalti Fragoso
- Department of Neurology, Universidade Metropolitana de Santos, 11070-100 Santos SP, São Paulo, Brazil
| | - Gençer Genç
- Department of Neurology, Şişli Etfal Training and Research Hospital, University of Health Sciences, 34371 Istanbul, Istanbul, Turkey
| | - Arantza Gorospe
- Department of Neurology, de Navarra University Hospital, 31008 Pamplona, Navarre, Spain
| | - Francisco Grandas
- Movement Disorders Unit, University General Hospital Gregorio Marañón, 28007 Madrid, Madrid, Spain
| | - Doreen Gruber
- Movement Disorders Clinic, 14547 Beelitz-Heilstätten, Brandenburg, Germany
| | - Mark Gudesblatt
- NYU Langone South Shore Neurologic Associates, Islip, NY 11751, USA
| | - Tanya Gurevich
- Movement Disorders Unit, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv University, 6423906 Tel Aviv, Tel Aviv District, Israel
| | - Johann Hagenah
- Department of Neurology, Westküstenklinikum Heide, 25746 Heide, Schleswig-Holstein, Germany
| | - Hasmet A Hanagasi
- Behavioral Neurology and Movement Disorders Unit, Department of Neurology, Istanbul Faculty of Medicine, Istanbul University, 34093 Istanbul, Istanbul, Turkey
| | - Sharon Hassin-Baer
- Sackler Faculty of Medicine, Tel Aviv University, 6997801 Tel Aviv, Tel Aviv District, Israel
- Movement Disorders Institute and Department of Neurology, Chaim Sheba Medical Center, 52621 Ramat-Gan, Tel Aviv District, Israel
| | - Robert A Hauser
- University of South Florida Parkinson’s Disease and Movement Disorders Center of Excellence, Tampa, FL 33612, USA
| | - Jorge Hernández-Vara
- Neurology Department, Vall d’Hebron University Hospital, Universitat Autònoma de Barcelona, 08035 Barcelona, Catalonia, Spain
| | - Birgit Herting
- Neurological Clinic, Diakonie-Klinikum Schwäbisch Hall, 74523 Schwäbisch Hall, Baden-Württemberg, Germany
| | - Vanessa K Hinson
- Department of Neurology, Medical University of South Carolina, Charleston, SC 29425, USA
| | - Elliot Hogg
- Department of Neurosurgery, Cedars-Sinai Medical Center, Movement Disorder Program, Los Angeles, CA 90048, USA
| | - Michele T Hu
- Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, University of Oxford, Oxford OX3 9DU, UK
| | - Eduardo Hummelgen
- Neurology Service, Hospital Angelina Caron, 83430-000 Curitiba, Paraná, Brazil
| | - Kelly Hussey
- University of South Florida Parkinson’s Disease and Movement Disorders Center of Excellence, Tampa, FL 33612, USA
| | - Jon Infante
- Service of Neurology, University Hospital ‘Marqués de Valdecilla (IDIVAL)’, University of Cantabria, and ‘Centro de Investigación Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED)’, 39008 Santander, Cantabria, Spain
| | - Stuart H Isaacson
- Parkinson’s Disease and Movement Disorders Center of Boca Raton, Boca Raton, FL 33486, USA
| | - Serge Jauma
- Neurology Service, Hospital Universitari de Bellvitge, 08907 Barcelona, Catalonia, Spain
| | | | - Gregor Kuhlenbäumer
- Department of Neurology, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Schleswig-Holstein, Germany
| | - Andrea Kühn
- Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité, University Medicine Berlin, 10117 Berlin, Berlin, Germany
| | - Irene Litvan
- Parkinson and Other Movement Disorders Center, University of California San Diego Health, La Jolla, San Diego, CA 92037, USA
| | - Lydia López-Manzanares
- Department of Neurology, Movement Disorders Unit, La Princesa University Hospital, 28006 Madrid, Madrid, Spain
| | - McKenzie Luxmore
- Department of Neurology, Duke University School of Medicine, Durham, NC 27710, USA
| | | | - Veronique Marcaud
- Department of Neurology, Saint Joseph Hospital, 75014 Paris, Île-de-France, France
| | - Katerina Markopoulou
- Department of Neurology, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Neurology, Pritzker School of Medicine, University of Chicago, Chicago, IL 60637, USA
| | - Connie Marras
- The Edmond J Safra Program in Parkinson’s Disease, Toronto Western Hospital, University of Toronto, Toronto, Ontario M5T 2S8, Canada
| | | | - Michele Matarazzo
- HM CINAC (Centro Integral de Neurociencias Abarca Campal), Fundación Hospitales de Madrid, Hospital Universitario HM Puerta del Sur, HM Hospitales, 28938 Madrid, Madrid, Spain
| | - Marcelo Merello
- Movement Disorders Service FLENI, CONICET, C1428 Buenos Aires, Ciudad Autónoma de Buenos Aires (CABA), Argentina
| | - Brit Mollenhauer
- Paracelsus-Elena-Klinik, 34128 Kassel, Hesse, Germany
- Department of Neurology, University Medical Centre Göttingen, 37075 Göttingen, Lower Saxony, Germany
| | - John C Morgan
- Movement & Memory Disorder Programs, Department of Neurology, Augusta University Medical Center, Augusta, GA 30912, USA
| | - Stephen Mullin
- Institute of Translational and Stratified Medicine, University of Plymouth School of Medicine, Plymouth, Devon PL6 8BU, UK
| | - Thomas Musacchio
- Department of Neurology, University Hospital of Würzburg, 97080 Würzburg, Bavaria, Germany
| | | | - Anna Negrotti
- Department of General and Specialized Medicine, Neurology Unit, University Hospital of Parma, 43126 Parma, Emilia-Romagna, Italy
| | | | - Zeev Nitsan
- Department of Neurology, Barzilai Medical Center, 78278 Ashkelon, Southern District, Israel
- Faculty of Health Sciences, Ben Gurion University of the Negev, 84105 Beer-Sheva, Southern District, Israel
| | - Nader Oskooilar
- Pharmacology Research Institute, Newport Beach, CA 92660, USA
| | - Özgür Öztop-Çakmak
- Department of Neurology, Koç University, 34450 Istanbul, Istanbul, Turkey
| | - Gian Pal
- Department of Neurology, Rutgers Robert Wood Johnson Medical School, New Brunswick, NJ 08901, USA
| | - Nicola Pavese
- Clinical Ageing Research Unit, Newcastle University, Newcastle Upon Tyne, Tyne and Wear NE4 5PL, UK
| | - Antonio Percesepe
- Department of Medicine and Surgery, University of Parma, 43126 Parma, Emilia-Romagna, Italy
| | - Tommaso Piccoli
- Unit of Neurology, Department of Biomedicine, Neurosciences and advanced Diagnostics (BiND), University of Palermo, 90127 Palermo, Sicily, Italy
| | - Carolina Pinto de Souza
- Department of Neurology, São Francisco Hospital, University of São Paulo, 01236-030 São Paulo, São Paulo, Brazil
| | - Tino Prell
- Department of Neurology, Jena University Hospital, 07747 Jena, Thuringia, Germany
- Department of Geriatrics, Halle University Hospital, 06120 Halle, Saxony-Anhalt, Germany
| | - Mark Pulera
- Pharmacology Research Institute, Encino, CA 91316, USA
| | - Jason Raw
- Clinical Research Unit, Pennine Acute Hospitals NHS Trust, Oldham, Greater Manchester OL1 2JH, UK
| | - Kathrin Reetz
- Department of Neurology, RWTH Aachen University, 52074 Aachen, North Rhine-Westphalia, Germany
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Research Centre Jülich, 52428 Jülich, North Rhine-Westphalia, Germany
| | - Johnathan Reiner
- Department of Neurology, Movement Disorders Clinic, Rabin Medical Center-Beilinson Hospital, 49100 Petach Tikva, Central District, Israel
- Sackler Faculty of Medicine, Tel Aviv University, 6997801 Tel Aviv, Tel Aviv District, Israel
| | - David Rosenberg
- Pharmacology Research Institute, Los Alamitos, CA 90720, USA
| | - Marta Ruiz-Lopez
- Department of Neurology, University Hospital Cruces, Biocruces Research Institute, 48903 Barakaldo, Basque Country, Spain
| | - Javier Ruiz Martinez
- Department of Neurology, Hospital Universitario Donostia, 20014 San Sebastian, Basque Country, Spain
| | - Esther Sammler
- Medical Research Council Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee DD1 5EH, UK
- Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK
| | | | | | - Ilana Schlesinger
- Rambam Health Care Campus, Technion Faculty of Medicine, 31096 Haifa, Haifa District, Israel
| | - Christine M Schofield
- Research and Development Unit, Royal Cornwall Hospitals Trust, Truro, Cornwall TR1 3LJ, UK
| | - Artur F Schumacher-Schuh
- Neurological Services, Clinical Hospital of Porto Alegre, 90035-903 Porto Alegre, Rio Grande do Sul, Brazil
| | - Burton Scott
- Department of Neurology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Ángel Sesar
- Department of Neurology, University Hospital of Santiago de Compostela, 15706 Santiago de Compostela, Galicia, Spain
| | - Stuart J Shafer
- Vero Beach Neurology and Research Institute, Vero Beach, FL 32960, USA
| | - Ray Sheridan
- Geriatric Medicine, Royal Devon and Exeter Hospital NHS Foundation Trust, Exeter, Devon EX2 5DW, UK
| | - Monty Silverdale
- Division of Neurology, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, University of Manchester, Manchester, Greater Manchester M6 8HD, UK
| | - Rani Sophia
- Department of Geriatric Medicine, Yeovil Hospital, Yeovil, Somerset BA21 4AT, UK
| | - Mariana Spitz
- Neurology, Pedro Ernesto University Hospital, 20551-030 Rio de Janeiro, Rio de Janeiro, Brazil
| | - Pantelis Stathis
- Department of Neurology, Mediterraneo Hospital, 166 75 Glyfada-Athens, Attica, Greece
| | - Fabrizio Stocchi
- University and Institute for Research and Medical Care, IRCCS San Raffaele, 00166 Rome, Lazio, Italy
| | - Michele Tagliati
- Nuffield Department of Clinical Neurosciences, Division of Clinical Neurology, University of Oxford, Oxford OX3 9DU, UK
| | - Yen F Tai
- Division of Medicine and Integrated Care, Charing Cross Hospital, Imperial College Healthcare Trust, London W6 8RF, UK
| | | | - Sven Thonke
- Department of Neurology, Klinikum Hanau, 63450 Hanau, Hesse, Germany
| | - Lars Tönges
- Department of Neurology, St. Josef-Hospital and Neurodegeneration Research, Protein Research Unit Ruhr (PURE), Ruhr University Bochum, 44791 Bochum, North Rhine-Westphalia, Germany
- Neurodegeneration Research, Protein Research Unit Ruhr (PURE), Ruhr University Bochum, 44791 Bochum, North Rhine-Westphalia, Germany
| | - Giulia Toschi
- Neurology Unit, Neuromotor and Rehabilitation Department, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, 42122 Reggio Emilia, Emilia-Romagna, Italy
| | - Vitor Tumas
- Department of Neurosciences and Behavioral Sciences, Ribeirao Preto Medical School of University of São Paulo, 14049-900 São Paulo, São Paulo, Brazil
| | - Peter Paul Urban
- Department of Neurology, Asklepios Klinik Barmbek, 22307 Hamburg, Hamburg, Germany
| | - Laura Vacca
- University and Institute for Research and Medical Care, IRCCS San Raffaele, 00166 Rome, Lazio, Italy
| | - Wim Vandenberghe
- Department of Neurology, University Hospitals Leuven, 3000 Leuven, Flanders, Belgium
- Department of Neurosciences, KU Leuven, 3000 Leuven, Flanders, Belgium
| | - Enza Maria Valente
- Neurogenetics Research Center, IRCCS Mondino Foundation, 27100 Pavia, Italy
- Department of Molecular Medicine, University of Pavia, 27100 Pavia, Lombardy, Italy
| | - Franco Valzania
- Neurology Unit, Neuromotor and Rehabilitation Department, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, 42122 Reggio Emilia, Emilia-Romagna, Italy
| | - Lydia Vela-Desojo
- Neurology Unit, Hospital Fundación Alcorcón, 28922 Madrid, Madrid, Spain
| | - Caroline Weill
- Neurogenetics Research Center, IRCCS Mondino Foundation, 27100 Pavia, Italy
| | - David Weise
- Department of Neurology, Asklepios Fachklinikum Stadtroda, 07646 Stadtroda, Thuringia, Germany
- Department of Neurology, University of Leipzig, 04103 Leipzig, Saxony, Germany
| | | | - Martin Wolz
- Department of Neurology, Elblandklinikum Meißen, 01662 Meißen, Saxony, Germany
| | - Gilad Yahalom
- Department of Neurology and the Movement Disorders Unit, Shaare Zedek Medical Center, 9103102 Jerusalem, Jerusalem District, Israel
| | - Gul Yalcin-Cakmakli
- Department of Neurology, Faculty of Medicine, Hacettepe University, 06100 Ankara, Ankara, Turkey
| | - Simone Zittel
- Department of Neurology, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Hamburg, Germany
| | - Yair Zlotnik
- Neurology Department, Soroka University Medical Center, 84101 Beer Sheva, Southern District, Israel
| | | | - Alexander Balck
- Institute of Neurogenetics, University of Lübeck, University Medical Center Schleswig-Holstein, 23538 Lübeck, Schleswig-Holstein, Germany
- Department of Neurology, University of Lübeck, 23562 Lübeck, Schleswig-Holstein, Germany
| | - Henrike Hanssen
- Institute of Neurogenetics, University of Lübeck, University Medical Center Schleswig-Holstein, 23538 Lübeck, Schleswig-Holstein, Germany
- Department of Neurology, University of Lübeck, 23562 Lübeck, Schleswig-Holstein, Germany
| | - Max Borsche
- Institute of Neurogenetics, University of Lübeck, University Medical Center Schleswig-Holstein, 23538 Lübeck, Schleswig-Holstein, Germany
- Department of Neurology, University of Lübeck, 23562 Lübeck, Schleswig-Holstein, Germany
| | - Lara M Lange
- Institute of Neurogenetics, University of Lübeck, University Medical Center Schleswig-Holstein, 23538 Lübeck, Schleswig-Holstein, Germany
- Department of Neurology, University of Lübeck, 23562 Lübeck, Schleswig-Holstein, Germany
| | - Ilona Csoti
- Neurology Service, Hospital Universitari de Bellvitge, 08907 Barcelona, Catalonia, Spain
| | - Katja Lohmann
- Institute of Neurogenetics, University of Lübeck, University Medical Center Schleswig-Holstein, 23538 Lübeck, Schleswig-Holstein, Germany
| | - Meike Kasten
- Institute of Neurogenetics, University of Lübeck, University Medical Center Schleswig-Holstein, 23538 Lübeck, Schleswig-Holstein, Germany
| | - Norbert Brüggemann
- Institute of Neurogenetics, University of Lübeck, University Medical Center Schleswig-Holstein, 23538 Lübeck, Schleswig-Holstein, Germany
- Department of Neurology, University of Lübeck, 23562 Lübeck, Schleswig-Holstein, Germany
| | - Arndt Rolfs
- CENTOGENE GmbH, 18055 Rostock, Mecklenburg-Vorpommern, Germany
- Department of Neurology, University of Rostock, 18057 Rostock, Mecklenburg-Vorpommern, Germany
| | - Christine Klein
- Institute of Neurogenetics, University of Lübeck, University Medical Center Schleswig-Holstein, 23538 Lübeck, Schleswig-Holstein, Germany
| | - Peter Bauer
- CENTOGENE GmbH, 18055 Rostock, Mecklenburg-Vorpommern, Germany
- Department of Internal Medicine, University of Rostock, 18057 Rostock, Mecklenburg-Vorpommern, Germany
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Ariz M, Martínez M, Alvarez I, Fernández-Seara MA, Castellanos G, Pastor P, Pastor MA, Ortiz de Solórzano C. Automatic Segmentation and Quantification of Nigrosome-1 Neuromelanin and Iron in MRI: A Candidate Biomarker for Parkinson's Disease. J Magn Reson Imaging 2024; 60:534-547. [PMID: 37915245 DOI: 10.1002/jmri.29073] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Revised: 10/04/2023] [Accepted: 10/05/2023] [Indexed: 11/03/2023] Open
Abstract
BACKGROUND There is a lack of automated tools for the segmentation and quantification of neuromelanin (NM) and iron in the nigrosome-1 (N1). Existing tools evaluate the N1 sign, i.e., the presence or absence of the "swallow-tail" in iron-sensitive MRI, or globally analyze the MRI signal in an area containing the N1, without providing a volumetric delineation. PURPOSE Present an automated method to segment the N1 and quantify differences in N1's NM and iron content between Parkinson's disease (PD) patients and healthy controls (HCs). Study whether N1 degeneration is clinically related to PD and could be used as a biomarker of the disease. STUDY TYPE Prospective. SUBJECTS Seventy-one PD (65.3 ± 10.3 years old, 34 female/37 male); 30 HC (62.7 ± 7.8 years old, 17 female/13 male). FIELD STRENGTH/SEQUENCE 3 T Anatomical T1-weighted MPRAGE, NM-MRI T1-weighted gradient with magnetization transfer, susceptibility-weighted imaging (SWI). ASSESSMENT N1 was automatically segmented in SWI images using a multi-image atlas, populated with healthy N1 structures manually annotated by a neurologist. Relative NM and iron content were quantified and their diagnostic performance assessed and compared with the substantia nigra pars compacta (SNc). The association between image parameters and clinically relevant variables was studied. STATISTICAL TESTS Nonparametric tests were used (Mann-Whitney's U, chi-square, and Friedman tests) at P = 0.05. RESULTS N1's relative NM content decreased and relative iron content increased in PD patients compared with HCs (NM-CRHC = 22.55 ± 1.49; NM-CRPD = 19.79 ± 1.92; NM-nVolHC = 2.69 × 10-5 ± 1.02 × 10-5; NM-nVolPD = 1.18 × 10-5 ± 0.96 × 10-5; Iron-CRHC = 10.51 ± 2.64; Iron-CRPD = 19.35 ± 7.88; Iron-nVolHC = 0.72 × 10-5 ± 0.81 × 10-5; Iron-nVolPD = 2.82 × 10-5 ± 2.04 × 10-5). Binary logistic regression analyses combining N1 and SNc image parameters yielded a top AUC = 0.955. Significant correlation was found between most N1 parameters and both disease duration (ρNM-CR = -0.31; ρiron-CR = 0.43; ρiron-nVol = 0.46) and the motor status (ρNM-nVol = -0.27; ρiron-CR = 0.33; ρiron-nVol = 0.28), suggesting NM reduction along with iron accumulation in N1 as the disease progresses. DATA CONCLUSION This method provides a fully automatic N1 segmentation, and the analyses performed reveal that N1 relative NM and iron quantification improves diagnostic performance and suggest a relative NM reduction along with a relative iron accumulation in N1 as the disease progresses. EVIDENCE LEVEL 1 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Mikel Ariz
- Ciberonc and Biomedical Engineering Program, CIMA University of Navarra, Pamplona, Spain
- Department of Electrical, Electronic and Communications Engineering, Public University of Navarre, Pamplona, Spain
| | - Martín Martínez
- Neuroimaging Laboratory, University of Navarra, School of Medicine, Pamplona, Spain
| | - Ignacio Alvarez
- Movement Disorders Unit, Neurology, University Hospital Mútua de Terrassa, Terrassa, Barcelona, Spain
| | - Maria A Fernández-Seara
- Department of Radiology, Clínica Universidad de Navarra, Pamplona, Spain
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
| | - Gabriel Castellanos
- Department of Physiological Sciences, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Pau Pastor
- Unit of Neurodegenerative Diseases, Department of Neurology, University Hospital Germans Trias i Pujol, and Germans Trias i Pujol Research Institute (IGTP), Badalona, Barcelona, Spain
| | - Maria A Pastor
- Neuroimaging Laboratory, University of Navarra, School of Medicine, Pamplona, Spain
- Movement Disorders Unit, Neurology, University of Navarra, Pamplona, Spain
| | - Carlos Ortiz de Solórzano
- Ciberonc and Biomedical Engineering Program, CIMA University of Navarra, Pamplona, Spain
- IdiSNA, Instituto de Investigación Sanitaria de Navarra, Pamplona, Spain
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Richardson A, Kundu A, Henao R, Lee T, Scott BL, Grewal DS, Fekrat S. Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network. Transl Vis Sci Technol 2024; 13:23. [PMID: 39136960 PMCID: PMC11323992 DOI: 10.1167/tvst.13.8.23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2024] [Accepted: 06/23/2024] [Indexed: 08/16/2024] Open
Abstract
Purpose Changes in retinal structure and microvasculature are connected to parallel changes in the brain. Two recent studies described machine learning algorithms trained on retinal images and quantitative data that identified Alzheimer's dementia and mild cognitive impairment with high accuracy. Prior studies also demonstrated retinal differences in individuals with PD. Herein, we developed a convolutional neural network (CNN) to classify multimodal retinal imaging from either a Parkinson's disease (PD) or control group. Methods We trained a CNN to receive retinal image inputs of optical coherence tomography (OCT) ganglion cell-inner plexiform layer (GC-IPL) thickness color maps, OCT angiography 6 × 6-mm en face macular images of the superficial capillary plexus, and ultra-widefield (UWF) fundus color and autofluorescence photographs to classify the retinal imaging as PD or control. The model consists of a shared pretrained VGG19 feature extractor and image-specific feature transformations which converge to a single output. Model results were assessed using receiver operating characteristic (ROC) curves and bootstrapped 95% confidence intervals for area under the ROC curve (AUC) values. Results In total, 371 eyes of 249 control subjects and 75 eyes of 52 PD subjects were used for training, validation, and testing. Our best CNN variant achieved an AUC of 0.918. UWF color photographs were the most effective imaging input, and GC-IPL thickness maps were the least contributory. Conclusions Using retinal images, our pilot CNN was able to identify individuals with PD and serves as a proof of concept to spur the collection of larger imaging datasets needed for clinical-grade algorithms. Translational Relevance Developing machine learning models for automated detection of Parkinson's disease from retinal imaging could lead to earlier and more widespread diagnoses.
