Neurology

Effects of cerebral infarction on cognitive and speech performance.

OBJECTIVE: The present study sought to identify the cognitive features and speech features that might distinguish cerebral infarction patients by using artificial intelligence. METHODS: A total of 117 patients were divided into the lacunar group, non-lacunar cerebral infarction group, and control group from Anhui No.2 Provincial People's Hospital. The cognitive features were created from the Chine...

Quantitative sleep EEG identifies CSF core biomarker-related subgroups in Alzheimer's disease.

Early detection and biological characterization of Alzheimer's disease (AD) remain challenging, as current diagnostic approaches rely on invasive cerebrospinal fluid (CSF) sampling or costly neuroimaging, limiting scalability. Sleep quantitative electroencephalography (qEEG) provides a non-invasive measure of brain function and may capture early AD-related neural alterations; however, the high dim...

Cascaded Deep Learning enables multimodal brain PET spatial normalization and quantification for Alzheimer's disease.

Semi-quantitative positron emission tomography (PET) analysis, particularly Centiloid and CenTauRz scaling, is essential for Alzheimer's disease (AD) ...

Machine learning and deep learning for neurological disease analysis: A systematic review across five major disorders.

Artificial Intelligence (AI) has become integral to the research of neurological diseases due to the rapid expansion of neuroimaging, clinical, physio...

Stacking machine learning model for risk stratification of acute respiratory distress syndrome after traumatic brain injury: a multicenter retrospective study.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a severe complication after traumatic brain injury (TBI), and early risk stratification may ...

Use of artificial intelligence in magnetic resonance imaging across the epileptic patient's journey: A meta-analysis of four clinical applications.

OBJECTIVE: The application of artificial intelligence/machine learning (AI/ML) to magnetic resonance imaging (MRI) promises to enhance and support cli...

Whole anterior visual pathway segmentation from high-resolution MRI using artificial intelligence.

OBJECTIVE: Manual segmentation of the whole anterior visual pathway (aVP) from high-resolution magnetic resonance imaging (MRI) is time-consuming and ...

Raw EEG as a viable alternative to engineered decompositions in anesthetic depth prediction.

PURPOSE: Assessing the depth of anesthesia remains a challenge in operating rooms worldwide, as hospitals often rely on proprietary monitors that are ...

Developing and Testing a Brief Mindfulness Just-in-Time Adaptive Intervention to Reduce Stress Among Caregivers of People With Dementia: Quasi-Experimental Study.

BACKGROUND: Dementia caregiving entails chronic, fluctuating stress with downstream risks to caregivers' mental health and quality of care. Mindfulnes...

Application of Focused Ultrasound in Alzheimer's Disease: A Bibliometric Analysis.

OBJECTIVE: This study uses bibliometric analysis and knowledge mapping methods to systematically explore the emerging research frontiers and developme...

Robust multi-centre interictal EEG biomarker for distinguishing epilepsy from mimickers.

OBJECTIVE: To develop and validate an interpretable multi-centre interictal EEG biomarker for distinguishing epilepsy from mimickers, addressing the c...

Uncertainty-aware multi-path framework with dynamic arbitration for Alzheimer's disease MRI classification.

As a progressive neurodegenerative disorder, Alzheimer's disease (AD) requires early and accurate diagnosis to delay pathological progression and impr...

Enhanced vertebrae localization in CT volumes: a two-stage deep learning framework.

BACKGROUND: Vertebral landmark localization in computed tomography (CT) volumes is crucial for spinal pathological diagnosis, postoperative assessment...

Structural damage assessment in the spine of patients with axial spondyloarthritis - results from an international OMERACT multi-reader exercise using MRI-based synthetic CT.

OBJECTIVE: To develop and perform preliminary cross-sectional validation of a magnetic resonance imaging (MRI)-based outcome measure for assessing spi...

Decoding neuron-specific lineage to identify diagnostic biomarkers and therapeutic targets for ischemic stroke.

Ischemic stroke (IS) imposes a major global health burden. To uncover new diagnostic and therapeutic targets, we profiled neuronal heterogeneity durin...

Prediction of an fMRI-based schizophrenia biomarker from EEG using dynamic functional connectivity: a simultaneous EEG-fMRI study.

OBJECTIVE: Recent advances in functional magnetic resonance imaging (fMRI) have identified brain functions associated with psychiatric disorders using...

Multimodal neuroimaging-based deep learning framework for pattern analysis and early prediction of neurodegenerative diseases.

Neurodegenerative diseases, such as Mild Cognitive Impairment (MCI) and Alzheimer's, pose significant challenges due to their progressive nature and l...

Resting-state EEG microstates as biomarkers for major depressive disorder.

OBJECTIVES: To investigate the temporal dynamics of resting-state electroencephalography (EEG) microstates in patients with Major Depressive Disorder ...

Machine Learning-Based Prediction of Independent Ambulation Following Intramedullary Spinal Cord Tumor Resection.

BACKGROUND AND OBJECTIVES: Intramedullary spinal cord tumor (IMSCT) resection carries a high risk of postoperative neurological deficit because of neu...

EEG-based dynamic emotion recognition using multi-scale wavelet transform with a Spatio-Temporal neural network.

Emotion recognition from EEG signals has been one of the most promising areas due to its potential in enhancing human-computer interaction, especially...