Neurology

Confused or not: decoding brain activity and recognizing confusion in reasoning learning using EEG.

Confusion is the primary epistemic emotion in the learning process, influencing students' engagement and whether they become frustrated or bored. However, research on confusion in learning is still in its early stages, and there is a need to better understand how to recognize it and what electroencephalography (EEG) signals indicate its occurrence. The present work investigates confusion during re...

Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification.

The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing efforts, neurological observations alongside clinical evaluations are considered to be potential solutions to address diagnostic problems. The Bipolar-Sc...

Electrical Stimulation Induced Current Distribution in Peripheral Nerves Varies Significantly with the Extent of Nerve Damage: A Computational Study Utilizing Convolutional Neural Network and Realistic Nerve Models.

Electrical stimulation of the peripheral nervous system is a promising therapeutic option for several conditions; however, its effects on tissue and t...

Deciphering multiple sclerosis disability with deep learning attention maps on clinical MRI.

The application of convolutional neural networks (CNNs) to MRI data has emerged as a promising approach to achieving unprecedented levels of accuracy ...

A multi-task and multi-channel convolutional neural network for semi-supervised neonatal artefact detection.

. Automated artefact detection in the neonatal electroencephalogram (EEG) is crucial for reliable automated EEG analysis, but limited availability of ...

Deep Learning Algorithm Enables Cerebral Venous Thrombosis Detection With Routine Brain Magnetic Resonance Imaging.

BACKGROUND: Cerebral venous thrombosis (CVT) is a rare cerebrovascular disease. Routine brain magnetic resonance imaging is commonly used to diagnose ...

Effects of Upper Limb Robot-Assisted Rehabilitation Compared with Conventional Therapy in Patients with Stroke: Preliminary Results on a Daily Task Assessed Using Motion Analysis.

Robotic rehabilitation of the upper limb has demonstrated promising results in terms of the improvement of arm function in post-stroke patients. The c...

Mammalian-brain-inspired neuromorphic motion-cognition nerve achieves cross-modal perceptual enhancement.

Perceptual enhancement of neural and behavioral response due to combinations of multisensory stimuli are found in many animal species across different...

Is the robotic rehabilitation that is added to intensive body rehabilitation effective for maximization of upper extremity motor recovery following a stroke? A randomized controlled study.

BACKGROUND: Trunk stabilization, which is a factor that directly affects the performance of affected upper-limb movements in stroke patients, is of cr...

Big Data in Stroke: How to Use Big Data to Make the Next Management Decision.

The last decade has seen significant advances in the accumulation of medical data, the computational techniques to analyze that data, and correspondin...

Learning-Based Motion-Intention Prediction for End-Point Control of Upper-Limb-Assistive Robots.

The lack of intuitive and active human-robot interaction makes it difficult to use upper-limb-assistive devices. In this paper, we propose a novel lea...

Biopsychosocial effects and experience of use of robotic and virtual reality devices in neuromotor rehabilitation: A study protocol.

BACKGROUND: Robot-assisted therapy (RAT) and virtual reality (VR)-based neuromotor rehabilitation have shown promising evidence in terms of patient's ...

A comprehensive characterization of hippocampal feature ensemble serves as individualized brain signature for Alzheimer's disease: deep learning analysis in 3238 participants worldwide.

OBJECTIVES: Hippocampal characterization is one of the most significant hallmarks of Alzheimer's disease (AD); rather, the single-level feature is ins...

External Validation of an Artificial Intelligence Device for Intracranial Hemorrhage Detection.

BACKGROUND: Artificial intelligence applications have gained traction in the field of cerebrovascular disease by assisting in the triage, classificati...

Toward explainable AI-empowered cognitive health assessment.

Explainable artificial intelligence (XAI) is of paramount importance to various domains, including healthcare, fitness, skill assessment, and personal...

[Robotic Diaphragm Plication].

Acquired unilateral hemidiaphragm elevation is characterised by dyspnoea, which is typically aggravated when lying down, bending over or during swimmi...

Automatic Detection of Alzheimer's Disease using Deep Learning Models and Neuro-Imaging: Current Trends and Future Perspectives.

Deep learning algorithms have a huge influence on tackling research issues in the field of medical image processing. It acts as a vital aid for the ra...

Non-invasive regional cerebral blood flow quantification in the 123I-IMP autoradiography using artificial neural network.

PURPOSE: Regional cerebral blood flow (rCBF) quantification using 123I-N-isopropyl-p-iodoamphetamine (123I-IMP) requires an invasive, one-time-only ar...

Diagnosis of Alzheimer Disease and Tauopathies on Whole-Slide Histopathology Images Using a Weakly Supervised Deep Learning Algorithm.

Neuropathologic assessment during autopsy is the gold standard for diagnosing neurodegenerative disorders. Neurodegenerative conditions, such as Alzhe...

iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction.

The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many in...