Neurology

Latest AI and machine learning research in neurology for healthcare professionals.

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BrainRVQ: A High-Fidelity EEG Foundation Model via Dual-Domain Residual Quantization and Hierarchical Autoregression

Developing foundation models for electroencephalography (EEG) remains challenging due to the signal's low signal-to-noise ratio and complex spectro-temporal non-stationarity. Existing approaches often overlook the hierarchical latent structure inherent in neural dynamics, leading to suboptimal reconstruction of fine-grained information. In this work, we propose BrainRVQ, a general-purpose EEG foun...

Feb 18 2026 2602.16951v1

TMS timed to interictal epileptiform discharges

Interictal epileptiform discharges (IEDs) are pathological hypersynchronous bursts of electrical brain activity that occur between seizures in patients with epilepsy. IEDs are caused by transient brain states that are difficult to predict, making them a challenging neurophysiological and technological case for brain-state-dependent stimulation. Administering stimulation at IED onset may provide in...

RosetteArray Platform for Quantitative High-Throughput Screening of Human Neurodevelopmental Risk

Neural organoids have revolutionized how human neurodevelopmental disorders (NDDs) are studied. Yet, their utility for screening chemical hazards and ...

p-Brain: An Automated MRI Pipeline for Cerebral Perfusion, Microvasculature, and Blood-Brain Barrier Permeability Estimation

We present p-Brain, an end-to-end neuroimaging analysis framework for reproducible, automated quantitative DCE-MRI analysis at scale. From standard ac...

Local REM sleep-N1-wake sleep stage mixing in narcolepsy type 1

Type 1 narcolepsy (NT1), a disorder caused by the loss of hypocretin/orexin transmission, is characterized by daytime sleepiness and symptoms where Ra...

MRC-GAT: A Meta-Relational Copula-Based Graph Attention Network for Interpretable Multimodal Alzheimer's Disease Diagnosis

Alzheimer's disease (AD) is a progressive neurodegenerative condition necessitating early and precise diagnosis to provide prompt clinical management....

Feb 17 2026 2602.15740v1
VideoSketcher: Video Models Prior Enable Versatile Sequential Sketch Generation

Sketching is inherently a sequential process, in which strokes are drawn in a meaningful order to explore and refine ideas. However, most generative m...

Feb 17 2026 2602.15819v1
Focused ultrasound neuromodulation of mediodorsal thalamus disrupts decision flexibility during reward learning

When learning to find the most beneficial course of action, the prefrontal cortex guides decisions by comparing estimates of the relative value of the...

StrokeNeXt: A Siamese-encoder Approach for Brain Stroke Classification in Computed Tomography Imagery

We present StrokeNeXt, a model for stroke classification in 2D Computed Tomography (CT) images. StrokeNeXt employs a dual-branch design with two ConvN...

Feb 16 2026 2602.15087v1
Parsing Neurometabolic Signatures of Multiple Sclerosis with MRSI and cPCA

Magnetic Resonance Spectroscopy Imaging (MRSI) offers spatially-resolved, neurometabolic information, acquired non-invasively at whole-brain scales fr...

Application of Explainable AI in Neuroscience: Enhancing Autism Screening

The main challenges in the life of a child with autism are difficulties in communication, behavior, and social interaction. Early diagnosis of this ne...

Mapping the Fascicular Morphology and Organization of the Human Sciatic Nerve via High-Resolution MicroCT Imaging

Objective: Implanted neuroprostheses can restore standing and walking after spinal cord injury and somatosensation after limb loss. Yet current approa...

Detection-Guided Artifact Removal for Clinical EEG: A Deep Learning Framework

Objective: We developed and validated a detection-guided artifact removal framework for clinical electroencephalography (EEG). The framework applies a...

Discovery of TDP-43 aggregation inhibitors via a hybrid machine learning framework

TAR DNA-binding protein 43 (TDP-43) aggregation is a hallmark of several neurodegenerative diseases, including amyotrophic lateral sclerosis and front...

Statistical Opportunities in Neuroimaging

Neuroimaging has profoundly enhanced our understanding of the human brain by characterizing its structure, function, and connectivity through modaliti...

Feb 13 2026 2602.12974v1
Learning Image-based Tree Crown Segmentation from Enhanced Lidar-based Pseudo-labels

Mapping individual tree crowns is essential for tasks such as maintaining urban tree inventories and monitoring forest health, which help us understan...

Feb 13 2026 2602.13022v1
Calmodulin controls spatial and temporal specificity of calcium-induced calcium release

Calcium dynamics controls learning and memory. Changes in calcium-induced calcium release (CICR), which is caused by opening ryanodine receptors (RyR)...

Treatment Effects of Cholinesterase Inhibitors in Alzheimer's Disease: a Causal Machine Learning Approach

INTRODUCTION: Treatment response in Alzheimer's disease (AD) varies substantially across patients, yet no validated frameworks exist to estimate heter...

Development and validation of neurological health score using machine learning algorithms

Neurological health score (NHS), indicating the health of brain and nervous system, helps in identifying high risk individuals, and in recommending li...

AI-powered Gradient Echo Plural Contrast Imaging (AI-GEPCI): a Comprehensive Multiparametric Neurological Protocol from a Single MRI Scan

Background: MRI plays an essential role in diagnosing and monitoring neurological diseases. Conventional protocols rely on multiple sequences to obtai...

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