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

Functional and Effective Connectivity

Mathys, Christoph Daniel
2009-01-01

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
2009
40
04
222
232
Stephan, K. E.; Kasper, L.; Brodersen, K. H.; Mathys, Christoph Daniel
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/47895
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