Latest AI and machine learning research in neurology for healthcare professionals.
Structural Magnetic Resonance Imaging (MRI) is widely used in neuroimaging research and clinical practice, but structural MRI volumes may retain facial and cranial anatomical information that raises privacy concerns. Existing deep learning-based brain extraction methods generally produce a single fixed output, limiting flexibility when different applications require different balances between priv...
Connected speech is essential for everyday communication, but clinical constraints and patient fatigue limit detailed evaluation in acute stroke (<1-week post-stroke). Bedside assessments may sample discourse but rarely quantify language impairment (LI) in connected speech, leaving patient communication poorly characterized. We analyzed brief story retellings from 86 patients with left-hemisphere ...
Accurate localization of the seizure onset zone (SOZ) is a central determinant of surgical outcome in drug-resistant focal epilepsy, yet identifying i...
Electroencephalography (EEG) is a promising tool for automated detection of mild cognitive impairment (MCI) and dementia, but comparisons across studi...
Synaptic vesicle glycoprotein 2C (SV2C) is a vesicular protein enriched in dopaminergic neurons of the basal ganglia that modulates dopamine storage a...
The population structure of an inbred population of 781 people on Norfolk Island in the Pacific, 318 of which are descendants of the original Mutineer...
Objective: Foundation models represent the next advancement in AI for EEG analysis; however current explainable AI techniques provide attribution scor...
Neurodegenexrative diseases such as Alzheimer's disease and Parkinson's disease are diagnosed most reliably only after substantial, often irreversible...
Peripheral nerves buried beneath intact tissue are difficult to visualize during surgery and remain inaccessible to white light wide-field imaging and...
Mechanistic interpretability of large language models lacks spatially resolved, falsifiable tools for testing whether internal components are speciali...
Fiber tractography's ability to reconstruct the brain's structural pathways, has made it a crucial component of modern neuroimaging, enabling detailed...
Magnetic Resonance Imaging (MRI) interpretation is fundamental to clinical decision-making, requiring radiologists to integrate multi-view anatomical ...
Sleep stage classification is important for the diagnosis and management of sleep disorders, yet most automatic staging studies evaluate models agains...
Alzheimer's disease is a leading cause of death with no cure. Therefore, early detection is critical to slow progression and preserve quality of life....
Parkinson's disease (PD) is the second most common neurodegenerative disorder. Typical machine learning screening methods require PD labels, but the a...
Mixed emotions represent a clinically relevant but still underexplored target for automatic emotion recognition. EEG provides millisecond-level access...
In medical applications, raw data is frequently associated with significant privacy concerns, lending particular importance to the encoding of summary...
Alzheimer's disease (AD) progression is a longitudinal process with subtle pathological cues in the early stages. Yet, computational constraints have ...
Neuroimaging and genetic testing are two important clinical references for nervous system diseases, offering complementary diagnostic information. How...
Background: Large language models have been proposed to improve patient comprehension of radiology reports. However, whether they improve objective un...