Neurology

Psychedelics align brain activity with context.

Psychedelics can profoundly alter consciousness by reorganizing brain connectivity1,2, producing acute experiences that shape lasting psychological change3,4. Psychedelic dynamics are commonly described as desynchronized or entropically disordered5,6, yet the brain organization underlying self-dissolving and boundary-dissolving experiences that participants often report7, and how context shapes th...

CSF Cytokine Network Organization Predicts Progression Independent of Relapse and MRI Activity in Multiple Sclerosis.

OBJECTIVE: Progression independent of relapse activity is a major determinant of long-term disability in multiple sclerosis, but its immunopathologic basis remains incompletely understood. We investigated whether relapse-independent progression in radiologically stable relapsing-remitting multiple sclerosis is associated with distinct cerebrospinal fluid inflammatory profiles and whether cytokine-...

Rational Design of Light-Driven Molecular Motors Enabled by a Data-Efficient Machine Learning Framework for Nonadiabatic Dynamics.

Light-driven molecular rotary motors convert photon energy into mechanical motion, but designing systems that combine high rotational speed with robus...

Mitochondria-Targeted Nanotherapies in Aging Neurodegenerative Disorders: Emerging Prospects and Clinical Potential.

Aging is a significant risk factor of neurodegenerative disorders (NDs) such as Huntington's, Alzheimer's, Parkinson's, amyotrophic lateral sclerosis ...

Disease-specific tau polymorphs are associated with unique protein networks across proteinopathies.

Tau protein aggregates adopt distinct conformations across tauopathies, yet the protein interactions engaged by disease-specific polymorphs remain poo...

Comparative outcomes of balloon-expandable versus self-expanding transcatheter aortic valves: insights from a large-scale German registry.

BACKGROUND: While transcatheter aortic valve replacement (TAVR) has become an established alternative to surgical aortic valve replacement (SAVR), the...

Characterization of Genetic Risk Factors of Cerebral Small Vessel Disease Based on Large-scale Transcriptome Profiling.

Cerebral small vessel disease (CSVD) is a major contributor to stroke and dementia, and it endangers the health of older individuals (>50 years old). ...

Quantification of practice effects in a self-administered online cognitive pre-screening test.

BACKGROUND: With growing prevalence of mild cognitive impairment and dementia, online self-administered pre-screening tests hold the promise to more o...

Big data in U.S. neuro-oncology: trends and translational priorities.

PURPOSE: Neuro-oncology generates complex clinical, imaging, and molecular data, yet datasets remain relatively small and fragmented across modalities...

Foundation Model Forecasting of Headache Days in People With Persisting Postconcussion Symptoms.

OBJECTIVES: We aimed to forecast headache in individuals with persisting postconcussion symptoms using foundation machine learning (ML) models and mul...

Deep Learning for Scalp-Level Nonlinear Source Separation in Electroencephalogram: A Comparative Evaluation of Spatiotemporal Architectures.

Electroencephalography (EEG) is a common technique to measure field potentials of various brain regions, and event-related potentials (ERPs) reflect s...

A Multidisciplinary Team-Based Large Language Model Framework for Predicting Postoperative Neurological Complications in Acute Type A Aortic Dissection: Model Development and Validation Study.

BACKGROUND: Postoperative neurological complications (PNCs) after acute type A aortic dissection (ATAAD) surgery are clinically emergent and require m...

NeuroSift for Task-Aware Quality Assurance of Multimedia Data in Remote Parkinson Disease Assessment: Machine Learning Model Development and Validation Study.

BACKGROUND: Automated multimedia analysis of remotely recorded tasks offers a scalable approach to screening and remote monitoring of movement disorde...

Translating cellular aging clocks into disease risk prediction.

Ding et al. mapped over 7,000 plasma proteins to more than 40 cell types and developed machine learning aging clocks across 60,000 individuals, demons...

Dataset distillation for efficient seizure prediction: tiny-scale data, comparable performance.

OBJECTIVE: Deep learning has shown significant potential in electroencephalogram (EEG)-based seizure prediction. However, translating these advances i...

A first realization of reinforcement learning-based closed-loop EEG-TMS.

Transcranial magnetic stimulation (TMS) is a powerful tool to investigate neurophysiology of the human brain and treat brain disorders. Traditionally,...

Connectome-based modeling of the suicidal brain: neurobiological substrates, clinical translation and future perspectives.

Suicide represents a global public health crisis, claiming over 700,000 lives worldwide every year. Deep understanding of the neurobiological mechanis...

Machine learning combined with fMRI identifies dynamic brain network alterations in post-stroke depression: A dual-center cross-sectional study.

OBJECTIVE: Post-stroke depression (PSD) is under-recognized and associated with poorer rehabilitation outcomes and quality of life. We characterized P...

Smartwatch-Based Screening to Identify Candidates for Deep Brain Stimulation in Parkinson's Disease.

INTRODUCTION: Identifying deep brain stimulation (DBS) candidates, particularly those without access to an advanced specialty center, presents ongoing...

EEG-based brain-computer interface (BCI) dataset for directional word recognition.

We present an EEG dataset recorded from 22 neurologically healthy volunteers (12 native Russian speakers and 10 native Spanish speakers) during overt ...