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Angelini L, Paparella G, Bologna M. Distinguishing essential tremor from Parkinson's disease: clinical and experimental tools. Expert Rev Neurother 2024:1-16. [PMID: 39016323 DOI: 10.1080/14737175.2024.2372339] [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: 04/25/2024] [Accepted: 06/20/2024] [Indexed: 07/18/2024]
Abstract
INTRODUCTION Essential tremor (ET) and Parkinson's disease (PD) are the most common causes of tremor and the most prevalent movement disorders, with overlapping clinical features that can lead to diagnostic challenges, especially in the early stages. AREAS COVERED In the present paper, the authors review the clinical and experimental studies and emphasized the major aspects to differentiate between ET and PD, with particular attention to cardinal phenomenological features of these two conditions. Ancillary and experimental techniques, including neurophysiology, neuroimaging, fluid biomarker evaluation, and innovative methods, are also discussed for their role in differential diagnosis between ET and PD. Special attention is given to investigations and tools applicable in the early stages of the diseases, when the differential diagnosis between the two conditions is more challenging. Furthermore, the authors discuss knowledge gaps and unsolved issues in the field. EXPERT OPINION Distinguishing ET and PD is crucial for prognostic purposes and appropriate treatment. Additionally, accurate diagnosis is critical for optimizing clinical and experimental research on pathophysiology and innovative therapies. In a few years, integrated technologies could enable accurate, reliable diagnosis from early disease stages or prodromal stages in at-risk populations, but further research combining different techniques is needed.
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Affiliation(s)
| | - Giulia Paparella
- IRCCS Neuromed, Pozzilli, (IS), Italy
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
| | - Matteo Bologna
- IRCCS Neuromed, Pozzilli, (IS), Italy
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
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Jiang W, Zhou H, Wu J, Chen H, Li L, Wu Y, Meng T, Zuo G, Fan W, Shi C. Short Step Length Estimation for Parkinson's Disease Patients by Using Fusion Data From Camera-IMU in Smart Glasses. IEEE Trans Biomed Eng 2024; 71:2265-2275. [PMID: 38376981 DOI: 10.1109/tbme.2024.3367923] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Shortened step length is a prominent motor abnormality in Parkinson's disease (PD) patients. Current methods for estimating short step length have the limitation of relying on laboratory scenarios, wearing multiple sensors, and inaccurate estimation results from a single sensor. In this paper, we proposed a novel method for estimating short step length for PD patients by fusing data from camera and inertial measurement units in smart glasses. A simultaneous localization and mapping technique and acceleration thresholding-based step detection technique were combined to realize the step length estimation. Two sets of experiments were conducted to demonstrate the performance of our method. In the first set of experiments with 12 healthy subjects, the proposed method demonstrated an average error of 8.44% across all experiments including six fixed step lengths below 30 cm. The second set of straightly walking experiments were implemented with 12 PD patients, the proposed method exhibited an average error of 4.27% compared to a standard gait evaluation technique in total walking distance. Notably, among the results of step lengths below 40 cm, our method agreed with the standard technique (R 2=0.8659). This study offers a promising approach for estimating short step length for PD patients during smart glasses-based gait training.
