Dyconnmap: Dynamic connectome mapping—A neuroimaging python module

Despite recent progress in the analysis of neuroimaging data sets, our comprehension of the main mechanisms and principles which govern human brain cognition and function remains incomplete. Network neuroscience makes substantial efforts to manipulate these challenges and provide real answers. For t...

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Veröffentlicht in:	Human brain mapping Jg. 42; H. 15; S. 4909 - 4939
Hauptverfasser:	Marimpis, Avraam D., Dimitriadis, Stavros I., Goebel, Rainer
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Hoboken, USA John Wiley & Sons, Inc 15.10.2021
Schlagworte:	Algorithms Alzheimer's disease Brain Brain - diagnostic imaging Brain - physiology Brain mapping Brain research chronnectomics Cognition complex networks Complex systems Complexity Connectome - methods Datasets dynamic connectivity EEG Electroencephalography Electroencephalography - methods Empirical analysis Evolution fMRI Functional anatomy functional connectivity Functional magnetic resonance imaging graph theory human connectome Humans Image processing Magnetic Resonance Imaging - methods Magnetoencephalography - methods Medical imaging MEG Modules Nerve Net - diagnostic imaging Nerve Net - physiology Nervous system Networks Neural networks Neuroimaging Neurosciences python Schizophrenia Spatio-Temporal Analysis statistical analysis Structure-function relationships Time Factors Time series fMRI python functional connectivity graph theory EEG dynamic connectivity chronnectomics complex networks statistical analysis human connectome MEG
ISSN:	1065-9471, 1097-0193, 1097-0193
Online-Zugang:	Volltext
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