Neurology

Domain-generalized Deep Learning for Improved Subject-independent Emotion Recognition Based on Electroencephalography.

Electroencephalography (EEG) provides high temporal resolution and noninvasiveness for a range of practical applications, including emotion recognition. However, inherent variability across subjects poses significant challenges to model generalizability. In this study, we systematically evaluated twelve approaches by combining four domain generalization (DG) techniques, Deep CORAL, GroupDRO, VREx,...

Bipartite Graph Adversarial Network for Subject-Independent Emotion Recognition.

Emotions play a vital role in connecting and sharing with others. However, individuals with emotional disorders face challenges in expressing their emotions, affecting their social lives. Current artificial intelligence tools support this problem by enabling the development of methods that recognize emotions from electroencephalographic (EEG) signals. However, the high variability across individua...

Advancing Remote Monitoring for Patients With Alzheimer Disease and Related Dementias: Systematic Review.

BACKGROUND: Using remote monitoring technology in the context of Alzheimer disease (AD) care presents exciting new opportunities to lessen caregiver s...

Single-cell mitochondrial morphomics reveals cellular heterogeneity and predicts complex I, III, and ATP synthase Inhibition responses.

Mitochondrial heterogeneity drives diverse cellular responses in neurodegenerative diseases, complicating the evaluation of mitochondrial dysfunction....

Development and validation of a deep learning model for diagnosing neuropathic corneal pain via in vivo confocal microscopy.

Neuropathic corneal pain (NCP) is an underdiagnosed ocular disorder caused by aberrant nociception and hypersensitivity of corneal nerves, often resul...

Generative Deep Learning Design of Single-Domain Antibodies Against Venezuelan Equine Encephalitis Virus.

BACKGROUND/OBJECTIVES: Venezuelan equine encephalitis virus (VEEV) represents a significant biothreat with no FDA-approved vaccine currently available...

Automated Risk Prediction of Post-Stroke Adverse Mental Outcomes Using Deep Learning Methods and Sequential Data.

Depression and anxiety are common comorbidities of stroke. Research has shown that about 30% of stroke survivors develop depression and about 20% deve...

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...

Interpretable Independent Recurrent Networks for Forecasting Stroke in Atrial Fibrillation.

BACKGROUND: Atrial fibrillation (AF) is a major risk factor for transient ischemic attack (TIA)/ischemic stroke (IS).

Electromechanical-assisted training for walking after stroke.

RATIONALE: Walking difficulties are common after a stroke. During rehabilitation, electromechanical and robotic gait-training devices can help improve...

Deep learning for cerebral vascular occlusion segmentation: A novel ConvNeXtV2 and GRN-integrated U-Net framework for diffusion-weighted imaging.

Cerebral vascular occlusion is a serious condition that can lead to stroke and permanent neurological damage due to insufficient oxygen and nutrients ...

A fully automatic radiomics pipeline for postoperative facial nerve function prediction of vestibular schwannoma.

Vestibular schwannoma (VS) is the most prevalent intracranial schwannoma. Surgery is one of the options for the treatment of VS, with the preservation...

Realistic Subject-Specific Simulation of Resting State Scalp EEG Based on Physiological Model.

Electroencephalography (EEG) recordings are widely used in neuroscience to identify healthy individual brain rhythms and to detect alterations associa...

Predicting dementia in people with Parkinson's disease.

Parkinson's disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing ...

DEMAC-Net: A Dual-Encoder Multiattention Collaborative Network for Cervical Nerve Pathway and Adjacent Anatomical Structure Segmentation.

OBJECTIVE: Currently, cervical anesthesia is performed using three main approaches: superficial cervical plexus block, deep cervical plexus block, and...

Predicting antiseizure medication response in newly diagnosed epilepsy using quantitative EEG and machine learning.

BACKGROUND: Predicting long-term outcomes in newly diagnosed epilepsy remains limited by reliance on clinical features and visual EEG interpretation. ...

Transforming 3D MRI to 2D Feature Maps Using Pre-Trained Models for Diagnosis of Attention Deficit Hyperactivity Disorder.

According to the World Health Organization (WHO), approximately 5% of children and 2.5% of adults suffer from attention deficit hyperactivity disorde...

Automated Early-Stage Glaucoma Detection Using a Robust Concatenated AI Model.

Glaucoma is a leading cause of irreversible blindness worldwide; therefore, detection of this disease in its early stage is crucial. However, previous...

The contribution of the locus coeruleus - norepinephrine system to the coupling between pupil-linked arousal and cortical state.

Understanding how pupil-linked arousal couples with cortical state is crucial for uncovering the neural mechanisms underlying brain state-dependent co...

Leveraging explainable artificial intelligence with ensemble of deep learning model for dementia prediction to enhance clinical decision support systems.

The prevalence of dementia is growing worldwide due to the fast ageing of the population. Dementia is an intricate illness that is frequently produced...