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Generalised filtering and stochastic DCM for fMRI.

Baojuan Li¹, Jean Daunizeau, Klaas E Stephan

¹The Wellcome Trust Centre for Neuroimaging, University College London, Queen Square, London WC1N 3BG, UK.

|February 12, 2011

Summary

This study validates stochastic dynamic causal models (DCMs) for fMRI data, showing they accurately estimate effective connectivity by accounting for neuronal and physiological noise. This approach offers a more plausible model of fMRI signal generation.

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Area of Science:

Neuroimaging
Computational Neuroscience
Systems Neuroscience

Background:

Dynamic Causal Models (DCMs) are used to infer effective connectivity from fMRI data.
Traditional DCMs often ignore random fluctuations (noise) in hidden neuronal and physiological states.
The validity of incorporating state-noise into DCMs requires empirical investigation.

Purpose of the Study:

To establish the validity of stochastic DCMs that accommodate random fluctuations in hidden neuronal and physiological states.
To compare deterministic and stochastic DCMs, and different stochastic DCM variants (generalised filtering vs. dynamic expectation maximisation).
To assess the face and construct validity of stochastic DCM inversion using simulated and real fMRI data.

Main Methods:

Characterization of state-noise by comparing log evidence of models with varying noise assumptions.
Validation using simulated fMRI data with and without state-noise.
Application to real fMRI data from an internet addiction study.

Main Results:

Stochastic DCM inversion is feasible with typical fMRI data.
State-noise exhibits non-trivial amplitude and smoothness.
Stochastic DCM demonstrates face validity, enabling distinction between noisy data and accurate effective connectivity estimation.
Relaxing conditional independence assumptions enhances construct validity, revealing group differences missed by variational schemes.

Conclusions:

Stochastic DCM provides a feasible and valid method for analyzing fMRI time series.
Accounting for endogenous fluctuations in neuronal and physiological states offers a more plausible account of fMRI signal generation.
This approach enhances the understanding of brain connectivity and group differences in neurological studies.