AIMC Topic: Brain Mapping
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Experimental Comparisons of Sparse Dictionary Learning and Independent Component Analysis for Brain Network Inference From fMRI Data.
IEEE transactions on bio-medical engineering
May 17, 2018
In this work, we conduct comprehensive comparisons between four variants of independent component analysis (ICA) methods and three variants of sparse dictionary learning (SDL) methods, both at the subject-level, by using synthesized fMRI data with gr...
Sharpening of Hierarchical Visual Feature Representations of Blurred Images.
eNeuro
May 8, 2018
The robustness of the visual system lies in its ability to perceive degraded images. This is achieved through interacting bottom-up, recurrent, and top-down pathways that process the visual input in concordance with stored prior information. The inte...
Muscleless motor synergies and actions without movements: From motor neuroscience to cognitive robotics.
Physics of life reviews
Apr 27, 2018
Emerging trends in neurosciences are providing converging evidence that cortical networks in predominantly motor areas are activated in several contexts related to 'action' that do not cause any overt movement. Indeed for any complex body, human or e...
Transferring and generalizing deep-learning-based neural encoding models across subjects.
NeuroImage
Apr 27, 2018
Recent studies have shown the value of using deep learning models for mapping and characterizing how the brain represents and organizes information for natural vision. However, modeling the relationship between deep learning models and the brain (or ...
The inhibitory effect of functional lesions on eloquent brain areas: from research bench to operating bed.
The International journal of neuroscience
Apr 23, 2018
Functioning, but injured cerebral connections are hypothesized to inhibit cortical plasticity. Study of neural networks can validate this hypothesis, and provide further practical clues for clinical and surgical options to restore function in eloque...
Computational mechanisms underlying cortical responses to the affordance properties of visual scenes.
PLoS computational biology
Apr 23, 2018
Biologically inspired deep convolutional neural networks (CNNs), trained for computer vision tasks, have been found to predict cortical responses with remarkable accuracy. However, the internal operations of these models remain poorly understood, and...
Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image.
Medical image analysis
Mar 30, 2018
Recently, more and more attention is drawn to the field of medical image synthesis across modalities. Among them, the synthesis of computed tomography (CT) image from T1-weighted magnetic resonance (MR) image is of great importance, although the mapp...
Abnormal brain structure as a potential biomarker for venous erectile dysfunction: evidence from multimodal MRI and machine learning.
European radiology
Mar 29, 2018
OBJECTIVES: To investigate the cerebral structural changes related to venous erectile dysfunction (VED) and the relationship of these changes to clinical symptoms and disorder duration and distinguish patients with VED from healthy controls using a m...
Age-related changes in the ease of dynamical transitions in human brain activity.
Human brain mapping
Mar 9, 2018
Executive functions, a set of cognitive processes that enable flexible behavioral control, are known to decay with aging. Because such complex mental functions are considered to rely on the dynamic coordination of functionally different neural system...
Clustering fMRI data with a robust unsupervised learning algorithm for neuroscience data mining.
Journal of neuroscience methods
Feb 20, 2018
BACKGROUND: Clustering approaches used in functional magnetic resonance imaging (fMRI) research use brain activity to divide the brain into various parcels with some degree of homogeneous characteristics, but choosing the appropriate clustering algor...
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