Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks
| Autor: | Katja Pinker-Domenig, Anke Meyer-Bäse, Ignacio A. Illán, Uwe Meyer-Bäse, Rodney G. Roberts, Marc B. I. Lobbes, Andreas Stadlbauer |
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| Přispěvatelé: | Beeldvorming, MUMC+: DA BV Medisch Specialisten Radiologie (9), RS: GROW - R3 - Innovative Cancer Diagnostics & Therapy |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
0209 industrial biotechnology
Computer science neural network CONTROLLABILITY Neuroscience (miscellaneous) 02 engineering and technology Network theory area aggregation pinning observability lcsh:RC321-571 03 medical and health sciences Cellular and Molecular Neuroscience 020901 industrial engineering & automation 0302 clinical medicine neurodegenerative disease SYSTEMS multi-time-scale brain network Biological neural network Observability lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Original Research COMPLEX NETWORKS Artificial neural network MODEL-REDUCTION business.industry Graph theory Complex network Controllability Graph (abstract data type) Artificial intelligence singular perturbations business synchronization 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | Frontiers in Computational Neuroscience, Vol 11 (2017) Frontiers in Computational Neuroscience Frontiers in Computational Neuroscience, 11:87. Frontiers Media S.A. |
| ISSN: | 1662-5188 |
| DOI: | 10.3389/fncom.2017.00087/full |
| Popis: | Neuroimaging in combination with graph theory has been successful in analyzing the functional connectome. However almost all analysis are performed based on static graph theory. The derived quantitative graph measures can only describe a snap shot of the disease over time. Neurodegenerative disease evolution is poorly understood and treatment strategies are consequently only of limited efficiency. Fusing modern dynamic graph network theory techniques and modeling strategies at different time scales with pinning observability of complex brain networks will lay the foundation for a transformational paradigm in neurodegnerative diseases research regarding disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. We model and analyze brain networks as two-time scale sparse dynamic graph networks with hubs (clusters) representing the fast sub-system and the interconnections between hubs the slow sub-system. Alterations in brain function as seen in dementia can be dynamically modeled by determining the clusters in which disturbance inputs have entered and the impact they have on the large-scale dementia dynamic system. Observing a small fraction of specific nodes in dementia networks such that the others can be recovered is accomplished by the novel concept of pinning observability. In addition, how to control this complex network seems to be crucial in understanding the progressive abnormal neural circuits in many neurodegenerative diseases. Detecting the controlling regions in the networks, which serve as key nodes to control the aberrant dynamics of the networks to a desired state and thus influence the progressive abnormal behavior, will have a huge impact in understanding and developing therapeutic solutions and also will provide useful information about the trajectory of the disease. In this paper, we present the theoretical framework and derive the necessary conditions for (1) area aggregation and time-scale modeling in brain networks and for (2) pinning observability of nodes in dynamic graph networks. Simulation examples are given to illustrate the theoretical concepts. |
| Databáze: | OpenAIRE |
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