Dependency Network Analysis (DEPNA) Reveals Context Related Influence of Brain Network Nodes.
Communication between and within brain regions is essential for information processing within functional networks. The current methods to determine the influence of one region on another are either based on temporal resolution, or require a predefined model for the connectivity direction. However these requirements are not always achieved, especially in fMRI studies, which have poor temporal resolution. We thus propose a new graph theory approach that focuses on the correlation influence between selected brain regions, entitled Dependency Network Analysis (DEPNA). Partial correlations are used to quantify the level of influence of each node during task performance. As a proof of concept, we conducted the DEPNA on simulated datasets and on two empirical motor and working memory fMRI tasks. The simulations revealed that the DEPNA correctly captures the network's hierarchy of influence. Applying DEPNA to the functional tasks reveals the dynamics between specific nodes as would be expected from prior knowledge. To conclude, we demonstrate that DEPNA can capture the most influencing nodes in the network, as they emerge during specific cognitive processes. This ability opens a new horizon for example in delineating critical nodes for specific clinical interventions.
Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.|School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel.|Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Yael
Y
Jacob
Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.|Biophysics Program, Stanford University, Stanford, CA, USA.
Yonatan
Y
Winetraub
Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.|Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.|The Steve Tisch School of Film and Television, Tel Aviv University, Tel Aviv, Israel.
Gal
G
Raz
Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
Eti
E
Ben-Simon
Department of Psychology, University of Haifa, Haifa, Israel.
Hadas
H
Okon-Singer
Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.|Center for the study of Movement, Cognition, and Mobility, Department of Neurology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Keren
K
Rosenberg-Katz
Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.|Functional Brain Center, Wohl Institute for Advanced Imaging, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.|Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.|The School of Psychological Sciences, Tel Aviv University, Tel Aviv, Israel.
Talma
T
Hendler
Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.|School of Physics and Astronomy, Tel Aviv University, Tel Aviv, Israel.|Center for Theoretical Biological Physics, Rice University, Houston, TX, USA.
Eshel
E
Ben-Jacob
D001921:Brain / Q000502:physiology*; D001931:Brain Mapping; D006801:Humans; D008279:Magnetic Resonance Imaging; D008570:Memory, Short-Term