Neurology

Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis

Accurate alignment of dysfluent speech with intended text is crucial for automating the diagnosis of neurodegenerative speech disorders. Traditional methods often fail to model phoneme similarities effectively, limiting their performance. In this work, we propose Neural LCS, a novel approach for dysfluent text-text and speech-text alignment. Neural LCS addresses key challenges, including partial...

Mitigating Confounding in Speech-Based Dementia Detection through Weight Masking

Deep transformer models have been used to detect linguistic anomalies in patient transcripts for early Alzheimer's disease (AD) screening. While pre-trained neural language models (LMs) fine-tuned on AD transcripts perform well, little research has explored the effects of the gender of the speakers represented by these transcripts. This work addresses gender confounding in dementia detection and...

Neuronal avalanches as a predictive biomarker of BCI performance: towards a tool to guide tailored training program

Brain-Computer Interfaces (BCIs) based on motor imagery (MI) hold promise for restoring control in individuals with motor impairments. However, up t...

The Latent Space Hypothesis: Toward Universal Medical Representation Learning

Medical data range from genomic sequences and retinal photographs to structured laboratory results and unstructured clinical narratives. Although th...

From Spikes to Speech: NeuroVoc -- A Biologically Plausible Vocoder Framework for Auditory Perception and Cochlear Implant Simulation

We present NeuroVoc, a flexible model-agnostic vocoder framework that reconstructs acoustic waveforms from simulated neural activity patterns using ...

Identifying Alzheimer's Disease Prediction Strategies of Convolutional Neural Network Classifiers using R2* Maps and Spectral Clustering

Deep learning models have shown strong performance in classifying Alzheimer's disease (AD) from R2* maps, but their decision-making remains opaque, ...

Delta-KNN: Improving Demonstration Selection in In-Context Learning for Alzheimer's Disease Detection

Alzheimer's Disease (AD) is a progressive neurodegenerative disorder that leads to dementia, and early intervention can greatly benefit from analyzi...

Adverse Outcome Pathway and Machine Learning to Predict Drug Induced Seizure Liability.

Central nervous system (CNS) drugs have the highest clinical attrition, often due to CNS-related toxicities such as drug-induced seizures (DIS). Early...

petBrain: A New Pipeline for Amyloid, Tau Tangles and Neurodegeneration Quantification Using PET and MRI

INTRODUCTION: Quantification of amyloid plaques (A), neurofibrillary tangles (T2), and neurodegeneration (N) using PET and MRI is critical for Alzhe...

A Pre-trained Framework for Multilingual Brain Decoding Using Non-invasive Recordings

Brain-computer interfaces (BCIs) with speech decoding from brain recordings have broad application potential in fields such as clinical rehabilitati...

Automated Measurement of Optic Nerve Sheath Diameter Using Ocular Ultrasound Video

Objective. Elevated intracranial pressure (ICP) is recognized as a biomarker of secondary brain injury, with a significant linear correlation observ...

HRTR: A Single-stage Transformer for Fine-grained Sub-second Action Segmentation in Stroke Rehabilitation

Stroke rehabilitation often demands precise tracking of patient movements to monitor progress, with complexities of rehabilitation exercises present...

Joint Modeling for Learning Decision-Making Dynamics in Behavioral Experiments

Major depressive disorder (MDD), a leading cause of disability and mortality, is associated with reward-processing abnormalities and concentration i...

Deep learning reveals pathology-confirmed neuroimaging signatures in Alzheimer's, vascular and Lewy body dementias.

Concurrent neurodegenerative and vascular pathologies pose a diagnostic challenge in the clinical setting, with histopathology remaining the definitiv...

Redefining diagnostic lesional status in temporal lobe epilepsy with artificial intelligence.

Despite decades of advancements in diagnostic MRI, 30%-50% of temporal lobe epilepsy (TLE) patients remain categorized as 'non-lesional' (i.e. MRI neg...

Large Language Models for EEG: A Comprehensive Survey and Taxonomy

The growing convergence between Large Language Models (LLMs) and electroencephalography (EEG) research is enabling new directions in neural decoding...

Predicting Postoperative Stroke in Elderly SICU Patients: An Interpretable Machine Learning Model Using MIMIC Data

Postoperative stroke remains a critical complication in elderly surgical intensive care unit (SICU) patients, contributing to prolonged hospitalizat...

Evaluating the Effectiveness of Pre-Trained Audio Embeddings for Classification of Parkinson's Disease Speech Data

Speech impairments are prevalent biomarkers for Parkinson's Disease (PD), motivating the development of diagnostic techniques using speech data for ...

A meaningful prediction of functional decline in amyotrophic lateral sclerosis based on multi-event survival analysis

Amyotrophic lateral sclerosis (ALS) is a degenerative disorder of motor neurons that causes progressive paralysis in patients. Current treatment opt...

CogniAlign: Word-Level Multimodal Speech Alignment with Gated Cross-Attention for Alzheimer's Detection

Early detection of cognitive disorders such as Alzheimer's disease is critical for enabling timely clinical intervention and improving patient outco...