Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine
Sep 4, 2015
BACKGROUND AND PURPOSE: We analyzed the ability of a machine learning approach that uses diffusion tensor imaging (DTI) structural connectomes to determine lateralization of epileptogenicity in temporal lobe epilepsy (TLE).
Brain development is adversely affected by preterm birth. Magnetic resonance image analysis has revealed a complex fusion of structural alterations across all tissue compartments that are apparent by term-equivalent age, persistent into adolescence a...
BACKGROUND: Epilepsy is one of the most common neurological disorders approximately one in every 100 people worldwide are suffering from it. Uncontrolled epilepsy poses a significant burden to society due to associated healthcare cost to treat and co...
BACKGROUND: The development of MRI based methods could prove extremely valuable for identification of reliable biomarkers to aid diagnosis of neurodegenerative diseases (NDs). A great deal of current research has been aimed at identification biomarke...
BACKGROUND: A brain-machine interface (BMI) should be able to help people with disabilities by replacing their lost motor functions. To replace lost functions, robot arms have been developed that are controlled by invasive neural signals. Although in...
Motivated by neuroscience discoveries during the last few years, many studies consider pulse-coupled neural networks with spike-timing as an essential component in information processing by the brain. There also exists some technical challenges while...
Neural networks : the official journal of the International Neural Network Society
Aug 18, 2015
The application of biologically inspired methods in design and control has a long tradition in robotics. Unlike previous approaches in this direction, the emerging field of neurorobotics not only mimics biological mechanisms at a relatively high leve...
A wide interest has been observed in the medical health care applications that interpret neuroimaging scans by machine learning systems. This research proposes an intelligent, automatic, accurate, and robust classification technique to classify the h...
PURPOSE: Multifractal applications to resting state functional MRI (rs-fMRI) time series for diagnosing Alzheimer's disease (AD) are still limited. We aim to address two issues: (I) if and what multifractal features are sufficiently discriminative to...
BACKGROUND: Electroencephalography (EEG) based sleep staging is commonly used in clinical routine. Feature extraction and representation plays a crucial role in EEG-based automatic classification of sleep stages. Sparse representation (SR) is a state...
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