Functional ultrasound (fUS) is a high-sensitivity neuroimaging technique that images cerebral blood volume changes, which reflect neuronal activity in the corresponding brain area. fUS measures hemodynamic changes which are typically modeled as the output of a linear time-invariant system, characterized by an impulse response known as the hemodynamic response function (HRF), and a binary representation of the stimulus signal as input. In this work, we quantify the difference between a linear and a nonlinear time-invariant HRF model in terms of data fitting and prediction performance. Our results on fUS data obtained from two mice reveal that: (a) including nonlinearities in the HRF achieves a significantly more precise modeling of the fUS signal compared to the linear assumption under certain stimulus conditions and (b) a second-order Volterra series approximation can be used to characterize the nonlinear model and predict responses to stimuli.

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Functional ultrasound (fUS) is an emerging technique that provides high sensitivity imaging of cerebral blood volume (CBV) changes. As increased metabolic demand of active tissue induces changes in CBV, these changes reflect neuronal activity in the corresponding brain area. The main advantages of this technique are that it can image the entire brain with unprecedented spatial (50-500um) and temporal resolution (10- 100ms), and that it constitutes a potentially portable solution, as opposed to functional magnetic resonance imaging (fMRI), the currently dominant modality in functional brain imaging. The high resolution as well as the plane-wave illumination lead to a large amount of raw ultrasound data per aquisition. The fundamental challenge is that fUS only provides an indirect measure of brain activity through the neurovascular coupling; this system is the link between the local neuronal activity and the resulting blood flow changes and has only partially known dynamic and nonlinear characteristics. Moreover, besides the activity of interest, fUS records a mixture of other ongoing brain activity, physiological artifacts and noise. The goal of this research is to develop tensor-based source separation techniques in order to estimate the brain’s hemodynamic response function (HRF) to stimuli and the activity of interest by learning its nonlinear coupling with the fUS signal. @en

The use of wireless acoustic sensor networks (WASNs) has received increased attention over the last decade. The advantages of WASNs over stand-alone multi-microphone devices are that the microphone array is not anymore limited by the dimensions of a single device, and that microphones can be placed at arbitrary locations. One of the disadvantages, however, is that for many applications, like beamforming, the clocks of all devices in the network need to be synchronised and that the microphone gains need to be equalised. In this paper we will prove that a specific class of beamformers is clock-offset and gain mismatch invariant. The parameters for these beamformers (acoustic transfer function and power spectral density matrices) can be estimated directly from the uncalibrated microphone signals, instead of first synchronising the clocks and equalising the gains and then estimating them. The resulting beamformers are applied to the non-calibrated microphone signals. We will substantiate, by means of computer simulations, that the proposed approach gives identical results compared to the setup where microphone signals are first calibrated, so that clock-offset compensation and microphone gain equalisation becomes unnecessary.@en