Neurology

From Articles to Canopies: Knowledge-Driven Pseudo-Labelling for Tree Species Classification using LLM Experts

Hyperspectral tree species classification is challenging due to limited and imbalanced class labels, spectral mixing (overlapping light signatures from multiple species), and ecological heterogeneity (variability among ecological systems). Addressing these challenges requires methods that integrate biological and structural characteristics of vegetation, such as canopy architecture and interspecif...

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 pathological speech data, limiting scalability across languages and clinical settings. We present a training-free method (no supervised severity model is trained; feature directions are estimated from healthy control speech using a pretrained forced aligner) ...

Proteomic profiling of CSF reveals stage-specific changes in Amyotrophic lateral sclerosis patients

Amyotrophic lateral sclerosis (ALS) is a rapidly progressing neurodegenerative disease with a heterogeneous clinical presentation, complicating early ...

Predicting Post-Traumatic Epilepsy from Clinical Records using Large Language Model Embeddings

Objective: Post-traumatic epilepsy (PTE) is a debilitating neurological disorder that develops after traumatic brain injury (TBI). Early prediction of...

Improved Multiscale Structural Mapping with Supervertex Vision Transformer for the Detection of Alzheimer's Disease Neurodegeneration

Alzheimer's disease (AD) confirmation often relies on positron emission tomography (PET) or cerebrospinal fluid (CSF) analysis, which are costly and i...

Neural mechanism of postural sway-related beta-band oscillations: a cortico-basal ganglia-thalamic network model of intermittent control

Recent EEG studies of human quiet stance have identified beta-band event-related desynchronization (beta-ERD) and synchronization (beta-ERS; post-move...

A 3D SAM-Based Progressive Prompting Framework for Multi-Task Segmentation of Radiotherapy-induced Normal Tissue Injuries in Limited-Data Settings

Radiotherapy-induced normal tissue injury is a clinically important complication, and accurate segmentation of injury regions from medical images coul...

ADP-DiT: Text-Guided Diffusion Transformer for Brain Image Generation in Alzheimer's Disease Progression

Alzheimer's disease (AD) progresses heterogeneously across individuals, motivating subject-specific synthesis of follow-up magnetic resonance imaging ...

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...

EEG-Based Multimodal Learning via Hyperbolic Mixture-of-Curvature Experts

Electroencephalography (EEG)-based multimodal learning integrates brain signals with complementary modalities to improve mental state assessment, prov...

Generative Anonymization in Event Streams

Neuromorphic vision sensors offer low latency and high dynamic range, but their deployment in public spaces raises severe data protection concerns. Re...

Classification of Epileptic iEEG using Topological Machine Learning

Epileptic seizure detection from EEG signals remains challenging due to the high dimensionality and nonlinear, potentially stochastic, dynamics of neu...

Multidomain Analysis of Clinical Cognitive Assessments and Imaging Data in Alzheimer's Disease Accurately Predicts Disease Stage and Grade Independent of Amyloid and Tau

Background Individual clinical cognitive assessments (CCA) for Alzheimer's disease (AD) provide broad disease stratification but are limited in sensit...

Identification and Analysis of Novel RNA Editing Sites in Neurodegenerative Diseases Using Machine Learning Approaches.

ABSTRACT Background: RNA editing is a post-transcriptional modification that alters the sequence of an RNA transcript. Two types of RNA editing were f...

Precision Synthesis of Multi-Tracer PET via VLM-Modulated Rectified Flow for Stratifying Mild Cognitive Impairment

The biological definition of Alzheimer's disease (AD) relies on multi-modal neuroimaging, yet the clinical utility of positron emission tomography (PE...

Sense Less, Infer More: Agentic Multimodal Transformers for Edge Medical Intelligence

Edge-based multimodal medical monitoring requires models that balance diagnostic accuracy with severe energy constraints. Continuous acquisition of EC...

EEG2Vision: A Multimodal EEG-Based Framework for 2D Visual Reconstruction in Cognitive Neuroscience

Reconstructing visual stimuli from non-invasive electroencephalography (EEG) remains challenging due to its low spatial resolution and high noise, par...

Brain3D: EEG-to-3D Decoding of Visual Representations via Multimodal Reasoning

Decoding visual information from electroencephalography (EEG) has recently achieved promising results, primarily focusing on reconstructing two-dimens...

Predicting Alzheimer's disease progression using rs-fMRI and a history-aware graph neural network

Alzheimer's disease (AD) is a neurodegenerative disorder that affects more than seven million people in the United States alone. AD currently has no c...

Reduced spread of nodes in spatial network models improves topology associated with increased computational capabilities

Biological neural networks are characterized by short average path lengths, high clustering, and modular and hierarchical architectures. These complex...