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Zhang W, Ling Y, Chen Z, Ren K, Chen S, Huang P, Tan Y. Wearable sensor-based quantitative gait analysis in Parkinson's disease patients with different motor subtypes. NPJ Digit Med 2024; 7:169. [PMID: 38926552 PMCID: PMC11208588 DOI: 10.1038/s41746-024-01163-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
Gait impairments are among the most common and disabling symptoms of Parkinson's disease and worsen as the disease progresses. Early detection and diagnosis of subtype-specific gait deficits, as well as progression monitoring, can help to implement effective and preventive personalized treatment for PD patients. Yet, the gait features have not been fully studied in PD and its motor subtypes. To characterize comprehensive and objective gait alterations and to identify the potential gait biomarkers for early diagnosis, subtype differentiation, and disease severity monitoring. We analyzed gait parameters related to upper/lower limbs, trunk and lumbar, and postural transitions from 24 tremor-dominant (TD) and 20 postural instability gait difficulty (PIGD) dominant PD patients who were in early stage and 39 matched healthy controls (HC) during the Timed Up and Go test using wearable sensors. Results show: (1) Both TD and PIGD groups showed restricted backswing range in bilateral lower extremities and more affected side (MAS) arm, reduced trunk and lumbar rotation range in the coronal plane, and low turning efficiency. The receiver operating characteristic (ROC) analysis revealed these objective gait features had high discriminative value in distinguishing both PD subtypes from the HC with the area under the curve (AUC) values of 0.7~0.9 (p < 0.01). (2) Subtle but measurable gait differences existed between TD and PIGD patients before the onset of clinically apparent gait impairment. (3) Specific gait parameters were significantly associated with disease severity in TD and PIGD subtypes. Objective gait biomarkers based on wearable sensors may facilitate timely and personalized gait treatments in PD subtypes through early diagnosis, subtype differentiation, and disease severity monitoring.
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Affiliation(s)
- Weishan Zhang
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yun Ling
- GYENNO SCIENCE Co., Ltd. Department of Research, Shenzhen, Guangdong, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Zhonglue Chen
- GYENNO SCIENCE Co., Ltd. Department of Research, Shenzhen, Guangdong, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Kang Ren
- GYENNO SCIENCE Co., Ltd. Department of Research, Shenzhen, Guangdong, China
- HUST-GYENNO CNS Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Shengdi Chen
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Pei Huang
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Yuyan Tan
- Department of Neurology and Institute of Neurology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
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Li H, Ma W, Li C, He Q, Zhou Y, Xie A. Combined diagnosis for Parkinson's disease via gait and eye movement disorders. Parkinsonism Relat Disord 2024; 123:106979. [PMID: 38669851 DOI: 10.1016/j.parkreldis.2024.106979] [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: 02/16/2024] [Revised: 04/14/2024] [Accepted: 04/21/2024] [Indexed: 04/28/2024]
Abstract
BACKGROUND AND OBJECTIVES With the discovery of the potential role of gait and eye movement disorders in Parkinson's disease (PD) recognition, we intend to investigate the combined diagnostic value of gait and eye movement disorders for PD. METHODS We enrolled some Chinese PD patients and healthy controls and separated them into the training and validation sets based on enrollment time. Performance in five oculomotor paradigms and in one gait paradigm was examined using an infrared eye tracking device and a wearable gait analysis device. We developed and validated a combined model for PD diagnosis via multivariate stepwise logistic regression analysis. Furthermore, subgroup comparisons and multi-model comparison were performed to assess its applicability and advantages. RESULTS A total of 145 PD patients and 80 healthy controls in China were recruited. The pro-saccade velocity, the trunk-sway max, and the turn mean angular velocity were finally screened out for the model development. Incorporating age factor, the ternary model demonstrated more satisfactory performance on ROC (AUC of 0.953 in the training set and AUC of 0.972 in the validation set), calibration curve, and decision curve. A nomogram was drawn to visualize the model. The combined model outperforms individual models with a broad application and the unique diagnostic value for early detection of PD patients, especially TD-PD patients. CONCLUSION We demonstrated the presence of gait and eye movement disorders, as well as the feasibility, applicability, and superiority of employing them together to diagnose PD.
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Affiliation(s)
- Han Li
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.
| | - Wenqi Ma
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.
| | - Chengqian Li
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China; Department of Neurology, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
| | - Qiqing He
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.
| | - Yuting Zhou
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China.
| | - Anmu Xie
- Department of Neurology, The Affiliated Hospital of Qingdao University, Qingdao, China; Institute of Cerebrovascular Diseases, The Affiliated Hospital of Qingdao University, Qingdao, China.
