Neurology

Predicting brain structural network using functional connectivity.

Uncovering the non-trivial brain structure-function relationship is fundamentally important for revealing organizational principles of human brain. However, it is challenging to infer a reliable relationship between individual brain structure and function, e.g., the relations between individual brain structural connectivity (SC) and functional connectivity (FC). Brain structure-function displays a...

Stretchable Temperature-Responsive Multimodal Neuromorphic Electronic Skin with Spontaneous Synaptic Plasticity Recovery.

Multimodal electronic skin devices capable of detecting multimodal signals provide the possibility for health monitoring. Sensing and memory for temperature and deformation by human skin are of great significance for the perception and monitoring of physiological changes of the human body. Electronic skin is highly expected to have similar functions as human skin. Here, by implementing intrinsical...

The clinical effects of brain-computer interface with robot on upper-limb function for post-stroke rehabilitation: a meta-analysis and systematic review.

PURPOSE: Many recent clinical studies have suggested that the combination of brain-computer interfaces (BCIs) can induce neurological recovery and imp...

Parallel transmission in a synthetic nerve.

Bioelectronic devices that are tetherless and soft are promising developments in medicine, robotics and chemical computing. Here, we describe bioinspi...

Socially assistive robots for people with dementia: Systematic review and meta-analysis of feasibility, acceptability and the effect on cognition, neuropsychiatric symptoms and quality of life.

BACKGROUND: There is increasing interest in using robots to support dementia care but little consensus on the evidence for their use. The aim of the s...

Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning.

OBJECTIVE: Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative el...

SCC-MPGCN: self-attention coherence clustering based on multi-pooling graph convolutional network for EEG emotion recognition.

The emotion recognition with electroencephalography (EEG) has been widely studied using the deep learning methods, but the topology of EEG channels is...

ClinicaDL: An open-source deep learning software for reproducible neuroimaging processing.

BACKGROUND AND OBJECTIVE: As deep learning faces a reproducibility crisis and studies on deep learning applied to neuroimaging are contaminated by met...

Automatic hemorrhage segmentation on head CT scan for traumatic brain injury using 3D deep learning model.

The most common cause of long-term disability and death in young adults is a traumatic brain injury. The decision for surgical intervention for cranio...

Deep Learning and Microscopic Imaging in the Nursing Process of Neurosurgery Operation.

Neurosurgery is mainly for the treatment of head trauma, cerebrovascular disease, brain tumors, and spinal cord disorders. These operations are diffic...

Deep Transfer Learning for Automatic Prediction of Hemorrhagic Stroke on CT Images.

Intracerebral hemorrhage (ICH) is the most common type of hemorrhagic stroke which occurs due to ruptures of weakened blood vessel in brain tissue. It...

Automated stain-free histomorphometry of peripheral nerve by contrast-enhancing techniques and artificial intelligence.

BACKGROUND: Traditional histopathologic evaluation of peripheral nerve using brightfield microscopy is resource-intensive, necessitating complex sampl...

Key Feature Extraction Method of Electroencephalogram Signal by Independent Component Analysis for Athlete Selection and Training.

Emotion is an important expression generated by human beings to external stimuli in the process of interaction with the external environment. It affec...

Time-Frequency Analysis of Scalp EEG With Hilbert-Huang Transform and Deep Learning.

Electroencephalography (EEG) is a brain imaging approach that has been widely used in neuroscience and clinical settings. The conventional EEG analyse...

Interpretability Analysis of One-Year Mortality Prediction for Stroke Patients Based on Deep Neural Network.

Clinically, physicians collect the benchmark medical data to establish archives for a stroke patient and then add the follow up data regularly. It has...

Implementing Critical Machine Learning (ML) Approaches for Generating Robust Discriminative Neuroimaging Representations Using Structural Equation Model (SEM).

Critical ML or CML is a critical approach development of the standard ML (SML) procedure. Conventional ML (ML) is being used in radiology departments ...

Telerobotic neurovascular interventions with magnetic manipulation.

Advances in robotic technology have been adopted in various subspecialties of both open and minimally invasive surgery, offering benefits such as enha...

Performance of Deep Learning Models in Forecasting Gait Trajectories of Children with Neurological Disorders.

Forecasted gait trajectories of children could be used as feedforward input to control lower limb robotic devices, such as exoskeletons and actuated o...

Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM).

Emotions are an essential part of daily human communication. The emotional states and dynamics of the brain can be linked by electroencephalography (E...

Using Robot-Based Variables during Upper Limb Robot-Assisted Training in Subacute Stroke Patients to Quantify Treatment Dose.

In post-stroke motor rehabilitation, treatment dose description is estimated approximately. The aim of this retrospective study was to quantify the tr...