Neurology

DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features

The detection of Alzheimers disease (AD) is considered crucial, as timely intervention can improve patient outcomes. Electroencephalogram (EEG)-based diagnosis has been recognized as a non-invasive, accessible, and cost-effective approach for AD detection; however, it faces challenges related to data availability, accuracy of modern deep learning methods, and the time-consuming nature of expert-ba...

Simulating the spectrum, not the syndrome: Large scale individualized modeling of oral reading in stroke aphasia

Computational models are a linchpin in our understanding of the neurocognitive basis of reading. These models can simulate idealized profiles of alexia syndromes, but in reality, individuals with alexia present with a wide range of mixed deficits rather than idealized syndromes. To provide a complete cognitive theory of reading, computational models must be able to account for this individual vari...

TRACE: Temporal Routing with Autoregressive Cross-channel Experts for EEG Representation Learning

Learning transferable representations for electroencephalography (EEG) remains challenging because EEG signals are inherently multi-channel and non-st...

When Brains Disagree: Biological Ambiguity Underlies the Challenge of Amyloid PET Synthesis from Structural MRI

Structural MRI-to-amyloid PET synthesis has been proposed as a non-invasive alternative for amyloid assessment in Alzheimer's disease (AD). However, r...

Optimization in Sparse 2D to Dense 3D Weakly Supervised Learning: Application to Multi-Label Segmentation of Large ex vivo MRI Data

INTRODUCTION | Fully supervised 3D segmentation of high-resolution ex vivo MRI is limited by the prohibitive cost of volumetric annotation, forcing re...

Digital Twins as Synthetic Controls in Single-Arm Trials

Single-arm trials are an important study design for evaluating drug efficacy and safety without enrolling patients into a control arm. Although they d...

Virtual screening and zebrafish phenotype-based evaluation argues against repurposing 4-phenylbutyrate for STXBP1-relateddisorders

Syntaxin-binding protein 1 (STXBP1) mutations lead to severe epilepsy, intellectual disability, developmental delay, and movement disorder. Effective ...

Structural brain networks shape individual-level progression of brain atrophy after stroke

Stroke starts as a focal vascular lesion, but its structural consequences often extend beyond the lesion site, resulting in distributed brain atrophy ...

Human intracranial correlates of dynamic coding in auditory working memory

Working memory (WM), the human brain's system for maintaining and manipulating information over short timescales, is critical for goal-directed behavi...

CFSPMNet: Cross-subject Fourier-guided Spatial-Patch Mamba Network for EEG Motor Imagery Decoding in Stroke Patients

Motor imagery electroencephalography (MI-EEG) decoding offers a non-invasive route for post-stroke rehabilitation, but cross-patient use remains diffi...

DANCE: Detect and Classify Events in EEG

Event identification in continuous neural recordings is a critical task in neuroscience. Decoding in EEG is dominated by classifying windows aligned t...

Toward Early Diagnosis and Therapeutic Discovery in CLN3 Disease: A Computational Biomarker Discovery Framework

Background CLN3 disease, also known as juvenile neuronal ceroid lipofuscinosis, is a rare and neurodegenerative disorder characterized by the accumula...

Generating synthetic tau-PET scans in Alzheimer's disease from MRI, blood biomarkers and demographics with deep learning

Tau protein aggregation in the brain is a hallmark of Alzheimer's disease (AD). Positron emission tomography (PET) is the only in vivo method to visua...

Multi-modal Ensemble Approach for Decoding Player Intentions in Table Tennis

This study aims to predict human intentions during intense sports activities, specifically in table tennis. Using a publicly available Real World Tabl...

Classification of Smartphone Interaction Using Multimodal Physiological Signals with a Brain-Body Spatio-Temporal Transformer

Distinct smartphone interaction behaviors, like short-form video scrolling and mobile gaming, elicit qualitatively different cognitive and physiologic...

Gene-Modulated Network Diffusion for Improved Modeling of Amyloid-β Spread in Alzheimer's Disease

Understanding the pathogenesis of amyloid-{beta} pathology in Alzheimer's Disease (AD) proves to be a challenge. In this work, we expand upon the appl...

How Much Does the Reduced EEG Montage Matter for Seizure Detection?: A Large-Cohort Simulation Study

Importance: Implantable sub-scalp EEG systems with a small number of channels have emerged as promising solutions for long-term seizure monitoring in ...

From Power Spectral Density to Wavelets: Improving Symbolic Representations of Electroencephalography Band Dynamics in the Weed Plot Framework

Electroencephalography (EEG) interpretation in clinical practice relies on the analysis of energy distribution across standard frequency bands. The We...

Head and Body Pose Classification for Understanding Sleep Behaviour in People Living with Dementia using Video and a Novel Multi-Head Attention-Driven Deep Learning Architecture

Sleep posture is known to be relevant to various sleep disorders, such as sleep apnea, but it is not often quantified in sleep monitoring systems. We ...

Hierarchical integration of multimodal clinical data to predict epilepsy surgery outcome

Background: Integrating multimodal data into medical artificial intelligence (AI) tools and evaluating whether they outperform human experts remains a...