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Peng Y, Ma C, Li M, Liu Y, Yu J, Pan L, Zhang Z. Intelligent devices for assessing essential tremor: a comprehensive review. J Neurol 2024:10.1007/s00415-024-12354-9. [PMID: 38816480 DOI: 10.1007/s00415-024-12354-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 03/25/2024] [Accepted: 03/26/2024] [Indexed: 06/01/2024]
Abstract
Essential tremor (ET) stands as the most prevalent movement disorder, characterized by rhythmic and involuntary shaking of body parts. Achieving an accurate and comprehensive assessment of tremor severity is crucial for effectively diagnosing and managing ET. Traditional methods rely on clinical observation and rating scales, which may introduce subjective biases and hinder continuous evaluation of disease progression. Recent research has explored new approaches to quantifying ET. A promising method involves the use of intelligent devices to facilitate objective and quantitative measurements. These devices include inertial measurement units, electromyography, video equipment, and electronic handwriting boards, and more. Their deployment enables real-time monitoring of human activity data, featuring portability and efficiency. This capability allows for more extensive research in this field and supports the shift from in-lab/clinic to in-home monitoring of ET symptoms. Therefore, this review provides an in-depth analysis of the application, current development, potential characteristics, and roles of intelligent devices in assessing ET.
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Affiliation(s)
- Yumeng Peng
- Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, Beijing, 100853, China
- Department of Neurology, 923th Hospital of the Joint Logistics Support Force of PLA, Nanning, 530021, China
- Chinese PLA Medical School, Beijing, 100853, China
| | - Chenbin Ma
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China
| | - Mengwei Li
- Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, Beijing, 100853, China
- Chinese PLA Medical School, Beijing, 100853, China
| | - Yunmo Liu
- Chinese PLA Medical School, Beijing, 100853, China
| | - Jinze Yu
- School of Computer Science and Engineering, Beihang University, Beijing, 100191, China
| | - Longsheng Pan
- Department of Neurosurgery, First Medical Center, PLA General Hospital, Beijing, 100853, China.
| | - Zhengbo Zhang
- Center for Artificial Intelligence in Medicine, Medical Innovation Research Department, PLA General Hospital, Beijing, 100853, China.
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Rios-Urrego CD, Rusz J, Orozco-Arroyave JR. Automatic speech-based assessment to discriminate Parkinson's disease from essential tremor with a cross-language approach. NPJ Digit Med 2024; 7:37. [PMID: 38368458 PMCID: PMC10874421 DOI: 10.1038/s41746-024-01027-6] [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: 07/07/2023] [Accepted: 02/05/2024] [Indexed: 02/19/2024] Open
Abstract
Parkinson's disease (PD) and essential tremor (ET) are prevalent movement disorders that mainly affect elderly people, presenting diagnostic challenges due to shared clinical features. While both disorders exhibit distinct speech patterns-hypokinetic dysarthria in PD and hyperkinetic dysarthria in ET-the efficacy of speech assessment for differentiation remains unexplored. Developing technology for automatic discrimination could enable early diagnosis and continuous monitoring. However, the lack of data for investigating speech behavior in these patients has inhibited the development of a framework for diagnostic support. In addition, phonetic variability across languages poses practical challenges in establishing a universal speech assessment system. Therefore, it is necessary to develop models robust to the phonetic variability present in different languages worldwide. We propose a method based on Gaussian mixture models to assess domain adaptation from models trained in German and Spanish to classify PD and ET patients in Czech. We modeled three different speech dimensions: articulation, phonation, and prosody and evaluated the models' performance in both bi-class and tri-class classification scenarios (with the addition of healthy controls). Our results show that a fusion of the three speech dimensions achieved optimal results in binary classification, with accuracies up to 81.4 and 86.2% for monologue and /pa-ta-ka/ tasks, respectively. In tri-class scenarios, incorporating healthy speech signals resulted in accuracies of 63.3 and 71.6% for monologue and /pa-ta-ka/ tasks, respectively. Our findings suggest that automated speech analysis, combined with machine learning is robust, accurate, and can be adapted to different languages to distinguish between PD and ET patients.
