Zusammenfassung
Neurophysiologische und bildgebende Verfahren zur Messung von Hirnaktivität, wie fMRI
oder EEG, werden in den Neurowissenschaften eingesetzt, um Prozesse funktioneller
Spezialisierung und funktioneller Integration im menschlichen Gehirn zu untersuchen.
Funktionelle Integration kann auf zwei verschiedene Arten beschrieben werden: funktionelle
Konnektivität und effektive Konnektivität. Während die funktionelle Konnektivität
lediglich statistische Abhängigkeiten zwischen Zeitreihen beschreibt, erfordert das
Konzept der effektiven Konnektivität ein mechanistisches Modell der kausalen Effekte,
die den beobachteten Daten zu Grunde liegen. Dieser Artikel fasst die konzeptionellen
und methodischen Grundlagen moderner Techniken für die Analyse funktioneller und effektiver
Konnektivität auf der Basis von fMRI und elektrophysiologischen Daten zusammen. Ein
besonderer Schwerpunkt liegt dabei auf dem Dynamic Causal Modelling (DCM), einem neuen
Verfahren zur Analyse nichtlinearer neuronaler Systeme. Diese Methode besitzt ein
vielversprechendes Potenzial für klinische Anwendungen, z. B. zur Entschlüsselung
pathophysiologischer Mechanismen bei Hirnerkrankungen und zur Etablierung neurophysiologisch
fundierter diagnostischer Klassifikationen.
Abstract
Neurophysiological and imaging procedures to measure brain activity, such as fMRI
or EEG, are employed in neuroscience to investigate processes of functional specialisation
and functional integration in the human brain. Functioal integration can be described
in two distinct ways: functional connectivity and effective connectivity. Whereas
functional connectivity merely describes the statistical dependence between two time
series, the concept of effective connectivity requires a mechanistic model of the
causative effects upon which the data to be observed are based. This article summarises
the conceptual and methodological principles of modern techniques for the analysis
of functional and effective connectivity on the basis of fMRI and electrophysiological
data. Particular emphasis is placed on dynamic causal modelling (DCM), a new procedure
for the analysis of non-linear neuronal systems. This method has a highly promising
potential for clinical applications, e. g., for decoding pathological mechanisms in
brain diseases and for the establishment of neurologically valid diagnostic classifications.
Schlüsselwörter
Dynamic Causal Modelling (DCM) - Modellvergleich - Bayes - synaptische Plastizität
Key words
dynamic causal modelling (DCM) - model comparisons - Bayesian procedure - synaptic
plasticity
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Korrespondenzadresse
Prof. Dr. Dr. med. K. E. Stephan
Laboratory for Social and Neural Systems Research
Institute for Empirical Research in Economics
University of Zurich
Switzerland
eMail: k.stephan@iew.uzh.ch
