Permanent URL to this publication: http://dx.doi.org/10.5167/uzh-64795
Friston, K J; Bastos, A; Litvak, V; Stephan, K E; Fries, P; Moran, R J (2012). DCM for complex-valued data: Cross-spectra, coherence and phase-delays. NeuroImage, 59(1):439-455.
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This note describes an extension of Bayesian model inversion procedures for the Dynamic Causal Modeling (DCM) of complex-valued data. Modeling complex data can be particularly useful in the analysis of multivariate ergodic (stationary) time-series. We illustrate this with a generalization of DCM for steady-state responses that models both the real and imaginary parts of sample cross-spectra. DCM allows one to infer underlying biophysical parameters generating data (like synaptic time constants, connection strengths and conduction delays). Because transfer functions and complex cross-spectra can be generated from these parameters, one can also describe the implicit system architecture in terms of conventional (linear systems) measures; like coherence, phase-delay or cross-correlation functions. Crucially, these measures can be derived in both sensor and source-space. In other words, one can examine the cross-correlation or phase-delay functions between hidden neuronal sources using non-invasive data and relate these functions to synaptic parameters and neuronal conduction delays. We illustrate these points using local field potential recordings from the subthalamic nucleus and globus pallidus, with a special focus on the relationship between conduction delays and the ensuing phase relationships and cross-correlation time lags between population activities.
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|Item Type:||Journal Article, refereed, original work|
|Communities & Collections:||04 Faculty of Medicine > Institute of Biomedical Engineering|
|Dewey Decimal Classification:||170 Ethics
610 Medicine & health
|Deposited On:||21 Sep 2012 13:08|
|Last Modified:||05 Apr 2016 15:58|
|Free access at:||PubMed ID. An embargo period may apply.|
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