Latest AI and machine learning research in neurology for healthcare professionals.
Dynamic brain networks are more effective than static networks in characterizing the evolving patterns of brain functional connectivity, making them a more promising tool for diagnosing neurodegenerative diseases. However, existing classification methods for dynamic brain networks often rely on sliding windows to extract multi-window features, leading to suboptimal performance due to the spatio-te...
In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT, which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts. We collated comprehensive data from a variety of news sou...
BACKGROUND: Migraine ranks as the second-leading cause of global neurological disability, affecting approximately 1.1 billion individuals worldwide wi...
The single-step biotransformation of the natural compound piperine into a known dual inhibitor of monoamine oxidase B (MAO-B) and catechol-O-methyltra...
BACKGROUND: While Parkinson's disease is a low dopamine neurodegenerative disorder, Schizophrenia is considered a high dopamine psychiatric disorder. ...
The 2D/3D heterojunction perovskite solar cells (PSCs) exhibit remarkable stability, but the quantum well in the 2D perovskite capping layer hinders t...
Migraine has an assumed polygenic basis, but the genetic risk variants identified in genome-wide association studies only explain a proportion of the ...
BACKGROUND: Artificial intelligence (AI) plays a promising role in ophthalmic imaging by providing innovative, non-invasive tools for the early detect...
Nuclear medicine is rapidly evolving with new molecular imaging targets and advanced computational tools that promise to enhance diagnostic precision ...
We propose a dynamic sensor selection approach for deep neural networks (DNNs), which is able to derive an optimal sensor subset selection for each sp...
Robust decoding performance is essential for the practical deployment of brain-computer interface (BCI) systems. Existing EEG decoding models often re...
OBJECTIVE: To determine whether a robotic-assisted intervention increases hip flexor muscle strength in people with spinal cord injury (SCI) and marke...
Narcolepsy is a chronic neurodegenerative disorder defined by the selective loss of orexin-producing neurons in the lateral hypothalamus, leading to e...
One of the areas where artificial intelligence (AI) technologies are used is the detection and diagnosis of mental disorders. AI approaches, including...
Epilepsy is one of the most frequently occurring neurological disorders that require early and accurate detection. This paper introduces a novel appro...
Motor imagery (MI) is a crucial research field within the brain-computer interface (BCI) domain. It enables patients with muscle or neural damage to c...
BACKGROUND AND AIMS: Stroke remains a leading cause of mortality and long-term disability worldwide, presenting a significant global health challenge....
Seated rehabilitation is essential in early-stage recovery for patients who can sit but cannot stand or walk. Robotic-based lower limb rehabilitation ...
BackgroundDancing may be protective for cognitive health among adults with mild cognitive impairment, Alzheimer's disease or dementia; however, additi...
Post-translational modifications (PTMs) play a crucial role in allowing cells to expand the functionality of their proteins and adaptively regulate th...