A hemodynamic model for layered BOLD signals

• Published in: NeuroImage (Orlando, Fla.) Vol. 125; pp. 556 - 570
• Main Authors: Heinzle, Jakob, Koopmans, Peter J., den Ouden, Hanneke E.M., Raman, Sudhir, Stephan, Klaas Enno
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 15-01-2016; Elsevier Limited
• Subjects: Algorithms; Bayesian model comparison; Blood; Brain - blood supply; Brain Mapping - methods; Cortical layers; Dynamic causal modeling; fMRI; Hemodynamics - physiology; Humans; Image Processing, Computer-Assisted - methods; Magnetic Resonance Imaging - methods; Models, Neurological; Models, Theoretical; NMR; Nuclear magnetic resonance; Oxygen - blood; Predictive coding; Standard deviation; Predictive coding; fMRI; Dynamic causal modeling; Bayesian model comparison; Cortical layers
• ISSN: 1053-8119; 1095-9572
• DOI: 10.1016/j.neuroimage.2015.10.025

High-resolution blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) at the sub-millimeter scale has become feasible with recent advances in MR technology. In principle, this would enable the study of layered cortical circuits, one of the fundaments of cortical computation. However, the spatial layout of cortical blood supply may become an important confound at such high resolution. In particular, venous blood draining back to the cortical surface perpendicularly to the layered structure is expected to influence the measured responses in different layers. Here, we present an extension of a hemodynamic model commonly used for analyzing fMRI data (in dynamic causal models or biophysical network models) that accounts for such blood draining effects by coupling local hemodynamics across layers. We illustrate the properties of the model and its inversion by a series of simulations and show that it successfully captures layered fMRI data obtained during a simple visual experiment. We conclude that for future studies of the dynamics of layered neuronal circuits with high-resolution fMRI, it will be pivotal to include effects of blood draining, particularly when trying to infer on the layer-specific connections in cortex — a theme of key relevance for brain disorders like schizophrenia and for theories of brain function such as predictive coding.
•We present a dynamic causal model for fMRI data of cortical layers.•The model includes blood draining from lower to upper cortical layers.•In simulations, neural coupling can be distinguished from blood draining effects.•On real data, models with blood draining excel over models without blood draining.