AIMC Topic: Brain

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Design and validation of an ontology-driven animal-free testing strategy for developmental neurotoxicity testing.

Toxicology and applied pharmacology
Developmental neurotoxicity entails one of the most complex areas in toxicology. Animal studies provide only limited information as to human relevance. A multitude of alternative models have been developed over the years, providing insights into mech...

Medical Image Synthesis with Deep Convolutional Adversarial Networks.

IEEE transactions on bio-medical engineering
Medical imaging plays a critical role in various clinical applications. However, due to multiple considerations such as cost and radiation dose, the acquisition of certain image modalities may be limited. Thus, medical image synthesis can be of great...

Learning Temporal Information for Brain-Computer Interface Using Convolutional Neural Networks.

IEEE transactions on neural networks and learning systems
Deep learning (DL) methods and architectures have been the state-of-the-art classification algorithms for computer vision and natural language processing problems. However, the successful application of these methods in motor imagery (MI) brain-compu...

A convolutional neural network to filter artifacts in spectroscopic MRI.

Magnetic resonance in medicine
PURPOSE: Proton MRSI is a noninvasive modality capable of generating volumetric maps of in vivo tissue metabolism without the need for ionizing radiation or injected contrast agent. Magnetic resonance spectroscopic imaging has been shown to be a viab...

Age-related changes in the ease of dynamical transitions in human brain activity.

Human brain mapping
Executive functions, a set of cognitive processes that enable flexible behavioral control, are known to decay with aging. Because such complex mental functions are considered to rely on the dynamic coordination of functionally different neural system...

Multineuron spike train analysis with R-convolution linear combination kernel.

Neural networks : the official journal of the International Neural Network Society
A spike train kernel provides an effective way of decoding information represented by a spike train. Some spike train kernels have been extended to multineuron spike trains, which are simultaneously recorded spike trains obtained from multiple neuron...

Investigating brain structural patterns in first episode psychosis and schizophrenia using MRI and a machine learning approach.

Psychiatry research. Neuroimaging
In this study, we employed the Maximum Uncertainty Linear Discriminant Analysis (MLDA) to investigate whether the structural brain patterns in first episode psychosis (FEP) patients would be more similar to patients with chronic schizophrenia (SCZ) o...

Motor-commands decoding using peripheral nerve signals: a review.

Journal of neural engineering
During the last few decades, substantial scientific and technological efforts have been focused on the development of neuroprostheses. The major emphasis has been on techniques for connecting the human nervous system with a robotic prosthesis via nat...

Classifying dynamic transitions in high dimensional neural mass models: A random forest approach.

PLoS computational biology
Neural mass models (NMMs) are increasingly used to uncover the large-scale mechanisms of brain rhythms in health and disease. The dynamics of these models is dependent upon the choice of parameters, and therefore it is crucial to be able to understan...

Multi-channel multi-scale fully convolutional network for 3D perivascular spaces segmentation in 7T MR images.

Medical image analysis
Accurate segmentation of perivascular spaces (PVSs) is an important step for quantitative study of PVS morphology. However, since PVSs are the thin tubular structures with relatively low contrast and also the number of PVSs is often large, it is chal...