AIMC Topic: Brain

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A Machine Learning Approach to the Detection of Pilot's Reaction to Unexpected Events Based on EEG Signals.

Computational intelligence and neuroscience
This work considers the problem of utilizing electroencephalographic signals for use in systems designed for monitoring and enhancing the performance of aircraft pilots. Systems with such capabilities are generally referred to as cognitive cockpits. ...

Brain-Wide Genome-Wide Association Study for Alzheimer's Disease via Joint Projection Learning and Sparse Regression Model.

IEEE transactions on bio-medical engineering
Brain-wide and genome-wide association (BW-GWA) study is presented in this paper to identify the associations between the brain imaging phenotypes (i.e., regional volumetric measures) and the genetic variants [i.e., single nucleotide polymorphism (SN...

KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images.

Magnetic resonance in medicine
PURPOSE: To demonstrate accurate MR image reconstruction from undersampled k-space data using cross-domain convolutional neural networks (CNNs) METHODS: Cross-domain CNNs consist of 3 components: (1) a deep CNN operating on the k-space (KCNN), (2) a ...

Scale-freeness or partial synchronization in neural mass phase oscillator networks: Pick one of two?

NeuroImage
Modeling and interpreting (partial) synchronous neural activity can be a challenge. We illustrate this by deriving the phase dynamics of two seminal neural mass models: the Wilson-Cowan firing rate model and the voltage-based Freeman model. We establ...

Deformable Image Registration Using a Cue-Aware Deep Regression Network.

IEEE transactions on bio-medical engineering
SIGNIFICANCE: Analysis of modern large-scale, multicenter or diseased data requires deformable registration algorithms that can cope with data of diverse nature.

Deep Residual Learning for Accelerated MRI Using Magnitude and Phase Networks.

IEEE transactions on bio-medical engineering
OBJECTIVE: Accelerated magnetic resonance (MR) image acquisition with compressed sensing (CS) and parallel imaging is a powerful method to reduce MR imaging scan time. However, many reconstruction algorithms have high computational costs. To address ...

Automatic Segmentation of Acute Ischemic Stroke From DWI Using 3-D Fully Convolutional DenseNets.

IEEE transactions on medical imaging
Acute ischemic stroke is recognized as a common cerebral vascular disease in aging people. Accurate diagnosis and timely treatment can effectively improve the blood supply of the ischemic area and reduce the risk of disability or even death. Understa...

Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning.

European radiology
OBJECTIVES: To investigate the cerebral structural changes related to venous erectile dysfunction (VED) and the relationship of these changes to clinical symptoms and disorder duration and distinguish patients with VED from healthy controls using a m...

Using human brain activity to guide machine learning.

Scientific reports
Machine learning is a field of computer science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate a human ability like adding a caption to a photo, driving a car, or playing a game. While the human bra...