Latest AI and machine learning research in neurology for healthcare professionals.
Identifying robust neuroimaging markers associated with schizophrenia is essential for advancing research and informing clinical understanding. However, a major obstacle to clinical translation is the limited ability of neuroimaging-based classification models to generalise across scanning sites. In this study, we first establish best performing within-site models, and then systematically investig...
ALS shows marked clinical heterogeneity, yet much real-world evidence remains trapped in unstructured reports. Here we introduce MEDSTREM, a large-language-model (LLM)-based agent that converts patient-sourced document images into standardized longitudinal electronic health records, enabling bottom-up cohort building and linkage to trials and multi-omics. By applying MEDSTREM to clinical report im...
Urination, a vital and conserved process of emptying urine from the urinary bladder in mammals, requires precise coordination between the bladder and ...
Focal cortical dysplasia (FCD) lesions in epilepsy FLAIR MRI are subtle and scarce, making joint image--mask generative modeling prone to instability ...
We introduce a reproducible, bias-resistant machine learning framework that integrates domain-informed feature engineering, nested cross-validation, a...
Significance: Quantitatively mapping both cerebral blood flow and tissue dynamics from laser speckle contrast imaging (LSCI) is powerful for studying ...
Electroencephalography (EEG) serves as a fundamental tool in modern neurology, cognitive neuroscience, and brain-computer interfaces, but its practica...
Recent electroencephalography (EEG) spatial super-resolution (SR) methods, while showing improved quality by either directly predicting missing signal...
Systematic assessment of organelle architectures, termed the organellome, offers valuable insights into cellular states and pathomechanisms, but remai...
Early and accurate diagnosis of Alzheimer's disease (AD) remains a critical challenge in neuroimaging-based clinical decision support systems. In this...
Understanding why patients with the same diagnosis exhibit markedly different disease progression--some progressing rapidly, others slowly, and throug...
Parkinson's disease (PD) is a chronic and complex neurodegenerative disorder influenced by genetic, clinical, and lifestyle factors. Predicting this d...
Generating clinical reports that summarize abnormal patterns, diagnostic findings, and clinical interpretations from long-term EEG recordings remains ...
Genome-wide association studies of physical activity traits have mapped numerous loci, yet the molecular mechanisms through which exercise influences ...
Mitochondrial and lysosomal dysfunction are central features of Parkinson's disease (PD) across major genetic forms including PRKN, SNCA, and LRRK2. W...
Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by widespread dysregulation of gene expression and regulatory pathways....
High-dimensional structural MRI (sMRI) images are widely used for Alzheimer's Disease (AD) diagnosis. Most existing methods for sMRI representation le...
Background Distinguishing individuals with cognitive decline (CD), including early Alzheimers disease, from cognitively normal (CN) individuals is ess...
Enabling natural communication through brain-computer interfaces (BCIs) remains one of the most profound challenges in neuroscience and neurotechnolog...
High-dimensional neuroimaging data presents challenges for assessing neurodegenerative diseases due to complex non-linear relationships. Variational A...