Article
Clinical Neurology
Shinuan Lin, Chao Gao, Hongxia Li, Pei Huang, Yun Ling, Zhonglue Chen, Kang Ren, Shengdi Chen
Summary: This study used machine learning based on gait and postural transition parameters to differentiate early-stage Parkinson's disease (PD) from essential tremor (ET). The results showed that by combining wearable sensors and machine learning, it is possible to successfully distinguish between these two disorders.
JOURNAL OF NEUROLOGY
(2023)
Article
Engineering, Biomedical
Junjie Li, Huaiyu Zhu, Jiaxiang Li, Haotian Wang, Bo Wang, Wei Luo, Yun Pan
Summary: This study proposes a wearable system for assessing upper limb tremor and differentiating between Parkinson's disease and essential tremor. The system collects tremor data from the wrist and fingers simultaneously and extracts multi-segment features to perform the differential diagnosis using support vector machine classifiers. The system demonstrated high accuracy in diagnosing Parkinson's disease versus essential tremor in real-world settings.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Clinical Neurology
Maria Giovanna Bianco, Andrea Quattrone, Alessia Sarica, Federica Aracri, Camilla Calomino, Maria Eugenia Caligiuri, Fabiana Novellino, Rita Nistico, Jolanda Buonocore, Marianna Crasa, Maria Grazia Vaccaro, Aldo Quattrone
Summary: This study aimed to compare the differences in MRI findings between essential tremor (ET) and rest tremor (rET) patients. The results showed that rET patients had increased roughness and mean curvature in certain frontal and temporal areas compared to healthy controls and ET patients, which were correlated with cognitive scores. Additionally, the cortical volume in the left pars opercularis was lower in rET patients than in ET patients. The machine learning approach successfully distinguished between rET and ET patients based on structural cortical features.
JOURNAL OF NEUROLOGY
(2023)
Article
Neurosciences
Xupo Xing, Ningdi Luo, Shun Li, Liche Zhou, Chengli Song, Jun Liu
Summary: This study evaluated seven predictive models using machine learning algorithms to differentiate between Parkinson's disease and essential tremor. The results showed that random forest and extreme gradient boosting models had the best predictive ability. The analysis also revealed that the dominant frequency and average amplitude of surface electromyogram signals from flexors, as well as resting and winging postures, had the greatest impact on the diagnosis of Parkinson's disease.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Clinical Neurology
Pan Xiao, Li Tao, Xiaoyu Zhang, Qin Li, Honge Gui, Bintao Xu, Xueyan Zhang, Wanlin He, Huiyue Chen, Hansheng Wang, Fajin Lv, Tianyou Luo, Oumei Cheng, Jin Luo, Yun Man, Zheng Xiao, Weidong Fang
Summary: Histogram analysis of rs-fMRI data is a promising method to identify ET patients and build potential diagnostic biomarkers. Multiple machine learning algorithms, such as SVM, LR, RF, and KNN, were used to achieve good classification performance. The most power-discriminative features were mainly located in the cerebello-thalamo-motor and non-motor cortical pathways.
FRONTIERS IN NEUROLOGY
(2023)
Article
Clinical Neurology
Valeria Sacca, Fabiana Novellino, Maria Salsone, Maurice Abou Jaoude, Andrea Quattrone, Carmelina Chiriaco, Jose L. M. Madrigal, Aldo Quattrone
Summary: This study aimed to investigate the applicability of machine learning on resting-state fMRI connectivity data for detecting functional changes in essential tremor (ET). A support vector machine with a radial kernel was trained on the average signals from 14 brain networks obtained from ET and healthy control subjects. The machine learning algorithm achieved an AUC of 0.75 and identified four networks (language, primary visual, cerebellum, and attention), which have significant roles in ET pathophysiology, as the most important features for classification. Univariate analysis could not find significant results between the two conditions.
NEUROLOGICAL SCIENCES
(2023)
Article
Clinical Neurology
Xueyan Zhang, Li Tao, Huiyue Chen, Xiaoyu Zhang, Hansheng Wang, Wanlin He, Qin Li, Fajin Lv, Tianyou Luo, Jin Luo, Yun Man, Zheng Xiao, Jun Cao, Weidong Fang
Summary: By combining MVPA with local brain functional connectivity, this study successfully identified depressed patients with essential tremor. The findings revealed distinct patterns of brain activity in depressed ET patients compared to non-depressed ET patients and healthy controls, shedding light on the underlying mechanisms of depression in ET.