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Affiliation(s)
- Alexander Richardson
- Duke Eye Center, Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA
- Department of Computer Science, Duke University, Durham, NC, USA
| | - Anita Kundu
- Duke Eye Center, Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA
| | - Ricardo Henao
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA
- Department of Computer Science, Duke University, Durham, NC, USA
| | - Terry Lee
- Duke Eye Center, Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA
| | - Burton L. Scott
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
| | - Dilraj S. Grewal
- Duke Eye Center, Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA
| | - Sharon Fekrat
- Duke Eye Center, Department of Ophthalmology, Duke University School of Medicine, Durham, NC, USA
- iMIND Research Group, Duke University School of Medicine, Durham, NC, USA
- Department of Neurology, Duke University School of Medicine, Durham, NC, USA
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Wang X, Li S, Pun CM, Guo Y, Xu F, Gao H, Lu H. A Parkinson's Auxiliary Diagnosis Algorithm Based on a Hyperparameter Optimization Method of Deep Learning. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2024; 21:912-923. [PMID: 37027659 DOI: 10.1109/tcbb.2023.3246961] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Parkinson's disease is a common mental disease in the world, especially in the middle-aged and elderly groups. Today, clinical diagnosis is the main diagnostic method of Parkinson's disease, but the diagnosis results are not ideal, especially in the early stage of the disease. In this paper, a Parkinson's auxiliary diagnosis algorithm based on a hyperparameter optimization method of deep learning is proposed for the Parkinson's diagnosis. The diagnosis system uses ResNet50 to achieve feature extraction and Parkinson's classification, mainly including speech signal processing part, algorithm improvement part based on Artificial Bee Colony algorithm (ABC) and optimizing the hyperparameters of ResNet50 part. The improved algorithm is called Gbest Dimension Artificial Bee Colony algorithm (GDABC), proposing "Range pruning strategy" which aims at narrowing the scope of search and "Dimension adjustment strategy" which is to adjust gbest dimension by dimension. The accuracy of the diagnosis system in the verification set of Mobile Device Voice Recordings at King's College London (MDVR-CKL) dataset can reach more than 96%. Compared with current Parkinson's sound diagnosis methods and other optimization algorithms, our auxiliary diagnosis system shows better classification performance on the dataset within limited time and resources.
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Adler CH, Halverson M, Zhang N, Shill HA, Driver-Dunckley E, Mehta SH, Atri A, Caviness JN, Serrano GE, Shprecher DR, Belden CM, Sabbagh MN, Long K, Beach TG. Conjugal Synucleinopathies: A Clinicopathologic Study. Mov Disord 2024; 39:1212-1217. [PMID: 38597193 PMCID: PMC11260251 DOI: 10.1002/mds.29783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 02/23/2024] [Accepted: 02/27/2024] [Indexed: 04/11/2024] Open
Abstract
BACKGROUND While preclinical studies have shown that alpha-synuclein can spread through cell-to-cell transmission whether it can be transmitted between humans is unknown. OBJECTIVES The aim was to assess the presence of a synucleinopathy in autopsied conjugal couples. METHODS Neuropathological findings in conjugal couples were categorized as Parkinson's disease (PD), dementia with Lewy bodies (DLB), Alzheimer's disease with Lewy bodies (ADLB), incidental Lewy body disease (ILBD), or no Lewy bodies. RESULTS Ninety conjugal couples were included; the mean age of death was 88.3 years; 32 couples had no Lewy bodies; 42 couples had 1 spouse with a synucleinopathy: 10 PD, 3 DLB, 13 ADLB, and 16 ILBD; 16 couples had both spouses with a synucleinopathy: in 4 couples both spouses had PD, 1 couple had PD and DLB, 4 couples had PD and ADLB, 2 couples had PD and ILBD, 1 couple had DLB and ADLB, in 3 couples both had ADLB, and 1 couple had ADLB and ILBD. No couples had both spouses with ILBD. CONCLUSIONS This large series of 90 autopsied conjugal couples found 16 conjugal couples with synucleinopathies, suggesting transmission of synucleinopathy between spouses is unlikely. © 2024 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Charles H. Adler
- Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic Arizona, Scottsdale, AZ
| | | | - Nan Zhang
- Department of Biostatistics, Mayo Clinic College of Medicine, Mayo Clinic Arizona, Scottsdale, AZ
| | | | | | | | - Alireza Atri
- Cleo Roberts Center, Banner Sun Health Research Institute, Sun City, AZ
- Center for Brain/Mind Medicine, Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | | | - Geidy E. Serrano
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ
| | | | | | | | - Kathy Long
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ
| | - Thomas G. Beach
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, AZ
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Zhu SG, Chen ZL, Xiao K, Wang ZW, Lu WB, Liu RP, Huang SS, Zhu JH, Zhang X, Wang JY. Association analyses of apolipoprotein E genotypes and cognitive performance in patients with Parkinson's disease. Eur J Med Res 2024; 29:334. [PMID: 38880878 PMCID: PMC11181540 DOI: 10.1186/s40001-024-01924-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2024] [Accepted: 06/06/2024] [Indexed: 06/18/2024] Open
Abstract
BACKGROUND Cognitive impairment is a common non-motor symptom of Parkinson's disease (PD). The apolipoprotein E (APOE) ε4 genotype increases the risk of Alzheimer's disease (AD). However, the effect of APOEε4 on cognitive function of PD patients remains unclear. In this study, we aimed to understand whether and how carrying APOEε4 affects cognitive performance in patients with early-stage and advanced PD. METHODS A total of 119 Chinese early-stage PD patients were recruited. Movement Disorder Society Unified Parkinson's Disease Rating Scale, Hamilton anxiety scale, Hamilton depression scale, non-motor symptoms scale, Mini-mental State Examination, Montreal Cognitive Assessment, and Fazekas scale were evaluated. APOE genotypes were determined by polymerase chain reactions and direct sequencing. Demographic and clinical information of 521 early-stage and 262 advanced PD patients were obtained from Parkinson's Progression Marker Initiative (PPMI). RESULTS No significant difference in cognitive performance was found between ApoEε4 carriers and non-carriers in early-stage PD patients from our cohort and PPMI. The cerebrospinal fluid (CSF) Amyloid Beta 42 (Aβ42) level was significantly lower in ApoEε4 carrier than non-carriers in early-stage PD patients from PPMI. In advanced PD patients from PPMI, the BJLOT, HVLT retention and SDMT scores seem to be lower in ApoEε4 carriers without reach the statistical significance. CONCLUSIONS APOEε4 carriage does not affect the cognitive performance of early-stage PD patients. However, it may promote the decline of CSF Aβ42 level and the associated amyloidopathy, which is likely to further contribute to the cognitive dysfunction of PD patients in the advanced stage.
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Affiliation(s)
- Shi-Guo Zhu
- Department of Neurology, Institute of Geriatric Neurology, the Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Zhu-Ling Chen
- Department of Neurology, Institute of Geriatric Neurology, the Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Ke Xiao
- Department of Neurology, Institute of Geriatric Neurology, the Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Zi-Wei Wang
- Department of Neurology, Institute of Geriatric Neurology, the Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Wen-Bin Lu
- Department of Neurology, Institute of Geriatric Neurology, the Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Rong-Pei Liu
- Department of Neurology, Institute of Geriatric Neurology, the Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Shi-Shi Huang
- Department of Neurology, Institute of Geriatric Neurology, the Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Jian-Hong Zhu
- Department of Preventive Medicine, Institute of Nutrition and Diseases, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
| | - Xiong Zhang
- Department of Neurology, Institute of Geriatric Neurology, the Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China.
| | - Jian-Yong Wang
- Department of Neurology, Institute of Geriatric Neurology, the Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China.
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9
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Yang J, Williams S, Hogg DC, Alty JE, Relton SD. Deep learning of Parkinson's movement from video, without human-defined measures. J Neurol Sci 2024; 463:123089. [PMID: 38991323 DOI: 10.1016/j.jns.2024.123089] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 06/05/2024] [Accepted: 06/05/2024] [Indexed: 07/13/2024]
Abstract
BACKGROUND The core clinical sign of Parkinson's disease (PD) is bradykinesia, for which a standard test is finger tapping: the clinician observes a person repetitively tap finger and thumb together. That requires an expert eye, a scarce resource, and even experts show variability and inaccuracy. Existing applications of technology to finger tapping reduce the tapping signal to one-dimensional measures, with researcher-defined features derived from those measures. OBJECTIVES (1) To apply a deep learning neural network directly to video of finger tapping, without human-defined measures/features, and determine classification accuracy for idiopathic PD versus controls. (2) To visualise the features learned by the model. METHODS 152 smartphone videos of 10s finger tapping were collected from 40 people with PD and 37 controls. We down-sampled pixel dimensions and videos were split into 1 s clips. A 3D convolutional neural network was trained on these clips. RESULTS For discriminating PD from controls, our model showed training accuracy 0.91, and test accuracy 0.69, with test precision 0.73, test recall 0.76 and test AUROC 0.76. We also report class activation maps for the five most predictive features. These show the spatial and temporal sections of video upon which the network focuses attention to make a prediction, including an apparent dropping thumb movement distinct for the PD group. CONCLUSIONS A deep learning neural network can be applied directly to standard video of finger tapping, to distinguish PD from controls, without a requirement to extract a one-dimensional signal from the video, or pre-define tapping features.
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Affiliation(s)
| | - Stefan Williams
- Leeds Institute of Health Sciences, University of Leeds, UK; Leeds Teaching Hospitals NHS Trust, UK.
| | | | - Jane E Alty
- Leeds Teaching Hospitals NHS Trust, UK; Wicking Dementia Research and Education Centre, University of Tasmania, Australia
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Furlepa M, Zhang YP, Lobanova E, Kahanawita L, Vivacqua G, Williams-Gray CH, Klenerman D. Single-molecule characterization of salivary protein aggregates from Parkinson's disease patients: a pilot study. Brain Commun 2024; 6:fcae178. [PMID: 38863577 PMCID: PMC11166177 DOI: 10.1093/braincomms/fcae178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 04/03/2024] [Accepted: 05/20/2024] [Indexed: 06/13/2024] Open
Abstract
Saliva is a convenient and accessible biofluid that has potential as a future diagnostic tool for Parkinson's disease. Candidate diagnostic tests for Parkinson's disease to date have predominantly focused on measurements of α-synuclein in CSF, but there is a need for accurate tests utilizing more easily accessible sample types. Prior studies utilizing saliva have used bulk measurements of salivary α-synuclein to provide diagnostic insight. Aggregate structure may influence the contribution of α-synuclein to disease pathology. Single-molecule approaches can characterize the structure of individual aggregates present in the biofluid and may, therefore, provide greater insight than bulk measurements. We have employed an antibody-based single-molecule pulldown assay to quantify salivary α-synuclein and amyloid-β peptide aggregate numbers and subsequently super-resolved captured aggregates using direct Stochastic Optical Reconstruction Microscopy to describe their morphological features. We show that the salivary α-synuclein aggregate/amyloid-β aggregate ratio is increased almost 2-fold in patients with Parkinson's disease (n = 20) compared with controls (n = 20, P < 0.05). Morphological information also provides insight, with saliva from patients with Parkinson's disease containing a greater proportion of larger and more fibrillar amyloid-β aggregates than control saliva (P < 0.05). Furthermore, the combination of count and morphology data provides greater diagnostic value than either measure alone, distinguishing between patients with Parkinson's disease (n = 17) and controls (n = 18) with a high degree of accuracy (area under the curve = 0.87, P < 0.001) and a larger dynamic range. We, therefore, demonstrate for the first time the application of highly sensitive single-molecule imaging techniques to saliva. In addition, we show that aggregates present within saliva retain relevant structural information, further expanding the potential utility of saliva-based diagnostic methods.
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Affiliation(s)
- Martin Furlepa
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0PY, UK
| | - Yu P Zhang
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- UK Dementia Research Institute at Cambridge, Cambridge CB2 0XY, UK
| | - Evgeniia Lobanova
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- UK Dementia Research Institute at Cambridge, Cambridge CB2 0XY, UK
| | - Lakmini Kahanawita
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0PY, UK
| | - Giorgio Vivacqua
- Microscopic and Ultrastructural Anatomy Research Unit-Integrated Research Centre (PRABB), Campus Biomedico University of Rome, 00128 Rome, Italy
- Department of Clinical Neurosciences, University of Cambridge, Cambridge CB2 0AH, UK
| | | | - David Klenerman
- Department of Chemistry, University of Cambridge, Cambridge CB2 1EW, UK
- UK Dementia Research Institute at Cambridge, Cambridge CB2 0XY, UK
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11
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Bartl M, Nilsson J, Dakna M, Weber S, Schade S, Xylaki M, Fernandes Gomes B, Ernst M, Muntean ML, Sixel-Döring F, Trenkwalder C, Zetterberg H, Brinkmalm A, Mollenhauer B. Lysosomal and synaptic dysfunction markers in longitudinal cerebrospinal fluid of de novo Parkinson's disease. NPJ Parkinsons Dis 2024; 10:102. [PMID: 38760408 PMCID: PMC11101466 DOI: 10.1038/s41531-024-00714-1] [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: 11/24/2023] [Accepted: 04/19/2024] [Indexed: 05/19/2024] Open
Abstract
Lysosomal and synaptic dysfunctions are hallmarks in neurodegeneration and potentially relevant as biomarkers, but data on early Parkinson's disease (PD) is lacking. We performed targeted mass spectrometry with an established protein panel, assessing autophagy and synaptic function in cerebrospinal fluid (CSF) of drug-naïve de novo PD, and sex-/age-matched healthy controls (HC) cross-sectionally (88 PD, 46 HC) and longitudinally (104 PD, 58 HC) over 10 years. Multiple markers of autophagy, synaptic plasticity, and secretory pathways were reduced in PD. We added samples from prodromal subjects (9 cross-sectional, 12 longitudinal) with isolated REM sleep behavior disorder, revealing secretogranin-2 already decreased compared to controls. Machine learning identified neuronal pentraxin receptor and neurosecretory protein VGF as most relevant for discriminating between groups. CSF levels of LAMP2, neuronal pentraxins, and syntaxins in PD correlated with clinical progression, showing predictive potential for motor- and non-motor symptoms as a valid basis for future drug trials.
