AIMC Topic: Brain Mapping

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BrainNET: Inference of Brain Network Topology Using Machine Learning.

Brain connectivity Oct 8, 2020
To develop a new functional magnetic resonance image (fMRI) network inference method, BrainNET, that utilizes an efficient machine learning algorithm to quantify contributions of various regions of interests (ROIs) in the brain to a specific ROI. B...
Humans Adolescent Computer Simulation Machine Learning Child Female Algorithms Brain Mapping Image Processing, Computer-Assisted Sensitivity and Specificity Brain Neural Pathways Male Magnetic Resonance Imaging Attention Deficit Disorder with Hyperactivity
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Reconstructing lost BOLD signal in individual participants using deep machine learning.

Nature communications Oct 7, 2020
Signal loss in blood oxygen level-dependent (BOLD) functional neuroimaging is common and can lead to misinterpretation of findings. Here, we reconstructed compromised fMRI signal using deep machine learning. We trained a model to learn principles gov...
Deep Learning Deep Brain Stimulation Humans Adolescent Healthy Volunteers Datasets as Topic Image Processing, Computer-Assisted Female Cerebral Cortex Magnetic Resonance Imaging Young Adult Models, Neurological Adult Parkinson Disease Male Brain Mapping Oxygen
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Differential Covariance: A New Method to Estimate Functional Connectivity in fMRI.

Neural computation Sep 18, 2020
Measuring functional connectivity from fMRI recordings is important in understanding processing in cortical networks. However, because the brain's connection pattern is complex, currently used methods are prone to producing false functional connectio...
Models, Neurological Brain Mapping Neural Pathways Nerve Net Brain Animals Humans Image Processing, Computer-Assisted Neural Networks, Computer Magnetic Resonance Imaging
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Disentangling time series between brain tissues improves fMRI data quality using a time-dependent deep neural network.

NeuroImage Sep 6, 2020
Functional MRI (fMRI) is a prominent imaging technique to probe brain function, however, a substantial proportion of noise from multiple sources influences the reliability and reproducibility of fMRI data analysis and limits its clinical applications...
Neural Pathways Image Processing, Computer-Assisted Reproducibility of Results Humans Magnetic Resonance Imaging Aged Neural Networks, Computer Brain Mapping Male Brain Artifacts Female
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fMRI volume classification using a 3D convolutional neural network robust to shifted and scaled neuronal activations.

NeuroImage Sep 5, 2020
Deep-learning methods based on deep neural networks (DNNs) have recently been successfully utilized in the analysis of neuroimaging data. A convolutional neural network (CNN) is a type of DNN that employs a convolution kernel that covers a local area...
Motor Activity Visual Perception Auditory Perception Magnetic Resonance Imaging Brain Mapping Attention Psychomotor Performance Humans Neural Networks, Computer Brain Young Adult Adult Male Support Vector Machine
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Motion opponency examined throughout visual cortex with multivariate pattern analysis of fMRI data.

Human brain mapping Sep 2, 2020
This study explores how the human brain solves the challenge of flicker noise in motion processing. Despite providing no useful directional motion information, flicker is common in the visual environment and exhibits omnidirectional motion energy whi...
Humans Multivariate Analysis Visual Cortex Motion Perception Adult Support Vector Machine Magnetic Resonance Imaging Brain Mapping
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Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity.

Neural plasticity Aug 24, 2020
The effects of acupuncture facilitating neural plasticity for treating diseases have been identified by clinical and experimental studies. In the last two decades, the application of neuroimaging techniques in acupuncture research provided visualized...
Neuroimaging Image Processing, Computer-Assisted Neuronal Plasticity Humans Brain Mapping Machine Learning Acupuncture Therapy Brain
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Passive, yet not inactive: robotic exoskeleton walking increases cortical activation dependent on task.

Journal of neuroengineering and rehabilitation Aug 10, 2020
BACKGROUND: Experimental designs using surrogate gait-like movements, such as in functional magnetic resonance imaging (MRI), cannot fully capture the cortical activation associated with overground gait. Overground gait in a robotic exoskeleton may b...
Adult Male Spectroscopy, Near-Infrared Magnetic Resonance Imaging Brain Mapping Robotics Walking Exercise Therapy Humans Brain Female Healthy Volunteers Exoskeleton Device
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The formation and use of hierarchical cognitive maps in the brain: A neural network model.

Network (Bristol, England) Aug 3, 2020
Many researchers have tried to model how environmental knowledge is learned by the brain and used in the form of cognitive maps. However, previous work was limited in various important ways: there was little consensus on how these cognitive maps were...
Humans Neural Networks, Computer Algorithms Brain Animals Cognition Brain Mapping
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Probing the neural dynamics of mnemonic representations after the initial consolidation.

NeuroImage Jul 31, 2020
Memories are not stored as static engrams, but as dynamic representations affected by processes occurring after initial encoding. Previous studies revealed changes in activity and mnemonic representations in visual processing areas, parietal lobe, an...
Memory Consolidation Adult Hippocampus Male Pattern Recognition, Visual Cerebral Cortex Humans Adolescent Spatial Memory Memory, Episodic Female Support Vector Machine Magnetic Resonance Imaging Association Learning Mental Recall Young Adult Brain Mapping Cues Image Interpretation, Computer-Assisted
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