Neurology

EEG-estimated functional connectivity, and not behavior, differentiates Parkinson's patients from health controls during the Simon conflict task

Neural biomarkers that can classify or predict disease are of broad interest to the neurological and psychiatric communities. Such biomarkers can be informative of disease state or treatment efficacy, even before there are changes in symptoms and/or behavior. This work investigates EEG-estimated functional connectivity (FC) as a Parkinson's Disease (PD) biomarker. Specifically, we investigate FC...

The Effect of Acute Stress on the Interpretability and Generalization of Schizophrenia Predictive Machine Learning Models

Introduction Schizophrenia is a severe mental disorder, and early diagnosis is key to improving outcomes. Its complexity makes predicting onset and progression challenging. EEG has emerged as a valuable tool for studying schizophrenia, with machine learning increasingly applied for diagnosis. This paper assesses the accuracy of ML models for predicting schizophrenia and examines the impact of st...

Seizure freedom after surgical resection of diffusion-weighted MRI abnormalities

Importance: Many individuals with drug-resistant epilepsy continue to have seizures after resective surgery. Accurate identification of focal brain ...

From Epilepsy Seizures Classification to Detection: A Deep Learning-based Approach for Raw EEG Signals

Epilepsy represents the most prevalent neurological disease in the world. One-third of people suffering from mesial temporal lobe epilepsy (MTLE) ex...

Brain-Aware Readout Layers in GNNs: Advancing Alzheimer's early Detection and Neuroimaging

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive memory and cognitive decline, affecting millions worldwide. Di...

GAMMA-PD: Graph-based Analysis of Multi-Modal Motor Impairment Assessments in Parkinson's Disease

The rapid advancement of medical technology has led to an exponential increase in multi-modal medical data, including imaging, genomics, and electro...

NECOMIMI: Neural-Cognitive Multimodal EEG-informed Image Generation with Diffusion Models

NECOMIMI (NEural-COgnitive MultImodal EEG-Informed Image Generation with Diffusion Models) introduces a novel framework for generating images direct...

Embolic Ischemic Cortical Stroke in a Young Flight Instructor with a Small Patent Foramen Ovale.

BACKGROUND: Stroke in young patients is frequently associated with a patent foramen ovale (PFO). Controversy exists over whether the PFO is a cause, a...

Cerebral microbleeds: Association with cognitive decline and pathology build-up

Cerebral microbleeds, markers of brain damage from vascular and amyloid pathologies, are linked to cognitive decline in aging, but their role in Alz...

Feature Estimation of Global Language Processing in EEG Using Attention Maps

Understanding the correlation between EEG features and cognitive tasks is crucial for elucidating brain function. Brain activity synchronizes during...

Latent Representation Learning for Multimodal Brain Activity Translation

Neuroscience employs diverse neuroimaging techniques, each offering distinct insights into brain activity, from electrophysiological recordings such...

A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications

In recent years, the field of electroencephalography (EEG) analysis has witnessed remarkable advancements, driven by the integration of machine lear...

NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human Connectomes

Although modern imaging technologies allow us to study connectivity between two distinct brain regions in-vivo, an in-depth understanding of how ana...

Towards Explainable Graph Neural Networks for Neurological Evaluation on EEG Signals

After an acute stroke, accurately estimating stroke severity is crucial for healthcare professionals to effectively manage patient's treatment. Grap...

Deep learning in template-free de novo biosynthetic pathway design of natural products.

Natural products (NPs) are indispensable in drug development, particularly in combating infections, cancer, and neurodegenerative diseases. However, t...

Predicting functional outcome in ischemic stroke patients using genetic, environmental, and clinical factors: a machine learning analysis of population-based prospective cohort study.

Ischemic stroke (IS) is a leading cause of adult disability that can severely compromise the quality of life for patients. Accurately predicting the I...

Differentially Private Multimodal Laplacian Dropout (DP-MLD) for EEG Representative Learning

Recently, multimodal electroencephalogram (EEG) learning has shown great promise in disease detection. At the same time, ensuring privacy in clinica...

Towards the Discovery of Down Syndrome Brain Biomarkers Using Generative Models

Brain imaging has allowed neuroscientists to analyze brain morphology in genetic and neurodevelopmental disorders, such as Down syndrome, pinpointin...

[An autoencoder model based on one-dimensional neural network for epileptic EEG anomaly detection].

OBJECTIVE: We propose an autoencoder model based on a one-dimensional convolutional neural network (1DCNN) as the feature extraction network for effic...

Contrasformer: A Brain Network Contrastive Transformer for Neurodegenerative Condition Identification

Understanding neurological disorder is a fundamental problem in neuroscience, which often requires the analysis of brain networks derived from funct...