Neurology

Integrating network, sequence and functional features using machine learning approaches towards identification of novel Alzheimer genes.

BACKGROUND: Alzheimer's disease (AD) is a complex progressive neurodegenerative disorder commonly characterized by short term memory loss. Presently no effective therapeutic treatments exist that can completely cure this disease. The cause of Alzheimer's is still unclear, however one of the other major factors involved in AD pathogenesis are the genetic factors and around 70 % risk of the disease ...

Response to "Letter to the editor: Robot training for hand motor recovery in subacute stroke patients: A randomized controlled trial".

Re-Evaluation of Old Findings on Stroke Volume Responses to Exercise and Recovery by Nitrous-Oxide Rebreathin.

It is important to verify the old findings of Cumming (1972) and Goldberg and Shephard (1980) who showed that stroke volume (SV) may be higher during ...

Semi-supervised learning of the electronic health record for phenotype stratification.

Patient interactions with health care providers result in entries to electronic health records (EHRs). EHRs were built for clinical and billing purpos...

Do children and adolescent ice hockey players with and without a history of concussion differ in robotic testing of sensory, motor and cognitive function?

BACKGROUND: KINARM end point robotic testing on a range of tasks evaluating sensory, motor and cognitive function in children/adolescents with no neur...

Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains r...

Robot-assisted and conventional therapies produce distinct rehabilitative trends in stroke survivors.

BACKGROUND: Comparing the efficacy of alternative therapeutic strategies for the rehabilitation of motor function in chronically impaired individuals ...

A robot-based behavioural task to quantify impairments in rapid motor decisions and actions after stroke.

BACKGROUND: Stroke can affect our ability to perform daily activities, although it can be difficult to identify the underlying functional impairment(s...

Stop! border ahead: Automatic detection of subthalamic exit during deep brain stimulation surgery.

BACKGROUND: Microelectrode recordings along preplanned trajectories are often used for accurate definition of the subthalamic nucleus (STN) borders du...

Diagnosis of Alzheimer's Disease Using View-Aligned Hypergraph Learning with Incomplete Multi-modality Data.

Effectively utilizing incomplete multi-modality data for diagnosis of Alzheimer's disease (AD) is still an area of active research. Several multi-view...

Early Diagnosis of Alzheimer's Disease by Joint Feature Selection and Classification on Temporally Structured Support Vector Machine.

The diagnosis of Alzheimer's disease (AD) from neuroimaging data at the pre-clinical stage has been intensively investigated because of the immense so...

Feature Selection Based on Iterative Canonical Correlation Analysis for Automatic Diagnosis of Parkinson's Disease.

Parkinson's disease (PD) is a major progressive neurodegenerative disorder. Accurate diagnosis of PD is crucial to control the symptoms appropriately....

Evaluation of extreme learning machine for classification of individual and combined finger movements using electromyography on amputees and non-amputees.

The success of myoelectric pattern recognition (M-PR) mostly relies on the features extracted and classifier employed. This paper proposes and evaluat...

Correlated EEG Signals Simulation Based on Artificial Neural Networks.

In recent years, simulation of the human electroencephalogram (EEG) data found its important role in medical domain and neuropsychology. In this paper...

Effect of nitazoxanide on albendazole pharmacokinetics in cerebrospinal fluid and plasma in rats.

Although albendazole is the drug-of-choice for the treatment of neurocysticercosis, its efficacy is limited due to its low bioavailability. An altern...

Parkinson's disease classification using gait analysis via deterministic learning.

Gait analysis plays an important role in maintaining the well-being of human mobility and health care, and is a valuable tool for obtaining quantitati...

Artificial neural network EMG classifier for functional hand grasp movements prediction.

Objective To design and implement an electromyography (EMG)-based controller for a hand robotic assistive device, which is able to classify the user's...

A Realistic Seizure Prediction Study Based on Multiclass SVM.

A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensi...

Robots to assist daily activities: views of older adults with Alzheimer's disease and their caregivers.

BACKGROUND: Robots have the potential to both enable older adults with dementia to perform daily activities with greater independence, and provide sup...

Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data.

Machine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require l...