Dynamic causal models of steady-state responses
2008

Dynamic Causal Models of Steady-State Responses

Sample size: 1 publication Evidence: moderate

Author Information

Author(s): R.J. Moran, K.E. Stephan, T. Seidenbecher, H.-C. Pape, R.J. Dolan, K.J. Friston

Primary Institution: Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London

Hypothesis

Can a dynamic causal model (DCM) effectively describe steady-state responses in electrophysiological data?

Conclusion

The study demonstrates that a dynamic causal model can accurately infer synaptic physiology and connectivity from cross-spectral density data.

Supporting Evidence

  • The model predicts cross-spectral density in multi-channel data.
  • Results showed increased coupling between the hippocampus and amygdala during fear conditioning.
  • The model was validated using both synthetic and real data.

Takeaway

This study shows how scientists can use a special model to understand how brain cells communicate during learning and memory.

Methodology

The study used a dynamic causal model to analyze local field potentials recorded from mice during a learning experiment.

Limitations

The model's assumptions may not hold in all experimental designs, and the analysis is based on a single animal's data.

Participant Demographics

Adult male C57B/6J mice, 10 to 12 weeks old.

Digital Object Identifier (DOI)

10.1016/j.neuroimage.2008.09.048

Want to read the original?

Access the complete publication on the publisher's website

View Original Publication