title: Investigating complex networks with inverse models: analytical aspects of spatial leakage and connectivity estimation.
abstract: Network theory and inverse modeling are two standard tools of applied physics, whose combination is needed when studying the dynamical organization of spatially distributed systems from indirect measurements. However, the associated connectivity estimation may be affected by spatial leakage, an artifact of inverse modeling that limits the interpretability of network analysis. This paper investigates general analytical aspects pertaining to this issue. First, the existence of spatial leakage is derived from the topological structure of inverse operators. Then the geometry of spatial leakage is modeled and used to define a geometric correction scheme, which limits spatial leakage effects in connectivity estimation. Finally, this new approach for network analysis is compared analytically to existing methods based on linear regressions, which are shown to yield biased coupling estimates.
authors: Laboratoire de Cartographie fonctionnelle du Cerveau, UNI-ULB Neurosciences Institute, Université libre de Bruxelles (ULB), Brussels, Belgium.
authors: Vincent
authors: V
authors: Wens
mesh_terms: D001921:Brain; D016014:Linear Models; D008959:Models, Neurological; D008962:Models, Theoretical; D009415:Nerve Net