FRONTIERS IN NEUROLOGY
(2022)
Article
Medicine, Research & Experimental
Peter Larner, Rachel Jonas, Claudia N. Gutierrez, Patrick McGarey, Joanna Lott, Shayan Moosa, W. Jeffrey Elias, James Daniero
Summary: The study aimed to determine whether two neurosurgical procedures, deep brain stimulation (DBS) and focused ultrasound (FUS), reduce vocal tremor in patients with essential tremor. The results showed that both DBS and FUS interventions had acoustical and perceptual benefits for vocal tremor, providing further evidence for the effectiveness of neurosurgical interventions in treating vocal tremor.
Article
Acoustics
Changwei Zhou, Yuanbo Wu, Ziqi Fan, Xiaojun Zhang, Di Wu, Zhi Tao
Summary: GTSL features, inspired by nonlinear phenomena in human phonation and incorporating auditory characteristics, quantify turbulent noise through nonlinear compression. In pathological voice detection, GTSL features outperform traditional features with higher accuracy with traditional machine learning algorithms.
Article
Engineering, Biomedical
Chenbin Ma, Deyu Li, Longsheng Pan, Xuemei Li, Chunyu Yin, Ailing Li, Zhengbo Zhang, Rui Zong
Summary: This study successfully assessed the severity of symptoms in ET patients using wearable sensor technology and machine learning methods, which could help clinicians automate scoring and improve disease management efficiency.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Integrative & Complementary Medicine
Koichi Tsunoda, Rika Kobayashi, Mihiro Takazawa, Yoko Morita
Summary: The study analyzed 26 patients with essential voice tremor and found that yokukansan was more effective than clonazepam in treating the condition, suggesting it could be the best choice for first-line treatment.
ALTERNATIVE THERAPIES IN HEALTH AND MEDICINE
(2021)
Article
Computer Science, Software Engineering
Julian D. Loaiza Duque, Antonio J. Sanchez Egea, Hernan A. Gonzalez Rojas, Pedro Chana-Cuevas, Joaquim J. Ferreira, Joao Costa
Summary: This presents a cost-effective, non-invasive, and easy-to-use tool that utilizes the 6-axis inertial sensor of a smartphone or specific wearable sensor, enhanced by machine learning, for early differential diagnosis of Parkinson's disease and Essential Tremor. A dedicated web server is used to extract kinematic indexes, implement machine learning models, and provide classification results to the app. Therefore, clinicians can use this app as a support tool in the clinic for motor evaluations and timely therapeutic responses in uncertain stages of the diseases.
Article
Neurosciences
Xueyan Zhang, Huiyue Chen, Xiaoyu Zhang, Hansheng Wang, Li Tao, Wanlin He, Qin Li, Oumei Cheng, Jing Luo, Yun Man, Zheng Xiao, Weidong Fang
Summary: This study investigated the potential use of machine-learning algorithms combined with whole-brain resting-state functional connectivity (RSFC) metrics to identify essential tremor (ET) patients and revealed the underlying brain network pathogenesis. The results showed that multiple machine-learning algorithms could effectively distinguish ET patients from healthy controls and identify the most discriminative features.
HUMAN BRAIN MAPPING
(2023)
Article
Clinical Neurology
Lena C. O'Flynn, Steven J. Frucht, Kristina Simonyan
Summary: This study aimed to explore the efficacy and central effects of sodium oxybate in patients with alcohol-responsive essential tremor of voice (ETv). Oral administration of sodium oxybate reduced ETv symptoms and modulated brain activity. These findings suggest that sodium oxybate may be an effective oral medication for the treatment of alcohol-responsive ETv patients.
