Large-scale DCMs for resting-state fMRI

This paper considers the identification of large graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, . This identification can be contrasted with methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fM...

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Veröffentlicht in:	Harvard data science review 2017-01, Vol.1 (3), p.222-241
Hauptverfasser:	Razi, Adeel, Seghier, Mohamed L., Zhou, Yuan, McColgan, Peter, Zeidman, Peter, Park, Hae-Jeong, Sporns, Olaf, Rees, Geraint, Friston, Karl J.
Format:	Artikel
Sprache:	eng
Schlagworte:	Bayesian analysis Bayesian inference Brain mapping Dynamic causal modeling Effective connectivity Empirical analysis Estimates fMRI Functional connectivity Functional magnetic resonance imaging Graph theory Graphs Large-scale networks Magnetic resonance imaging Mathematical models METHODS Model reduction Neural networks Neurodevelopmental disorders Parameterization Psychopathology Resting state
Online-Zugang:	Volltext
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