Parkinson's Disease

Accurate Tracking of Locomotory Kinematics in Mice Moving Freely in Three-Dimensional Environments.

Marker-based motion capture (MBMC) is a powerful tool for precise, high-speed, three-dimensional tracking of animal movements, enabling detailed study of behaviors ranging from subtle limb trajectories to broad spatial exploration. Despite its proven utility in larger animals, MBMC has remained underutilized in mice due to the difficulty of robust marker attachment during unrestricted behavior. In...

Computer-Based Tracking of Microsurgical Instruments: A Novel Assessment Tool for Robot-Assisted and Conventional Microsurgery.

BACKGROUND: Efficient and objective tools for self-assessment of microsurgical skills are needed to ensure high-quality microsurgical training and optimized use of surgeons' time and resources. In addition, the successful clinical integration of microsurgical robots in operating rooms will critically depend on effective training and evaluation strategies for microsurgeons, necessitating the develo...

Divide and Conquer: Automatic Detection of the Thalamus to Empower DBS Physiological Navigation to the Subthalamic Region.

OBJECTIVE: To identify thalamic electrophysiological activity along the trajectory to the subthalamic region using micro-electrode recordings in deep ...

Uncovering injury-specific proteomic signatures and neurodegenerative risks in single and repetitive traumatic brain injury.

Traumatic brain injury (TBI) is a major public health concern associated with an increased risk of neurodegenerative diseases including Alzheimer's di...

A New Insight in Cellular and Molecular Signaling Regulation for Neural Differentiation Program.

Numerous neurological conditions impact the brain, spinal cord, and nerves, including neurodegenerative diseases such as Alzheimer's and Parkinson's d...

Quantum-classical deep learning hybrid architecture with graphene-printed low-cost capacitive sensor for essential tremor detection.

This study presents a novel hardware and software architecture combining capacitive sensors, quantum-inspired algorithms, and deep learning applied to...

Multi-Scale Temporal Analysis with a Dual-Branch Attention Network for Interpretable Gait-Based Classification of Neurodegenerative Diseases.

The accurate diagnosis of neurodegenerative diseases (NDDs), such as Amyotrophic Lateral Sclerosis (ALS), Huntington's Disease (HD), and Parkinson's D...

Decoding Parkinson's Diagnosis: An OCT-Based Explainable AI with SHAP/LIME Transparency from the Persian Cohort Study.

BACKGROUND: Parkinson's disease (PD) diagnosis remains challenging due to subjective clinical assessments and late-stage symptom manifestation. Retina...

FedOcw: optimized federated learning for cross-lingual speech-based Parkinson's disease detection.

Accurate detection of Parkinson's disease (PD) through speech analysis holds great promise for early diagnosis and improved patient management. Howeve...

Clinical correlates of data-driven subtypes of deep gray matter atrophy and dopamine availability in early Parkinson's disease.

Recent machine-learning techniques may be useful to identify subtypes with distinct spatial patterns of biomarker abnormality in the various neurodege...

POC-CSP: a novel parameterised and orthogonally-constrained neural network layer for learning common spatial patterns (CSP) in EEG signals.

. Common spatial patterns (CSPs) has been established as a powerful feature extraction method in EEG signal processing with machine learning, but it h...

Deep-learning-based Partial Volume Correction in 99mTc-TRODAT-1 SPECT for Parkinson's Disease: A Preliminary Study on Clinical Translation.

Tc-TRODAT-1 SPECT is effective for the early detection of Parkinson's disease (PD). However, SPECT images suffer from severe partial volume effect, wh...

Thalamic neural activity and epileptic network analysis using stereoelectroencephalography: a prospective study protocol.

INTRODUCTION: Epilepsy is a prevalent chronic neurological disorder, with approximately one-third of patients experiencing intractable epilepsy, often...

Using machine learning to identify Parkinson's disease severity subtypes with multimodal data.

BACKGROUND: Classifying and predicting Parkinson's disease (PD) is challenging because of its diverse subtypes based on severity levels. Currently, id...

In vivo and in silico models of Drosophila for Parkinson's disease.

The fruit fly Drosophila melanogaster has emerged as an important model organism to shed light on neurodegeneration. Parkinson's disease (PD) is the s...

Estimating motor symptom presence and severity in Parkinson's disease from wrist accelerometer time series using ROCKET and InceptionTime.

Parkinson's disease (PD) is a neurodegenerative condition characterized by frequently changing motor symptoms, necessitating continuous symptom monito...

Microglia-drive IRF8 upregulates complement pathway in Parkinson's disease.

Parkinson's disease (PD) is a widespread degenerative disorder of the central nervous system. The gradual degeneration of dopaminergic neurons in the ...

Prediction of 123I-FP-CIT SPECT Results from First Acquired Projections Using Artificial Intelligence.

123I-FP-CIT dopamine transporter imaging is commonly used for the diagnosis of Parkinsonian syndromes in patients whose clinical presentation is atyp...

Personalized medication recommendations for Parkinson's disease patients using gated recurrent units and SHAP interpretability.

Managing Parkinson's disease (PD) through medication can be challenging due to varying symptoms and disease duration. This study aims to demonstrate t...

Decoding dynamic brain networks in Parkinson's disease with temporal attention.

Detecting brief, clinically meaningful changes in brain activity is crucial for understanding neurological disorders. Conventional imaging analyses of...