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Affiliation(s)
- Michael Bartl
- Department of Neurology, University Medical Center Goettingen, Goettingen, Germany.
- Institute for Neuroimmunology and Multiple Sclerosis Research, University Medical Center Goettingen, Goettingen, Germany.
| | - Johanna Nilsson
- Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Mohammed Dakna
- Department of Neurology, University Medical Center Goettingen, Goettingen, Germany
| | - Sandrina Weber
- Department of Neurology, University Medical Center Goettingen, Goettingen, Germany
| | | | - Mary Xylaki
- Department of Neurology, University Medical Center Goettingen, Goettingen, Germany
| | - Bárbara Fernandes Gomes
- Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Marielle Ernst
- Institute of Diagnostic and Interventional Neuroradiology, University Medical Center Goettingen, Goettingen, Germany
| | | | - Friederike Sixel-Döring
- Paracelsus-Elena-Klinik, Kassel, Germany
- Department of Neurology, Philipps-University, Marburg, Germany
| | - Claudia Trenkwalder
- Paracelsus-Elena-Klinik, Kassel, Germany
- Department of Neurosurgery, University Medical Center Goettingen, Goettingen, Germany
| | - Henrik Zetterberg
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- UK Dementia Research Institute at UCL, London, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- Hong Kong Center for Neurodegenerative Diseases, Hong Kong, China
| | - Ann Brinkmalm
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Brit Mollenhauer
- Department of Neurology, University Medical Center Goettingen, Goettingen, Germany
- Paracelsus-Elena-Klinik, Kassel, Germany
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12
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Wang M, Zhao X, Li F, Wu L, Li Y, Tang R, Yao J, Lin S, Zheng Y, Ling Y, Ren K, Chen Z, Yin X, Wang Z, Gao Z, Zhang X. Using sustained vowels to identify patients with mild Parkinson's disease in a Chinese dataset. Front Aging Neurosci 2024; 16:1377442. [PMID: 38765774 PMCID: PMC11102047 DOI: 10.3389/fnagi.2024.1377442] [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: 01/27/2024] [Accepted: 04/15/2024] [Indexed: 05/22/2024] Open
Abstract
Introduction Parkinson's disease (PD) is the second most common neurodegenerative disease and affects millions of people. Accurate diagnosis and subsequent treatment in the early stages can slow down disease progression. However, making an accurate diagnosis of PD at an early stage is challenging. Previous studies have revealed that even for movement disorder specialists, it was difficult to differentiate patients with PD from healthy individuals until the average modified Hoehn-Yahr staging (mH&Y) reached 1.8. Recent researches have shown that dysarthria provides good indicators for computer-assisted diagnosis of patients with PD. However, few studies have focused on diagnosing patients with PD in the early stages, specifically those with mH&Y ≤ 1.5. Method We used a machine learning algorithm to analyze voice features and developed diagnostic models for differentiating between healthy controls (HCs) and patients with PD, and for differentiating between HCs and patients with mild PD (mH&Y ≤ 1.5). The models were independently validated using separate datasets. Results Our results demonstrate that, a remarkable diagnostic performance of the model in identifying patients with mild PD (mH&Y ≤ 1.5) and HCs, with area under the ROC curve 0.93 (95% CI: 0.851.00), accuracy 0.85, sensitivity 0.95, and specificity 0.75. Conclusion The results of our study are helpful for screening PD in the early stages in the community and primary medical institutions where there is a lack of movement disorder specialists and special equipment.
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Affiliation(s)
- Miao Wang
- Department of Geriatric Neurology, The Second Medical Center and National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Xingli Zhao
- Department of Geriatric Neurology, The Second Medical Center and National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Fengzhu Li
- Department of Geriatric Neurology, The Second Medical Center and National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Lingyu Wu
- Gyenno Science Co., Ltd., Shenzhen, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Yifan Li
- Department of Geriatric Neurology, The Second Medical Center and National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Ruonan Tang
- Department of Geriatric Neurology, The Second Medical Center and National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Jiarui Yao
- Department of Geriatric Neurology, The Second Medical Center and National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Shinuan Lin
- Gyenno Science Co., Ltd., Shenzhen, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Yuan Zheng
- Gyenno Science Co., Ltd., Shenzhen, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Yun Ling
- Gyenno Science Co., Ltd., Shenzhen, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Kang Ren
- Gyenno Science Co., Ltd., Shenzhen, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Zhonglue Chen
- Gyenno Science Co., Ltd., Shenzhen, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Xi Yin
- Department of Geriatric Neurology, The Second Medical Center and National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Zhenfu Wang
- Department of Geriatric Neurology, The Second Medical Center and National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Zhongbao Gao
- Department of Geriatric Neurology, The Second Medical Center and National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
| | - Xi Zhang
- Department of Geriatric Neurology, The Second Medical Center and National Clinical Research Center for Geriatric Disease, Chinese PLA General Hospital, Beijing, China
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13
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Chougar L, Faucher A, Faouzi J, Lejeune FX, Gama Lobo G, Jovanovic C, Cormier F, Dupont G, Vidailhet M, Corvol JC, Colliot O, Lehéricy S, Grabli D, Degos B. Contribution of MRI for the Early Diagnosis of Parkinsonism in Patients with Diagnostic Uncertainty. Mov Disord 2024; 39:825-835. [PMID: 38486423 DOI: 10.1002/mds.29760] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 01/16/2024] [Accepted: 02/16/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND International clinical criteria are the reference for the diagnosis of degenerative parkinsonism in clinical research, but they may lack sensitivity and specificity in the early stages. OBJECTIVES To determine whether magnetic resonance imaging (MRI) analysis, through visual reading or machine-learning approaches, improves diagnostic accuracy compared with clinical diagnosis at an early stage in patients referred for suspected degenerative parkinsonism. MATERIALS Patients with initial diagnostic uncertainty between Parkinson's disease (PD), progressive supranuclear palsy (PSP), and multisystem atrophy (MSA), with brain MRI performed at the initial visit (V1) and available 2-year follow-up (V2), were included. We evaluated the accuracy of the diagnosis established based on: (1) the international clinical diagnostic criteria for PD, PSP, and MSA at V1 ("Clin1"); (2) MRI visual reading blinded to the clinical diagnosis ("MRI"); (3) both MRI visual reading and clinical criteria at V1 ("MRI and Clin1"), and (4) a machine-learning algorithm ("Algorithm"). The gold standard diagnosis was established by expert consensus after a 2-year follow-up. RESULTS We recruited 113 patients (53 with PD, 31 with PSP, and 29 with MSA). Considering the whole population, compared with clinical criteria at the initial visit ("Clin1": balanced accuracy, 66.2%), MRI visual reading showed a diagnostic gain of 14.3% ("MRI": 80.5%; P = 0.01), increasing to 19.2% when combined with the clinical diagnosis at the initial visit ("MRI and Clin1": 85.4%; P < 0.0001). The algorithm achieved a diagnostic gain of 9.9% ("Algorithm": 76.1%; P = 0.08). CONCLUSION Our study shows the use of MRI analysis, whether by visual reading or machine-learning methods, for early differentiation of parkinsonism. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Lydia Chougar
- Department of Neuroradiology, Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Paris, France
- ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France
- ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France
- Department of Neuroradiology, Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Paris, France
| | - Alice Faucher
- Assistance Publique Hôpitaux de Paris, Service de Neurologie, Hôpital Avicenne, Hôpitaux Universitaires de Paris Seine-Saint-Denis, Sorbonne Paris Nord, NS-PARK/FCRIN Network, Bobigny, France
| | - Johann Faouzi
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
- CREST, ENSAI, Campus de Ker-Lann, Bruz, France
| | - François-Xavier Lejeune
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, Paris, France
- ICM, Data Analysis Core (DAC), Paris, France
| | - Gonçalo Gama Lobo
- Neuroradiology Department, Centro Hospitalar Universitário de Lisboa Central, Lisboa, Portugal
| | - Carna Jovanovic
- Neurology Clinic, University Clinical Center of Serbia, Belgrade, Serbia
| | - Florence Cormier
- Département de Neurologie, Hôpital Pitié-Salpêtrière, Assistance Publique Hôpitaux de Paris, Clinique des Mouvements Anormaux, Clinical Investigation Center for Neurosciences, Paris, France
| | - Gwendoline Dupont
- Université de Bourgogne, Dijon, France
- Département de Neurologie, Centre Hospitalier Universitaire François Mitterrand, Dijon, France
| | - Marie Vidailhet
- ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, Paris, France
| | - Jean-Christophe Corvol
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, Paris, France
- Département de Neurologie, Hôpital Pitié-Salpêtrière, Assistance Publique Hôpitaux de Paris, Clinique des Mouvements Anormaux, Clinical Investigation Center for Neurosciences, Paris, France
| | - Olivier Colliot
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stéphane Lehéricy
- ICM, Centre de NeuroImagerie de Recherche-CENIR, Paris, France
- ICM, Team "Movement Investigations and Therapeutics" (MOV'IT), Paris, France
- Department of Neuroradiology, Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, AP-HP, Hôpital de la Pitié Salpêtrière, DMU DIAMENT, Paris, France
| | - David Grabli
- Sorbonne Université, Institut du Cerveau-Paris Brain Institute-ICM, CNRS, Inserm, Paris, France
- Département de Neurologie, Hôpital Pitié-Salpêtrière, Assistance Publique Hôpitaux de Paris, Clinique des Mouvements Anormaux, Clinical Investigation Center for Neurosciences, Paris, France
| | - Bertrand Degos
- Assistance Publique Hôpitaux de Paris, Service de Neurologie, Hôpital Avicenne, Hôpitaux Universitaires de Paris Seine-Saint-Denis, Sorbonne Paris Nord, NS-PARK/FCRIN Network, Bobigny, France
- Dynamics and Pathophysiology of Neuronal Networks Team, Center for Interdisciplinary Research in Biology, Collège de France, CNRS UMR7241/INSERM U1050, Université PSL, Paris, France
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14
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Costanzo M, Galosi E, De Bartolo MI, Gallo G, Leodori G, Belvisi D, Conte A, Fabbrini G, Truini A, Berardelli A, Vivacqua G. Evaluating the Diagnostic Potential of Combined Salivary and Skin Biomarkers in Parkinson's Disease. Int J Mol Sci 2024; 25:4823. [PMID: 38732041 PMCID: PMC11084721 DOI: 10.3390/ijms25094823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 04/17/2024] [Accepted: 04/26/2024] [Indexed: 05/13/2024] Open
Abstract
Oligomeric alpha-synuclein (α-syn) in saliva and phosphorylated α-syn deposits in the skin have emerged as promising diagnostic biomarkers for Parkinson's disease (PD). This study aimed to assess and compare the diagnostic value of these biomarkers in discriminating between 38 PD patients and 24 healthy subjects (HSs) using easily accessible biological samples. Additionally, the study sought to determine the diagnostic potential of combining these biomarkers and to explore their correlations with clinical features. Salivary oligomeric α-syn levels were quantified using competitive ELISA, while skin biopsies were analyzed through immunofluorescence to detect phosphorylated α-syn at Ser129 (p-S129). Both biomarkers individually were accurate in discriminating PD patients from HSs, with a modest agreement between them. The combined positivity of salivary α-syn oligomers and skin p-S129 aggregates differentiated PD patients from HSs with an excellent discriminative ability with an AUC of 0.9095. The modest agreement observed between salivary and skin biomarkers individually suggests that they may reflect different aspects of PD pathology, thus providing complementary information when combined. This study's results highlight the potential of utilizing a multimodal biomarker approach to enhance diagnostic accuracy in PD.
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Affiliation(s)
- Matteo Costanzo
- Department of Human Neuroscience, Sapienza University of Rome, Viale dell’Università 30, 00185 Rome, Italy; (M.C.); (E.G.); (G.L.); (A.C.); (G.F.); (A.T.); (A.B.)
- Department of Neuroscience, Istituto Superiore di Sanità, Viale Regina Elena 299, 00161 Rome, Italy
| | - Eleonora Galosi
- Department of Human Neuroscience, Sapienza University of Rome, Viale dell’Università 30, 00185 Rome, Italy; (M.C.); (E.G.); (G.L.); (A.C.); (G.F.); (A.T.); (A.B.)
| | | | - Gaetano Gallo
- Unità Operativa Complessa Neurologia, Ospedali Riuniti Padova Sud, Via Albere 30, 35043 Padova, Italy;
| | - Giorgio Leodori
- Department of Human Neuroscience, Sapienza University of Rome, Viale dell’Università 30, 00185 Rome, Italy; (M.C.); (E.G.); (G.L.); (A.C.); (G.F.); (A.T.); (A.B.)
- IRCCS Neuromed, Via Atinense 18, 86077 Isernia, Italy;
| | - Daniele Belvisi
- Department of Human Neuroscience, Sapienza University of Rome, Viale dell’Università 30, 00185 Rome, Italy; (M.C.); (E.G.); (G.L.); (A.C.); (G.F.); (A.T.); (A.B.)
- IRCCS Neuromed, Via Atinense 18, 86077 Isernia, Italy;
| | - Antonella Conte
- Department of Human Neuroscience, Sapienza University of Rome, Viale dell’Università 30, 00185 Rome, Italy; (M.C.); (E.G.); (G.L.); (A.C.); (G.F.); (A.T.); (A.B.)
- IRCCS Neuromed, Via Atinense 18, 86077 Isernia, Italy;
| | - Giovanni Fabbrini
- Department of Human Neuroscience, Sapienza University of Rome, Viale dell’Università 30, 00185 Rome, Italy; (M.C.); (E.G.); (G.L.); (A.C.); (G.F.); (A.T.); (A.B.)
- IRCCS Neuromed, Via Atinense 18, 86077 Isernia, Italy;
| | - Andrea Truini
- Department of Human Neuroscience, Sapienza University of Rome, Viale dell’Università 30, 00185 Rome, Italy; (M.C.); (E.G.); (G.L.); (A.C.); (G.F.); (A.T.); (A.B.)
| | - Alfredo Berardelli
- Department of Human Neuroscience, Sapienza University of Rome, Viale dell’Università 30, 00185 Rome, Italy; (M.C.); (E.G.); (G.L.); (A.C.); (G.F.); (A.T.); (A.B.)
- IRCCS Neuromed, Via Atinense 18, 86077 Isernia, Italy;
| | - Giorgio Vivacqua
- Department of Experimental Morphology and Microscopy-Integrated Research Center (PRAAB), Campus Biomedico University of Rome, 00128 Rome, Italy;
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15
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Hermann MG, Schröter N, Rau A, Reisert M, Jarc N, Rijntjes M, Hosp JA, Reinacher PC, Jost WH, Urbach H, Weiller C, Coenen VA, Sajonz BEA. The connection of motor improvement after deep brain stimulation in Parkinson's disease and microstructural integrity of the substantia nigra and subthalamic nucleus. Neuroimage Clin 2024; 42:103607. [PMID: 38643635 PMCID: PMC11046219 DOI: 10.1016/j.nicl.2024.103607] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 04/15/2024] [Accepted: 04/15/2024] [Indexed: 04/23/2024]
Abstract
BACKGROUND Nigrostriatal microstructural integrity has been suggested as a biomarker for levodopa response in Parkinson's disease (PD), which is a strong predictor for motor response to deep brain stimulation (DBS) of the subthalamic nucleus (STN). This study aimed to explore the impact of microstructural integrity of the substantia nigra (SN), STN, and putamen on motor response to STN-DBS using diffusion microstructure imaging. METHODS Data was collected from 23 PD patients (mean age 63 ± 7, 6 females) who underwent STN-DBS, had preoperative 3 T diffusion magnetic resonance imaging including multishell diffusion-weighted MRI with b-values of 1000 and 2000 s/mm2 and records of motor improvement available. RESULTS The association between a poorer DBS-response and increased free interstitial fluid showed notable effect sizes (rho > |0.4|) in SN and STN, but not in putamen. However, this did not reach significance after Bonferroni correction and controlling for sex and age. CONCLUSION Microstructural integrity of SN and STN are potential biomarkers for the prediction of therapy efficacy following STN-DBS, but further studies are required to confirm these associations.
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Affiliation(s)
- Marco G Hermann
- Department of Stereotactic and Functional Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Nils Schröter
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Alexander Rau
- Department of Neuroradiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Department of Diagnostic and Interventional Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Marco Reisert
- Department of Stereotactic and Functional Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Medical Physics, Department of Radiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Nadja Jarc
- Department of Stereotactic and Functional Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Michel Rijntjes
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Jonas A Hosp
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Peter C Reinacher
- Department of Stereotactic and Functional Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Fraunhofer Institute for Laser Technology (ILT), Aachen, Germany
| | | | - Horst Urbach
- Department of Neuroradiology, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Cornelius Weiller
- Department of Neurology and Clinical Neuroscience, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Volker A Coenen
- Department of Stereotactic and Functional Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany; Center for Deep Brain Stimulation, University of Freiburg, Germany
| | - Bastian E A Sajonz
- Department of Stereotactic and Functional Neurosurgery, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany.
