Neurology

Association of initial core volume on non-contrast CT using a deep learning algorithm with clinical outcomes in acute ischemic stroke: a potential tool for selection and prognosis?

BACKGROUND: In an extended time window, contrast-based neuroimaging is valuable for treatment selection or prognosis in patients with stroke undergoing reperfusion treatment. However, its immediate availability remains limited, especially in resource-constrained regions. We sought to evaluate the association of initial core volume (ICV) measured on non-contrast computed tomography (NCCT) by a deep...

The Insomnia EEG Score: a new tool for the classification of people with poor sleep.

Quantitative features could help objectively identify and grade insomnia severity, though there is currently no pathophysiological biomarker of insomnia. In this study, we used the largest cohort of individuals with and without insomnia to date to train a model capable of distinguishing people with insomnia from those without insomnia. We identified 720 spectral, 606 spindle, and 16 macro-architec...

Multimodal cardiovascular risk profiling using self-supervised learning of polysomnography.

STUDY OBJECTIVES: Polysomnography (PSG) provides a comprehensive assessment of brain, cardiac, and respiratory activity during sleep. While it is wide...

Statistically valid explainable black-box machine learning: applications in sex classification across species using brain imaging.

Sex classification using neuroimaging data has the potential to revolutionize personalized diagnostics by revealing subtle structural brain difference...

Differential diagnosis of migraine and tension-type headache based on brain volumes using machine learning methods.

PURPOSE: Structural brain changes in migraine and tension-type headache (TTH) have mostly been investigated using voxel-based morphometry or regional ...

Enhanced cortical connectivity during passive robotic-assisted gait training in individuals with spinal cord injury: An EEG study.

BACKGROUND: Robotic-assisted gait training (RAGT) has emerged as a promising strategy to promote neuroplasticity and motor recovery in individuals wit...

Lower pre-treatment TMS-evoked cortical reactivity and alpha-band oscillatory dynamics predict efficacy of primary motor cortex neuromodulation for chronic pain.

Repetitive transcranial magnetic stimulation (rTMS) to the primary motor cortex (M1) provides significant pain relief in ∼45% of chronic pain patients...

Drawn-on-skin electronic tattoo as a closed-loop sensing-stimulation system for the muscles.

Simultaneous electromyography (EMG) sensing and closed-loop electrical stimulation (ES) could enable interactive muscle training, motor rehabilitation...

Linking neurological status to functional outcomes in spinal cord injury: a multi-class, task-specific approach.

Neural Reconstruction in Disorders of Consciousness: Prospects and Emerging Challenges.

Reconstructing mental experience from brain activity is becoming increasingly feasible through advances in neuroimaging and deep learning. Neural sign...

Prediction of acetylcholinesterase inhibition associated with Alzheimer's disease using hybrid descriptor and graph-based machine learning models.

Accurate prediction of acetylcholinesterase (AChE) inhibitory activity is important in drug discovery and environmental toxicology because AChE inhibi...

Multimodal Machine Learning for Diabetic Peripheral Neuropathy Diagnosis: Methodological Advances and Future Directions.

MRI-Guided High-Intensity Focused Ultrasound in Movement Disorders: Targeting Pathologic Brain Circuits With Precision Imaging, From the AJR Special Series on Critical Anatomy.

MRI-guided high-intensity focused ultrasound (MRgHIFU) has emerged as an alternative to other neuromodulatory interventions for patients with medicall...

Interpretable deep learning reveals spatiotemporal MRI features of brain aging that align with neurodegeneration.

Cortical thinning and atrophy are hallmarks of brain aging that have been characterized using magnetic resonance imaging (MRI). Brain aging involves m...

Bidirectional Mamba-2 Boosts EEG Super-Resolution via Regression and Diffusion.

MOTIVATIONS: Electroencephalography (EEG) is a non-invasive method that records brain electrical activity from scalp electrodes, offering millisecond ...

MAISNet: A Multi-Species Integrated Graph Neural Network for Acetylcholinesterase Inhibitor Screening.

MOTIVATION: Global population aging has led to a rapid increase in neurodegenerative disorders such as alzheimer's disease (AD). Although existing dru...

A novel machine learning approach for prediction of postoperative delirium using multi-domain features of high-density EEG.

Postoperative delirium (POD) poses a significant risk to patients, and accurate prediction of postoperative delirium can provide guidance for pos...

Predicting post-stroke functional outcome using explainable machine learning and integrated data.

Functional outcome after acute ischemic stroke (AIS) varies widely, and existing prognostic scores may not capture complex relationships. We evaluated...

Feature selection leads to divergent neurobiological interpretations of brain-based machine learning biomarkers.

A central objective in human neuroimaging is to understand the neurobiology underlying cognition and mental health. Machine learning models trained on...

Malignant Cerebral Edema after Large-Core Anterior Circulation Stroke in the Thrombectomy Era: Prediction and Neurocritical Care Pathways.

Large-core anterior circulation ischemic stroke (LCIS) complicated by malignant cerebral edema (MCE) remains a leading cause of early death and profou...