Neurology

Next-generation computational strategies for neurodegenerative biomarkers: Multi-omics integration, AI, and molecular modeling.

Neurodegenerative diseases (NDs) are progressively debilitating conditions driven by complex molecular perturbations and selective neuronal loss. Conventional approaches to discovering biomarkers, using single-omics or empirical screening, often fail to capture the multi-factorial nature of these disorders. It is now possible to integrate large-scale omics data with structural and molecular modeli...

No Normal Brain: How Demographic Exclusion Undermines Neuroimaging AI Validity.

BACKGROUND AND OBJECTIVE: Neuroimaging AI systems increasingly influence clinical decisions, yet demographic exclusions in training datasets may compromise their scientific validity and clinical safety. This analysis examines how demographic concentration in major neuroimaging repositories undermines the fundamental neurobiological principle that there is no single "normal" brain, potentially crea...

Differentiating bipolar disorder and schizophrenia using sleep EEG power and coherence features: A machine learning approach based on polysomnography.

Differentiating between bipolar disorder (BD) and schizophrenia (SZ) is challenging due to overlapping clinical symptoms and shared genetic risks, res...

Large language models with image processing in automated Cobb angle.

BACKGROUND: The degree of scoliosis is assessed through the Cobb angle, which quantifies severity and is measured by clinicians on radiographs. With t...

AI in epilepsy neuroimaging.

PURPOSE OF REVIEW: Recent advances in the capabilities and usability of artificial intelligence (AI) architectures coupled with increased availability...

A Wearable Brain-Computer Interface for Mitigating Car Sickness via Attention Shifting.

Car sickness, an enormous vehicular travel challenge, affects a significant proportion of the population. Pharmacological interventions are limited by...

Achieving more human brain-like vision via human EEG representational alignment.

Despite advancements in artificial intelligence, object recognition models still lag behind in emulating visual information processing in human brains...

Higher artificial intelligence-ECG atrial fibrillation prediction model output and estimated physiologic aging predict higher risk of adverse vascular events in patients with migraine.

OBJECTIVE: To investigate the ability of artificial intelligence-enabled electrocardiogram (AI-ECG) atrial fibrillation (AF) prediction model output a...

Multimodal graph fusion-based GCN for Alzheimer's disease diagnosis using fMRI and T1-weighted MRI.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder marked by both structural atrophy and functional dysregulation in the brain, yet ...

Brain structural and functional alterations in adolescents with borderline personality disorder: A systematic review and a research agenda.

Adolescent borderline personality disorder (aBPD) is linked to severe psychological problems and social dysfunction in the affected adolescents. Howev...

Multi-branch convolutional neural network and intracranial EEG high-frequency oscillations predict post-surgical seizure outcomes.

OBJECTIVE: Pathological High-Frequency Oscillations (HFOs) identify epileptogenic cortex, but their surgical utility is unproven. Current epilepsy sur...

Electroencephalography-Based Machine Learning Models for Predicting Ketogenic Diet Outcomes in Pediatric Drug-Resistant Epilepsy.

BACKGROUND: Ketogenic diet therapy (KDT) is an established treatment for drug-resistant epilepsy (DRE); however, methods for predicting its effectiven...

Subregional limbic radiomics on FDG-PET provides accurate early detection of Alzheimer's disease.

BACKGROUND: To investigate the radiomics features of the hippocampus and the amygdala subregions in FDG-PET images that can best differentiate Mild Co...

Gut-brain axis-mediated mechanisms and immune regulatory pathways in vascular dementia: insights into microbiota-derived metabolites and novel therapeutic strategies.

BACKGROUND: Vascular dementia (VaD), known for cognitive issues and cerebrovascular irregularities, is a common dementia type, but its molecular under...

Privacy-preserving multimodal fusion for Alzheimer's staging: A federated vision transformer framework with explainable AI.

Accurate, early-stage staging of Alzheimer's disease (AD) is critical for therapeutic intervention but is hampered by data privacy regulations, multim...

Motor imagery EEG signal classification using minimally random convolutional kernel transform and hybrid deep learning.

The brain-computer interface (BCI) establishes a non-muscle channel that enables direct communication between the human body and an external device. E...

Artificial intelligence-driven nano-enhanced stem cell therapy for neurodegenerative diseases: from rational design to clinical translation.

Neurodegenerative diseases (NDs) are progressive and incurable central nervous system disorders characterized by the accumulation of pathological prot...

Machine Learning for Predicting Stroke Risk Stratification Using Multiomics Data: Systematic Review.

BACKGROUND: Stroke is a complex, multidimensional disorder influenced by interacting inflammatory, immune, coagulation, endothelial, and metabolic pat...

Bimodal EEG-fNIRS and Deep Learning for Classifying Intensity-Dependent Cortical Auditory Evoked Responses.

Detection of intensity-dependent cortical auditory evoked responses using electroencephalography (EEG) is essential in clinical audiology and research...

Interpretable Machine Learning for Stroke Recovery: Predicting Discharge and 3-Month Functional Outcomes.

IntroductionStroke is a leading cause of disability worldwide. This study uses Machine Learning models to investigate factors influencing modified Ran...