Latest AI and machine learning research in neurology for healthcare professionals.
BACKGROUND: Intellectual disability (ID), autism, and epilepsy share frequent yet variable comorbidities with one another. In order to better understand potential genetic divergence underlying this variable risk, we studied genes responsible for monogenic IDs, grouped according to their autism and epilepsy comorbidities.
Functional magnetic resonance imaging (fMRI) is one of the most promising noninvasive techniques for early Alzheimer's disease (AD) diagnosis. In this paper, we explore the application of different machine learning techniques to the classification of fMRI data for this purpose. The functional images were first preprocessed using the statistical parametric mapping toolbox to output individual maps ...
CONTEXT: Spinal cord injury (SCI) affects more than 2.5 million people worldwide, often leading to severe disability. Thus, a proper management of ind...
BACKGROUND: Electroencephalogram (EEG) and electromyogram (EMG) recordings are often used in rodents to study sleep architecture and sleep-associated ...
The MIT-Skywalker is a novel robotic device developed for the rehabilitation or habilitation of gait and balance after a neurological injury. It repre...
Understanding the progression of chronic diseases can empower the sufferers in taking proactive care. To predict the disease status in the future time...
Multivariate pattern analysis techniques have been increasingly used over the past decade to derive highly sensitive and specific biomarkers of diseas...
This paper describes in detail the design of the specialized text predictor for patients with Huntington's disease. The main aim of the specialized te...
Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity ...
OBJECTIVE: To examine the outcomes of home-based robot-guided therapy and compare it to laboratory-based robot-guided therapy for the treatment of imp...
While there is an extensive number of studies on the development and evaluation of electromyography (EMG)- and force-based control interfaces for assi...
In this paper we propose an exoskeleton control method for adaptive learning of assistive joint torque profiles in periodic tasks. We use human muscle...
Diagnosis, clinical management and research of psychiatric disorders remain subjective - largely guided by historically developed categories which may...
In this study several investigations and tests were performed to determine the antioxidant activity and the acetylcholinesterase and tyrosinase inhibi...
A novel solution to compensate hand grasping abilities is proposed for chronic stroke patients. The goal is to provide the patients with a wearable ro...
Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebro...
Predictions of patient outcomes after a given therapy are fundamental to medical practice. We employ a machine learning approach towards predicting th...
Parkinson's disease (PD) is a movement disorder that affects the patient's nervous system and health-care applications mostly uses wearable sensors to...
Identifying diffuse axonal injury (DAI) in patients with traumatic brain injury (TBI) presenting with normal appearing radiological MRI presents a sig...