Neurology

Random forest feature selection, fusion and ensemble strategy: Combining multiple morphological MRI measures to discriminate among healhy elderly, MCI, cMCI and alzheimer's disease patients: From the alzheimer's disease neuroimaging initiative (ADNI) database.

BACKGROUND: In the era of computer-assisted diagnostic tools for various brain diseases, Alzheimer's disease (AD) covers a large percentage of neuroimaging research, with the main scope being its use in daily practice. However, there has been no study attempting to simultaneously discriminate among Healthy Controls (HC), early mild cognitive impairment (MCI), late MCI (cMCI) and stable AD, using f...

Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine: a case study.

In this work, we have used a time-frequency domain analysis method called discrete wavelet transform (DWT) technique. This method stand out compared to other proposed methods because of its algorithmic elegance and accuracy. A wavelet is a mathematical function based on time-frequency analysis in signal processing. It is useful particularly because it allows a weak signal to be recovered from a no...

Intrinsic interactive reinforcement learning - Using error-related potentials for real world human-robot interaction.

Reinforcement learning (RL) enables robots to learn its optimal behavioral strategy in dynamic environments based on feedback. Explicit human feedback...

Neural Network Modeling of AChE Inhibition by New Carbazole-Bearing Oxazolones.

Acetylcholine esterase (AChE) is one of the targeted enzymes in the therapy of important neurodegenerative diseases such as Alzheimer's disease. Many ...

Emergent coordination underlying learning to reach to grasp with a brain-machine interface.

The development of coordinated reach-to-grasp movement has been well studied in infants and children. However, the role of motor cortex during this de...

Modeling spike-wave discharges by a complex network of neuronal oscillators.

PURPOSE: The organization of neural networks and the mechanisms, which generate the highly stereotypical for absence epilepsy spike-wave discharges (S...

Epileptic Seizure Prediction Using Big Data and Deep Learning: Toward a Mobile System.

BACKGROUND: Seizure prediction can increase independence and allow preventative treatment for patients with epilepsy. We present a proof-of-concept fo...

Netrin-1 Ameliorates Blood-Brain Barrier Impairment Secondary to Ischemic Stroke via the Activation of PI3K Pathway.

Secondary impairment of blood-brain barrier (BBB) occurs in the remote thalamus after ischemic stroke. Netrin-1, an axonal guidance molecule, presents...

Metabolism of Dopamine in Nucleus Accumbens Astrocytes Is Preserved in Aged Mice Exposed to MPTP.

Parkinson disease (PD) is prevalent in elderly individuals and is characterized by selective degeneration of igrotriatal opmine (NSDA) neurons. Intere...

Computer-Assisted Recovery of Threatened Plants: Keys for Breaking Seed Dormancy of .

Many endangered plants such as (Apiaceae) present a poor germination rate. This fact could be due to intrinsic and extrinsic seed variability influen...

Ensemble of random forests One vs. Rest classifiers for MCI and AD prediction using ANOVA cortical and subcortical feature selection and partial least squares.

BACKGROUND: Alzheimer's disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. M...

Machine learning shows association between genetic variability in and cerebral connectivity in preterm infants.

Preterm infants show abnormal structural and functional brain development, and have a high risk of long-term neurocognitive problems. The molecular an...

Bullying incidences identification within an immersive environment using HD EEG-based analysis: A Swarm Decomposition and Deep Learning approach.

Bullying is an everlasting phenomenon and the first, yet difficult, step towards the solution is its detection. Conventional approaches for bullying i...

A bicentric controlled study on the effects of aquatic Ai Chi in Parkinson disease.

OBJECTIVES: Various exercise strategies have been suggested to address movement deficits in order to improve motor function and quality of life for in...

Nonlinear predictive control for adaptive adjustments of deep brain stimulation parameters in basal ganglia-thalamic network.

The efficacy of deep brain stimulation (DBS) for Parkinson's disease (PD) depends in part on the post-operative programming of stimulation parameters....

Automatic spinal cord localization, robust to MRI contrasts using global curve optimization.

During the last two decades, MRI has been increasingly used for providing valuable quantitative information about spinal cord morphometry, such as qua...

Tensor Decomposition of Gait Dynamics in Parkinson's Disease.

OBJECTIVE: The study of gait in Parkinson's disease is important because it can provide insights into the complex neural system and physiological beha...

Robotic assisted gait as a tool for rehabilitation of individuals with spinal cord injury: a systematic review.

BACKGROUND: Spinal cord injury (SCI) is characterized by a total or partial deficit of sensory and motor pathways. Impairments of this injury compromi...

"I Treat Him as a Normal Patient": Unveiling the Normalization Coping Strategy Among Formal Caregivers of Persons With Dementia and Its Implications for Person-Centered Care.

INTRODUCTION: Currently, 47 million people have dementia, worldwide, often requiring paid care by formal caregivers. Research regarding family caregiv...

Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines.

BACKGROUND AND PURPOSE: In this study we attempt to automatically classify individual patients with different parkinsonian disorders, making use of pa...