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16
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Adler CH, Serrano GE, Shill HA, Driver-Dunckley E, Mehta SH, Zhang N, Glass M, Sue LI, Intorcia A, Beach TG. Symmetry of synuclein density in autopsied Parkinson's disease submandibular glands. Neurosci Lett 2024; 825:137702. [PMID: 38395191 PMCID: PMC10942751 DOI: 10.1016/j.neulet.2024.137702] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Revised: 02/16/2024] [Accepted: 02/20/2024] [Indexed: 02/25/2024]
Abstract
BACKGROUND Peripheral tissue biopsy in Parkinson's disease (PD) may be valuable for clinical care, biomarker validation, and as research enrollment criteria. OBJECTIVE Determine whether submandibular gland pathologic alpha-synuclein (aSyn) density is symmetrical and whether previous needle biopsy caused tissue damage. METHODS Thirty autopsy-confirmed PD cases having fixed submandibular gland tissue from one side and frozen submandibular gland tissue from the contralateral side were studied. Tissue was stained for phosphorylated aSyn and density (0-4 semiquantitative scale) was determined. Three previously biopsied cases were also assessed for tissue damage at subsequent autopsy. RESULTS Mean (SD) age was 80.9 (5.5) years and disease duration 12.5 (9.3). Submandibular gland aSyn staining had a mean score of 2.13 for both the initially fixed and the initially frozen submandibular glands. The correlation between aSyn density of the two sides was r = 0.63. Correlation of aSyn density, in the originally fixed submandibular gland, with disease duration was good (r = 0.49, p =.006). No permanent tissue damage was found in the three previously biopsied cases. CONCLUSIONS This study found good correlation between aSyn density in both submandibular glands of patients with PD and found no evidence of significant tissue damage in previously biopsied subjects.
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Affiliation(s)
- Charles H Adler
- Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic Arizona, Scottsdale, AZ, USA.
| | - Geidy E Serrano
- Civin Laboratory of Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, USA
| | | | - Erika Driver-Dunckley
- Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Shyamal H Mehta
- Department of Neurology, Mayo Clinic College of Medicine, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Nan Zhang
- Department of Biostatistics, Mayo Clinic College of Medicine, Mayo Clinic Arizona, Scottsdale, AZ, USA
| | - Michael Glass
- Civin Laboratory of Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, USA
| | - Lucia I Sue
- Civin Laboratory of Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, USA
| | - Anthony Intorcia
- Civin Laboratory of Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, USA
| | - Thomas G Beach
- Civin Laboratory of Neuropathology, Banner Sun Health Research Institute, Sun City, AZ, USA
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17
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Li K, Wang P, Li W, Yan JH, Ge YL, Zhang JR, Wang F, Mao CJ, Liu CF. The association between plasma GPNMB and Parkinson's disease and multiple system atrophy. Parkinsonism Relat Disord 2024; 120:106001. [PMID: 38217954 DOI: 10.1016/j.parkreldis.2024.106001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/03/2024] [Accepted: 01/06/2024] [Indexed: 01/15/2024]
Abstract
AIMS Parkinson's disease (PD), as the second most common neurodegenerative disorder, often presents diagnostic challenges in differentiation from other forms of Parkinsonism. Recent studies have reported an association between plasma glycoprotein nonmetastatic melanoma protein B (pGPNMB) and PD. METHODS A retrospective study was conducted, comprising 401 PD patients, 111 multiple system atrophy (MSA) patients, 13 progressive supranuclear palsy (PSP) patients and 461 healthy controls from the Chinese Han population, with an assessment of pGPNMB levels. RESULTS The study revealed that pGPNMB concentrations were significantly lower in PD and MSA patients compared to controls (area under the receiver operating characteristics curve (AUC) 0.62 and 0.74, respectively, P < 0.0001 for both), but no difference was found in PSP patients compared to controls (P > 0.05). Interestingly, the level of pGPNMB was significantly higher in PD patients than in MSA patients (AUC = 0.63, P < 0.0001). Furthermore, the study explored the association between pGPNMB levels and disease severity in PD and MSA patients, revealing a positive correlation in PD patients but not in MSA patients with both disease severity and cognitive impairment. CONCLUSION This study successfully replicated prior findings, demonstrating an association between pGPNMB levels and disease severity, and also identified a correlation with cognitive impairment in PD patients of the Chinese Han population. Additionally, this study is the first to identify a significant difference in pGPNMB levels between MSA, PD, and normal controls. The data provide new evidence supporting the potential role of pGPNMB in the diagnosis and differential diagnosis of Parkinsonism.
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Affiliation(s)
- Kai Li
- Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Puzhi Wang
- Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Wen Li
- Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Jia-Hui Yan
- Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Yi-Lun Ge
- Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Jin-Ru Zhang
- Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China
| | - Fen Wang
- Jiangsu Key Laboratory of Neuropsychiatric Diseases and Institute of Neuroscience, Soochow University, Suzhou, China.
| | - Cheng-Jie Mao
- Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China.
| | - Chun-Feng Liu
- Department of Neurology and Suzhou Clinical Research Center of Neurological Disease, The Second Affiliated Hospital of Soochow University, Suzhou, China; Jiangsu Key Laboratory of Neuropsychiatric Diseases and Institute of Neuroscience, Soochow University, Suzhou, China.
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Huang J, Yuan X, Chen L, Hu B, Wang H, Wang Y, Huang W. Pathological α-synuclein detected by real-time quaking-induced conversion in synucleinopathies. Exp Gerontol 2024; 187:112366. [PMID: 38280659 DOI: 10.1016/j.exger.2024.112366] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2023] [Revised: 01/10/2024] [Accepted: 01/21/2024] [Indexed: 01/29/2024]
Abstract
synucleinopathies are diseases characterized by the aggregation of α-synuclein (α-syn), which forms fibrils through misfolding and accumulates in a prion-like manner. To detect the presence of these α-syn aggregates in clinical samples, seed amplification assays (SAAs) have been developed. These SAAs are capable of amplifying the α-syn seeds, allowing for their detection. αSyn-SAAs have been reported under the names 'protein misfolding cyclic amplification' (αSyn-PMCA) and 'real-time quaking-induced conversion'α-Syn-RT-QuIC. The α-Syn RT-QuIC, in particular, has been adapted to amplify and detect α-syn aggregates in various biospecimens, including cerebrospinal fluid (CSF), skin, nasal brushing, serum and saliva. The α-syn RT-QuIC assay has demonstrated good sensitivity and specificity in detecting pathological α-syn, particularly in Parkinson's disease (PD) and dementia with Lewy bodies (DLB) cases, with an accuracy rate of up to 80 %. Additionally, differential diagnosis between DLB and PD, as well as PD and multiple system atrophy (MSA), can be achieved by utilizing certain kinetic thioflavin T (ThT) parameters and other parameters. Moreover, the positive detection of α-syn in the prodromal stage of synucleinopathies provides an opportunity for early intervention and management. In summary, the development of the α-syn RT-QuIC assay has greatly contributed to the field of synucleinopathies. Therefore, we review the development of α-syn RT-QuIC assay and describe in detail the recent advancements of α-syn RT-QuIC assay for detecting pathological α-syn in synucleinopathies.
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Affiliation(s)
- Juan Huang
- Department of Neurology, Second Affiliated Hospital of Nanchang University, Jiangxi Medical College, Nanchang University, China
| | - Xingxing Yuan
- Department of Anesthesiology, Hunan Provincial People's Hospital, The First Affiliated Hospital of Hunan Normal University, China
| | - Lin Chen
- Department of Neurology, Second Affiliated Hospital of Nanchang University, Jiangxi Medical College, Nanchang University, China
| | - Binbin Hu
- Department of Neurology, Second Affiliated Hospital of Nanchang University, Jiangxi Medical College, Nanchang University, China
| | - Hui Wang
- Department of Neurology, Second Affiliated Hospital of Nanchang University, Jiangxi Medical College, Nanchang University, China
| | - Ye Wang
- Department of Neurology, Second Affiliated Hospital of Nanchang University, Jiangxi Medical College, Nanchang University, China.
| | - Wei Huang
- Department of Neurology, Second Affiliated Hospital of Nanchang University, Jiangxi Medical College, Nanchang University, China.
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Ruppert-Junck MC, Kräling G, Greuel A, Tittgemeyer M, Timmermann L, Drzezga A, Eggers C, Pedrosa D. Random forest analysis of midbrain hypometabolism using [ 18F]-FDG PET identifies Parkinson's disease at the subject-level. Front Comput Neurosci 2024; 18:1328699. [PMID: 38384375 PMCID: PMC10879348 DOI: 10.3389/fncom.2024.1328699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Accepted: 01/22/2024] [Indexed: 02/23/2024] Open
Abstract
Parkinson's disease (PD) is currently diagnosed largely on the basis of expert judgement with neuroimaging serving only as a supportive tool. In a recent study, we identified a hypometabolic midbrain cluster, which includes parts of the substantia nigra, as the best differentiating metabolic feature for PD-patients based on group comparison of [18F]-fluorodeoxyglucose ([18F]-FDG) PET scans. Longitudinal analyses confirmed progressive metabolic changes in this region and, an independent study showed great potential of nigral metabolism for diagnostic workup of parkinsonian syndromes. In this study, we applied a machine learning approach to evaluate midbrain metabolism measured by [18F]-FDG PET as a diagnostic marker for PD. In total, 51 mid-stage PD-patients and 16 healthy control subjects underwent high-resolution [18F]-FDG PET. Normalized tracer update values of the midbrain cluster identified by between-group comparison were extracted voxel-wise from individuals' scans. Extracted uptake values were subjected to a random forest feature classification algorithm. An adapted leave-one-out cross validation approach was applied for testing robustness of the model for differentiating between patients and controls. Performance of the model across all runs was evaluated by calculating sensitivity, specificity and model accuracy for the validation data set and the percentage of correctly categorized subjects for test data sets. The random forest feature classification of voxel-based uptake values from the midbrain cluster identified patients in the validation data set with an average sensitivity of 0.91 (Min: 0.82, Max: 0.94). For all 67 runs, in which each of the individuals was treated once as test data set, the test data set was correctly categorized by our model. The applied feature importance extraction consistently identified a subset of voxels within the midbrain cluster with highest importance across all runs which spatially converged with the left substantia nigra. Our data suggest midbrain metabolism measured by [18F]-FDG PET as a promising diagnostic imaging tool for PD. Given its close relationship to PD pathophysiology and very high discriminatory accuracy, this approach could help to objectify PD diagnosis and enable more accurate classification in relation to clinical trials, which could also be applicable to patients with prodromal disease.
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Affiliation(s)
- Marina C. Ruppert-Junck
- Department of Neurology, Philipps-University of Marburg, Marburg, Germany
- Clinic for Neurology, University Hospital Gießen and Marburg GmbH, Marburg, Germany
- Center for Mind, Brain and Behavior, Philipps-University of Marburg and Justus-Liebig University Gießen, Marburg, Germany
| | - Gunter Kräling
- Clinic for Neurology, University Hospital Gießen and Marburg GmbH, Marburg, Germany
| | - Andrea Greuel
- Department of Psychiatry, Psychotherapy and Psychosomatics, Vivantes Hospital Neukölln, Berlin, Germany
| | - Marc Tittgemeyer
- Max Planck Institute for Metabolism Research, Cologne, Germany
- Cluster of Excellence in Cellular Stress and Aging Associated Disease (CECAD), Cologne, Germany
| | - Lars Timmermann
- Department of Neurology, Philipps-University of Marburg, Marburg, Germany
- Clinic for Neurology, University Hospital Gießen and Marburg GmbH, Marburg, Germany
- Center for Mind, Brain and Behavior, Philipps-University of Marburg and Justus-Liebig University Gießen, Marburg, Germany
| | - Alexander Drzezga
- Cluster of Excellence in Cellular Stress and Aging Associated Disease (CECAD), Cologne, Germany
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-2), Research Center Jülich, Jülich, Germany
- Multimodal Neuroimaging Group, Department of Nuclear Medicine, Medical Faculty, University Hospital Cologne, Cologne, Germany
| | - Carsten Eggers
- Department of Neurology, Philipps-University of Marburg, Marburg, Germany
- Department of Neurology, Knappschaftskrankenhaus Bottrop, Bottrop, Germany
| | - David Pedrosa
- Department of Neurology, Philipps-University of Marburg, Marburg, Germany
- Clinic for Neurology, University Hospital Gießen and Marburg GmbH, Marburg, Germany
- Center for Mind, Brain and Behavior, Philipps-University of Marburg and Justus-Liebig University Gießen, Marburg, Germany
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20
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Sun J, Cong C, Li X, Zhou W, Xia R, Liu H, Wang Y, Xu Z, Chen X. Identification of Parkinson's disease and multiple system atrophy using multimodal PET/MRI radiomics. Eur Radiol 2024; 34:662-672. [PMID: 37535155 DOI: 10.1007/s00330-023-10003-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 05/08/2023] [Accepted: 06/06/2023] [Indexed: 08/04/2023]
Abstract
OBJECTIVES To construct a machine learning model for differentiating Parkinson's disease (PD) and multiple system atrophy (MSA) by using multimodal PET/MRI radiomics and clinical characteristics. METHODS One hundred and nineteen patients (81 with PD and 38 with MSA) underwent brain PET/CT and MRI to obtain metabolic images ([18F]FDG, [11C]CFT PET) and structural MRI (T1WI, T2WI, and T2-FLAIR). Image analysis included automatic segmentation on MRI, co-registration of PET images onto the corresponding MRI. Radiomics features were then extracted from the putamina and caudate nuclei and selected to construct predictive models. Moreover, based on PET/MRI radiomics and clinical characteristics, we developed a nomogram. Receiver operating characteristic (ROC) curves were performed to evaluate the performance of the models. Decision curve analysis (DCA) was employed to access the clinical usefulness of the models. RESULTS The combined PET/MRI radiomics model of five sequences outperformed monomodal radiomics models alone. Further, PET/MRI radiomics-clinical combined model could perfectly distinguish PD from MSA (AUC = 0.993), which outperformed the clinical model (AUC = 0.923, p = 0.028) in training set, with no significant difference in test set (AUC = 0.860 vs 0.917, p = 0.390). However, no significant difference was found between PET/MRI radiomics-clinical model and PET/MRI radiomics model in training (AUC = 0.988, p = 0.276) and test sets (AUC = 0.860 vs 0.845, p = 0.632). DCA demonstrated the highest clinical benefit of PET/MRI radiomics-clinical model. CONCLUSIONS Our study indicates that multimodal PET/MRI radiomics could achieve promising performance to differentiate between PD and MSA in clinics. CLINICAL RELEVANCE STATEMENT This study developed an optimal radiomics signature and construct model to distinguish PD from MSA by multimodal PET/MRI imaging methods in clinics for parkinsonian syndromes, which achieved an excellent performance. KEY POINTS •Multimodal PET/MRI radiomics from putamina and caudate nuclei increase the diagnostic efficiency for distinguishing PD from MSA. •The radiomics-based nomogram was developed to differentiate between PD and MSA. •Combining PET/MRI radiomics-clinical model achieved promising performance to identify PD and MSA.
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Affiliation(s)
- Jinju Sun
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Chao Cong
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
- School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, China
| | - Xinpeng Li
- Department of Neurology, Daping Hospital, Army Medical University, Chongqing, China
| | - Weicheng Zhou
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Renxiang Xia
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | | | - Yi Wang
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Zhiqiang Xu
- Department of Neurology, Daping Hospital, Army Medical University, Chongqing, China.
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China.
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China.
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Savoie FA, Arpin DJ, Vaillancourt DE. Magnetic Resonance Imaging and Nuclear Imaging of Parkinsonian Disorders: Where do we go from here? Curr Neuropharmacol 2024; 22:1583-1605. [PMID: 37533246 DOI: 10.2174/1570159x21666230801140648] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 01/11/2023] [Accepted: 01/13/2023] [Indexed: 08/04/2023] Open
Abstract
Parkinsonian disorders are a heterogeneous group of incurable neurodegenerative diseases that significantly reduce quality of life and constitute a substantial economic burden. Nuclear imaging (NI) and magnetic resonance imaging (MRI) have played and continue to play a key role in research aimed at understanding and monitoring these disorders. MRI is cheaper, more accessible, nonirradiating, and better at measuring biological structures and hemodynamics than NI. NI, on the other hand, can track molecular processes, which may be crucial for the development of efficient diseasemodifying therapies. Given the strengths and weaknesses of NI and MRI, how can they best be applied to Parkinsonism research going forward? This review aims to examine the effectiveness of NI and MRI in three areas of Parkinsonism research (differential diagnosis, prodromal disease identification, and disease monitoring) to highlight where they can be most impactful. Based on the available literature, MRI can assist with differential diagnosis, prodromal disease identification, and disease monitoring as well as NI. However, more work is needed, to confirm the value of MRI for monitoring prodromal disease and predicting phenoconversion. Although NI can complement or be a substitute for MRI in all the areas covered in this review, we believe that its most meaningful impact will emerge once reliable Parkinsonian proteinopathy tracers become available. Future work in tracer development and high-field imaging will continue to influence the landscape for NI and MRI.
