Extracting hemodynamic activity with low-rank spatial signatures in functional ultrasound using tensor decompositions

Abstract

Functional ultrasound (fUS) is a neuroimaging modality that indirectly measures local neuronal activity by imaging cerebral blood volume fluctuations. However, accurately estimating neuronal activity from fUS measurements remains an open challenge. Hemodynamic changes are often modeled as the output of a system characterized by the hemodynamic response function (HRF), with neuronal activations as input. In this work, we propose a model for fUS measurements that assumes that hemodynamic activity has a low-rank spatial characterization. Starting from the tensor block term decomposition, we propose a method to estimate the spatial signatures, the HRF and the neuronal activation signals. This method is entirely data-driven and can be applied to entire fUS datasets. After an investigation using simulations, application to task experiment data of a mouse verified that activity that is spatially low rank and temporally correlated with the stimulus can be extracted in expected regions, which opens up the way to application on resting state data.