Abstract
Functional ultrasound (fUS) is an exciting new neuroimaging technique that is able to record brain activity similar to functional magnetic resonance imaging, yet with higher spatiotemporal resolution and at lower cost. We consider the problem of jointly estimating the underlying neural sources and the hemodynamic response function (HRF) from fUS recordings. We propose to model the measured voxel time-series as a convolutive mixture of multiple source signals and solve the blind deconvolution problem via block-term decomposition. This allows us to estimate both the source time courses and a different HRF for each voxel and source combination, which accounts for the variability of HRF across different brain regions and events respectively. The proposed approach is proven to be robust against noise via simulations and further validated on real fUS data by performing a visual experiment on a mouse. The obtained results show that the proposed method is able to recover the timings of the visual paradigm.
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