Intelligibility of speech in Parkinson's disease relies on anatomically segregated subthalamic beta oscillations

Avantaggiato, F.; Farokhniaee, AmirAli; Bandini, Andrea; Palmisano, Chiara; Hanafi, Ibrahem; Pezzoli, Gianni; Mazzoni, Annalisa; Isaias, Ioannis U.

doi:10.1016/j.nbd.2023.106239

Cited by 6 publications

(4 citation statements)

References 76 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To assess the relationship among subthalamic neural activity, speech production, and intelligibility, Avantaggiato et al [62] investigated bilateral and STN local field potentials (LFPs) in 9 PD patients chronically implanted with DBS during open reading. The spectral features of LFP in the STN were analyzed, then correlated with clinical scores and speech intelligibility levels.…”

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction

Jumanto,

Rofik,

Sugiharti

et al. 2024

J. Inf. Syst. Eng. Bus. Intell.

View full text Add to dashboard Cite

Background: Parkinson's disease (PD) is a critical neurodegenerative disorder affecting the central nervous system and often causing impaired movement and cognitive function in patients. In addition, its diagnosis in the early stages requires a complex and time-consuming process because all existing tests such as electroencephalography or blood examinations lack effectiveness and accuracy. Several studies explored PD prediction using sound, with a specific focus on the development of classification models to enhance accuracy. The majority of these neglected crucial aspects including feature extraction and proper parameter tuning, leading to low accuracy. Objective: This study aims to optimize performance of voice-based PD prediction through feature extraction, with the goal of reducing data dimensions and improving model computational efficiency. Additionally, appropriate parameters will be selected for enhancement of the ability of the model to identify both PD cases and healthy individuals. Methods: The proposed new model applied an OpenML dataset comprising voice recordings from 31 individuals, namely 23 PD patients and 8 healthy participants. The experimental process included the initial use of the SVM algorithm, followed by implementing PCA for feature extraction to enhance machine learning accuracy. Subsequently, data balancing with SMOTE was conducted, and GridSearchCV was used to identify the best parameter combination based on the predicted model characteristics. Result: Evaluation of the proposed model showed an impressive accuracy of 97.44%, sensitivity of 100%, and specificity of 85.71%. This excellent result was achieved with a limited dataset and a 10-fold cross-validation tuning, rendering the model sensitive to the training data. Conclusion: This study successfully enhanced the prediction model accuracy through the SVM+PCA+GridSearchCV+CV method. However, future investigations should consider an appropriate number of folds for a small dataset, explore alternative cross-validation methods, and expand the dataset to enhance model generalizability. Keywords: GridSearchCV, Parkinson Disaese, SVM, PCA, SMOTE, Voice/Speech

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction

Jumanto,

Rofik,

Sugiharti

et al. 2024

J. Inf. Syst. Eng. Bus. Intell.

View full text Add to dashboard Cite

show abstract

“…Furthermore, exercise increases the circulating levels of endocannabinoids. After both aerobic and resistance exercise tasks, there is an increased expression of cannabinoid receptors in the brain, including the PAG, in healthy uninjured animals, but also humans [95].…”

Section: Physical Approachesmentioning

confidence: 99%

Psycho-Neuroendocrinology in the Rehabilitation Field: Focus on the Complex Interplay between Stress and Pain

Bonanno,

Papa,

Cerasa

et al. 2024

Medicina

View full text Add to dashboard Cite

Chronic stress and chronic pain share neuro-anatomical, endocrinological, and biological features. However, stress prepares the body for challenging situations or mitigates tissue damage, while pain is an unpleasant sensation due to nociceptive receptor stimulation. When pain is chronic, it might lead to an allostatic overload in the body and brain due to the chronic dysregulation of the physiological systems that are normally involved in adapting to environmental challenges. Managing stress and chronic pain (CP) in neurorehabilitation presents a significant challenge for healthcare professionals and researchers, as there is no definitive and effective solution for these issues. Patients suffering from neurological disorders often complain of CP, which significantly reduces their quality of life. The aim of this narrative review is to examine the correlation between stress and pain and their potential negative impact on the rehabilitation process. Moreover, we described the most relevant interventions used to manage stress and pain in the neurological population. In conclusion, this review sheds light on the connection between chronic stress and chronic pain and their impact on the neurorehabilitation pathway. Our results emphasize the need for tailored rehabilitation protocols to effectively manage pain, improve treatment adherence, and ensure comprehensive patient care.

show abstract

“…Functional connectivity analysis reveals TENS strengthens top-down connectivity from the periaqueductal gray and prefrontal cortex to the anterior cingulate cortex [59]. Electroencephalogram (EEG) shows TENS increases alpha and beta oscillations that correlate with analgesia [60]. Molecular studies indicate TENS increases endogenous opioid and serotonin levels in brain regions involved in descending modulation [61].…”

Section: Tensmentioning

confidence: 99%

Multimodal non-invasive non-pharmacological therapies for chronic pain: mechanisms and progress

Shi,

2023

BMC Med

View full text Add to dashboard Cite

Background Chronic pain conditions impose significant burdens worldwide. Pharmacological treatments like opioids have limitations. Non-invasive non-pharmacological therapies (NINPT) encompass diverse interventions including physical, psychological, complementary and alternative approaches, and other innovative techniques that provide analgesic options for chronic pain without medications. Main body This review elucidates the mechanisms of major NINPT modalities and synthesizes evidence for their clinical potential across chronic pain populations. NINPT leverages peripheral, spinal, and supraspinal mechanisms to restore normal pain processing and limit central sensitization. However, heterogeneity in treatment protocols and individual responses warrants optimization through precision medicine approaches. Conclusion Future adoption of NINPT requires addressing limitations in standardization and accessibility as well as synergistic combination with emerging therapies. Overall, this review highlights the promise of NINPT as a valuable complementary option ready for integration into contemporary pain medicine paradigms to improve patient care and outcomes.

show abstract

Intelligibility of speech in Parkinson's disease relies on anatomically segregated subthalamic beta oscillations

Cited by 6 publications

References 76 publications

Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction

Optimizing Support Vector Machine Performance for Parkinson's Disease Diagnosis Using GridSearchCV and PCA-Based Feature Extraction

Psycho-Neuroendocrinology in the Rehabilitation Field: Focus on the Complex Interplay between Stress and Pain

Multimodal non-invasive non-pharmacological therapies for chronic pain: mechanisms and progress

Contact Info

Product

Resources

About