AIMC Topic: Brain Mapping
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Extraction of the cardiac waveform from simultaneous multislice fMRI data using slice sorted averaging and a deep learning reconstruction filter.
NeuroImage
May 23, 2019
Cardiac signal contamination has long confounded the analysis of blood-oxygenation-level-dependent (BOLD) functional magnetic resonance imaging (fMRI). Cardiac pulsation results in significant BOLD signal changes, especially in and around blood vesse...
Topological correction of infant white matter surfaces using anatomically constrained convolutional neural network.
NeuroImage
May 18, 2019
Reconstruction of accurate cortical surfaces without topological errors (i.e., handles and holes) from infant brain MR images is very important in early brain development studies. However, infant brain MR images typically suffer extremely low tissue ...
A multi-objective optimization approach for brain MRI segmentation using fuzzy entropy clustering and region-based active contour methods.
Magnetic resonance imaging
May 17, 2019
In this paper, we present a new multi-objective optimization approach for segmentation of Magnetic Resonance Imaging (MRI) of the human brain. The proposed algorithm not only takes advantages but also solves major drawbacks of two well-known compleme...
Variational autoencoder: An unsupervised model for encoding and decoding fMRI activity in visual cortex.
NeuroImage
May 16, 2019
Goal-driven and feedforward-only convolutional neural networks (CNN) have been shown to be able to predict and decode cortical responses to natural images or videos. Here, we explored an alternative deep neural network, variational auto-encoder (VAE)...
A semi-blind online dictionary learning approach for fMRI data.
Journal of neuroscience methods
May 11, 2019
BACKGROUND: Online dictionary learning (ODL) has been applied to extract brain networks from functional magnetic resonance imaging (fMRI) data in recent year. Moreover, the supervised dictionary learning (SDL) that fixed the task stimulus curves as p...
Adaptive neural network classifier for decoding MEG signals.
NeuroImage
May 4, 2019
We introduce two Convolutional Neural Network (CNN) classifiers optimized for inferring brain states from magnetoencephalographic (MEG) measurements. Network design follows a generative model of the electromagnetic (EEG and MEG) brain signals allowin...
Spectral and Temporal Feature Learning With Two-Stream Neural Networks for Mental Workload Assessment.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Apr 26, 2019
People's mental workload profoundly affects their work efficiency and health. Mental workload assessment can be used to effectively avoid serious accidents caused by excessive mental workload. Both electroencephalogram (EEG) spectral features and its...
A simulation-based approach to improve decoded neurofeedback performance.
NeuroImage
Apr 4, 2019
The neural correlates of specific brain functions such as visual orientation tuning and individual finger movements can be revealed using multivoxel pattern analysis (MVPA) of fMRI data. Neurofeedback based on these distributed patterns of brain acti...
Predicting anxiety from wholebrain activity patterns to emotional faces in young adults: a machine learning approach.
NeuroImage. Clinical
Apr 3, 2019
BACKGROUND: It is becoming increasingly clear that pathophysiological processes underlying psychiatric disorders categories are heterogeneous on many levels, including symptoms, disease course, comorbidity and biological underpinnings. This heterogen...
DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping.
NeuroImage
Mar 29, 2019
Quantitative susceptibility mapping (QSM) is based on magnetic resonance imaging (MRI) phase measurements and has gained broad interest because it yields relevant information on biological tissue properties, predominantly myelin, iron and calcium in ...
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