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Affiliation(s)
- Félix-Antoine Savoie
- Department of Applied Physiology and Kinesiology, Laboratory for Rehabilitation Neuroscience, University of Florida, Gainesville, FL, USA
| | - David J Arpin
- Department of Applied Physiology and Kinesiology, Laboratory for Rehabilitation Neuroscience, University of Florida, Gainesville, FL, USA
| | - David E Vaillancourt
- Department of Applied Physiology and Kinesiology, Laboratory for Rehabilitation Neuroscience, University of Florida, Gainesville, FL, USA
- Department of Neurology, Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
- J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, FL, USA
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22
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Keavney JL, Mathur S, Schroeder K, Merrell R, Castillo-Torres SA, Gao V, Crotty GF, Schwarzschild MA, Poma JM. Perspectives of People At-Risk on Parkinson's Prevention Research. JOURNAL OF PARKINSON'S DISEASE 2024; 14:399-414. [PMID: 38489198 DOI: 10.3233/jpd-230436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/17/2024]
Abstract
The movement toward prevention trials in people at-risk for Parkinson's disease (PD) is rapidly becoming a reality. The authors of this article include a genetically at-risk advocate with the LRRK2 G2019 S variant and two patients with rapid eye movement sleep behavior disorder (RBD), one of whom has now been diagnosed with PD. These authors participated as speakers, panelists, and moderators in the "Planning for Prevention of Parkinson's: A Trial Design Forum" hosted by Massachusetts General Hospital in 2021 and 2022. Other authors include a young onset person with Parkinson's (PwP) and retired family physician, an expert in patient engagement in Parkinson's, and early career and veteran movement disorders clinician researchers. Several themes emerged from the at-risk participant voice concerning the importance of early intervention, the legitimacy of their input in decision-making, and the desire for transparent communication and feedback throughout the entire research study process. Challenges and opportunities in the current environment include lack of awareness among primary care physicians and general neurologists about PD risk, legal and psychological implications of risk disclosure, limited return of individual research study results, and undefined engagement and integration of individuals at-risk into the broader Parkinson's community. Incorporating the perspectives of individuals at-risk as well as those living with PD at this early stage of prevention trial development is crucial to success.
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Affiliation(s)
- Jessi L Keavney
- Parkinson's Foundation, Parkinson's Advocates in Research Program, Pendergrass, GA, USA
| | | | - Karlin Schroeder
- Parkinson's Foundation, Associate Vice President of Community Engagement, New York, NY, USA
| | | | - Sergio A Castillo-Torres
- Edmond J. Safra Fellow in Movement Disorders, Servicio de Movimientos Anormales, Fleni, Buenos Aires, Argentina
| | - Virginia Gao
- Movement Disorders Fellow, Columbia University Irving Medical Center and Weill Cornell Medicine, New York, NY, USA
| | - Grace F Crotty
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael A Schwarzschild
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - John M Poma
- Parkinson's Foundation, People with Parkinson's Advisory Council, Glen Allen, VA, USA
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23
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Tran KK, Lee PY, Finkelstein DI, McKendrick AM, Nguyen BN, Bui BV, Nguyen CT. Altered Outer Retinal Structure, Electrophysiology and Visual Perception in Parkinson's Disease. JOURNAL OF PARKINSON'S DISEASE 2024; 14:167-180. [PMID: 38189711 PMCID: PMC10836541 DOI: 10.3233/jpd-230293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/19/2023] [Indexed: 01/09/2024]
Abstract
BACKGROUND Visual biomarkers of Parkinson's disease (PD) are attractive as the retina is an outpouching of the brain. Although inner retinal neurodegeneration in PD is well-established this has overlap with other neurodegenerative diseases and thus outer retinal (photoreceptor) measures warrant further investigation. OBJECTIVE To examine in a cross-sectional study whether clinically implementable measures targeting outer retinal function and structure can differentiate PD from healthy ageing and whether these are sensitive to intraday levodopa (L-DOPA) dosing. METHODS Centre-surround perceptual contrast suppression, macular visual field sensitivity, colour discrimination, light-adapted electroretinography and optical coherence tomography (OCT) were tested in PD participants (n = 16) and controls (n = 21). Electroretinography and OCT were conducted before and after midday L-DOPA in PD participants, or repeated after ∼2 hours in controls. RESULTS PD participants had decreased center-surround contrast suppression (p < 0.01), reduced macular visual field sensitivity (p < 0.05), color vision impairment (p < 0.01) photoreceptor dysfunction (a-wave, p < 0.01) and photoreceptor neurodegeneration (outer nuclear layer thinning, p < 0.05), relative to controls. Effect size comparison between inner and outer retinal parameters showed that photoreceptor metrics were similarly robust in differentiating the PD group from age-matched controls as inner retinal changes. Electroretinography and OCT were unaffected by L-DOPA treatment or time. CONCLUSIONS We show that outer retinal outcomes of photoreceptoral dysfunction (decreased cone function and impaired color vision) and degeneration (i.e., outer nuclear layer thinning) were equivalent to inner retinal metrics at differentiating PD from healthy age-matched adults. These findings suggest outer retinal metrics may serve as useful biomarkers for PD.
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Affiliation(s)
- Katie K.N. Tran
- Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Pei Ying Lee
- Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - David I. Finkelstein
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Allison M. McKendrick
- Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, VIC, Australia
- Division of Optometry, School of Allied Health, The University of Western Australia, Crawley, WA, Australia
- Lions Eye Institute, Nedlands, WA, Australia
| | - Bao N. Nguyen
- Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Bang V. Bui
- Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Christine T.O. Nguyen
- Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, VIC, Australia
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Huang X, Kanthasamy AG. Seeding and amplifying parkinsonism research. Parkinsonism Relat Disord 2023; 117:105934. [PMID: 37989652 DOI: 10.1016/j.parkreldis.2023.105934] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/23/2023]
Affiliation(s)
- Xuemei Huang
- University Distinguished Professor, Departments of Neurology, Neurosurgery, Pharmacology, Radiology & Kinesiology, Translational Brain Research Center, Penn State Health-Milton S. Hershey Medical Center, 500 University Drive, H037, Hershey, PA, 17033-0850, USA.
| | - Anumantha G Kanthasamy
- Parkinson's Research and Georgia Research Alliance Eminent Scholar, Director, Isakson Center for Neurological Disease Research, University of Georgia. College of Veterinary Medicine, Athens, GA, 30602, USA.
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Fernandes Gomes B, Farris CM, Ma Y, Concha-Marambio L, Lebovitz R, Nellgård B, Dalla K, Constantinescu J, Constantinescu R, Gobom J, Andreasson U, Zetterberg H, Blennow K. α-Synuclein seed amplification assay as a diagnostic tool for parkinsonian disorders. Parkinsonism Relat Disord 2023; 117:105807. [PMID: 37591709 DOI: 10.1016/j.parkreldis.2023.105807] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Revised: 08/09/2023] [Accepted: 08/13/2023] [Indexed: 08/19/2023]
Abstract
INTRODUCTION Synucleinopathies such as Parkinson's disease (PD) and multiple system atrophy (MSA) can be challenging to diagnose due to the symptom overlap with, for example, atypical parkinsonisms like progressive supranuclear palsy (PSP) and corticobasal degeneration (CBD). Seed amplification assays (SAA), developed for the detection of α-synuclein (αSyn) aggregates in CSF, have been successful when used as a biomarker evaluation for synucleinopathies. In this study, we investigated the potential of this assay to not only detect αSyn seeds in CSF, but also discriminate between movement disorders. METHODS The αSyn-SAA was tested in a Scandinavian cohort composed of 129 CSF samples from patients with PD (n = 55), MSA (n = 27), CBD (n = 7), and PSP (n = 16), as well as healthy controls (HC, n = 24). RESULTS The αSyn seed amplification assay (αSyn-SAA) was able to correctly identify all PD samples as positive (sensitivity of 100%) while also discriminating the PD group from HC (70.8% specificity, p < 0.0001) and tauopathies [CBD (71% specificity) and PSP (75% specificity), p < 0.0001)]. The αSyn-SAA was also able to identify almost all MSA samples as positive for αSyn aggregation (sensitivity of 92.6%). In general, this assay is able to discriminate between the synucleinopathies and tauopathies analyzed herein (p < 0.0001) despite the overlapping symptoms in these diseases. CONCLUSION These findings suggest the αSyn-SAA is a useful diagnostic tool for differentiating between different parkinsonian disorders, although further optimization may be needed.
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Affiliation(s)
- Bárbara Fernandes Gomes
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden.
| | | | - Yihua Ma
- R&D Unit, Amprion Inc., San Diego, CA, 92121, USA
| | | | | | - Bengt Nellgård
- Department of Anesthesiology and Intensive Care, Institute of Clinical Sciences, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | - Keti Dalla
- Department of Anesthesiology and Intensive Care, Institute of Clinical Sciences, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden
| | | | - Radu Constantinescu
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Johan Gobom
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Ulf Andreasson
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden; Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK; UK Dementia Research Institute at UCL, London, UK; Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China; Wisconsin Alzheimer's Disease Research Center, University of Wisconsin School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53792, USA
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden; Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
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Park S, No C, Kim S, Han K, Jung JM, Kwon KY, Lee M. A multimodal screening system for elderly neurological diseases based on deep learning. Sci Rep 2023; 13:21013. [PMID: 38030653 PMCID: PMC10687257 DOI: 10.1038/s41598-023-48071-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2023] [Accepted: 11/22/2023] [Indexed: 12/01/2023] Open
Abstract
In this paper, we propose a deep-learning-based algorithm for screening neurological diseases. We proposed various examination protocols for screening neurological diseases and collected data by video-recording persons performing these protocols. We converted video data into human landmarks that capture action information with a much smaller data dimension. We also used voice data which are also effective indicators of neurological disorders. We designed a subnetwork for each protocol to extract features from landmarks or voice and a feature aggregator that combines all the information extracted from the protocols to make a final decision. Multitask learning was applied to screen two neurological diseases. To capture meaningful information about these human landmarks and voices, we applied various pre-trained models to extract preliminary features. The spatiotemporal characteristics of landmarks are extracted using a pre-trained graph neural network, and voice features are extracted using a pre-trained time-delay neural network. These extracted high-level features are then passed onto the subnetworks and an additional feature aggregator that are simultaneously trained. We also used various data augmentation techniques to overcome the shortage of data. Using a frame-length staticizer that considers the characteristics of the data, we can capture momentary tremors without wasting information. Finally, we examine the effectiveness of different protocols and different modalities (different body parts and voice) through extensive experiments. The proposed method achieves AUC scores of 0.802 for stroke and 0.780 for Parkinson's disease, which is effective for a screening system.
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Affiliation(s)
- Sangyoung Park
- Department of Electrical and Electronic Engineering, Hanyang University ERICA, Ansan, 15588, South Korea
| | - Changho No
- Department of Electrical and Electronic Engineering, Hanyang University ERICA, Ansan, 15588, South Korea
| | - Sora Kim
- Department of Electrical and Electronic Engineering, Hanyang University ERICA, Ansan, 15588, South Korea
| | - Kyoungmin Han
- Department of Electrical and Electronic Engineering, Hanyang University ERICA, Ansan, 15588, South Korea
| | - Jin-Man Jung
- Department of Neurology, Korea University Ansan Hospital, Ansan, 15355, South Korea
| | - Kyum-Yil Kwon
- Department of Neurology, Soonchunhyang University Seoul Hospital, Seoul, 04401, South Korea
| | - Minsik Lee
- Department of Electrical and Electronic Engineering, Hanyang University ERICA, Ansan, 15588, South Korea.
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Christodoulou CC, Onisiforou A, Zanos P, Papanicolaou EZ. Unraveling the transcriptomic signatures of Parkinson's disease and major depression using single-cell and bulk data. Front Aging Neurosci 2023; 15:1273855. [PMID: 38020762 PMCID: PMC10664927 DOI: 10.3389/fnagi.2023.1273855] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Accepted: 10/18/2023] [Indexed: 12/01/2023] Open
Abstract
Background Motor symptoms are well-characterized in Parkinson's disease (PD). However, non-motor symptoms, such as depression, are commonly observed and can appear up to 10 years before motor features, resulting in one-third of individuals being misdiagnosed with a neuropsychiatric disorder. Thus, identifying diagnostic biomarkers is crucial for accurate PD diagnosis during its prodromal or early stages. Methods We employed an integrative approach, combining single nucleus RNA and bulk mRNA transcriptomics to perform comparative molecular signatures analysis between PD and major depressive disorder (MDD). We examined 39,834 nuclei from PD (GSE202210) and 32,707 nuclei from MDD (GSE144136) in the dorsolateral prefrontal cortex (dlPFC) of Brodmann area 9. Additionally, we analyzed bulk mRNA peripheral blood samples from PD compared to controls (GSE49126, GSE72267), as well as MDD compared to controls (GSE39653). Results Our findings show a higher proportion of astrocytes, and oligodendrocyte cells in the dlPFC of individuals with PD vs. MDD. The excitatory to inhibitory neurons (E/I) ratio analysis indicates that MDD has a ratio close to normal 80/20, while PD has a ratio of 62/38, indicating increased inhibition in the dlPFC. Microglia displayed the most pronounced differences in gene expression profiles between the two conditions. In PD, microglia display a pro-inflammatory phenotype, while in MDD, they regulate synaptic transmission through oligodendrocyte-microglia crosstalk. Analysis of bulk mRNA blood samples revealed that the COL5A, MID1, ZNF148, and CD22 genes were highly expressed in PD, whereas the DENR and RNU1G2 genes were highly expressed in MDD. CD22 is involved in B-cell activation and the negative regulation of B-cell receptor signaling. Additionally, CD86, which provides co-stimulatory signals for T-cell activation and survival, was found to be a commonly differentially expressed gene in both conditions. Pathway analysis revealed several immune-related pathways common in both conditions, including the complement and coagulation cascade, and B-cell receptor signaling. Discussion This study demonstrates that bulk peripheral immune cells play a role in both conditions, but neuroinflammation in the dlPFC specifically manifests in PD as evidenced by the analysis of single nucleus dlPFC datasets. Integrating these two omics levels offers a better understanding of the shared and distinct molecular pathophysiology of PD and MDD in both the periphery and the brain. These findings could lead to potential diagnostic biomarkers, improving accuracy and guiding pharmacological treatments.
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Affiliation(s)
- Christiana C. Christodoulou
- Neuroepidemiology Department, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus Institute of Neurology and Genetics Is a Full Member of the European Reference Network-Rare Neurological Diseases (ERN-RND), Tübingen, Germany
| | - Anna Onisiforou
- Translational Neuropharmacology Laboratory, Department of Psychology, University of Cyprus, Nicosia, Cyprus
| | - Panos Zanos
- Translational Neuropharmacology Laboratory, Department of Psychology, University of Cyprus, Nicosia, Cyprus
| | - Eleni Zamba Papanicolaou
- Neuroepidemiology Department, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus
- The Cyprus Institute of Neurology and Genetics Is a Full Member of the European Reference Network-Rare Neurological Diseases (ERN-RND), Tübingen, Germany
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Siderowf A, Concha-Marambio L, Marek K, Soto C. α-synuclein seed amplification in Parkinson's disease - Authors' reply. Lancet Neurol 2023; 22:985-986. [PMID: 37863605 DOI: 10.1016/s1474-4422(23)00371-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 09/22/2023] [Indexed: 10/22/2023]
Affiliation(s)
- Andrew Siderowf
- Department of Neurology, Penn Perelman School of Medicine, Philadelphia, PA 19107, USA.
| | | | - Kenneth Marek
- Institute for Neurodegenerative Disorders, New Haven, CT, USA
| | - Claudio Soto
- Department of Neurology, University of Texas McGovern Medical School at Houston, TX, USA
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Pitton Rissardo J, Fornari Caprara AL. Cardiac 123I-Metaiodobenzylguanidine (MIBG) Scintigraphy in Parkinson's Disease: A Comprehensive Review. Brain Sci 2023; 13:1471. [PMID: 37891838 PMCID: PMC10605004 DOI: 10.3390/brainsci13101471] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Revised: 09/23/2023] [Accepted: 10/13/2023] [Indexed: 10/29/2023] Open
Abstract
Cardiac sympathetic denervation, as documented on 123I-metaiodobenzylguanidine (MIBG) myocardial scintigraphy, is relatively sensitive and specific for distinguishing Parkinson's disease (PD) from other neurodegenerative causes of parkinsonism. The present study aims to comprehensively review the literature regarding the use of cardiac MIBG in PD. MIBG is an analog to norepinephrine. They share the same uptake, storage, and release mechanisms. An abnormal result in the cardiac MIBG uptake in individuals with parkinsonism can be an additional criterion for diagnosing PD. However, a normal result of cardiac MIBG in individuals with suspicious parkinsonian syndrome does not exclude the diagnosis of PD. The findings of cardiac MIBG studies contributed to elucidating the pathophysiology of PD. We investigated the sensitivity and specificity of cardiac MIBG scintigraphy in PD. A total of 54 studies with 3114 individuals diagnosed with PD were included. The data were described as means with a Hoehn and Yahr stage of 2.5 and early and delayed registration H/M ratios of 1.70 and 1.51, respectively. The mean cutoff for the early and delayed phases were 1.89 and 1.86. The sensitivity for the early and delayed phases was 0.81 and 0.83, respectively. The specificity for the early and delayed phases were 0.86 and 0.80, respectively.
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Chen M, Sun Z, Xin T, Chen Y, Su F. An Interpretable Deep Learning Optimized Wearable Daily Detection System for Parkinson's Disease. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3937-3946. [PMID: 37695969 DOI: 10.1109/tnsre.2023.3314100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2023]
Abstract
Walking detection in the daily life of patients with Parkinson's disease (PD) is of great significance for tracking the progress of the disease. This study aims to implement an accurate, objective, and passive detection algorithm optimized based on an interpretable deep learning architecture for the daily walking of patients with PD and to explore the most representative spatiotemporal motor features. Five inertial measurement units attached to the wrist, ankle, and waist are used to collect motion data from 100 subjects during a 10-meter walking test. The raw data of each sensor are subjected to the continuous wavelet transform to train the classification model of the constructed 6-channel convolutional neural network (CNN). The results show that the sensor located at the waist has the best classification performance with an accuracy of 98.01%±0.85% and the area under the receiver operating characteristic curve (AUC) of 0.9981±0.0017 under ten-fold cross-validation. The gradient-weighted class activation mapping shows that the feature points with greater contribution to PD were concentrated in the lower frequency band (0.5~3Hz) compared with healthy controls. The visual maps of the 3D CNN show that only three out of the six time series have a greater contribution, which is used as a basis to further optimize the model input, greatly reducing the raw data processing costs (50%) while ensuring its performance (AUC=0.9929±0.0019). To the best of our knowledge, this is the first study to consider the visual interpretation-based optimization of an intelligent classification model in the intelligent diagnosis of PD.
