AIMC Topic: Brain Mapping
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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 ...
Accelerating GluCEST imaging using deep learning for B correction.
Magnetic resonance in medicine
Apr 17, 2020
PURPOSE: Glutamate weighted Chemical Exchange Saturation Transfer (GluCEST) MRI is a noninvasive technique for mapping parenchymal glutamate in the brain. Because of the sensitivity to field (B ) inhomogeneity, the total acquisition time is prolonged...
From spatial navigation via visual construction to episodic memory and imagination.
Biological cybernetics
Apr 13, 2020
This hybrid of review and personal essay argues that models of visual construction are essential to extend spatial navigation models to models that link episodic memory and imagination. The starting point is the TAM-WG model, combining the Taxon Affo...
Multivariate patterns of EEG microstate parameters and their role in the discrimination of patients with schizophrenia from healthy controls.
Psychiatry research
Apr 6, 2020
Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of large scale brain networks. Using machine learning techniques, we explored whether abnormalities in microstates can be used to classify patients with s...
Achieving affective human-virtual agent communication by enabling virtual agents to imitate positive expressions.
Scientific reports
Apr 6, 2020
Affective communication, communicating with emotion, during face-to-face communication is critical for social interaction. Advances in artificial intelligence have made it essential to develop affective human-virtual agent communication. A person's b...
Topics and trends in artificial intelligence assisted human brain research.
PloS one
Apr 6, 2020
Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous gro...
Feature optimization method for machine learning-based diagnosis of schizophrenia using magnetoencephalography.
Journal of neuroscience methods
Mar 19, 2020
BACKGROUND: When many features and a small number of clinical data exist, previous studies have used a few top-ranked features from the Fisher's discriminant ratio (FDR) for feature selection. However, there are many similarities between selected fea...
Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques.
NeuroImage. Clinical
Mar 7, 2020
Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, which is diagnosed using subjective symptom reports. Machine learning classifiers have been utilized to assist in the development of ...
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