Neurology

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

We thank the readers for their thoughtful reading of our review, "Artificial Intelligence for Neuroimaging in Pediatric Cancer", and for their interest in discussing aspects of segmentation performance metrics [...].

Prediction of mild cognitive impairment using blood multi-omics data.

Mild cognitive impairment (MCI) represents an initial phase of memory or other cognitive function decline and is viewed as an intermediary stage between normal aging and Alzheimer's disease (AD), the most prevalent type of dementia. Individuals with MCI face a heightened risk of progressing to AD, and early detection of MCI can facilitate the prevention of such progression through timely intervent...

A data-centric and interpretable EEG framework for depression severity grading using SHAP-based insights.

BACKGROUND: Major Depressive Disorder is a leading cause of disability worldwide. An accurate assessment of depression severity is critical for diagno...

Sex-related differences and associated transcriptional signatures in the brain ventricular system and cerebrospinal fluid development in full-term neonates.

BACKGROUND: The cerebrospinal fluid (CSF) is known to serve as a unique environment for neurodevelopment, with specific proteins secreted by epithelia...

Speech-based digital cognitive assessments for detection of mild cognitive impairment: Validation against paper-based neurocognitive assessment scores.

BackgroundCognitive decline in Alzheimer's disease (AD) often includes speech impairments, where subtle changes may precede clinical dementia onset. A...

Deep learning-based classification and segmentation of interictal epileptiform discharges using multichannel electroencephalography.

OBJECTIVE: This study was undertaken to develop a deep learning framework that can classify and segment interictal epileptiform discharges (IEDs) in m...

Forecasting epileptic seizures with wearable devices: A hybrid short- and long-horizon pseudo-prospective approach.

OBJECTIVE: Seizure unpredictability can be debilitating and dangerous for people with epilepsy. Accurate seizure forecasters could improve quality of ...

Inorganic Biomaterials Inducing Scaffolds Pre-Neuralization for Infarcted Myocardium Repair.

Neural networks are found to play an important role in monitoring and coordinating cardiac physiological activities. However, the clinical use of neur...

Developing a ground truth for a convolutional neural network-based segmentation of anatomical structures in endoscopic ear surgery videos.

OBJECTIVE: This study aimed to develop a ground truth for a convolutional neural network-based segmentation of the stapes, chorda tympani and facial n...

Multimodal Explainable Artificial Intelligence for Prognostic Stratification of Glioblastoma Patients.

Glioblastoma is the most common and aggressive malignant adult tumor of the central nervous system, with a grim prognosis and heterogeneous morphologi...

Electroencephalography microstates predict 12-h abstinence-induced craving changes in young smokers.

Nicotine abstinence inhibits the function of the mesolimbic dopamine system to enhance craving. EEG microstates may provide spatiotemporal characteris...

The Role of Hydrogen Sulfide in the Localization and Structural-Functional Organization of p53 Following Traumatic Brain Injury: Development of a YOLO Model for Detection and Quantification of Apoptotic Nuclei.

Traumatic brain injury (TBI) triggers a cascade of molecular and cellular disturbances, including apoptosis, inflammation, and destabilization of neur...

Predictive machine learning model for 30-day hospital readmissions in a tertiary healthcare setting.

MOTIVATION: Hospital readmissions represent a major challenge for healthcare systems due to their impact on patient outcomes and associated costs. As ...

Improving automatic cerebral 3D-2D CTA-DSA registration.

PURPOSE: Stroke remains a leading cause of morbidity and mortality worldwide, despite advances in treatment modalities. Endovascular thrombectomy (EVT...

A Wearable Ultra-Low-Power System for EEG-based Speech-Imagery Interfaces.

Speech imagery-the process of mentally simulating speech without vocalization-is a promising approach for brain-computer interfaces (BCIs), enabling a...

DenoiseMamba: An Innovative Approach for EEG Artifact Removal Leveraging Mamba and CNN.

Electroencephalography (EEG) is a widely used tool for monitoring brain activity, but it is often disturbed by various artifacts, such as electrooculo...

SSC-SleepNet: A Siamese-Based Automatic Sleep Staging Model with Improved N1 Sleep Detection.

Automatic sleep staging from single-channel electroencephalography (EEG) using artificial intelligence (AI) is emerging as an alternative to costly an...

Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform.

Post-COVID-19, depression rates have risen sharply, increasing the need for early diagnosis using electroencephalogram (EEG) and deep learning. To tac...

Development of a non-contrast CT-based radiomics nomogram for early prediction of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage.

BACKGROUNDS: Delayed cerebral ischemia (DCI) is a significant complication following aneurysmal subarachnoid hemorrhage (aSAH), leading to poor progno...

Automated detection of severe cerebral edema using explainable deep transfer learning after hypoxic ischemic brain injury.

BACKGROUND: Substantial gaps exist in the neuroprognostication of cardiac arrest patients who remain comatose after the restoration of spontaneous cir...