Latest AI and machine learning research in neurology for healthcare professionals.
Decoding brain activity from electroencephalography (EEG) is crucial for neuroscience and clinical applications. Among recent advances in deep learning for EEG, geometric learning stands out as its theoretical underpinnings on symmetric positive definite (SPD) allows revealing structural connectivity analysis in a physics-grounded manner. However, current SPD-based methods focus predominantly on s...
This paper proposes a novel medical image segmentation framework, MNAS-Unet, which combines Monte Carlo Tree Search (MCTS) and Neural Architecture Search (NAS). MNAS-Unet dynamically explores promising network architectures through MCTS, significantly enhancing the efficiency and accuracy of architecture search. It also optimizes the DownSC and UpSC unit structures, enabling fast and precise model...
Neurons in the cerebral cortex are organized topographically. In the primate visual cortex, neighboring neurons often respond to similar stimulus para...
Disease progression modeling provides a robust framework to identify long-term disease trajectories from short-term biomarker data. It is a valuable t...
Multi-tracer positron emission tomography (PET) provides critical insights into diverse neuropathological processes such as tau accumulation, neuroinf...
Restoring communication for people with dysarthria secondary to pontine stroke remains a critical challenge. Intracortical brain-computer interfaces (...
An electroencephalogram (EEG) records the spatially averaged electrical activity of neurons in the brain, measured from the human scalp. Prior studies...
Quantifying uncertainty in clinical predictions is critical for high-stakes diagnosis tasks. Conformal prediction offers a principled approach by prov...
Temporal information in structured electronic health records (EHRs) is often lost in sparse one-hot or count-based representations, while sequence mod...
Deep learning models for medical image analysis often act as black boxes, seldom aligning with clinical guidelines or explicitly linking decisions to ...
Background Epilepsy is a common neurologic disorder characterized by recurrent, unprovoked seizures. Epilepsy manifests as different seizure types and...
Importance: Large language models (LLMs) offer potential decision support, but their accuracy varies. Prompt engineering can generally enhance LLM beh...
Background: Advances in wearable devices and machine-learning-based ECG analysis enable highly accurate detection of atrial fibrillation (AF) outside ...
Neural progenitor cell (NPC) transplantation holds immense promise for neurodegenerative and traumatic central nervous system (CNS) pathologies. Howev...
Alzheimer's disease (AD) alters brain electrophysiology and disrupts multichannel EEG dynamics, making accurate and clinically useful EEG-based diagno...
Deep Generative models (DGMs) play two key roles in modern machine learning: (i) producing new information (e.g., image synthesis) and (ii) reducing d...
Abstract Background Neurodegenerative diseases, including Alzheimer's disease (AD), exhibit substantial clinical and molecular heterogeneity, complica...
Alternative splicing is a core determinant of cell-type-specific gene expression in humans, and its dysregulation contributes to many diseases includi...
Electroencephalography (EEG) foundation models (EFMs) have achieved strong performance under full fine-tuning but exhibit poor generalization when sub...
Alzheimer's disease (AD) and Lewy body dementia (LBD) present overlapping clinical features yet require distinct diagnostic strategies. While neuroima...