Neurology

An efficient brain-heart coupling learning system for emotion recognition.

In neuroscience, cardiac sympathetic-vagal nerve activity and central nervous system activity in the brain work in concert to maintain the body's response to emotional perception. Therefore, the efficient mining of brain-heart driving patterns in emotion perception is conducive to the development of emotion recognition system. However, the dynamic correlation between the brain and heart poses a ch...

HyCoSwin-PD: An explainable hybrid ConvNeXtV2-Swin transformer framework for Parkinson's disease detection from neuroimaging.

Accurate detection of Parkinson's disease (PD) from structural MRI remains a significant challenge due to the diffuse and heterogeneous nature of PD-related neuroanatomical alterations. This study introduces HyCoSwin-PD, an advanced hybrid deep learning framework that integrates ConvNeXt-V2 and Swin Transformer to jointly model fine-grained local morphology and hierarchical global context. ConvNeX...

Integrated cortical-cognitive signatures identified by machine learning enable early detection of MCI in type 2 diabetes.

Type 2 Diabetes Mellitus (T2DM) confers a significant risk for Mild Cognitive Impairment (MCI), yet robust biomarkers for early detection remain limit...

Chronological Diagnostic Algorithm Predicting Neuropathology in Parkinsonism.

OBJECTIVE: Pre-mortem diagnosis of parkinsonism is often challenging due to atypical presentations, overlapping syndromes, and co-pathologies. This st...

Dose reduction for synaptic density PET imaging in Parkinson's disease.

OBJECTIVE: Dynamic PET imaging with 11C-UCB-J enables in vivo quantification of synaptic vesicle glycoprotein 2A (SV2A), with prior reports of lower s...

Epilepsy: Modern Treatment Approaches, Clinical Aspects, and Patent Status.

Epilepsy is a chronic neurological disorder causing recurrent seizures. Improved diagnosis and management, including high-resolution imaging, genetic ...

Autophagy-exosome crosstalk in neurodegeneration: Mechanisms and therapeutic opportunities.

Neurodegenerative diseases (NDs), including Alzheimer's, Parkinson's, Huntington's, amyotrophic lateral sclerosis, and multiple sclerosis, share a com...

Dynamic functional connectivity changes in noise-induced hearing loss: a resting-state fMRI study with machine learning-based classification.

OBJECTIVE: Noise-induced hearing loss impacts brain health and cognition, with dynamic functional connectivity analysis offering a promising but under...

Letter to the editor regarding "Development of a machine learning-based predictive model for long-term adverse outcomes in neonatal bacterial meningitis".

A time-frequency cross-attention network model for epileptic seizure detection.

Epilepsy is a chronic neurological disease that profoundly impacts patients' daily lives. Electroencephalography (EEG) serves as a crucial tool for th...

A Machine learning pipeline to investigate tissue ingrowth in cerebral aneurysms using preclinical animal models.

Cerebral aneurysm is a life-threatening condition characterized by the formation of a saccular bulge in brain blood vessels, which can rupture and lea...

Spinal disease image segmentation technology integrating U-ResNet and shape-aware attention.

The incidence of spinal diseases is rising and affecting younger people, making early and accurate diagnosis based on medical imaging crucial for trea...

Estimating cognitive function score from mild cognitive impairment to moderate dementia using a hybrid model combining plasma biomarkers with electroencephalogram signal.

BackgroundPredicting cognitive function across dementia stages remains challenging. Plasma biomarkers and electroencephalogram (EEG) features may prov...

Decoding epilepsy's molecular blueprint: Machine learning unravels transcriptomic subtypes and regulatory networks.

OBJECTIVE: Drug-resistant epilepsy (DRE) affects approximately one-third of patients with epilepsy. The molecular heterogeneity underlying DRE remains...

Integrated network toxicology and computational profiling of acetyl tributyl citrate-related mechanisms in Alzheimer's disease.

BackgroundAcetyl tributyl citrate (ATBC), an eco-friendly plasticizer, exhibits poorly characterized neurotoxic effects.ObjectiveWe integrated network...

Sub-band Embedding Based EEG Spatio-Temporal Activity Representation for Emotion Recognition.

EEG-based emotion recognition is a crucial task with significant implications for mental health monitoring, affective computing, and clinical decision...

Automated CTA-Derived Collateral Grading and Morphologic Metrics for Enhanced Prediction of Post-Stroke Outcomes.

BACKGROUND AND PURPOSE: Collateral circulation is a key determinant of treatment response and outcomes in acute ischemic stroke (AIS), yet its assessm...

A neurofeedback-guided EEG and BCI framework for personalized attention rehabilitation in ADHD.

The integration of game-based cognitive training with electroencephalography (EEG)-based brain-computer interaction (BCI) has demonstrated potential f...

Predictive modeling for early diagnosis of dementia using sequential data analysis and data mining.

Dementia, a progressively debilitating neurodegenerative disorder, poses a growing challenge to global healthcare due to its subtle early onset and la...

Machine-learning prediction and risk stratification of 12-month cognitive decline in Alzheimer's disease using routine clinical and MRI data.

Early identification of patients with Alzheimer's disease (AD) who will experience near-term cognitive decline can support trial enrichment and risk-s...