AIMC Topic: Brain

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A Novel Sparse Dictionary Learning Separation (SDLS) Model With Adaptive Dictionary Mutual Incoherence Constraint for fMRI Data Analysis.

IEEE transactions on bio-medical engineering
OBJECTIVE: Many studies have shown that the independence assumption in the widely-used ICAs is not adaptive enough for brain functional networks (BFN) detection due to the complex brain hemodynamics, functional integration, artifacts embedded in func...

Control-group feature normalization for multivariate pattern analysis of structural MRI data using the support vector machine.

NeuroImage
Normalization of feature vector values is a common practice in machine learning. Generally, each feature value is standardized to the unit hypercube or by normalizing to zero mean and unit variance. Classification decisions based on support vector ma...

A multi-layer network approach to MEG connectivity analysis.

NeuroImage
Recent years have shown the critical importance of inter-regional neural network connectivity in supporting healthy brain function. Such connectivity is measurable using neuroimaging techniques such as MEG, however the richness of the electrophysiolo...

Function approximation in inhibitory networks.

Neural networks : the official journal of the International Neural Network Society
In performance-optimized artificial neural networks, such as convolutional networks, each neuron makes excitatory connections with some of its targets and inhibitory connections with others. In contrast, physiological neurons are typically either exc...

Atoms of recognition in human and computer vision.

Proceedings of the National Academy of Sciences of the United States of America
Discovering the visual features and representations used by the brain to recognize objects is a central problem in the study of vision. Recently, neural network models of visual object recognition, including biological and deep network models, have s...

Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.

IEEE transactions on medical imaging
We propose a novel segmentation approach based on deep 3D convolutional encoder networks with shortcut connections and apply it to the segmentation of multiple sclerosis (MS) lesions in magnetic resonance images. Our model is a neural network that co...

Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks.

IEEE transactions on medical imaging
Cerebral microbleeds (CMBs) are small haemorrhages nearby blood vessels. They have been recognized as important diagnostic biomarkers for many cerebrovascular diseases and cognitive dysfunctions. In current clinical routine, CMBs are manually labelle...

Physicochemical property profile for brain permeability: comparative study by different approaches.

Journal of drug targeting
A comparative study of classification models of brain penetration by different approaches was carried out on a training set of 1000 chemicals and drugs, and an external test set of 100 drugs. Ten approaches were applied in this work: seven medicinal ...

Segmenting Brain Tissues from Chinese Visible Human Dataset by Deep-Learned Features with Stacked Autoencoder.

BioMed research international
Cryosection brain images in Chinese Visible Human (CVH) dataset contain rich anatomical structure information of tissues because of its high resolution (e.g., 0.167 mm per pixel). Fast and accurate segmentation of these images into white matter, gray...

State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

NeuroImage
Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over ti...