Parkinson's Disease

A Comparative Evaluation of Structural MRI Foundation Models for Brain Age Regression and Sex Classification

Foundation models pre-trained on large neuroimaging datasets offer a promising approach to overcome the limited sample sizes typical of mental health imaging studies, yet their generalization across diverse clinical populations remains unclear. We present the first systematic benchmark of four publicly available structural MRI foundation models - AnatCL, BrainIAC, 3D-Neuro-SimCLR, and SwinBrain - ...

Automatic segmentation of choroid plexus using deep learning across neurodegenerative diagnoses in the multi-site COMPASS-ND Study

The choroid plexus (ChP) plays a central role in cerebrospinal fluid production, immune signaling, and metabolic clearance, and has emerged as a potential imaging biomarker of neurodegeneration. However, accurate and scalable quantification of ChP volume remains challenging due to its complex morphology and low contrast on conventional MRI. The Automatic Segmentation of Choroid Plexus (ASCHOPLEX),...

Comparison of Machine Learning Surrogate Models for Prediction of Single-Fiber Activation in Deep Brain Stimulation

Machine-learning surrogate models are positioned to help optimize deep brain stimulation (DBS) usage by predicting neural activation in response to el...

Smartphone Placement Recognition during Walking: Performance Determinants and Real-World Generalizability

The opportunity to collect movement data from smartphones for prolonged periods has opened new perspectives in the field of clinical movement analysis...

Plasmatic immune extracellular vesicle profiles identify prodromal and early stages of Parkinson's disease

Extracellular vesicles (EVs) hold promise as minimally invasive biomarkers for neurodegenerative proteinopathies, but disease- and stage-specific prof...

AI-based discovery of functional boundaries in the human brain from intraoperative electrophysiology

Neurosurgical and neuromodulation therapies such as deep brain stimulation (DBS) require millimeter-level accuracy to effectively target functional br...

A biologically annotated neural network for proteomic discovery in Parkinsons disease

Machine learning models that can utilize high-dimensional data to make predictions and derive biological insights can improve understanding of disease...

Artificial Intelligence, LLM-based generation of synthetic patients with Parkinson's Disease: towards a digital twin paradigm for in silico studies

Heterogeneity in sporadic Parkinson's Disease (PD) is a critical problem that drives variable rates of progression and treatment response and complica...

MTCurv: Deep learning for direct microtubule curvature mapping in noisy fluorescence microscopy images

Accurate quantification of the geometry of curvilinear biological structures is essential for understanding cellular mechanics and disease-related mor...

SEAL: Semantic-aware Single-image Sticker Personalization with a Large-scale Sticker-tag Dataset

Synthesizing a target concept from a single reference image is challenging in diffusion-based personalized text-to-image generation, particularly for ...

Parkinson's Disease Detection via Self-Supervised Dual-Channel Cross-Attention on Bilateral Wrist-Worn IMU Signals

Parkinson's disease (PD) is a chronic neurodegenerative disease. It shows multiple motor symptoms such as tremor, bradykinesia, postural instability, ...

STEP-PD: Stage-Aware and Explainable Parkinson's Disease Severity Classification Using Multimodal Clinical Assessments

Parkinson's disease (PD) is a progressive disorder in which symptom burden and functional impairment evolve over time, making severity staging essenti...

Training-Free Cross-Lingual Dysarthria Severity Assessment via Phonological Subspace Analysis in Self-Supervised Speech Representations

Dysarthric speech severity assessment typically requires either trained clinicians or supervised machine learning models built from labelled pathologi...

Engaging patient communities in intracranial neuroscience research

Patients who participate in intracranial neuroscience research make invaluable contributions to our understanding of the brain, accelerating the devel...

Chain of Modality: From Static Fusion to Dynamic Orchestration in Omni-MLLMs

Omni-modal Large Language Models (Omni-MLLMs) promise a unified integration of diverse sensory streams. However, recent evaluations reveal a critical ...

Explainable machine learning identifies candidate shared neuroanatomical features in Alzheimer's and Parkinson's via importance inversion transfer

Despite significant neurobiological and pathological overlaps, Alzheimer's (AD) and Parkinson's (PD)-the primary threats to healthy aging-are still ma...

UDAPose: Unsupervised Domain Adaptation for Low-Light Human Pose Estimation

Low-visibility scenarios, such as low-light conditions, pose significant challenges to human pose estimation due to the scarcity of annotated low-ligh...

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 stimulation (aDBS) for Parkinson's disease (PD). In a t...

Adaptive Learned Image Compression with Graph Neural Networks

Efficient image compression relies on modeling both local and global redundancy. Most state-of-the-art (SOTA) learned image compression (LIC) methods ...

Vision-based Deep Learning Analysis of Unordered Biomedical Tabular Datasets via Optimal Spatial Cartography

Tabular data are central to biomedical research, from liquid biopsy and bulk and single-cell transcriptomics to electronic health records and phenotyp...