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Affiliation(s)
| | - Jan Rusz
- Department of Circuit Theory, Czech Technical University in Prague, Prague, Czech Republic.
| | - Juan Rafael Orozco-Arroyave
- GITA Lab, Faculty of Engineering, University of Antioquia, Medellín, Colombia.
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany.
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Robertson-Dick EE, Timm EC, Pal G, Ouyang B, Liu Y, Berry-Kravis E, Hall DA, O’Keefe JA. Digital gait markers to potentially distinguish fragile X-associated tremor/ataxia syndrome, Parkinson's disease, and essential tremor. Front Neurol 2023; 14:1308698. [PMID: 38162443 PMCID: PMC10755476 DOI: 10.3389/fneur.2023.1308698] [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: 10/06/2023] [Accepted: 11/22/2023] [Indexed: 01/03/2024] Open
Abstract
Background Fragile X-associated tremor/ataxia syndrome (FXTAS), a neurodegenerative disease that affects carriers of a 55-200 CGG repeat expansion in the fragile X messenger ribonucleoprotein 1 (FMR1) gene, may be given an incorrect initial diagnosis of Parkinson's disease (PD) or essential tremor (ET) due to overlapping motor symptoms. It is critical to characterize distinct phenotypes in FXTAS compared to PD and ET to improve diagnostic accuracy. Fast as possible (FP) speed and dual-task (DT) paradigms have the potential to distinguish differences in gait performance between the three movement disorders. Therefore, we sought to compare FXTAS, PD, and ET patients using quantitative measures of functional mobility and gait under self-selected (SS) speed, FP, and DT conditions. Methods Participants with FXTAS (n = 22), PD (n = 23), ET (n = 20), and controls (n = 20) underwent gait testing with an inertial sensor system (APDM™). An instrumented Timed Up and Go test (i-TUG) was used to measure movement transitions, and a 2-min walk test (2MWT) was used to measure gait and turn variables under SS, FP, and DT conditions, and dual-task costs (DTC) were calculated. ANOVA and multinomial logistic regression analyses were performed. Results PD participants had reduced stride lengths compared to FXTAS and ET participants under SS and DT conditions, longer turn duration than ET participants during the FP task, and less arm symmetry than ET participants in SS gait. They also had greater DTC for stride length and velocity compared to FXTAS participants. On the i-TUG, PD participants had reduced sit-to-stand peak velocity compared to FXTAS and ET participants. Stride length and arm symmetry index during the DT 2MWT was able to distinguish FXTAS and ET from PD, such that participants with shorter stride lengths were more likely to have a diagnosis of PD and those with greater arm asymmetry were more likely to be diagnosed with PD. No gait or i-TUG parameters distinguished FXTAS from ET participants in the regression model. Conclusion This is the first quantitative study demonstrating distinct gait and functional mobility profiles in FXTAS, PD, and ET which may assist in more accurate and timely diagnosis.
