Neurology

Lanthanide-Doped Organic Framework Sensor Array Coupled with Machine Learning for Minimally Invasive Glioma Diagnosis via Cerebrospinal Fluid Biopsy.

Glioblastoma multiforme (GBM), the most malignant subtype of glioma, poses significant diagnostic challenges due to limitations in current methods, such as invasive histopathological examination and costly, lab-restricted biomarker detection technologies. Herein, we report a lanthanide (Tb3+)-doped organic framework-based sensor array for minimally invasive, sensitive, and accurate glioma diagnosi...

Exploring feature importance in machine learning for neuroimaging traits in Alzheimer's disease across a multiethnic cohort.

BackgroundAlzheimer's disease (AD) affects 55 million people worldwide, projected to reach 139 million by 2050; yet, most machine learning (ML)-based AD classifiers have been developed in Non-Hispanic White (NHW) cohorts, limiting generalizability.ObjectiveAssess ethnic differences in AD prediction using classification performance and feature importance derived from multimodal neuroimaging biomark...

Balance biomarker for early differentiation of Parkinson's disease and multiple system atrophy with parkinsonian type.

BACKGROUND: Parkinson's disease (PD) and multiple system atrophy with parkinsonian type (MSA-P) share various motor and nonmotor symptoms, complicatin...

Transcriptomic analysis and machine learning have identified shared diagnostic genes and a possible mechanism linking bipolar disorder and epilepsy.

It is well known that bipolar disorder (BD) and epilepsy (EP) are common neurological diseases. The objective of this study was to screen for potentia...

Inferring preoperative cognitive function from intraoperative electroencephalography in elderly patients using machine learning.

To develop and evaluate machine learning (ML) models that infer preoperative cognitive function from intraoperative electroencephalography (EEG). This...

AI-Driven Mental Health Support for Caregivers of Individuals With Alzheimer Disease: Systematic Literature Review and Development of a Conceptual Framework.

BACKGROUND: Caregivers supporting individuals with Alzheimer disease and related dementias (AD/ADRD) frequently encounter prolonged emotional strain, ...

Leveraging Naturalistic Driving Digital Biomarkers for Early Mild Cognitive Impairment Detection: Deep Learning Strategies.

BACKGROUND: Alzheimer disease and related dementias are increasing worldwide, with early detection during the mild cognitive impairment (MCI) stage cr...

Current State of Artificial Intelligence Adoption and Implementation in Neuroradiology Departments: Insights from a U.S. National Survey.

BACKGROUND AND PURPOSE: Artificial intelligence (AI) is rapidly transforming medical imaging, yet its integration into neuroradiology remains uneven. ...

Mixture-of-Skip-Connection Deep Learning Model to Classify Stroke Severity from Diffusion Weighted Imaging Based on NIHSS.

The National Institutes of Health Stroke Scale (NIHSS) is a quantitative tool, grading neurological deficits and guiding acute stroke management; howe...

Single-cell and multi-omics analysis identifies mitophagy-related biomarkers and therapeutic targets in ischemic stroke.

Ischemic stroke (IS) remains a leading cause of death and disability, with limited effective treatments in the acute phase. Mitophagy, the selective d...

Unveiling state-specific neural dynamics in migraine with and without depressive symptom: a hidden Markov model and interpretable machine learning approach.

Integrating contrastive cross-modal attention and stacked GRU for hand function rehabilitation robot control.

With the intensification of population aging and the increasing incidence of neurological diseases, the demand for precise and intelligent control tec...

Unbiased quantification of persistent postural and motor deficits following spinal cord injury in mice.

Spinal cord injury (SCI) causes multifaceted postural and motor impairments that are challenging to quantify. Conventional behavioral tests, such as t...

Differential quadruple pattern: A new EEG signal classification framework.

EEG signals are the letters of the brain and reflect neural activity. Abnormal EEG patterns indicate brain disorders such as epilepsy. Recently, machi...

Forecasting-based biomedical time-series data synthesis for open data and robust AI.

The limited data availability due to strict privacy regulations and significant resource demands severely constrains biomedical time-series AI develop...

Neuroimaging insights into the neurophysiological subtypes of major depressive disorder.

Major depressive disorder (MDD) is a highly heterogeneous condition that limits the reliability of symptom-based diagnosis and treatment selection. In...

Evaluating the potential of applying artificial intelligence in the diagnosis of major depressive disorder: Neuroimaging and clinical behavioral markers.

BACKGROUND: Consensus exists that point-of-care in scalable capabilities are required to improve the timeliness and accuracy of Major Depressive Disor...

Using machine learning to reveal two distinct neuroanatomical subtypes of first-episode, drug-naïve major depressive disorder: Evidence from the REST-meta-MDD project.

BACKGROUND: Major depressive disorder (MDD) is a highly heterogeneous condition, complicating biomarker discovery and precision medicine. Identifying ...

Derivation of machine learning brain aging biomarkers for a set of forty thousand functional connectomes.

Various Magnetic Resonance Imaging modalities were developed to explore the brain. Among them, functional MRI is of key importance for studying brain ...

Improved image quality and reduced acquisition time in brain MRI using deep learning-based reconstruction: A quantitative and subjective assessment compared to standard MPRAGE in 0.55 T MRI.

PURPOSE: To assess the impact of deep learning (DL)-based image reconstruction on quantitative and subjective image quality in brain MRI at 0.55 T by ...