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Choudhury P, Zhang N, Adler CH, Chen K, Belden C, Driver-Dunckley E, Mehta SH, Shprecher DR, Serrano G, Shill HA, Beach TG, Atri A. Longitudinal motor decline in dementia with Lewy bodies, Parkinson disease dementia, and Alzheimer's dementia in a community autopsy cohort. Alzheimers Dement 2023; 19:4377-4387. [PMID: 37422286 PMCID: PMC10592344 DOI: 10.1002/alz.13357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 05/04/2023] [Accepted: 05/05/2023] [Indexed: 07/10/2023]
Abstract
INTRODUCTION We examined the progression of extrapyramidal symptoms and signs in autopsy-confirmed dementia with Lewy bodies (DLB), Parkinson's disease dementia (PDD), and Alzheimer's disease dementia (AD). METHODS Longitudinal data were obtained from Arizona Study of Aging and Neurodegenerative Disease, with PDD (n = 98), AD (n = 47) and DLB (n = 48) further sub-grouped as with or without parkinsonism (DLB+ and DLB-). Within-group Unified Parkinson's Disease Rating Scale (UPDRS) -II and UPDRS-III trajectories were analyzed using non-linear mixed effects models. RESULTS In DLB, 65.6% had parkinsonism. Baseline UPDRS-II and III scores (off-stage) were highest (P < 0.001) for PDD (mean ± SD 14.3 ± 7.8 and 27.4 ± 16.3), followed by DLB+ (6.0 ± 8.8 and 17.2 ± 17.1), DLB- (1.1 ± 1.3 and 3.3 ± 5.5) and AD (3.2 ± 6.1 and 8.2 ± 13.6). Compared to PDD, the DLB+ group had faster UPDRS-III progression over 8-years (Cohen's-d range 0.98 to 2.79, P < 0.001), driven by gait (P < 0.001) and limb bradykinesia (P = 0.02) subscales. DISCUSSION Motor deficits progress faster in DLB+ than PDD, providing insights about expected changes in motor function. HIGHLIGHTS Dementia with Lewy bodies has faster motor progression than Parkinson's disease dementia Linear and non-linear mixed modeling analysis of longitudinal data was utilized Findings have implications for clinical prognostication and trial design.
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Affiliation(s)
- Parichita Choudhury
- Cleo Roberts Center, Banner Sun Health Research Institute, Sun City, Arizona, 85351, USA
| | - Nan Zhang
- Department of Quantitative Health Sciences, Mayo Clinic College of Medicine, Scottsdale, Arizona, 85259, USA
| | - Charles H. Adler
- Department of Neurology, Mayo Clinic College of Medicine, Scottsdale, Arizona, 85259, USA
| | - Kewei Chen
- Computational Imaging Lab, Banner Alzheimer’s Institute, Phoenix, Arizona, 85006, USA
| | - Christine Belden
- Cleo Roberts Center, Banner Sun Health Research Institute, Sun City, Arizona, 85351, USA
| | - Erika Driver-Dunckley
- Department of Neurology, Mayo Clinic College of Medicine, Scottsdale, Arizona, 85259, USA
| | - Shyamal H. Mehta
- Computational Imaging Lab, Banner Alzheimer’s Institute, Phoenix, Arizona, 85006, USA
| | - David R. Shprecher
- Cleo Roberts Center, Banner Sun Health Research Institute, Sun City, Arizona, 85351, USA
| | - Geidy Serrano
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, Arizona, 85351, USA
| | - Holly A. Shill
- Department of Neurology, Barrow Neurological Institute, Phoenix, Arizona, 85013, USA
| | - Thomas G. Beach
- Civin Laboratory for Neuropathology, Banner Sun Health Research Institute, Sun City, Arizona, 85351, USA
| | - Alireza Atri
- Cleo Roberts Center, Banner Sun Health Research Institute, Sun City, Arizona, 85351, USA
- Center for Brain/Mind Medicine & Department of Neurology, Brigham and Women’s Hospital, Boston, MA, 02115, USA
- Harvard Medical School, Boston, MA, 02115, USA
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Prajjwal P, Flores Sanga HS, Acharya K, Tango T, John J, Rodriguez RS, Dheyaa Marsool Marsool M, Sulaimanov M, Ahmed A, Hussin OA. Parkinson's disease updates: Addressing the pathophysiology, risk factors, genetics, diagnosis, along with the medical and surgical treatment. Ann Med Surg (Lond) 2023; 85:4887-4902. [PMID: 37811009 PMCID: PMC10553032 DOI: 10.1097/ms9.0000000000001142] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2023] [Accepted: 07/31/2023] [Indexed: 10/10/2023] Open
Abstract
After only Alzheimer's disease (AD), Parkinson's disease (PD) is the second most prevalent neurodegenerative disease. The incidence of this disease increases with age, especially for those above 70 years old. There are many risk factors that are well-established in the contribution to the development of PD, such as age, gender, ethnicity, rapid eye movement sleep disorder, high consumption of dairy products, traumatic brain injury, genetics, and pesticides/herbicides. Interestingly, smoking, consumption of caffeine, and physical activities are the protective factors of PD. A deficiency of dopamine in the substantia nigra of the brainstem is the main pathology. This, subsequently, alters the neurotransmitter, causing an imbalance between excitatory and inhibitory signals. In addition, genetics is also involved in the pathogenesis of the disease. As a result, patients exhibit characteristic motor symptoms such as tremors, stiffness, bradykinesia, and postural instability, along with non-motor symptoms, including dementia, urinary incontinence, sleeping disturbances, and orthostatic hypotension. PD may resemble other diseases; therefore, it is important to pay attention to the diagnosis criteria. Parkinson's disease dementia can share common features with AD; this can include behavioral as well as psychiatric symptoms, in addition to the pathology being protein aggregate accumulation in the brain. For PD management, the administration of pharmacological treatment depends on the motor symptoms experienced by the patients. Non-pharmacological treatment plays a role as adjuvant therapy, while surgical management is indicated in chronic cases. This paper aims to review the etiology, risk factors, protective factors, pathophysiology, signs and symptoms, associated conditions, and management of PD.
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Affiliation(s)
| | - Herson S Flores Sanga
- Department of Telemedicine, Hospital Nacional Carlos Alberto Seguin Escobedo, Arequipa, Peru
| | - Kirtish Acharya
- Maharaja Krishna Chandra Gajapati Medical College and Hospital, Brahmapur, Odisha
| | - Tamara Tango
- Faculty of Medicine Universitas, Jakarta, Indonesia
| | - Jobby John
- Dr. Somervell Memorial CSI Medical College and Hospital, Neyyāttinkara, Kerala, India
| | | | | | | | - Aneeqa Ahmed
- Shadan Hospital and Institute of Medical Sciences, Hyderabad, Telangana
| | - Omniat A. Hussin
- Department of Medicine, Sudan Academy of Sciences, Khartoum, Sudan
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Maple-Grødem J, Ushakova A, Pedersen KF, Tysnes OB, Alves G, Lange J. Identification of diagnostic and prognostic biomarkers of PD using a multiplex proteomics approach. Neurobiol Dis 2023; 186:106281. [PMID: 37673381 DOI: 10.1016/j.nbd.2023.106281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/29/2023] [Accepted: 09/02/2023] [Indexed: 09/08/2023] Open
Abstract
Given the complexity of Parkinson's disease (PD), achieving acceptable diagnostic and prognostic accuracy will require the support of a panel of diverse biomarkers. We used Proximity extension assays to measure a panel of 92 proteins in CSF of 120 newly diagnosed PD patients and 45 control subjects without neurological disease. From 75 proteins detectable in the CSF of >90% of the subjects, regularized regression analysis identified four proteins (β-NGF, CD38, tau and NCAN) as downregulated in newly diagnosed PD patients (age at diagnosis 67.2 ± 9.4 years) compared to controls (age 65.4 ± 10.9 years). Higher tau (β -0.82 transformed MMSE points/year, 95% CI -1.37 to -0.27, P = 0.005) was also linked to faster cognitive decline over the first ten years after PD diagnosis. These findings provide insights into multiple aspects of PD pathophysiology and may serve as the foundation for identifying new biomarkers and therapeutic targets.
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Affiliation(s)
- Jodi Maple-Grødem
- Centre for Movement Disorders, Centre for Brain Health, Stavanger University Hospital, Stavanger, Norway; Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, Stavanger, Norway.
| | - Anastasia Ushakova
- Section of Biostatistics, Department of Research, Stavanger University Hospital, Stavanger, Norway.
| | - Kenn Freddy Pedersen
- Centre for Movement Disorders, Centre for Brain Health, Stavanger University Hospital, Stavanger, Norway; Department of Neurology, Stavanger University Hospital, Stavanger, Norway.
| | - Ole-Bjørn Tysnes
- Department of Neurology, Haukeland University Hospital, Bergen, Norway; Department of Clinical Medicine, University of Bergen, Bergen, Norway.
| | - Guido Alves
- Centre for Movement Disorders, Centre for Brain Health, Stavanger University Hospital, Stavanger, Norway; Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, Stavanger, Norway; Department of Neurology, Stavanger University Hospital, Stavanger, Norway.
| | - Johannes Lange
- Centre for Movement Disorders, Centre for Brain Health, Stavanger University Hospital, Stavanger, Norway; Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, Stavanger, Norway.
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Liu WM, Yeh CL, Chen PW, Lin CW, Liu AB. Keystroke Biometrics as a Tool for the Early Diagnosis and Clinical Assessment of Parkinson's Disease. Diagnostics (Basel) 2023; 13:3061. [PMID: 37835803 PMCID: PMC10572112 DOI: 10.3390/diagnostics13193061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 09/22/2023] [Accepted: 09/23/2023] [Indexed: 10/15/2023] Open
Abstract
(1) Background: Parkinson's disease (PD) is the second most common neurodegenerative disease. Early diagnosis and reliable clinical assessments are essential for appropriate therapy and improving patients' quality of life. Keystroke biometrics, which capture unique typing behavior, have shown potential for early PD diagnosis. This study aimed to evaluate keystroke biometric parameters from two datasets to identify indicators that can effectively distinguish de novo PD patients from healthy controls. (2) Methods: Data from natural typing tasks in Physionet were analyzed to estimate keystroke biometric parameters. The parameters investigated included alternating-finger tapping (afTap) and standard deviations of interkey latencies (ILSD) and release latencies (RLSD). Sensitivity rates were calculated to assess the discriminatory ability of these parameters. (3) Results: Significant differences were observed in three parameters, namely afTap, ILSD, and RLSD, between de novo PD patients and healthy controls. The sensitivity rates were high, with values of 83%, 88%, and 96% for afTap, ILSD, and RLSD, respectively. Correlation analysis revealed a significantly negative correlation between typing speed and number of words typed with the standard motor assessment for PD, UPDRS-III, in patients with early PD. (4) Conclusions: Simple algorithms utilizing keystroke biometric parameters can serve as effective screening tests in distinguishing de novo PD patients from healthy controls. Moreover, typing speed and number of words typed were identified as reliable tools for assessing clinical statuses in PD patients. These findings underscore the potential of keystroke biometrics for early PD diagnosis and clinical severity assessment.
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Affiliation(s)
- Wei-Min Liu
- Department of Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chiayi 621301, Taiwan; (W.-M.L.); (C.-L.Y.)
| | - Che-Lun Yeh
- Department of Computer Science and Information Engineering, Advanced Institute of Manufacturing with High-Tech Innovations, National Chung Cheng University, Chiayi 621301, Taiwan; (W.-M.L.); (C.-L.Y.)
| | - Po-Wei Chen
- Department of Physical Medicine and Rehabilitation, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 970473, Taiwan;
| | - Che-Wei Lin
- Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701401, Taiwan;
| | - An-Bang Liu
- Department of Medicine, School of Medicine, Tzu Chi University, Hualien 970374, Taiwan
- Department of Neurology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, Hualien 970473, Taiwan
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Khan AF, Adewale Q, Lin SJ, Baumeister TR, Zeighami Y, Carbonell F, Palomero-Gallagher N, Iturria-Medina Y. Patient-specific models link neurotransmitter receptor mechanisms with motor and visuospatial axes of Parkinson's disease. Nat Commun 2023; 14:6009. [PMID: 37752107 PMCID: PMC10522603 DOI: 10.1038/s41467-023-41677-w] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 09/08/2023] [Indexed: 09/28/2023] Open
Abstract
Parkinson's disease involves multiple neurotransmitter systems beyond the classical dopaminergic circuit, but their influence on structural and functional alterations is not well understood. Here, we use patient-specific causal brain modeling to identify latent neurotransmitter receptor-mediated mechanisms contributing to Parkinson's disease progression. Combining the spatial distribution of 15 receptors from post-mortem autoradiography with 6 neuroimaging-derived pathological factors, we detect a diverse set of receptors influencing gray matter atrophy, functional activity dysregulation, microstructural degeneration, and dendrite and dopaminergic transporter loss. Inter-individual variability in receptor mechanisms correlates with symptom severity along two distinct axes, representing motor and psychomotor symptoms with large GABAergic and glutamatergic contributions, and cholinergically-dominant visuospatial, psychiatric and memory dysfunction. Our work demonstrates that receptor architecture helps explain multi-factorial brain re-organization, and suggests that distinct, co-existing receptor-mediated processes underlie Parkinson's disease.
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Affiliation(s)
- Ahmed Faraz Khan
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada
| | - Quadri Adewale
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada
| | - Sue-Jin Lin
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada
| | - Tobias R Baumeister
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada
| | - Yashar Zeighami
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, QC, Canada
| | | | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany
- Cécile and Oskar Vogt Institute of Brain Research, Medical Faculty, Heinrich-Heine University, Düsseldorf, Germany
- Department of Psychiatry, Psychotherapy, and Psychosomatics, Medical Faculty, RWTH Aachen, and JARA - Translational Brain Medicine, Aachen, Germany
| | - Yasser Iturria-Medina
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, QC, Canada.
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, QC, Canada.
- Ludmer Centre for Neuroinformatics & Mental Health, Montreal, QC, Canada.
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Taha HB. Rethinking the reliability and accuracy of biomarkers in CNS-originating EVs for Parkinson's disease and multiple system atrophy. Front Neurol 2023; 14:1192115. [PMID: 37731853 PMCID: PMC10507694 DOI: 10.3389/fneur.2023.1192115] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/02/2023] [Indexed: 09/22/2023] Open
Affiliation(s)
- Hash Brown Taha
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Integrative Physiology, University of Colorado Boulder, Boulder, CO, United States
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Xylaki M, Chopra A, Weber S, Bartl M, Outeiro TF, Mollenhauer B. Extracellular Vesicles for the Diagnosis of Parkinson's Disease: Systematic Review and Meta-Analysis. Mov Disord 2023; 38:1585-1597. [PMID: 37449706 DOI: 10.1002/mds.29497] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 05/23/2023] [Accepted: 05/24/2023] [Indexed: 07/18/2023] Open
Abstract
Parkinson's disease (PD) biomarkers are needed by both clinicians and researchers (for diagnosis, identifying study populations, and monitoring therapeutic response). Imaging, genetic, and biochemical biomarkers have been widely studied. In recent years, extracellular vesicles (EVs) have become a promising material for biomarker development. Proteins and molecular material from any organ, including the central nervous system, can be packed into EVs and transported to the periphery into easily obtainable biological specimens like blood, urine, and saliva. We performed a systematic review and meta-analysis of articles (published before November 15, 2022) reporting biomarker assessment in EVs in PD patients and healthy controls (HCs). Biomarkers were analyzed using random effects meta-analysis and the calculated standardized mean difference (Std.MD). Several proteins and ribonucleic acids have been identified in EVs in PD patients, but only α-synuclein (aSyn) and leucine-rich repeat kinase 2 (LRRK2) were reported in sufficient studies (n = 24 and 6, respectively) to perform a meta-analysis. EV aSyn was significantly increased in neuronal L1 cell adhesion molecule (L1CAM)-positive blood EVs in PD patients compared to HCs (Std.MD = 1.84, 95% confidence interval = 0.76-2.93, P = 0.0009). Further analysis of the biological sample and EV isolation method indicated that L1CAM-IP (immunoprecipitation) directly from plasma was the best isolation method for assessing aSyn in PD patients. Upcoming neuroprotective clinical trials immediately need peripheral biomarkers for identifying individuals at risk of developing PD. Overall, the improved sensitivity of assays means they can identify biomarkers in blood that reflect changes in the brain. CNS-derived EVs in blood will likely play a major role in biomarker development in the coming years. © 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)
- Mary Xylaki
- Department of Neurology, University Medical Center Goettingen, Goettingen, Germany
| | - Avika Chopra
- Department of Experimental Neurodegeneration, Center for Biostructural Imaging of Neurodegeneration, University Medical Center Goettingen, Goettingen, Germany
| | - Sandrina Weber
- Department of Neurology, University Medical Center Goettingen, Goettingen, Germany
| | - Michael Bartl
- Department of Neurology, University Medical Center Goettingen, Goettingen, Germany
| | - Tiago F Outeiro
- Department of Experimental Neurodegeneration, Center for Biostructural Imaging of Neurodegeneration, University Medical Center Goettingen, Goettingen, Germany
- Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle, Upon Tyne, United Kingdom
- Max Planck Institute for Multidisciplinary Sciences, Goettingen, Germany
- Scientific Employee with an Honorary Contract at German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany
| | - Brit Mollenhauer
- Department of Neurology, University Medical Center Goettingen, Goettingen, Germany
- Scientific Employee with an Honorary Contract at German Center for Neurodegenerative Diseases (DZNE), Goettingen, Germany
- Paracelsus-Elena-Klinik, Kassel, Germany
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Park DG, Kim JY, Kim MS, Kim MH, An YS, Chang J, Yoon JH. Neurofilament light chain and cardiac MIBG uptake as predictors for phenoconversion in isolated REM sleep behavior disorder. J Neurol 2023; 270:4393-4402. [PMID: 37233802 DOI: 10.1007/s00415-023-11785-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 05/15/2023] [Accepted: 05/16/2023] [Indexed: 05/27/2023]
Abstract
BACKGROUND Isolated rapid-eye-movement (REM) sleep behavior disorder (iRBD) is considered as a prodromal stage of either multiple system atrophy (MSA) or Lewy body disease (LBD; Parkinson's disease and dementia with Lewy bodies). However, current knowledge is limited in predicting and differentiating the type of future phenoconversion in iRBD patients. We investigated the role of plasma neurofilament light chain (NfL) and cardiac metaiodobenzylguanidine (MIBG) uptake as predictors for phenoconversion. METHODS Forty patients with iRBD were enrolled between April 2018 and October 2019 and prospectively followed every 3 months to determine phenoconversion to either MSA or LBD. Plasma NfL levels were measured at enrollment. Cardiac MIBG uptake and striatal dopamine transporter uptake were assessed at baseline. RESULTS Patients were followed for a median of 2.92 years. Four patients converted to MSA and 7 to LBD. Plasma NfL level at baseline was significantly higher in future MSA-converters (median 23.2 pg/mL) when compared with the rest of the samples (median 14.1 pg/mL, p = 0.003). NfL level above 21.3 pg/mL predicted phenoconversion to MSA with the sensitivity of 100% and specificity of 94.3%. Baseline MIBG heart-to-mediastinum ratio of LBD-converters (median 1.10) was significantly lower when compared with the rest (median 2.00, p < 0.001). Heart-to-mediastinum ratio below 1.545 predicted phenoconversion to LBD with the sensitivity of 100% and specificity of 92.9%. CONCLUSIONS Plasma NfL and cardiac MIBG uptake may be useful biomarkers in predicting phenoconversion of iRBD. Elevated plasma NfL levels may suggest imminent phenoconversion to MSA, whereas low cardiac MIBG uptake suggests phenoconversion to LBD.
