Abstract
Dynamic causal modeling (DCM)—a framework for inferring hidden neuronal states from brain activity measurements (e. g., fMRI) and their context-dependent modulation—was developed for human neuroimaging, and has not been optimized for non-human primate (NHP) studies, which are usually done under anesthesia. Animal neuroimaging studies offer the potential to improve effective connectivity modeling using DCM through combining functional imaging with invasive procedures such as in vivo optogenetic or electrical stimulation. Employing a Bayesian approach, model parameters are estimated based on prior knowledge of conditions that might be related to neural and BOLD dynamics (e.g., requires empirical knowledge about the range of plausible parameter values). As such, we address the following questions in this review: What factors need to be considered when applying DCM to NHP data? What differences in functional networks, cerebrovascular architecture and physiology exist between human and NHPs that are relevant for DCM application? How do anesthetics affect vascular physiology, BOLD contrast, and neural dynamics—particularly, effective communication within, and between networks? Considering the factors that are relevant for DCM application to NHP neuroimaging, we propose a strategy for modeling effective connectivity under anesthesia using an integrated physiologic-stochastic DCM (IPS-DCM).
Highlights
Neuroimaging analyses in humans and non-human primates (NHP) have become increasingly sophisticated
We discuss: (1) what factors need to be considered when applying dynamic causal modeling (DCM) to NHP; and (2) considering said factors, what strategies can one implement when modeling effective connectivity to fMRI data recorded under anesthesia
The latter is an important consideration in NHP fMRI, since most imaging experiments are done under anesthesia and anesthetics have been demonstrated to impart changes in BOLD and neural dynamics, the inhibitory drive (Martin et al, 2006; Masamoto et al, 2007; Moran et al, 2011; Aksenov et al, 2015; Paasonen et al, 2018)
Summary
Animal neuroimaging studies offer the potential to improve effective connectivity modeling using DCM through combining functional imaging with invasive procedures such as in vivo optogenetic or electrical stimulation. Employing a Bayesian approach, model parameters are estimated based on prior knowledge of conditions that might be related to neural and BOLD dynamics (e.g., requires empirical knowledge about the range of plausible parameter values). What differences in functional networks, cerebrovascular architecture and physiology exist between human and NHPs that are relevant for DCM application? How do anesthetics affect vascular physiology, BOLD contrast, and neural dynamics—effective communication within, and between networks? Considering the factors that are relevant for DCM application to NHP neuroimaging, we propose a strategy for modeling effective connectivity under anesthesia using an integrated physiologic-stochastic DCM (IPS-DCM)
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