Latest AI and machine learning research in neurology for healthcare professionals.
Modelling human cortical microcircuitry in vitro requires platforms that recapitulate both the compositional complexity and spatial architecture of developing neural tissue. Current organoid and assembloid models often rely on the bulk fusion of pre-differentiated, region-specific cells, lacking the capacity for emergent spatial co-differentiation and microenvironment-driven multiscale organisatio...
Quantifying variability in a target population relative to a reference population is central to many scientific and clinical problems (e.g., diseased vs. healthy). Yet, without paired data and in the presence of heterogeneous target variation, existing methods struggle to separate multiple modes of target-specific variation. We propose \textit{CASL-VAE}, a deep contrastive latent variable model th...
Forecasting the future anatomy of slow-evolving neurodegenerative diseases could enable earlier, more targeted intervention and improve clinical trial...
Background Machine learning (ML) models for traumatic brain injury (TBI) prediction increasingly demand extensive data, computational resources, and e...
Background: Emerging artificial intelligence and machine learning (AI/ML) tools can help generate robust knowledge to support precision rehabilitation...
We propose a study protocol for routine clinical electroencephalograms (EEGs) from public hospitals, which represents a vast resource for neuroscience...
Background: Hypertension is a modifiable risk factor for dementia, yet the comparative effectiveness of angiotensin receptor blockers (ARBs) versus an...
Accurate and scalable assessment of quantitative neuroimaging biomarkers, such as white matter hyperintensities (WMH) and hippocampal (HIP) volumes, i...
Background: Lewy body diseases (LBD) collectively share alpha-synuclein Lewy pathology, yet present wide clinical heterogeneity, with overlapping moto...
Transformer-based deep learning models have shown great potential for decoding visual EEG signals. However, their internal attention mechanisms are of...
Brain areas differ in their inherent susceptibility to focal seizures, but the principles governing this risk remain unclear. While prior work has foc...
Medical imaging pipelines routinely copy single-channel grayscale data into three identical RGB channels before classification, usually without justif...
While generative models enable encoding of complex neuroimaging data for feature generation and reconstruction, developing optimal architectural frame...
In longitudinal Alzheimer's disease (AD) diagnosis support, clinical and imaging information is often collected at irregular visits. Integrating these...
Exogenous opioids that activate mu-opioid receptors (MORs) in nociceptive circuits mediate transient pain relief lasting minutes to hours but have mor...
Human brain magnetic resonance imaging (MRI) revolutionized our ability to non-invasively probe individual differences in neuroanatomy. These anatomic...
Abstract Objective To validate a neonatal seizure detection algorithm that is based on extracted clinical features of the aEEG and CSA on a cohort of ...
Stroke is a leading cause of death and long-term disability worldwide, affecting approximately 15 million individuals annually. Prompt and accurate su...
Abstract Background: Lacunes are 3-15 mm cavities originating from small perforating artery disease and are a hallmark of cerebral small vessel diseas...
Longitudinal dementia progression prediction is essential for clinical decision-making. However, models often degrade on external cohorts due to syste...