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Affiliation(s)
- Erin E. Robertson-Dick
- Department of Anatomy and Cell Biology, Rush University Medical Center, Chicago, IL, United States
| | - Emily C. Timm
- Department of Anatomy and Cell Biology, Rush University Medical Center, Chicago, IL, United States
| | - Gian Pal
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Bichun Ouyang
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Yuanqing Liu
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Elizabeth Berry-Kravis
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
- Department of Pediatrics, Rush University Medical Center, Chicago, IL, United States
- Department of Biochemistry, Rush University Medical Center, Chicago, IL, United States
| | - Deborah A. Hall
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
| | - Joan A. O’Keefe
- Department of Anatomy and Cell Biology, Rush University Medical Center, Chicago, IL, United States
- Department of Neurological Sciences, Rush University Medical Center, Chicago, IL, United States
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Liu ST, Horng JL, Lin LY, Chou MY. Fenpropathrin causes alterations in locomotion and social behaviors in zebrafish (Danio rerio). AQUATIC TOXICOLOGY (AMSTERDAM, NETHERLANDS) 2023; 265:106756. [PMID: 37952273 DOI: 10.1016/j.aquatox.2023.106756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 11/01/2023] [Accepted: 11/03/2023] [Indexed: 11/14/2023]
Abstract
Fenpropathrin is one of the widely used pyrethroid pesticides in agriculture and is frequently detected in the environment, groundwater, and food. While fenpropathrin was found to have neurotoxic effects in mammals, it remains unclear whether it has similar effects on fish. Here, we used adult zebrafish to investigate the impacts of fenpropathrin on fish social behaviors and neural activity. Exposure of adult zebrafish to 500 ppb of fenpropathrin for 72 h increased anxiety levels but decreased physical fitness, as measured by a novel tank diving test and swimming tunnel test. Fish exposed to fenpropathrin appeared to spend more time in the conspecific zone of the tank, possibly seeking greater comfort from their companions. Although learning, memory, and aggressive behavior did not change, fish exposed to fenpropathrin appeared to have shorter fighting durations. The immunocytochemical results showed the tyrosine hydroxylase antibody-labeled dopaminergic neurons in the teleost posterior tuberculum decreased in the zebrafish brain. According to a quantitative polymerase chain reaction (qPCR) analysis of the brain, exposure to fenpropathrin resulted in a decrease in the messenger (m)RNA expression of monoamine oxidase (mao), an enzyme that facilitates the deamination of dopamine. In contrast, the mRNA expression of the sncga gene, which may trigger Parkinson's disease, was found to have increased. There were no changes observed in expressions of genes related to antioxidants and apoptosis between the control and fenpropathrin-exposed groups. We provide evidence to demonstrate the defect of the neurotoxicity of fenpropathrin toward dopaminergic neurons in adult zebrafish.
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Affiliation(s)
- Sian-Tai Liu
- Department of Life Science, National Taiwan University, Taipei City, Taiwan
| | - Jiun-Lin Horng
- Department of Anatomy and Cell Biology, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan
| | - Li-Yih Lin
- Department of Life Science, School of Life Sciences, National Taiwan Normal University, Taipei City, Taiwan
| | - Ming-Yi Chou
- Department of Life Science, National Taiwan University, Taipei City, Taiwan.
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Toader C, Dobrin N, Brehar FM, Popa C, Covache-Busuioc RA, Glavan LA, Costin HP, Bratu BG, Corlatescu AD, Popa AA, Ciurea AV. From Recognition to Remedy: The Significance of Biomarkers in Neurodegenerative Disease Pathology. Int J Mol Sci 2023; 24:16119. [PMID: 38003309 PMCID: PMC10671641 DOI: 10.3390/ijms242216119] [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: 10/10/2023] [Revised: 10/28/2023] [Accepted: 11/06/2023] [Indexed: 11/26/2023] Open
Abstract
With the inexorable aging of the global populace, neurodegenerative diseases (NDs) like Alzheimer's disease (AD), Parkinson's disease (PD), and amyotrophic lateral sclerosis (ALS) pose escalating challenges, which are underscored by their socioeconomic repercussions. A pivotal aspect in addressing these challenges lies in the elucidation and application of biomarkers for timely diagnosis, vigilant monitoring, and effective treatment modalities. This review delineates the quintessence of biomarkers in the realm of NDs, elucidating various classifications and their indispensable roles. Particularly, the quest for novel biomarkers in AD, transcending traditional markers in PD, and the frontier of biomarker research in ALS are scrutinized. Emergent susceptibility and trait markers herald a new era of personalized medicine, promising enhanced treatment initiation especially in cases of SOD1-ALS. The discourse extends to diagnostic and state markers, revolutionizing early detection and monitoring, alongside progression markers that unveil the trajectory of NDs, propelling forward the potential for tailored interventions. The synergy between burgeoning technologies and innovative techniques like -omics, histologic assessments, and imaging is spotlighted, underscoring their pivotal roles in biomarker discovery. Reflecting on the progress hitherto, the review underscores the exigent need for multidisciplinary collaborations to surmount the challenges ahead, accelerate biomarker discovery, and herald a new epoch of understanding and managing NDs. Through a panoramic lens, this article endeavors to provide a comprehensive insight into the burgeoning field of biomarkers in NDs, spotlighting the promise they hold in transforming the diagnostic landscape, enhancing disease management, and illuminating the pathway toward efficacious therapeutic interventions.
