Neurology

GenEEG: Improving epileptic EEG detection through patient-adaptive latent diffusion and continual learning.

Automated seizure detection systems face significant challenges due to the limited availability of clinical EEG data, a substantial class imbalance between seizure and non-seizure recordings, considerable variability among patients, and the issue of catastrophic forgetting in sequential multi-patient learning. These issues greatly limit the effectiveness of machine learning models in monitoring an...

Graph attention network with comorbidity connectivity embedding for post-traumatic epilepsy risk prediction using sparse time-series electronic health records.

BACKGROUND: Traumatic brain injury (TBI) is a major risk factor for neurological disorders, including post-traumatic epilepsy (PTE), a debilitating condition associated with significant long-term consequences. The prognosis of PTE occurrence remains challenging due to the complex pathophysiology of PTE and the impracticality of traditional blood biomarker- or imaging-based screening for large popu...

Causal models and network instability in neurological and psychiatric disorders: Computational and clinical perspectives.

Self-diagnosis-the capacity of a system to detect and correct its own failures-is a defining property of adaptive systems. In the brain, recursive sel...

Tenuigenin ameliorates Alzheimer's disease by targeting MAP2K1: integrated evidence from network pharmacology and experimental validation.

OBJECTIVE: Alzheimer's disease (AD) is a prevalent neurodegenerative disorder primarily characterized by progressive cognitive impairment and synaptic...

Prediction of postoperative delirium after cardiac surgery by the interplay between preoperative plasma p-tau181 and IL-6 and heart-brain axis related factors: results from the prospective observational study FINDERI.

Postoperative delirium (POD) following cardiac surgery is a severe complication. There is evidence of a link between neuroinflammation and neurodegene...

Support vector machine classification of 18F-FDG PET scans across subtypes of amyotrophic lateral sclerosis.

PURPOSE: While 18F-FDG PET imaging has demonstrated diagnostic value in people with Amyotrophic Lateral Sclerosis (PwALS) and group-level differences ...

Deep learning based treatment remission prediction to transcranial direct current stimulation in bipolar depression using EEG power spectral density.

Bipolar disorder is characterized by marked changes in mood and activity levels and is a leading cause of disability worldwide. We sought to investiga...

Wirelessly transmitted subthalamic nucleus signals decode endogenous pain levels in Parkinson's disease patients.

Pain is a prominent non-motor symptom of Parkinson's disease (PD); it may appear in various levels (elevated or diminished) during waking hours and su...

A clinical-AI correlation for integrating artificial intelligence into stroke care: a systematized literature review and practice framework.

BACKGROUND AND PURPOSE: The rapid integration of artificial intelligence (AI) into stroke care has outpaced many clinicians' ability to critically eva...

Decoding aroma perception of grilled lamb skewers: an EEG-MambaFusionNet framework integrating TDS and GC-IMS.

This study investigates the dynamic evolution of aroma perception in grilled lamb skewers from raw to well-done stages and its corresponding neural si...

Complexity of olfactory-evoked EEG as an evidence-based marker of Alzheimer's disease.

Olfactory impairment is an early symptom of Alzheimer's disease (AD). However, currently used olfactory task-based functional magnetic resonance imagi...

Advances in occult cancer screening for patients with unprovoked venous thromboembolism: A narrative review of epidemiology, risk, and clinical strategies.

BACKGROUND: Venous thromboembolism (VTE) and cancer exhibit a bidirectional correlation. The probability of detecting occult cancer in unprovoked VTE ...

Developmental Brain Age Estimation From MRI Data: A Systematic Review of Deep Learning Approaches and Open Datasets.

Brain age is an emerging concept that reflects complex, time-dependent changes in brain structure, identifying departures from expected neurodevelopme...

SynSeg: A synthetic data-driven approach for robust subcellular structure segmentation.

Accurate subcellular segmentation is crucial for understanding cellular processes, but traditional methods struggle with noise and complex structures....

Identification of alkaline phosphatase as a putative biomarker of anti-NGF treatment-associated arthropathies: Machine learning-assisted analyses of clinical trial data.

OBJECTIVE: Nerve growth factor (NGF) inhibitors have been shown to provide pain relief in patients with osteoarthritis but are associated with adjudic...

Association of Brain Age With Physical Disability and Cognitive Impairment in People With Multiple Sclerosis of the Same Age.

BACKGROUND AND OBJECTIVES: The brain-predicted age difference (brain-PAD) is a novel marker of neurodegeneration in multiple sclerosis (MS). Brain-PAD...

Deciphering the rules of regulated cell death subroutines by in silico methods.

BACKGROUND: Regulated cell death (RCD) is essential for organismal development, as it plays key roles in organ formation, pathogen defense, and the ma...

A multi-scale deep CNN based on attention mechanism for EEG emotion recognition.

BACKGROUND: Recognizing emotion is a crucial project within the domain of brain-computer interface technology. Recently, researchers have found that d...

Global trends and collaboration networks in radiology: A bibliometric analysis of the 500 most-cited articles in web of science.

OBJECTIVE: This study examined global research trends in Radiology, Nuclear Medicine, and Medical Imaging by analyzing the 500 most-cited articles in ...

Cerebral blood flow and modern approaches for clinical assessment & monitoring: a view to the future.

Cerebral blood flow (CBF) is under homeostatic control via cerebral autoregulation, maintaining a constant blood supply to brain parenchyma by integra...