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In Silico Clinical Trials for Cardiovascular Disease
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A hemodynamic model for layered BOLD signals.

Jakob Heinzle1, Peter J Koopmans2, Hanneke E M den Ouden3

  • 1Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich & ETH Zurich, Switzerland.

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|October 21, 2015
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Summary
This summary is machine-generated.

High-resolution functional MRI (fMRI) can now study brain layers. A new model accounts for blood flow effects, improving analysis of layered neuronal circuits and brain disorders.

Keywords:
Bayesian model comparisonCortical layersDynamic causal modelingPredictive codingfMRI

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Area of Science:

  • Neuroimaging
  • Systems Neuroscience
  • Biophysics

Background:

  • Recent MR technology advances enable sub-millimeter resolution BOLD fMRI.
  • Studying layered cortical circuits is crucial for understanding computation.
  • Cortical blood supply layout can confound high-resolution fMRI data, especially venous drainage.

Purpose of the Study:

  • To develop and validate a hemodynamic model accounting for cross-layer blood draining effects in fMRI.
  • To improve the analysis of high-resolution fMRI data for layered cortical circuits.

Main Methods:

  • Extension of a hemodynamic model to couple local hemodynamics across cortical layers.
  • Model inversion and validation using simulations.
  • Application to layered fMRI data from a visual experiment.

Main Results:

  • The extended model successfully captures cross-layer hemodynamic influences.
  • Simulations demonstrate the model's ability to account for blood draining effects.
  • The model accurately represents layered fMRI data.

Conclusions:

  • Accounting for blood draining is essential for high-resolution fMRI studies of layered neuronal circuits.
  • This model is crucial for inferring layer-specific cortical connectivity.
  • Findings are relevant for understanding brain disorders and theories like predictive coding.