Parkinson's Disease

Artificial intelligence for gait and balance in neurological disorders: a scoping review of clinical applications and technologies.

BACKGROUND: Artificial intelligence (AI) has rapidly emerged within healthcare systems and neurological rehabilitation with the potential to revolutionize clinical decision-making and therapeutic strategies. However, a comprehensive understanding of how AI is currently applied to gait and balance rehabilitation in stroke, Parkinson's disease (PD), and multiple sclerosis (MS) is still lacking. OBJE...

Frequency- and Network-Specific Changes in Functional Connectivity Reflect Pathophysiological Mechanisms across Parkinson's Disease Stages.

OBJECTIVE: Parkinson's disease (PD) is increasingly conceptualized as a disorder of large-scale brain networks, yet whether and how frequency-specific functional connectivity reorganizes across stages remains poorly understood. In this study, we used high-density electroencephalography (EEG) to characterize cortico-cortical functional connectivity across the clinical spectrum of PD. METHODS: We pe...

Deep Learning-Based Estimation of Ground Reaction Forces in Parkinsonian Gait Using an Optimized Set of IMU Data.

Accurate gait analysis in Parkinson's disease (PD) typically relies on laboratory-based systems to capture biomechanical data, such as ground reaction...

A stacked multi-classifier for multi-modal data fusion in transcranial sonography-based Parkinson's disease assessment.

Parkinson's disease (PD) is a catastrophic neurodegenerative disorder and a major culprit of neurological disability worldwide. Accurate diagnosis of ...

In-vivo iron mapping in patients with Parkinson's disease using deep learning-based susceptibility source separation MRI.

Parkinson's disease (PD) involves pathological iron accumulation, yet MRI metrics, such as R2* or magnetic susceptibility (χ), lack mechanistic specif...

Multi-dimensional risk prediction and genetic architecture of aspiration pneumonia: a population-based analysis.

BACKGROUND: Aspiration pneumonia represents a significant but understudied cause of morbidity and mortality, particularly among aging populations with...

A Multistage Virtual Screening Strategy Integrating Molecular Similarity, Deep Learning Scoring, and Molecular Docking toward the Discovery of Novel LRRK2 Inhibitors.

Leucine-rich repeat kinase 2 (LRRK2) has emerged as an attractive molecular target for Parkinson's disease therapeutics. To discover novel and potent ...

OCT-based optic neuropathy diagnosis using explainable and privacy-preserving machine learning.

Glaucoma shares similarities with neurodegenerative conditions like dementia, Parkinson's disease, and ischaemic optic neuropathy, which affect ocular...

Machine learning-based optimization of dual subthalamic nucleus and substantia nigra targeting in deep brain stimulation.

Advances in deep brain stimulation lead technology have created new opportunities for multi-site network modulation, including applications for freezi...

EEG microstate dynamics and metal metabolism in Parkinson's disease dementia: an exploratory study with machine learning-assisted sensitivity analysis.

BACKGROUND: Electroencephalogram (EEG) microstates effectively characterise cognitive-related brain networks, and metal homeostasis is crucial for mai...

A machine learning approach for early Parkinson's disease diagnosis based on brain texture features from T1-weighted imaging.

OBJECTIVE: To investigate the diagnostic value of subcortical texture features from T1-weighted MRI combined with machine learning for early Parkinson...

Explainable Machine Learning for Parkinson's Disease Screening Using Spiral Drawing Test.

Parkinson's disease (PD) is a common neurological disorder that can severely affect the patient's quality of life. The Archimedean spiral drawing test...

Integrative multi-omics analysis reveals host-microbiome metabolic alterations and candidate biomarkers in Parkinson's disease.

Alterations in the gut microbiome have been increasingly implicated in Parkinson's disease (PD), but the associated metabolic changes remain incomplet...

Mapping heterogeneous predictors of problematic smartphone use in Chinese adolescents: A machine learning approach with a nationally representative sample.

Problematic smartphone use (PSU) has become a significant global public health issue among children and adolescents. While previous research has ident...

Comparing Stakeholders' Perspectives on Parkinson Disease Management and Digital Technologies: Exploratory International Survey.

BACKGROUND: Parkinson disease (PD) is a progressive neurodegenerative disorder that poses complex challenges for persons with PD, informal caregivers,...

Parkinson disease severity detection based On OPtFuzNet with fused features.

BACKGROUND: Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting millions of people worldwide. It severely impairs patients'...

Use of machine learning and voice for multiclass classification of Parkinson's disease, chronic obstructive pulmonary disease, and healthy controls.

Parkinson's disease (PD) and chronic obstructive pulmonary disease (COPD) are prevalent conditions with substantial impact on quality of life and heal...

Proto-memformer: deformable memory transformer for Parkinson's MRI classification.

This paper proposes a Prototype-Guided Deformable Memory Transformer (Proto-MemFormer) model for Parkinson's Disease (PD) MRI classification. In the e...

N2G calibrator: a cross-subject adversarial learning framework for neural signal-driven gait tracking in Parkinson's disease.

Adaptive deep brain stimulation (aDBS) has enabled machine learning models to track motor states from neural signals with improved accuracy, aiming to...

Aging beyond diagnosis: the MRI brain age gap across disorders.

The brain age gap (BAG), the difference between magnetic resonance imaging-predicted brain age and chronological age, is a proposed marker of neurobio...