AIMC Topic: Brain

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3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution.

Sensors (Basel, Switzerland)
The diagnosis of brain pathologies usually involves imaging to analyze the condition of the brain. Magnetic resonance imaging (MRI) technology is widely used in brain disorder diagnosis. The image quality of MRI depends on the magnetostatic field str...

Predictive Visual Motion Extrapolation Emerges Spontaneously and without Supervision at Each Layer of a Hierarchical Neural Network with Spike-Timing-Dependent Plasticity.

The Journal of neuroscience : the official journal of the Society for Neuroscience
The fact that the transmission and processing of visual information in the brain takes time presents a problem for the accurate real-time localization of a moving object. One way this problem might be solved is extrapolation: using an object's past t...

Machine learning for detection of interictal epileptiform discharges.

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
The electroencephalogram (EEG) is a fundamental tool in the diagnosis and classification of epilepsy. In particular, Interictal Epileptiform Discharges (IEDs) reflect an increased likelihood of seizures and are routinely assessed by visual analysis o...

Brain graph super-resolution using adversarial graph neural network with application to functional brain connectivity.

Medical image analysis
Brain image analysis has advanced substantially in recent years with the proliferation of neuroimaging datasets acquired at different resolutions. While research on brain image super-resolution has undergone a rapid development in the recent years, b...

Improving brain age estimates with deep learning leads to identification of novel genetic factors associated with brain aging.

Neurobiology of aging
To study genetic factors associated with brain aging, we first need to quantify brain aging. Statistical models have been created for estimating the apparent age of the brain, or predicted brain age (PBA), using imaging data. Recent studies have refi...

Hybrid memristor-CMOS neurons for in-situ learning in fully hardware memristive spiking neural networks.

Science bulletin
Spiking neural network, inspired by the human brain, consisting of spiking neurons and plastic synapses, is a promising solution for highly efficient data processing in neuromorphic computing. Recently, memristor-based neurons and synapses are becomi...

Whole-brain functional MRI registration based on a semi-supervised deep learning model.

Medical physics
PURPOSE: Traditional registration of functional magnetic resonance images (fMRI) is typically achieved through registering their coregistered structural MRI. However, it cannot achieve accurate performance in that functional units which are not neces...

Information seeking criteria: artificial intelligence, economics, psychology, and neuroscience.

Reviews in the neurosciences
There has been an enormous amount of interest in how the brain seeks information. The study of this issue is a rapidly growing field in neuroscience. Information seeking is to make informative choices among multiple alternatives. A central issue in i...

A graph neural network framework for causal inference in brain networks.

Scientific reports
A central question in neuroscience is how self-organizing dynamic interactions in the brain emerge on their relatively static structural backbone. Due to the complexity of spatial and temporal dependencies between different brain areas, fully compreh...