Neurology

Identification and Validation of Glycosylation‑Related Genes in Ischemic Stroke Based on Bioinformatics and Machine Learning.

Ischemic stroke (IS) constitutes a severe neurological disorder with restricted treatment alternatives. Recent investigations have disclosed that glycosylation is closely associated with the occurrence and outcome of IS. Nevertheless, data on the transcriptomic dynamics of glycosylation in IS are lacking. The objective of this study was to undertake a comprehensive exploration of glycosylation-rel...

A swin transformer and CNN fusion framework for accurate Parkinson disease classification in MRI.

Parkinson's disease ranks as the second most prevalent neurological disorder after Alzheimer's disease. Convolutional neural networks (CNNs) have been extensively employed in Parkinson's disease (PD) detection using MR images. However, CNN models generally focus on local features while prone to capture global representations. On the other hand, the vision transformer (ViT) excels at capturing glob...

Application of deep learning reconstruction combined with time-resolved post-processing method to improve image quality in CTA derived from low-dose cerebral CT perfusion data.

BACKGROUND: To assess the effect of the combination of deep learning reconstruction (DLR) and time-resolved maximum intensity projection (tMIP) or tim...

Alzheimer's disease knowledge graph enhances knowledge discovery and disease prediction.

OBJECTIVE: To construct an Alzheimer's Disease Knowledge Graph (ADKG) by extracting and integrating relationships among Alzheimer's disease (AD), gene...

SMANet: A Model Combining SincNet, Multi-Branch Spatial-Temporal CNN, and Attention Mechanism for Motor Imagery BCI.

Building a brain-computer interface (BCI) based on motor imagery (MI) requires accurately decoding MI tasks, which poses a significant challenge due t...

Flexor Synergy Assessment and Therapy for Persons With Stroke Using the ULIX Low Impedance Robot.

The flexor synergy after stroke results in involuntary activation of distal muscles when lifting the shoulder against gravity. This contributes to imp...

Tiny Convolutional Neural Network with Supervised Contrastive Learning for Epileptic Seizure Prediction.

Automatic seizure prediction based on ElectroEncephaloGraphy (EEG) ensures the safety of patients with epilepsy and mitigates anxiety. In recent years...

Interictal network dysfunction and cognitive impairment in epilepsy.

Epilepsy is diagnosed when neural networks become capable of generating excessive or hypersynchronous activity patterns that result in observable seiz...

A Deep Learning Approach for Nerve Injury Classification in Brachial Plexopathies Using Magnetic Resonance Neurography with Modified Hiking Optimization Algorithm.

RATIONALE AND OBJECTIVES: Brachial plexopathies (BPs) encompass a complex spectrum of nerve injuries affecting motor and sensory function in the upper...

Enhanced glaucoma classification through advanced segmentation by integrating cup-to-disc ratio and neuro-retinal rim features.

Glaucoma is a progressive eye condition caused by high intraocular fluid pressure, damaging the optic nerve, leading to gradual, irreversible vision l...

Retinal Ischemic Perivascular Lesions (RIPLs) as Potential Biomarkers for Systemic Vascular Diseases: A Narrative Review of the Literature.

Retinal ischemic perivascular lesions (RIPLs) are characteristic focal thinning of the inner nuclear layer, with an upward expansion of the outer nucl...

Demonstration of impaired facial emotion perception in temporal lobe epilepsy by theta responses in EEG.

OBJECTIVE: Temporale lobe and occipito-temporal cortical areas play an important role in facial emotion perception (FEP). FEP might be represented by ...

The role of artificial intelligence in diagnostic neurosurgery: a systematic review.

BACKGROUND: Artificial intelligence (AI) is increasingly applied in diagnostic neurosurgery, enhancing precision and decision-making in neuro-oncology...

A novel diagnosis method utilizing MDBO-SVM and imaging genetics for Alzheimer's disease.

Alzheimer's disease (AD) is the most common neurodegenerative disorder, yet its underlying mechanisms remain elusive. Early and accurate diagnosis is ...

Application and optimization of the U-Net++ model for cerebral artery segmentation based on computed tomographic angiography images.

Accurate segmentation of cerebral arteries on computed tomography angiography (CTA) images is essential for the diagnosis and management of cerebrovas...

Altered static and dynamic functional network connectivity and combined Machine learning in asthma.

Asthma is a reversible disease characterized by airflow limitation and chronic airway inflammation. Previous neuroimaging studies have shown structura...

Therapeutic potential of enzymes, neurosteroids, and synthetic steroids in neurodegenerative disorders: A critical review.

Neurodegenerative disorders present a significant challenge to healthcare systems, mainly due to the limited availability of effective treatment optio...

Current and Emerging Precision Therapies for Developmental and Epileptic Encephalopathies.

Developmental and epileptic encephalopathies (DEEs) are severe neurological disorders characterized by childhood-onset seizures and significant develo...

Parkinson's disease detection using inceptionV3: A Deep learning approach.

Parkinson's disease (PD) is a neurodegenerative condition that progressively affects motor function and causes tremors, rigidity, and bradykinesia. De...

FusionXNet: enhancing EEG-based seizure prediction with integrated convolutional and Transformer architectures.

. Effective seizure prediction can reduce patient burden, improve clinical treatment accuracy, and lower healthcare costs. However, existing deep lear...