Latest AI and machine learning research in neurology for healthcare professionals.
BACKGROUND: Accumulations of AD and LATE-NC both contribute to changes in hippocampal volume, possibly via distinct and/or overlapping mechanisms. Microglia-driven inflammation is a shared pathway associated with both AD and LATE-NC. However, the extent to which microglia inflammation is associated with hippocampal volume is less understood. OBJECTIVE: Examine the relationship between AD and LATE-...
Biological heterogeneity in Alzheimer's Disease (AD) poses a critical diagnostic challenge, particularly for traditional linear methods that fail to capture non-linear neural dynamics. To address this, we propose a diagnostic framework utilizing the Large Brain Model (LaBraM), pretrained on over 2,500 hours of EEG data. By integrating these high-dimensional latent embeddings with a non-linear Rand...
Background: Distinguishing epilepsy from functional/dissociative seizures (FDS) is an ongoing diagnostic challenge. Using a well-controlled clinical E...
Background Large language models are increasingly proposed to post-edit decoded text in communication brain-computer interfaces and augmentative commu...
Neuropsychiatric symptoms (NPS) are increasingly recognized as critical components of the disease progression in Alzheimer's disease (AD), yet their r...
Intracranial electroencephalography (iEEG) provides temporally precise and spatially specific access to neural activity from focal and deep brain regi...
Objective: Diffusion-weighted MRI (DWI-MRI) is the gold standard for visualizing and quantifying acute ischaemic stroke (AIS). Although deep learning ...
Existing action quality assessment (AQA) datasets and methods rely primarily on visual inputs such as RGB and pose, overlooking physiological dynamics...
Spoken-language analysis via prompt-based domain-adaptive models is a promising direction for low-resource, non-invasive dementia screening, but such ...
Background: Artificial intelligence (AI) systems for glaucoma diagnosis and prognostication from visual fields (VF) are under active development, yet ...
Accurate stroke lesion segmentation is essential for large-scale neuroimaging studies, yet manual delineation remains labor-intensive, and existing au...
Aging is a progressive decline in biological function that is proposed to be driven by the accumulation of epigenetic noise and the loss of epigenetic...
Background and Objectives: Word-finding difficulty is common in healthy aging and in neurologic disorders, including temporal lobe epilepsy (TLE) and ...
Accurate characterisation of the haemodynamic response function (HRF) is central to interpreting blood-oxygen-level-dependent (BOLD) signals in functi...
Electroencephalography (EEG) is a widely used window into human brain function, but most EEG models remain tied to a one-dataset-one-model supervised ...
EEG foundation models pretrained via self-supervised learning promise transferable representations, but their generalization remains limited, especial...
Brain stroke, known for its high mortality and incidence rates, poses significant health risks and requires rapid intervention for survival. Early dia...
Segmenting ischemic stroke lesions on T1-weighted (T1w) MRI acquired across different scanners and protocols without intensity standardization is diff...
Background Glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 (SGLT2) inhibitors are increasingly used for type 2 di...
Chronic fatigue, characterized by persistent physical and/or mental exhaustion, is a frequent and debilitating symptom in medicine. Despite its impact...