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Affiliation(s)
- Corneliu Toader
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (C.T.); (L.A.G.); (H.P.C.); (B.-G.B.); (A.D.C.); (A.V.C.)
- Department of Vascular Neurosurgery, National Institute of Neurology and Neurovascular Diseases, 077160 Bucharest, Romania
| | - Nicolaie Dobrin
- Department of Neurosurgery, Clinical Emergency Hospital “Prof. Dr. Nicolae Oblu”, 700309 Iasi, Romania
| | - Felix-Mircea Brehar
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (C.T.); (L.A.G.); (H.P.C.); (B.-G.B.); (A.D.C.); (A.V.C.)
- Department of Neurosurgery, Clinical Emergency Hospital “Bagdasar-Arseni”, 041915 Bucharest, Romania
| | - Constantin Popa
- Department of Neurology, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania
- Department of Neurology, National Institute of Neurology and Neurovascular Diseases, 077160 Bucharest, Romania
- Medical Science Section, Romanian Academy, 060021 Bucharest, Romania
| | - Razvan-Adrian Covache-Busuioc
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (C.T.); (L.A.G.); (H.P.C.); (B.-G.B.); (A.D.C.); (A.V.C.)
| | - Luca Andrei Glavan
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (C.T.); (L.A.G.); (H.P.C.); (B.-G.B.); (A.D.C.); (A.V.C.)
| | - Horia Petre Costin
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (C.T.); (L.A.G.); (H.P.C.); (B.-G.B.); (A.D.C.); (A.V.C.)
| | - Bogdan-Gabriel Bratu
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (C.T.); (L.A.G.); (H.P.C.); (B.-G.B.); (A.D.C.); (A.V.C.)
| | - Antonio Daniel Corlatescu
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (C.T.); (L.A.G.); (H.P.C.); (B.-G.B.); (A.D.C.); (A.V.C.)
| | - Andrei Adrian Popa
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (C.T.); (L.A.G.); (H.P.C.); (B.-G.B.); (A.D.C.); (A.V.C.)
| | - Alexandru Vlad Ciurea
- Department of Neurosurgery, “Carol Davila” University of Medicine and Pharmacy, 020021 Bucharest, Romania; (C.T.); (L.A.G.); (H.P.C.); (B.-G.B.); (A.D.C.); (A.V.C.)
