Latest AI and machine learning research in neurology for healthcare professionals.
Accurate multimodal Cognitive Workload Recognition (CWR) remains challenging due to the difficulty of modeling cross-modal relationships between Electroencephalography (EEG) and Functional Magnetic Resonance Imaging (fMRI) data. Additionally, the inherent heterogeneity of these physiological signals-each capturing distinct neural characteristics-complicates unified feature extraction. To address t...
Brain imaging genetics aims to uncover the pathological mechanisms and improve the diagnosis of brain diseases, particularly neurodegenerative disorders. While deep learning has advanced feature extraction and association modeling in this field, there are still two major challenges: extracting meaningful information from long genetic sequences and establishing causal relationships among genetics, ...
Acetylcholine (ACh) is a critical neurotransmitter that regulates diverse physiological functions, such as cognition and muscle contraction, through s...
Traumatic brain injury (TBI) initiates a complex cascade of neuroinflammatory and metabolic disturbances that exacerbate neuronal loss and neurologica...
PURPOSE: There has been significant progress in detecting Alzheimer's disease (AD) using retinal imaging. We developed an ensemble learning-based deep...
Octopuses are capable of remarkably intricate movements without a skeletal framework, making them a compelling model for the design of soft robotic ar...
PURPOSE: Seizure recurrence, often presenting as clusters, is a major clinical concern linked to increased morbidity. The immediate postictal period i...
The recurrence of cerebral aneurysms after coil embolization remains a significant concern in clinical practice. This study introduced a novel approac...
BACKGROUND: Alzheimer's disease (AD) is increasingly recognized as a multifactorial network disorder in which amyloid and tau pathology interact with ...
The use of artificial intelligence for emotion recognition is the focus of improving human-computer interaction. Recently, deep learning has been wide...
BackgroundHuntington's disease (HD) is a hereditary neurodegenerative disorder, with pathological changes detectable by MRI before symptom onset. Quan...
Alzheimer's disease (AD) is characterized by progressive brain network disintegration, yet quantifying this process at an individual level remains cha...
OBJECTIVE: This study aims to develop an advanced deep learning framework to overcome the challenges associated with real-time ultrasound monitoring o...
Schizophrenia is a complex psychiatric disorder marked by cognitive and perceptual disruptions, for which electroencephalography (EEG) provides a valu...
BACKGROUND AND OBJECTIVES: Identifying surgical candidates who are prone to poor outcomes is crucial for adapting treatment and ensuring optimal outco...
Although modern imaging methods enable in-vivo examination of connections between distinct brain areas, we still lack a comprehensive understanding of...
Protein function is inherently spatial: the same molecule can elicit distinct biological outcomes depending on its localization, interacting partners,...
Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized by dopaminergic neuronal loss and α-synuclein pathology, yet the ro...
OBJECTIVES: Amyloid-β (Aβ) PET is crucial for diagnosing and monitoring Alzheimer's disease (AD), but its high cost and radiation exposure limit its u...
The paper presents novel Universum-enhanced classifiers: the Universum Generalized Eigenvalue Proximal Support Vector Machine (U-GEPSVM) and the Impro...