Neurology

Parkinson's Disease

Latest AI and machine learning research in parkinson's disease for healthcare professionals.

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Invasive and Non-Invasive Neural Decoding of Motor Performance in Parkinson's Disease for Personalized Deep Brain Stimulation

Decoding motor performance from brain signals offers promising avenues for adaptive deep brain stimu...

Adaptive Learned Image Compression with Graph Neural Networks

Efficient image compression relies on modeling both local and global redundancy. Most state-of-the-a...

When AI Shows Its Work, Is It Actually Working? Step-Level Evaluation Reveals Frontier Language Models Frequently Bypass Their Own Reasoning

Language models increasingly "show their work" by writing step-by-step reasoning before answering. B...

ChArtist: Generating Pictorial Charts with Unified Spatial and Subject Control

A pictorial chart is an effective medium for visual storytelling, seamlessly integrating visual elem...

Characterizing EEG Spectro-Temporal Variability Signatures in Alzheimer's and Parkinson's Disease

We present an EEG-based approach to characterize disease-related spectro-temporal signatures in Alzh...

Gait-Related Digital Mobility Outcomes in Parkinson's Disease: New Insights into Convergent Validity?

Objective: In Parkinson's disease (PD), gait-related digital mobility outcomes (DMOs) show promise f...

Topological descriptors of foot clearance gait dynamics improve differential diagnosis of Parkinsonism

Differential diagnosis among parkinsonian syndromes remains a clinical challenge due to overlapping ...

Massive-scale single-nucleus multi-omics identifies novel rare noncoding drivers of Parkinson's disease

Most genetic variants contributing to complex diseases reside in the noncoding genome. While common ...

PreSight: Preoperative Outcome Prediction for Parkinson's Disease via Region-Prior Morphometry and Patient-Specific Weighting

Preoperative improvement rate prediction for Parkinson's disease surgery is clinically important yet...

Parkinson's Disease motor and non-motor progression models emerge from pathway-level transcriptomics

Background Prognosis and therapeutic management in Parkinson's disease is a challenging task by its ...

Fair feature attribution for multi-output prediction: a Shapley-based perspective

In this article, we provide an axiomatic characterization of feature attribution for multi-output pr...

Focused ultrasound neuromodulation of mediodorsal thalamus disrupts decision flexibility during reward learning

When learning to find the most beneficial course of action, the prefrontal cortex guides decisions b...

Reproducible symptom subtypes of depression identified using unsupervised machine learning

Depression is a heterogeneous disorder, often diagnosed based on symptom co-occurrence. However, ind...

Development and validation of neurological health score using machine learning algorithms

Neurological health score (NHS), indicating the health of brain and nervous system, helps in identif...

α-Synuclein Strain Dynamics Correlate with Cognitive Shifts in Parkinson's Disease

-Synuclein (-syn) strains can serve as discriminators between Parkinson's disease (PD) and related -...

Attention-Based Deep Learning for Early Parkinson's Disease Detection with Tabular Biomedical Data

Early and accurate detection of Parkinson's disease (PD) remains a critical challenge in medical dia...

A Hybrid CNN and ML Framework for Multi-modal Classification of Movement Disorders Using MRI and Brain Structural Features

Atypical Parkinsonian Disorders (APD), also known as Parkinson-plus syndrome, are a group of neurode...

Uncertainty-aware personalized estimation of Parkinsons disease severity from longitudinal speech

Parkinsons disease is a progressive neurological disorder characterized by motor impairments whose s...

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