- Medical Science Section, Romanian Academy, 060021 Bucharest, Romania
- Neurosurgery Department, Sanador Clinical Hospital, 010991 Bucharest, Romania
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Lai H, Li XY, Xu F, Zhu J, Li X, Song Y, Wang X, Wang Z, Wang C. Applications of Machine Learning to Diagnosis of Parkinson's Disease. Brain Sci 2023; 13:1546. [PMID: 38002506 PMCID: PMC10670005 DOI: 10.3390/brainsci13111546] [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: 09/27/2023] [Revised: 10/28/2023] [Accepted: 10/31/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Accurate diagnosis of Parkinson's disease (PD) is challenging due to its diverse manifestations. Machine learning (ML) algorithms can improve diagnostic precision, but their generalizability across medical centers in China is underexplored. OBJECTIVE To assess the accuracy of an ML algorithm for PD diagnosis, trained and tested on data from different medical centers in China. METHODS A total of 1656 participants were included, with 1028 from Beijing (training set) and 628 from Fuzhou (external validation set). Models were trained using the least absolute shrinkage and selection operator-logistic regression (LASSO-LR), decision tree (DT), random forest (RF), eXtreme gradient boosting (XGboost), support vector machine (SVM), and k-nearest neighbor (KNN) techniques. Hyperparameters were optimized using five-fold cross-validation and grid search techniques. Model performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity (recall), specificity, precision, and F1 score. Variable importance was assessed for all models. RESULTS SVM demonstrated the best differentiation between healthy controls (HCs) and PD patients (AUC: 0.928, 95% CI: 0.908-0.947; accuracy: 0.844, 95% CI: 0.814-0.871; sensitivity: 0.826, 95% CI: 0.786-0.866; specificity: 0.861, 95% CI: 0.820-0.898; precision: 0.849, 95% CI: 0.807-0.891; F1 score: 0.837, 95% CI: 0.803-0.868) in the validation set. Constipation, olfactory decline, and daytime somnolence significantly influenced predictability. CONCLUSION We identified multiple pivotal variables and SVM as a precise and clinician-friendly ML algorithm for prediction of PD in Chinese patients.
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Affiliation(s)
- Hong Lai
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
- Department of Neurology, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Xu-Ying Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
| | - Fanxi Xu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
| | - Junge Zhu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
| | - Xian Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
| | - Yang Song
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
| | - Xianlin Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
| | - Zhanjun Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
| | - Chaodong Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
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Guerra A, D'Onofrio V, Ferreri F, Bologna M, Antonini A. Objective measurement versus clinician-based assessment for Parkinson's disease. Expert Rev Neurother 2023; 23:689-702. [PMID: 37366316 DOI: 10.1080/14737175.2023.2229954] [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: 04/28/2023] [Revised: 06/18/2023] [Accepted: 06/22/2023] [Indexed: 06/28/2023]
Abstract
INTRODUCTION Although clinician-based assessment through standardized clinical rating scales is currently the gold standard for quantifying motor impairment in Parkinson's disease (PD), it is not without limitations, including intra- and inter-rater variability and a degree of approximation. There is increasing evidence supporting the use of objective motion analyses to complement clinician-based assessment. Objective measurement tools hold significant potential for improving the accuracy of clinical and research-based evaluations of patients. AREAS COVERED The authors provide several examples from the literature demonstrating how different motion measurement tools, including optoelectronics, contactless and wearable systems allow for both the objective quantification and monitoring of key motor symptoms (such as bradykinesia, rigidity, tremor, and gait disturbances), and the identification of motor fluctuations in PD patients. Furthermore, they discuss how, from a clinician's perspective, objective measurements can help in various stages of PD management. EXPERT OPINION In our opinion, sufficient evidence supports the assertion that objective monitoring systems enable accurate evaluation of motor symptoms and complications in PD. A range of devices can be utilized not only to support diagnosis but also to monitor motor symptom during the disease progression and can become relevant in the therapeutic decision-making process.
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Affiliation(s)
- Andrea Guerra
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
| | | | - Florinda Ferreri
- Unit of Neurology, Unit of Clinical Neurophysiology, Study Center of Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
- Department of Clinical Neurophysiology, Kuopio University Hospital, University of Eastern Finland, Kuopio, Finland
| | - Matteo Bologna
- Department of Human Neurosciences, Sapienza University of Rome, Rome, Italy
- IRCCS Neuromed, Pozzilli, Italy
| | - Angelo Antonini
- Parkinson and Movement Disorder Unit, Study Center on Neurodegeneration (CESNE), Department of Neuroscience, University of Padua, Padua, Italy
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