Improved 7 Tesla resting-state fMRI connectivity measurements by cluster-based modeling of respiratory volume and heart rate effects.

Several strategies have been proposed to model and remove physiological noise from resting-state fMRI (rs-fMRI) data, particularly at ultrahigh fields (7 T), including contributions from respiratory volume (RV) and heart rate (HR) signal fluctuations. Recent studies suggest that these contributions are highly variable across subjects and that physiological noise correction may thus benefit from optimization at the subject or even voxel level. Here, we systematically investigated the impact of the degree of spatial specificity (group, subject, newly proposed cluster, and voxel levels) on the optimization of RV and HR models. For each degree of spatial specificity, we measured the fMRI signal variance explained (VE) by each model, as well as the functional connectivity underlying three well-known resting-state networks (RSNs) obtained from the fMRI data after removal of RV+HR contributions. Whole-brain, high-resolution rs-fMRI data were acquired from twelve healthy volunteers at 7 T, while simultaneously recording their cardiac and respiratory signals. Although VE increased with spatial specificity up to the voxel level, the accuracy of functional connectivity measurements improved only up to the cluster level, and subsequently decreased at the voxel level. This suggests that voxelwise modeling over-fits to local fluctuations with no physiological meaning. In conclusion, our results indicate that 7 T rs-fMRI connectivity measurements improve if a cluster-based physiological noise correction approach is employed in order to take into account the individual spatial variability in the HR and RV contributions.

Black-box model reduction of the C. Elegans nervous system.

In recent years, modeling neurons and neuronal collections with high accuracy have become central issues of neuroscience. The development of efficient algorithms for their simulation as well as the increase in computational power and parallelization need to keep up with the quantity and complexity of novel recordings and reconstructions reported by the experimental neuroscientists. The extraction of low-order equivalents that capture the essential aspects of the high-accuracy models is an essential part of the simulation process. The complexity of these models require the use of black-box data-oriented reduction approaches. We create a detailed model of the nervous system of a very known organism, C. Elegans, and show that it can be reduced using a modified data-driven model reduction method up to the order of 4 with very little loss in accuracy. The reduced model is able to predict the behaviour of the original for time ranges beyond the data used for the reduction.