ArticleBrain sciences2019
Uncovering Dynamic Brain Reconfiguration in MEG Working Memory
Article in Brain sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Article
- NeuMapper: A scalable computational framework for multiscale exploration of the brain's dynamical organization.Network neuroscience (Cambridge, Mass.) · 2022Article
- Association between the total amount of electromagnetic cortical neuronal activity and a decline in motivation.Physiological reports · 2021Article
- Simplicial and topological descriptions of human brain dynamics.Network neuroscience (Cambridge, Mass.) · 2021Article
- Manifold Learning of Dynamic Functional Connectivity Reliably Identifies Functionally Consistent Coupling Patterns in Human Brains.Brain sciences · 2019Article
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Authors and funding
3 authors.
Funding
Abstract
The increasing availability of high temporal resolution neuroimaging data has increased the efforts to understand the dynamics of neural functions. Until recently, there are few studies on generative models supporting classification and prediction of neural systems compared to the description of the architecture. However, the requirement of collapsing data spatially and temporally in the state-of-the art methods to analyze functional magnetic resonance imaging (fMRI), electroencephalogram (EEG) and magnetoencephalography (MEG) data cause loss of important information. In this study, we addressed this issue using a topological data analysis (TDA) method, called Mapper, which visualizes evolving patterns of brain activity as a mathematical graph. Accordingly, we analyzed preprocessed MEG data of 83 subjects from Human Connectome Project (HCP) collected during working memory
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