Neurology

Neuronal network-based mathematical modeling of perceived verticality in acute unilateral vestibular lesions: from nerve to thalamus and cortex.

Acute unilateral lesions of vestibular graviceptive pathways from the otolith organs and semicircular canals via vestibular nuclei and the thalamus to the parieto-insular vestibular cortex regularly cause deviations of perceived verticality in the frontal roll plane. These tilts are ipsilateral in peripheral and in ponto-medullary lesions and contralateral in ponto-mesencephalic lesions. Unilatera...

Epileptic seizure anticipation and localisation of epileptogenic region using EEG signals.

Electric activity of brain gets disturbed prior to epileptic seizure onset. Early prediction of an upcoming seizure can help to increase effectiveness of antiepileptic drugs. The scalp electroencephalogram signals contain information about the dynamics of brain and have been used to predict an upcoming seizure and localise its zone. The objective of this paper is to localise the epileptogenic regi...

Concussion classification via deep learning using whole-brain white matter fiber strains.

Developing an accurate and reliable injury predictor is central to the biomechanical studies of traumatic brain injury. State-of-the-art efforts conti...

Integrating spatial-anatomical regularization and structure sparsity into SVM: Improving interpretation of Alzheimer's disease classification.

In recent years, machine learning approaches have been successfully applied to the field of neuroimaging for classification and regression tasks. Howe...

Prediction of central neuropathic pain in spinal cord injury based on EEG classifier.

OBJECTIVES: To create a classifier based on electroencephalography (EEG) to identify spinal cord injured (SCI) participants at risk of developing cent...

Reshaping Movement Distributions With Limit-Push Robotic Training.

High-cost situations need to be avoided. However, occasionally, cost may only be learned by experience. Here, we tested whether an artificially induce...

A computational framework for the detection of subcortical brain dysmaturation in neonatal MRI using 3D Convolutional Neural Networks.

Deep neural networks are increasingly being used in both supervised learning for classification tasks and unsupervised learning to derive complex patt...

Tensor-driven extraction of developmental features from varying paediatric EEG datasets.

OBJECTIVE: Constant changes in developing children's brains can pose a challenge in EEG dependant technologies. Advancing signal processing methods to...

Factors associated with dementia in elderly.

We analyzed the factors associated with dementia in the elderly attended at a memory outpatient clinic of the University of Southern Santa Catarina (U...

Generalized Recurrent Neural Network accommodating Dynamic Causal Modeling for functional MRI analysis.

Dynamic Causal Modeling (DCM) is an advanced biophysical model which explicitly describes the entire process from experimental stimuli to functional m...

Automated comprehensive Adolescent Idiopathic Scoliosis assessment using MVC-Net.

Automated quantitative estimation of spinal curvature is an important task for the ongoing evaluation and treatment planning of Adolescent Idiopathic ...

A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals.

The electroencephalogram (EEG) is the most prominent means to study epilepsy and capture changes in electrical brain activity that could declare an im...

Differential Effects of Simulated Cortical Network Lesions on Synchrony and EEG Complexity.

Brain function has been proposed to arise as a result of the coordinated activity between distributed brain areas. An important issue in the study of ...

PLGA Microspheres Loaded with β-Cyclodextrin Complexes of Epigallocatechin-3-Gallate for the Anti-Inflammatory Properties in Activated Microglial Cells.

Although epigallocatechin-3-gallate (EG) is well-known as a potent antioxidant and free radical scavenger for neurodegenerative diseases, it still has...

The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users.

This work aims at corroborating the importance and efficacy of mutual learning in motor imagery (MI) brain-computer interface (BCI) by leveraging the ...

Recovery and compensation after robotic assisted gait training in chronic stroke survivors.

Gait re-education is a primary rehabilitation goal after stroke. In the last decades, robots with different mechanical structures have been extensive...

Robot-Guided Neuronavigated Repetitive Transcranial Magnetic Stimulation (rTMS) in Central Neuropathic Pain.

OBJECTIVES: To confirm and extend previous results involving repetitive transcranial magnetic stimulation (rTMS) aimed at alleviating refractory centr...

Model-based and Model-free Machine Learning Techniques for Diagnostic Prediction and Classification of Clinical Outcomes in Parkinson's Disease.

In this study, we apply a multidisciplinary approach to investigate falls in PD patients using clinical, demographic and neuroimaging data from two in...

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram.

Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients wit...

A fresh look at functional link neural network for motor imagery-based brain-computer interface.

BACKGROUND: Artificial neural networks (ANNs) are one of the widely used classifiers in the brain-computer interface (BCI) systems-based on noninvasiv...