Neurology

Neural operator-based digital twins for modeling amyloid-$β$ and tau propagation and treatment optimization in Alzheimer's disease

Accurately predicting the spatiotemporal evolution of amyloid-$β$ and tau proteins at the individual level is critical for improving the diagnosis and treatment of Alzheimer's disease. We consider the problem of constructing patient-specific digital twins that model the propagation of these biomarkers on the cortical surface using reaction--diffusion dynamics. A major challenge is that the underly...

Acute Ischemic Stroke Detection on Non-Contrast CT: A Deep Learning Approach

Acute ischemic stroke (AIS) is a leading cause of disability and death while effective treatment requires quick and accurate diagnosis. Non-contrast CT (NCCT) is widely used in the initial screening of AIS, but stroke detection is challenging because early changes on NCCT are subtle or indistinguishable. Using hyperacute NCCTs as inputs and diffusion-weighted MRI as ground truth, we trained a deep...

PIGMENT: A deep learning framework for Porcine Immunohistochemistry seGMENTation

Traumatic brain injury produces widespread axonal damage can be assessed histologically using amyloid precursor protein (APP) immunohistochemistry, wh...

ComCat: Combating Covariate Effects in Brain Analysis

As neuroimaging analysis shifts toward large-scale, multi-site studies, managing the unwanted variability introduced by combining heterogeneous datase...

Graph-based characterization of in vitro neuronal network maturation using machine learning and digital holographic microscopy

Significance: Digital Holographic Microscopy (DHM) provides label-free quantitative phase images (QPIs) of living cells and has become a powerful tool...

Insect-inspired, efficient event-based classification of tactile features

Tactile sensing enables humans and animals to detect and discriminate features during exploration and guide context appropriate actions. Compared to c...

Machine learning-based modeling to predict inhibitors for targets of Alzheimer's Disease

Alzheimer's Disease is a chronic neurodegenerative disorder projected to affect 115 million people by 2050, driven by mechanisms like the cholinergic ...

Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning

Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but ho...

Symptom-based phenotype discovery in motor neuron disease using natural language processing of electronic health records

Background: Motor neuron disease (MND) is a fatal neurodegenerative condition with significant clinical heterogeneity that is incompletely captured by...

Image-based deep learning for emergency electrocardiogram classification

Automated electrocardiogram analysis has advanced largely through digital waveforms, yet many emergency-care workflows rely on ECGs available only as ...

MCH-Guard: Multimodal Machine Learning Framework for Risk Stratification of Cerebral Microhemorrhage Risk in the Alzheimer's Disease Neuroimaging Initiative

Background: Efficient cerebral microhemorrhage (MCH) monitoring is critical for anti-amyloid therapy safety due to ARIA-H risk. We developed MCH-Guard...

Optimal Practice Schedules in a Dual-Rate Model of Motor Adaptation, and Their Recovery by Reinforcement Learning

A clinician guiding a stroke patient through a 45-minute rehabilitation session, a coach planning a training day, a teacher choosing the order of prac...

Foundation Models for Epileptogenic Zone Identification in Drug-Resistant Epilepsy

Accurate identification of the epileptogenic zone (EZ) is essential for seizure freedom after resective surgery in drug-resistant epilepsy, yet seizur...

Machine learning evaluation of gene expression-based ALS subtypes across brain and blood tissues

The clinical and molecular heterogeneity observed in amyotrophic lateral sclerosis (ALS) presents a challenge for diagnosis, prognosis, and treatment....

FeatureMSEA: Metabolic Feature-based Metabolite Set Enrichment Analysis

Liquid chromatography-mass spectrometry (LC-MS) untargeted metabolomics detects thousands of metabolic features, but converting these chemical signals...

Predicting Motor Recovery After Stroke: Utility and Limits of Corticospinal Tract Biomarkers

Background: Corticospinal tract (CST) damage is a major cause of post-stroke motor deficits. However, it remains unclear which estimates of CST damage...

Human Intuition vs. Computational Precision: Neurologists, Feature-based Models, and Deep Learning for Stroke Prognosis

Background: Prognostication in large vessel occlusion (LVO) stroke remains challenging. Although several prognostic models exist, their comparison to ...

Adaptive Neural Reorganization Enables Real-Time Finger-Level Robotic Control in BCI-Naïve Stroke Survivors

Restoring hand function remains a major challenge for individuals with motor impairments following stroke. Noninvasive brain-computer interfaces (BCIs...

Looking beyond stereotyped neuron structures reveals links between beading and morphological rearrangements in aging phenotypes.

Understanding how neuronal morphology changes during aging and acute stress is essential for elucidating mechanisms of neurodegeneration. The highly b...

Quantum machine learning for detection of sleep deprivation from EEG signals

Approximately 50% of the population in India is estimated to experience sleep-related disorders. Sleep deprivation is a prevalent condition that adver...