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Affiliation(s)
- Don Gueu Park
- Department of Neurology, Ajou University School of Medicine, 164, Worldcup-Ro, Songjae Hall, Suwon-Si, Gyeonggi-Do, 16499, South Korea
| | - Ju Yeong Kim
- Department of Biomedical Sciences, Ajou University School of Medicine, Suwon-Si, Republic of Korea
| | - Min Seung Kim
- Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong, South Korea
| | - Mi Hee Kim
- Department of Neurology, Ajou University School of Medicine, 164, Worldcup-Ro, Songjae Hall, Suwon-Si, Gyeonggi-Do, 16499, South Korea
| | - Young-Sil An
- Department of Nuclear Medicine, Ajou University School of Medicine, Suwon-Si, Republic of Korea
| | - Jaerak Chang
- Department of Biomedical Sciences, Ajou University School of Medicine, Suwon-Si, Republic of Korea.
- Department of Brain Science, Ajou University School of Medicine, 164, Worldcup-Ro, Songjae Hall, Suwon-Si, Gyeonggi-Do, 16499, South Korea.
| | - Jung Han Yoon
- Department of Neurology, Ajou University School of Medicine, 164, Worldcup-Ro, Songjae Hall, Suwon-Si, Gyeonggi-Do, 16499, South Korea.
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Qi R, Sammler E, Gonzalez-Hunt CP, Barraza I, Pena N, Rouanet JP, Naaldijk Y, Goodson S, Fuzzati M, Blandini F, Erickson KI, Weinstein AM, Lutz MW, Kwok JB, Halliday GM, Dzamko N, Padmanabhan S, Alcalay RN, Waters C, Hogarth P, Simuni T, Smith D, Marras C, Tonelli F, Alessi DR, West AB, Shiva S, Hilfiker S, Sanders LH. A blood-based marker of mitochondrial DNA damage in Parkinson's disease. Sci Transl Med 2023; 15:eabo1557. [PMID: 37647388 PMCID: PMC11135133 DOI: 10.1126/scitranslmed.abo1557] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Accepted: 08/11/2023] [Indexed: 09/01/2023]
Abstract
Parkinson's disease (PD) is the most common neurodegenerative movement disorder, and neuroprotective or disease-modifying interventions remain elusive. High-throughput markers aimed at stratifying patients on the basis of shared etiology are required to ensure the success of disease-modifying therapies in clinical trials. Mitochondrial dysfunction plays a prominent role in the pathogenesis of PD. Previously, we found brain region-specific accumulation of mitochondrial DNA (mtDNA) damage in PD neuronal culture and animal models, as well as in human PD postmortem brain tissue. To investigate mtDNA damage as a potential blood-based marker for PD, we describe herein a PCR-based assay (Mito DNADX) that allows for the accurate real-time quantification of mtDNA damage in a scalable platform. We found that mtDNA damage was increased in peripheral blood mononuclear cells derived from patients with idiopathic PD and those harboring the PD-associated leucine-rich repeat kinase 2 (LRRK2) G2019S mutation in comparison with age-matched controls. In addition, mtDNA damage was elevated in non-disease-manifesting LRRK2 mutation carriers, demonstrating that mtDNA damage can occur irrespective of a PD diagnosis. We further established that Lrrk2 G2019S knock-in mice displayed increased mtDNA damage, whereas Lrrk2 knockout mice showed fewer mtDNA lesions in the ventral midbrain, compared with wild-type control mice. Furthermore, a small-molecule kinase inhibitor of LRRK2 mitigated mtDNA damage in a rotenone PD rat midbrain neuron model and in idiopathic PD patient-derived lymphoblastoid cell lines. Quantifying mtDNA damage using the Mito DNADX assay may have utility as a candidate marker of PD and for measuring the pharmacodynamic response to LRRK2 kinase inhibitors.
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Affiliation(s)
- Rui Qi
- Departments of Neurology and Pathology, Duke University School of Medicine, Durham, NC 27710, USA
- Duke Center for Neurodegeneration and Neurotherapeutics, Duke University, Durham, NC 27710, USA
| | - Esther Sammler
- Molecular and Clinical Medicine, Ninewells Hospital and Medical School, University of Dundee, Dundee DD1 9SY, UK
- Medical Research Council Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee, DD1 5EH UK
| | - Claudia P. Gonzalez-Hunt
- Departments of Neurology and Pathology, Duke University School of Medicine, Durham, NC 27710, USA
- Duke Center for Neurodegeneration and Neurotherapeutics, Duke University, Durham, NC 27710, USA
| | - Ivana Barraza
- Departments of Neurology and Pathology, Duke University School of Medicine, Durham, NC 27710, USA
- Duke Center for Neurodegeneration and Neurotherapeutics, Duke University, Durham, NC 27710, USA
| | - Nicholas Pena
- Departments of Neurology and Pathology, Duke University School of Medicine, Durham, NC 27710, USA
- Duke Center for Neurodegeneration and Neurotherapeutics, Duke University, Durham, NC 27710, USA
| | - Jeremy P. Rouanet
- Departments of Neurology and Pathology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Yahaira Naaldijk
- Department of Anesthesiology and Department of Physiology, Pharmacology and Neuroscience, Rutgers New Jersey Medical School, Newark, NJ 07103, USA
| | - Steven Goodson
- Departments of Neurology and Pathology, Duke University School of Medicine, Durham, NC 27710, USA
- Duke Center for Neurodegeneration and Neurotherapeutics, Duke University, Durham, NC 27710, USA
| | - Marie Fuzzati
- IRCCS Mondino Foundation, National Institute of Neurology, Pavia 27100, Italy
| | - Fabio Blandini
- Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan 20122, Italy
- Department of Brain and Behavioral Sciences, University of Pavia, 27100 Pavia, Italy
| | - Kirk I. Erickson
- Department of Psychology, University of Pittsburgh, Pittsburgh, PA 15213, USA
- AdventHealth Research Institute, Neuroscience, Orlando, FL 32804, USA
| | - Andrea M. Weinstein
- Department of Psychiatry, School of Medicine, University of Pittsburgh, PA 15213, USA
| | - Michael W. Lutz
- Departments of Neurology and Pathology, Duke University School of Medicine, Durham, NC 27710, USA
| | - John B. Kwok
- School of Medical Sciences, Faculty of Medicine and Health and the Brain and Mind Centre, University of Sydney, Camperdown, New South Wales 2050, Australia
| | - Glenda M. Halliday
- School of Medical Sciences, Faculty of Medicine and Health and the Brain and Mind Centre, University of Sydney, Camperdown, New South Wales 2050, Australia
| | - Nicolas Dzamko
- School of Medical Sciences, Faculty of Medicine and Health and the Brain and Mind Centre, University of Sydney, Camperdown, New South Wales 2050, Australia
| | - Shalini Padmanabhan
- Michael J. Fox Foundation for Parkinson’s Research, Grand Central Station, P.O. Box 4777, New York, NY 10120, USA
| | - Roy N. Alcalay
- Columbia University Irving Medical Center, New York, NY 10032, USA
- Movement Disorders Unit, Neurological Institute, Tel Aviv Sourasky Medical Centre, Sackler School of Medicine, Sagol School of Neurosciences, Tel Aviv University, Tel Aviv, Israel
| | - Cheryl Waters
- Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Penelope Hogarth
- Departments of Molecular and Medical Genetics and Neurology, Oregon Health and Science University, Portland, OR 97239, USA
| | - Tanya Simuni
- Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Danielle Smith
- Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Connie Marras
- Edmond J. Safra Program in Parkinson’s Disease, Toronto Western Hospital, University Health Network, University of Toronto, Toronto, Canada
| | - Francesca Tonelli
- Medical Research Council Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee, DD1 5EH UK
| | - Dario R. Alessi
- Medical Research Council Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee, DD1 5EH UK
| | - Andrew B. West
- Duke Center for Neurodegeneration and Neurotherapeutics, Duke University, Durham, NC 27710, USA
- Department of Pharmacology and Cancer Biology, Duke University School of Medicine, Durham, NC 27710, USA
| | - Sruti Shiva
- Department of Pharmacology and Chemical Biology and Medicine, Vascular Medicine Institute, University of Pittsburgh, Pittsburgh, PA 15260, USA
| | - Sabine Hilfiker
- Department of Anesthesiology and Department of Physiology, Pharmacology and Neuroscience, Rutgers New Jersey Medical School, Newark, NJ 07103, USA
| | - Laurie H. Sanders
- Departments of Neurology and Pathology, Duke University School of Medicine, Durham, NC 27710, USA
- Duke Center for Neurodegeneration and Neurotherapeutics, Duke University, Durham, NC 27710, USA
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Zenesini C, Belotti LMB, Baccari F, Baldin E, Ridley B, Calandra-Buonaura G, Cortelli P, D'Alessandro R, Nonino F, Vignatelli L. Validation of Administrative Health Data Algorithms for Identifying Persons with Parkinson's Disease and the 10-Year Prevalence Trend in Bologna, Italy. Neuroepidemiology 2023; 57:336-344. [PMID: 37549643 DOI: 10.1159/000533362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/27/2023] [Indexed: 08/09/2023] Open
Abstract
INTRODUCTION Health administrative databases are widely used for the estimation of the prevalence of Parkinson's disease (PD). Few in general, and none used in Italy, have been validated by testing their diagnostic accuracy. The primary objective was to validate two algorithms for the identification of persons with PD using clinical diagnosis as the reference standard on an Italian sample of people with PD. The second objective was to estimate 10-year trends in PD prevalence in the Bologna Local Health Trust from 2010 to 2019. METHODS Two algorithms (index tests) applied to health administrative databases (hospital discharge, drug prescriptions, exemptions for medical costs) were validated against clinical diagnosis of PD by an expert neurologist (reference standard) in a cohort of consecutive outpatients. Sensitivity and specificity with relative 95% confidence intervals (CIs) were calculated. The prevalence of PD in a specific year was estimated as the ratio between the number of subjects fulfilling any criteria of the algorithm with better diagnostic accuracy and the total population in the same year (×1,000), stratified by age, sex, and district of residence. RESULTS The two algorithms showed high accuracy for identifying patients with PD: one with greater sensitivity of 94.2% (CI: 88.4-97.6) and the other with greater specificity of 98.1% (CI: 97.7-98.5). For the estimation of prevalence, we chose the most specific algorithm with the fewest total number of misclassified cases. We identified 3,798 people with PD as of December 31, 2019, corresponding to a prevalence of 4.3 per 1,000 inhabitants (CI: 4.2-4.4). Prevalence was higher in males (4.7, CI: 4.5-5.0) than females (3.8, CI: 3.7-4.0) and increased with age. The crude prevalence over time was slightly elevated as it followed a progressive aging of the population. When stratifying the prevalence for age groups, we did not observe a trend except in the 45-64 year category where we observed an increasing trend over time. CONCLUSION Algorithms based on administrative data are accurate when detecting people with PD in the Italian public health system. In a large northern Italian population, increased prevalence of about 10% was observed in the decade 2010-2019 and is explained by increased life expectancy. These data may be useful in planning the allocation of health care resources for people with PD.
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Affiliation(s)
- Corrado Zenesini
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | | | - Flavia Baccari
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Elisa Baldin
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Ben Ridley
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Giovanna Calandra-Buonaura
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
- Dipartimento di Scienze Biomediche e NeuroMotorie, Università Degli Studi di Bologna, Bologna, Italy
| | - Pietro Cortelli
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
- Dipartimento di Scienze Biomediche e NeuroMotorie, Università Degli Studi di Bologna, Bologna, Italy
| | | | - Francesco Nonino
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Luca Vignatelli
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
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Li W, Tang Y, Peng L, Wang Z, Hu S, Gao X. The reconfiguration pattern of individual brain metabolic connectome for Parkinson's disease identification. MedComm (Beijing) 2023; 4:e305. [PMID: 37388240 PMCID: PMC10300308 DOI: 10.1002/mco2.305] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 05/14/2023] [Accepted: 05/22/2023] [Indexed: 07/01/2023] Open
Abstract
18F-Fluorodeoxyglucose positron emission tomography (18F-FDG PET) is widely employed to reveal metabolic abnormalities linked to Parkinson's disease (PD) at a systemic level. However, the individual metabolic connectome details with PD based on 18F-FDG PET remain largely unknown. To alleviate this issue, we derived a novel brain network estimation method for individual metabolic connectome, that is, Jensen-Shannon Divergence Similarity Estimation (JSSE). Further, intergroup difference between the individual's metabolic brain network and its global/local graph metrics was analyzed to investigate the metabolic connectome's alterations. To further improve the PD diagnosis performance, multiple kernel support vector machine (MKSVM) is conducted for identifying PD from normal control (NC), which combines both topological metrics and connection. Resultantly, PD individuals showed higher nodal topological properties (including assortativity, modularity score, and characteristic path length) than NC individuals, whereas global efficiency and synchronization were lower. Moreover, 45 most significant connections were affected. Further, consensus connections in occipital, parietal, and frontal regions were decrease in PD while increase in subcortical, temporal, and prefrontal regions. The abnormal metabolic network measurements depicted an ideal classification in identifying PD of NC with an accuracy up to 91.84%. The JSSE method identified the individual-level metabolic connectome of 18F-FDG PET, providing more dimensional and systematic mechanism insights for PD.
