Dyconnmap: Dynamic connectome mapping—A neuroimaging python module

• Published in: Human brain mapping Vol. 42; no. 15; pp. 4909 - 4939
• Main Authors: Marimpis, Avraam D., Dimitriadis, Stavros I., Goebel, Rainer
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 15.10.2021
• Subjects: 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

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...