Parkinson's Disease

Robust Neural Stimulation Response Modeling Through Meta-Learning and Pretraining

Objective: Model-based closed-loop neural stimulation holds promise for therapeutic applications ranging from Parkinson's disease to sensory restoration, but deployment has been limited by two obstacles: 1) forecasting models for predicting the consequences of stimulation fail catastrophically on a meaningful fraction of sessions, and 2) per-session calibration requirements are often incompatible ...

A Hierarchical Synergistic Deep Learning Framework Integrating Composition, Structure, and Ionic Transport for Solid-State Electrolyte Discovery

Inorganic solid-state electrolytes must combine high room-temperature ionic conductivity, a wide electrochemical window, excellent electronic insulation, and favorable mechanical compliance. Single models struggle to support reliable multi-objective screening across vast chemical spaces because of training-data distribution mismatch, cross-property dataset heterogeneity, and scarce kinetic transpo...

Cell type-resolved chromatin accessibility clocks for brain aging

Aging is a progressive decline in biological function that is proposed to be driven by the accumulation of epigenetic noise and the loss of epigenetic...

Aggregate, Don't Adapt: Subject-Level Posterior Aggregation and Transductive Calibration for Cross-Site Parkinsonian Gait Severity

We describe the winning entry to the MoCha 2026 Benchmark and Challenge on Parkinsonian Gait, which predicts MDS-UPDRS gait severity from canonicalize...

Discovery of Selective Small-Molecule Ligands of SV2C by AI-Enhanced Virtual Screening and Experimental Validation

Synaptic vesicle glycoprotein 2C (SV2C) is a vesicular protein enriched in dopaminergic neurons of the basal ganglia that modulates dopamine storage a...

Data-driven techniques for translational neuroscience and personalized neuro-health

Neurodegenexrative diseases such as Alzheimer's disease and Parkinson's disease are diagnosed most reliably only after substantial, often irreversible...

Label-Free Parkinson's Disease Screening from Face and Voice through Mechanistic Interpretability

Parkinson's disease (PD) is the second most common neurodegenerative disorder. Typical machine learning screening methods require PD labels, but the a...

Is Self-Pretraining really useful to improve diagnosis in medical Time Series?

Inspired by recent evidence that transformer architectures benefit from Self-PreTraining (SPT) on long-context benchmarks, we investigate whether simi...

NeuroAid An Open-Data Multimodal Screening Framework for Parkinson's and Depression Risk Estimation

Neurological and mental-health conditions such as Parkinson's disease (PD) and major depressive disorder (MDD) impose a substantial and growing global...

REDDI: A Riemannian Ensemble Learning Framework for Interpretable Differential Diagnosis of Neurodegenerative Diseases

Neurodegenerative diseases such as Mild Cognitive Impairment (MCI), Multiple Sclerosis (MS), Parkinson s Disease (PD), and Amyotrophic Lateral Scleros...

HAFI-VLM: A Frequency Perspective for Diagnosing and Enhancing Visual Perception in Vision-Language Models

Vision-language models (VLMs) remain unreliable when predictions require fine-grained visual evidence. We identify a previously overlooked cause: spec...

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...

Struct-GStream: Towards Efficient Free-Viewpoint Video Streaming at Low-Bitrates with Structured 3D Gaussians

Constructing photorealistic Free-Viewpoint Videos (FVVs) of dynamic scenes from a set of posed 2D images has been an intriguing yet challenging task i...

Beyond Facial Consistency: Personalized Person Image Generation with Holistic Identity Preservation

Personalized person image generation requires preserving subject identity across both local facial details and broader appearance cues. Existing metho...

Diagnosing Rejection Collapse via Uncertainty Decomposition

Standard uncertainty-informed rejection can unexpectedly trigger severe performance collapse, exposing localized vulnerabilities that common machine l...

Interpretable gene networks from single-cell foundation models reveal conserved neurogenic dysfunction in Parkinson's disease

Interpreting large-scale singlecell transcriptomic data remains a major challenge for understanding disease mechanisms. Recent single-cell foundation ...

An Explainable AI-Driven Classification Framework for Parkinson's Disease Detection via Acoustic Speech Features: A Comparative Machine Learning Study

Parkinsons Disease (PD) is a progressive neurodegenerative disorder which significantly affects motor function, daily coordination and verbal communic...

Machine learning-based neuroimaging for prediction of deep brain stimulation outcomes in movement disorders: Systematic review and meta-analysis

Abstract Background: Deep Brain Stimulation (DBS) surgery is a treatment of choice for movement disorders, and utilizes an implanted electrical pulse ...

A Preoperative Electroencephalography Signature for Predicting Treatment Response to Deep Brain Stimulation in Obsessive-Compulsive Disorder

Deep brain stimulation (DBS) is effective for treatment-refractory obsessive-compulsive disorder (OCD), but outcomes are heterogeneous and non-respond...

Bifurcation Structure and Cross Nuclei Universality Govern Frequency-Selective Deep Brain Stimulation

High-frequency deep brain stimulation (DBS, >90 Hz) reliably suppresses Parkinsonian motor symptoms, whereas sub-therapeutic frequencies (<60 Hz) wors...