AIMC Topic: Brain Mapping
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Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain.
eLife
Jan 18, 2021
We have developed an open-source software called bi-channel image registration and deep-learning segmentation (BIRDS) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image preprocess...
xQSM: quantitative susceptibility mapping with octave convolutional and noise-regularized neural networks.
NMR in biomedicine
Dec 27, 2020
Quantitative susceptibility mapping (QSM) provides a valuable MRI contrast mechanism that has demonstrated broad clinical applications. However, the image reconstruction of QSM is challenging due to its ill-posed dipole inversion process. In this stu...
Interpretability of Spatiotemporal Dynamics of the Brain Processes Followed by Mindfulness Intervention in a Brain-Inspired Spiking Neural Network Architecture.
Sensors (Basel, Switzerland)
Dec 21, 2020
Mindfulness training is associated with improvements in psychological wellbeing and cognition, yet the specific underlying neurophysiological mechanisms underpinning these changes are uncertain. This study uses a novel brain-inspired artificial neura...
Finding Distributed Needles in Neural Haystacks.
The Journal of neuroscience : the official journal of the Society for Neuroscience
Dec 17, 2020
The human cortex encodes information in complex networks that can be anatomically dispersed and variable in their microstructure across individuals. Using simulations with neural network models, we show that contemporary statistical methods for funct...
Deep learning networks reflect cytoarchitectonic features used in brain mapping.
Scientific reports
Dec 16, 2020
The distribution of neurons in the cortex (cytoarchitecture) differs between cortical areas and constitutes the basis for structural maps of the human brain. Deep learning approaches provide a promising alternative to overcome throughput limitations ...
DIKA-Nets: Domain-invariant knowledge-guided attention networks for brain skull stripping of early developing macaques.
NeuroImage
Dec 15, 2020
As non-human primates, macaques have a close phylogenetic relationship to human beings and have been proven to be a valuable and widely used animal model in human neuroscience research. Accurate skull stripping (aka. brain extraction) of brain magnet...
Changes to information in working memory depend on distinct removal operations.
Nature communications
Dec 7, 2020
Holding information in working memory is essential for cognition, but removing unwanted thoughts is equally important. Here we use multivariate pattern analyses of brain activity to demonstrate the successful manipulation and removal of information f...
Multimodal magnetic resonance imaging correlates of motor outcome after stroke using machine learning.
Neuroscience letters
Nov 6, 2020
This study applied machine learning regression to predict motor function after stroke based on multimodal magnetic resonance imaging. Fifty-four stroke patients, who underwent T1 weighted, diffusion tensor, and resting state functional magnetic reson...
Reliability of robotic transcranial magnetic stimulation motor mapping.
Journal of neurophysiology
Nov 4, 2020
Robotic transcranial magnetic stimulation (TMS) is a noninvasive and safe tool that produces cortical motor maps using neuronavigational and neuroanatomical images. Motor maps are individualized representations of the primary motor cortex (M1) topogr...
Multi-band MR fingerprinting (MRF) ASL imaging using artificial-neural-network trained with high-fidelity experimental data.
Magnetic resonance in medicine
Oct 26, 2020
PURPOSE: We aim to leverage the power of deep-learning with high-fidelity training data to improve the reliability and processing speed of hemodynamic mapping with MR fingerprinting (MRF) arterial spin labeling (ASL).
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