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Affiliation(s)
- Weikai Li
- College of Mathematics and StatisticsChongqing Jiaotong UniversityChongqingChina
- Department of Nuclear Medicine (PET Center)XiangYa HospitalChangshaHunanChina
- Department of PET/MRShanghai Universal Medical Imaging Diagnostic CenterShanghaiChina
- MIIT Key Laboratory of Pattern Analysis and Machine IntelligenceNanjing University of Aeronautics and AstronauticsNanjingChina
| | - Yongxiang Tang
- Department of Nuclear Medicine (PET Center)XiangYa HospitalChangshaHunanChina
| | - Liling Peng
- Department of PET/MRShanghai Universal Medical Imaging Diagnostic CenterShanghaiChina
| | - Zhengxia Wang
- School of Computer Science and Cyberspace SecurityHainan UniversityHainanChina
| | - Shuo Hu
- Department of Nuclear Medicine (PET Center)XiangYa HospitalChangshaHunanChina
- Key Laboratory of Biological Nanotechnology of National Health CommissionXiangYa HospitalCentral South UniversityChangshaHunanChina
| | - Xin Gao
- Department of PET/MRShanghai Universal Medical Imaging Diagnostic CenterShanghaiChina
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Chompoopong P, Reiter-Campeau S. Recent updates in autonomic research: orthostatic hypotension and cognitive function in Parkinson disease and multiple system atrophy, the skin as a window into synuclein pathology, and RFC1 repeat expansions in hereditary sensory autonomic neuropathies. Clin Auton Res 2023; 33:387-389. [PMID: 37493897 DOI: 10.1007/s10286-023-00968-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Accepted: 07/15/2023] [Indexed: 07/27/2023]
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Virameteekul S, de Pablo-Fernández E. Variability in the Accuracy of Clinical Diagnosis of Early Parkinson's Disease. Mov Disord 2023; 38:1574-1575. [PMID: 37565405 DOI: 10.1002/mds.29553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 06/30/2023] [Indexed: 08/12/2023] Open
Affiliation(s)
- Sasivimol Virameteekul
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, United Kingdom
- Reta Lila Weston Institute of Neurological Studies, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Eduardo de Pablo-Fernández
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, United Kingdom
- Reta Lila Weston Institute of Neurological Studies, UCL Queen Square Institute of Neurology, London, United Kingdom
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Klietz M, Mahmoudi N, Maudsley AA, Sheriff S, Bronzlik P, Almohammad M, Nösel P, Wegner F, Höglinger GU, Lanfermann H, Ding XQ. Whole-Brain Magnetic Resonance Spectroscopy Reveals Distinct Alterations in Neurometabolic Profile in Progressive Supranuclear Palsy. Mov Disord 2023; 38:1503-1514. [PMID: 37289057 DOI: 10.1002/mds.29456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 03/16/2023] [Accepted: 05/09/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND Progressive supranuclear palsy (PSP) is an atypical Parkinsonian syndrome characterized by supranuclear gaze palsy, early postural instability, and a frontal dysexecutive syndrome. Contrary to normal brain magnetic resonance imaging in Parkinson's disease (PD), PSP shows specific cerebral atrophy patterns and alterations, but these findings are not present in every patient, and it is still unclear if these signs are also detectable in early disease stages. OBJECTIVE The aim of the present study was to analyze the metabolic profile of patients with clinically diagnosed PSP in comparison with matched healthy volunteers and PD patients using whole-brain magnetic resonance spectroscopic imaging (wbMRSI). METHODS Thirty-nine healthy controls (HCs), 29 PD, and 22 PSP patients underwent wbMRSI. PSP and PD patients were matched for age and handedness with HCs. Clinical characterization was performed using the Movement Disorder Society Unified Parkinson's Disease Rating Scale, PSP rating scale, and DemTect (test for cognitive assessment). RESULTS In PSP patients a significant reduction in N-acetyl-aspartate (NAA) was detected in all brain lobes. Fractional volume of the cerebrospinal fluid significantly increased in PSP patients compared to PD and healthy volunteers. CONCLUSIONS In PSP much more neuronal degeneration and cerebral atrophy have been detected compared with PD. The most relevant alteration is the decrease in NAA in all lobes of the brain, which also showed a partial correlation with clinical symptoms. However, more studies are needed to confirm the additional value of wbMRSI in clinical practice. © 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)
- Martin Klietz
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | - Nima Mahmoudi
- Department of Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Andrew A Maudsley
- Department of Radiology, University of Miami School of Medicine, Miami, Florida, USA
| | - Sulaiman Sheriff
- Department of Radiology, University of Miami School of Medicine, Miami, Florida, USA
| | - Paul Bronzlik
- Department of Neuroradiology, Hannover Medical School, Hannover, Germany
| | | | - Patrick Nösel
- Department of Neuroradiology, Hannover Medical School, Hannover, Germany
| | - Florian Wegner
- Department of Neurology, Hannover Medical School, Hannover, Germany
| | | | | | - Xiao-Qi Ding
- Department of Neuroradiology, Hannover Medical School, Hannover, Germany
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Coughlin DG, Irwin DJ. Fluid and Biopsy Based Biomarkers in Parkinson's Disease. Neurotherapeutics 2023; 20:932-954. [PMID: 37138160 PMCID: PMC10457253 DOI: 10.1007/s13311-023-01379-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/11/2023] [Indexed: 05/05/2023] Open
Abstract
Several advances in fluid and tissue-based biomarkers for use in Parkinson's disease (PD) and other synucleinopathies have been made in the last several years. While work continues on species of alpha-synuclein (aSyn) and other proteins which can be measured from spinal fluid and plasma samples, immunohistochemistry and immunofluorescence from peripheral tissue biopsies and alpha-synuclein seeding amplification assays (aSyn-SAA: including real-time quaking induced conversion (RT-QuIC) and protein misfolding cyclic amplification (PMCA)) now offer a crucial advancement in their ability to identify aSyn species in PD patients in a categorical fashion (i.e., of aSyn + vs aSyn -); to augment clinical diagnosis however, aSyn-specific assays that have quantitative relevance to pathological burden remain an unmet need. Alzheimer's disease (AD) co-pathology is commonly found postmortem in PD, especially in those who develop dementia, and dementia with Lewy bodies (DLB). Biofluid biomarkers for tau and amyloid beta species can detect AD co-pathology in PD and DLB, which does have relevance for prognosis, but further work is needed to understand the interplay of aSyn tau, amyloid beta, and other pathological changes to generate comprehensive biomarker profiles for patients in a manner translatable to clinical trial design and individualized therapies.
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Affiliation(s)
- David G Coughlin
- Department of Neurosciences, University of California San Diego, 9444 Medical Center Drive, ECOB 03-021, MCC 0886, La Jolla, CA, 92037, USA.
| | - David J Irwin
- Department of Neurology, University of Pennsylvania, Philadelphia, PA, 19104, USA
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Kurbatskaya A, Jaramillo-Jimenez A, Ochoa-Gomez JF, Bronnick K, Fernandez-Quilez A. Machine Learning-Based Detection of Parkinson's Disease From Resting-State EEG: A Multi-Center Study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083565 DOI: 10.1109/embc40787.2023.10340700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Resting-state EEG (rs-EEG) has been demonstrated to aid in Parkinson's disease (PD) diagnosis. In particular, the power spectral density (PSD) of low-frequency bands (δ and θ) and high-frequency bands (α and β) has been shown to be significantly different in patients with PD as compared to subjects without PD (non-PD). However, rs-EEG feature extraction and the interpretation thereof can be time-intensive and prone to examiner variability. Machine learning (ML) has the potential to automatize the analysis of rs-EEG recordings and provides a supportive tool for clinicians to ease their workload. In this work, we use rs-EEG recordings of 84 PD and 85 non-PD subjects pooled from four datasets obtained at different centers. We propose an end-to-end pipeline consisting of preprocessing, extraction of PSD features from clinically-validated frequency bands, and feature selection. Following, we assess the classification ability of the features via ML algorithms to stratify between PD and non-PD subjects. Further, we evaluate the effect of feature harmonization, given the multi-center nature of the datasets. Our validation results show, on average, an improvement in PD detection ability (69.6% vs. 75.5% accuracy) by logistic regression when harmonizing the features and performing univariate feature selection (k = 202 features). Our final results show an average global accuracy of 72.2% with balanced accuracy results for all the centers included in the study: 60.6%, 68.7%, 77.7%, and 82.2%, respectively.Clinical relevance- We present an end-to-end pipeline to extract clinically relevant features from rs-EEG recordings that can facilitate the analysis and detection of PD. Further, we provide an ML system that shows a good performance in detecting PD, even in the presence of centers with different acquisition protocols. Our results show the relevance of harmonizing features and provide a good starting point for future studies focusing on rs-EEG analysis and multi-center data.
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Lan TT, Chang L, Hou LW, Wang ZZ, Li DC, Ren ZH, Gu T, Wang JW, Chen GS. Serum metabolomics analysis revealed metabolic disorders in Parkinson's disease. Medicine (Baltimore) 2023; 102:e33715. [PMID: 37335671 DOI: 10.1097/md.0000000000033715] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/21/2023] Open
Abstract
BACKGROUND Parkinson's disease (PD) is by now the second of the most prevalent neurodegenerative diseases in the world, and its incidence is increasing rapidly as the global population ages, with 14.2 million PD patients expected worldwide by 2040. METHODS We gathered a completion of 45 serum samples, including 15 of healthy controls and 30 from the PD group. We used non-targeted metabolomics analysis based on liquid chromatography-mass spectrometry to identify the molecular changes in PD patients, and conducted bioinformatics analysis on this basis to explore the possible pathogenesis of PD. RESULTS We found significant metabolomics changes in the levels of 30 metabolites in PD patients compared with healthy controls. CONCLUSION Lipids and lipid-like molecules accounted for the majority of the 30 differentially expressed metabolites. Also, pathway enrichment analysis showed significant enrichment in sphingolipid metabolic pathway. These assessments can improve our perception on the underlying mechanism of PD as well as facilitate a better targeting on therapeutic interventions.
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Affiliation(s)
- Tian-Tian Lan
- Clinical Medical College of Ningxia Medical University, Yinchuan, China
| | - Le Chang
- Clinical Medical College of Ningxia Medical University, Yinchuan, China
| | - Li-Wei Hou
- People's Hospital of Ningxia Hui Autonomous Region, Yinchuan, China
| | - Zhen-Zhen Wang
- Clinical Medical College of Ningxia Medical University, Yinchuan, China
| | - Dong-Chu Li
- Clinical Medical College of Ningxia Medical University, Yinchuan, China
| | - Zi-Han Ren
- Clinical Medical College of Ningxia Medical University, Yinchuan, China
| | - Tao Gu
- Clinical Medical College of Ningxia Medical University, Yinchuan, China
| | - Jian-Wen Wang
- Clinical Medical College of Ningxia Medical University, Yinchuan, China
| | - Gui-Sheng Chen
- Department of Neurology, Ningxia Medical University General Hospital, Yinchuan, China
- Cranial Laboratory of Ningxia Medical University, Yinchuan, China
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Tran KKN, Wong VHY, Hoang A, Finkelstein DI, Bui BV, Nguyen CTO. Retinal alpha-synuclein accumulation correlates with retinal dysfunction and structural thinning in the A53T mouse model of Parkinson's disease. Front Neurosci 2023; 17:1146979. [PMID: 37214398 PMCID: PMC10196133 DOI: 10.3389/fnins.2023.1146979] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2023] [Accepted: 04/17/2023] [Indexed: 05/24/2023] Open
Abstract
Abnormal alpha-synuclein (α-SYN) protein deposition has long been recognized as one of the pathological hallmarks of Parkinson's disease's (PD). This study considers the potential utility of PD retinal biomarkers by investigating retinal changes in a well characterized PD model of α-SYN overexpression and how these correspond to the presence of retinal α-SYN. Transgenic A53T homozygous (HOM) mice overexpressing human α-SYN and wildtype (WT) control littermates were assessed at 4, 6, and 14 months of age (male and female, n = 15-29 per group). In vivo retinal function (electroretinography, ERG) and structure (optical coherence tomography, OCT) were recorded, and retinal immunohistochemistry and western blot assays were performed to examine retinal α-SYN and tyrosine hydroxylase. Compared to WT controls, A53T mice exhibited reduced light-adapted (cone photoreceptor and bipolar cell amplitude, p < 0.0001) ERG responses and outer retinal thinning (outer plexiform layer, outer nuclear layer, p < 0.0001) which correlated with elevated levels of α-SYN. These retinal signatures provide a high throughput means to study α-SYN induced neurodegeneration and may be useful in vivo endpoints for PD drug discovery.
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Affiliation(s)
- Katie K. N. Tran
- Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Vickie H. Y. Wong
- Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Anh Hoang
- Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - David I. Finkelstein
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Bang V. Bui
- Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, VIC, Australia
| | - Christine T. O. Nguyen
- Department of Optometry and Vision Sciences, The University of Melbourne, Parkville, VIC, Australia
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Chahine LM, Beach TG, Adler CH, Hepker M, Kanthasamy A, Appel S, Pritzkow S, Pinho M, Mosovsky S, Serrano GE, Coffey C, Brumm MC, Oliveira LMA, Eberling J, Mollenhauer B. Central and peripheral α-synuclein in Parkinson disease detected by seed amplification assay. Ann Clin Transl Neurol 2023; 10:696-705. [PMID: 36972727 PMCID: PMC10187727 DOI: 10.1002/acn3.51753] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/29/2023] [Accepted: 02/10/2023] [Indexed: 03/29/2023] Open
Abstract
OBJECTIVES Detection of α-synuclein aggregates by seed amplification is a promising Parkinson disease biomarker assay. Understanding intraindividual relationships of α-synuclein measures could inform optimal biomarker development. The objectives were to test accuracy of α-synuclein seed amplification assay in central (cerebrospinal fluid) and peripheral (submandibular gland) sources, compare to total α-synuclein measures, and investigate within-subject relationships. METHODS The Systemic Synuclein Sampling Study aimed to characterize α-synuclein in multiple tissues and biofluids within Parkinson disease subjects (n = 59) and compared to healthy controls (n = 21). Motor and non-motor measures and dopamine transporter scans were obtained. Four measures of α-synuclein were compared: seed amplification assay in cerebrospinal fluid and formalin-fixed paraffin-embedded submandibular gland, total α-synuclein quantified in biofluids using enzyme-linked immunoassay, and aggregated α-synuclein in submandibular gland detected by immunohistochemistry. Accuracy of seed amplification assay for Parkinson disease diagnosis was examined and within-subject α-synuclein measures were compared. RESULTS Sensitivity and specificity of α-synuclein seed amplification assay for Parkinson disease diagnosis was 92.6% and 90.5% in cerebrospinal fluid, and 73.2% and 78.6% in submandibular gland, respectively. 25/38 (65.8%) Parkinson disease participants were positive for both cerebrospinal fluid and submandibular gland seed amplification assay. Comparing accuracy for Parkinson disease diagnosis of different α-synuclein measures, cerebrospinal fluid seed amplification assay was the highest (Youden Index = 83.1%). 98.3% of all Parkinson disease cases had ≥1 measure of α-synuclein positive. INTERPRETATION α-synuclein seed amplification assay (cerebrospinal fluid>submandibular gland) had higher sensitivity and specificity compared to total α-synuclein measures, and within-subject relationships of central and peripheral α-synuclein measures emerged.
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Affiliation(s)
- Lana M. Chahine
- Department of NeurologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | | | - Charles H. Adler
- Department of NeurologyMayo Clinic College of MedicineScottsdaleArizonaUSA
| | | | - Anumantha Kanthasamy
- Center for Brain Science and Neurodegenerative Diseases, Department of Physiology and PharmacologyUniversity of GeorgiaAthensGeorgiaUSA
| | - Scott Appel
- Biostatistics Analysis CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sandra Pritzkow
- Department of NeurologyUniversity of Texas, McGovern Medical SchoolHoustonTexasUSA
| | - Michelle Pinho
- Department of NeurologyUniversity of Texas, McGovern Medical SchoolHoustonTexasUSA
| | - Sherri Mosovsky
- Department of NeurologyUniversity of PittsburghPittsburghPennsylvaniaUSA
| | | | - Christopher Coffey
- Banner Sun Health Research InstituteSun CityArizonaUSA
- Department of BiostatisticsUniversity of Iowa College of Public HealthIowa CityIowaUSA
| | - Michael C. Brumm
- Department of BiostatisticsUniversity of Iowa College of Public HealthIowa CityIowaUSA
| | - Luis M. A. Oliveira
- Banner Sun Health Research InstituteSun CityArizonaUSA
- The Michael J. Fox Foundation for Parkinson's ResearchNew YorkNew YorkUSA
| | - Jamie Eberling
- Banner Sun Health Research InstituteSun CityArizonaUSA
- The Michael J. Fox Foundation for Parkinson's ResearchNew YorkNew YorkUSA
| | - Brit Mollenhauer
- Center of Parkinsonism and Movement Disorders, Department of NeurologyParacelsus‐Elena Klinik Kassel and University Medical Center GöttingenGöttingenGermany
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Af Bjerkén S, Axelsson J, Larsson A, Flygare C, Remes J, Strandberg S, Eriksson L, Bäckström D, Jakobson Mo S. Reliability and validity of visual analysis of [ 18 F]FE-PE2I PET/CT in early Parkinsonian disease. Nucl Med Commun 2023; 44:397-406. [PMID: 36862448 DOI: 10.1097/mnm.0000000000001679] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/03/2023]
Abstract
OBJECTIVE [ 18 F]FE-PE2I (FE-PE2I) is a new radiotracer for dopamine transporter (DAT) imaging with PET. The aim of this study was to evaluate the visual interpretation of FE-PE2I images for the diagnosis of idiopathic Parkinsonian syndrome (IPS). The inter-rater variability, sensitivity, specificity, and diagnostic accuracy for visual interpretation of striatal FE-PE2I compared to [ 123 I]FP-CIT (FP-CIT) single-photon emission computed tomography (SPECT) was evaluated. METHODS Thirty patients with newly onset parkinsonism and 32 healthy controls with both an FE-PE2I and FP-CIT were included in the study. Four patients had normal DAT imaging, of which three did not fulfil the IPS criteria at the clinical reassessment after 2 years. Six raters evaluated the DAT images blinded to the clinical diagnosis, interpreting the image as being 'normal' or 'pathological', and assessed the degree of DAT-reduction in the caudate and putamen. The inter-rater agreement was assessed with intra-class correlation and Cronbach's α . For calculation of sensitivity and specificity, DAT images were defined as correctly classified if categorized as normal or pathological by ≥4/6 raters. RESULTS The overall agreement in visual evaluation of the FE-PE2I- and FP-CIT images was high for the IPS patients ( α = 0.960 and 0.898, respectively), but lower in healthy controls (FE-PE2I: α = 0.693, FP-CIT: α = 0.657). Visual interpretation gave high sensitivity (both 0.96) but lower specificity (FE-PE2I: 0.86, FP-CIT: 0.63) with an accuracy of 90% for FE-PE2I and 77% for FP-CIT. CONCLUSION Visual evaluation of FE-PE2I PET imaging demonstrates high reliability and diagnostic accuracy for IPS.
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Affiliation(s)
- Sara Af Bjerkén
- Department of Integrative Medical Biology
- Department of Clinical Science, Neurosciences
| | - Jan Axelsson
- Department of Radiation Sciences, Radiation Physics
- Umeå Center for Functional Brain Imaging (UFBI)
| | - Anne Larsson
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, Umeå, Sweden
| | - Carolina Flygare
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, Umeå, Sweden
| | - Jussi Remes
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, Umeå, Sweden
| | - Sara Strandberg
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, Umeå, Sweden
| | | | | | - Susanna Jakobson Mo
- Umeå Center for Functional Brain Imaging (UFBI)
- Department of Radiation Sciences, Diagnostic Radiology, Umeå University, Umeå, Sweden
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