Latest AI and machine learning research in neurology for healthcare professionals.
The increasing prevalence of neurological disorders highlights the need for human in vitro systems that recapitulate key mechanisms of neurodegeneration and neuroinflammation. Although induced pluripotent stem cell (iPSC)-derived organoids and spheroids have advanced structural modelling of the human brain, platforms capable of robustly capturing neuronal electrophysiology in 3D remain limited. He...
Huntington's disease (HD) presents a heterogeneous neurodegenerative course, with motor, cognitive, and functional symptoms progressing differently across individuals. This atypical progression complicates the definition of discrete disease stages, hindering understanding of disease trajectories, timely pa- tient care, and therapy development. Consequently, current clinical staging systems rely he...
Chemotherapy-induced peripheral neuropathy (CIPN) is a common and painful side effect of paclitaxel (PTX) treatment. The most common measures of painf...
Specialist epilepsy expertise is scarce in resource-constrained settings, making LLM-based decision support attractive for frontline clinicians managi...
Artificial intelligence is transforming our capability to solve biological challenges. In dimensionality bottleneck regimes exacerbated by high-dimens...
Echo Planar Imaging (EPI) is the standard acquisition technique for diffusion and functional neuroimaging, enabling rapid imaging but suffering from g...
Non-suicidal self-injury (NSSI) among adolescents is a prevalent mental health problem and an important indicator of potential suicide risk. Early obj...
Derived from previous observations on equal and cross-frequency coupling, we evaluated the proposal that equal and cross-frequency phase synchronizati...
Purpose: EEG-based brain-machine interfaces (BMIs) may support assistive technologies for individuals with stroke-related motor impairment by translat...
Accurately predicting the temporal evolution of clinical biomarkers is crucial for the early diagnosis and management of neurodegenerative diseases su...
Positron Emission Tomography (PET) reveals brain metabolism and is clinically central to neurodegenerative disease assessment, yet existing 3D brain f...
Precise prognostication in acute brain injury is limited by a lack of reliable biomarkers of consciousness available to clinicians at the bedside. The...
Sleep deprivation impairs vigilance and cognitive function, yet jointly identifying the sleep condition (normal vs deprived) and the eye state (open v...
Brain MRI poses a fundamental challenge for machine learning: models must learn from high-dimensional 3D data spanning multiple co-registered modaliti...
Importance: Machine-learning models for ischemic stroke risk prediction are rarely validated across ancestrally distinct cohorts, and the contribution...
ABSTRACT Dementia classification in heterogeneous populations is complicated by the influence of education, language, socioeconomic position and healt...
Early and scalable detection of mild cognitive impairment (MCI) remains an unresolved clinical challenge. Existing EEG-based screening approaches are ...
Brain-Computer Interface (BCI) based on electroencephalography (EEG) enables direct interaction between the brain and external environments and has si...
Zero-shot visual decoding from electroencephalography (EEG) aims to infer visual semantics from non-invasive neural recordings, but remains challengin...
Tailoring stroke rehabilitation requires assessing how movements are organized, not merely if they succeed. Currently, this assessment is a rate-limit...