Neurology

Movement related activity in the μ band of the human EEG during a robot-based proprioceptive task.

Innovative research in the fields of prosthetic, neurorehabilitation, motor control and human physiology has been focusing on the study of proprioception, the sense through which we perceive the position and movement of our body, and great achievements have been obtained regarding its assessment and characterization. However, how proprioceptive signals are combined with other sensory modalities an...

Robot-assisted locomotor training did not improve walking function in patients with chronic incomplete spinal cord injury: A randomized clinical trial.

OBJECTIVE: To assess the effects of robot-assisted locomotor training in patients with chronic incomplete spinal cord injury.

Machine Learning-Based Model for Prediction of Outcomes in Acute Stroke.

Background and Purpose- The prediction of long-term outcomes in ischemic stroke patients may be useful in treatment decisions. Machine learning techni...

Machine learning-based preoperative predictive analytics for lumbar spinal stenosis.

OBJECTIVEPatient-reported outcome measures (PROMs) following decompression surgery for lumbar spinal stenosis (LSS) demonstrate considerable heterogen...

Fusing autonomy and sociality via embodied emergence and development of behaviour and cognition from fetal period.

Human-centred AI/Robotics are quickly becoming important. Their core claim is that AI systems or robots must be designed and work for the benefits of ...

A neurocognitive investigation of the impact of socializing with a robot on empathy for pain.

To what extent can humans form social relationships with robots? In the present study, we combined functional neuroimaging with a robot socializing in...

Intracerebral EEG Artifact Identification Using Convolutional Neural Networks.

Manual and semi-automatic identification of artifacts and unwanted physiological signals in large intracerebral electroencephalographic (iEEG) recordi...

Automated Neuron Detection in High-Content Fluorescence Microscopy Images Using Machine Learning.

The study of neuronal morphology in relation to function, and the development of effective medicines to positively impact this relationship in patient...

Fused Group Lasso Regularized Multi-Task Feature Learning and Its Application to the Cognitive Performance Prediction of Alzheimer's Disease.

Alzheimer's disease (AD) is characterized by gradual neurodegeneration and loss of brain function, especially for memory during early stages. Regressi...

The Residual Center of Mass: An Image Descriptor for the Diagnosis of Alzheimer Disease.

A crucial quest in neuroimaging is the discovery of image features (biomarkers) associated with neurodegenerative disorders. Recent works show that su...

Using deep learning to probe the neural code for images in primary visual cortex.

Primary visual cortex (V1) is the first stage of cortical image processing, and major effort in systems neuroscience is devoted to understanding how i...

Machine Learning Approaches to Analyze Speech-Evoked Neurophysiological Responses.

Purpose Speech-evoked neurophysiological responses are often collected to answer clinically and theoretically driven questions concerning speech and l...

Rhythmic robotic training enhances motor skills of both rhythmic and discrete upper-limb movements after stroke: a longitudinal pilot study.

Discrete and rhythmic movements are two fundamental motor primitives being, at least partially, controlled by separate neural circuitries. After a str...

Whole-brain structural magnetic resonance imaging-based classification of primary dysmenorrhea in pain-free phase: a machine learning study.

To develop a machine learning model to investigate the discriminative power of whole-brain gray-matter (GM) images derived from primary dysmenorrhea (...

Machine learning-based prediction of clinical pain using multimodal neuroimaging and autonomic metrics.

Although self-report pain ratings are the gold standard in clinical pain assessment, they are inherently subjective in nature and significantly influe...

Deep Learning Natural Language Processing Successfully Predicts the Cerebrovascular Cause of Transient Ischemic Attack-Like Presentations.

Background and Purpose- Triaging of referrals to transient ischemic attack (TIA) clinics is aided by risk stratification. Deep learning-based natural ...

Cranial Nerves: Phylogeny and Ontogeny.

This Special Issue, entitled "Cranial Nerves: phylogeny, ontogeny, morphology and clinical significance," has been divided into two consecutive volume...

Extensive Lymph Node Dissection Around the Left Laryngeal Nerve Achieved With Robot-assisted Thoracoscopic Esophagectomy.

BACKGROUND/AIM: The potential advantages of robot-assisted thoracoscopic esophagectomy (RATE) have yet to be verified. This study focused on the degre...

Predicting Future Cardiovascular Events in Patients With Peripheral Artery Disease Using Electronic Health Record Data.

BACKGROUND: Patients with peripheral artery disease (PAD) are at risk of major adverse cardiac and cerebrovascular events. There are no readily availa...

The role of robotic gait training and tDCS in Friedrich ataxia rehabilitation: A case report.

RATIONALE: Friedrich ataxia (FA) is the most common inherited neurodegenerative cerebellar ataxic syndrome. In patients with FA, physiotherapy is high...