Neurology

Interpretable EEG biomarkers for neurological disease models in mice using bag-of-waves classifiers

Electroencephalograms (EEGs) are time-series records of the electrical potential from collective neural activity in the brain. EEG waveform patterns—rhythmic and irregular oscillations and transient patterns of sharp waves or spikes—are potential phenotypical biomarkers, reflecting genotype-specific neural activity. This is especially relevant to diagnosing epilepsy without direct seizure observat...

Trustworthy Sleep Staging from EEG: Deep Ensembles, MC Dropout, and Predictive Calibration

Reliable sleep stage classification from EEG signals is critical for the development of clinical decision support systems. However, many deep learning models lack mechanisms for estimating predictive uncertainty, which is important for trust and interpretability. This work explores the use of Monte Carlo Dropout and Deep Ensembles to estimate uncertainty in automatic sleep staging. We apply these ...

Peripheral immune patterns enable robust cross-platform prediction of ALS onset and progression

Amyotrophic lateral sclerosis (ALS) progression rates vary dramatically between patients, yet the basis of this heterogeneity remains elusive, with no...

Subtype-Specific Roles of Nigrostriatal Dopaminergic Neurons in Motor and Associative Learning

Nigrostriatal dopaminergic neurons (DANs) in the substantia nigra pars compacta (SNc) comprise distinct subtypes defined by unique gene expression pro...

Imagined Speech Reconstruction with 3D Neural Metabolism and Large Language Model Integration

Cognitive linguistics posits that language underpins human thought, and this principle has influenced the study and development of large language mode...

ROSMAP-Compass: a data-harmonised, AI-ready atlas of 22 million single nuclei from the ROSMAP cohort

The Religious Orders Study and Memory and Aging Project (ROSMAP) cohort has generated the world’s most comprehensive single-cell transcriptomic resour...

An Explainable Web-Based Diagnostic System for Alzheimer’s Disease Using XRAI and Deep Learning on Brain MRI

Background Alzheimer’s disease (AD) is a progressive neurodegenerative condition marked by cognitive decline and memory loss. Despite advancements in ...

Hand Drawing Image based Causal Representation Learning for Robust Parkinson’s Disease Feature Extraction and Detection

Being an irreversible disorder regarding the human motor-system, Parkinson’s Disease(PD) has been a threat to many neurological patients, especially d...

Dynamic Graphs Analysis of EEG

In this study, we investigate the use of temporal dynamics in brain connectivity for the classification of electroencephalography (EEG) signals using ...

NiCLIP: Neuroimaging contrastive language-image pretraining model for predicting text from brain activation images

Predicting cognitive processes from brain activation maps has remained an open question within the neuroscience community for many years. Meta-analyti...

Human CYP2C9 metabolism of organophosphorus pesticides and nerve agent surrogates

Of the Cytochrome P450 enzymes, the CYP2C9 variant is very important and is the cytochrome P450 (CYP) involved in the metabolism of several human drug...

Mapping the Cerebello-Hippocampal Circuit: Normative Patterns and Sex-Dependent Connectivity

Cerebello-hippocampal (CB-HP) interactions have been implicated in spatial abilities and reinforcement learning, yet their relationship to behavior an...

In Silico Design of APOE ɛ4 Interaction Inhibitor Peptides for Alzheimer’s Disease

Protein-protein interactions (PPIs) are essential for cellular functions, and their aberrant formation contributes to neurodegenerative diseases. Alzh...

Personalized real-time inference of momentary excitability from human EEG

The efficacy of transcranial magnetic stimulation (TMS) is often limited by non-adaptive protocols that disregard instantaneous brain states, potentia...

The Use of Artificial Intelligence In Magnetic Resonance Imaging of Epilepsy: A Systematic Review and Meta-Analysis

The application of artificial intelligence (AI)/machine learning (ML) to MRI can be a powerful tool to streamline clinical decision-making, yet variab...

Design and Implementation of a Decision Making System for Controlling a Hand Exoskeleton Based on EEG/EMG Signals

This paper presents an approach of combining Electroencephalography (EEG) and Electromyography (EMG) signals to create a hybrid Brain Interface Comput...

Alzheimer’s subtypes A supervised, unsupervised, multimodal, multilayered embedded recursive (SUMMER) AI study

Since Alzheimer’s disease (AD) is a heterogeneous disease, different subtypes may have distinct biological, genetic, and clinical characteristics, req...

Virtual Brain Inference (VBI): A flexible and integrative toolkit for efficient probabilistic inference on virtual brain models

Network neuroscience has proven essential for understanding the principles and mechanisms underlying complex brain (dys)function and cognition. In thi...

Graph-Based Modeling of Alzheimer’s Protein Interactions via Spiking Neural, Hyperdimensional Encoding, and Scalable Ray-Based Learning

This study introduces a novel computational framework for predicting protein-protein interactions (PPIs) in Alzheimer’s disease by integrating biologi...

An Open-Source Deep Learning-Based Toolbox for Automated Auditory Brainstem Response Analyses (ABRA)

Hearing loss is a pervasive global health challenge with profound impacts on communication, cognitive function, and quality of life. Recent studies ha...