Neurology

Deep learning on brief interictal intracranial recordings can accurately characterize seizure onset zones.

OBJECTIVE: Epilepsy is a debilitating disorder affecting more than 50 million people worldwide, and one third of patients continue to have seizures despite maximal medical management. If patients' seizures localize to a discrete brain region, termed a seizure onset zone, resection may be curative. Localization is often confirmed with stereotactic electroencephalography; however, this may require p...

Neuronal network analysis during short time transcranial direct current stimulation with permanent EEG measurement in early childhood: a feasibility study.

Transcranial direct current stimulation (tDCS) as a non-invasive stimulation is still in the experimental stage for many psychiatric disorders even in adults. The use of tDCS provides an opportunity to influence neural networks and their functional connectivity. How tDCS affects cortical networks and how it influences the functional connectivity of the developing brain is largely unknown. The elec...

Development and validation of a quick screening tool for predicting neck pain patients benefiting from spinal manipulation: a machine learning study.

BACKGROUND: Neck pain (NP) ranks among the leading causes of years lived with disability worldwide. While spinal manipulation is a common physical the...

A Pilot Study Comparing Speech Characteristics in People With Parkinson's Disease and Controls Dancing Weekly Over 5-Years.

INTRODUCTION: Parkinson's Disease (PD) is a neurodegenerative disorder that affects motor and non-motor functions. Speech impairments, such as reduced...

Emerging blood biomarkers in Alzheimer's disease: a proteomic perspective.

Early detection of Alzheimer's disease (AD) remains a formidable clinical challenge, but emerging blood-based assays show promise for identifying at-r...

AI-assisted multi-OMICS analysis reveals new markers for the prediction of AD.

Alzheimer's Disease (AD) is the most prevalent neurodegenerative disorder, characterized by progressive cognitive decline. Early and accurate diagnosi...

Advances in Diagnostic Approaches for Alzheimer's Disease: From Biomarkers to Deep Learning Technology.

Alzheimer's disease (AD) is a devastating neurological disorder that affects humans and is a major contributor to dementia. It is characterized by cog...

Neurostimulation for the Management of Epilepsy: Advances in Targeted Therapy.

BACKGROUND: Epilepsy is a multifaceted neurological disorder marked by seizures that can present with a wide range of symptoms. Despite the prevalent ...

Machine Learning for Missing Data Imputation in Alzheimer's Research: Predicting Medial Temporal Lobe Flexibility.

BACKGROUND: Alzheimer's disease (AD) begins years before symptoms appear, making early detection essential. The medial temporal lobe (MTL) is one of t...

iSeizdiag: toward the framework development of epileptic seizure detection for healthcare.

INTRODUCTION: The seizure episodes result from abnormal and excessive electrical discharges by a group of brain cells. EEG framework-based signal acqu...

Deep learning-based automated segmentation for the quantitative diagnosis of cerebral small vessel disease via multisequence MRI.

OBJECTIVE: Existing visual scoring systems for cerebral small vessel disease (CSVD) cannot assess the global lesion load accurately and quantitatively...

Cerebral gray matter volume identifies healthy older drivers with a critical decline in driving safety performance using actual vehicles on a closed-circuit course.

INTRODUCTION: Identifying older drivers at risk of critical decline in driving safety performance (DSP) is essential for traffic safety. Regional cere...

Neuropsychological and clinical variables associated with cognitive trajectories in patients with Alzheimer's disease.

BACKGROUND: The NeuroArtP3 (NET-2018-12366666) is a multicenter study funded by the Italian Ministry of Health. The aim of the project is to identify ...

Enhanced visibility graph for EEG classification.

Electroencephalography (EEG) holds immense potential for decoding complex brain patterns associated with cognitive states and neurological conditions....

Heterogeneity in the association between internet use and dementia among older adults: A machine-learning analysis.

BACKGROUND & AIMS: Internet use among older adults may reduce the risk of dementia, but it remains unknown how the effects vary across individuals. Th...

Gait analysis for Parkinson's disease using multiscale entropy.

Parkinson's disease (PD) is a progressive neurodegenerative disorder marked by motor dysfunction and complex gait abnormalities. Traditional linear me...

EEG Connectivity as Predictor of ICAN ADHD Children's Improvement After Completion of Theta Beta Ratio Neurofeedback: Machine Learning Analyses.

Attention deficit hyperactivity disorder is a prevalent syndrome that costs billions of dollars annually. Finding meaningful interventions based upon ...

EEG microstate analysis in trigeminal neuralgia: identifying potential biomarkers for enhanced diagnostic accuracy.

OBJECTIVE: This study investigated EEG microstate dynamics in trigeminal neuralgia (TN) patients to understand the central nervous system's contributi...

Computational Risk Stratification of Preclinical Alzheimer's in Younger Adults.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that often begins decades before clinical symptoms manifest. Early detection rem...

Comment on Dalboni da Rocha et al. Artificial Intelligence for Neuroimaging in Pediatric Cancer. 2025, , 622.

As researchers actively working in the field of pediatric neuroimaging and artificial intelligence, we were pleased to see your recent review, Artific...