Neurology

Machine Learning on Dynamic Functional Connectivity: Promise, Pitfalls, and Interpretations.

An unprecedented amount of existing functional Magnetic Resonance Imaging (fMRI) data provides a new opportunity to understand how functional fluctuations relate to human cognition/behavior using data-driven approaches. To this end, tremendous efforts have been made in machine learning to decode cognitive states from evolving volumetric images of blood-oxygen-level-dependent (BOLD) signals. Howeve...

Deep learning-based diagnostic classification of multiple sclerosis using multicenter optical coherence tomography data.

BACKGROUND: Multiple sclerosis (MS) is a chronic inflammatory disorder of the central nervous system, where timely and accurate diagnosis is essential for effective management. Optical coherence tomography (OCT) enables non-invasive evaluation of retinal changes that may serve as biomarkers for MS. Unlike other ophthalmologic diseases, raw cross-sectional OCT images in MS show subtle alterations o...

Characterization of cutaneous wound healing in swine.

The porcine excisional wound model is widely regarded as the most translationally relevant preclinical platform for studying human skin wound healing ...

Identification of mitochondrial dysfunction-related biomarkers and immune infiltration in liver ischemia-reperfusion injury via integrated bioinformatics and machine learning.

BACKGROUND: Mitochondrial dysfunction contributes to the pathogenesis of multiple diseases. This study explores the involvement of mitochondrial dysfu...

De novo identification of potent ingredients for proteasome activation in MT101-5 using an AI-driven approach.

Parkinson's disease (PD) is a neurodegenerative disorder characterized by the intracellular accumulation of α-synuclein (α-syn) protein within Lewy bo...

Data-driven subgroups for 3-year risk stratification of incident diabetes and complications in diabetes-free Chinese adults.

BACKGROUND: Substantial metabolic heterogeneity exists prior to the development of diabetes, creating opportunities for earlier and more precise inter...

Integration of Multi-Omics and Machine Learning Identifies TGFB1 and SERPINE1 as Biomarkers of Vascular Smooth Muscle Cell Senescence in Intracranial Aneurysms.

Intracranial aneurysms (IAs) are severe cerebrovascular disorders lacking effective gene therapy or pharmacological interventions. Vascular smooth mus...

Teravoxel Microscopy Image Analysis for Neurological Diseases.

Light-sheet fluorescence microscopy (LSFM) has emerged as a revolutionary imaging modality for investigating intact three-dimensional brain structures...

Unsupervised Disentanglement of Brain Heterogeneity for Identifying Subtypes of Alzheimer's Disease.

Neuroanatomical heterogeneity in Alzheimer's disease (AD) hinders precision diagnosis and treatment, as distinct brain phenotypes may correspond to di...

Digitized Canine Olfaction and Multimodal Biosensing for Breath-Based Multi-Cancer Detection: A Hypothesis-Driven Perspective.

Multi-Cancer Early Detection (MCED) is critical for reducing cancer mortality, however current screening technologies have limitations in accessibilit...

EEG foundation models: A critical review of current progress and future directions.

PREMISE: Patterns of electrical brain activity recorded via electroencephalography (EEG) offer immense value for scientific and clinical investigation...

A Comparative Evaluation of 7T MRI for Epilepsy with Deep-Learning-Based Image Reconstruction and Dynamic Parallel Transmission.

OBJECTIVES: 7T MRI enhances lesion detection in epilepsy but is limited by radiofrequency transmission field (B1+) inhomogeneity and long scan times. ...

Alzheimer's disease prediction using deep learning and XAI based interpretable feature selection from blood gene expression data.

Alzheimer's disease (AD), a type of neurodegenerative disorder, has seen an increase in cases over the past decade, necessitating the construction of ...

The use of the odds ratio product and self-reported data to detect comorbid insomnia and sleep apnea.

STUDY OBJECTIVES: To evaluate the utility of the odds ratio product (ORP) in differentiating comorbid insomnia and sleep apnea (COMISA) from obstructi...

Health-oriented sleep states: making sleep states reflect health conditions.

STUDY OBJECTIVES: The rich information in sleep offers insights into brain function and overall health. The current guidelines for sleep staging by th...

MS-STFNN: A multi-scale spatio-temporal fusion neural network for fMRI-based depression diagnosis.

Major depressive disorder (MDD) is a common mental disorder. Current clinical diagnosis primarily relies on subjective scales, underscoring an urgent ...

Elucidating the toxicological impact and mechanism of plasticizers exposure on Alzheimer's disease through network toxicology and molecular docking.

This study aimed to explore the underlying mechanisms and key targets of widely used plasticizers, including dimethyl phthalate (DMP), diethyl phthala...

Prenatal alcohol exposure and the retrosplenial cortex: Structural and functional mechanisms across species.

The retrosplenial cortex (RSC) is a structurally and functionally complex hub with broad connectivity, integrating sensory inputs, cognitive states, a...

Lesion network mapping in pediatric epilepsy.

OBJECTIVE: To describe the current use, limitations, and future directions of lesion network mapping in pediatric epilepsy. METHODS: Narrative review ...

Exploring the potential of explainable deep learning for EEG-based cognitive decline prediction.

OBJECTIVE: Detecting Alzheimer's disease (AD) at an early stage is essential for administering effective treatments and preventing neuronal damage. Un...