A generative model of whole-brain effective connectivity

• Published in: NeuroImage (Orlando, Fla.) Vol. 179; pp. 505 - 529
• Main Authors: Frässle, Stefan, Lomakina, Ekaterina I., Kasper, Lars, Manjaly, Zina M., Leff, Alex, Pruessmann, Klaas P., Buhmann, Joachim M., Stephan, Klaas E.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2018; Elsevier Limited
• Subjects: Bayes Theorem; Bayesian analysis; Bayesian regression; Brain; Brain - physiology; Brain mapping; Computational neuroscience; Connectome - methods; Connectomics; Data processing; Dynamic causal modeling; Effective connectivity; Entropy; Estimates; Functional magnetic resonance imaging; Generative model; Humans; Magnetic Resonance Imaging - methods; Medical imaging; Models, Neurological; Models, Theoretical; Nerve Net - physiology; Neural networks; Neuroimaging; Parameter estimation; Phenotyping; Sparsity; Bayesian regression; Dynamic causal modeling; Sparsity; Connectomics; Generative model; Effective connectivity

The development of whole-brain models that can infer effective (directed) connection strengths from fMRI data represents a central challenge for computational neuroimaging. A recently introduced generative model of fMRI data, regression dynamic causal modeling (rDCM), moves towards this goal as it s...