Kolloquium 25.07.2017 16:30 s.t. in room 222, Institute of Applied Physics, Corrensstraße 2/4

Static and Dynamic Functional Brain Connectivity at Sensor and Source level: Evidences from EEG-MEG Group Analysis

Dr. Stavros Dimitriadis, Aristotle University of Thessaloniki

The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then thresholding them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes The adaptation of both bivariate (mutual information) and multivariate (Granger causality) connectivity estimators to quantify the synchronization between multichannel recordings yields a fully connected, weighted, (a)symmetric functional connectivity graph (FCG), representing the associations among all brain areas. The aforementioned procedure leads to an extremely dense network of tens up to a few hundreds of weights. Therefore, this FCG must be filtered out so that the “true” connectivity pattern can emerge.  For that reason, statistical filtering based on surrogates analysis and also topological filtering based on the maximization of information flow in the network under the constraint of the wiring cost (Dimitriadis et al., 2017) should be adopted to get a subject and condition specific functional connectivity pattern.
The whole methodology relies on data-driven techniques without using a priori information for the subjects e.g. labels. Subject-specific approaches increase the reproducibility of the dataset avoiding optimizing it independently for each study. Complementary, one can add more subjects to the original cohort without re-optimizing the parameters, an approach that can lead to controversy findings to the original study.
I will demonstrate how different connectivity estimators in both static and dynamic functional connectivity with the incorporation of arbitrary statistical and topological filtering schemes can give contradictory results. The selection of appropriate surrogates and data-driven topological filtering scheme will give more reproducible and stable results. The whole analysis will focus on electro/magneto-encephalography (EEG/MEG) at resting-state in normal populations and in mild cognitive impairments (MCI) subjects. First evidences of similarities of connectivity patterns between sensor and source-level will be also demonstrated.

References:

[1] Dimitriadis SI et al., . Topological Filtering of Dynamic Functional Brain Networks Unfolds Informative Chronnectomics: A Novel Data-Driven Thresholding Scheme Based on Orthogonal Minimal Spanning Trees (OMSTs). Front. Neuroinform., 26 April 2017 | https://doi.org/10.3389/fninf.2017.00028

Einladender: Prof. C. Wolters

All Termine SS 2017

Highlights der Physik

Struktur & Symmetrie

19. - 23. September 2017, Münster

Die 17. Ausgabe der „Highlights der Physik“ Ausstellung findet diemal in Münster statt. Das Thema Struktur und Symmerie hat starke Berührungspunkte mit Nonlinear Science. Weitere Informationen finden Sie auf der offiziellen Webseite der Veranstaltung  Link zur Webseite

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Das Center for Nonlinear Science (CeNoS) ist eine zentrale wissenschaftliche Einrichtung der Westfälischen Wilhelms-Universität Münster und besteht aus den beteiligten Arbeitsgruppen die im Moment sechs verschiedenen Fachbereichen angehören. Mitglied kann jede an der Thematik interessierte Arbeitsgruppe oder jede/r interessierte Wissenschafter/in der Universität Münster werden.Das CeNoS versteht sich als Dach für die grundlagenorientierte Forschung und Lehre an Fragestellungen zu nichtlinearen Systemen sowie für Anwendungen der Ergebnisse in verschiedenen Gebieten. Darüber hinaus dient es als Forum des interdisziplinären Dialogs zwischen Wissenschaftlern und Wissenschaftlerinnen verschiedener wissenschaftlicher Fachdisziplinen.