MOVEMENT DISORDERS
(2023)
Article
Clinical Neurology
Fabiana Novellino, Valeria Sacca, Maria Salsone, Giuseppe Nicoletti, Andrea Quattrone, Carmelina Chiriaco, Jose L. M. Madrigal, Aldo Quattrone
Summary: This study explored the cognitive functioning of ET patients without dementia and found that cognitive impairments involve memory, executive function, and language domains. The MRI analysis revealed a correlation between cognitive performances and widespread brain areas including the cerebellum, frontal cortices, cingulate cortices, and temporal cortex.
NEUROLOGICAL SCIENCES
(2022)
Article
Otorhinolaryngology
Maria Nicastri, Ilaria Giallini, Bianca Maria Serena Inguscio, Rosaria Turchetta, Letizia Guerzoni, Domenico Cuda, Ginevra Portanova, Giovanni Ruoppolo, Hilal Dincer D'Alessandro, Patrizia Mancini
Summary: The present study aimed to investigate the unique contribution of auditory selective attention (ASA) to the linguistic levels achieved by a group of cochlear implanted (CI) children. Results showed that ASA skills significantly contributed to linguistic skills, with bilateral CI children performing better. The importance of early assessment of ASA skills and the potential positive impact on language development were highlighted.
EUROPEAN ARCHIVES OF OTO-RHINO-LARYNGOLOGY
(2023)
Article
Computer Science, Information Systems
Luca Pietrosanti, Alexandre Calado, Cristiano Maria Verrelli, Antonio Pisani, Antonio Suppa, Francesco Fattapposta, Alessandro Zampogna, Martina Patera, Viviana Rosati, Franco Giannini, Giovanni Saggio
Summary: Parkinson's disease (PD) causes a range of symptoms, including motor impairments. Recent studies have utilized technology-based systems, particularly wearable devices, to objectively measure gait capabilities and differences between PD patients and healthy individuals. This study focused on the harmonic content of upper limb swing during walking, which had not been previously studied. Using IMU sensors, the walking capabilities of PD patients (both newly diagnosed and those under chronic dopaminergic treatment in an off-therapy state) and healthy individuals were measured. The collected data were FFT transformed and analyzed for frequency content. The results showed objectively evidenced upper limb rigidity in PD patients, which was correlated to lower harmonic contents.
Article
Clinical Neurology
Francesco Asci, Marco Falletti, Alessandro Zampogna, Martina Patera, Mark Hallett, John Rothwell, Antonio Suppa
Summary: Asci et al. investigate the pathophysiology of rigidity in Parkinson's disease using a robotic device to measure objective rigidity and its velocity-dependent features. They clarify the role of long-latency reflexes in the development of rigidity. The study highlights the need for innovative methods to objectively measure parkinsonian rigidity and to understand the different biomechanical and neurophysiological factors contributing to this clinical sign.
Article
Neurosciences
Giulia Paparella, Martina De Riggi, Antonio Cannavacciuolo, Donato Colella, Davide Costa, Daniele Birreci, Massimiliano Passaretti, Luca Angelini, Andrea Guerra, Alfredo Berardelli, Matteo Bologna
Summary: Through a study involving 33 healthy subjects, it was found that unilateral motor practice leads to improved performance of both the trained and untrained contralateral limbs. The transfer of this skill is asymmetric and relates to the modulation of specific inhibitory interhemispheric connections.
Article
Clinical Neurology
Matteo Bologna, Alberto J. Espay, Alfonso Fasano, Giulia Paparella, Mark Hallett, Alfredo Berardelli
MOVEMENT DISORDERS
(2023)
Article
Chemistry, Analytical
Giovanni Costantini, Valerio Cesarini, Pietro Di Leo, Federica Amato, Antonio Suppa, Francesco Asci, Antonio Pisani, Alessandra Calculli, Giovanni Saggio
Summary: This study analyzed the voice characteristics of Parkinson's disease patients using machine learning techniques, and compared different feature selection and classification algorithms. The results showed that both feature-based machine learning and deep learning achieved comparable results in terms of classification, with KNN, SVM, and naive Bayes classifiers performing similarly. The superiority of CFS as the best feature selector was more evident, and the selected features acted as relevant vocal biomarkers capable of differentiating healthy subjects, early untreated PD patients, and mid-advanced L-Dopa treated patients.
