Neurology

Acute and sub-acute stroke lesion segmentation from multimodal MRI.

BACKGROUND AND OBJECTIVE: Acute stroke lesion segmentation tasks are of great clinical interest as they can help doctors make better informed time-critical treatment decisions. Magnetic resonance imaging (MRI) is time demanding but can provide images that are considered the gold standard for diagnosis. Automated stroke lesion segmentation can provide with an estimate of the location and volume of ...

Towards a brain-based predictome of mental illness.

Neuroimaging-based approaches have been extensively applied to study mental illness in recent years and have deepened our understanding of both cognitively healthy and disordered brain structure and function. Recent advancements in machine learning techniques have shown promising outcomes for individualized prediction and characterization of patients with psychiatric disorders. Studies have utiliz...

Reaching exercise for chronic paretic upper extremity after stroke using a novel rehabilitation robot with arm-weight support and concomitant electrical stimulation and vibration: before-and-after feasibility trial.

BACKGROUND: Our group developed a rehabilitation robot to assist with repetitive, active reaching movement of a paretic upper extremity. The robot is ...

A new lower limb portable exoskeleton for gait assistance in neurological patients: a proof of concept study.

BACKGROUND: Few portable exoskeletons following the assist-as-needed concept have been developed for patients with neurological disorders. Thus, the m...

Prediction of early neurological deterioration in acute minor ischemic stroke by machine learning algorithms.

OBJECTIVES: A significant proportion of patients with acute minor stroke have unfavorable functional outcome due to early neurological deterioration (...

Grab-AD: Generalizability and reproducibility of altered brain activity and diagnostic classification in Alzheimer's Disease.

Alzheimer's disease (AD) is associated with disruptions in brain activity and networks. However, there is substantial inconsistency among studies that...

Parkinson's Disease EMG Data Augmentation and Simulation with DCGANs and Style Transfer.

This paper proposes two new data augmentation approaches based on Deep Convolutional Generative Adversarial Networks (DCGANs) and Style Transfer for a...

Recognition of Patient Groups with Sleep Related Disorders using Bio-signal Processing and Deep Learning.

Accurately diagnosing sleep disorders is essential for clinical assessments and treatments. Polysomnography (PSG) has long been used for detection of ...

The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study.

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radio...

Working memory load-dependent changes in cortical network connectivity estimated by machine learning.

Working memory engages multiple distributed brain networks to support goal-directed behavior and higher order cognition. Dysfunction in working memory...

Social Cognition in the Age of Human-Robot Interaction.

Artificial intelligence advances have led to robots endowed with increasingly sophisticated social abilities. These machines speak to our innate desir...

Olle Höök Lectureship 2019: The changing world of stroke rehabilitation.

The paper presents a summary of the Olle Höök lecture, which was presented at the Baltic North-Sea Forum in Oslo, Sweden, in October 2019. The paper a...

Validation of machine learning models to detect amyloid pathologies across institutions.

Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) are the most commonly used method in Alz...

Real-Time Hand Gesture Recognition Using Surface Electromyography and Machine Learning: A Systematic Literature Review.

Today, daily life is composed of many computing systems, therefore interacting with them in a natural way makes the communication process more comfort...

Analyzing the Effectiveness of the Brain-Computer Interface for Task Discerning Based on Machine Learning.

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classifi...

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT).

The Drosophila model has been invaluable for the study of neurological function and for understanding the molecular and cellular mechanisms that under...

Gait-Based Machine Learning for Classifying Patients with Different Types of Mild Cognitive Impairment.

Mild cognitive impairment (MCI) may be caused by Alzheimer's disease, Parkinson's disease (PD), cerebrovascular accident, nutritional or metabolic dis...

From a deep learning model back to the brain-Identifying regional predictors and their relation to aging.

We present a Deep Learning framework for the prediction of chronological age from structural magnetic resonance imaging scans. Previous findings assoc...

Key components of mechanical work predict outcomes in robotic stroke therapy.

BACKGROUND: Clinical practice typically emphasizes active involvement during therapy. However, traditional approaches can offer only general guidance ...

AI approach of cycle-consistent generative adversarial networks to synthesize PET images to train computer-aided diagnosis algorithm for dementia.

OBJECTIVE: An artificial intelligence (AI)-based algorithm typically requires a considerable amount of training data; however, few training images are...