Neurology

Deep Learning Reconstruction on Quantitative Analysis in Brain Tumors With Diffusion-Weighted Imaging and Dynamic Susceptibility Contrast Imaging.

BACKGROUND: Although deep learning reconstruction (DLR) has been shown to improve image quality in MRI, its impact on quantitative physiologic parameters derived from diffusion-weighted imaging (DWI) and dynamic susceptibility contrast (DSC) perfusion in brain tumor imaging remains unclear. PURPOSE: To evaluate the impact of DLR on quantitative parameters derived from DWI and DSC in patients with ...

Predicting Behavioral Reliance on AI-Based Depression Treatment Recommendations Among Engineering Graduate Students: A Pilot Study Using EEG Signals.

OCCUPATIONAL APPLICATIONSThis pilot study demonstrates the feasibility of using EEG-derived features to characterize behavioral reliance among engineering graduate students interacting with AI-labeled recommendations. Engineering professionals frequently engage with AI-supported decision systems in safety-critical and cognitively demanding contexts. In such occupational environments, inappropriate...

Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.

Artificial intelligence (AI) is transforming biomarker discovery in neurology by overcoming key limitations of conventional approaches that are often ...

Modeling inter-slice dependencies with temporal graph learning for Alzheimer's disease.

Accurate and early diagnosis of Alzheimer's disease (AD) remains a major clinical challenge, particularly in distinguishing mild cognitive impairment ...

Geometry-aware PointNet for rapid prediction of cerebral aneurysm hemodynamics.

BACKGROUND AND OBJECTIVE: Cerebral aneurysms affect 2-5% of the global population and pose a significant health risk upon rupture. While computational...

MS360°: a conceptual digital-first, data-driven hybrid care framework for personalised multiple sclerosis management.

This perspective introduces MS360°, a conceptual hybrid care model for the management of multiple sclerosis (MS). It integrates traditional on-site as...

Machine Learning-Integrated Pharmacophore, DFT Analysis, and molecular dynamics of Diosmetin as a potent ache inhibitor with neuroprotective activity in a Scopolamine-Induced alzheimer's zebrafish model.

Alzheimer's Disease (AD) is a degenerative disorder of the brain that causes a gradual loss of cognitive function. The cholinergic hypothesis suggests...

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, su...

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 ...

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...