Parkinson's Disease

Enhancing Asleep Deep Brain Stimulation Targeting for Essential Tremor Using Machine Learning: The OPTIVIM Phase 2 Study.

BACKGROUND AND OBJECTIVES: Deep brain stimulation (DBS) is an effective treatment of essential tremor, but the optimal target and how to reach it with the best accuracy remain controversial. The OptimDBS algorithm is based on Kernel Ridge Regression and was trained on a database of patients who underwent DBS with optimal postoperative outcomes. Using 3-dimensional T1-weighted MRI as the only input...

Gait analysis and prediction in Parkinson's disease using rhythmic auditory stimulation: A data-driven approach with machine learning models.

BACKGROUND: Gait disturbance is a core feature of Parkinson's disease (PD), contributing to mobility loss and fall risk. Rhythmic auditory stimulation (RAS) can improve gait, yet response varies across patients. This study evaluated RAS effects on gait and developed a model to predict responders. METHODS: Three hundred PD patients were enrolled and assigned to RAS or control groups. Gait data were...

A Causal Validation augmented Temporal Convolutional Framework for Brain Effective Connectivity Networks Estimation.

Advancements in neuroimaging have facilitated unprecedented insights into brain connectivity, making the study of brain effective connectivity network...

Examination of simple artificial intelligence-based analysis of dopamine transporter scintigraphy for supporting a diagnosis of Parkinson's disease.

INTRODUCTION: In the Movement Disorder Society criteria for the diagnosis of Parkinson's disease (PD), evaluation of the presynaptic dopamine system s...

Utility of Baseline Pathological, Neuroimaging and Clinical Markers for Prognosis in Early Parkinson's Disease.

BackgroundCurrently, prognosis of Parkinson's Disease (PD) is limited. Emerging literature highlights potential of multi-modal biomarkers and neuroima...

Deep machine learning methods for Parkinson's disease diagnosis: a new direction in decision-making systems.

Parkinson's disease (PD) is a progressive neurodegenerative disorder primarily characterized by the gradual loss of dopamine-producing neurons in the ...

Closed-Loop Control of Epilepsy Based on Reinforcement Learning.

This study proposes a novel adaptive DBS control strategy for epilepsy treatment based on deep reinforcement learning. By establishing a random distur...

Artificial intelligence in paediatric respiratory medicine.

Proponents of artificial intelligence (AI) believe that it will revolutionise the modern world, affecting how healthcare is delivered and improve both...

Structure and function of voltage-gated sodium channel Nav1.6: Involvement in the pathological process of neural injury.

The voltage-gated sodium channel Nav1.6, encoded by the sodium voltage-gated channel alpha subunit 8 gene, is a crucial regulator of neuronal excitabi...

Technical Feasibility of Quantitative Susceptibility Mapping Radiomics for Predicting Deep Brain Stimulation Outcomes in Parkinson Disease.

BACKGROUND AND OBJECTIVES: Parkinson disease (PD) patients with motor complications are often considered for deep brain stimulation (DBS) surgery. Pre...

Enhancing interpretability of AI with radiomics-based deep neural network: proof of concept in the classification of Parkinsonian syndromes with 18F-FDG PET imaging.

OBJECTIVE: Interpretability and reproducibility remain major challenges in applying deep neural network (DNN) to neuroimaging-based diagnosis. This st...

From resting-state to movement: The role of cortical networks in parkinsonian gait.

BACKGROUND: Gait impairments in Parkinson's disease (PD) arise from disruptions in automatic motor control, requiring compensatory engagement of corti...

Machine Learning-Driven Radiomic Profiling of Thalamus-Amygdala Nuclei for Prediction of Postoperative Delirium After STN-DBS in Parkinson's Disease Patients: A Pilot Study.

Postoperative delirium is a common complication following sub-thalamic nucleus deep brain stimulation surgery in Parkinson's disease patients. Postope...

Predicting Cognition and Affective Changes in Newly Diagnosed Parkinson's Disease Through Longitudinal Data-Driven Clustering.

Background: Although primarily characterised as a motor disorder, Parkinson's Disease (PD) also presents with non-motor symptoms, including cognitive ...

Tuning antibody stability and function by rational designs of framework mutations.

Artificial intelligence and machine learning models have been developed to engineer antibodies for specific recognition of antigens. These approaches,...

The role of neuro-imaging in multiple system atrophy.

Neuroimaging plays a crucial role in diagnosing multiple system atrophy and monitoring progressive neurodegeneration in this fatal disease. Advanced M...

Characteristics and Validity of Commercially Available Technologies Analyzing Voice Features to Assess Parkinson's Disease.

BACKGROUND: Interest in technologies for quantitative assessment of Parkinson's disease (PD) is growing, particularly those enabling voice and speech ...

An Efficient FoG-M3 Method for Self-Adaptive Labeling and Predicting Freezing of Gait.

Freezing of gait (FoG) is a common motor impairment that occurs as Parkinson's disease patients enter the moderate stage or higher, leading to a high ...

Brain region localization: a rapid Parkinson's disease detection method based on EEG signals.

Parkinson's disease (PD) is a prevalent neurodegenerative disorder worldwide, often progressing to mild cognitive impairment (MCI) and dementia. Clini...

Recognition of Parkinson disease using Kriging Empirical Mode Decomposition via deep learning techniques.

UNLABELLED: Parkinson's disorder (PD) is a chronic, irreversible neurological disorder that is hard to identify and manage.