Article
Biochemistry & Molecular Biology
Claudia Piervincenzi, Antonio Suppa, Nikolaos Petsas, Andrea Fabbrini, Alessandro Trebbastoni, Francesco Asci, Costanza Gianni, Alfredo Berardelli, Patrizia Pantano
Summary: This study used multimodal magnetic resonance imaging to investigate the relationship between parkinsonism and brain structure and function in patients with frontotemporal degeneration (FTD). The results showed that patients with and without parkinsonism exhibited similar patterns of cortical thinning and reduced thalamic volume, but only patients with parkinsonism showed reduced putaminal volume and decreased connectivity between the supplementary motor area and putamen. These findings suggest that FTD patients with parkinsonism have specific neurodegenerative processes in the corticobasal ganglia-thalamocortical motor loops.
Article
Clinical Neurology
Matteo Costanzo, Carolina Cutrona, Giorgio Leodori, Maria Ilenia De Bartolo, Andrea Fabbrini, Giorgio Vivacqua, Antonella Conte, Giovanni Fabbrini, Alfredo Berardelli, Daniele Belvisi
Summary: This study aimed to characterize the clinical features of upper limb tremor during walking (TW) in Parkinson's disease (PD) patients. TW was found to be a frequent sign in PD patients, and its distribution and severity were similar to rest and re-emergent tremor but different from postural tremor. TW may be considered as a clinical variant of rest tremor.
MOVEMENT DISORDERS CLINICAL PRACTICE
(2023)
Article
Clinical Neurology
Francesco Asci, Giulia Di Stefano, Alessandro Di Santo, Edoardo Bianchini, Caterina Leone, Silvia La Cesa, Alessandro Zampogna, Giorgio Cruccu, Antonio Suppa
Summary: This neurophysiological study aimed to investigate M1 plasticity in patients with chronic pain using a non-invasive transcranial magnetic stimulation protocol. The results showed that M1 plasticity was altered in patients with chronic pain, possibly reflecting abnormal pain-motor integration processes.
CLINICAL NEUROPHYSIOLOGY
(2023)
Article
Clinical Neurology
A. Conte, N. De Stefano, A. Nicoletti, V. Caso, M. Mancuso, Alfredo Berardelli, G. Defazio
Summary: According to the analysis, Italian neurological research ranks fifth globally and third in Europe from 2020 to 2023.
NEUROLOGICAL SCIENCES
(2023)
Article
Chemistry, Multidisciplinary
Valerio Cesarini, Giovanni Saggio, Antonio Suppa, Francesco Asci, Antonio Pisani, Alessandra Calculli, Rayan Fayad, Mohamad Hajj-Hassan, Giovanni Costantini
Summary: Parkinson's Disease and Adductor-type Spasmodic Dysphonia are two neurological disorders that greatly impact the quality of life of millions of patients worldwide. In this study, the researchers aimed to develop a robust Machine Learning approach for multi-class classification using voice features. Different feature selection methods and classifiers were compared, and the results showed that spectral, cepstral, prosodic, and voicing-related features were the most relevant, with the Genetic Algorithm performing as the most effective feature selector. The Genetic Algorithm + Naive Bayes approach achieved high accuracy in multi-class voice analysis.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Information Systems
Luca Pietrosanti, Cristiano Maria Verrelli, Franco Giannini, Antonio Suppa, Francesco Fattapposta, Alessandro Zampogna, Martina Patera, Viviana Rosati, Giovanni Saggio
Summary: Parkinson's disease is a chronic neurodegenerative disorder that affects movement and is evaluated subjectively by clinicians. To provide more objective assessments, researchers have developed technology-based systems to measure motor symptoms, but there has been a lack of focus on the importance of upper limb swing during walking.
Article
Engineering, Electrical & Electronic
Giovanni Saggio, Alexandre Calado, Vito Errico, Bor-Shing Lin, I-Jung Lee
Summary: Sensory gloves are capable of measuring finger movements and are useful in multiple applications. This study proposes a testing procedure for assessing the measurements under dynamic conditions using two types of sensory gloves. The results show the feasibility of measuring dynamic finger movements and the differences in measurement repeatability and reliability between the two types of gloves.
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT
(2023)
