AIMC Topic: Brain Mapping

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Multi-Atlas Segmentation of Anatomical Brain Structures Using Hierarchical Hypergraph Learning.

IEEE transactions on neural networks and learning systems Sep 5, 2019
Accurate segmentation of anatomical brain structures is crucial for many neuroimaging applications, e.g., early brain development studies and the study of imaging biomarkers of neurodegenerative diseases. Although multi-atlas segmentation (MAS) has a...
Humans Deep Learning Brain Mapping Image Interpretation, Computer-Assisted Neural Networks, Computer Databases, Factual Brain
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Removing segmentation inconsistencies with semi-supervised non-adjacency constraint.

Medical image analysis Aug 31, 2019
The advent of deep learning has pushed medical image analysis to new levels, rapidly replacing more traditional machine learning and computer vision pipelines. However segmenting and labelling anatomical regions remains challenging owing to appearanc...
Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Brain Mapping Supervised Machine Learning Humans Tomography, X-Ray Computed
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Adversarial learning for mono- or multi-modal registration.

Medical image analysis Aug 24, 2019
This paper introduces an unsupervised adversarial similarity network for image registration. Unlike existing deep learning registration methods, our approach can train a deformable registration network without the need of ground-truth deformations an...
Humans Brain Mapping Neural Networks, Computer Magnetic Resonance Imaging Tomography, X-Ray Computed Unsupervised Machine Learning Image Processing, Computer-Assisted
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Toward an interpretable Alzheimer's disease diagnostic model with regional abnormality representation via deep learning.

NeuroImage Aug 22, 2019
In this paper, we propose a novel method for magnetic resonance imaging based Alzheimer's disease (AD) or mild cognitive impairment (MCI) diagnosis that systematically integrates voxel-based, region-based, and patch-based approaches into a unified fr...
Brain Deep Learning Cognitive Dysfunction Humans Alzheimer Disease Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Brain Mapping
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Multiscale brain MRI super-resolution using deep 3D convolutional networks.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Aug 14, 2019
The purpose of super-resolution approaches is to overcome the hardware limitations and the clinical requirements of imaging procedures by reconstructing high-resolution images from low-resolution acquisitions using post-processing methods. Super-reso...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Brain Mapping Neural Networks, Computer Imaging, Three-Dimensional Datasets as Topic Image Enhancement Humans
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Dynamic network modeling and dimensionality reduction for human ECoG activity.

Journal of neural engineering Aug 14, 2019
OBJECTIVE: Developing dynamic network models for multisite electrocorticogram (ECoG) activity can help study neural representations and design neurotechnologies in humans given the clinical promise of ECoG. However, dynamic network models have so far...
Nerve Net Electrocorticography Male Middle Aged Neural Networks, Computer Humans Brain Mapping Brain Aged Female Young Adult Adult
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Interpretable, highly accurate brain decoding of subtly distinct brain states from functional MRI using intrinsic functional networks and long short-term memory recurrent neural networks.

NeuroImage Jul 27, 2019
Decoding brain functional states underlying cognitive processes from functional MRI (fMRI) data using multivariate pattern analysis (MVPA) techniques has achieved promising performance for characterizing brain activation patterns and providing neurof...
Brain Mapping Brain Humans Adult Neural Networks, Computer Nerve Net Magnetic Resonance Imaging
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Region-of-interest undersampled MRI reconstruction: A deep convolutional neural network approach.

Magnetic resonance imaging Jul 25, 2019
Compressive sensing enables fast magnetic resonance imaging (MRI) reconstruction with undersampled k-space data. However, in most existing MRI reconstruction models, the whole MR image is targeted and reconstructed without taking specific tissue regi...
Humans Neural Networks, Computer Computers Models, Statistical Brain Brain Mapping Data Compression Image Processing, Computer-Assisted Magnetic Resonance Imaging
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Spread of α-synuclein pathology through the brain connectome is modulated by selective vulnerability and predicted by network analysis.

Nature neuroscience Jul 25, 2019
Studies of patients afflicted by neurodegenerative diseases suggest that misfolded proteins spread through the brain along anatomically connected networks, prompting progressive decline. Recently, mouse models have recapitulated the cell-to-cell tran...
Animals Cell Death Male Linear Models Brain Mapping Connectome Models, Neurological Leucine-Rich Repeat Serine-Threonine Protein Kinase-2 Mice alpha-Synuclein Mice, Inbred C57BL Neural Networks, Computer Neurons Brain Female
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Maximal flexibility in dynamic functional connectivity with critical dynamics revealed by fMRI data analysis and brain network modelling.

Journal of neural engineering Jul 23, 2019
OBJECTIVE: The exploration of time-varying functional connectivity (FC) through human neuroimaging techniques provides important new insights on the spatio-temporal organization of functional communication in the brain's networks and its alterations ...
Humans Neural Networks, Computer Brain Mapping Nerve Net Male Data Analysis Adolescent Brain Magnetic Resonance Imaging Female
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