AIMC Topic: Brain

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Neuromorphic device architectures with global connectivity through electrolyte gating.

Nature communications
Information processing in the brain takes place in a network of neurons that are connected with each other by an immense number of synapses. At the same time, neurons are immersed in a common electrochemical environment, and global parameters such as...

Defining a multimodal signature of remote sports concussions.

The European journal of neuroscience
Sports-related concussions lead to persistent anomalies of the brain structure and function that interact with the effects of normal ageing. Although post-mortem investigations have proposed a bio-signature of remote concussions, there is still no cl...

Oxygen extraction fraction mapping at 3 Tesla using an artificial neural network: A feasibility study.

Magnetic resonance in medicine
PURPOSE: The oxygen extraction fraction (OEF) is an important biomarker for tissue-viability. MRI enables noninvasive estimation of the OEF based on the blood-oxygenation-level-dependent (BOLD) effect. Quantitative OEF-mapping is commonly applied usi...

Emergence of ultrafast sparsely synchronized rhythms and their responses to external stimuli in an inhomogeneous small-world complex neuronal network.

Neural networks : the official journal of the International Neural Network Society
We consider an inhomogeneous small-world network (SWN) composed of inhibitory short-range (SR) and long-range (LR) interneurons, and investigate the effect of network architecture on emergence of synchronized brain rhythms by varying the fraction of ...

VoxResNet: Deep voxelwise residual networks for brain segmentation from 3D MR images.

NeuroImage
Segmentation of key brain tissues from 3D medical images is of great significance for brain disease diagnosis, progression assessment and monitoring of neurologic conditions. While manual segmentation is time-consuming, laborious, and subjective, aut...

A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease.

Neuroscience letters
Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-feature...

Improving automated multiple sclerosis lesion segmentation with a cascaded 3D convolutional neural network approach.

NeuroImage
In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is ...

Immunological Approach for Full NURBS Reconstruction of Outline Curves from Noisy Data Points in Medical Imaging.

IEEE/ACM transactions on computational biology and bioinformatics
Curve reconstruction from data points is an important issue for advanced medical imaging techniques, such as computer tomography (CT) and magnetic resonance imaging (MRI). The most powerful fitting functions for this purpose are the NURBS (non-unifor...

Discriminating cognitive status in Parkinson's disease through functional connectomics and machine learning.

Scientific reports
There is growing interest in the potential of neuroimaging to help develop non-invasive biomarkers in neurodegenerative diseases. In this study, connection-wise patterns of functional connectivity were used to distinguish Parkinson's disease patients...