AIMC Topic: Brain Mapping
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Functional magnetic resonance imaging multivoxel pattern analysis reveals neuronal substrates for collaboration and competition with myopic and predictive strategic reasoning.
Human brain mapping
Jul 7, 2020
Competition and collaboration are strategies that can be used to optimize the outcomes of social interactions. Research into the neuronal substrates underlying these aspects of social behavior has been limited due to the difficulty in distinguishing ...
Brain-optimized extraction of complex sound features that drive continuous auditory perception.
PLoS computational biology
Jul 2, 2020
Understanding how the human brain processes auditory input remains a challenge. Traditionally, a distinction between lower- and higher-level sound features is made, but their definition depends on a specific theoretical framework and might not match ...
Investigating the challenges and generalizability of deep learning brain conductivity mapping.
Physics in medicine and biology
Jun 26, 2020
To investigate deep learning electrical properties tomography (EPT) for application on different simulated and in-vivo datasets, including pathologies for brain conductivity reconstructions, 3D patch-based convolutional neural networks were trained t...
Identifying psychosis spectrum youth using support vector machines and cerebral blood perfusion as measured by arterial spin labeled fMRI.
NeuroImage. Clinical
Jun 4, 2020
Altered cerebral blood flow (CBF), as measured by arterial spin labelling (ASL), has been observed in several psychiatric conditions, but is a generally underutilized MRI technique, especially in the study of psychosis spectrum (PS) symptoms. We aime...
Nonlinear ICA of fMRI reveals primitive temporal structures linked to rest, task, and behavioral traits.
NeuroImage
May 30, 2020
Accumulating evidence from whole brain functional magnetic resonance imaging (fMRI) suggests that the human brain at rest is functionally organized in a spatially and temporally constrained manner. However, because of their complexity, the fundamenta...
Classifying creativity: Applying machine learning techniques to divergent thinking EEG data.
NeuroImage
May 28, 2020
Prior research has shown that greater EEG alpha power (8-13 Hz) is characteristic of more creative individuals, and more creative task conditions. The present study investigated the potential for machine learning to classify more and less creative b...
Working memory load-dependent changes in cortical network connectivity estimated by machine learning.
NeuroImage
May 1, 2020
Working memory engages multiple distributed brain networks to support goal-directed behavior and higher order cognition. Dysfunction in working memory has been associated with cognitive impairment in neuropsychiatric disorders. It is important to cha...
Designing weighted correlation kernels in convolutional neural networks for functional connectivity based brain disease diagnosis.
Medical image analysis
Apr 23, 2020
Functional connectivity networks (FCNs) based on functional magnetic resonance imaging (fMRI) have been widely applied to analyzing and diagnosing brain diseases, such as Alzheimer's disease (AD) and its prodrome stage, i.e., mild cognitive impairmen...
Bio-inspired multi-scale fusion.
Biological cybernetics
Apr 22, 2020
We reveal how implementing the homogeneous, multi-scale mapping frameworks observed in the mammalian brain's mapping systems radically improves the performance of a range of current robotic localization techniques. Roboticists have developed a range ...
Classifying heterogeneous presentations of PTSD via the default mode, central executive, and salience networks with machine learning.
NeuroImage. Clinical
Apr 22, 2020
Intrinsic connectivity networks (ICNs), including the default mode network (DMN), the central executive network (CEN), and the salience network (SN) have been shown to be aberrant in patients with posttraumatic stress disorder (PTSD). The purpose of ...
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