Latest AI and machine learning research in neurology for healthcare professionals.
Background: Current diagnostic criteria for multiple sclerosis (MS) rely on white matter lesions (WMLs), which are not specific and often occur in other disorders. Microstructural abnormalities in normal-appearing white matter (NAWM) may provide complementary information beyond focal lesions. However, the diagnostic use of NAWM in MS remains limited because a reproducible, diagnostically specific ...
Multimodal neuroimaging provides complementary insights for Alzheimer's disease diagnosis, yet clinical datasets frequently suffer from missing modalities. We propose ACADiff, a framework that synthesizes missing brain imaging modalities through adaptive clinical-aware diffusion. ACADiff learns mappings between incomplete multimodal observations and target modalities by progressively denoising lat...
Hereditary cerebellar ataxias (HCAs) are rare neurodegenerative disorders characterised by progressive motor impairment and overlapping clinical pheno...
Objective: In Parkinson's disease (PD), gait-related digital mobility outcomes (DMOs) show promise for monitoring mobility decline, but convergent val...
Magnetoencephalography (MEG) and electroencephalography (EEG) source imaging requires solving an ill-posed inverse problem for which numerous algorith...
Motor imagery (MI) brain-computer interfaces (BCIs) are promising technologies for neurorehabilitation. In this context, deep learning (DL) models are...
Multiple sclerosis (MS) is a chronic neurodegenerative disease characterised by progressive neurological disability and heterogeneous symptom trajecto...
Resting-state electroencephalography (EEG) has been proposed as a scalable source of biomarkers for chronic pain, but its clinical potential remains u...
Alzheimer's disease (AD) is a complex neurodegenerative disorder with multifactorial etiology and widespread molecular manifestations. Investigating m...
Human brain function emerges from dynamic reconfigurations of large-scale neural networks. While population-level reference charts have transformed th...
Multiple Sclerosis (MS) is a chronic autoimmune disease of the central nervous system whose molecular mechanisms remain incompletely understood. In th...
Accurate longitudinal analysis of brain MRI is often hindered by evolving lesions, which bias automated neuroimaging pipelines. While deep generative ...
Quantifying task difficulty remains an open theoretical problem in neuroscience and artificial intelligence. While difficulty is often treated as a sc...
Most genetic variants contributing to complex diseases reside in the noncoding genome. While common variants uncovered by genome-wide association stud...
Sense of agency (SoA), the experience of controlling one's actions and their consequences, is crucial for self-representation and adaptive goal-direct...
Arterial spin labeling (ASL) perfusion MRI allows direct quantification of regional cerebral blood flow (CBF) without exogenous contrast, enabling non...
Pretraining for electroencephalogram (EEG) foundation models has predominantly relied on self-supervised masked reconstruction, a paradigm largely ada...
Introduction: The eligibility of anti-amyloid disease-modifying therapies (DMTs) and their integration into clinical practice in some institutions req...
Background: Cognitive decline and dementia represent major public health challenges in aging populations. Natural language processing (NLP)-augmented ...
Background: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive decline, memory impairment, and functional deteriorati...